diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 663cfb1be..7e4487ac8 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -57,7 +57,7 @@ jobs: - name: Installation - *nix if: runner.os == 'Linux' run: | - python -m pip install --upgrade pip wheel + python -m pip install --upgrade pip==23.0.1 wheel==0.38.4 export LD_LIBRARY_PATH=${HOME}/dependencies/lib:$LD_LIBRARY_PATH export TA_LIBRARY_PATH=${HOME}/dependencies/lib export TA_INCLUDE_PATH=${HOME}/dependencies/include @@ -163,7 +163,7 @@ jobs: rm /usr/local/bin/python3.11-config || true brew install hdf5 c-blosc - python -m pip install --upgrade pip wheel + python -m pip install --upgrade pip==23.0.1 wheel==0.38.4 export LD_LIBRARY_PATH=${HOME}/dependencies/lib:$LD_LIBRARY_PATH export TA_LIBRARY_PATH=${HOME}/dependencies/lib export TA_INCLUDE_PATH=${HOME}/dependencies/include @@ -352,7 +352,7 @@ jobs: - name: Installation - *nix if: runner.os == 'Linux' run: | - python -m pip install --upgrade pip wheel + python -m pip install --upgrade pip==23.0.1 wheel==0.38.4 export LD_LIBRARY_PATH=${HOME}/dependencies/lib:$LD_LIBRARY_PATH export TA_LIBRARY_PATH=${HOME}/dependencies/lib export TA_INCLUDE_PATH=${HOME}/dependencies/include @@ -425,7 +425,7 @@ jobs: python setup.py sdist bdist_wheel - name: Publish to PyPI (Test) - uses: pypa/gh-action-pypi-publish@v1.7.1 + uses: pypa/gh-action-pypi-publish@v1.8.5 if: (github.event_name == 'release') with: user: __token__ @@ -433,7 +433,7 @@ jobs: repository_url: https://test.pypi.org/legacy/ - name: Publish to PyPI - uses: pypa/gh-action-pypi-publish@v1.7.1 + uses: pypa/gh-action-pypi-publish@v1.8.5 if: (github.event_name == 'release') with: user: __token__ diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index bc2e0bc0d..0031300cd 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -13,12 +13,12 @@ repos: - id: mypy exclude: build_helpers additional_dependencies: - - types-cachetools==5.3.0.4 + - types-cachetools==5.3.0.5 - types-filelock==3.2.7 - - types-requests==2.28.11.15 - - types-tabulate==0.9.0.1 - - types-python-dateutil==2.8.19.10 - - SQLAlchemy==2.0.5.post1 + - types-requests==2.28.11.17 + - types-tabulate==0.9.0.2 + - types-python-dateutil==2.8.19.12 + - SQLAlchemy==2.0.10 # stages: [push] - repo: https://github.com/pycqa/isort diff --git a/Dockerfile b/Dockerfile index 6a4a168c1..ee8b3f0a8 100644 --- a/Dockerfile +++ b/Dockerfile @@ -1,4 +1,4 @@ -FROM python:3.10.10-slim-bullseye as base +FROM python:3.10.11-slim-bullseye as base # Setup env ENV LANG C.UTF-8 @@ -25,7 +25,7 @@ FROM base as python-deps RUN apt-get update \ && apt-get -y install build-essential libssl-dev git libffi-dev libgfortran5 pkg-config cmake gcc \ && apt-get clean \ - && pip install --upgrade pip + && pip install --upgrade pip==23.0.1 wheel==0.38.4 # Install TA-lib COPY build_helpers/* /tmp/ diff --git a/README.md b/README.md index c8bc50dac..57c4e3a52 100644 --- a/README.md +++ b/README.md @@ -210,6 +210,6 @@ To run this bot we recommend you a cloud instance with a minimum of: - [Python >= 3.8](http://docs.python-guide.org/en/latest/starting/installation/) - [pip](https://pip.pypa.io/en/stable/installing/) - [git](https://git-scm.com/book/en/v2/Getting-Started-Installing-Git) -- [TA-Lib](https://mrjbq7.github.io/ta-lib/install.html) +- [TA-Lib](https://ta-lib.github.io/ta-lib-python/) - [virtualenv](https://virtualenv.pypa.io/en/stable/installation.html) (Recommended) - [Docker](https://www.docker.com/products/docker) (Recommended) diff --git a/build_helpers/TA_Lib-0.4.25-cp310-cp310-win_amd64.whl b/build_helpers/TA_Lib-0.4.25-cp310-cp310-win_amd64.whl deleted file mode 100644 index c6435da0d..000000000 Binary files a/build_helpers/TA_Lib-0.4.25-cp310-cp310-win_amd64.whl and /dev/null differ diff --git a/build_helpers/TA_Lib-0.4.25-cp311-cp311-win_amd64.whl b/build_helpers/TA_Lib-0.4.25-cp311-cp311-win_amd64.whl deleted file mode 100644 index 4d55d18c8..000000000 Binary files a/build_helpers/TA_Lib-0.4.25-cp311-cp311-win_amd64.whl and /dev/null differ diff --git a/build_helpers/TA_Lib-0.4.25-cp38-cp38-win_amd64.whl b/build_helpers/TA_Lib-0.4.25-cp38-cp38-win_amd64.whl deleted file mode 100644 index f2806db80..000000000 Binary files a/build_helpers/TA_Lib-0.4.25-cp38-cp38-win_amd64.whl and /dev/null differ diff --git a/build_helpers/TA_Lib-0.4.25-cp39-cp39-win_amd64.whl b/build_helpers/TA_Lib-0.4.25-cp39-cp39-win_amd64.whl deleted file mode 100644 index 0d4ceb3b4..000000000 Binary files a/build_helpers/TA_Lib-0.4.25-cp39-cp39-win_amd64.whl and /dev/null differ diff --git a/build_helpers/TA_Lib-0.4.26-cp310-cp310-win_amd64.whl b/build_helpers/TA_Lib-0.4.26-cp310-cp310-win_amd64.whl new file mode 100644 index 000000000..466455a8c Binary files /dev/null and b/build_helpers/TA_Lib-0.4.26-cp310-cp310-win_amd64.whl differ diff --git a/build_helpers/TA_Lib-0.4.26-cp311-cp311-win_amd64.whl b/build_helpers/TA_Lib-0.4.26-cp311-cp311-win_amd64.whl new file mode 100644 index 000000000..16f4f411a Binary files /dev/null and b/build_helpers/TA_Lib-0.4.26-cp311-cp311-win_amd64.whl differ diff --git a/build_helpers/TA_Lib-0.4.26-cp38-cp38-win_amd64.whl b/build_helpers/TA_Lib-0.4.26-cp38-cp38-win_amd64.whl new file mode 100644 index 000000000..324502fb8 Binary files /dev/null and b/build_helpers/TA_Lib-0.4.26-cp38-cp38-win_amd64.whl differ diff --git a/build_helpers/TA_Lib-0.4.26-cp39-cp39-win_amd64.whl b/build_helpers/TA_Lib-0.4.26-cp39-cp39-win_amd64.whl new file mode 100644 index 000000000..389403041 Binary files /dev/null and b/build_helpers/TA_Lib-0.4.26-cp39-cp39-win_amd64.whl differ diff --git a/build_helpers/install_ta-lib.sh b/build_helpers/install_ta-lib.sh index 079d578b4..005d9abca 100755 --- a/build_helpers/install_ta-lib.sh +++ b/build_helpers/install_ta-lib.sh @@ -8,8 +8,8 @@ if [ -n "$2" ] || [ ! -f "${INSTALL_LOC}/lib/libta_lib.a" ]; then tar zxvf ta-lib-0.4.0-src.tar.gz cd ta-lib \ && sed -i.bak "s|0.00000001|0.000000000000000001 |g" src/ta_func/ta_utility.h \ - && curl 'http://git.savannah.gnu.org/gitweb/?p=config.git;a=blob_plain;f=config.guess;hb=HEAD' -o config.guess \ - && curl 'http://git.savannah.gnu.org/gitweb/?p=config.git;a=blob_plain;f=config.sub;hb=HEAD' -o config.sub \ + && curl 'https://raw.githubusercontent.com/gcc-mirror/gcc/master/config.guess' -o config.guess \ + && curl 'https://raw.githubusercontent.com/gcc-mirror/gcc/master/config.sub' -o config.sub \ && ./configure --prefix=${INSTALL_LOC}/ \ && make if [ $? -ne 0 ]; then diff --git a/build_helpers/install_windows.ps1 b/build_helpers/install_windows.ps1 index 36606cc3a..3e7df5dfc 100644 --- a/build_helpers/install_windows.ps1 +++ b/build_helpers/install_windows.ps1 @@ -1,21 +1,21 @@ # Downloads don't work automatically, since the URL is regenerated via javascript. # Downloaded from https://www.lfd.uci.edu/~gohlke/pythonlibs/#ta-lib -python -m pip install --upgrade pip wheel +python -m pip install --upgrade pip==23.0.1 wheel==0.38.4 $pyv = python -c "import sys; print(f'{sys.version_info.major}.{sys.version_info.minor}')" if ($pyv -eq '3.8') { - pip install build_helpers\TA_Lib-0.4.25-cp38-cp38-win_amd64.whl + pip install build_helpers\TA_Lib-0.4.26-cp38-cp38-win_amd64.whl } if ($pyv -eq '3.9') { - pip install build_helpers\TA_Lib-0.4.25-cp39-cp39-win_amd64.whl + pip install build_helpers\TA_Lib-0.4.26-cp39-cp39-win_amd64.whl } if ($pyv -eq '3.10') { - pip install build_helpers\TA_Lib-0.4.25-cp310-cp310-win_amd64.whl + pip install build_helpers\TA_Lib-0.4.26-cp310-cp310-win_amd64.whl } if ($pyv -eq '3.11') { - pip install build_helpers\TA_Lib-0.4.25-cp311-cp311-win_amd64.whl + pip install build_helpers\TA_Lib-0.4.26-cp311-cp311-win_amd64.whl } pip install -r requirements-dev.txt pip install -e . diff --git a/build_helpers/publish_docker_arm64.sh b/build_helpers/publish_docker_arm64.sh index 696f5bc48..8f0de2cc9 100755 --- a/build_helpers/publish_docker_arm64.sh +++ b/build_helpers/publish_docker_arm64.sh @@ -12,6 +12,7 @@ TAG=$(echo "${BRANCH_NAME}" | sed -e "s/\//_/g") TAG_PLOT=${TAG}_plot TAG_FREQAI=${TAG}_freqai TAG_FREQAI_RL=${TAG_FREQAI}rl +TAG_FREQAI_TORCH=${TAG_FREQAI}torch TAG_PI="${TAG}_pi" TAG_ARM=${TAG}_arm @@ -42,9 +43,9 @@ if [ $? -ne 0 ]; then return 1 fi -docker build --cache-from freqtrade:${TAG_ARM} --build-arg sourceimage=${CACHE_IMAGE} --build-arg sourcetag=${TAG_ARM} -t freqtrade:${TAG_PLOT_ARM} -f docker/Dockerfile.plot . -docker build --cache-from freqtrade:${TAG_ARM} --build-arg sourceimage=${CACHE_IMAGE} --build-arg sourcetag=${TAG_ARM} -t freqtrade:${TAG_FREQAI_ARM} -f docker/Dockerfile.freqai . -docker build --cache-from freqtrade:${TAG_ARM} --build-arg sourceimage=${CACHE_IMAGE} --build-arg sourcetag=${TAG_ARM} -t freqtrade:${TAG_FREQAI_RL_ARM} -f docker/Dockerfile.freqai_rl . +docker build --build-arg sourceimage=freqtrade --build-arg sourcetag=${TAG_ARM} -t freqtrade:${TAG_PLOT_ARM} -f docker/Dockerfile.plot . +docker build --build-arg sourceimage=freqtrade --build-arg sourcetag=${TAG_ARM} -t freqtrade:${TAG_FREQAI_ARM} -f docker/Dockerfile.freqai . +docker build --build-arg sourceimage=freqtrade --build-arg sourcetag=${TAG_FREQAI_ARM} -t freqtrade:${TAG_FREQAI_RL_ARM} -f docker/Dockerfile.freqai_rl . # Tag image for upload and next build step docker tag freqtrade:$TAG_ARM ${CACHE_IMAGE}:$TAG_ARM @@ -84,6 +85,10 @@ docker manifest push -p ${IMAGE_NAME}:${TAG_FREQAI} docker manifest create ${IMAGE_NAME}:${TAG_FREQAI_RL} ${CACHE_IMAGE}:${TAG_FREQAI_RL} ${CACHE_IMAGE}:${TAG_FREQAI_RL_ARM} docker manifest push -p ${IMAGE_NAME}:${TAG_FREQAI_RL} +# Create special Torch tag - which is identical to the RL tag. +docker manifest create ${IMAGE_NAME}:${TAG_FREQAI_TORCH} ${CACHE_IMAGE}:${TAG_FREQAI_RL} ${CACHE_IMAGE}:${TAG_FREQAI_RL_ARM} +docker manifest push -p ${IMAGE_NAME}:${TAG_FREQAI_TORCH} + # copy images to ghcr.io alias crane="docker run --rm -i -v $(pwd)/.crane:/home/nonroot/.docker/ gcr.io/go-containerregistry/crane" @@ -92,10 +97,11 @@ chmod a+rwx .crane echo "${GHCR_TOKEN}" | crane auth login ghcr.io -u "${GHCR_USERNAME}" --password-stdin -crane copy ${IMAGE_NAME}:${TAG} ${GHCR_IMAGE_NAME}:${TAG} -crane copy ${IMAGE_NAME}:${TAG_PLOT} ${GHCR_IMAGE_NAME}:${TAG_PLOT} -crane copy ${IMAGE_NAME}:${TAG_FREQAI} ${GHCR_IMAGE_NAME}:${TAG_FREQAI} crane copy ${IMAGE_NAME}:${TAG_FREQAI_RL} ${GHCR_IMAGE_NAME}:${TAG_FREQAI_RL} +crane copy ${IMAGE_NAME}:${TAG_FREQAI_RL} ${GHCR_IMAGE_NAME}:${TAG_FREQAI_TORCH} +crane copy ${IMAGE_NAME}:${TAG_FREQAI} ${GHCR_IMAGE_NAME}:${TAG_FREQAI} +crane copy ${IMAGE_NAME}:${TAG_PLOT} ${GHCR_IMAGE_NAME}:${TAG_PLOT} +crane copy ${IMAGE_NAME}:${TAG} ${GHCR_IMAGE_NAME}:${TAG} # Tag as latest for develop builds if [ "${TAG}" = "develop" ]; then diff --git a/build_helpers/publish_docker_multi.sh b/build_helpers/publish_docker_multi.sh index 27fa06b95..72b20ac5d 100755 --- a/build_helpers/publish_docker_multi.sh +++ b/build_helpers/publish_docker_multi.sh @@ -58,9 +58,9 @@ fi # Tag image for upload and next build step docker tag freqtrade:$TAG ${CACHE_IMAGE}:$TAG -docker build --cache-from freqtrade:${TAG} --build-arg sourceimage=${CACHE_IMAGE} --build-arg sourcetag=${TAG} -t freqtrade:${TAG_PLOT} -f docker/Dockerfile.plot . -docker build --cache-from freqtrade:${TAG} --build-arg sourceimage=${CACHE_IMAGE} --build-arg sourcetag=${TAG} -t freqtrade:${TAG_FREQAI} -f docker/Dockerfile.freqai . -docker build --cache-from freqtrade:${TAG_FREQAI} --build-arg sourceimage=${CACHE_IMAGE} --build-arg sourcetag=${TAG_FREQAI} -t freqtrade:${TAG_FREQAI_RL} -f docker/Dockerfile.freqai_rl . +docker build --build-arg sourceimage=freqtrade --build-arg sourcetag=${TAG} -t freqtrade:${TAG_PLOT} -f docker/Dockerfile.plot . +docker build --build-arg sourceimage=freqtrade --build-arg sourcetag=${TAG} -t freqtrade:${TAG_FREQAI} -f docker/Dockerfile.freqai . +docker build --build-arg sourceimage=freqtrade --build-arg sourcetag=${TAG_FREQAI} -t freqtrade:${TAG_FREQAI_RL} -f docker/Dockerfile.freqai_rl . docker tag freqtrade:$TAG_PLOT ${CACHE_IMAGE}:$TAG_PLOT docker tag freqtrade:$TAG_FREQAI ${CACHE_IMAGE}:$TAG_FREQAI diff --git a/docs/assets/freqai_pytorch-diagram.png b/docs/assets/freqai_pytorch-diagram.png new file mode 100644 index 000000000..f48ebae25 Binary files /dev/null and b/docs/assets/freqai_pytorch-diagram.png differ diff --git a/docs/backtesting.md b/docs/backtesting.md index 0227df3f6..166c2b28b 100644 --- a/docs/backtesting.md +++ b/docs/backtesting.md @@ -274,19 +274,20 @@ A backtesting result will look like that: | XRP/BTC | 35 | 0.66 | 22.96 | 0.00114897 | 11.48 | 3:49:00 | 12 0 23 34.3 | | ZEC/BTC | 22 | -0.46 | -10.18 | -0.00050971 | -5.09 | 2:22:00 | 7 0 15 31.8 | | TOTAL | 429 | 0.36 | 152.41 | 0.00762792 | 76.20 | 4:12:00 | 186 0 243 43.4 | -========================================================= EXIT REASON STATS ========================================================== -| Exit Reason | Exits | Wins | Draws | Losses | -|:-------------------|--------:|------:|-------:|--------:| -| trailing_stop_loss | 205 | 150 | 0 | 55 | -| stop_loss | 166 | 0 | 0 | 166 | -| exit_signal | 56 | 36 | 0 | 20 | -| force_exit | 2 | 0 | 0 | 2 | ====================================================== LEFT OPEN TRADES REPORT ====================================================== | Pair | Entries | Avg Profit % | Cum Profit % | Tot Profit BTC | Tot Profit % | Avg Duration | Win Draw Loss Win% | |:---------|---------:|---------------:|---------------:|-----------------:|---------------:|:---------------|--------------------:| | ADA/BTC | 1 | 0.89 | 0.89 | 0.00004434 | 0.44 | 6:00:00 | 1 0 0 100 | | LTC/BTC | 1 | 0.68 | 0.68 | 0.00003421 | 0.34 | 2:00:00 | 1 0 0 100 | | TOTAL | 2 | 0.78 | 1.57 | 0.00007855 | 0.78 | 4:00:00 | 2 0 0 100 | +==================== EXIT REASON STATS ==================== +| Exit Reason | Exits | Wins | Draws | Losses | +|:-------------------|--------:|------:|-------:|--------:| +| trailing_stop_loss | 205 | 150 | 0 | 55 | +| stop_loss | 166 | 0 | 0 | 166 | +| exit_signal | 56 | 36 | 0 | 20 | +| force_exit | 2 | 0 | 0 | 2 | + ================== SUMMARY METRICS ================== | Metric | Value | |-----------------------------+---------------------| diff --git a/docs/bot-basics.md b/docs/bot-basics.md index 1aa8f3085..ef5e6900b 100644 --- a/docs/bot-basics.md +++ b/docs/bot-basics.md @@ -60,10 +60,10 @@ This loop will be repeated again and again until the bot is stopped. * Load historic data for configured pairlist. * Calls `bot_start()` once. -* Calls `bot_loop_start()` once. * Calculate indicators (calls `populate_indicators()` once per pair). * Calculate entry / exit signals (calls `populate_entry_trend()` and `populate_exit_trend()` once per pair). * Loops per candle simulating entry and exit points. + * Calls `bot_loop_start()` strategy callback. * Check for Order timeouts, either via the `unfilledtimeout` configuration, or via `check_entry_timeout()` / `check_exit_timeout()` strategy callbacks. * Calls `adjust_entry_price()` strategy callback for open entry orders. * Check for trade entry signals (`enter_long` / `enter_short` columns). diff --git a/docs/configuration.md b/docs/configuration.md index 8a1aeb40e..cf3872f1c 100644 --- a/docs/configuration.md +++ b/docs/configuration.md @@ -138,7 +138,7 @@ Mandatory parameters are marked as **Required**, which means that they are requi | `stake_currency` | **Required.** Crypto-currency used for trading.
**Datatype:** String | `stake_amount` | **Required.** Amount of crypto-currency your bot will use for each trade. Set it to `"unlimited"` to allow the bot to use all available balance. [More information below](#configuring-amount-per-trade).
**Datatype:** Positive float or `"unlimited"`. | `tradable_balance_ratio` | Ratio of the total account balance the bot is allowed to trade. [More information below](#configuring-amount-per-trade).
*Defaults to `0.99` 99%).*
**Datatype:** Positive float between `0.1` and `1.0`. -| `available_capital` | Available starting capital for the bot. Useful when running multiple bots on the same exchange account.[More information below](#configuring-amount-per-trade).
**Datatype:** Positive float. +| `available_capital` | Available starting capital for the bot. Useful when running multiple bots on the same exchange account. [More information below](#configuring-amount-per-trade).
**Datatype:** Positive float. | `amend_last_stake_amount` | Use reduced last stake amount if necessary. [More information below](#configuring-amount-per-trade).
*Defaults to `false`.*
**Datatype:** Boolean | `last_stake_amount_min_ratio` | Defines minimum stake amount that has to be left and executed. Applies only to the last stake amount when it's amended to a reduced value (i.e. if `amend_last_stake_amount` is set to `true`). [More information below](#configuring-amount-per-trade).
*Defaults to `0.5`.*
**Datatype:** Float (as ratio) | `amount_reserve_percent` | Reserve some amount in min pair stake amount. The bot will reserve `amount_reserve_percent` + stoploss value when calculating min pair stake amount in order to avoid possible trade refusals.
*Defaults to `0.05` (5%).*
**Datatype:** Positive Float as ratio. @@ -155,25 +155,25 @@ Mandatory parameters are marked as **Required**, which means that they are requi | `trailing_stop_positive_offset` | Offset on when to apply `trailing_stop_positive`. Percentage value which should be positive. More details in the [stoploss documentation](stoploss.md#trailing-stop-loss-only-once-the-trade-has-reached-a-certain-offset). [Strategy Override](#parameters-in-the-strategy).
*Defaults to `0.0` (no offset).*
**Datatype:** Float | `trailing_only_offset_is_reached` | Only apply trailing stoploss when the offset is reached. [stoploss documentation](stoploss.md). [Strategy Override](#parameters-in-the-strategy).
*Defaults to `false`.*
**Datatype:** Boolean | `fee` | Fee used during backtesting / dry-runs. Should normally not be configured, which has freqtrade fall back to the exchange default fee. Set as ratio (e.g. 0.001 = 0.1%). Fee is applied twice for each trade, once when buying, once when selling.
**Datatype:** Float (as ratio) -| `futures_funding_rate` | User-specified funding rate to be used when historical funding rates are not available from the exchange. This does not overwrite real historical rates. It is recommended that this be set to 0 unless you are testing a specific coin and you understand how the funding rate will affect freqtrade's profit calculations. [More information here](leverage.md#unavailable-funding-rates)
*Defaults to None.*
**Datatype:** Float +| `futures_funding_rate` | User-specified funding rate to be used when historical funding rates are not available from the exchange. This does not overwrite real historical rates. It is recommended that this be set to 0 unless you are testing a specific coin and you understand how the funding rate will affect freqtrade's profit calculations. [More information here](leverage.md#unavailable-funding-rates)
*Defaults to `None`.*
**Datatype:** Float | `trading_mode` | Specifies if you want to trade regularly, trade with leverage, or trade contracts whose prices are derived from matching cryptocurrency prices. [leverage documentation](leverage.md).
*Defaults to `"spot"`.*
**Datatype:** String | `margin_mode` | When trading with leverage, this determines if the collateral owned by the trader will be shared or isolated to each trading pair [leverage documentation](leverage.md).
**Datatype:** String | `liquidation_buffer` | A ratio specifying how large of a safety net to place between the liquidation price and the stoploss to prevent a position from reaching the liquidation price [leverage documentation](leverage.md).
*Defaults to `0.05`.*
**Datatype:** Float | | **Unfilled timeout** | `unfilledtimeout.entry` | **Required.** How long (in minutes or seconds) the bot will wait for an unfilled entry order to complete, after which the order will be cancelled and repeated at current (new) price, as long as there is a signal. [Strategy Override](#parameters-in-the-strategy).
**Datatype:** Integer | `unfilledtimeout.exit` | **Required.** How long (in minutes or seconds) the bot will wait for an unfilled exit order to complete, after which the order will be cancelled and repeated at current (new) price, as long as there is a signal. [Strategy Override](#parameters-in-the-strategy).
**Datatype:** Integer -| `unfilledtimeout.unit` | Unit to use in unfilledtimeout setting. Note: If you set unfilledtimeout.unit to "seconds", "internals.process_throttle_secs" must be inferior or equal to timeout [Strategy Override](#parameters-in-the-strategy).
*Defaults to `minutes`.*
**Datatype:** String +| `unfilledtimeout.unit` | Unit to use in unfilledtimeout setting. Note: If you set unfilledtimeout.unit to "seconds", "internals.process_throttle_secs" must be inferior or equal to timeout [Strategy Override](#parameters-in-the-strategy).
*Defaults to `"minutes"`.*
**Datatype:** String | `unfilledtimeout.exit_timeout_count` | How many times can exit orders time out. Once this number of timeouts is reached, an emergency exit is triggered. 0 to disable and allow unlimited order cancels. [Strategy Override](#parameters-in-the-strategy).
*Defaults to `0`.*
**Datatype:** Integer | | **Pricing** -| `entry_pricing.price_side` | Select the side of the spread the bot should look at to get the entry rate. [More information below](#buy-price-side).
*Defaults to `same`.*
**Datatype:** String (either `ask`, `bid`, `same` or `other`). +| `entry_pricing.price_side` | Select the side of the spread the bot should look at to get the entry rate. [More information below](#entry-price).
*Defaults to `"same"`.*
**Datatype:** String (either `ask`, `bid`, `same` or `other`). | `entry_pricing.price_last_balance` | **Required.** Interpolate the bidding price. More information [below](#entry-price-without-orderbook-enabled). -| `entry_pricing.use_order_book` | Enable entering using the rates in [Order Book Entry](#entry-price-with-orderbook-enabled).
*Defaults to `True`.*
**Datatype:** Boolean +| `entry_pricing.use_order_book` | Enable entering using the rates in [Order Book Entry](#entry-price-with-orderbook-enabled).
*Defaults to `true`.*
**Datatype:** Boolean | `entry_pricing.order_book_top` | Bot will use the top N rate in Order Book "price_side" to enter a trade. I.e. a value of 2 will allow the bot to pick the 2nd entry in [Order Book Entry](#entry-price-with-orderbook-enabled).
*Defaults to `1`.*
**Datatype:** Positive Integer | `entry_pricing. check_depth_of_market.enabled` | Do not enter if the difference of buy orders and sell orders is met in Order Book. [Check market depth](#check-depth-of-market).
*Defaults to `false`.*
**Datatype:** Boolean | `entry_pricing. check_depth_of_market.bids_to_ask_delta` | The difference ratio of buy orders and sell orders found in Order Book. A value below 1 means sell order size is greater, while value greater than 1 means buy order size is higher. [Check market depth](#check-depth-of-market)
*Defaults to `0`.*
**Datatype:** Float (as ratio) -| `exit_pricing.price_side` | Select the side of the spread the bot should look at to get the exit rate. [More information below](#exit-price-side).
*Defaults to `same`.*
**Datatype:** String (either `ask`, `bid`, `same` or `other`). +| `exit_pricing.price_side` | Select the side of the spread the bot should look at to get the exit rate. [More information below](#exit-price-side).
*Defaults to `"same"`.*
**Datatype:** String (either `ask`, `bid`, `same` or `other`). | `exit_pricing.price_last_balance` | Interpolate the exiting price. More information [below](#exit-price-without-orderbook-enabled). -| `exit_pricing.use_order_book` | Enable exiting of open trades using [Order Book Exit](#exit-price-with-orderbook-enabled).
*Defaults to `True`.*
**Datatype:** Boolean +| `exit_pricing.use_order_book` | Enable exiting of open trades using [Order Book Exit](#exit-price-with-orderbook-enabled).
*Defaults to `true`.*
**Datatype:** Boolean | `exit_pricing.order_book_top` | Bot will use the top N rate in Order Book "price_side" to exit. I.e. a value of 2 will allow the bot to pick the 2nd ask rate in [Order Book Exit](#exit-price-with-orderbook-enabled)
*Defaults to `1`.*
**Datatype:** Positive Integer | `custom_price_max_distance_ratio` | Configure maximum distance ratio between current and custom entry or exit price.
*Defaults to `0.02` 2%).*
**Datatype:** Positive float | | **TODO** @@ -199,10 +199,10 @@ Mandatory parameters are marked as **Required**, which means that they are requi | `exchange.ccxt_sync_config` | Additional CCXT parameters passed to the regular (sync) ccxt instance. Parameters may differ from exchange to exchange and are documented in the [ccxt documentation](https://ccxt.readthedocs.io/en/latest/manual.html#instantiation)
**Datatype:** Dict | `exchange.ccxt_async_config` | Additional CCXT parameters passed to the async ccxt instance. Parameters may differ from exchange to exchange and are documented in the [ccxt documentation](https://ccxt.readthedocs.io/en/latest/manual.html#instantiation)
**Datatype:** Dict | `exchange.markets_refresh_interval` | The interval in minutes in which markets are reloaded.
*Defaults to `60` minutes.*
**Datatype:** Positive Integer -| `exchange.skip_pair_validation` | Skip pairlist validation on startup.
*Defaults to `false`
**Datatype:** Boolean -| `exchange.skip_open_order_update` | Skips open order updates on startup should the exchange cause problems. Only relevant in live conditions.
*Defaults to `false`
**Datatype:** Boolean +| `exchange.skip_pair_validation` | Skip pairlist validation on startup.
*Defaults to `false`*
**Datatype:** Boolean +| `exchange.skip_open_order_update` | Skips open order updates on startup should the exchange cause problems. Only relevant in live conditions.
*Defaults to `false`*
**Datatype:** Boolean | `exchange.unknown_fee_rate` | Fallback value to use when calculating trading fees. This can be useful for exchanges which have fees in non-tradable currencies. The value provided here will be multiplied with the "fee cost".
*Defaults to `None`
**Datatype:** float -| `exchange.log_responses` | Log relevant exchange responses. For debug mode only - use with care.
*Defaults to `false`
**Datatype:** Boolean +| `exchange.log_responses` | Log relevant exchange responses. For debug mode only - use with care.
*Defaults to `false`*
**Datatype:** Boolean | `experimental.block_bad_exchanges` | Block exchanges known to not work with freqtrade. Leave on default unless you want to test if that exchange works now.
*Defaults to `true`.*
**Datatype:** Boolean | | **Plugins** | `edge.*` | Please refer to [edge configuration document](edge.md) for detailed explanation of all possible configuration options. @@ -213,7 +213,7 @@ Mandatory parameters are marked as **Required**, which means that they are requi | `telegram.token` | Your Telegram bot token. Only required if `telegram.enabled` is `true`.
**Keep it in secret, do not disclose publicly.**
**Datatype:** String | `telegram.chat_id` | Your personal Telegram account id. Only required if `telegram.enabled` is `true`.
**Keep it in secret, do not disclose publicly.**
**Datatype:** String | `telegram.balance_dust_level` | Dust-level (in stake currency) - currencies with a balance below this will not be shown by `/balance`.
**Datatype:** float -| `telegram.reload` | Allow "reload" buttons on telegram messages.
*Defaults to `True`.
**Datatype:** boolean +| `telegram.reload` | Allow "reload" buttons on telegram messages.
*Defaults to `true`.
**Datatype:** boolean | `telegram.notification_settings.*` | Detailed notification settings. Refer to the [telegram documentation](telegram-usage.md) for details.
**Datatype:** dictionary | `telegram.allow_custom_messages` | Enable the sending of Telegram messages from strategies via the dataprovider.send_msg() function.
**Datatype:** Boolean | | **Webhook** diff --git a/docs/faq.md b/docs/faq.md index b52a77c6b..7b8cc2580 100644 --- a/docs/faq.md +++ b/docs/faq.md @@ -142,6 +142,13 @@ To fix this, redefine order types in the strategy to use "limit" instead of "mar The same fix should be applied in the configuration file, if order types are defined in your custom config rather than in the strategy. +### I'm trying to start the bot live, but get an API permission error + +Errors like `Invalid API-key, IP, or permissions for action` mean exactly what they actually say. +Your API key is either invalid (copy/paste error? check for leading/trailing spaces in the config), expired, or the IP you're running the bot from is not enabled in the Exchange's API console. +Usually, the permission "Spot Trading" (or the equivalent in the exchange you use) will be necessary. +Futures will usually have to be enabled specifically. + ### How do I search the bot logs for something? By default, the bot writes its log into stderr stream. This is implemented this way so that you can easily separate the bot's diagnostics messages from Backtesting, Edge and Hyperopt results, output from other various Freqtrade utility sub-commands, as well as from the output of your custom `print()`'s you may have inserted into your strategy. So if you need to search the log messages with the grep utility, you need to redirect stderr to stdout and disregard stdout. diff --git a/docs/freqai-configuration.md b/docs/freqai-configuration.md index 886dc2338..e7aca20be 100644 --- a/docs/freqai-configuration.md +++ b/docs/freqai-configuration.md @@ -52,7 +52,7 @@ The FreqAI strategy requires including the following lines of code in the standa return dataframe - def feature_engineering_expand_all(self, dataframe, period, **kwargs): + def feature_engineering_expand_all(self, dataframe: DataFrame, period, **kwargs) -> DataFrame: """ *Only functional with FreqAI enabled strategies* This function will automatically expand the defined features on the config defined @@ -77,7 +77,7 @@ The FreqAI strategy requires including the following lines of code in the standa return dataframe - def feature_engineering_expand_basic(self, dataframe, **kwargs): + def feature_engineering_expand_basic(self, dataframe: DataFrame, **kwargs) -> DataFrame: """ *Only functional with FreqAI enabled strategies* This function will automatically expand the defined features on the config defined @@ -101,7 +101,7 @@ The FreqAI strategy requires including the following lines of code in the standa dataframe["%-raw_price"] = dataframe["close"] return dataframe - def feature_engineering_standard(self, dataframe, **kwargs): + def feature_engineering_standard(self, dataframe: DataFrame, **kwargs) -> DataFrame: """ *Only functional with FreqAI enabled strategies* This optional function will be called once with the dataframe of the base timeframe. @@ -122,7 +122,7 @@ The FreqAI strategy requires including the following lines of code in the standa dataframe["%-hour_of_day"] = (dataframe["date"].dt.hour + 1) / 25 return dataframe - def set_freqai_targets(self, dataframe, **kwargs): + def set_freqai_targets(self, dataframe: DataFrame, **kwargs) -> DataFrame: """ *Only functional with FreqAI enabled strategies* Required function to set the targets for the model. @@ -139,6 +139,7 @@ The FreqAI strategy requires including the following lines of code in the standa / dataframe["close"] - 1 ) + return dataframe ``` Notice how the `feature_engineering_*()` is where [features](freqai-feature-engineering.md#feature-engineering) are added. Meanwhile `set_freqai_targets()` adds the labels/targets. A full example strategy is available in `templates/FreqaiExampleStrategy.py`. @@ -236,3 +237,161 @@ If you want to predict multiple targets you must specify all labels in the same df['&s-up_or_down'] = np.where( df["close"].shift(-100) > df["close"], 'up', 'down') df['&s-up_or_down'] = np.where( df["close"].shift(-100) == df["close"], 'same', df['&s-up_or_down']) ``` + +## PyTorch Module + +### Quick start + +The easiest way to quickly run a pytorch model is with the following command (for regression task): + +```bash +freqtrade trade --config config_examples/config_freqai.example.json --strategy FreqaiExampleStrategy --freqaimodel PyTorchMLPRegressor --strategy-path freqtrade/templates +``` + +!!! note "Installation/docker" + The PyTorch module requires large packages such as `torch`, which should be explicitly requested during `./setup.sh -i` by answering "y" to the question "Do you also want dependencies for freqai-rl or PyTorch (~700mb additional space required) [y/N]?". + Users who prefer docker should ensure they use the docker image appended with `_freqaitorch`. + +### Structure + +#### Model + +You can construct your own Neural Network architecture in PyTorch by simply defining your `nn.Module` class inside your custom [`IFreqaiModel` file](#using-different-prediction-models) and then using that class in your `def train()` function. Here is an example of logistic regression model implementation using PyTorch (should be used with nn.BCELoss criterion) for classification tasks. + +```python + +class LogisticRegression(nn.Module): + def __init__(self, input_size: int): + super().__init__() + # Define your layers + self.linear = nn.Linear(input_size, 1) + self.activation = nn.Sigmoid() + + def forward(self, x: torch.Tensor) -> torch.Tensor: + # Define the forward pass + out = self.linear(x) + out = self.activation(out) + return out + +class MyCoolPyTorchClassifier(BasePyTorchClassifier): + """ + This is a custom IFreqaiModel showing how a user might setup their own + custom Neural Network architecture for their training. + """ + + @property + def data_convertor(self) -> PyTorchDataConvertor: + return DefaultPyTorchDataConvertor(target_tensor_type=torch.float) + + def __init__(self, **kwargs) -> None: + super().__init__(**kwargs) + config = self.freqai_info.get("model_training_parameters", {}) + self.learning_rate: float = config.get("learning_rate", 3e-4) + self.model_kwargs: Dict[str, Any] = config.get("model_kwargs", {}) + self.trainer_kwargs: Dict[str, Any] = config.get("trainer_kwargs", {}) + + def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any: + """ + User sets up the training and test data to fit their desired model here + :param data_dictionary: the dictionary holding all data for train, test, + labels, weights + :param dk: The datakitchen object for the current coin/model + """ + + class_names = self.get_class_names() + self.convert_label_column_to_int(data_dictionary, dk, class_names) + n_features = data_dictionary["train_features"].shape[-1] + model = LogisticRegression( + input_dim=n_features + ) + model.to(self.device) + optimizer = torch.optim.AdamW(model.parameters(), lr=self.learning_rate) + criterion = torch.nn.CrossEntropyLoss() + init_model = self.get_init_model(dk.pair) + trainer = PyTorchModelTrainer( + model=model, + optimizer=optimizer, + criterion=criterion, + model_meta_data={"class_names": class_names}, + device=self.device, + init_model=init_model, + data_convertor=self.data_convertor, + **self.trainer_kwargs, + ) + trainer.fit(data_dictionary, self.splits) + return trainer + +``` + +#### Trainer + +The `PyTorchModelTrainer` performs the idiomatic PyTorch train loop: +Define our model, loss function, and optimizer, and then move them to the appropriate device (GPU or CPU). Inside the loop, we iterate through the batches in the dataloader, move the data to the device, compute the prediction and loss, backpropagate, and update the model parameters using the optimizer. + +In addition, the trainer is responsible for the following: + - saving and loading the model + - converting the data from `pandas.DataFrame` to `torch.Tensor`. + +#### Integration with Freqai module + +Like all freqai models, PyTorch models inherit `IFreqaiModel`. `IFreqaiModel` declares three abstract methods: `train`, `fit`, and `predict`. we implement these methods in three levels of hierarchy. +From top to bottom: + +1. `BasePyTorchModel` - Implements the `train` method. all `BasePyTorch*` inherit it. responsible for general data preparation (e.g., data normalization) and calling the `fit` method. Sets `device` attribute used by children classes. Sets `model_type` attribute used by the parent class. +2. `BasePyTorch*` - Implements the `predict` method. Here, the `*` represents a group of algorithms, such as classifiers or regressors. responsible for data preprocessing, predicting, and postprocessing if needed. +3. `PyTorch*Classifier` / `PyTorch*Regressor` - implements the `fit` method. responsible for the main train flaw, where we initialize the trainer and model objects. + +![image](assets/freqai_pytorch-diagram.png) + +#### Full example + +Building a PyTorch regressor using MLP (multilayer perceptron) model, MSELoss criterion, and AdamW optimizer. + +```python +class PyTorchMLPRegressor(BasePyTorchRegressor): + def __init__(self, **kwargs) -> None: + super().__init__(**kwargs) + config = self.freqai_info.get("model_training_parameters", {}) + self.learning_rate: float = config.get("learning_rate", 3e-4) + self.model_kwargs: Dict[str, Any] = config.get("model_kwargs", {}) + self.trainer_kwargs: Dict[str, Any] = config.get("trainer_kwargs", {}) + + def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any: + n_features = data_dictionary["train_features"].shape[-1] + model = PyTorchMLPModel( + input_dim=n_features, + output_dim=1, + **self.model_kwargs + ) + model.to(self.device) + optimizer = torch.optim.AdamW(model.parameters(), lr=self.learning_rate) + criterion = torch.nn.MSELoss() + init_model = self.get_init_model(dk.pair) + trainer = PyTorchModelTrainer( + model=model, + optimizer=optimizer, + criterion=criterion, + device=self.device, + init_model=init_model, + target_tensor_type=torch.float, + **self.trainer_kwargs, + ) + trainer.fit(data_dictionary) + return trainer +``` + +Here we create a `PyTorchMLPRegressor` class that implements the `fit` method. The `fit` method specifies the training building blocks: model, optimizer, criterion, and trainer. We inherit both `BasePyTorchRegressor` and `BasePyTorchModel`, where the former implements the `predict` method that is suitable for our regression task, and the latter implements the train method. + +??? Note "Setting Class Names for Classifiers" + When using classifiers, the user must declare the class names (or targets) by overriding the `IFreqaiModel.class_names` attribute. This is achieved by setting `self.freqai.class_names` in the FreqAI strategy inside the `set_freqai_targets` method. + + For example, if you are using a binary classifier to predict price movements as up or down, you can set the class names as follows: + ```python + def set_freqai_targets(self, dataframe: DataFrame, metadata: Dict, **kwargs) -> DataFrame: + self.freqai.class_names = ["down", "up"] + dataframe['&s-up_or_down'] = np.where(dataframe["close"].shift(-100) > + dataframe["close"], 'up', 'down') + + return dataframe + ``` + To see a full example, you can refer to the [classifier test strategy class](https://github.com/freqtrade/freqtrade/blob/develop/tests/strategy/strats/freqai_test_classifier.py). diff --git a/docs/freqai-feature-engineering.md b/docs/freqai-feature-engineering.md index 6389bd9e5..82b7569a5 100644 --- a/docs/freqai-feature-engineering.md +++ b/docs/freqai-feature-engineering.md @@ -6,8 +6,8 @@ Low level feature engineering is performed in the user strategy within a set of | Function | Description | |---------------|-------------| -| `feature_engineering__expand_all()` | This optional function will automatically expand the defined features on the config defined `indicator_periods_candles`, `include_timeframes`, `include_shifted_candles`, and `include_corr_pairs`. -| `feature_engineering__expand_basic()` | This optional function will automatically expand the defined features on the config defined `include_timeframes`, `include_shifted_candles`, and `include_corr_pairs`. Note: this function does *not* expand across `include_periods_candles`. +| `feature_engineering_expand_all()` | This optional function will automatically expand the defined features on the config defined `indicator_periods_candles`, `include_timeframes`, `include_shifted_candles`, and `include_corr_pairs`. +| `feature_engineering_expand_basic()` | This optional function will automatically expand the defined features on the config defined `include_timeframes`, `include_shifted_candles`, and `include_corr_pairs`. Note: this function does *not* expand across `include_periods_candles`. | `feature_engineering_standard()` | This optional function will be called once with the dataframe of the base timeframe. This is the final function to be called, which means that the dataframe entering this function will contain all the features and columns from the base asset created by the other `feature_engineering_expand` functions. This function is a good place to do custom exotic feature extractions (e.g. tsfresh). This function is also a good place for any feature that should not be auto-expanded upon (e.g., day of the week). | `set_freqai_targets()` | Required function to set the targets for the model. All targets must be prepended with `&` to be recognized by the FreqAI internals. @@ -16,7 +16,7 @@ Meanwhile, high level feature engineering is handled within `"feature_parameters It is advisable to start from the template `feature_engineering_*` functions in the source provided example strategy (found in `templates/FreqaiExampleStrategy.py`) to ensure that the feature definitions are following the correct conventions. Here is an example of how to set the indicators and labels in the strategy: ```python - def feature_engineering_expand_all(self, dataframe, period, metadata, **kwargs): + def feature_engineering_expand_all(self, dataframe: DataFrame, period, metadata, **kwargs) -> DataFrame: """ *Only functional with FreqAI enabled strategies* This function will automatically expand the defined features on the config defined @@ -67,7 +67,7 @@ It is advisable to start from the template `feature_engineering_*` functions in return dataframe - def feature_engineering_expand_basic(self, dataframe, metadata, **kwargs): + def feature_engineering_expand_basic(self, dataframe: DataFrame, metadata, **kwargs) -> DataFrame: """ *Only functional with FreqAI enabled strategies* This function will automatically expand the defined features on the config defined @@ -96,7 +96,7 @@ It is advisable to start from the template `feature_engineering_*` functions in dataframe["%-raw_price"] = dataframe["close"] return dataframe - def feature_engineering_standard(self, dataframe, metadata, **kwargs): + def feature_engineering_standard(self, dataframe: DataFrame, metadata, **kwargs) -> DataFrame: """ *Only functional with FreqAI enabled strategies* This optional function will be called once with the dataframe of the base timeframe. @@ -122,7 +122,7 @@ It is advisable to start from the template `feature_engineering_*` functions in dataframe["%-hour_of_day"] = (dataframe["date"].dt.hour + 1) / 25 return dataframe - def set_freqai_targets(self, dataframe, metadata, **kwargs): + def set_freqai_targets(self, dataframe: DataFrame, metadata, **kwargs) -> DataFrame: """ *Only functional with FreqAI enabled strategies* Required function to set the targets for the model. @@ -181,15 +181,14 @@ You can ask for each of the defined features to be included also for informative In total, the number of features the user of the presented example strat has created is: length of `include_timeframes` * no. features in `feature_engineering_expand_*()` * length of `include_corr_pairlist` * no. `include_shifted_candles` * length of `indicator_periods_candles` $= 3 * 3 * 3 * 2 * 2 = 108$. +### Gain finer control over `feature_engineering_*` functions with `metadata` - ### Gain finer control over `feature_engineering_*` functions with `metadata` +All `feature_engineering_*` and `set_freqai_targets()` functions are passed a `metadata` dictionary which contains information about the `pair`, `tf` (timeframe), and `period` that FreqAI is automating for feature building. As such, a user can use `metadata` inside `feature_engineering_*` functions as criteria for blocking/reserving features for certain timeframes, periods, pairs etc. - All `feature_engineering_*` and `set_freqai_targets()` functions are passed a `metadata` dictionary which contains information about the `pair`, `tf` (timeframe), and `period` that FreqAI is automating for feature building. As such, a user can use `metadata` inside `feature_engineering_*` functions as criteria for blocking/reserving features for certain timeframes, periods, pairs etc. - - ```py -def feature_engineering_expand_all(self, dataframe, period, metadata, **kwargs): - if metadata["tf"] == "1h": - dataframe["%-roc-period"] = ta.ROC(dataframe, timeperiod=period) +```python +def feature_engineering_expand_all(self, dataframe: DataFrame, period, metadata, **kwargs) -> DataFrame: + if metadata["tf"] == "1h": + dataframe["%-roc-period"] = ta.ROC(dataframe, timeperiod=period) ``` This will block `ta.ROC()` from being added to any timeframes other than `"1h"`. diff --git a/docs/freqai-parameter-table.md b/docs/freqai-parameter-table.md index f67ea8541..1487b92c2 100644 --- a/docs/freqai-parameter-table.md +++ b/docs/freqai-parameter-table.md @@ -46,7 +46,7 @@ Mandatory parameters are marked as **Required** and have to be set in one of the | `outlier_protection_percentage` | Enable to prevent outlier detection methods from discarding too much data. If more than `outlier_protection_percentage` % of points are detected as outliers by the SVM or DBSCAN, FreqAI will log a warning message and ignore outlier detection, i.e., the original dataset will be kept intact. If the outlier protection is triggered, no predictions will be made based on the training dataset.
**Datatype:** Float.
Default: `30`. | `reverse_train_test_order` | Split the feature dataset (see below) and use the latest data split for training and test on historical split of the data. This allows the model to be trained up to the most recent data point, while avoiding overfitting. However, you should be careful to understand the unorthodox nature of this parameter before employing it.
**Datatype:** Boolean.
Default: `False` (no reversal). | `shuffle_after_split` | Split the data into train and test sets, and then shuffle both sets individually.
**Datatype:** Boolean.
Default: `False`. -| `buffer_train_data_candles` | Cut `buffer_train_data_candles` off the beginning and end of the training data *after* the indicators were populated. The main example use is when predicting maxima and minima, the argrelextrema function cannot know the maxima/minima at the edges of the timerange. To improve model accuracy, it is best to compute argrelextrema on the full timerange and then use this function to cut off the edges (buffer) by the kernel. In another case, if the targets are set to a shifted price movement, this buffer is unnecessary because the shifted candles at the end of the timerange will be NaN and FreqAI will automatically cut those off of the training dataset.
**Datatype:** Boolean.
Default: `False`. +| `buffer_train_data_candles` | Cut `buffer_train_data_candles` off the beginning and end of the training data *after* the indicators were populated. The main example use is when predicting maxima and minima, the argrelextrema function cannot know the maxima/minima at the edges of the timerange. To improve model accuracy, it is best to compute argrelextrema on the full timerange and then use this function to cut off the edges (buffer) by the kernel. In another case, if the targets are set to a shifted price movement, this buffer is unnecessary because the shifted candles at the end of the timerange will be NaN and FreqAI will automatically cut those off of the training dataset.
**Datatype:** Integer.
Default: `0`. ### Data split parameters @@ -85,6 +85,28 @@ Mandatory parameters are marked as **Required** and have to be set in one of the | `net_arch` | Network architecture which is well described in [`stable_baselines3` doc](https://stable-baselines3.readthedocs.io/en/master/guide/custom_policy.html#examples). In summary: `[, dict(vf=[], pi=[])]`. By default this is set to `[128, 128]`, which defines 2 shared hidden layers with 128 units each. | `randomize_starting_position` | Randomize the starting point of each episode to avoid overfitting.
**Datatype:** bool.
Default: `False`. | `drop_ohlc_from_features` | Do not include the normalized ohlc data in the feature set passed to the agent during training (ohlc will still be used for driving the environment in all cases)
**Datatype:** Boolean.
**Default:** `False` +| `progress_bar` | Display a progress bar with the current progress, elapsed time and estimated remaining time.
**Datatype:** Boolean.
Default: `False`. + +### PyTorch parameters + +#### general + +| Parameter | Description | +|------------|-------------| +| | **Model training parameters within the `freqai.model_training_parameters` sub dictionary** +| `learning_rate` | Learning rate to be passed to the optimizer.
**Datatype:** float.
Default: `3e-4`. +| `model_kwargs` | Parameters to be passed to the model class.
**Datatype:** dict.
Default: `{}`. +| `trainer_kwargs` | Parameters to be passed to the trainer class.
**Datatype:** dict.
Default: `{}`. + +#### trainer_kwargs + +| Parameter | Description | +|------------|-------------| +| | **Model training parameters within the `freqai.model_training_parameters.model_kwargs` sub dictionary** +| `max_iters` | The number of training iterations to run. iteration here refers to the number of times we call self.optimizer.step(). used to calculate n_epochs.
**Datatype:** int.
Default: `100`. +| `batch_size` | The size of the batches to use during training..
**Datatype:** int.
Default: `64`. +| `max_n_eval_batches` | The maximum number batches to use for evaluation..
**Datatype:** int, optional.
Default: `None`. + ### Additional parameters diff --git a/docs/freqai-reinforcement-learning.md b/docs/freqai-reinforcement-learning.md index ed6a41825..962827348 100644 --- a/docs/freqai-reinforcement-learning.md +++ b/docs/freqai-reinforcement-learning.md @@ -37,7 +37,7 @@ freqtrade trade --freqaimodel ReinforcementLearner --strategy MyRLStrategy --con where `ReinforcementLearner` will use the templated `ReinforcementLearner` from `freqai/prediction_models/ReinforcementLearner` (or a custom user defined one located in `user_data/freqaimodels`). The strategy, on the other hand, follows the same base [feature engineering](freqai-feature-engineering.md) with `feature_engineering_*` as a typical Regressor. The difference lies in the creation of the targets, Reinforcement Learning doesn't require them. However, FreqAI requires a default (neutral) value to be set in the action column: ```python - def set_freqai_targets(self, dataframe, **kwargs): + def set_freqai_targets(self, dataframe, **kwargs) -> DataFrame: """ *Only functional with FreqAI enabled strategies* Required function to set the targets for the model. @@ -53,17 +53,19 @@ where `ReinforcementLearner` will use the templated `ReinforcementLearner` from # For RL, there are no direct targets to set. This is filler (neutral) # until the agent sends an action. dataframe["&-action"] = 0 + return dataframe ``` -Most of the function remains the same as for typical Regressors, however, the function above shows how the strategy must pass the raw price data to the agent so that it has access to raw OHLCV in the training environment: +Most of the function remains the same as for typical Regressors, however, the function below shows how the strategy must pass the raw price data to the agent so that it has access to raw OHLCV in the training environment: ```python - def feature_engineering_standard(self, dataframe, **kwargs): + def feature_engineering_standard(self, dataframe: DataFrame, **kwargs) -> DataFrame: # The following features are necessary for RL models dataframe[f"%-raw_close"] = dataframe["close"] dataframe[f"%-raw_open"] = dataframe["open"] dataframe[f"%-raw_high"] = dataframe["high"] dataframe[f"%-raw_low"] = dataframe["low"] + return dataframe ``` Finally, there is no explicit "label" to make - instead it is necessary to assign the `&-action` column which will contain the agent's actions when accessed in `populate_entry/exit_trends()`. In the present example, the neutral action to 0. This value should align with the environment used. FreqAI provides two environments, both use 0 as the neutral action. @@ -180,7 +182,7 @@ As you begin to modify the strategy and the prediction model, you will quickly r # you can use feature values from dataframe # Assumes the shifted RSI indicator has been generated in the strategy. - rsi_now = self.raw_features[f"%-rsi-period-10_shift-1_{pair}_" + rsi_now = self.raw_features[f"%-rsi-period_10_shift-1_{pair}_" f"{self.config['timeframe']}"].iloc[self._current_tick] # reward agent for entering trades diff --git a/docs/freqai-running.md b/docs/freqai-running.md index 1eaee1bf2..f3ccc546f 100644 --- a/docs/freqai-running.md +++ b/docs/freqai-running.md @@ -128,6 +128,9 @@ The FreqAI specific parameter `label_period_candles` defines the offset (number You can choose to adopt a continual learning scheme by setting `"continual_learning": true` in the config. By enabling `continual_learning`, after training an initial model from scratch, subsequent trainings will start from the final model state of the preceding training. This gives the new model a "memory" of the previous state. By default, this is set to `False` which means that all new models are trained from scratch, without input from previous models. +???+ danger "Continual learning enforces a constant parameter space" + Since `continual_learning` means that the model parameter space *cannot* change between trainings, `principal_component_analysis` is automatically disabled when `continual_learning` is enabled. Hint: PCA changes the parameter space and the number of features, learn more about PCA [here](freqai-feature-engineering.md#data-dimensionality-reduction-with-principal-component-analysis). + ## Hyperopt You can hyperopt using the same command as for [typical Freqtrade hyperopt](hyperopt.md): diff --git a/docs/includes/protections.md b/docs/includes/protections.md index e0ad8189f..12af081c0 100644 --- a/docs/includes/protections.md +++ b/docs/includes/protections.md @@ -149,7 +149,7 @@ The below example assumes a timeframe of 1 hour: * Locks each pair after selling for an additional 5 candles (`CooldownPeriod`), giving other pairs a chance to get filled. * Stops trading for 4 hours (`4 * 1h candles`) if the last 2 days (`48 * 1h candles`) had 20 trades, which caused a max-drawdown of more than 20%. (`MaxDrawdown`). * Stops trading if more than 4 stoploss occur for all pairs within a 1 day (`24 * 1h candles`) limit (`StoplossGuard`). -* Locks all pairs that had 4 Trades within the last 6 hours (`6 * 1h candles`) with a combined profit ratio of below 0.02 (<2%) (`LowProfitPairs`). +* Locks all pairs that had 2 Trades within the last 6 hours (`6 * 1h candles`) with a combined profit ratio of below 0.02 (<2%) (`LowProfitPairs`). * Locks all pairs for 2 candles that had a profit of below 0.01 (<1%) within the last 24h (`24 * 1h candles`), a minimum of 4 trades. ``` python diff --git a/docs/installation.md b/docs/installation.md index 6e8488b9f..11de20e83 100644 --- a/docs/installation.md +++ b/docs/installation.md @@ -52,7 +52,7 @@ These requirements apply to both [Script Installation](#script-installation) and * [pip](https://pip.pypa.io/en/stable/installing/) * [git](https://git-scm.com/book/en/v2/Getting-Started-Installing-Git) * [virtualenv](https://virtualenv.pypa.io/en/stable/installation.html) (Recommended) -* [TA-Lib](https://mrjbq7.github.io/ta-lib/install.html) (install instructions [below](#install-ta-lib)) +* [TA-Lib](https://ta-lib.github.io/ta-lib-python/) (install instructions [below](#install-ta-lib)) ### Install code @@ -210,7 +210,7 @@ sudo ./build_helpers/install_ta-lib.sh ##### TA-Lib manual installation -Official webpage: https://mrjbq7.github.io/ta-lib/install.html +[Official installation guide](https://ta-lib.github.io/ta-lib-python/install.html) ```bash wget http://prdownloads.sourceforge.net/ta-lib/ta-lib-0.4.0-src.tar.gz diff --git a/docs/producer-consumer.md b/docs/producer-consumer.md index 88e34d0d6..0bd52ac93 100644 --- a/docs/producer-consumer.md +++ b/docs/producer-consumer.md @@ -42,14 +42,14 @@ Enable subscribing to an instance by adding the `external_message_consumer` sect | `producers` | **Required.** List of producers
**Datatype:** Array. | `producers.name` | **Required.** Name of this producer. This name must be used in calls to `get_producer_pairs()` and `get_producer_df()` if more than one producer is used.
**Datatype:** string | `producers.host` | **Required.** The hostname or IP address from your producer.
**Datatype:** string -| `producers.port` | **Required.** The port matching the above host.
**Datatype:** string +| `producers.port` | **Required.** The port matching the above host.
*Defaults to `8080`.*
**Datatype:** Integer | `producers.secure` | **Optional.** Use ssl in websockets connection. Default False.
**Datatype:** string | `producers.ws_token` | **Required.** `ws_token` as configured on the producer.
**Datatype:** string | | **Optional settings** | `wait_timeout` | Timeout until we ping again if no message is received.
*Defaults to `300`.*
**Datatype:** Integer - in seconds. -| `wait_timeout` | Ping timeout
*Defaults to `10`.*
**Datatype:** Integer - in seconds. +| `ping_timeout` | Ping timeout
*Defaults to `10`.*
**Datatype:** Integer - in seconds. | `sleep_time` | Sleep time before retrying to connect.
*Defaults to `10`.*
**Datatype:** Integer - in seconds. -| `remove_entry_exit_signals` | Remove signal columns from the dataframe (set them to 0) on dataframe receipt.
*Defaults to `10`.*
**Datatype:** Integer - in seconds. +| `remove_entry_exit_signals` | Remove signal columns from the dataframe (set them to 0) on dataframe receipt.
*Defaults to `false`.*
**Datatype:** Boolean. | `message_size_limit` | Size limit per message
*Defaults to `8`.*
**Datatype:** Integer - Megabytes. Instead of (or as well as) calculating indicators in `populate_indicators()` the follower instance listens on the connection to a producer instance's messages (or multiple producer instances in advanced configurations) and requests the producer's most recently analyzed dataframes for each pair in the active whitelist. diff --git a/docs/requirements-docs.txt b/docs/requirements-docs.txt index d384a7ec5..91b0e993b 100644 --- a/docs/requirements-docs.txt +++ b/docs/requirements-docs.txt @@ -1,6 +1,6 @@ markdown==3.3.7 mkdocs==1.4.2 -mkdocs-material==9.1.2 +mkdocs-material==9.1.7 mdx_truly_sane_lists==1.3 -pymdown-extensions==9.10 +pymdown-extensions==9.11 jinja2==3.1.2 diff --git a/docs/rest-api.md b/docs/rest-api.md index 5f604ef43..860a44499 100644 --- a/docs/rest-api.md +++ b/docs/rest-api.md @@ -9,9 +9,6 @@ This same command can also be used to update freqUI, should there be a new relea Once the bot is started in trade / dry-run mode (with `freqtrade trade`) - the UI will be available under the configured port below (usually `http://127.0.0.1:8080`). -!!! info "Alpha release" - FreqUI is still considered an alpha release - if you encounter bugs or inconsistencies please open a [FreqUI issue](https://github.com/freqtrade/frequi/issues/new/choose). - !!! Note "developers" Developers should not use this method, but instead use the method described in the [freqUI repository](https://github.com/freqtrade/frequi) to get the source-code of freqUI. diff --git a/docs/stoploss.md b/docs/stoploss.md index 7af717955..8fc73be21 100644 --- a/docs/stoploss.md +++ b/docs/stoploss.md @@ -23,10 +23,22 @@ These modes can be configured with these values: 'stoploss_on_exchange_limit_ratio': 0.99 ``` -!!! Note - Stoploss on exchange is only supported for Binance (stop-loss-limit), Huobi (stop-limit), Kraken (stop-loss-market, stop-loss-limit), Gate (stop-limit), and Kucoin (stop-limit and stop-market) as of now. - Do not set too low/tight stoploss value if using stop loss on exchange! - If set to low/tight then you have greater risk of missing fill on the order and stoploss will not work. +Stoploss on exchange is only supported for the following exchanges, and not all exchanges support both stop-limit and stop-market. +The Order-type will be ignored if only one mode is available. + +| Exchange | stop-loss type | +|----------|-------------| +| Binance | limit | +| Binance Futures | market, limit | +| Huobi | limit | +| kraken | market, limit | +| Gate | limit | +| Okx | limit | +| Kucoin | stop-limit, stop-market| + +!!! Note "Tight stoploss" + Do not set too low/tight stoploss value when using stop loss on exchange! + If set to low/tight you will have greater risk of missing fill on the order and stoploss will not work. ### stoploss_on_exchange and stoploss_on_exchange_limit_ratio @@ -197,11 +209,6 @@ You can also keep a static stoploss until the offset is reached, and then trail If `trailing_only_offset_is_reached = True` then the trailing stoploss is only activated once the offset is reached. Until then, the stoploss remains at the configured `stoploss`. This option can be used with or without `trailing_stop_positive`, but uses `trailing_stop_positive_offset` as offset. -``` python - trailing_stop_positive_offset = 0.011 - trailing_only_offset_is_reached = True -``` - Configuration (offset is buy-price + 3%): ``` python diff --git a/docs/strategy-advanced.md b/docs/strategy-advanced.md index cbb71e810..a93dcecdf 100644 --- a/docs/strategy-advanced.md +++ b/docs/strategy-advanced.md @@ -1,21 +1,21 @@ # Advanced Strategies This page explains some advanced concepts available for strategies. -If you're just getting started, please be familiar with the methods described in the [Strategy Customization](strategy-customization.md) documentation and with the [Freqtrade basics](bot-basics.md) first. +If you're just getting started, please familiarize yourself with the [Freqtrade basics](bot-basics.md) and methods described in [Strategy Customization](strategy-customization.md) first. -[Freqtrade basics](bot-basics.md) describes in which sequence each method described below is called, which can be helpful to understand which method to use for your custom needs. +The call sequence of the methods described here is covered under [bot execution logic](bot-basics.md#bot-execution-logic). Those docs are also helpful in deciding which method is most suitable for your customisation needs. !!! Note - All callback methods described below should only be implemented in a strategy if they are actually used. + Callback methods should *only* be implemented if a strategy uses them. !!! Tip - You can get a strategy template containing all below methods by running `freqtrade new-strategy --strategy MyAwesomeStrategy --template advanced` + Start off with a strategy template containing all available callback methods by running `freqtrade new-strategy --strategy MyAwesomeStrategy --template advanced` ## Storing information Storing information can be accomplished by creating a new dictionary within the strategy class. -The name of the variable can be chosen at will, but should be prefixed with `cust_` to avoid naming collisions with predefined strategy variables. +The name of the variable can be chosen at will, but should be prefixed with `custom_` to avoid naming collisions with predefined strategy variables. ```python class AwesomeStrategy(IStrategy): diff --git a/docs/strategy-callbacks.md b/docs/strategy-callbacks.md index f1cdc9f3b..855f2353b 100644 --- a/docs/strategy-callbacks.md +++ b/docs/strategy-callbacks.md @@ -43,7 +43,7 @@ class AwesomeStrategy(IStrategy): if self.config['runmode'].value in ('live', 'dry_run'): # Assign this to the class by using self.* # can then be used by populate_* methods - self.cust_remote_data = requests.get('https://some_remote_source.example.com') + self.custom_remote_data = requests.get('https://some_remote_source.example.com') ``` @@ -51,7 +51,8 @@ During hyperopt, this runs only once at startup. ## Bot loop start -A simple callback which is called once at the start of every bot throttling iteration (roughly every 5 seconds, unless configured differently). +A simple callback which is called once at the start of every bot throttling iteration in dry/live mode (roughly every 5 +seconds, unless configured differently) or once per candle in backtest/hyperopt mode. This can be used to perform calculations which are pair independent (apply to all pairs), loading of external data, etc. ``` python @@ -61,11 +62,12 @@ class AwesomeStrategy(IStrategy): # ... populate_* methods - def bot_loop_start(self, **kwargs) -> None: + def bot_loop_start(self, current_time: datetime, **kwargs) -> None: """ Called at the start of the bot iteration (one loop). Might be used to perform pair-independent tasks (e.g. gather some remote resource for comparison) + :param current_time: datetime object, containing the current datetime :param **kwargs: Ensure to keep this here so updates to this won't break your strategy. """ if self.config['runmode'].value in ('live', 'dry_run'): @@ -350,7 +352,7 @@ class AwesomeStrategy(IStrategy): # Convert absolute price to percentage relative to current_rate if stoploss_price < current_rate: - return (stoploss_price / current_rate) - 1 + return stoploss_from_absolute(stoploss_price, current_rate, is_short=trade.is_short) # return maximum stoploss value, keeping current stoploss price unchanged return 1 diff --git a/docs/strategy_migration.md b/docs/strategy_migration.md index 22e3d2c22..5ef7a5a4c 100644 --- a/docs/strategy_migration.md +++ b/docs/strategy_migration.md @@ -578,7 +578,7 @@ def populate_any_indicators( Features will now expand automatically. As such, the expansion loops, as well as the `{pair}` / `{timeframe}` parts will need to be removed. ``` python linenums="1" - def feature_engineering_expand_all(self, dataframe, period, **kwargs): + def feature_engineering_expand_all(self, dataframe, period, **kwargs) -> DataFrame:: """ *Only functional with FreqAI enabled strategies* This function will automatically expand the defined features on the config defined @@ -638,7 +638,7 @@ Features will now expand automatically. As such, the expansion loops, as well as Basic features. Make sure to remove the `{pair}` part from your features. ``` python linenums="1" - def feature_engineering_expand_basic(self, dataframe, **kwargs): + def feature_engineering_expand_basic(self, dataframe: DataFrame, **kwargs) -> DataFrame:: """ *Only functional with FreqAI enabled strategies* This function will automatically expand the defined features on the config defined @@ -673,7 +673,7 @@ Basic features. Make sure to remove the `{pair}` part from your features. ### FreqAI - feature engineering standard ``` python linenums="1" - def feature_engineering_standard(self, dataframe, **kwargs): + def feature_engineering_standard(self, dataframe: DataFrame, **kwargs) -> DataFrame: """ *Only functional with FreqAI enabled strategies* This optional function will be called once with the dataframe of the base timeframe. @@ -704,7 +704,7 @@ Basic features. Make sure to remove the `{pair}` part from your features. Targets now get their own, dedicated method. ``` python linenums="1" - def set_freqai_targets(self, dataframe, **kwargs): + def set_freqai_targets(self, dataframe: DataFrame, **kwargs) -> DataFrame: """ *Only functional with FreqAI enabled strategies* Required function to set the targets for the model. diff --git a/docs/telegram-usage.md b/docs/telegram-usage.md index dc0ab0976..e6017e271 100644 --- a/docs/telegram-usage.md +++ b/docs/telegram-usage.md @@ -191,7 +191,8 @@ official commands. You can ask at any moment for help with `/help`. | **Metrics** | | `/profit []` | Display a summary of your profit/loss from close trades and some stats about your performance, over the last n days (all trades by default) | `/performance` | Show performance of each finished trade grouped by pair -| `/balance` | Show account balance per currency +| `/balance` | Show bot managed balance per currency +| `/balance full` | Show account balance per currency | `/daily ` | Shows profit or loss per day, over the last n days (n defaults to 7) | `/weekly ` | Shows profit or loss per week, over the last n weeks (n defaults to 8) | `/monthly ` | Shows profit or loss per month, over the last n months (n defaults to 6) @@ -202,7 +203,6 @@ official commands. You can ask at any moment for help with `/help`. | `/blacklist [pair]` | Show the current blacklist, or adds a pair to the blacklist. | `/edge` | Show validated pairs by Edge if it is enabled. - ## Telegram commands in action Below, example of Telegram message you will receive for each command. @@ -279,6 +279,7 @@ Return a summary of your profit/loss and performance. > ∙ `33.095 EUR` > > **Total Trade Count:** `138` +> **Bot started:** `2022-07-11 18:40:44` > **First Trade opened:** `3 days ago` > **Latest Trade opened:** `2 minutes ago` > **Avg. Duration:** `2:33:45` @@ -292,6 +293,7 @@ The relative profit of `15.2 Σ%` is be based on the starting capital - so in th Starting capital is either taken from the `available_capital` setting, or calculated by using current wallet size - profits. Profit Factor is calculated as gross profits / gross losses - and should serve as an overall metric for the strategy. Max drawdown corresponds to the backtesting metric `Absolute Drawdown (Account)` - calculated as `(Absolute Drawdown) / (DrawdownHigh + startingBalance)`. +Bot started date will refer to the date the bot was first started. For older bots, this will default to the first trade's open date. ### /forceexit diff --git a/docs/windows_installation.md b/docs/windows_installation.md index 43d6728ee..0327f21e5 100644 --- a/docs/windows_installation.md +++ b/docs/windows_installation.md @@ -24,9 +24,9 @@ git clone https://github.com/freqtrade/freqtrade.git Install ta-lib according to the [ta-lib documentation](https://github.com/mrjbq7/ta-lib#windows). -As compiling from source on windows has heavy dependencies (requires a partial visual studio installation), there is also a repository of unofficial pre-compiled windows Wheels [here](https://www.lfd.uci.edu/~gohlke/pythonlibs/#ta-lib), which need to be downloaded and installed using `pip install TA_Lib-0.4.25-cp38-cp38-win_amd64.whl` (make sure to use the version matching your python version). +As compiling from source on windows has heavy dependencies (requires a partial visual studio installation), Freqtrade provides these dependencies (in the binary wheel format) for the latest 3 Python versions (3.8, 3.9, 3.10 and 3.11) and for 64bit Windows. +These Wheels are also used by CI running on windows, and are therefore tested together with freqtrade. -Freqtrade provides these dependencies for the latest 3 Python versions (3.8, 3.9, 3.10 and 3.11) and for 64bit Windows. Other versions must be downloaded from the above link. ``` powershell @@ -45,8 +45,6 @@ freqtrade The above installation script assumes you're using powershell on a 64bit windows. Commands for the legacy CMD windows console may differ. -> Thanks [Owdr](https://github.com/Owdr) for the commands. Source: [Issue #222](https://github.com/freqtrade/freqtrade/issues/222) - ### Error during installation on Windows ``` bash diff --git a/freqtrade/__init__.py b/freqtrade/__init__.py index 6ba045adf..f8818c35c 100644 --- a/freqtrade/__init__.py +++ b/freqtrade/__init__.py @@ -1,5 +1,5 @@ """ Freqtrade bot """ -__version__ = '2023.3.dev' +__version__ = '2023.5.dev' if 'dev' in __version__: from pathlib import Path diff --git a/freqtrade/commands/analyze_commands.py b/freqtrade/commands/analyze_commands.py index 20afa7ffd..e928ccad7 100644 --- a/freqtrade/commands/analyze_commands.py +++ b/freqtrade/commands/analyze_commands.py @@ -40,8 +40,8 @@ def setup_analyze_configuration(args: Dict[str, Any], method: RunMode) -> Dict[s if (not Path(signals_file).exists()): raise OperationalException( - (f"Cannot find latest backtest signals file: {signals_file}." - "Run backtesting with `--export signals`.") + f"Cannot find latest backtest signals file: {signals_file}." + "Run backtesting with `--export signals`." ) return config diff --git a/freqtrade/commands/arguments.py b/freqtrade/commands/arguments.py index 794b96de2..8287879c4 100644 --- a/freqtrade/commands/arguments.py +++ b/freqtrade/commands/arguments.py @@ -46,7 +46,7 @@ ARGS_LIST_FREQAIMODELS = ["freqaimodel_path", "print_one_column", "print_coloriz ARGS_LIST_HYPEROPTS = ["hyperopt_path", "print_one_column", "print_colorized"] -ARGS_BACKTEST_SHOW = ["exportfilename", "backtest_show_pair_list"] +ARGS_BACKTEST_SHOW = ["exportfilename", "backtest_show_pair_list", "backtest_breakdown"] ARGS_LIST_EXCHANGES = ["print_one_column", "list_exchanges_all"] diff --git a/freqtrade/commands/data_commands.py b/freqtrade/commands/data_commands.py index 1e74e1036..bcef1c252 100644 --- a/freqtrade/commands/data_commands.py +++ b/freqtrade/commands/data_commands.py @@ -204,11 +204,14 @@ def start_list_data(args: Dict[str, Any]) -> None: pair, timeframe, candle_type, *dhc.ohlcv_data_min_max(pair, timeframe, candle_type) ) for pair, timeframe, candle_type in paircombs] + print(tabulate([ (pair, timeframe, candle_type, start.strftime(DATETIME_PRINT_FORMAT), end.strftime(DATETIME_PRINT_FORMAT)) - for pair, timeframe, candle_type, start, end in paircombs1 + for pair, timeframe, candle_type, start, end in sorted( + paircombs1, + key=lambda x: (x[0], timeframe_to_minutes(x[1]), x[2])) ], headers=("Pair", "Timeframe", "Type", 'From', 'To'), tablefmt='psql', stralign='right')) diff --git a/freqtrade/configuration/config_validation.py b/freqtrade/configuration/config_validation.py index 606f081ef..0ee48cf91 100644 --- a/freqtrade/configuration/config_validation.py +++ b/freqtrade/configuration/config_validation.py @@ -27,10 +27,7 @@ def _extend_validator(validator_class): if 'default' in subschema: instance.setdefault(prop, subschema['default']) - for error in validate_properties( - validator, properties, instance, schema, - ): - yield error + yield from validate_properties(validator, properties, instance, schema) return validators.extend( validator_class, {'properties': set_defaults} diff --git a/freqtrade/configuration/timerange.py b/freqtrade/configuration/timerange.py index adc5e65df..0c2f0d1b8 100644 --- a/freqtrade/configuration/timerange.py +++ b/freqtrade/configuration/timerange.py @@ -116,7 +116,7 @@ class TimeRange: :param text: value from --timerange :return: Start and End range period """ - if text is None: + if not text: return TimeRange(None, None, 0, 0) syntax = [(r'^-(\d{8})$', (None, 'date')), (r'^(\d{8})-$', ('date', None)), diff --git a/freqtrade/constants.py b/freqtrade/constants.py index 46e9b5cd4..b8e240419 100644 --- a/freqtrade/constants.py +++ b/freqtrade/constants.py @@ -36,9 +36,10 @@ AVAILABLE_PAIRLISTS = ['StaticPairList', 'VolumePairList', 'ProducerPairList', ' 'AgeFilter', 'OffsetFilter', 'PerformanceFilter', 'PrecisionFilter', 'PriceFilter', 'RangeStabilityFilter', 'ShuffleFilter', 'SpreadFilter', 'VolatilityFilter'] -AVAILABLE_PROTECTIONS = ['CooldownPeriod', 'LowProfitPairs', 'MaxDrawdown', 'StoplossGuard'] -AVAILABLE_DATAHANDLERS_TRADES = ['json', 'jsongz', 'hdf5'] -AVAILABLE_DATAHANDLERS = AVAILABLE_DATAHANDLERS_TRADES + ['feather', 'parquet'] +AVAILABLE_PROTECTIONS = ['CooldownPeriod', + 'LowProfitPairs', 'MaxDrawdown', 'StoplossGuard'] +AVAILABLE_DATAHANDLERS_TRADES = ['json', 'jsongz', 'hdf5', 'feather'] +AVAILABLE_DATAHANDLERS = AVAILABLE_DATAHANDLERS_TRADES + ['parquet'] BACKTEST_BREAKDOWNS = ['day', 'week', 'month'] BACKTEST_CACHE_AGE = ['none', 'day', 'week', 'month'] BACKTEST_CACHE_DEFAULT = 'day' @@ -63,6 +64,7 @@ USERPATH_FREQAIMODELS = 'freqaimodels' TELEGRAM_SETTING_OPTIONS = ['on', 'off', 'silent'] WEBHOOK_FORMAT_OPTIONS = ['form', 'json', 'raw'] FULL_DATAFRAME_THRESHOLD = 100 +CUSTOM_TAG_MAX_LENGTH = 255 ENV_VAR_PREFIX = 'FREQTRADE__' @@ -597,7 +599,8 @@ CONF_SCHEMA = { "model_type": {"type": "string", "default": "PPO"}, "policy_type": {"type": "string", "default": "MlpPolicy"}, "net_arch": {"type": "array", "default": [128, 128]}, - "randomize_startinng_position": {"type": "boolean", "default": False}, + "randomize_starting_position": {"type": "boolean", "default": False}, + "progress_bar": {"type": "boolean", "default": True}, "model_reward_parameters": { "type": "object", "properties": { diff --git a/freqtrade/data/btanalysis.py b/freqtrade/data/btanalysis.py index 3567f4112..c5905acde 100644 --- a/freqtrade/data/btanalysis.py +++ b/freqtrade/data/btanalysis.py @@ -246,14 +246,8 @@ def _load_backtest_data_df_compatibility(df: pd.DataFrame) -> pd.DataFrame: """ Compatibility support for older backtest data. """ - df['open_date'] = pd.to_datetime(df['open_date'], - utc=True, - infer_datetime_format=True - ) - df['close_date'] = pd.to_datetime(df['close_date'], - utc=True, - infer_datetime_format=True - ) + df['open_date'] = pd.to_datetime(df['open_date'], utc=True) + df['close_date'] = pd.to_datetime(df['close_date'], utc=True) # Compatibility support for pre short Columns if 'is_short' not in df.columns: df['is_short'] = False diff --git a/freqtrade/data/converter.py b/freqtrade/data/converter.py index 7ce98de42..2d3855d87 100644 --- a/freqtrade/data/converter.py +++ b/freqtrade/data/converter.py @@ -34,7 +34,7 @@ def ohlcv_to_dataframe(ohlcv: list, timeframe: str, pair: str, *, cols = DEFAULT_DATAFRAME_COLUMNS df = DataFrame(ohlcv, columns=cols) - df['date'] = to_datetime(df['date'], unit='ms', utc=True, infer_datetime_format=True) + df['date'] = to_datetime(df['date'], unit='ms', utc=True) # Some exchanges return int values for Volume and even for OHLC. # Convert them since TA-LIB indicators used in the strategy assume floats diff --git a/freqtrade/data/dataprovider.py b/freqtrade/data/dataprovider.py index 3991432a4..d05ee5db7 100644 --- a/freqtrade/data/dataprovider.py +++ b/freqtrade/data/dataprovider.py @@ -21,6 +21,7 @@ from freqtrade.exchange import Exchange, timeframe_to_seconds from freqtrade.exchange.types import OrderBook from freqtrade.misc import append_candles_to_dataframe from freqtrade.rpc import RPCManager +from freqtrade.rpc.rpc_types import RPCAnalyzedDFMsg from freqtrade.util import PeriodicCache @@ -118,8 +119,7 @@ class DataProvider: :param new_candle: This is a new candle """ if self.__rpc: - self.__rpc.send_msg( - { + msg: RPCAnalyzedDFMsg = { 'type': RPCMessageType.ANALYZED_DF, 'data': { 'key': pair_key, @@ -127,7 +127,7 @@ class DataProvider: 'la': datetime.now(timezone.utc) } } - ) + self.__rpc.send_msg(msg) if new_candle: self.__rpc.send_msg({ 'type': RPCMessageType.NEW_CANDLE, diff --git a/freqtrade/data/history/featherdatahandler.py b/freqtrade/data/history/featherdatahandler.py index 22a6805e7..28a12fb29 100644 --- a/freqtrade/data/history/featherdatahandler.py +++ b/freqtrade/data/history/featherdatahandler.py @@ -4,7 +4,7 @@ from typing import Optional from pandas import DataFrame, read_feather, to_datetime from freqtrade.configuration import TimeRange -from freqtrade.constants import DEFAULT_DATAFRAME_COLUMNS, TradeList +from freqtrade.constants import DEFAULT_DATAFRAME_COLUMNS, DEFAULT_TRADES_COLUMNS, TradeList from freqtrade.enums import CandleType from .idatahandler import IDataHandler @@ -63,10 +63,7 @@ class FeatherDataHandler(IDataHandler): pairdata.columns = self._columns pairdata = pairdata.astype(dtype={'open': 'float', 'high': 'float', 'low': 'float', 'close': 'float', 'volume': 'float'}) - pairdata['date'] = to_datetime(pairdata['date'], - unit='ms', - utc=True, - infer_datetime_format=True) + pairdata['date'] = to_datetime(pairdata['date'], unit='ms', utc=True) return pairdata def ohlcv_append( @@ -92,12 +89,11 @@ class FeatherDataHandler(IDataHandler): :param data: List of Lists containing trade data, column sequence as in DEFAULT_TRADES_COLUMNS """ - # filename = self._pair_trades_filename(self._datadir, pair) + filename = self._pair_trades_filename(self._datadir, pair) + self.create_dir_if_needed(filename) - raise NotImplementedError() - # array = pa.array(data) - # array - # feather.write_feather(data, filename) + tradesdata = DataFrame(data, columns=DEFAULT_TRADES_COLUMNS) + tradesdata.to_feather(filename, compression_level=9, compression='lz4') def trades_append(self, pair: str, data: TradeList): """ @@ -116,14 +112,13 @@ class FeatherDataHandler(IDataHandler): :param timerange: Timerange to load trades for - currently not implemented :return: List of trades """ - raise NotImplementedError() - # filename = self._pair_trades_filename(self._datadir, pair) - # tradesdata = misc.file_load_json(filename) + filename = self._pair_trades_filename(self._datadir, pair) + if not filename.exists(): + return [] - # if not tradesdata: - # return [] + tradesdata = read_feather(filename) - # return tradesdata + return tradesdata.values.tolist() @classmethod def _get_file_extension(cls): diff --git a/freqtrade/data/history/jsondatahandler.py b/freqtrade/data/history/jsondatahandler.py index f016c0ec1..ed7a33f8e 100644 --- a/freqtrade/data/history/jsondatahandler.py +++ b/freqtrade/data/history/jsondatahandler.py @@ -75,10 +75,7 @@ class JsonDataHandler(IDataHandler): return DataFrame(columns=self._columns) pairdata = pairdata.astype(dtype={'open': 'float', 'high': 'float', 'low': 'float', 'close': 'float', 'volume': 'float'}) - pairdata['date'] = to_datetime(pairdata['date'], - unit='ms', - utc=True, - infer_datetime_format=True) + pairdata['date'] = to_datetime(pairdata['date'], unit='ms', utc=True) return pairdata def ohlcv_append( diff --git a/freqtrade/data/history/parquetdatahandler.py b/freqtrade/data/history/parquetdatahandler.py index 57581861d..e6b2481d2 100644 --- a/freqtrade/data/history/parquetdatahandler.py +++ b/freqtrade/data/history/parquetdatahandler.py @@ -62,10 +62,7 @@ class ParquetDataHandler(IDataHandler): pairdata.columns = self._columns pairdata = pairdata.astype(dtype={'open': 'float', 'high': 'float', 'low': 'float', 'close': 'float', 'volume': 'float'}) - pairdata['date'] = to_datetime(pairdata['date'], - unit='ms', - utc=True, - infer_datetime_format=True) + pairdata['date'] = to_datetime(pairdata['date'], unit='ms', utc=True) return pairdata def ohlcv_append( diff --git a/freqtrade/exchange/__init__.py b/freqtrade/exchange/__init__.py index b815fb3ee..8092d5af8 100644 --- a/freqtrade/exchange/__init__.py +++ b/freqtrade/exchange/__init__.py @@ -6,17 +6,18 @@ from freqtrade.exchange.exchange import Exchange from freqtrade.exchange.binance import Binance from freqtrade.exchange.bitpanda import Bitpanda from freqtrade.exchange.bittrex import Bittrex +from freqtrade.exchange.bitvavo import Bitvavo from freqtrade.exchange.bybit import Bybit from freqtrade.exchange.coinbasepro import Coinbasepro -from freqtrade.exchange.exchange_utils import (amount_to_contract_precision, amount_to_contracts, - amount_to_precision, available_exchanges, - ccxt_exchanges, contracts_to_amount, - date_minus_candles, is_exchange_known_ccxt, - market_is_active, price_to_precision, - timeframe_to_minutes, timeframe_to_msecs, - timeframe_to_next_date, timeframe_to_prev_date, - timeframe_to_seconds, validate_exchange, - validate_exchanges) +from freqtrade.exchange.exchange_utils import (ROUND_DOWN, ROUND_UP, amount_to_contract_precision, + amount_to_contracts, amount_to_precision, + available_exchanges, ccxt_exchanges, + contracts_to_amount, date_minus_candles, + is_exchange_known_ccxt, market_is_active, + price_to_precision, timeframe_to_minutes, + timeframe_to_msecs, timeframe_to_next_date, + timeframe_to_prev_date, timeframe_to_seconds, + validate_exchange, validate_exchanges) from freqtrade.exchange.gate import Gate from freqtrade.exchange.hitbtc import Hitbtc from freqtrade.exchange.huobi import Huobi diff --git a/freqtrade/exchange/binance.py b/freqtrade/exchange/binance.py index 9580bc690..7ac496f62 100644 --- a/freqtrade/exchange/binance.py +++ b/freqtrade/exchange/binance.py @@ -23,7 +23,7 @@ class Binance(Exchange): _ft_has: Dict = { "stoploss_on_exchange": True, "stoploss_order_types": {"limit": "stop_loss_limit"}, - "order_time_in_force": ['GTC', 'FOK', 'IOC'], + "order_time_in_force": ["GTC", "FOK", "IOC", "PO"], "ohlcv_candle_limit": 1000, "trades_pagination": "id", "trades_pagination_arg": "fromId", @@ -31,6 +31,7 @@ class Binance(Exchange): } _ft_has_futures: Dict = { "stoploss_order_types": {"limit": "stop", "market": "stop_market"}, + "order_time_in_force": ["GTC", "FOK", "IOC"], "tickers_have_price": False, "floor_leverage": True, "stop_price_type_field": "workingType", diff --git a/freqtrade/exchange/binance_leverage_tiers.json b/freqtrade/exchange/binance_leverage_tiers.json index 07fdcb5a4..0b9be0f55 100644 --- a/freqtrade/exchange/binance_leverage_tiers.json +++ b/freqtrade/exchange/binance_leverage_tiers.json @@ -999,15 +999,15 @@ "currency": "USDT", "minNotional": 5000.0, "maxNotional": 10000.0, - "maintenanceMarginRate": 0.0065, + "maintenanceMarginRate": 0.006, "maxLeverage": 50.0, "info": { "bracket": "2", "initialLeverage": "50", "notionalCap": "10000", "notionalFloor": "5000", - "maintMarginRatio": "0.0065", - "cum": "7.5" + "maintMarginRatio": "0.006", + "cum": "5.0" } }, { @@ -1023,7 +1023,7 @@ "notionalCap": "50000", "notionalFloor": "10000", "maintMarginRatio": "0.01", - "cum": "42.5" + "cum": "45.0" } }, { @@ -1039,7 +1039,7 @@ "notionalCap": "250000", "notionalFloor": "50000", "maintMarginRatio": "0.02", - "cum": "542.5" + "cum": "545.0" } }, { @@ -1055,77 +1055,77 @@ "notionalCap": "1000000", "notionalFloor": "250000", "maintMarginRatio": "0.05", - "cum": "8042.5" + "cum": "8045.0" } }, { "tier": 6.0, "currency": "USDT", "minNotional": 1000000.0, - "maxNotional": 2000000.0, + "maxNotional": 5000000.0, "maintenanceMarginRate": 0.1, "maxLeverage": 5.0, "info": { "bracket": "6", "initialLeverage": "5", - "notionalCap": "2000000", + "notionalCap": "5000000", "notionalFloor": "1000000", "maintMarginRatio": "0.1", - "cum": "58042.5" + "cum": "58045.0" } }, { "tier": 7.0, "currency": "USDT", - "minNotional": 2000000.0, - "maxNotional": 5000000.0, + "minNotional": 5000000.0, + "maxNotional": 10000000.0, "maintenanceMarginRate": 0.125, "maxLeverage": 4.0, "info": { "bracket": "7", "initialLeverage": "4", - "notionalCap": "5000000", - "notionalFloor": "2000000", + "notionalCap": "10000000", + "notionalFloor": "5000000", "maintMarginRatio": "0.125", - "cum": "108042.5" + "cum": "183045.0" } }, { "tier": 8.0, "currency": "USDT", - "minNotional": 5000000.0, - "maxNotional": 10000000.0, + "minNotional": 10000000.0, + "maxNotional": 20000000.0, "maintenanceMarginRate": 0.15, "maxLeverage": 3.0, "info": { "bracket": "8", "initialLeverage": "3", - "notionalCap": "10000000", - "notionalFloor": "5000000", + "notionalCap": "20000000", + "notionalFloor": "10000000", "maintMarginRatio": "0.15", - "cum": "233042.5" + "cum": "433045.0" } }, { "tier": 9.0, "currency": "USDT", - "minNotional": 10000000.0, - "maxNotional": 20000000.0, + "minNotional": 20000000.0, + "maxNotional": 30000000.0, "maintenanceMarginRate": 0.25, "maxLeverage": 2.0, "info": { "bracket": "9", "initialLeverage": "2", - "notionalCap": "20000000", - "notionalFloor": "10000000", + "notionalCap": "30000000", + "notionalFloor": "20000000", "maintMarginRatio": "0.25", - "cum": "1233042.5" + "cum": "2433045.0" } }, { "tier": 10.0, "currency": "USDT", - "minNotional": 20000000.0, + "minNotional": 30000000.0, "maxNotional": 50000000.0, "maintenanceMarginRate": 0.5, "maxLeverage": 1.0, @@ -1133,9 +1133,9 @@ "bracket": "10", "initialLeverage": "1", "notionalCap": "50000000", - "notionalFloor": "20000000", + "notionalFloor": "30000000", "maintMarginRatio": "0.5", - "cum": "6233042.5" + "cum": "9933045.0" } } ], @@ -1274,13 +1274,13 @@ "tier": 3.0, "currency": "USDT", "minNotional": 25000.0, - "maxNotional": 200000.0, + "maxNotional": 600000.0, "maintenanceMarginRate": 0.05, "maxLeverage": 10.0, "info": { "bracket": "3", "initialLeverage": "10", - "notionalCap": "200000", + "notionalCap": "600000", "notionalFloor": "25000", "maintMarginRatio": "0.05", "cum": "650.0" @@ -1289,65 +1289,65 @@ { "tier": 4.0, "currency": "USDT", - "minNotional": 200000.0, - "maxNotional": 500000.0, + "minNotional": 600000.0, + "maxNotional": 1600000.0, "maintenanceMarginRate": 0.1, "maxLeverage": 5.0, "info": { "bracket": "4", "initialLeverage": "5", - "notionalCap": "500000", - "notionalFloor": "200000", + "notionalCap": "1600000", + "notionalFloor": "600000", "maintMarginRatio": "0.1", - "cum": "10650.0" + "cum": "30650.0" } }, { "tier": 5.0, "currency": "USDT", - "minNotional": 500000.0, - "maxNotional": 1000000.0, + "minNotional": 1600000.0, + "maxNotional": 2000000.0, "maintenanceMarginRate": 0.125, "maxLeverage": 4.0, "info": { "bracket": "5", "initialLeverage": "4", - "notionalCap": "1000000", - "notionalFloor": "500000", + "notionalCap": "2000000", + "notionalFloor": "1600000", "maintMarginRatio": "0.125", - "cum": "23150.0" + "cum": "70650.0" } }, { "tier": 6.0, "currency": "USDT", - "minNotional": 1000000.0, - "maxNotional": 3000000.0, + "minNotional": 2000000.0, + "maxNotional": 6000000.0, "maintenanceMarginRate": 0.25, "maxLeverage": 2.0, "info": { "bracket": "6", "initialLeverage": "2", - "notionalCap": "3000000", - "notionalFloor": "1000000", + "notionalCap": "6000000", + "notionalFloor": "2000000", "maintMarginRatio": "0.25", - "cum": "148150.0" + "cum": "320650.0" } }, { "tier": 7.0, "currency": "USDT", - "minNotional": 3000000.0, - "maxNotional": 5000000.0, + "minNotional": 6000000.0, + "maxNotional": 10000000.0, "maintenanceMarginRate": 0.5, "maxLeverage": 1.0, "info": { "bracket": "7", "initialLeverage": "1", - "notionalCap": "5000000", - "notionalFloor": "3000000", + "notionalCap": "10000000", + "notionalFloor": "6000000", "maintMarginRatio": "0.5", - "cum": "898150.0" + "cum": "1820650.0" } } ], @@ -1743,6 +1743,120 @@ } } ], + "AMB/USDT:USDT": [ + { + "tier": 1.0, + "currency": "USDT", + "minNotional": 0.0, + "maxNotional": 5000.0, + "maintenanceMarginRate": 0.02, + "maxLeverage": 20.0, + "info": { + "bracket": "1", + "initialLeverage": "20", + "notionalCap": "5000", + "notionalFloor": "0", + "maintMarginRatio": "0.02", + "cum": "0.0" + } + }, + { + "tier": 2.0, + "currency": "USDT", + "minNotional": 5000.0, + "maxNotional": 25000.0, + "maintenanceMarginRate": 0.025, + "maxLeverage": 15.0, + "info": { + "bracket": "2", + "initialLeverage": "15", + "notionalCap": "25000", + "notionalFloor": "5000", + "maintMarginRatio": "0.025", + "cum": "25.0" + } + }, + { + "tier": 3.0, + "currency": "USDT", + "minNotional": 25000.0, + "maxNotional": 200000.0, + "maintenanceMarginRate": 0.05, + "maxLeverage": 10.0, + "info": { + "bracket": "3", + "initialLeverage": "10", + "notionalCap": "200000", + "notionalFloor": "25000", + "maintMarginRatio": "0.05", + "cum": "650.0" + } + }, + { + "tier": 4.0, + "currency": "USDT", + "minNotional": 200000.0, + "maxNotional": 500000.0, + "maintenanceMarginRate": 0.1, + "maxLeverage": 5.0, + "info": { + "bracket": "4", + "initialLeverage": "5", + "notionalCap": "500000", + "notionalFloor": "200000", + "maintMarginRatio": "0.1", + "cum": "10650.0" + } + }, + { + "tier": 5.0, + "currency": "USDT", + "minNotional": 500000.0, + "maxNotional": 1000000.0, + "maintenanceMarginRate": 0.125, + "maxLeverage": 4.0, + "info": { + "bracket": "5", + "initialLeverage": "4", + "notionalCap": "1000000", + "notionalFloor": "500000", + "maintMarginRatio": "0.125", + "cum": "23150.0" + } + }, + { + "tier": 6.0, + "currency": "USDT", + "minNotional": 1000000.0, + "maxNotional": 3000000.0, + "maintenanceMarginRate": 0.25, + "maxLeverage": 2.0, + "info": { + "bracket": "6", + "initialLeverage": "2", + "notionalCap": "3000000", + "notionalFloor": "1000000", + "maintMarginRatio": "0.25", + "cum": "148150.0" + } + }, + { + "tier": 7.0, + "currency": "USDT", + "minNotional": 3000000.0, + "maxNotional": 5000000.0, + "maintenanceMarginRate": 0.5, + "maxLeverage": 1.0, + "info": { + "bracket": "7", + "initialLeverage": "1", + "notionalCap": "5000000", + "notionalFloor": "3000000", + "maintMarginRatio": "0.5", + "cum": "898150.0" + } + } + ], "ANC/BUSD:BUSD": [ { "tier": 1.0, @@ -2304,10 +2418,10 @@ "minNotional": 0.0, "maxNotional": 5000.0, "maintenanceMarginRate": 0.02, - "maxLeverage": 20.0, + "maxLeverage": 25.0, "info": { "bracket": "1", - "initialLeverage": "20", + "initialLeverage": "25", "notionalCap": "5000", "notionalFloor": "0", "maintMarginRatio": "0.02", @@ -2320,10 +2434,10 @@ "minNotional": 5000.0, "maxNotional": 25000.0, "maintenanceMarginRate": 0.025, - "maxLeverage": 10.0, + "maxLeverage": 20.0, "info": { "bracket": "2", - "initialLeverage": "10", + "initialLeverage": "20", "notionalCap": "25000", "notionalFloor": "5000", "maintMarginRatio": "0.025", @@ -2334,13 +2448,13 @@ "tier": 3.0, "currency": "USDT", "minNotional": 25000.0, - "maxNotional": 100000.0, + "maxNotional": 600000.0, "maintenanceMarginRate": 0.05, - "maxLeverage": 8.0, + "maxLeverage": 10.0, "info": { "bracket": "3", - "initialLeverage": "8", - "notionalCap": "100000", + "initialLeverage": "10", + "notionalCap": "600000", "notionalFloor": "25000", "maintMarginRatio": "0.05", "cum": "650.0" @@ -2349,49 +2463,65 @@ { "tier": 4.0, "currency": "USDT", - "minNotional": 100000.0, - "maxNotional": 250000.0, + "minNotional": 600000.0, + "maxNotional": 1600000.0, "maintenanceMarginRate": 0.1, "maxLeverage": 5.0, "info": { "bracket": "4", "initialLeverage": "5", - "notionalCap": "250000", - "notionalFloor": "100000", + "notionalCap": "1600000", + "notionalFloor": "600000", "maintMarginRatio": "0.1", - "cum": "5650.0" + "cum": "30650.0" } }, { "tier": 5.0, "currency": "USDT", - "minNotional": 250000.0, - "maxNotional": 1000000.0, + "minNotional": 1600000.0, + "maxNotional": 2000000.0, "maintenanceMarginRate": 0.125, - "maxLeverage": 2.0, + "maxLeverage": 4.0, "info": { "bracket": "5", - "initialLeverage": "2", - "notionalCap": "1000000", - "notionalFloor": "250000", + "initialLeverage": "4", + "notionalCap": "2000000", + "notionalFloor": "1600000", "maintMarginRatio": "0.125", - "cum": "11900.0" + "cum": "70650.0" } }, { "tier": 6.0, "currency": "USDT", - "minNotional": 1000000.0, - "maxNotional": 3000000.0, + "minNotional": 2000000.0, + "maxNotional": 6000000.0, + "maintenanceMarginRate": 0.25, + "maxLeverage": 2.0, + "info": { + "bracket": "6", + "initialLeverage": "2", + "notionalCap": "6000000", + "notionalFloor": "2000000", + "maintMarginRatio": "0.25", + "cum": "320650.0" + } + }, + { + "tier": 7.0, + "currency": "USDT", + "minNotional": 6000000.0, + "maxNotional": 10000000.0, "maintenanceMarginRate": 0.5, "maxLeverage": 1.0, "info": { - "bracket": "6", + "bracket": "7", "initialLeverage": "1", - "notionalCap": "3000000", - "notionalFloor": "1000000", + "notionalCap": "10000000", + "notionalFloor": "6000000", "maintMarginRatio": "0.5", - "cum": "386900.0" + "cum": "1820650.0" } } ], @@ -2546,13 +2676,13 @@ "tier": 3.0, "currency": "USDT", "minNotional": 25000.0, - "maxNotional": 300000.0, + "maxNotional": 900000.0, "maintenanceMarginRate": 0.02, "maxLeverage": 20.0, "info": { "bracket": "3", "initialLeverage": "20", - "notionalCap": "300000", + "notionalCap": "900000", "notionalFloor": "25000", "maintMarginRatio": "0.02", "cum": "150.0" @@ -2561,39 +2691,39 @@ { "tier": 4.0, "currency": "USDT", - "minNotional": 300000.0, - "maxNotional": 1200000.0, + "minNotional": 900000.0, + "maxNotional": 1800000.0, "maintenanceMarginRate": 0.05, "maxLeverage": 10.0, "info": { "bracket": "4", "initialLeverage": "10", - "notionalCap": "1200000", - "notionalFloor": "300000", + "notionalCap": "1800000", + "notionalFloor": "900000", "maintMarginRatio": "0.05", - "cum": "9150.0" + "cum": "27150.0" } }, { "tier": 5.0, "currency": "USDT", - "minNotional": 1200000.0, - "maxNotional": 3000000.0, + "minNotional": 1800000.0, + "maxNotional": 4800000.0, "maintenanceMarginRate": 0.1, "maxLeverage": 5.0, "info": { "bracket": "5", "initialLeverage": "5", - "notionalCap": "3000000", - "notionalFloor": "1200000", + "notionalCap": "4800000", + "notionalFloor": "1800000", "maintMarginRatio": "0.1", - "cum": "69150.0" + "cum": "117150.0" } }, { "tier": 6.0, "currency": "USDT", - "minNotional": 3000000.0, + "minNotional": 4800000.0, "maxNotional": 6000000.0, "maintenanceMarginRate": 0.125, "maxLeverage": 4.0, @@ -2601,9 +2731,9 @@ "bracket": "6", "initialLeverage": "4", "notionalCap": "6000000", - "notionalFloor": "3000000", + "notionalFloor": "4800000", "maintMarginRatio": "0.125", - "cum": "144150.0" + "cum": "237150.0" } }, { @@ -2619,7 +2749,7 @@ "notionalCap": "18000000", "notionalFloor": "6000000", "maintMarginRatio": "0.25", - "cum": "894150.0" + "cum": "987150.0" } }, { @@ -2635,7 +2765,7 @@ "notionalCap": "30000000", "notionalFloor": "18000000", "maintMarginRatio": "0.5", - "cum": "5394150.0" + "cum": "5487150.0" } } ], @@ -2737,6 +2867,136 @@ } } ], + "ARB/USDT:USDT": [ + { + "tier": 1.0, + "currency": "USDT", + "minNotional": 0.0, + "maxNotional": 5000.0, + "maintenanceMarginRate": 0.006, + "maxLeverage": 50.0, + "info": { + "bracket": "1", + "initialLeverage": "50", + "notionalCap": "5000", + "notionalFloor": "0", + "maintMarginRatio": "0.006", + "cum": "0.0" + } + }, + { + "tier": 2.0, + "currency": "USDT", + "minNotional": 5000.0, + "maxNotional": 50000.0, + "maintenanceMarginRate": 0.01, + "maxLeverage": 25.0, + "info": { + "bracket": "2", + "initialLeverage": "25", + "notionalCap": "50000", + "notionalFloor": "5000", + "maintMarginRatio": "0.01", + "cum": "20.0" + } + }, + { + "tier": 3.0, + "currency": "USDT", + "minNotional": 50000.0, + "maxNotional": 400000.0, + "maintenanceMarginRate": 0.025, + "maxLeverage": 20.0, + "info": { + "bracket": "3", + "initialLeverage": "20", + "notionalCap": "400000", + "notionalFloor": "50000", + "maintMarginRatio": "0.025", + "cum": "770.0" + } + }, + { + "tier": 4.0, + "currency": "USDT", + "minNotional": 400000.0, + "maxNotional": 800000.0, + "maintenanceMarginRate": 0.05, + "maxLeverage": 10.0, + "info": { + "bracket": "4", + "initialLeverage": "10", + "notionalCap": "800000", + "notionalFloor": "400000", + "maintMarginRatio": "0.05", + "cum": "10770.0" + } + }, + { + "tier": 5.0, + "currency": "USDT", + "minNotional": 800000.0, + "maxNotional": 2000000.0, + "maintenanceMarginRate": 0.1, + "maxLeverage": 5.0, + "info": { + "bracket": "5", + "initialLeverage": "5", + "notionalCap": "2000000", + "notionalFloor": "800000", + "maintMarginRatio": "0.1", + "cum": "50770.0" + } + }, + { + "tier": 6.0, + "currency": "USDT", + "minNotional": 2000000.0, + "maxNotional": 5000000.0, + "maintenanceMarginRate": 0.125, + "maxLeverage": 4.0, + "info": { + "bracket": "6", + "initialLeverage": "4", + "notionalCap": "5000000", + "notionalFloor": "2000000", + "maintMarginRatio": "0.125", + "cum": "100770.0" + } + }, + { + "tier": 7.0, + "currency": "USDT", + "minNotional": 5000000.0, + "maxNotional": 12000000.0, + "maintenanceMarginRate": 0.25, + "maxLeverage": 2.0, + "info": { + "bracket": "7", + "initialLeverage": "2", + "notionalCap": "12000000", + "notionalFloor": "5000000", + "maintMarginRatio": "0.25", + "cum": "725770.0" + } + }, + { + "tier": 8.0, + "currency": "USDT", + "minNotional": 12000000.0, + "maxNotional": 20000000.0, + "maintenanceMarginRate": 0.5, + "maxLeverage": 1.0, + "info": { + "bracket": "8", + "initialLeverage": "1", + "notionalCap": "20000000", + "notionalFloor": "12000000", + "maintMarginRatio": "0.5", + "cum": "3725770.0" + } + } + ], "ARPA/USDT:USDT": [ { "tier": 1.0, @@ -2760,10 +3020,10 @@ "minNotional": 5000.0, "maxNotional": 25000.0, "maintenanceMarginRate": 0.025, - "maxLeverage": 10.0, + "maxLeverage": 15.0, "info": { "bracket": "2", - "initialLeverage": "10", + "initialLeverage": "15", "notionalCap": "25000", "notionalFloor": "5000", "maintMarginRatio": "0.025", @@ -2774,13 +3034,13 @@ "tier": 3.0, "currency": "USDT", "minNotional": 25000.0, - "maxNotional": 100000.0, + "maxNotional": 600000.0, "maintenanceMarginRate": 0.05, - "maxLeverage": 8.0, + "maxLeverage": 10.0, "info": { "bracket": "3", - "initialLeverage": "8", - "notionalCap": "100000", + "initialLeverage": "10", + "notionalCap": "600000", "notionalFloor": "25000", "maintMarginRatio": "0.05", "cum": "650.0" @@ -2789,49 +3049,65 @@ { "tier": 4.0, "currency": "USDT", - "minNotional": 100000.0, - "maxNotional": 250000.0, + "minNotional": 600000.0, + "maxNotional": 1600000.0, "maintenanceMarginRate": 0.1, "maxLeverage": 5.0, "info": { "bracket": "4", "initialLeverage": "5", - "notionalCap": "250000", - "notionalFloor": "100000", + "notionalCap": "1600000", + "notionalFloor": "600000", "maintMarginRatio": "0.1", - "cum": "5650.0" + "cum": "30650.0" } }, { "tier": 5.0, "currency": "USDT", - "minNotional": 250000.0, - "maxNotional": 1000000.0, + "minNotional": 1600000.0, + "maxNotional": 2000000.0, "maintenanceMarginRate": 0.125, - "maxLeverage": 2.0, + "maxLeverage": 4.0, "info": { "bracket": "5", - "initialLeverage": "2", - "notionalCap": "1000000", - "notionalFloor": "250000", + "initialLeverage": "4", + "notionalCap": "2000000", + "notionalFloor": "1600000", "maintMarginRatio": "0.125", - "cum": "11900.0" + "cum": "70650.0" } }, { "tier": 6.0, "currency": "USDT", - "minNotional": 1000000.0, - "maxNotional": 3000000.0, + "minNotional": 2000000.0, + "maxNotional": 6000000.0, + "maintenanceMarginRate": 0.25, + "maxLeverage": 2.0, + "info": { + "bracket": "6", + "initialLeverage": "2", + "notionalCap": "6000000", + "notionalFloor": "2000000", + "maintMarginRatio": "0.25", + "cum": "320650.0" + } + }, + { + "tier": 7.0, + "currency": "USDT", + "minNotional": 6000000.0, + "maxNotional": 10000000.0, "maintenanceMarginRate": 0.5, "maxLeverage": 1.0, "info": { - "bracket": "6", + "bracket": "7", "initialLeverage": "1", - "notionalCap": "3000000", - "notionalFloor": "1000000", + "notionalCap": "10000000", + "notionalFloor": "6000000", "maintMarginRatio": "0.5", - "cum": "386900.0" + "cum": "1820650.0" } } ], @@ -3364,10 +3640,10 @@ "minNotional": 0.0, "maxNotional": 5000.0, "maintenanceMarginRate": 0.02, - "maxLeverage": 25.0, + "maxLeverage": 8.0, "info": { "bracket": "1", - "initialLeverage": "25", + "initialLeverage": "8", "notionalCap": "5000", "notionalFloor": "0", "maintMarginRatio": "0.02", @@ -3380,10 +3656,10 @@ "minNotional": 5000.0, "maxNotional": 25000.0, "maintenanceMarginRate": 0.025, - "maxLeverage": 15.0, + "maxLeverage": 7.0, "info": { "bracket": "2", - "initialLeverage": "15", + "initialLeverage": "7", "notionalCap": "25000", "notionalFloor": "5000", "maintMarginRatio": "0.025", @@ -3396,10 +3672,10 @@ "minNotional": 25000.0, "maxNotional": 100000.0, "maintenanceMarginRate": 0.05, - "maxLeverage": 10.0, + "maxLeverage": 6.0, "info": { "bracket": "3", - "initialLeverage": "10", + "initialLeverage": "6", "notionalCap": "100000", "notionalFloor": "25000", "maintMarginRatio": "0.05", @@ -3458,13 +3734,13 @@ "tier": 7.0, "currency": "BUSD", "minNotional": 3000000.0, - "maxNotional": 8000000.0, + "maxNotional": 4000000.0, "maintenanceMarginRate": 0.5, "maxLeverage": 1.0, "info": { "bracket": "7", "initialLeverage": "1", - "notionalCap": "8000000", + "notionalCap": "4000000", "notionalFloor": "3000000", "maintMarginRatio": "0.5", "cum": "949400.0" @@ -3639,14 +3915,14 @@ "currency": "USDT", "minNotional": 0.0, "maxNotional": 5000.0, - "maintenanceMarginRate": 0.0065, - "maxLeverage": 25.0, + "maintenanceMarginRate": 0.006, + "maxLeverage": 50.0, "info": { "bracket": "1", - "initialLeverage": "25", + "initialLeverage": "50", "notionalCap": "5000", "notionalFloor": "0", - "maintMarginRatio": "0.0065", + "maintMarginRatio": "0.006", "cum": "0.0" } }, @@ -3655,14 +3931,14 @@ "currency": "USDT", "minNotional": 5000.0, "maxNotional": 25000.0, - "maintenanceMarginRate": 0.0075, - "maxLeverage": 20.0, + "maintenanceMarginRate": 0.007, + "maxLeverage": 30.0, "info": { "bracket": "2", - "initialLeverage": "20", + "initialLeverage": "30", "notionalCap": "25000", "notionalFloor": "5000", - "maintMarginRatio": "0.0075", + "maintMarginRatio": "0.007", "cum": "5.0" } }, @@ -3670,70 +3946,70 @@ "tier": 3.0, "currency": "USDT", "minNotional": 25000.0, - "maxNotional": 50000.0, + "maxNotional": 400000.0, "maintenanceMarginRate": 0.01, - "maxLeverage": 18.0, + "maxLeverage": 25.0, "info": { "bracket": "3", - "initialLeverage": "18", - "notionalCap": "50000", + "initialLeverage": "25", + "notionalCap": "400000", "notionalFloor": "25000", "maintMarginRatio": "0.01", - "cum": "67.5" + "cum": "80.0" } }, { "tier": 4.0, "currency": "USDT", - "minNotional": 50000.0, - "maxNotional": 250000.0, + "minNotional": 400000.0, + "maxNotional": 600000.0, "maintenanceMarginRate": 0.02, - "maxLeverage": 15.0, + "maxLeverage": 20.0, "info": { "bracket": "4", - "initialLeverage": "15", - "notionalCap": "250000", - "notionalFloor": "50000", + "initialLeverage": "20", + "notionalCap": "600000", + "notionalFloor": "400000", "maintMarginRatio": "0.02", - "cum": "567.5" + "cum": "4080.0" } }, { "tier": 5.0, "currency": "USDT", - "minNotional": 250000.0, - "maxNotional": 1000000.0, + "minNotional": 600000.0, + "maxNotional": 1200000.0, "maintenanceMarginRate": 0.05, "maxLeverage": 10.0, "info": { "bracket": "5", "initialLeverage": "10", - "notionalCap": "1000000", - "notionalFloor": "250000", + "notionalCap": "1200000", + "notionalFloor": "600000", "maintMarginRatio": "0.05", - "cum": "8067.5" + "cum": "22080.0" } }, { "tier": 6.0, "currency": "USDT", - "minNotional": 1000000.0, - "maxNotional": 2000000.0, + "minNotional": 1200000.0, + "maxNotional": 3200000.0, "maintenanceMarginRate": 0.1, "maxLeverage": 5.0, "info": { "bracket": "6", "initialLeverage": "5", - "notionalCap": "2000000", - "notionalFloor": "1000000", + "notionalCap": "3200000", + "notionalFloor": "1200000", "maintMarginRatio": "0.1", - "cum": "58067.5" + "cum": "82080.0" } }, { "tier": 7.0, "currency": "USDT", - "minNotional": 2000000.0, + "minNotional": 3200000.0, "maxNotional": 5000000.0, "maintenanceMarginRate": 0.125, "maxLeverage": 4.0, @@ -3741,9 +4017,9 @@ "bracket": "7", "initialLeverage": "4", "notionalCap": "5000000", - "notionalFloor": "2000000", + "notionalFloor": "3200000", "maintMarginRatio": "0.125", - "cum": "108067.5" + "cum": "162080.0" } }, { @@ -3751,15 +4027,15 @@ "currency": "USDT", "minNotional": 5000000.0, "maxNotional": 10000000.0, - "maintenanceMarginRate": 0.1665, + "maintenanceMarginRate": 0.15, "maxLeverage": 3.0, "info": { "bracket": "8", "initialLeverage": "3", "notionalCap": "10000000", "notionalFloor": "5000000", - "maintMarginRatio": "0.1665", - "cum": "315567.5" + "maintMarginRatio": "0.15", + "cum": "287080.0" } }, { @@ -3775,7 +4051,7 @@ "notionalCap": "15000000", "notionalFloor": "10000000", "maintMarginRatio": "0.25", - "cum": "1150567.5" + "cum": "1287080.0" } }, { @@ -3791,7 +4067,7 @@ "notionalCap": "20000000", "notionalFloor": "15000000", "maintMarginRatio": "0.5", - "cum": "4900567.5" + "cum": "5037080.0" } } ], @@ -4763,15 +5039,15 @@ "currency": "USDT", "minNotional": 5000.0, "maxNotional": 10000.0, - "maintenanceMarginRate": 0.0065, + "maintenanceMarginRate": 0.006, "maxLeverage": 50.0, "info": { "bracket": "2", "initialLeverage": "50", "notionalCap": "10000", "notionalFloor": "5000", - "maintMarginRatio": "0.0065", - "cum": "7.5" + "maintMarginRatio": "0.006", + "cum": "5.0" } }, { @@ -4787,7 +5063,7 @@ "notionalCap": "50000", "notionalFloor": "10000", "maintMarginRatio": "0.01", - "cum": "42.5" + "cum": "45.0" } }, { @@ -4803,7 +5079,7 @@ "notionalCap": "250000", "notionalFloor": "50000", "maintMarginRatio": "0.02", - "cum": "542.5" + "cum": "545.0" } }, { @@ -4819,77 +5095,77 @@ "notionalCap": "1000000", "notionalFloor": "250000", "maintMarginRatio": "0.05", - "cum": "8042.5" + "cum": "8045.0" } }, { "tier": 6.0, "currency": "USDT", "minNotional": 1000000.0, - "maxNotional": 2000000.0, + "maxNotional": 5000000.0, "maintenanceMarginRate": 0.1, "maxLeverage": 5.0, "info": { "bracket": "6", "initialLeverage": "5", - "notionalCap": "2000000", + "notionalCap": "5000000", "notionalFloor": "1000000", "maintMarginRatio": "0.1", - "cum": "58042.5" + "cum": "58045.0" } }, { "tier": 7.0, "currency": "USDT", - "minNotional": 2000000.0, - "maxNotional": 5000000.0, + "minNotional": 5000000.0, + "maxNotional": 10000000.0, "maintenanceMarginRate": 0.125, "maxLeverage": 4.0, "info": { "bracket": "7", "initialLeverage": "4", - "notionalCap": "5000000", - "notionalFloor": "2000000", + "notionalCap": "10000000", + "notionalFloor": "5000000", "maintMarginRatio": "0.125", - "cum": "108042.5" + "cum": "183045.0" } }, { "tier": 8.0, "currency": "USDT", - "minNotional": 5000000.0, - "maxNotional": 10000000.0, + "minNotional": 10000000.0, + "maxNotional": 20000000.0, "maintenanceMarginRate": 0.15, "maxLeverage": 3.0, "info": { "bracket": "8", "initialLeverage": "3", - "notionalCap": "10000000", - "notionalFloor": "5000000", + "notionalCap": "20000000", + "notionalFloor": "10000000", "maintMarginRatio": "0.15", - "cum": "233042.5" + "cum": "433045.0" } }, { "tier": 9.0, "currency": "USDT", - "minNotional": 10000000.0, - "maxNotional": 20000000.0, + "minNotional": 20000000.0, + "maxNotional": 30000000.0, "maintenanceMarginRate": 0.25, "maxLeverage": 2.0, "info": { "bracket": "9", "initialLeverage": "2", - "notionalCap": "20000000", - "notionalFloor": "10000000", + "notionalCap": "30000000", + "notionalFloor": "20000000", "maintMarginRatio": "0.25", - "cum": "1233042.5" + "cum": "2433045.0" } }, { "tier": 10.0, "currency": "USDT", - "minNotional": 20000000.0, + "minNotional": 30000000.0, "maxNotional": 50000000.0, "maintenanceMarginRate": 0.5, "maxLeverage": 1.0, @@ -4897,9 +5173,9 @@ "bracket": "10", "initialLeverage": "1", "notionalCap": "50000000", - "notionalFloor": "20000000", + "notionalFloor": "30000000", "maintMarginRatio": "0.5", - "cum": "6233042.5" + "cum": "9933045.0" } } ], @@ -5325,7 +5601,7 @@ } } ], - "BTC/USDT:USDT-230331": [ + "BTC/USDT:USDT-230630": [ { "tier": 1.0, "currency": "USDT", @@ -5740,10 +6016,10 @@ "minNotional": 0.0, "maxNotional": 5000.0, "maintenanceMarginRate": 0.02, - "maxLeverage": 20.0, + "maxLeverage": 25.0, "info": { "bracket": "1", - "initialLeverage": "20", + "initialLeverage": "25", "notionalCap": "5000", "notionalFloor": "0", "maintMarginRatio": "0.02", @@ -5756,10 +6032,10 @@ "minNotional": 5000.0, "maxNotional": 25000.0, "maintenanceMarginRate": 0.025, - "maxLeverage": 10.0, + "maxLeverage": 20.0, "info": { "bracket": "2", - "initialLeverage": "10", + "initialLeverage": "20", "notionalCap": "25000", "notionalFloor": "5000", "maintMarginRatio": "0.025", @@ -5770,13 +6046,13 @@ "tier": 3.0, "currency": "USDT", "minNotional": 25000.0, - "maxNotional": 100000.0, + "maxNotional": 300000.0, "maintenanceMarginRate": 0.05, - "maxLeverage": 8.0, + "maxLeverage": 10.0, "info": { "bracket": "3", - "initialLeverage": "8", - "notionalCap": "100000", + "initialLeverage": "10", + "notionalCap": "300000", "notionalFloor": "25000", "maintMarginRatio": "0.05", "cum": "650.0" @@ -5785,33 +6061,33 @@ { "tier": 4.0, "currency": "USDT", - "minNotional": 100000.0, - "maxNotional": 250000.0, + "minNotional": 300000.0, + "maxNotional": 800000.0, "maintenanceMarginRate": 0.1, "maxLeverage": 5.0, "info": { "bracket": "4", "initialLeverage": "5", - "notionalCap": "250000", - "notionalFloor": "100000", + "notionalCap": "800000", + "notionalFloor": "300000", "maintMarginRatio": "0.1", - "cum": "5650.0" + "cum": "15650.0" } }, { "tier": 5.0, "currency": "USDT", - "minNotional": 250000.0, + "minNotional": 800000.0, "maxNotional": 1000000.0, "maintenanceMarginRate": 0.125, - "maxLeverage": 2.0, + "maxLeverage": 4.0, "info": { "bracket": "5", - "initialLeverage": "2", + "initialLeverage": "4", "notionalCap": "1000000", - "notionalFloor": "250000", + "notionalFloor": "800000", "maintMarginRatio": "0.125", - "cum": "11900.0" + "cum": "35650.0" } }, { @@ -5819,15 +6095,31 @@ "currency": "USDT", "minNotional": 1000000.0, "maxNotional": 3000000.0, + "maintenanceMarginRate": 0.25, + "maxLeverage": 2.0, + "info": { + "bracket": "6", + "initialLeverage": "2", + "notionalCap": "3000000", + "notionalFloor": "1000000", + "maintMarginRatio": "0.25", + "cum": "160650.0" + } + }, + { + "tier": 7.0, + "currency": "USDT", + "minNotional": 3000000.0, + "maxNotional": 5000000.0, "maintenanceMarginRate": 0.5, "maxLeverage": 1.0, "info": { - "bracket": "6", + "bracket": "7", "initialLeverage": "1", - "notionalCap": "3000000", - "notionalFloor": "1000000", + "notionalCap": "5000000", + "notionalFloor": "3000000", "maintMarginRatio": "0.5", - "cum": "386900.0" + "cum": "910650.0" } } ], @@ -5838,10 +6130,10 @@ "minNotional": 0.0, "maxNotional": 5000.0, "maintenanceMarginRate": 0.02, - "maxLeverage": 20.0, + "maxLeverage": 25.0, "info": { "bracket": "1", - "initialLeverage": "20", + "initialLeverage": "25", "notionalCap": "5000", "notionalFloor": "0", "maintMarginRatio": "0.02", @@ -5854,10 +6146,10 @@ "minNotional": 5000.0, "maxNotional": 25000.0, "maintenanceMarginRate": 0.025, - "maxLeverage": 10.0, + "maxLeverage": 20.0, "info": { "bracket": "2", - "initialLeverage": "10", + "initialLeverage": "20", "notionalCap": "25000", "notionalFloor": "5000", "maintMarginRatio": "0.025", @@ -5868,13 +6160,13 @@ "tier": 3.0, "currency": "USDT", "minNotional": 25000.0, - "maxNotional": 100000.0, + "maxNotional": 300000.0, "maintenanceMarginRate": 0.05, - "maxLeverage": 8.0, + "maxLeverage": 10.0, "info": { "bracket": "3", - "initialLeverage": "8", - "notionalCap": "100000", + "initialLeverage": "10", + "notionalCap": "300000", "notionalFloor": "25000", "maintMarginRatio": "0.05", "cum": "650.0" @@ -5883,33 +6175,33 @@ { "tier": 4.0, "currency": "USDT", - "minNotional": 100000.0, - "maxNotional": 250000.0, + "minNotional": 300000.0, + "maxNotional": 800000.0, "maintenanceMarginRate": 0.1, "maxLeverage": 5.0, "info": { "bracket": "4", "initialLeverage": "5", - "notionalCap": "250000", - "notionalFloor": "100000", + "notionalCap": "800000", + "notionalFloor": "300000", "maintMarginRatio": "0.1", - "cum": "5650.0" + "cum": "15650.0" } }, { "tier": 5.0, "currency": "USDT", - "minNotional": 250000.0, + "minNotional": 800000.0, "maxNotional": 1000000.0, "maintenanceMarginRate": 0.125, - "maxLeverage": 2.0, + "maxLeverage": 4.0, "info": { "bracket": "5", - "initialLeverage": "2", + "initialLeverage": "4", "notionalCap": "1000000", - "notionalFloor": "250000", + "notionalFloor": "800000", "maintMarginRatio": "0.125", - "cum": "11900.0" + "cum": "35650.0" } }, { @@ -5917,15 +6209,31 @@ "currency": "USDT", "minNotional": 1000000.0, "maxNotional": 3000000.0, + "maintenanceMarginRate": 0.25, + "maxLeverage": 2.0, + "info": { + "bracket": "6", + "initialLeverage": "2", + "notionalCap": "3000000", + "notionalFloor": "1000000", + "maintMarginRatio": "0.25", + "cum": "160650.0" + } + }, + { + "tier": 7.0, + "currency": "USDT", + "minNotional": 3000000.0, + "maxNotional": 5000000.0, "maintenanceMarginRate": 0.5, "maxLeverage": 1.0, "info": { - "bracket": "6", + "bracket": "7", "initialLeverage": "1", - "notionalCap": "3000000", - "notionalFloor": "1000000", + "notionalCap": "5000000", + "notionalFloor": "3000000", "maintMarginRatio": "0.5", - "cum": "386900.0" + "cum": "910650.0" } } ], @@ -5936,10 +6244,10 @@ "minNotional": 0.0, "maxNotional": 5000.0, "maintenanceMarginRate": 0.02, - "maxLeverage": 20.0, + "maxLeverage": 25.0, "info": { "bracket": "1", - "initialLeverage": "20", + "initialLeverage": "25", "notionalCap": "5000", "notionalFloor": "0", "maintMarginRatio": "0.02", @@ -5952,10 +6260,10 @@ "minNotional": 5000.0, "maxNotional": 25000.0, "maintenanceMarginRate": 0.025, - "maxLeverage": 10.0, + "maxLeverage": 20.0, "info": { "bracket": "2", - "initialLeverage": "10", + "initialLeverage": "20", "notionalCap": "25000", "notionalFloor": "5000", "maintMarginRatio": "0.025", @@ -5966,13 +6274,13 @@ "tier": 3.0, "currency": "USDT", "minNotional": 25000.0, - "maxNotional": 100000.0, + "maxNotional": 600000.0, "maintenanceMarginRate": 0.05, - "maxLeverage": 8.0, + "maxLeverage": 10.0, "info": { "bracket": "3", - "initialLeverage": "8", - "notionalCap": "100000", + "initialLeverage": "10", + "notionalCap": "600000", "notionalFloor": "25000", "maintMarginRatio": "0.05", "cum": "650.0" @@ -5981,49 +6289,65 @@ { "tier": 4.0, "currency": "USDT", - "minNotional": 100000.0, - "maxNotional": 250000.0, + "minNotional": 600000.0, + "maxNotional": 1600000.0, "maintenanceMarginRate": 0.1, "maxLeverage": 5.0, "info": { "bracket": "4", "initialLeverage": "5", - "notionalCap": "250000", - "notionalFloor": "100000", + "notionalCap": "1600000", + "notionalFloor": "600000", "maintMarginRatio": "0.1", - "cum": "5650.0" + "cum": "30650.0" } }, { "tier": 5.0, "currency": "USDT", - "minNotional": 250000.0, - "maxNotional": 1000000.0, + "minNotional": 1600000.0, + "maxNotional": 2000000.0, "maintenanceMarginRate": 0.125, - "maxLeverage": 2.0, + "maxLeverage": 4.0, "info": { "bracket": "5", - "initialLeverage": "2", - "notionalCap": "1000000", - "notionalFloor": "250000", + "initialLeverage": "4", + "notionalCap": "2000000", + "notionalFloor": "1600000", "maintMarginRatio": "0.125", - "cum": "11900.0" + "cum": "70650.0" } }, { "tier": 6.0, "currency": "USDT", - "minNotional": 1000000.0, - "maxNotional": 3000000.0, + "minNotional": 2000000.0, + "maxNotional": 6000000.0, + "maintenanceMarginRate": 0.25, + "maxLeverage": 2.0, + "info": { + "bracket": "6", + "initialLeverage": "2", + "notionalCap": "6000000", + "notionalFloor": "2000000", + "maintMarginRatio": "0.25", + "cum": "320650.0" + } + }, + { + "tier": 7.0, + "currency": "USDT", + "minNotional": 6000000.0, + "maxNotional": 10000000.0, "maintenanceMarginRate": 0.5, "maxLeverage": 1.0, "info": { - "bracket": "6", + "bracket": "7", "initialLeverage": "1", - "notionalCap": "3000000", - "notionalFloor": "1000000", + "notionalCap": "10000000", + "notionalFloor": "6000000", "maintMarginRatio": "0.5", - "cum": "386900.0" + "cum": "1820650.0" } } ], @@ -6980,10 +7304,10 @@ "minNotional": 0.0, "maxNotional": 5000.0, "maintenanceMarginRate": 0.02, - "maxLeverage": 20.0, + "maxLeverage": 25.0, "info": { "bracket": "1", - "initialLeverage": "20", + "initialLeverage": "25", "notionalCap": "5000", "notionalFloor": "0", "maintMarginRatio": "0.02", @@ -6996,10 +7320,10 @@ "minNotional": 5000.0, "maxNotional": 25000.0, "maintenanceMarginRate": 0.025, - "maxLeverage": 10.0, + "maxLeverage": 20.0, "info": { "bracket": "2", - "initialLeverage": "10", + "initialLeverage": "20", "notionalCap": "25000", "notionalFloor": "5000", "maintMarginRatio": "0.025", @@ -7010,13 +7334,13 @@ "tier": 3.0, "currency": "USDT", "minNotional": 25000.0, - "maxNotional": 100000.0, + "maxNotional": 300000.0, "maintenanceMarginRate": 0.05, - "maxLeverage": 8.0, + "maxLeverage": 10.0, "info": { "bracket": "3", - "initialLeverage": "8", - "notionalCap": "100000", + "initialLeverage": "10", + "notionalCap": "300000", "notionalFloor": "25000", "maintMarginRatio": "0.05", "cum": "650.0" @@ -7025,33 +7349,33 @@ { "tier": 4.0, "currency": "USDT", - "minNotional": 100000.0, - "maxNotional": 250000.0, + "minNotional": 300000.0, + "maxNotional": 800000.0, "maintenanceMarginRate": 0.1, "maxLeverage": 5.0, "info": { "bracket": "4", "initialLeverage": "5", - "notionalCap": "250000", - "notionalFloor": "100000", + "notionalCap": "800000", + "notionalFloor": "300000", "maintMarginRatio": "0.1", - "cum": "5650.0" + "cum": "15650.0" } }, { "tier": 5.0, "currency": "USDT", - "minNotional": 250000.0, + "minNotional": 800000.0, "maxNotional": 1000000.0, "maintenanceMarginRate": 0.125, - "maxLeverage": 2.0, + "maxLeverage": 4.0, "info": { "bracket": "5", - "initialLeverage": "2", + "initialLeverage": "4", "notionalCap": "1000000", - "notionalFloor": "250000", + "notionalFloor": "800000", "maintMarginRatio": "0.125", - "cum": "11900.0" + "cum": "35650.0" } }, { @@ -7059,15 +7383,31 @@ "currency": "USDT", "minNotional": 1000000.0, "maxNotional": 3000000.0, + "maintenanceMarginRate": 0.25, + "maxLeverage": 2.0, + "info": { + "bracket": "6", + "initialLeverage": "2", + "notionalCap": "3000000", + "notionalFloor": "1000000", + "maintMarginRatio": "0.25", + "cum": "160650.0" + } + }, + { + "tier": 7.0, + "currency": "USDT", + "minNotional": 3000000.0, + "maxNotional": 5000000.0, "maintenanceMarginRate": 0.5, "maxLeverage": 1.0, "info": { - "bracket": "6", + "bracket": "7", "initialLeverage": "1", - "notionalCap": "3000000", - "notionalFloor": "1000000", + "notionalCap": "5000000", + "notionalFloor": "3000000", "maintMarginRatio": "0.5", - "cum": "386900.0" + "cum": "910650.0" } } ], @@ -7160,10 +7500,10 @@ "minNotional": 0.0, "maxNotional": 5000.0, "maintenanceMarginRate": 0.02, - "maxLeverage": 20.0, + "maxLeverage": 10.0, "info": { "bracket": "1", - "initialLeverage": "20", + "initialLeverage": "10", "notionalCap": "5000", "notionalFloor": "0", "maintMarginRatio": "0.02", @@ -7176,10 +7516,10 @@ "minNotional": 5000.0, "maxNotional": 25000.0, "maintenanceMarginRate": 0.025, - "maxLeverage": 10.0, + "maxLeverage": 8.0, "info": { "bracket": "2", - "initialLeverage": "10", + "initialLeverage": "8", "notionalCap": "25000", "notionalFloor": "5000", "maintMarginRatio": "0.025", @@ -7192,10 +7532,10 @@ "minNotional": 25000.0, "maxNotional": 100000.0, "maintenanceMarginRate": 0.05, - "maxLeverage": 8.0, + "maxLeverage": 6.0, "info": { "bracket": "3", - "initialLeverage": "8", + "initialLeverage": "6", "notionalCap": "100000", "notionalFloor": "25000", "maintMarginRatio": "0.05", @@ -7222,13 +7562,13 @@ "tier": 5.0, "currency": "BUSD", "minNotional": 250000.0, - "maxNotional": 1000000.0, + "maxNotional": 500000.0, "maintenanceMarginRate": 0.125, "maxLeverage": 2.0, "info": { "bracket": "5", "initialLeverage": "2", - "notionalCap": "1000000", + "notionalCap": "500000", "notionalFloor": "250000", "maintMarginRatio": "0.125", "cum": "11900.0" @@ -7237,17 +7577,17 @@ { "tier": 6.0, "currency": "BUSD", - "minNotional": 1000000.0, - "maxNotional": 5000000.0, + "minNotional": 500000.0, + "maxNotional": 1000000.0, "maintenanceMarginRate": 0.5, "maxLeverage": 1.0, "info": { "bracket": "6", "initialLeverage": "1", - "notionalCap": "5000000", - "notionalFloor": "1000000", + "notionalCap": "1000000", + "notionalFloor": "500000", "maintMarginRatio": "0.5", - "cum": "386900.0" + "cum": "199400.0" } } ], @@ -7340,10 +7680,10 @@ "minNotional": 0.0, "maxNotional": 5000.0, "maintenanceMarginRate": 0.02, - "maxLeverage": 20.0, + "maxLeverage": 25.0, "info": { "bracket": "1", - "initialLeverage": "20", + "initialLeverage": "25", "notionalCap": "5000", "notionalFloor": "0", "maintMarginRatio": "0.02", @@ -7356,10 +7696,10 @@ "minNotional": 5000.0, "maxNotional": 25000.0, "maintenanceMarginRate": 0.025, - "maxLeverage": 10.0, + "maxLeverage": 20.0, "info": { "bracket": "2", - "initialLeverage": "10", + "initialLeverage": "20", "notionalCap": "25000", "notionalFloor": "5000", "maintMarginRatio": "0.025", @@ -7370,13 +7710,13 @@ "tier": 3.0, "currency": "USDT", "minNotional": 25000.0, - "maxNotional": 100000.0, + "maxNotional": 600000.0, "maintenanceMarginRate": 0.05, - "maxLeverage": 8.0, + "maxLeverage": 10.0, "info": { "bracket": "3", - "initialLeverage": "8", - "notionalCap": "100000", + "initialLeverage": "10", + "notionalCap": "600000", "notionalFloor": "25000", "maintMarginRatio": "0.05", "cum": "650.0" @@ -7385,49 +7725,65 @@ { "tier": 4.0, "currency": "USDT", - "minNotional": 100000.0, - "maxNotional": 250000.0, + "minNotional": 600000.0, + "maxNotional": 1600000.0, "maintenanceMarginRate": 0.1, "maxLeverage": 5.0, "info": { "bracket": "4", "initialLeverage": "5", - "notionalCap": "250000", - "notionalFloor": "100000", + "notionalCap": "1600000", + "notionalFloor": "600000", "maintMarginRatio": "0.1", - "cum": "5650.0" + "cum": "30650.0" } }, { "tier": 5.0, "currency": "USDT", - "minNotional": 250000.0, - "maxNotional": 1000000.0, + "minNotional": 1600000.0, + "maxNotional": 2000000.0, "maintenanceMarginRate": 0.125, - "maxLeverage": 2.0, + "maxLeverage": 4.0, "info": { "bracket": "5", - "initialLeverage": "2", - "notionalCap": "1000000", - "notionalFloor": "250000", + "initialLeverage": "4", + "notionalCap": "2000000", + "notionalFloor": "1600000", "maintMarginRatio": "0.125", - "cum": "11900.0" + "cum": "70650.0" } }, { "tier": 6.0, "currency": "USDT", - "minNotional": 1000000.0, - "maxNotional": 5000000.0, + "minNotional": 2000000.0, + "maxNotional": 6000000.0, + "maintenanceMarginRate": 0.25, + "maxLeverage": 2.0, + "info": { + "bracket": "6", + "initialLeverage": "2", + "notionalCap": "6000000", + "notionalFloor": "2000000", + "maintMarginRatio": "0.25", + "cum": "320650.0" + } + }, + { + "tier": 7.0, + "currency": "USDT", + "minNotional": 6000000.0, + "maxNotional": 10000000.0, "maintenanceMarginRate": 0.5, "maxLeverage": 1.0, "info": { - "bracket": "6", + "bracket": "7", "initialLeverage": "1", - "notionalCap": "5000000", - "notionalFloor": "1000000", + "notionalCap": "10000000", + "notionalFloor": "6000000", "maintMarginRatio": "0.5", - "cum": "386900.0" + "cum": "1820650.0" } } ], @@ -7634,10 +7990,10 @@ "minNotional": 0.0, "maxNotional": 5000.0, "maintenanceMarginRate": 0.02, - "maxLeverage": 15.0, + "maxLeverage": 25.0, "info": { "bracket": "1", - "initialLeverage": "15", + "initialLeverage": "25", "notionalCap": "5000", "notionalFloor": "0", "maintMarginRatio": "0.02", @@ -7650,10 +8006,10 @@ "minNotional": 5000.0, "maxNotional": 25000.0, "maintenanceMarginRate": 0.025, - "maxLeverage": 10.0, + "maxLeverage": 20.0, "info": { "bracket": "2", - "initialLeverage": "10", + "initialLeverage": "20", "notionalCap": "25000", "notionalFloor": "5000", "maintMarginRatio": "0.025", @@ -7664,13 +8020,13 @@ "tier": 3.0, "currency": "USDT", "minNotional": 25000.0, - "maxNotional": 200000.0, + "maxNotional": 300000.0, "maintenanceMarginRate": 0.05, - "maxLeverage": 8.0, + "maxLeverage": 10.0, "info": { "bracket": "3", - "initialLeverage": "8", - "notionalCap": "200000", + "initialLeverage": "10", + "notionalCap": "300000", "notionalFloor": "25000", "maintMarginRatio": "0.05", "cum": "650.0" @@ -7679,49 +8035,65 @@ { "tier": 4.0, "currency": "USDT", - "minNotional": 200000.0, - "maxNotional": 500000.0, + "minNotional": 300000.0, + "maxNotional": 800000.0, "maintenanceMarginRate": 0.1, "maxLeverage": 5.0, "info": { "bracket": "4", "initialLeverage": "5", - "notionalCap": "500000", - "notionalFloor": "200000", + "notionalCap": "800000", + "notionalFloor": "300000", "maintMarginRatio": "0.1", - "cum": "10650.0" + "cum": "15650.0" } }, { "tier": 5.0, "currency": "USDT", - "minNotional": 500000.0, + "minNotional": 800000.0, "maxNotional": 1000000.0, "maintenanceMarginRate": 0.125, - "maxLeverage": 2.0, + "maxLeverage": 4.0, "info": { "bracket": "5", - "initialLeverage": "2", + "initialLeverage": "4", "notionalCap": "1000000", - "notionalFloor": "500000", + "notionalFloor": "800000", "maintMarginRatio": "0.125", - "cum": "23150.0" + "cum": "35650.0" } }, { "tier": 6.0, "currency": "USDT", "minNotional": 1000000.0, + "maxNotional": 3000000.0, + "maintenanceMarginRate": 0.25, + "maxLeverage": 2.0, + "info": { + "bracket": "6", + "initialLeverage": "2", + "notionalCap": "3000000", + "notionalFloor": "1000000", + "maintMarginRatio": "0.25", + "cum": "160650.0" + } + }, + { + "tier": 7.0, + "currency": "USDT", + "minNotional": 3000000.0, "maxNotional": 5000000.0, "maintenanceMarginRate": 0.5, "maxLeverage": 1.0, "info": { - "bracket": "6", + "bracket": "7", "initialLeverage": "1", "notionalCap": "5000000", - "notionalFloor": "1000000", + "notionalFloor": "3000000", "maintMarginRatio": "0.5", - "cum": "398150.0" + "cum": "910650.0" } } ], @@ -8025,14 +8397,14 @@ "currency": "USDT", "minNotional": 0.0, "maxNotional": 5000.0, - "maintenanceMarginRate": 0.0065, + "maintenanceMarginRate": 0.006, "maxLeverage": 50.0, "info": { "bracket": "1", "initialLeverage": "50", "notionalCap": "5000", "notionalFloor": "0", - "maintMarginRatio": "0.0065", + "maintMarginRatio": "0.006", "cum": "0.0" } }, @@ -8041,14 +8413,14 @@ "currency": "USDT", "minNotional": 5000.0, "maxNotional": 25000.0, - "maintenanceMarginRate": 0.0075, + "maintenanceMarginRate": 0.007, "maxLeverage": 40.0, "info": { "bracket": "2", "initialLeverage": "40", "notionalCap": "25000", "notionalFloor": "5000", - "maintMarginRatio": "0.0075", + "maintMarginRatio": "0.007", "cum": "5.0" } }, @@ -8056,112 +8428,112 @@ "tier": 3.0, "currency": "USDT", "minNotional": 25000.0, - "maxNotional": 50000.0, + "maxNotional": 600000.0, "maintenanceMarginRate": 0.01, "maxLeverage": 25.0, "info": { "bracket": "3", "initialLeverage": "25", - "notionalCap": "50000", + "notionalCap": "600000", "notionalFloor": "25000", "maintMarginRatio": "0.01", - "cum": "67.5" + "cum": "80.0" } }, { "tier": 4.0, "currency": "USDT", - "minNotional": 50000.0, - "maxNotional": 250000.0, + "minNotional": 600000.0, + "maxNotional": 900000.0, "maintenanceMarginRate": 0.025, "maxLeverage": 20.0, "info": { "bracket": "4", "initialLeverage": "20", - "notionalCap": "250000", - "notionalFloor": "50000", + "notionalCap": "900000", + "notionalFloor": "600000", "maintMarginRatio": "0.025", - "cum": "817.5" + "cum": "9080.0" } }, { "tier": 5.0, "currency": "USDT", - "minNotional": 250000.0, - "maxNotional": 1000000.0, + "minNotional": 900000.0, + "maxNotional": 1800000.0, "maintenanceMarginRate": 0.05, "maxLeverage": 10.0, "info": { "bracket": "5", "initialLeverage": "10", - "notionalCap": "1000000", - "notionalFloor": "250000", + "notionalCap": "1800000", + "notionalFloor": "900000", "maintMarginRatio": "0.05", - "cum": "7067.5" + "cum": "31580.0" } }, { "tier": 6.0, "currency": "USDT", - "minNotional": 1000000.0, - "maxNotional": 2000000.0, + "minNotional": 1800000.0, + "maxNotional": 4800000.0, "maintenanceMarginRate": 0.1, "maxLeverage": 5.0, "info": { "bracket": "6", "initialLeverage": "5", - "notionalCap": "2000000", - "notionalFloor": "1000000", + "notionalCap": "4800000", + "notionalFloor": "1800000", "maintMarginRatio": "0.1", - "cum": "57067.5" + "cum": "121580.0" } }, { "tier": 7.0, "currency": "USDT", - "minNotional": 2000000.0, - "maxNotional": 5000000.0, + "minNotional": 4800000.0, + "maxNotional": 6000000.0, "maintenanceMarginRate": 0.125, "maxLeverage": 4.0, "info": { "bracket": "7", "initialLeverage": "4", - "notionalCap": "5000000", - "notionalFloor": "2000000", + "notionalCap": "6000000", + "notionalFloor": "4800000", "maintMarginRatio": "0.125", - "cum": "107067.5" + "cum": "241580.0" } }, { "tier": 8.0, "currency": "USDT", - "minNotional": 5000000.0, - "maxNotional": 10000000.0, + "minNotional": 6000000.0, + "maxNotional": 18000000.0, "maintenanceMarginRate": 0.25, "maxLeverage": 2.0, "info": { "bracket": "8", "initialLeverage": "2", - "notionalCap": "10000000", - "notionalFloor": "5000000", + "notionalCap": "18000000", + "notionalFloor": "6000000", "maintMarginRatio": "0.25", - "cum": "732067.5" + "cum": "991580.0" } }, { "tier": 9.0, "currency": "USDT", - "minNotional": 10000000.0, - "maxNotional": 20000000.0, + "minNotional": 18000000.0, + "maxNotional": 30000000.0, "maintenanceMarginRate": 0.5, "maxLeverage": 1.0, "info": { "bracket": "9", "initialLeverage": "1", - "notionalCap": "20000000", - "notionalFloor": "10000000", + "notionalCap": "30000000", + "notionalFloor": "18000000", "maintMarginRatio": "0.5", - "cum": "3232067.5" + "cum": "5491580.0" } } ], @@ -8706,13 +9078,13 @@ "tier": 3.0, "currency": "USDT", "minNotional": 25000.0, - "maxNotional": 100000.0, + "maxNotional": 600000.0, "maintenanceMarginRate": 0.05, "maxLeverage": 10.0, "info": { "bracket": "3", "initialLeverage": "10", - "notionalCap": "100000", + "notionalCap": "600000", "notionalFloor": "25000", "maintMarginRatio": "0.05", "cum": "700.0" @@ -8721,49 +9093,65 @@ { "tier": 4.0, "currency": "USDT", - "minNotional": 100000.0, - "maxNotional": 250000.0, + "minNotional": 600000.0, + "maxNotional": 1600000.0, "maintenanceMarginRate": 0.1, "maxLeverage": 5.0, "info": { "bracket": "4", "initialLeverage": "5", - "notionalCap": "250000", - "notionalFloor": "100000", + "notionalCap": "1600000", + "notionalFloor": "600000", "maintMarginRatio": "0.1", - "cum": "5700.0" + "cum": "30700.0" } }, { "tier": 5.0, "currency": "USDT", - "minNotional": 250000.0, - "maxNotional": 1000000.0, + "minNotional": 1600000.0, + "maxNotional": 2000000.0, "maintenanceMarginRate": 0.125, - "maxLeverage": 2.0, + "maxLeverage": 4.0, "info": { "bracket": "5", - "initialLeverage": "2", - "notionalCap": "1000000", - "notionalFloor": "250000", + "initialLeverage": "4", + "notionalCap": "2000000", + "notionalFloor": "1600000", "maintMarginRatio": "0.125", - "cum": "11950.0" + "cum": "70700.0" } }, { "tier": 6.0, "currency": "USDT", - "minNotional": 1000000.0, - "maxNotional": 50000000.0, + "minNotional": 2000000.0, + "maxNotional": 6000000.0, + "maintenanceMarginRate": 0.25, + "maxLeverage": 2.0, + "info": { + "bracket": "6", + "initialLeverage": "2", + "notionalCap": "6000000", + "notionalFloor": "2000000", + "maintMarginRatio": "0.25", + "cum": "320700.0" + } + }, + { + "tier": 7.0, + "currency": "USDT", + "minNotional": 6000000.0, + "maxNotional": 10000000.0, "maintenanceMarginRate": 0.5, "maxLeverage": 1.0, "info": { - "bracket": "6", + "bracket": "7", "initialLeverage": "1", - "notionalCap": "50000000", - "notionalFloor": "1000000", + "notionalCap": "10000000", + "notionalFloor": "6000000", "maintMarginRatio": "0.5", - "cum": "386950.0" + "cum": "1820700.0" } } ], @@ -9017,15 +9405,15 @@ "currency": "USDT", "minNotional": 5000.0, "maxNotional": 10000.0, - "maintenanceMarginRate": 0.0065, + "maintenanceMarginRate": 0.006, "maxLeverage": 50.0, "info": { "bracket": "2", "initialLeverage": "50", "notionalCap": "10000", "notionalFloor": "5000", - "maintMarginRatio": "0.0065", - "cum": "7.5" + "maintMarginRatio": "0.006", + "cum": "5.0" } }, { @@ -9041,7 +9429,7 @@ "notionalCap": "50000", "notionalFloor": "10000", "maintMarginRatio": "0.01", - "cum": "42.5" + "cum": "45.0" } }, { @@ -9057,7 +9445,7 @@ "notionalCap": "250000", "notionalFloor": "50000", "maintMarginRatio": "0.02", - "cum": "542.5" + "cum": "545.0" } }, { @@ -9073,77 +9461,77 @@ "notionalCap": "1000000", "notionalFloor": "250000", "maintMarginRatio": "0.05", - "cum": "8042.5" + "cum": "8045.0" } }, { "tier": 6.0, "currency": "USDT", "minNotional": 1000000.0, - "maxNotional": 2000000.0, + "maxNotional": 5000000.0, "maintenanceMarginRate": 0.1, "maxLeverage": 5.0, "info": { "bracket": "6", "initialLeverage": "5", - "notionalCap": "2000000", + "notionalCap": "5000000", "notionalFloor": "1000000", "maintMarginRatio": "0.1", - "cum": "58042.5" + "cum": "58045.0" } }, { "tier": 7.0, "currency": "USDT", - "minNotional": 2000000.0, - "maxNotional": 5000000.0, + "minNotional": 5000000.0, + "maxNotional": 10000000.0, "maintenanceMarginRate": 0.125, "maxLeverage": 4.0, "info": { "bracket": "7", "initialLeverage": "4", - "notionalCap": "5000000", - "notionalFloor": "2000000", + "notionalCap": "10000000", + "notionalFloor": "5000000", "maintMarginRatio": "0.125", - "cum": "108042.5" + "cum": "183045.0" } }, { "tier": 8.0, "currency": "USDT", - "minNotional": 5000000.0, - "maxNotional": 10000000.0, + "minNotional": 10000000.0, + "maxNotional": 20000000.0, "maintenanceMarginRate": 0.15, "maxLeverage": 3.0, "info": { "bracket": "8", "initialLeverage": "3", - "notionalCap": "10000000", - "notionalFloor": "5000000", + "notionalCap": "20000000", + "notionalFloor": "10000000", "maintMarginRatio": "0.15", - "cum": "233042.5" + "cum": "433045.0" } }, { "tier": 9.0, "currency": "USDT", - "minNotional": 10000000.0, - "maxNotional": 20000000.0, + "minNotional": 20000000.0, + "maxNotional": 30000000.0, "maintenanceMarginRate": 0.25, "maxLeverage": 2.0, "info": { "bracket": "9", "initialLeverage": "2", - "notionalCap": "20000000", - "notionalFloor": "10000000", + "notionalCap": "30000000", + "notionalFloor": "20000000", "maintMarginRatio": "0.25", - "cum": "1233042.5" + "cum": "2433045.0" } }, { "tier": 10.0, "currency": "USDT", - "minNotional": 20000000.0, + "minNotional": 30000000.0, "maxNotional": 50000000.0, "maintenanceMarginRate": 0.5, "maxLeverage": 1.0, @@ -9151,9 +9539,9 @@ "bracket": "10", "initialLeverage": "1", "notionalCap": "50000000", - "notionalFloor": "20000000", + "notionalFloor": "30000000", "maintMarginRatio": "0.5", - "cum": "6233042.5" + "cum": "9933045.0" } } ], @@ -9277,15 +9665,15 @@ "currency": "USDT", "minNotional": 5000.0, "maxNotional": 10000.0, - "maintenanceMarginRate": 0.0065, + "maintenanceMarginRate": 0.006, "maxLeverage": 50.0, "info": { "bracket": "2", "initialLeverage": "50", "notionalCap": "10000", "notionalFloor": "5000", - "maintMarginRatio": "0.0065", - "cum": "7.5" + "maintMarginRatio": "0.006", + "cum": "5.0" } }, { @@ -9301,7 +9689,7 @@ "notionalCap": "50000", "notionalFloor": "10000", "maintMarginRatio": "0.01", - "cum": "42.5" + "cum": "45.0" } }, { @@ -9317,7 +9705,7 @@ "notionalCap": "250000", "notionalFloor": "50000", "maintMarginRatio": "0.02", - "cum": "542.5" + "cum": "545.0" } }, { @@ -9333,77 +9721,77 @@ "notionalCap": "1000000", "notionalFloor": "250000", "maintMarginRatio": "0.05", - "cum": "8042.5" + "cum": "8045.0" } }, { "tier": 6.0, "currency": "USDT", "minNotional": 1000000.0, - "maxNotional": 2000000.0, + "maxNotional": 5000000.0, "maintenanceMarginRate": 0.1, "maxLeverage": 5.0, "info": { "bracket": "6", "initialLeverage": "5", - "notionalCap": "2000000", + "notionalCap": "5000000", "notionalFloor": "1000000", "maintMarginRatio": "0.1", - "cum": "58042.5" + "cum": "58045.0" } }, { "tier": 7.0, "currency": "USDT", - "minNotional": 2000000.0, - "maxNotional": 5000000.0, + "minNotional": 5000000.0, + "maxNotional": 10000000.0, "maintenanceMarginRate": 0.125, "maxLeverage": 4.0, "info": { "bracket": "7", "initialLeverage": "4", - "notionalCap": "5000000", - "notionalFloor": "2000000", + "notionalCap": "10000000", + "notionalFloor": "5000000", "maintMarginRatio": "0.125", - "cum": "108042.5" + "cum": "183045.0" } }, { "tier": 8.0, "currency": "USDT", - "minNotional": 5000000.0, - "maxNotional": 10000000.0, + "minNotional": 10000000.0, + "maxNotional": 20000000.0, "maintenanceMarginRate": 0.15, "maxLeverage": 3.0, "info": { "bracket": "8", "initialLeverage": "3", - "notionalCap": "10000000", - "notionalFloor": "5000000", + "notionalCap": "20000000", + "notionalFloor": "10000000", "maintMarginRatio": "0.15", - "cum": "233042.5" + "cum": "433045.0" } }, { "tier": 9.0, "currency": "USDT", - "minNotional": 10000000.0, - "maxNotional": 20000000.0, + "minNotional": 20000000.0, + "maxNotional": 30000000.0, "maintenanceMarginRate": 0.25, "maxLeverage": 2.0, "info": { "bracket": "9", "initialLeverage": "2", - "notionalCap": "20000000", - "notionalFloor": "10000000", + "notionalCap": "30000000", + "notionalFloor": "20000000", "maintMarginRatio": "0.25", - "cum": "1233042.5" + "cum": "2433045.0" } }, { "tier": 10.0, "currency": "USDT", - "minNotional": 20000000.0, + "minNotional": 30000000.0, "maxNotional": 50000000.0, "maintenanceMarginRate": 0.5, "maxLeverage": 1.0, @@ -9411,9 +9799,9 @@ "bracket": "10", "initialLeverage": "1", "notionalCap": "50000000", - "notionalFloor": "20000000", + "notionalFloor": "30000000", "maintMarginRatio": "0.5", - "cum": "6233042.5" + "cum": "9933045.0" } } ], @@ -9741,7 +10129,7 @@ } } ], - "ETH/USDT:USDT-230331": [ + "ETH/USDT:USDT-230630": [ { "tier": 1.0, "currency": "USDT", @@ -11036,10 +11424,10 @@ "minNotional": 0.0, "maxNotional": 5000.0, "maintenanceMarginRate": 0.02, - "maxLeverage": 20.0, + "maxLeverage": 10.0, "info": { "bracket": "1", - "initialLeverage": "20", + "initialLeverage": "10", "notionalCap": "5000", "notionalFloor": "0", "maintMarginRatio": "0.02", @@ -11052,10 +11440,10 @@ "minNotional": 5000.0, "maxNotional": 25000.0, "maintenanceMarginRate": 0.025, - "maxLeverage": 10.0, + "maxLeverage": 8.0, "info": { "bracket": "2", - "initialLeverage": "10", + "initialLeverage": "8", "notionalCap": "25000", "notionalFloor": "5000", "maintMarginRatio": "0.025", @@ -11068,10 +11456,10 @@ "minNotional": 25000.0, "maxNotional": 100000.0, "maintenanceMarginRate": 0.05, - "maxLeverage": 8.0, + "maxLeverage": 6.0, "info": { "bracket": "3", - "initialLeverage": "8", + "initialLeverage": "6", "notionalCap": "100000", "notionalFloor": "25000", "maintMarginRatio": "0.05", @@ -11098,13 +11486,13 @@ "tier": 5.0, "currency": "BUSD", "minNotional": 250000.0, - "maxNotional": 1000000.0, + "maxNotional": 500000.0, "maintenanceMarginRate": 0.125, "maxLeverage": 2.0, "info": { "bracket": "5", "initialLeverage": "2", - "notionalCap": "1000000", + "notionalCap": "500000", "notionalFloor": "250000", "maintMarginRatio": "0.125", "cum": "11900.0" @@ -11113,17 +11501,17 @@ { "tier": 6.0, "currency": "BUSD", - "minNotional": 1000000.0, - "maxNotional": 3000000.0, + "minNotional": 500000.0, + "maxNotional": 1000000.0, "maintenanceMarginRate": 0.5, "maxLeverage": 1.0, "info": { "bracket": "6", "initialLeverage": "1", - "notionalCap": "3000000", - "notionalFloor": "1000000", + "notionalCap": "1000000", + "notionalFloor": "500000", "maintMarginRatio": "0.5", - "cum": "386900.0" + "cum": "199400.0" } } ], @@ -11329,14 +11717,14 @@ "currency": "USDT", "minNotional": 0.0, "maxNotional": 5000.0, - "maintenanceMarginRate": 0.0065, + "maintenanceMarginRate": 0.006, "maxLeverage": 50.0, "info": { "bracket": "1", "initialLeverage": "50", "notionalCap": "5000", "notionalFloor": "0", - "maintMarginRatio": "0.0065", + "maintMarginRatio": "0.006", "cum": "0.0" } }, @@ -11353,103 +11741,103 @@ "notionalCap": "50000", "notionalFloor": "5000", "maintMarginRatio": "0.01", - "cum": "17.5" + "cum": "20.0" } }, { "tier": 3.0, "currency": "USDT", "minNotional": 50000.0, - "maxNotional": 200000.0, + "maxNotional": 900000.0, "maintenanceMarginRate": 0.025, "maxLeverage": 20.0, "info": { "bracket": "3", "initialLeverage": "20", - "notionalCap": "200000", + "notionalCap": "900000", "notionalFloor": "50000", "maintMarginRatio": "0.025", - "cum": "767.5" + "cum": "770.0" } }, { "tier": 4.0, "currency": "USDT", - "minNotional": 200000.0, - "maxNotional": 400000.0, + "minNotional": 900000.0, + "maxNotional": 1800000.0, "maintenanceMarginRate": 0.05, "maxLeverage": 10.0, "info": { "bracket": "4", "initialLeverage": "10", - "notionalCap": "400000", - "notionalFloor": "200000", + "notionalCap": "1800000", + "notionalFloor": "900000", "maintMarginRatio": "0.05", - "cum": "5767.5" + "cum": "23270.0" } }, { "tier": 5.0, "currency": "USDT", - "minNotional": 400000.0, - "maxNotional": 1000000.0, + "minNotional": 1800000.0, + "maxNotional": 4800000.0, "maintenanceMarginRate": 0.1, "maxLeverage": 5.0, "info": { "bracket": "5", "initialLeverage": "5", - "notionalCap": "1000000", - "notionalFloor": "400000", + "notionalCap": "4800000", + "notionalFloor": "1800000", "maintMarginRatio": "0.1", - "cum": "25767.5" + "cum": "113270.0" } }, { "tier": 6.0, "currency": "USDT", - "minNotional": 1000000.0, - "maxNotional": 5000000.0, + "minNotional": 4800000.0, + "maxNotional": 6000000.0, "maintenanceMarginRate": 0.125, "maxLeverage": 4.0, "info": { "bracket": "6", "initialLeverage": "4", - "notionalCap": "5000000", - "notionalFloor": "1000000", + "notionalCap": "6000000", + "notionalFloor": "4800000", "maintMarginRatio": "0.125", - "cum": "50767.5" + "cum": "233270.0" } }, { "tier": 7.0, "currency": "USDT", - "minNotional": 5000000.0, - "maxNotional": 6000000.0, + "minNotional": 6000000.0, + "maxNotional": 18000000.0, "maintenanceMarginRate": 0.25, "maxLeverage": 2.0, "info": { "bracket": "7", "initialLeverage": "2", - "notionalCap": "6000000", - "notionalFloor": "5000000", + "notionalCap": "18000000", + "notionalFloor": "6000000", "maintMarginRatio": "0.25", - "cum": "675767.5" + "cum": "983270.0" } }, { "tier": 8.0, "currency": "USDT", - "minNotional": 6000000.0, - "maxNotional": 10000000.0, + "minNotional": 18000000.0, + "maxNotional": 30000000.0, "maintenanceMarginRate": 0.5, "maxLeverage": 1.0, "info": { "bracket": "8", "initialLeverage": "1", - "notionalCap": "10000000", - "notionalFloor": "6000000", + "notionalCap": "30000000", + "notionalFloor": "18000000", "maintMarginRatio": "0.5", - "cum": "2175767.5" + "cum": "5483270.0" } } ], @@ -11460,10 +11848,10 @@ "minNotional": 0.0, "maxNotional": 5000.0, "maintenanceMarginRate": 0.02, - "maxLeverage": 20.0, + "maxLeverage": 8.0, "info": { "bracket": "1", - "initialLeverage": "20", + "initialLeverage": "8", "notionalCap": "5000", "notionalFloor": "0", "maintMarginRatio": "0.02", @@ -11476,10 +11864,10 @@ "minNotional": 5000.0, "maxNotional": 25000.0, "maintenanceMarginRate": 0.025, - "maxLeverage": 10.0, + "maxLeverage": 7.0, "info": { "bracket": "2", - "initialLeverage": "10", + "initialLeverage": "7", "notionalCap": "25000", "notionalFloor": "5000", "maintMarginRatio": "0.025", @@ -11492,10 +11880,10 @@ "minNotional": 25000.0, "maxNotional": 100000.0, "maintenanceMarginRate": 0.05, - "maxLeverage": 8.0, + "maxLeverage": 6.0, "info": { "bracket": "3", - "initialLeverage": "8", + "initialLeverage": "6", "notionalCap": "100000", "notionalFloor": "25000", "maintMarginRatio": "0.05", @@ -11538,13 +11926,13 @@ "tier": 6.0, "currency": "BUSD", "minNotional": 1000000.0, - "maxNotional": 5000000.0, + "maxNotional": 1500000.0, "maintenanceMarginRate": 0.5, "maxLeverage": 1.0, "info": { "bracket": "6", "initialLeverage": "1", - "notionalCap": "5000000", + "notionalCap": "1500000", "notionalFloor": "1000000", "maintMarginRatio": "0.5", "cum": "386900.0" @@ -11672,10 +12060,10 @@ "minNotional": 0.0, "maxNotional": 5000.0, "maintenanceMarginRate": 0.02, - "maxLeverage": 20.0, + "maxLeverage": 25.0, "info": { "bracket": "1", - "initialLeverage": "20", + "initialLeverage": "25", "notionalCap": "5000", "notionalFloor": "0", "maintMarginRatio": "0.02", @@ -11688,10 +12076,10 @@ "minNotional": 5000.0, "maxNotional": 25000.0, "maintenanceMarginRate": 0.025, - "maxLeverage": 15.0, + "maxLeverage": 20.0, "info": { "bracket": "2", - "initialLeverage": "15", + "initialLeverage": "20", "notionalCap": "25000", "notionalFloor": "5000", "maintMarginRatio": "0.025", @@ -11702,13 +12090,13 @@ "tier": 3.0, "currency": "USDT", "minNotional": 25000.0, - "maxNotional": 100000.0, + "maxNotional": 480000.0, "maintenanceMarginRate": 0.05, "maxLeverage": 10.0, "info": { "bracket": "3", "initialLeverage": "10", - "notionalCap": "100000", + "notionalCap": "480000", "notionalFloor": "25000", "maintMarginRatio": "0.05", "cum": "650.0" @@ -11717,49 +12105,65 @@ { "tier": 4.0, "currency": "USDT", - "minNotional": 100000.0, - "maxNotional": 250000.0, + "minNotional": 480000.0, + "maxNotional": 1280000.0, "maintenanceMarginRate": 0.1, "maxLeverage": 5.0, "info": { "bracket": "4", "initialLeverage": "5", - "notionalCap": "250000", - "notionalFloor": "100000", + "notionalCap": "1280000", + "notionalFloor": "480000", "maintMarginRatio": "0.1", - "cum": "5650.0" + "cum": "24650.0" } }, { "tier": 5.0, "currency": "USDT", - "minNotional": 250000.0, - "maxNotional": 1000000.0, + "minNotional": 1280000.0, + "maxNotional": 1600000.0, "maintenanceMarginRate": 0.125, - "maxLeverage": 2.0, + "maxLeverage": 4.0, "info": { "bracket": "5", - "initialLeverage": "2", - "notionalCap": "1000000", - "notionalFloor": "250000", + "initialLeverage": "4", + "notionalCap": "1600000", + "notionalFloor": "1280000", "maintMarginRatio": "0.125", - "cum": "11900.0" + "cum": "56650.0" } }, { "tier": 6.0, "currency": "USDT", - "minNotional": 1000000.0, - "maxNotional": 5000000.0, + "minNotional": 1600000.0, + "maxNotional": 4800000.0, + "maintenanceMarginRate": 0.25, + "maxLeverage": 2.0, + "info": { + "bracket": "6", + "initialLeverage": "2", + "notionalCap": "4800000", + "notionalFloor": "1600000", + "maintMarginRatio": "0.25", + "cum": "256650.0" + } + }, + { + "tier": 7.0, + "currency": "USDT", + "minNotional": 4800000.0, + "maxNotional": 8000000.0, "maintenanceMarginRate": 0.5, "maxLeverage": 1.0, "info": { - "bracket": "6", + "bracket": "7", "initialLeverage": "1", - "notionalCap": "5000000", - "notionalFloor": "1000000", + "notionalCap": "8000000", + "notionalFloor": "4800000", "maintMarginRatio": "0.5", - "cum": "386900.0" + "cum": "1456650.0" } } ], @@ -12073,6 +12477,120 @@ } } ], + "HFT/USDT:USDT": [ + { + "tier": 1.0, + "currency": "USDT", + "minNotional": 0.0, + "maxNotional": 5000.0, + "maintenanceMarginRate": 0.02, + "maxLeverage": 20.0, + "info": { + "bracket": "1", + "initialLeverage": "20", + "notionalCap": "5000", + "notionalFloor": "0", + "maintMarginRatio": "0.02", + "cum": "0.0" + } + }, + { + "tier": 2.0, + "currency": "USDT", + "minNotional": 5000.0, + "maxNotional": 25000.0, + "maintenanceMarginRate": 0.025, + "maxLeverage": 15.0, + "info": { + "bracket": "2", + "initialLeverage": "15", + "notionalCap": "25000", + "notionalFloor": "5000", + "maintMarginRatio": "0.025", + "cum": "25.0" + } + }, + { + "tier": 3.0, + "currency": "USDT", + "minNotional": 25000.0, + "maxNotional": 200000.0, + "maintenanceMarginRate": 0.05, + "maxLeverage": 10.0, + "info": { + "bracket": "3", + "initialLeverage": "10", + "notionalCap": "200000", + "notionalFloor": "25000", + "maintMarginRatio": "0.05", + "cum": "650.0" + } + }, + { + "tier": 4.0, + "currency": "USDT", + "minNotional": 200000.0, + "maxNotional": 500000.0, + "maintenanceMarginRate": 0.1, + "maxLeverage": 5.0, + "info": { + "bracket": "4", + "initialLeverage": "5", + "notionalCap": "500000", + "notionalFloor": "200000", + "maintMarginRatio": "0.1", + "cum": "10650.0" + } + }, + { + "tier": 5.0, + "currency": "USDT", + "minNotional": 500000.0, + "maxNotional": 1000000.0, + "maintenanceMarginRate": 0.125, + "maxLeverage": 4.0, + "info": { + "bracket": "5", + "initialLeverage": "4", + "notionalCap": "1000000", + "notionalFloor": "500000", + "maintMarginRatio": "0.125", + "cum": "23150.0" + } + }, + { + "tier": 6.0, + "currency": "USDT", + "minNotional": 1000000.0, + "maxNotional": 3000000.0, + "maintenanceMarginRate": 0.25, + "maxLeverage": 2.0, + "info": { + "bracket": "6", + "initialLeverage": "2", + "notionalCap": "3000000", + "notionalFloor": "1000000", + "maintMarginRatio": "0.25", + "cum": "148150.0" + } + }, + { + "tier": 7.0, + "currency": "USDT", + "minNotional": 3000000.0, + "maxNotional": 5000000.0, + "maintenanceMarginRate": 0.5, + "maxLeverage": 1.0, + "info": { + "bracket": "7", + "initialLeverage": "1", + "notionalCap": "5000000", + "notionalFloor": "3000000", + "maintMarginRatio": "0.5", + "cum": "898150.0" + } + } + ], "HIGH/USDT:USDT": [ { "tier": 1.0, @@ -12194,10 +12712,10 @@ "minNotional": 0.0, "maxNotional": 5000.0, "maintenanceMarginRate": 0.02, - "maxLeverage": 15.0, + "maxLeverage": 10.0, "info": { "bracket": "1", - "initialLeverage": "15", + "initialLeverage": "10", "notionalCap": "5000", "notionalFloor": "0", "maintMarginRatio": "0.02", @@ -12210,10 +12728,10 @@ "minNotional": 5000.0, "maxNotional": 25000.0, "maintenanceMarginRate": 0.025, - "maxLeverage": 10.0, + "maxLeverage": 8.0, "info": { "bracket": "2", - "initialLeverage": "10", + "initialLeverage": "8", "notionalCap": "25000", "notionalFloor": "5000", "maintMarginRatio": "0.025", @@ -12224,13 +12742,13 @@ "tier": 3.0, "currency": "USDT", "minNotional": 25000.0, - "maxNotional": 120000.0, + "maxNotional": 300000.0, "maintenanceMarginRate": 0.05, - "maxLeverage": 8.0, + "maxLeverage": 6.0, "info": { "bracket": "3", - "initialLeverage": "8", - "notionalCap": "120000", + "initialLeverage": "6", + "notionalCap": "300000", "notionalFloor": "25000", "maintMarginRatio": "0.05", "cum": "650.0" @@ -12239,49 +12757,65 @@ { "tier": 4.0, "currency": "USDT", - "minNotional": 120000.0, - "maxNotional": 300000.0, + "minNotional": 300000.0, + "maxNotional": 800000.0, "maintenanceMarginRate": 0.1, "maxLeverage": 5.0, "info": { "bracket": "4", "initialLeverage": "5", - "notionalCap": "300000", - "notionalFloor": "120000", + "notionalCap": "800000", + "notionalFloor": "300000", "maintMarginRatio": "0.1", - "cum": "6650.0" + "cum": "15650.0" } }, { "tier": 5.0, "currency": "USDT", - "minNotional": 300000.0, + "minNotional": 800000.0, "maxNotional": 1000000.0, "maintenanceMarginRate": 0.125, - "maxLeverage": 2.0, + "maxLeverage": 4.0, "info": { "bracket": "5", - "initialLeverage": "2", + "initialLeverage": "4", "notionalCap": "1000000", - "notionalFloor": "300000", + "notionalFloor": "800000", "maintMarginRatio": "0.125", - "cum": "14150.0" + "cum": "35650.0" } }, { "tier": 6.0, "currency": "USDT", "minNotional": 1000000.0, - "maxNotional": 3000000.0, + "maxNotional": 1500000.0, + "maintenanceMarginRate": 0.25, + "maxLeverage": 2.0, + "info": { + "bracket": "6", + "initialLeverage": "2", + "notionalCap": "1500000", + "notionalFloor": "1000000", + "maintMarginRatio": "0.25", + "cum": "160650.0" + } + }, + { + "tier": 7.0, + "currency": "USDT", + "minNotional": 1500000.0, + "maxNotional": 2000000.0, "maintenanceMarginRate": 0.5, "maxLeverage": 1.0, "info": { - "bracket": "6", + "bracket": "7", "initialLeverage": "1", - "notionalCap": "3000000", - "notionalFloor": "1000000", + "notionalCap": "2000000", + "notionalFloor": "1500000", "maintMarginRatio": "0.5", - "cum": "389150.0" + "cum": "535650.0" } } ], @@ -12481,104 +13015,6 @@ } } ], - "ICP/BUSD:BUSD": [ - { - "tier": 1.0, - "currency": "BUSD", - "minNotional": 0.0, - "maxNotional": 5000.0, - "maintenanceMarginRate": 0.02, - "maxLeverage": 20.0, - "info": { - "bracket": "1", - "initialLeverage": "20", - "notionalCap": "5000", - "notionalFloor": "0", - "maintMarginRatio": "0.02", - "cum": "0.0" - } - }, - { - "tier": 2.0, - "currency": "BUSD", - "minNotional": 5000.0, - "maxNotional": 25000.0, - "maintenanceMarginRate": 0.025, - "maxLeverage": 10.0, - "info": { - "bracket": "2", - "initialLeverage": "10", - "notionalCap": "25000", - "notionalFloor": "5000", - "maintMarginRatio": "0.025", - "cum": "25.0" - } - }, - { - "tier": 3.0, - "currency": "BUSD", - "minNotional": 25000.0, - "maxNotional": 100000.0, - "maintenanceMarginRate": 0.05, - "maxLeverage": 8.0, - "info": { - "bracket": "3", - "initialLeverage": "8", - "notionalCap": "100000", - "notionalFloor": "25000", - "maintMarginRatio": "0.05", - "cum": "650.0" - } - }, - { - "tier": 4.0, - "currency": "BUSD", - "minNotional": 100000.0, - "maxNotional": 250000.0, - "maintenanceMarginRate": 0.1, - "maxLeverage": 5.0, - "info": { - "bracket": "4", - "initialLeverage": "5", - "notionalCap": "250000", - "notionalFloor": "100000", - "maintMarginRatio": "0.1", - "cum": "5650.0" - } - }, - { - "tier": 5.0, - "currency": "BUSD", - "minNotional": 250000.0, - "maxNotional": 1000000.0, - "maintenanceMarginRate": 0.125, - "maxLeverage": 2.0, - "info": { - "bracket": "5", - "initialLeverage": "2", - "notionalCap": "1000000", - "notionalFloor": "250000", - "maintMarginRatio": "0.125", - "cum": "11900.0" - } - }, - { - "tier": 6.0, - "currency": "BUSD", - "minNotional": 1000000.0, - "maxNotional": 3000000.0, - "maintenanceMarginRate": 0.5, - "maxLeverage": 1.0, - "info": { - "bracket": "6", - "initialLeverage": "1", - "notionalCap": "3000000", - "notionalFloor": "1000000", - "maintMarginRatio": "0.5", - "cum": "386900.0" - } - } - ], "ICP/USDT:USDT": [ { "tier": 1.0, @@ -12586,10 +13022,10 @@ "minNotional": 0.0, "maxNotional": 5000.0, "maintenanceMarginRate": 0.01, - "maxLeverage": 20.0, + "maxLeverage": 25.0, "info": { "bracket": "1", - "initialLeverage": "20", + "initialLeverage": "25", "notionalCap": "5000", "notionalFloor": "0", "maintMarginRatio": "0.01", @@ -12602,10 +13038,10 @@ "minNotional": 5000.0, "maxNotional": 25000.0, "maintenanceMarginRate": 0.025, - "maxLeverage": 10.0, + "maxLeverage": 20.0, "info": { "bracket": "2", - "initialLeverage": "10", + "initialLeverage": "20", "notionalCap": "25000", "notionalFloor": "5000", "maintMarginRatio": "0.025", @@ -12616,13 +13052,13 @@ "tier": 3.0, "currency": "USDT", "minNotional": 25000.0, - "maxNotional": 100000.0, + "maxNotional": 600000.0, "maintenanceMarginRate": 0.05, - "maxLeverage": 8.0, + "maxLeverage": 10.0, "info": { "bracket": "3", - "initialLeverage": "8", - "notionalCap": "100000", + "initialLeverage": "10", + "notionalCap": "600000", "notionalFloor": "25000", "maintMarginRatio": "0.05", "cum": "700.0" @@ -12631,49 +13067,65 @@ { "tier": 4.0, "currency": "USDT", - "minNotional": 100000.0, - "maxNotional": 250000.0, + "minNotional": 600000.0, + "maxNotional": 1600000.0, "maintenanceMarginRate": 0.1, "maxLeverage": 5.0, "info": { "bracket": "4", "initialLeverage": "5", - "notionalCap": "250000", - "notionalFloor": "100000", + "notionalCap": "1600000", + "notionalFloor": "600000", "maintMarginRatio": "0.1", - "cum": "5700.0" + "cum": "30700.0" } }, { "tier": 5.0, "currency": "USDT", - "minNotional": 250000.0, - "maxNotional": 1000000.0, + "minNotional": 1600000.0, + "maxNotional": 2000000.0, "maintenanceMarginRate": 0.125, - "maxLeverage": 2.0, + "maxLeverage": 4.0, "info": { "bracket": "5", - "initialLeverage": "2", - "notionalCap": "1000000", - "notionalFloor": "250000", + "initialLeverage": "4", + "notionalCap": "2000000", + "notionalFloor": "1600000", "maintMarginRatio": "0.125", - "cum": "11950.0" + "cum": "70700.0" } }, { "tier": 6.0, "currency": "USDT", - "minNotional": 1000000.0, - "maxNotional": 30000000.0, + "minNotional": 2000000.0, + "maxNotional": 6000000.0, + "maintenanceMarginRate": 0.25, + "maxLeverage": 2.0, + "info": { + "bracket": "6", + "initialLeverage": "2", + "notionalCap": "6000000", + "notionalFloor": "2000000", + "maintMarginRatio": "0.25", + "cum": "320700.0" + } + }, + { + "tier": 7.0, + "currency": "USDT", + "minNotional": 6000000.0, + "maxNotional": 10000000.0, "maintenanceMarginRate": 0.5, "maxLeverage": 1.0, "info": { - "bracket": "6", + "bracket": "7", "initialLeverage": "1", - "notionalCap": "30000000", - "notionalFloor": "1000000", + "notionalCap": "10000000", + "notionalFloor": "6000000", "maintMarginRatio": "0.5", - "cum": "386950.0" + "cum": "1820700.0" } } ], @@ -12775,105 +13227,7 @@ } } ], - "IMX/USDT:USDT": [ - { - "tier": 1.0, - "currency": "USDT", - "minNotional": 0.0, - "maxNotional": 5000.0, - "maintenanceMarginRate": 0.01, - "maxLeverage": 20.0, - "info": { - "bracket": "1", - "initialLeverage": "20", - "notionalCap": "5000", - "notionalFloor": "0", - "maintMarginRatio": "0.01", - "cum": "0.0" - } - }, - { - "tier": 2.0, - "currency": "USDT", - "minNotional": 5000.0, - "maxNotional": 25000.0, - "maintenanceMarginRate": 0.025, - "maxLeverage": 10.0, - "info": { - "bracket": "2", - "initialLeverage": "10", - "notionalCap": "25000", - "notionalFloor": "5000", - "maintMarginRatio": "0.025", - "cum": "75.0" - } - }, - { - "tier": 3.0, - "currency": "USDT", - "minNotional": 25000.0, - "maxNotional": 100000.0, - "maintenanceMarginRate": 0.05, - "maxLeverage": 8.0, - "info": { - "bracket": "3", - "initialLeverage": "8", - "notionalCap": "100000", - "notionalFloor": "25000", - "maintMarginRatio": "0.05", - "cum": "700.0" - } - }, - { - "tier": 4.0, - "currency": "USDT", - "minNotional": 100000.0, - "maxNotional": 250000.0, - "maintenanceMarginRate": 0.1, - "maxLeverage": 5.0, - "info": { - "bracket": "4", - "initialLeverage": "5", - "notionalCap": "250000", - "notionalFloor": "100000", - "maintMarginRatio": "0.1", - "cum": "5700.0" - } - }, - { - "tier": 5.0, - "currency": "USDT", - "minNotional": 250000.0, - "maxNotional": 1000000.0, - "maintenanceMarginRate": 0.125, - "maxLeverage": 2.0, - "info": { - "bracket": "5", - "initialLeverage": "2", - "notionalCap": "1000000", - "notionalFloor": "250000", - "maintMarginRatio": "0.125", - "cum": "11950.0" - } - }, - { - "tier": 6.0, - "currency": "USDT", - "minNotional": 1000000.0, - "maxNotional": 5000000.0, - "maintenanceMarginRate": 0.5, - "maxLeverage": 1.0, - "info": { - "bracket": "6", - "initialLeverage": "1", - "notionalCap": "5000000", - "notionalFloor": "1000000", - "maintMarginRatio": "0.5", - "cum": "386950.0" - } - } - ], - "INJ/USDT:USDT": [ + "ID/USDT:USDT": [ { "tier": 1.0, "currency": "USDT", @@ -12896,10 +13250,10 @@ "minNotional": 5000.0, "maxNotional": 25000.0, "maintenanceMarginRate": 0.025, - "maxLeverage": 10.0, + "maxLeverage": 15.0, "info": { "bracket": "2", - "initialLeverage": "10", + "initialLeverage": "15", "notionalCap": "25000", "notionalFloor": "5000", "maintMarginRatio": "0.025", @@ -12910,13 +13264,13 @@ "tier": 3.0, "currency": "USDT", "minNotional": 25000.0, - "maxNotional": 100000.0, + "maxNotional": 200000.0, "maintenanceMarginRate": 0.05, - "maxLeverage": 8.0, + "maxLeverage": 10.0, "info": { "bracket": "3", - "initialLeverage": "8", - "notionalCap": "100000", + "initialLeverage": "10", + "notionalCap": "200000", "notionalFloor": "25000", "maintMarginRatio": "0.05", "cum": "650.0" @@ -12925,33 +13279,33 @@ { "tier": 4.0, "currency": "USDT", - "minNotional": 100000.0, - "maxNotional": 250000.0, + "minNotional": 200000.0, + "maxNotional": 500000.0, "maintenanceMarginRate": 0.1, "maxLeverage": 5.0, "info": { "bracket": "4", "initialLeverage": "5", - "notionalCap": "250000", - "notionalFloor": "100000", + "notionalCap": "500000", + "notionalFloor": "200000", "maintMarginRatio": "0.1", - "cum": "5650.0" + "cum": "10650.0" } }, { "tier": 5.0, "currency": "USDT", - "minNotional": 250000.0, + "minNotional": 500000.0, "maxNotional": 1000000.0, "maintenanceMarginRate": 0.125, - "maxLeverage": 2.0, + "maxLeverage": 4.0, "info": { "bracket": "5", - "initialLeverage": "2", + "initialLeverage": "4", "notionalCap": "1000000", - "notionalFloor": "250000", + "notionalFloor": "500000", "maintMarginRatio": "0.125", - "cum": "11900.0" + "cum": "23150.0" } }, { @@ -12959,15 +13313,259 @@ "currency": "USDT", "minNotional": 1000000.0, "maxNotional": 3000000.0, + "maintenanceMarginRate": 0.25, + "maxLeverage": 2.0, + "info": { + "bracket": "6", + "initialLeverage": "2", + "notionalCap": "3000000", + "notionalFloor": "1000000", + "maintMarginRatio": "0.25", + "cum": "148150.0" + } + }, + { + "tier": 7.0, + "currency": "USDT", + "minNotional": 3000000.0, + "maxNotional": 5000000.0, "maintenanceMarginRate": 0.5, "maxLeverage": 1.0, "info": { - "bracket": "6", + "bracket": "7", "initialLeverage": "1", - "notionalCap": "3000000", - "notionalFloor": "1000000", + "notionalCap": "5000000", + "notionalFloor": "3000000", "maintMarginRatio": "0.5", - "cum": "386900.0" + "cum": "898150.0" + } + } + ], + "IMX/USDT:USDT": [ + { + "tier": 1.0, + "currency": "USDT", + "minNotional": 0.0, + "maxNotional": 5000.0, + "maintenanceMarginRate": 0.01, + "maxLeverage": 25.0, + "info": { + "bracket": "1", + "initialLeverage": "25", + "notionalCap": "5000", + "notionalFloor": "0", + "maintMarginRatio": "0.01", + "cum": "0.0" + } + }, + { + "tier": 2.0, + "currency": "USDT", + "minNotional": 5000.0, + "maxNotional": 25000.0, + "maintenanceMarginRate": 0.025, + "maxLeverage": 20.0, + "info": { + "bracket": "2", + "initialLeverage": "20", + "notionalCap": "25000", + "notionalFloor": "5000", + "maintMarginRatio": "0.025", + "cum": "75.0" + } + }, + { + "tier": 3.0, + "currency": "USDT", + "minNotional": 25000.0, + "maxNotional": 600000.0, + "maintenanceMarginRate": 0.05, + "maxLeverage": 10.0, + "info": { + "bracket": "3", + "initialLeverage": "10", + "notionalCap": "600000", + "notionalFloor": "25000", + "maintMarginRatio": "0.05", + "cum": "700.0" + } + }, + { + "tier": 4.0, + "currency": "USDT", + "minNotional": 600000.0, + "maxNotional": 1600000.0, + "maintenanceMarginRate": 0.1, + "maxLeverage": 5.0, + "info": { + "bracket": "4", + "initialLeverage": "5", + "notionalCap": "1600000", + "notionalFloor": "600000", + "maintMarginRatio": "0.1", + "cum": "30700.0" + } + }, + { + "tier": 5.0, + "currency": "USDT", + "minNotional": 1600000.0, + "maxNotional": 2000000.0, + "maintenanceMarginRate": 0.125, + "maxLeverage": 4.0, + "info": { + "bracket": "5", + "initialLeverage": "4", + "notionalCap": "2000000", + "notionalFloor": "1600000", + "maintMarginRatio": "0.125", + "cum": "70700.0" + } + }, + { + "tier": 6.0, + "currency": "USDT", + "minNotional": 2000000.0, + "maxNotional": 6000000.0, + "maintenanceMarginRate": 0.25, + "maxLeverage": 2.0, + "info": { + "bracket": "6", + "initialLeverage": "2", + "notionalCap": "6000000", + "notionalFloor": "2000000", + "maintMarginRatio": "0.25", + "cum": "320700.0" + } + }, + { + "tier": 7.0, + "currency": "USDT", + "minNotional": 6000000.0, + "maxNotional": 10000000.0, + "maintenanceMarginRate": 0.5, + "maxLeverage": 1.0, + "info": { + "bracket": "7", + "initialLeverage": "1", + "notionalCap": "10000000", + "notionalFloor": "6000000", + "maintMarginRatio": "0.5", + "cum": "1820700.0" + } + } + ], + "INJ/USDT:USDT": [ + { + "tier": 1.0, + "currency": "USDT", + "minNotional": 0.0, + "maxNotional": 5000.0, + "maintenanceMarginRate": 0.02, + "maxLeverage": 25.0, + "info": { + "bracket": "1", + "initialLeverage": "25", + "notionalCap": "5000", + "notionalFloor": "0", + "maintMarginRatio": "0.02", + "cum": "0.0" + } + }, + { + "tier": 2.0, + "currency": "USDT", + "minNotional": 5000.0, + "maxNotional": 25000.0, + "maintenanceMarginRate": 0.025, + "maxLeverage": 20.0, + "info": { + "bracket": "2", + "initialLeverage": "20", + "notionalCap": "25000", + "notionalFloor": "5000", + "maintMarginRatio": "0.025", + "cum": "25.0" + } + }, + { + "tier": 3.0, + "currency": "USDT", + "minNotional": 25000.0, + "maxNotional": 600000.0, + "maintenanceMarginRate": 0.05, + "maxLeverage": 10.0, + "info": { + "bracket": "3", + "initialLeverage": "10", + "notionalCap": "600000", + "notionalFloor": "25000", + "maintMarginRatio": "0.05", + "cum": "650.0" + } + }, + { + "tier": 4.0, + "currency": "USDT", + "minNotional": 600000.0, + "maxNotional": 1600000.0, + "maintenanceMarginRate": 0.1, + "maxLeverage": 5.0, + "info": { + "bracket": "4", + "initialLeverage": "5", + "notionalCap": "1600000", + "notionalFloor": "600000", + "maintMarginRatio": "0.1", + "cum": "30650.0" + } + }, + { + "tier": 5.0, + "currency": "USDT", + "minNotional": 1600000.0, + "maxNotional": 2000000.0, + "maintenanceMarginRate": 0.125, + "maxLeverage": 4.0, + "info": { + "bracket": "5", + "initialLeverage": "4", + "notionalCap": "2000000", + "notionalFloor": "1600000", + "maintMarginRatio": "0.125", + "cum": "70650.0" + } + }, + { + "tier": 6.0, + "currency": "USDT", + "minNotional": 2000000.0, + "maxNotional": 6000000.0, + "maintenanceMarginRate": 0.25, + "maxLeverage": 2.0, + "info": { + "bracket": "6", + "initialLeverage": "2", + "notionalCap": "6000000", + "notionalFloor": "2000000", + "maintMarginRatio": "0.25", + "cum": "320650.0" + } + }, + { + "tier": 7.0, + "currency": "USDT", + "minNotional": 6000000.0, + "maxNotional": 10000000.0, + "maintenanceMarginRate": 0.5, + "maxLeverage": 1.0, + "info": { + "bracket": "7", + "initialLeverage": "1", + "notionalCap": "10000000", + "notionalFloor": "6000000", + "maintMarginRatio": "0.5", + "cum": "1820650.0" } } ], @@ -13379,6 +13977,120 @@ } } ], + "JOE/USDT:USDT": [ + { + "tier": 1.0, + "currency": "USDT", + "minNotional": 0.0, + "maxNotional": 5000.0, + "maintenanceMarginRate": 0.02, + "maxLeverage": 20.0, + "info": { + "bracket": "1", + "initialLeverage": "20", + "notionalCap": "5000", + "notionalFloor": "0", + "maintMarginRatio": "0.02", + "cum": "0.0" + } + }, + { + "tier": 2.0, + "currency": "USDT", + "minNotional": 5000.0, + "maxNotional": 25000.0, + "maintenanceMarginRate": 0.025, + "maxLeverage": 15.0, + "info": { + "bracket": "2", + "initialLeverage": "15", + "notionalCap": "25000", + "notionalFloor": "5000", + "maintMarginRatio": "0.025", + "cum": "25.0" + } + }, + { + "tier": 3.0, + "currency": "USDT", + "minNotional": 25000.0, + "maxNotional": 200000.0, + "maintenanceMarginRate": 0.05, + "maxLeverage": 10.0, + "info": { + "bracket": "3", + "initialLeverage": "10", + "notionalCap": "200000", + "notionalFloor": "25000", + "maintMarginRatio": "0.05", + "cum": "650.0" + } + }, + { + "tier": 4.0, + "currency": "USDT", + "minNotional": 200000.0, + "maxNotional": 500000.0, + "maintenanceMarginRate": 0.1, + "maxLeverage": 5.0, + "info": { + "bracket": "4", + "initialLeverage": "5", + "notionalCap": "500000", + "notionalFloor": "200000", + "maintMarginRatio": "0.1", + "cum": "10650.0" + } + }, + { + "tier": 5.0, + "currency": "USDT", + "minNotional": 500000.0, + "maxNotional": 1000000.0, + "maintenanceMarginRate": 0.125, + "maxLeverage": 4.0, + "info": { + "bracket": "5", + "initialLeverage": "4", + "notionalCap": "1000000", + "notionalFloor": "500000", + "maintMarginRatio": "0.125", + "cum": "23150.0" + } + }, + { + "tier": 6.0, + "currency": "USDT", + "minNotional": 1000000.0, + "maxNotional": 3000000.0, + "maintenanceMarginRate": 0.25, + "maxLeverage": 2.0, + "info": { + "bracket": "6", + "initialLeverage": "2", + "notionalCap": "3000000", + "notionalFloor": "1000000", + "maintMarginRatio": "0.25", + "cum": "148150.0" + } + }, + { + "tier": 7.0, + "currency": "USDT", + "minNotional": 3000000.0, + "maxNotional": 5000000.0, + "maintenanceMarginRate": 0.5, + "maxLeverage": 1.0, + "info": { + "bracket": "7", + "initialLeverage": "1", + "notionalCap": "5000000", + "notionalFloor": "3000000", + "maintMarginRatio": "0.5", + "cum": "898150.0" + } + } + ], "KAVA/USDT:USDT": [ { "tier": 1.0, @@ -14116,20 +14828,20 @@ "tier": 6.0, "currency": "BUSD", "minNotional": 1000000.0, - "maxNotional": 2000000.0, + "maxNotional": 1500000.0, "maintenanceMarginRate": 0.5, "maxLeverage": 1.0, "info": { "bracket": "6", "initialLeverage": "1", - "notionalCap": "2000000", + "notionalCap": "1500000", "notionalFloor": "1000000", "maintMarginRatio": "0.5", "cum": "386885.0" } } ], - "LINA/USDT:USDT": [ + "LEVER/USDT:USDT": [ { "tier": 1.0, "currency": "USDT", @@ -14152,10 +14864,10 @@ "minNotional": 5000.0, "maxNotional": 25000.0, "maintenanceMarginRate": 0.025, - "maxLeverage": 10.0, + "maxLeverage": 15.0, "info": { "bracket": "2", - "initialLeverage": "10", + "initialLeverage": "15", "notionalCap": "25000", "notionalFloor": "5000", "maintMarginRatio": "0.025", @@ -14166,13 +14878,13 @@ "tier": 3.0, "currency": "USDT", "minNotional": 25000.0, - "maxNotional": 100000.0, + "maxNotional": 200000.0, "maintenanceMarginRate": 0.05, - "maxLeverage": 8.0, + "maxLeverage": 10.0, "info": { "bracket": "3", - "initialLeverage": "8", - "notionalCap": "100000", + "initialLeverage": "10", + "notionalCap": "200000", "notionalFloor": "25000", "maintMarginRatio": "0.05", "cum": "650.0" @@ -14181,33 +14893,33 @@ { "tier": 4.0, "currency": "USDT", - "minNotional": 100000.0, - "maxNotional": 250000.0, + "minNotional": 200000.0, + "maxNotional": 500000.0, "maintenanceMarginRate": 0.1, "maxLeverage": 5.0, "info": { "bracket": "4", "initialLeverage": "5", - "notionalCap": "250000", - "notionalFloor": "100000", + "notionalCap": "500000", + "notionalFloor": "200000", "maintMarginRatio": "0.1", - "cum": "5650.0" + "cum": "10650.0" } }, { "tier": 5.0, "currency": "USDT", - "minNotional": 250000.0, + "minNotional": 500000.0, "maxNotional": 1000000.0, "maintenanceMarginRate": 0.125, - "maxLeverage": 2.0, + "maxLeverage": 4.0, "info": { "bracket": "5", - "initialLeverage": "2", + "initialLeverage": "4", "notionalCap": "1000000", - "notionalFloor": "250000", + "notionalFloor": "500000", "maintMarginRatio": "0.125", - "cum": "11900.0" + "cum": "23150.0" } }, { @@ -14215,15 +14927,145 @@ "currency": "USDT", "minNotional": 1000000.0, "maxNotional": 3000000.0, + "maintenanceMarginRate": 0.25, + "maxLeverage": 2.0, + "info": { + "bracket": "6", + "initialLeverage": "2", + "notionalCap": "3000000", + "notionalFloor": "1000000", + "maintMarginRatio": "0.25", + "cum": "148150.0" + } + }, + { + "tier": 7.0, + "currency": "USDT", + "minNotional": 3000000.0, + "maxNotional": 5000000.0, "maintenanceMarginRate": 0.5, "maxLeverage": 1.0, "info": { - "bracket": "6", + "bracket": "7", "initialLeverage": "1", - "notionalCap": "3000000", - "notionalFloor": "1000000", + "notionalCap": "5000000", + "notionalFloor": "3000000", "maintMarginRatio": "0.5", - "cum": "386900.0" + "cum": "898150.0" + } + } + ], + "LINA/USDT:USDT": [ + { + "tier": 1.0, + "currency": "USDT", + "minNotional": 0.0, + "maxNotional": 5000.0, + "maintenanceMarginRate": 0.02, + "maxLeverage": 25.0, + "info": { + "bracket": "1", + "initialLeverage": "25", + "notionalCap": "5000", + "notionalFloor": "0", + "maintMarginRatio": "0.02", + "cum": "0.0" + } + }, + { + "tier": 2.0, + "currency": "USDT", + "minNotional": 5000.0, + "maxNotional": 25000.0, + "maintenanceMarginRate": 0.025, + "maxLeverage": 20.0, + "info": { + "bracket": "2", + "initialLeverage": "20", + "notionalCap": "25000", + "notionalFloor": "5000", + "maintMarginRatio": "0.025", + "cum": "25.0" + } + }, + { + "tier": 3.0, + "currency": "USDT", + "minNotional": 25000.0, + "maxNotional": 900000.0, + "maintenanceMarginRate": 0.05, + "maxLeverage": 10.0, + "info": { + "bracket": "3", + "initialLeverage": "10", + "notionalCap": "900000", + "notionalFloor": "25000", + "maintMarginRatio": "0.05", + "cum": "650.0" + } + }, + { + "tier": 4.0, + "currency": "USDT", + "minNotional": 900000.0, + "maxNotional": 2400000.0, + "maintenanceMarginRate": 0.1, + "maxLeverage": 5.0, + "info": { + "bracket": "4", + "initialLeverage": "5", + "notionalCap": "2400000", + "notionalFloor": "900000", + "maintMarginRatio": "0.1", + "cum": "45650.0" + } + }, + { + "tier": 5.0, + "currency": "USDT", + "minNotional": 2400000.0, + "maxNotional": 3000000.0, + "maintenanceMarginRate": 0.125, + "maxLeverage": 4.0, + "info": { + "bracket": "5", + "initialLeverage": "4", + "notionalCap": "3000000", + "notionalFloor": "2400000", + "maintMarginRatio": "0.125", + "cum": "105650.0" + } + }, + { + "tier": 6.0, + "currency": "USDT", + "minNotional": 3000000.0, + "maxNotional": 9000000.0, + "maintenanceMarginRate": 0.25, + "maxLeverage": 2.0, + "info": { + "bracket": "6", + "initialLeverage": "2", + "notionalCap": "9000000", + "notionalFloor": "3000000", + "maintMarginRatio": "0.25", + "cum": "480650.0" + } + }, + { + "tier": 7.0, + "currency": "USDT", + "minNotional": 9000000.0, + "maxNotional": 15000000.0, + "maintenanceMarginRate": 0.5, + "maxLeverage": 1.0, + "info": { + "bracket": "7", + "initialLeverage": "1", + "notionalCap": "15000000", + "notionalFloor": "9000000", + "maintMarginRatio": "0.5", + "cum": "2730650.0" } } ], @@ -14363,15 +15205,15 @@ "currency": "USDT", "minNotional": 5000.0, "maxNotional": 10000.0, - "maintenanceMarginRate": 0.0065, + "maintenanceMarginRate": 0.006, "maxLeverage": 50.0, "info": { "bracket": "2", "initialLeverage": "50", "notionalCap": "10000", "notionalFloor": "5000", - "maintMarginRatio": "0.0065", - "cum": "7.5" + "maintMarginRatio": "0.006", + "cum": "5.0" } }, { @@ -14387,7 +15229,7 @@ "notionalCap": "50000", "notionalFloor": "10000", "maintMarginRatio": "0.01", - "cum": "42.5" + "cum": "45.0" } }, { @@ -14403,7 +15245,7 @@ "notionalCap": "250000", "notionalFloor": "50000", "maintMarginRatio": "0.02", - "cum": "542.5" + "cum": "545.0" } }, { @@ -14419,77 +15261,77 @@ "notionalCap": "1000000", "notionalFloor": "250000", "maintMarginRatio": "0.05", - "cum": "8042.5" + "cum": "8045.0" } }, { "tier": 6.0, "currency": "USDT", "minNotional": 1000000.0, - "maxNotional": 2000000.0, + "maxNotional": 5000000.0, "maintenanceMarginRate": 0.1, "maxLeverage": 5.0, "info": { "bracket": "6", "initialLeverage": "5", - "notionalCap": "2000000", + "notionalCap": "5000000", "notionalFloor": "1000000", "maintMarginRatio": "0.1", - "cum": "58042.5" + "cum": "58045.0" } }, { "tier": 7.0, "currency": "USDT", - "minNotional": 2000000.0, - "maxNotional": 5000000.0, + "minNotional": 5000000.0, + "maxNotional": 10000000.0, "maintenanceMarginRate": 0.125, "maxLeverage": 4.0, "info": { "bracket": "7", "initialLeverage": "4", - "notionalCap": "5000000", - "notionalFloor": "2000000", + "notionalCap": "10000000", + "notionalFloor": "5000000", "maintMarginRatio": "0.125", - "cum": "108042.5" + "cum": "183045.0" } }, { "tier": 8.0, "currency": "USDT", - "minNotional": 5000000.0, - "maxNotional": 10000000.0, + "minNotional": 10000000.0, + "maxNotional": 20000000.0, "maintenanceMarginRate": 0.15, "maxLeverage": 3.0, "info": { "bracket": "8", "initialLeverage": "3", - "notionalCap": "10000000", - "notionalFloor": "5000000", + "notionalCap": "20000000", + "notionalFloor": "10000000", "maintMarginRatio": "0.15", - "cum": "233042.5" + "cum": "433045.0" } }, { "tier": 9.0, "currency": "USDT", - "minNotional": 10000000.0, - "maxNotional": 20000000.0, + "minNotional": 20000000.0, + "maxNotional": 30000000.0, "maintenanceMarginRate": 0.25, "maxLeverage": 2.0, "info": { "bracket": "9", "initialLeverage": "2", - "notionalCap": "20000000", - "notionalFloor": "10000000", + "notionalCap": "30000000", + "notionalFloor": "20000000", "maintMarginRatio": "0.25", - "cum": "1233042.5" + "cum": "2433045.0" } }, { "tier": 10.0, "currency": "USDT", - "minNotional": 20000000.0, + "minNotional": 30000000.0, "maxNotional": 50000000.0, "maintenanceMarginRate": 0.5, "maxLeverage": 1.0, @@ -14497,9 +15339,9 @@ "bracket": "10", "initialLeverage": "1", "notionalCap": "50000000", - "notionalFloor": "20000000", + "notionalFloor": "30000000", "maintMarginRatio": "0.5", - "cum": "6233042.5" + "cum": "9933045.0" } } ], @@ -15031,15 +15873,15 @@ "currency": "USDT", "minNotional": 5000.0, "maxNotional": 10000.0, - "maintenanceMarginRate": 0.0065, + "maintenanceMarginRate": 0.006, "maxLeverage": 50.0, "info": { "bracket": "2", "initialLeverage": "50", "notionalCap": "10000", "notionalFloor": "5000", - "maintMarginRatio": "0.0065", - "cum": "7.5" + "maintMarginRatio": "0.006", + "cum": "5.0" } }, { @@ -15055,7 +15897,7 @@ "notionalCap": "50000", "notionalFloor": "10000", "maintMarginRatio": "0.01", - "cum": "42.5" + "cum": "45.0" } }, { @@ -15071,7 +15913,7 @@ "notionalCap": "250000", "notionalFloor": "50000", "maintMarginRatio": "0.02", - "cum": "542.5" + "cum": "545.0" } }, { @@ -15087,77 +15929,77 @@ "notionalCap": "1000000", "notionalFloor": "250000", "maintMarginRatio": "0.05", - "cum": "8042.5" + "cum": "8045.0" } }, { "tier": 6.0, "currency": "USDT", "minNotional": 1000000.0, - "maxNotional": 2000000.0, + "maxNotional": 5000000.0, "maintenanceMarginRate": 0.1, "maxLeverage": 5.0, "info": { "bracket": "6", "initialLeverage": "5", - "notionalCap": "2000000", + "notionalCap": "5000000", "notionalFloor": "1000000", "maintMarginRatio": "0.1", - "cum": "58042.5" + "cum": "58045.0" } }, { "tier": 7.0, "currency": "USDT", - "minNotional": 2000000.0, - "maxNotional": 5000000.0, + "minNotional": 5000000.0, + "maxNotional": 10000000.0, "maintenanceMarginRate": 0.125, "maxLeverage": 4.0, "info": { "bracket": "7", "initialLeverage": "4", - "notionalCap": "5000000", - "notionalFloor": "2000000", + "notionalCap": "10000000", + "notionalFloor": "5000000", "maintMarginRatio": "0.125", - "cum": "108042.5" + "cum": "183045.0" } }, { "tier": 8.0, "currency": "USDT", - "minNotional": 5000000.0, - "maxNotional": 10000000.0, + "minNotional": 10000000.0, + "maxNotional": 20000000.0, "maintenanceMarginRate": 0.15, "maxLeverage": 3.0, "info": { "bracket": "8", "initialLeverage": "3", - "notionalCap": "10000000", - "notionalFloor": "5000000", + "notionalCap": "20000000", + "notionalFloor": "10000000", "maintMarginRatio": "0.15", - "cum": "233042.5" + "cum": "433045.0" } }, { "tier": 9.0, "currency": "USDT", - "minNotional": 10000000.0, - "maxNotional": 20000000.0, + "minNotional": 20000000.0, + "maxNotional": 30000000.0, "maintenanceMarginRate": 0.25, "maxLeverage": 2.0, "info": { "bracket": "9", "initialLeverage": "2", - "notionalCap": "20000000", - "notionalFloor": "10000000", + "notionalCap": "30000000", + "notionalFloor": "20000000", "maintMarginRatio": "0.25", - "cum": "1233042.5" + "cum": "2433045.0" } }, { "tier": 10.0, "currency": "USDT", - "minNotional": 20000000.0, + "minNotional": 30000000.0, "maxNotional": 50000000.0, "maintenanceMarginRate": 0.5, "maxLeverage": 1.0, @@ -15165,107 +16007,9 @@ "bracket": "10", "initialLeverage": "1", "notionalCap": "50000000", - "notionalFloor": "20000000", + "notionalFloor": "30000000", "maintMarginRatio": "0.5", - "cum": "6233042.5" - } - } - ], - "LUNA2/BUSD:BUSD": [ - { - "tier": 1.0, - "currency": "BUSD", - "minNotional": 0.0, - "maxNotional": 5000.0, - "maintenanceMarginRate": 0.02, - "maxLeverage": 20.0, - "info": { - "bracket": "1", - "initialLeverage": "20", - "notionalCap": "5000", - "notionalFloor": "0", - "maintMarginRatio": "0.02", - "cum": "0.0" - } - }, - { - "tier": 2.0, - "currency": "BUSD", - "minNotional": 5000.0, - "maxNotional": 25000.0, - "maintenanceMarginRate": 0.025, - "maxLeverage": 10.0, - "info": { - "bracket": "2", - "initialLeverage": "10", - "notionalCap": "25000", - "notionalFloor": "5000", - "maintMarginRatio": "0.025", - "cum": "25.0" - } - }, - { - "tier": 3.0, - "currency": "BUSD", - "minNotional": 25000.0, - "maxNotional": 100000.0, - "maintenanceMarginRate": 0.05, - "maxLeverage": 8.0, - "info": { - "bracket": "3", - "initialLeverage": "8", - "notionalCap": "100000", - "notionalFloor": "25000", - "maintMarginRatio": "0.05", - "cum": "650.0" - } - }, - { - "tier": 4.0, - "currency": "BUSD", - "minNotional": 100000.0, - "maxNotional": 250000.0, - "maintenanceMarginRate": 0.1, - "maxLeverage": 5.0, - "info": { - "bracket": "4", - "initialLeverage": "5", - "notionalCap": "250000", - "notionalFloor": "100000", - "maintMarginRatio": "0.1", - "cum": "5650.0" - } - }, - { - "tier": 5.0, - "currency": "BUSD", - "minNotional": 250000.0, - "maxNotional": 1000000.0, - "maintenanceMarginRate": 0.125, - "maxLeverage": 2.0, - "info": { - "bracket": "5", - "initialLeverage": "2", - "notionalCap": "1000000", - "notionalFloor": "250000", - "maintMarginRatio": "0.125", - "cum": "11900.0" - } - }, - { - "tier": 6.0, - "currency": "BUSD", - "minNotional": 1000000.0, - "maxNotional": 5000000.0, - "maintenanceMarginRate": 0.5, - "maxLeverage": 1.0, - "info": { - "bracket": "6", - "initialLeverage": "1", - "notionalCap": "5000000", - "notionalFloor": "1000000", - "maintMarginRatio": "0.5", - "cum": "386900.0" + "cum": "9933045.0" } } ], @@ -15617,14 +16361,14 @@ "currency": "USDT", "minNotional": 0.0, "maxNotional": 5000.0, - "maintenanceMarginRate": 0.02, - "maxLeverage": 25.0, + "maintenanceMarginRate": 0.01, + "maxLeverage": 50.0, "info": { "bracket": "1", - "initialLeverage": "25", + "initialLeverage": "50", "notionalCap": "5000", "notionalFloor": "0", - "maintMarginRatio": "0.02", + "maintMarginRatio": "0.01", "cum": "0.0" } }, @@ -15633,95 +16377,111 @@ "currency": "USDT", "minNotional": 5000.0, "maxNotional": 25000.0, - "maintenanceMarginRate": 0.025, - "maxLeverage": 20.0, + "maintenanceMarginRate": 0.02, + "maxLeverage": 25.0, "info": { "bracket": "2", - "initialLeverage": "20", + "initialLeverage": "25", "notionalCap": "25000", "notionalFloor": "5000", - "maintMarginRatio": "0.025", - "cum": "25.0" + "maintMarginRatio": "0.02", + "cum": "50.0" } }, { "tier": 3.0, "currency": "USDT", "minNotional": 25000.0, - "maxNotional": 600000.0, - "maintenanceMarginRate": 0.05, - "maxLeverage": 10.0, + "maxNotional": 900000.0, + "maintenanceMarginRate": 0.025, + "maxLeverage": 20.0, "info": { "bracket": "3", - "initialLeverage": "10", - "notionalCap": "600000", + "initialLeverage": "20", + "notionalCap": "900000", "notionalFloor": "25000", - "maintMarginRatio": "0.05", - "cum": "650.0" + "maintMarginRatio": "0.025", + "cum": "175.0" } }, { "tier": 4.0, "currency": "USDT", - "minNotional": 600000.0, - "maxNotional": 1600000.0, - "maintenanceMarginRate": 0.1, - "maxLeverage": 5.0, + "minNotional": 900000.0, + "maxNotional": 1800000.0, + "maintenanceMarginRate": 0.05, + "maxLeverage": 10.0, "info": { "bracket": "4", - "initialLeverage": "5", - "notionalCap": "1600000", - "notionalFloor": "600000", - "maintMarginRatio": "0.1", - "cum": "30650.0" + "initialLeverage": "10", + "notionalCap": "1800000", + "notionalFloor": "900000", + "maintMarginRatio": "0.05", + "cum": "22675.0" } }, { "tier": 5.0, "currency": "USDT", - "minNotional": 1600000.0, - "maxNotional": 3000000.0, - "maintenanceMarginRate": 0.125, - "maxLeverage": 4.0, + "minNotional": 1800000.0, + "maxNotional": 4800000.0, + "maintenanceMarginRate": 0.1, + "maxLeverage": 5.0, "info": { "bracket": "5", - "initialLeverage": "4", - "notionalCap": "3000000", - "notionalFloor": "1600000", - "maintMarginRatio": "0.125", - "cum": "70650.0" + "initialLeverage": "5", + "notionalCap": "4800000", + "notionalFloor": "1800000", + "maintMarginRatio": "0.1", + "cum": "112675.0" } }, { "tier": 6.0, "currency": "USDT", - "minNotional": 3000000.0, + "minNotional": 4800000.0, "maxNotional": 6000000.0, - "maintenanceMarginRate": 0.25, - "maxLeverage": 2.0, + "maintenanceMarginRate": 0.125, + "maxLeverage": 4.0, "info": { "bracket": "6", - "initialLeverage": "2", + "initialLeverage": "4", "notionalCap": "6000000", - "notionalFloor": "3000000", - "maintMarginRatio": "0.25", - "cum": "445650.0" + "notionalFloor": "4800000", + "maintMarginRatio": "0.125", + "cum": "232675.0" } }, { "tier": 7.0, "currency": "USDT", "minNotional": 6000000.0, - "maxNotional": 10000000.0, + "maxNotional": 18000000.0, + "maintenanceMarginRate": 0.25, + "maxLeverage": 2.0, + "info": { + "bracket": "7", + "initialLeverage": "2", + "notionalCap": "18000000", + "notionalFloor": "6000000", + "maintMarginRatio": "0.25", + "cum": "982675.0" + } + }, + { + "tier": 8.0, + "currency": "USDT", + "minNotional": 18000000.0, + "maxNotional": 30000000.0, "maintenanceMarginRate": 0.5, "maxLeverage": 1.0, "info": { - "bracket": "7", + "bracket": "8", "initialLeverage": "1", - "notionalCap": "10000000", - "notionalFloor": "6000000", + "notionalCap": "30000000", + "notionalFloor": "18000000", "maintMarginRatio": "0.5", - "cum": "1945650.0" + "cum": "5482675.0" } } ], @@ -15876,13 +16636,13 @@ "tier": 3.0, "currency": "USDT", "minNotional": 25000.0, - "maxNotional": 50000.0, + "maxNotional": 600000.0, "maintenanceMarginRate": 0.01, "maxLeverage": 25.0, "info": { "bracket": "3", "initialLeverage": "25", - "notionalCap": "50000", + "notionalCap": "600000", "notionalFloor": "25000", "maintMarginRatio": "0.01", "cum": "80.0" @@ -15891,97 +16651,97 @@ { "tier": 4.0, "currency": "USDT", - "minNotional": 50000.0, - "maxNotional": 400000.0, + "minNotional": 600000.0, + "maxNotional": 900000.0, "maintenanceMarginRate": 0.025, "maxLeverage": 20.0, "info": { "bracket": "4", "initialLeverage": "20", - "notionalCap": "400000", - "notionalFloor": "50000", + "notionalCap": "900000", + "notionalFloor": "600000", "maintMarginRatio": "0.025", - "cum": "830.0" + "cum": "9080.0" } }, { "tier": 5.0, "currency": "USDT", - "minNotional": 400000.0, - "maxNotional": 800000.0, + "minNotional": 900000.0, + "maxNotional": 1800000.0, "maintenanceMarginRate": 0.05, "maxLeverage": 10.0, "info": { "bracket": "5", "initialLeverage": "10", - "notionalCap": "800000", - "notionalFloor": "400000", + "notionalCap": "1800000", + "notionalFloor": "900000", "maintMarginRatio": "0.05", - "cum": "10830.0" + "cum": "31580.0" } }, { "tier": 6.0, "currency": "USDT", - "minNotional": 800000.0, - "maxNotional": 2000000.0, + "minNotional": 1800000.0, + "maxNotional": 4800000.0, "maintenanceMarginRate": 0.1, "maxLeverage": 5.0, "info": { "bracket": "6", "initialLeverage": "5", - "notionalCap": "2000000", - "notionalFloor": "800000", + "notionalCap": "4800000", + "notionalFloor": "1800000", "maintMarginRatio": "0.1", - "cum": "50830.0" + "cum": "121580.0" } }, { "tier": 7.0, "currency": "USDT", - "minNotional": 2000000.0, - "maxNotional": 5000000.0, + "minNotional": 4800000.0, + "maxNotional": 6000000.0, "maintenanceMarginRate": 0.125, "maxLeverage": 4.0, "info": { "bracket": "7", "initialLeverage": "4", - "notionalCap": "5000000", - "notionalFloor": "2000000", + "notionalCap": "6000000", + "notionalFloor": "4800000", "maintMarginRatio": "0.125", - "cum": "100830.0" + "cum": "241580.0" } }, { "tier": 8.0, "currency": "USDT", - "minNotional": 5000000.0, - "maxNotional": 12000000.0, + "minNotional": 6000000.0, + "maxNotional": 18000000.0, "maintenanceMarginRate": 0.25, "maxLeverage": 2.0, "info": { "bracket": "8", "initialLeverage": "2", - "notionalCap": "12000000", - "notionalFloor": "5000000", + "notionalCap": "18000000", + "notionalFloor": "6000000", "maintMarginRatio": "0.25", - "cum": "725830.0" + "cum": "991580.0" } }, { "tier": 9.0, "currency": "USDT", - "minNotional": 12000000.0, - "maxNotional": 20000000.0, + "minNotional": 18000000.0, + "maxNotional": 30000000.0, "maintenanceMarginRate": 0.5, "maxLeverage": 1.0, "info": { "bracket": "9", "initialLeverage": "1", - "notionalCap": "20000000", - "notionalFloor": "12000000", + "notionalCap": "30000000", + "notionalFloor": "18000000", "maintMarginRatio": "0.5", - "cum": "3725830.0" + "cum": "5491580.0" } } ], @@ -16105,14 +16865,14 @@ "currency": "USDT", "minNotional": 0.0, "maxNotional": 5000.0, - "maintenanceMarginRate": 0.01, - "maxLeverage": 25.0, + "maintenanceMarginRate": 0.006, + "maxLeverage": 50.0, "info": { "bracket": "1", - "initialLeverage": "25", + "initialLeverage": "50", "notionalCap": "5000", "notionalFloor": "0", - "maintMarginRatio": "0.01", + "maintMarginRatio": "0.006", "cum": "0.0" } }, @@ -16121,79 +16881,111 @@ "currency": "USDT", "minNotional": 5000.0, "maxNotional": 25000.0, - "maintenanceMarginRate": 0.025, - "maxLeverage": 20.0, + "maintenanceMarginRate": 0.01, + "maxLeverage": 25.0, "info": { "bracket": "2", - "initialLeverage": "20", + "initialLeverage": "25", "notionalCap": "25000", "notionalFloor": "5000", - "maintMarginRatio": "0.025", - "cum": "75.0" + "maintMarginRatio": "0.01", + "cum": "20.0" } }, { "tier": 3.0, "currency": "USDT", "minNotional": 25000.0, - "maxNotional": 100000.0, - "maintenanceMarginRate": 0.05, - "maxLeverage": 10.0, + "maxNotional": 450000.0, + "maintenanceMarginRate": 0.025, + "maxLeverage": 20.0, "info": { "bracket": "3", - "initialLeverage": "10", - "notionalCap": "100000", + "initialLeverage": "20", + "notionalCap": "450000", "notionalFloor": "25000", - "maintMarginRatio": "0.05", - "cum": "700.0" + "maintMarginRatio": "0.025", + "cum": "395.0" } }, { "tier": 4.0, "currency": "USDT", - "minNotional": 100000.0, - "maxNotional": 250000.0, - "maintenanceMarginRate": 0.1, - "maxLeverage": 5.0, + "minNotional": 450000.0, + "maxNotional": 900000.0, + "maintenanceMarginRate": 0.05, + "maxLeverage": 10.0, "info": { "bracket": "4", - "initialLeverage": "5", - "notionalCap": "250000", - "notionalFloor": "100000", - "maintMarginRatio": "0.1", - "cum": "5700.0" + "initialLeverage": "10", + "notionalCap": "900000", + "notionalFloor": "450000", + "maintMarginRatio": "0.05", + "cum": "11645.0" } }, { "tier": 5.0, "currency": "USDT", - "minNotional": 250000.0, - "maxNotional": 1000000.0, - "maintenanceMarginRate": 0.125, - "maxLeverage": 2.0, + "minNotional": 900000.0, + "maxNotional": 2400000.0, + "maintenanceMarginRate": 0.1, + "maxLeverage": 5.0, "info": { "bracket": "5", - "initialLeverage": "2", - "notionalCap": "1000000", - "notionalFloor": "250000", - "maintMarginRatio": "0.125", - "cum": "11950.0" + "initialLeverage": "5", + "notionalCap": "2400000", + "notionalFloor": "900000", + "maintMarginRatio": "0.1", + "cum": "56645.0" } }, { "tier": 6.0, "currency": "USDT", - "minNotional": 1000000.0, - "maxNotional": 5000000.0, + "minNotional": 2400000.0, + "maxNotional": 3000000.0, + "maintenanceMarginRate": 0.125, + "maxLeverage": 4.0, + "info": { + "bracket": "6", + "initialLeverage": "4", + "notionalCap": "3000000", + "notionalFloor": "2400000", + "maintMarginRatio": "0.125", + "cum": "116645.0" + } + }, + { + "tier": 7.0, + "currency": "USDT", + "minNotional": 3000000.0, + "maxNotional": 9000000.0, + "maintenanceMarginRate": 0.25, + "maxLeverage": 2.0, + "info": { + "bracket": "7", + "initialLeverage": "2", + "notionalCap": "9000000", + "notionalFloor": "3000000", + "maintMarginRatio": "0.25", + "cum": "491645.0" + } + }, + { + "tier": 8.0, + "currency": "USDT", + "minNotional": 9000000.0, + "maxNotional": 15000000.0, "maintenanceMarginRate": 0.5, "maxLeverage": 1.0, "info": { - "bracket": "6", + "bracket": "8", "initialLeverage": "1", - "notionalCap": "5000000", - "notionalFloor": "1000000", + "notionalCap": "15000000", + "notionalFloor": "9000000", "maintMarginRatio": "0.5", - "cum": "386950.0" + "cum": "2741645.0" } } ], @@ -16204,10 +16996,10 @@ "minNotional": 0.0, "maxNotional": 5000.0, "maintenanceMarginRate": 0.02, - "maxLeverage": 20.0, + "maxLeverage": 25.0, "info": { "bracket": "1", - "initialLeverage": "20", + "initialLeverage": "25", "notionalCap": "5000", "notionalFloor": "0", "maintMarginRatio": "0.02", @@ -16220,10 +17012,10 @@ "minNotional": 5000.0, "maxNotional": 25000.0, "maintenanceMarginRate": 0.025, - "maxLeverage": 10.0, + "maxLeverage": 20.0, "info": { "bracket": "2", - "initialLeverage": "10", + "initialLeverage": "20", "notionalCap": "25000", "notionalFloor": "5000", "maintMarginRatio": "0.025", @@ -16234,13 +17026,13 @@ "tier": 3.0, "currency": "USDT", "minNotional": 25000.0, - "maxNotional": 100000.0, + "maxNotional": 600000.0, "maintenanceMarginRate": 0.05, - "maxLeverage": 8.0, + "maxLeverage": 10.0, "info": { "bracket": "3", - "initialLeverage": "8", - "notionalCap": "100000", + "initialLeverage": "10", + "notionalCap": "600000", "notionalFloor": "25000", "maintMarginRatio": "0.05", "cum": "650.0" @@ -16249,49 +17041,65 @@ { "tier": 4.0, "currency": "USDT", - "minNotional": 100000.0, - "maxNotional": 250000.0, + "minNotional": 600000.0, + "maxNotional": 1600000.0, "maintenanceMarginRate": 0.1, "maxLeverage": 5.0, "info": { "bracket": "4", "initialLeverage": "5", - "notionalCap": "250000", - "notionalFloor": "100000", + "notionalCap": "1600000", + "notionalFloor": "600000", "maintMarginRatio": "0.1", - "cum": "5650.0" + "cum": "30650.0" } }, { "tier": 5.0, "currency": "USDT", - "minNotional": 250000.0, - "maxNotional": 1000000.0, + "minNotional": 1600000.0, + "maxNotional": 2000000.0, "maintenanceMarginRate": 0.125, - "maxLeverage": 2.0, + "maxLeverage": 4.0, "info": { "bracket": "5", - "initialLeverage": "2", - "notionalCap": "1000000", - "notionalFloor": "250000", + "initialLeverage": "4", + "notionalCap": "2000000", + "notionalFloor": "1600000", "maintMarginRatio": "0.125", - "cum": "11900.0" + "cum": "70650.0" } }, { "tier": 6.0, "currency": "USDT", - "minNotional": 1000000.0, - "maxNotional": 3000000.0, + "minNotional": 2000000.0, + "maxNotional": 6000000.0, + "maintenanceMarginRate": 0.25, + "maxLeverage": 2.0, + "info": { + "bracket": "6", + "initialLeverage": "2", + "notionalCap": "6000000", + "notionalFloor": "2000000", + "maintMarginRatio": "0.25", + "cum": "320650.0" + } + }, + { + "tier": 7.0, + "currency": "USDT", + "minNotional": 6000000.0, + "maxNotional": 10000000.0, "maintenanceMarginRate": 0.5, "maxLeverage": 1.0, "info": { - "bracket": "6", + "bracket": "7", "initialLeverage": "1", - "notionalCap": "3000000", - "notionalFloor": "1000000", + "notionalCap": "10000000", + "notionalFloor": "6000000", "maintMarginRatio": "0.5", - "cum": "386900.0" + "cum": "1820650.0" } } ], @@ -16302,10 +17110,10 @@ "minNotional": 0.0, "maxNotional": 5000.0, "maintenanceMarginRate": 0.02, - "maxLeverage": 20.0, + "maxLeverage": 8.0, "info": { "bracket": "1", - "initialLeverage": "20", + "initialLeverage": "8", "notionalCap": "5000", "notionalFloor": "0", "maintMarginRatio": "0.02", @@ -16318,10 +17126,10 @@ "minNotional": 5000.0, "maxNotional": 25000.0, "maintenanceMarginRate": 0.025, - "maxLeverage": 10.0, + "maxLeverage": 7.0, "info": { "bracket": "2", - "initialLeverage": "10", + "initialLeverage": "7", "notionalCap": "25000", "notionalFloor": "5000", "maintMarginRatio": "0.025", @@ -16334,10 +17142,10 @@ "minNotional": 25000.0, "maxNotional": 100000.0, "maintenanceMarginRate": 0.05, - "maxLeverage": 8.0, + "maxLeverage": 6.0, "info": { "bracket": "3", - "initialLeverage": "8", + "initialLeverage": "6", "notionalCap": "100000", "notionalFloor": "25000", "maintMarginRatio": "0.05", @@ -16380,13 +17188,13 @@ "tier": 6.0, "currency": "BUSD", "minNotional": 1000000.0, - "maxNotional": 5000000.0, + "maxNotional": 3000000.0, "maintenanceMarginRate": 0.5, "maxLeverage": 1.0, "info": { "bracket": "6", "initialLeverage": "1", - "notionalCap": "5000000", + "notionalCap": "3000000", "notionalFloor": "1000000", "maintMarginRatio": "0.5", "cum": "386900.0" @@ -16968,13 +17776,13 @@ "tier": 3.0, "currency": "USDT", "minNotional": 25000.0, - "maxNotional": 100000.0, + "maxNotional": 900000.0, "maintenanceMarginRate": 0.05, "maxLeverage": 8.0, "info": { "bracket": "3", "initialLeverage": "8", - "notionalCap": "100000", + "notionalCap": "900000", "notionalFloor": "25000", "maintMarginRatio": "0.05", "cum": "650.0" @@ -16983,49 +17791,65 @@ { "tier": 4.0, "currency": "USDT", - "minNotional": 100000.0, - "maxNotional": 250000.0, + "minNotional": 900000.0, + "maxNotional": 2400000.0, "maintenanceMarginRate": 0.1, "maxLeverage": 5.0, "info": { "bracket": "4", "initialLeverage": "5", - "notionalCap": "250000", - "notionalFloor": "100000", + "notionalCap": "2400000", + "notionalFloor": "900000", "maintMarginRatio": "0.1", - "cum": "5650.0" + "cum": "45650.0" } }, { "tier": 5.0, "currency": "USDT", - "minNotional": 250000.0, - "maxNotional": 1000000.0, + "minNotional": 2400000.0, + "maxNotional": 3000000.0, "maintenanceMarginRate": 0.125, - "maxLeverage": 2.0, + "maxLeverage": 4.0, "info": { "bracket": "5", - "initialLeverage": "2", - "notionalCap": "1000000", - "notionalFloor": "250000", + "initialLeverage": "4", + "notionalCap": "3000000", + "notionalFloor": "2400000", "maintMarginRatio": "0.125", - "cum": "11900.0" + "cum": "105650.0" } }, { "tier": 6.0, "currency": "USDT", - "minNotional": 1000000.0, - "maxNotional": 3000000.0, + "minNotional": 3000000.0, + "maxNotional": 9000000.0, + "maintenanceMarginRate": 0.25, + "maxLeverage": 2.0, + "info": { + "bracket": "6", + "initialLeverage": "2", + "notionalCap": "9000000", + "notionalFloor": "3000000", + "maintMarginRatio": "0.25", + "cum": "480650.0" + } + }, + { + "tier": 7.0, + "currency": "USDT", + "minNotional": 9000000.0, + "maxNotional": 15000000.0, "maintenanceMarginRate": 0.5, "maxLeverage": 1.0, "info": { - "bracket": "6", + "bracket": "7", "initialLeverage": "1", - "notionalCap": "3000000", - "notionalFloor": "1000000", + "notionalCap": "15000000", + "notionalFloor": "9000000", "maintMarginRatio": "0.5", - "cum": "386900.0" + "cum": "2730650.0" } } ], @@ -17164,13 +17988,13 @@ "tier": 3.0, "currency": "USDT", "minNotional": 25000.0, - "maxNotional": 100000.0, + "maxNotional": 600000.0, "maintenanceMarginRate": 0.05, "maxLeverage": 10.0, "info": { "bracket": "3", "initialLeverage": "10", - "notionalCap": "100000", + "notionalCap": "600000", "notionalFloor": "25000", "maintMarginRatio": "0.05", "cum": "700.0" @@ -17179,49 +18003,65 @@ { "tier": 4.0, "currency": "USDT", - "minNotional": 100000.0, - "maxNotional": 250000.0, + "minNotional": 600000.0, + "maxNotional": 1600000.0, "maintenanceMarginRate": 0.1, "maxLeverage": 5.0, "info": { "bracket": "4", "initialLeverage": "5", - "notionalCap": "250000", - "notionalFloor": "100000", + "notionalCap": "1600000", + "notionalFloor": "600000", "maintMarginRatio": "0.1", - "cum": "5700.0" + "cum": "30700.0" } }, { "tier": 5.0, "currency": "USDT", - "minNotional": 250000.0, - "maxNotional": 1000000.0, + "minNotional": 1600000.0, + "maxNotional": 2000000.0, "maintenanceMarginRate": 0.125, - "maxLeverage": 2.0, + "maxLeverage": 4.0, "info": { "bracket": "5", - "initialLeverage": "2", - "notionalCap": "1000000", - "notionalFloor": "250000", + "initialLeverage": "4", + "notionalCap": "2000000", + "notionalFloor": "1600000", "maintMarginRatio": "0.125", - "cum": "11950.0" + "cum": "70700.0" } }, { "tier": 6.0, "currency": "USDT", - "minNotional": 1000000.0, - "maxNotional": 5000000.0, + "minNotional": 2000000.0, + "maxNotional": 6000000.0, + "maintenanceMarginRate": 0.25, + "maxLeverage": 2.0, + "info": { + "bracket": "6", + "initialLeverage": "2", + "notionalCap": "6000000", + "notionalFloor": "2000000", + "maintMarginRatio": "0.25", + "cum": "320700.0" + } + }, + { + "tier": 7.0, + "currency": "USDT", + "minNotional": 6000000.0, + "maxNotional": 10000000.0, "maintenanceMarginRate": 0.5, "maxLeverage": 1.0, "info": { - "bracket": "6", + "bracket": "7", "initialLeverage": "1", - "notionalCap": "5000000", - "notionalFloor": "1000000", + "notionalCap": "10000000", + "notionalFloor": "6000000", "maintMarginRatio": "0.5", - "cum": "386950.0" + "cum": "1820700.0" } } ], @@ -17574,10 +18414,10 @@ "minNotional": 0.0, "maxNotional": 5000.0, "maintenanceMarginRate": 0.02, - "maxLeverage": 11.0, + "maxLeverage": 8.0, "info": { "bracket": "1", - "initialLeverage": "11", + "initialLeverage": "8", "notionalCap": "5000", "notionalFloor": "0", "maintMarginRatio": "0.02", @@ -17590,10 +18430,10 @@ "minNotional": 5000.0, "maxNotional": 25000.0, "maintenanceMarginRate": 0.025, - "maxLeverage": 10.0, + "maxLeverage": 7.0, "info": { "bracket": "2", - "initialLeverage": "10", + "initialLeverage": "7", "notionalCap": "25000", "notionalFloor": "5000", "maintMarginRatio": "0.025", @@ -17606,10 +18446,10 @@ "minNotional": 25000.0, "maxNotional": 100000.0, "maintenanceMarginRate": 0.05, - "maxLeverage": 8.0, + "maxLeverage": 6.0, "info": { "bracket": "3", - "initialLeverage": "8", + "initialLeverage": "6", "notionalCap": "100000", "notionalFloor": "25000", "maintMarginRatio": "0.05", @@ -17652,13 +18492,13 @@ "tier": 6.0, "currency": "BUSD", "minNotional": 1000000.0, - "maxNotional": 5000000.0, + "maxNotional": 1500000.0, "maintenanceMarginRate": 0.5, "maxLeverage": 1.0, "info": { "bracket": "6", "initialLeverage": "1", - "notionalCap": "5000000", + "notionalCap": "1500000", "notionalFloor": "1000000", "maintMarginRatio": "0.5", "cum": "386900.0" @@ -18073,6 +18913,120 @@ } } ], + "RDNT/USDT:USDT": [ + { + "tier": 1.0, + "currency": "USDT", + "minNotional": 0.0, + "maxNotional": 5000.0, + "maintenanceMarginRate": 0.02, + "maxLeverage": 20.0, + "info": { + "bracket": "1", + "initialLeverage": "20", + "notionalCap": "5000", + "notionalFloor": "0", + "maintMarginRatio": "0.02", + "cum": "0.0" + } + }, + { + "tier": 2.0, + "currency": "USDT", + "minNotional": 5000.0, + "maxNotional": 25000.0, + "maintenanceMarginRate": 0.025, + "maxLeverage": 15.0, + "info": { + "bracket": "2", + "initialLeverage": "15", + "notionalCap": "25000", + "notionalFloor": "5000", + "maintMarginRatio": "0.025", + "cum": "25.0" + } + }, + { + "tier": 3.0, + "currency": "USDT", + "minNotional": 25000.0, + "maxNotional": 200000.0, + "maintenanceMarginRate": 0.05, + "maxLeverage": 10.0, + "info": { + "bracket": "3", + "initialLeverage": "10", + "notionalCap": "200000", + "notionalFloor": "25000", + "maintMarginRatio": "0.05", + "cum": "650.0" + } + }, + { + "tier": 4.0, + "currency": "USDT", + "minNotional": 200000.0, + "maxNotional": 500000.0, + "maintenanceMarginRate": 0.1, + "maxLeverage": 5.0, + "info": { + "bracket": "4", + "initialLeverage": "5", + "notionalCap": "500000", + "notionalFloor": "200000", + "maintMarginRatio": "0.1", + "cum": "10650.0" + } + }, + { + "tier": 5.0, + "currency": "USDT", + "minNotional": 500000.0, + "maxNotional": 1000000.0, + "maintenanceMarginRate": 0.125, + "maxLeverage": 4.0, + "info": { + "bracket": "5", + "initialLeverage": "4", + "notionalCap": "1000000", + "notionalFloor": "500000", + "maintMarginRatio": "0.125", + "cum": "23150.0" + } + }, + { + "tier": 6.0, + "currency": "USDT", + "minNotional": 1000000.0, + "maxNotional": 3000000.0, + "maintenanceMarginRate": 0.25, + "maxLeverage": 2.0, + "info": { + "bracket": "6", + "initialLeverage": "2", + "notionalCap": "3000000", + "notionalFloor": "1000000", + "maintMarginRatio": "0.25", + "cum": "148150.0" + } + }, + { + "tier": 7.0, + "currency": "USDT", + "minNotional": 3000000.0, + "maxNotional": 5000000.0, + "maintenanceMarginRate": 0.5, + "maxLeverage": 1.0, + "info": { + "bracket": "7", + "initialLeverage": "1", + "notionalCap": "5000000", + "notionalFloor": "3000000", + "maintMarginRatio": "0.5", + "cum": "898150.0" + } + } + ], "REEF/USDT:USDT": [ { "tier": 1.0, @@ -18208,13 +19162,13 @@ "tier": 3.0, "currency": "USDT", "minNotional": 25000.0, - "maxNotional": 200000.0, + "maxNotional": 600000.0, "maintenanceMarginRate": 0.05, "maxLeverage": 10.0, "info": { "bracket": "3", "initialLeverage": "10", - "notionalCap": "200000", + "notionalCap": "600000", "notionalFloor": "25000", "maintMarginRatio": "0.05", "cum": "650.0" @@ -18223,65 +19177,65 @@ { "tier": 4.0, "currency": "USDT", - "minNotional": 200000.0, - "maxNotional": 500000.0, + "minNotional": 600000.0, + "maxNotional": 1600000.0, "maintenanceMarginRate": 0.1, "maxLeverage": 5.0, "info": { "bracket": "4", "initialLeverage": "5", - "notionalCap": "500000", - "notionalFloor": "200000", + "notionalCap": "1600000", + "notionalFloor": "600000", "maintMarginRatio": "0.1", - "cum": "10650.0" + "cum": "30650.0" } }, { "tier": 5.0, "currency": "USDT", - "minNotional": 500000.0, - "maxNotional": 1000000.0, + "minNotional": 1600000.0, + "maxNotional": 2000000.0, "maintenanceMarginRate": 0.125, "maxLeverage": 4.0, "info": { "bracket": "5", "initialLeverage": "4", - "notionalCap": "1000000", - "notionalFloor": "500000", + "notionalCap": "2000000", + "notionalFloor": "1600000", "maintMarginRatio": "0.125", - "cum": "23150.0" + "cum": "70650.0" } }, { "tier": 6.0, "currency": "USDT", - "minNotional": 1000000.0, - "maxNotional": 3000000.0, + "minNotional": 2000000.0, + "maxNotional": 6000000.0, "maintenanceMarginRate": 0.25, "maxLeverage": 2.0, "info": { "bracket": "6", "initialLeverage": "2", - "notionalCap": "3000000", - "notionalFloor": "1000000", + "notionalCap": "6000000", + "notionalFloor": "2000000", "maintMarginRatio": "0.25", - "cum": "148150.0" + "cum": "320650.0" } }, { "tier": 7.0, "currency": "USDT", - "minNotional": 3000000.0, - "maxNotional": 5000000.0, + "minNotional": 6000000.0, + "maxNotional": 10000000.0, "maintenanceMarginRate": 0.5, "maxLeverage": 1.0, "info": { "bracket": "7", "initialLeverage": "1", - "notionalCap": "5000000", - "notionalFloor": "3000000", + "notionalCap": "10000000", + "notionalFloor": "6000000", "maintMarginRatio": "0.5", - "cum": "898150.0" + "cum": "1820650.0" } } ], @@ -18290,80 +19244,112 @@ "tier": 1.0, "currency": "USDT", "minNotional": 0.0, - "maxNotional": 25000.0, - "maintenanceMarginRate": 0.03, - "maxLeverage": 20.0, + "maxNotional": 5000.0, + "maintenanceMarginRate": 0.02, + "maxLeverage": 25.0, "info": { "bracket": "1", - "initialLeverage": "20", - "notionalCap": "25000", + "initialLeverage": "25", + "notionalCap": "5000", "notionalFloor": "0", - "maintMarginRatio": "0.03", + "maintMarginRatio": "0.02", "cum": "0.0" } }, { "tier": 2.0, "currency": "USDT", - "minNotional": 25000.0, - "maxNotional": 100000.0, - "maintenanceMarginRate": 0.05, - "maxLeverage": 10.0, + "minNotional": 5000.0, + "maxNotional": 25000.0, + "maintenanceMarginRate": 0.025, + "maxLeverage": 20.0, "info": { "bracket": "2", - "initialLeverage": "10", - "notionalCap": "100000", - "notionalFloor": "25000", - "maintMarginRatio": "0.05", - "cum": "500.0" + "initialLeverage": "20", + "notionalCap": "25000", + "notionalFloor": "5000", + "maintMarginRatio": "0.025", + "cum": "25.0" } }, { "tier": 3.0, "currency": "USDT", - "minNotional": 100000.0, - "maxNotional": 250000.0, - "maintenanceMarginRate": 0.1, - "maxLeverage": 5.0, + "minNotional": 25000.0, + "maxNotional": 600000.0, + "maintenanceMarginRate": 0.05, + "maxLeverage": 10.0, "info": { "bracket": "3", - "initialLeverage": "5", - "notionalCap": "250000", - "notionalFloor": "100000", - "maintMarginRatio": "0.1", - "cum": "5500.0" + "initialLeverage": "10", + "notionalCap": "600000", + "notionalFloor": "25000", + "maintMarginRatio": "0.05", + "cum": "650.0" } }, { "tier": 4.0, "currency": "USDT", - "minNotional": 250000.0, - "maxNotional": 1000000.0, - "maintenanceMarginRate": 0.125, - "maxLeverage": 2.0, + "minNotional": 600000.0, + "maxNotional": 1600000.0, + "maintenanceMarginRate": 0.1, + "maxLeverage": 5.0, "info": { "bracket": "4", - "initialLeverage": "2", - "notionalCap": "1000000", - "notionalFloor": "250000", - "maintMarginRatio": "0.125", - "cum": "11750.0" + "initialLeverage": "5", + "notionalCap": "1600000", + "notionalFloor": "600000", + "maintMarginRatio": "0.1", + "cum": "30650.0" } }, { "tier": 5.0, "currency": "USDT", - "minNotional": 1000000.0, - "maxNotional": 3000000.0, + "minNotional": 1600000.0, + "maxNotional": 2000000.0, + "maintenanceMarginRate": 0.125, + "maxLeverage": 4.0, + "info": { + "bracket": "5", + "initialLeverage": "4", + "notionalCap": "2000000", + "notionalFloor": "1600000", + "maintMarginRatio": "0.125", + "cum": "70650.0" + } + }, + { + "tier": 6.0, + "currency": "USDT", + "minNotional": 2000000.0, + "maxNotional": 6000000.0, + "maintenanceMarginRate": 0.25, + "maxLeverage": 2.0, + "info": { + "bracket": "6", + "initialLeverage": "2", + "notionalCap": "6000000", + "notionalFloor": "2000000", + "maintMarginRatio": "0.25", + "cum": "320650.0" + } + }, + { + "tier": 7.0, + "currency": "USDT", + "minNotional": 6000000.0, + "maxNotional": 10000000.0, "maintenanceMarginRate": 0.5, "maxLeverage": 1.0, "info": { - "bracket": "5", + "bracket": "7", "initialLeverage": "1", - "notionalCap": "3000000", - "notionalFloor": "1000000", + "notionalCap": "10000000", + "notionalFloor": "6000000", "maintMarginRatio": "0.5", - "cum": "386750.0" + "cum": "1820650.0" } } ], @@ -18896,10 +19882,10 @@ "minNotional": 0.0, "maxNotional": 5000.0, "maintenanceMarginRate": 0.02, - "maxLeverage": 20.0, + "maxLeverage": 8.0, "info": { "bracket": "1", - "initialLeverage": "20", + "initialLeverage": "8", "notionalCap": "5000", "notionalFloor": "0", "maintMarginRatio": "0.02", @@ -18912,10 +19898,10 @@ "minNotional": 5000.0, "maxNotional": 25000.0, "maintenanceMarginRate": 0.025, - "maxLeverage": 10.0, + "maxLeverage": 7.0, "info": { "bracket": "2", - "initialLeverage": "10", + "initialLeverage": "7", "notionalCap": "25000", "notionalFloor": "5000", "maintMarginRatio": "0.025", @@ -18928,10 +19914,10 @@ "minNotional": 25000.0, "maxNotional": 100000.0, "maintenanceMarginRate": 0.05, - "maxLeverage": 8.0, + "maxLeverage": 6.0, "info": { "bracket": "3", - "initialLeverage": "8", + "initialLeverage": "6", "notionalCap": "100000", "notionalFloor": "25000", "maintMarginRatio": "0.05", @@ -18974,13 +19960,13 @@ "tier": 6.0, "currency": "BUSD", "minNotional": 1000000.0, - "maxNotional": 5000000.0, + "maxNotional": 1500000.0, "maintenanceMarginRate": 0.5, "maxLeverage": 1.0, "info": { "bracket": "6", "initialLeverage": "1", - "notionalCap": "5000000", + "notionalCap": "1500000", "notionalFloor": "1000000", "maintMarginRatio": "0.5", "cum": "386900.0" @@ -19678,10 +20664,10 @@ "minNotional": 50000.0, "maxNotional": 150000.0, "maintenanceMarginRate": 0.02, - "maxLeverage": 20.0, + "maxLeverage": 25.0, "info": { "bracket": "2", - "initialLeverage": "20", + "initialLeverage": "25", "notionalCap": "150000", "notionalFloor": "50000", "maintMarginRatio": "0.02", @@ -19692,13 +20678,13 @@ "tier": 3.0, "currency": "USDT", "minNotional": 150000.0, - "maxNotional": 250000.0, + "maxNotional": 900000.0, "maintenanceMarginRate": 0.025, - "maxLeverage": 15.0, + "maxLeverage": 20.0, "info": { "bracket": "3", - "initialLeverage": "15", - "notionalCap": "250000", + "initialLeverage": "20", + "notionalCap": "900000", "notionalFloor": "150000", "maintMarginRatio": "0.025", "cum": "1250.0" @@ -19707,81 +20693,81 @@ { "tier": 4.0, "currency": "USDT", - "minNotional": 250000.0, - "maxNotional": 1000000.0, + "minNotional": 900000.0, + "maxNotional": 1800000.0, "maintenanceMarginRate": 0.05, "maxLeverage": 10.0, "info": { "bracket": "4", "initialLeverage": "10", - "notionalCap": "1000000", - "notionalFloor": "250000", + "notionalCap": "1800000", + "notionalFloor": "900000", "maintMarginRatio": "0.05", - "cum": "7500.0" + "cum": "23750.0" } }, { "tier": 5.0, "currency": "USDT", - "minNotional": 1000000.0, - "maxNotional": 2000000.0, + "minNotional": 1800000.0, + "maxNotional": 4800000.0, "maintenanceMarginRate": 0.1, "maxLeverage": 5.0, "info": { "bracket": "5", "initialLeverage": "5", - "notionalCap": "2000000", - "notionalFloor": "1000000", + "notionalCap": "4800000", + "notionalFloor": "1800000", "maintMarginRatio": "0.1", - "cum": "57500.0" + "cum": "113750.0" } }, { "tier": 6.0, "currency": "USDT", - "minNotional": 2000000.0, - "maxNotional": 5000000.0, + "minNotional": 4800000.0, + "maxNotional": 6000000.0, "maintenanceMarginRate": 0.125, "maxLeverage": 4.0, "info": { "bracket": "6", "initialLeverage": "4", - "notionalCap": "5000000", - "notionalFloor": "2000000", + "notionalCap": "6000000", + "notionalFloor": "4800000", "maintMarginRatio": "0.125", - "cum": "107500.0" + "cum": "233750.0" } }, { "tier": 7.0, "currency": "USDT", - "minNotional": 5000000.0, - "maxNotional": 10000000.0, + "minNotional": 6000000.0, + "maxNotional": 18000000.0, "maintenanceMarginRate": 0.25, "maxLeverage": 2.0, "info": { "bracket": "7", "initialLeverage": "2", - "notionalCap": "10000000", - "notionalFloor": "5000000", + "notionalCap": "18000000", + "notionalFloor": "6000000", "maintMarginRatio": "0.25", - "cum": "732500.0" + "cum": "983750.0" } }, { "tier": 8.0, "currency": "USDT", - "minNotional": 10000000.0, - "maxNotional": 20000000.0, + "minNotional": 18000000.0, + "maxNotional": 30000000.0, "maintenanceMarginRate": 0.5, "maxLeverage": 1.0, "info": { "bracket": "8", "initialLeverage": "1", - "notionalCap": "20000000", - "notionalFloor": "10000000", + "notionalCap": "30000000", + "notionalFloor": "18000000", "maintMarginRatio": "0.5", - "cum": "3232500.0" + "cum": "5483750.0" } } ], @@ -20200,10 +21186,10 @@ "minNotional": 0.0, "maxNotional": 5000.0, "maintenanceMarginRate": 0.02, - "maxLeverage": 20.0, + "maxLeverage": 10.0, "info": { "bracket": "1", - "initialLeverage": "20", + "initialLeverage": "10", "notionalCap": "5000", "notionalFloor": "0", "maintMarginRatio": "0.02", @@ -20216,10 +21202,10 @@ "minNotional": 5000.0, "maxNotional": 25000.0, "maintenanceMarginRate": 0.025, - "maxLeverage": 10.0, + "maxLeverage": 8.0, "info": { "bracket": "2", - "initialLeverage": "10", + "initialLeverage": "8", "notionalCap": "25000", "notionalFloor": "5000", "maintMarginRatio": "0.025", @@ -20232,10 +21218,10 @@ "minNotional": 25000.0, "maxNotional": 100000.0, "maintenanceMarginRate": 0.05, - "maxLeverage": 8.0, + "maxLeverage": 6.0, "info": { "bracket": "3", - "initialLeverage": "8", + "initialLeverage": "6", "notionalCap": "100000", "notionalFloor": "25000", "maintMarginRatio": "0.05", @@ -20278,13 +21264,13 @@ "tier": 6.0, "currency": "USDT", "minNotional": 1000000.0, - "maxNotional": 3000000.0, + "maxNotional": 1500000.0, "maintenanceMarginRate": 0.5, "maxLeverage": 1.0, "info": { "bracket": "6", "initialLeverage": "1", - "notionalCap": "3000000", + "notionalCap": "1500000", "notionalFloor": "1000000", "maintMarginRatio": "0.5", "cum": "386900.0" @@ -20520,104 +21506,6 @@ } ], "SUSHI/USDT:USDT": [ - { - "tier": 1.0, - "currency": "USDT", - "minNotional": 0.0, - "maxNotional": 5000.0, - "maintenanceMarginRate": 0.01, - "maxLeverage": 20.0, - "info": { - "bracket": "1", - "initialLeverage": "20", - "notionalCap": "5000", - "notionalFloor": "0", - "maintMarginRatio": "0.01", - "cum": "0.0" - } - }, - { - "tier": 2.0, - "currency": "USDT", - "minNotional": 5000.0, - "maxNotional": 25000.0, - "maintenanceMarginRate": 0.025, - "maxLeverage": 10.0, - "info": { - "bracket": "2", - "initialLeverage": "10", - "notionalCap": "25000", - "notionalFloor": "5000", - "maintMarginRatio": "0.025", - "cum": "75.0" - } - }, - { - "tier": 3.0, - "currency": "USDT", - "minNotional": 25000.0, - "maxNotional": 100000.0, - "maintenanceMarginRate": 0.05, - "maxLeverage": 8.0, - "info": { - "bracket": "3", - "initialLeverage": "8", - "notionalCap": "100000", - "notionalFloor": "25000", - "maintMarginRatio": "0.05", - "cum": "700.0" - } - }, - { - "tier": 4.0, - "currency": "USDT", - "minNotional": 100000.0, - "maxNotional": 250000.0, - "maintenanceMarginRate": 0.1, - "maxLeverage": 5.0, - "info": { - "bracket": "4", - "initialLeverage": "5", - "notionalCap": "250000", - "notionalFloor": "100000", - "maintMarginRatio": "0.1", - "cum": "5700.0" - } - }, - { - "tier": 5.0, - "currency": "USDT", - "minNotional": 250000.0, - "maxNotional": 1000000.0, - "maintenanceMarginRate": 0.125, - "maxLeverage": 2.0, - "info": { - "bracket": "5", - "initialLeverage": "2", - "notionalCap": "1000000", - "notionalFloor": "250000", - "maintMarginRatio": "0.125", - "cum": "11950.0" - } - }, - { - "tier": 6.0, - "currency": "USDT", - "minNotional": 1000000.0, - "maxNotional": 5000000.0, - "maintenanceMarginRate": 0.5, - "maxLeverage": 1.0, - "info": { - "bracket": "6", - "initialLeverage": "1", - "notionalCap": "5000000", - "notionalFloor": "1000000", - "maintMarginRatio": "0.5", - "cum": "386950.0" - } - } - ], - "SXP/USDT:USDT": [ { "tier": 1.0, "currency": "USDT", @@ -20654,13 +21542,13 @@ "tier": 3.0, "currency": "USDT", "minNotional": 25000.0, - "maxNotional": 100000.0, + "maxNotional": 600000.0, "maintenanceMarginRate": 0.05, "maxLeverage": 10.0, "info": { "bracket": "3", "initialLeverage": "10", - "notionalCap": "100000", + "notionalCap": "600000", "notionalFloor": "25000", "maintMarginRatio": "0.05", "cum": "700.0" @@ -20669,49 +21557,179 @@ { "tier": 4.0, "currency": "USDT", - "minNotional": 100000.0, - "maxNotional": 250000.0, + "minNotional": 600000.0, + "maxNotional": 1600000.0, "maintenanceMarginRate": 0.1, "maxLeverage": 5.0, "info": { "bracket": "4", "initialLeverage": "5", - "notionalCap": "250000", - "notionalFloor": "100000", + "notionalCap": "1600000", + "notionalFloor": "600000", "maintMarginRatio": "0.1", - "cum": "5700.0" + "cum": "30700.0" } }, { "tier": 5.0, "currency": "USDT", - "minNotional": 250000.0, - "maxNotional": 1000000.0, + "minNotional": 1600000.0, + "maxNotional": 2000000.0, "maintenanceMarginRate": 0.125, - "maxLeverage": 2.0, + "maxLeverage": 4.0, "info": { "bracket": "5", - "initialLeverage": "2", - "notionalCap": "1000000", - "notionalFloor": "250000", + "initialLeverage": "4", + "notionalCap": "2000000", + "notionalFloor": "1600000", "maintMarginRatio": "0.125", - "cum": "11950.0" + "cum": "70700.0" } }, { "tier": 6.0, "currency": "USDT", - "minNotional": 1000000.0, - "maxNotional": 5000000.0, + "minNotional": 2000000.0, + "maxNotional": 6000000.0, + "maintenanceMarginRate": 0.25, + "maxLeverage": 2.0, + "info": { + "bracket": "6", + "initialLeverage": "2", + "notionalCap": "6000000", + "notionalFloor": "2000000", + "maintMarginRatio": "0.25", + "cum": "320700.0" + } + }, + { + "tier": 7.0, + "currency": "USDT", + "minNotional": 6000000.0, + "maxNotional": 10000000.0, "maintenanceMarginRate": 0.5, "maxLeverage": 1.0, "info": { - "bracket": "6", + "bracket": "7", "initialLeverage": "1", - "notionalCap": "5000000", - "notionalFloor": "1000000", + "notionalCap": "10000000", + "notionalFloor": "6000000", "maintMarginRatio": "0.5", - "cum": "386950.0" + "cum": "1820700.0" + } + } + ], + "SXP/USDT:USDT": [ + { + "tier": 1.0, + "currency": "USDT", + "minNotional": 0.0, + "maxNotional": 5000.0, + "maintenanceMarginRate": 0.02, + "maxLeverage": 15.0, + "info": { + "bracket": "1", + "initialLeverage": "15", + "notionalCap": "5000", + "notionalFloor": "0", + "maintMarginRatio": "0.02", + "cum": "0.0" + } + }, + { + "tier": 2.0, + "currency": "USDT", + "minNotional": 5000.0, + "maxNotional": 25000.0, + "maintenanceMarginRate": 0.025, + "maxLeverage": 10.0, + "info": { + "bracket": "2", + "initialLeverage": "10", + "notionalCap": "25000", + "notionalFloor": "5000", + "maintMarginRatio": "0.025", + "cum": "25.0" + } + }, + { + "tier": 3.0, + "currency": "USDT", + "minNotional": 25000.0, + "maxNotional": 600000.0, + "maintenanceMarginRate": 0.05, + "maxLeverage": 8.0, + "info": { + "bracket": "3", + "initialLeverage": "8", + "notionalCap": "600000", + "notionalFloor": "25000", + "maintMarginRatio": "0.05", + "cum": "650.0" + } + }, + { + "tier": 4.0, + "currency": "USDT", + "minNotional": 600000.0, + "maxNotional": 1600000.0, + "maintenanceMarginRate": 0.1, + "maxLeverage": 5.0, + "info": { + "bracket": "4", + "initialLeverage": "5", + "notionalCap": "1600000", + "notionalFloor": "600000", + "maintMarginRatio": "0.1", + "cum": "30650.0" + } + }, + { + "tier": 5.0, + "currency": "USDT", + "minNotional": 1600000.0, + "maxNotional": 2000000.0, + "maintenanceMarginRate": 0.125, + "maxLeverage": 4.0, + "info": { + "bracket": "5", + "initialLeverage": "4", + "notionalCap": "2000000", + "notionalFloor": "1600000", + "maintMarginRatio": "0.125", + "cum": "70650.0" + } + }, + { + "tier": 6.0, + "currency": "USDT", + "minNotional": 2000000.0, + "maxNotional": 6000000.0, + "maintenanceMarginRate": 0.25, + "maxLeverage": 2.0, + "info": { + "bracket": "6", + "initialLeverage": "2", + "notionalCap": "6000000", + "notionalFloor": "2000000", + "maintMarginRatio": "0.25", + "cum": "320650.0" + } + }, + { + "tier": 7.0, + "currency": "USDT", + "minNotional": 6000000.0, + "maxNotional": 6500000.0, + "maintenanceMarginRate": 0.5, + "maxLeverage": 1.0, + "info": { + "bracket": "7", + "initialLeverage": "1", + "notionalCap": "6500000", + "notionalFloor": "6000000", + "maintMarginRatio": "0.5", + "cum": "1820650.0" } } ], @@ -20950,10 +21968,10 @@ "minNotional": 0.0, "maxNotional": 5000.0, "maintenanceMarginRate": 0.02, - "maxLeverage": 20.0, + "maxLeverage": 8.0, "info": { "bracket": "1", - "initialLeverage": "20", + "initialLeverage": "8", "notionalCap": "5000", "notionalFloor": "0", "maintMarginRatio": "0.02", @@ -20966,10 +21984,10 @@ "minNotional": 5000.0, "maxNotional": 25000.0, "maintenanceMarginRate": 0.025, - "maxLeverage": 10.0, + "maxLeverage": 7.0, "info": { "bracket": "2", - "initialLeverage": "10", + "initialLeverage": "7", "notionalCap": "25000", "notionalFloor": "5000", "maintMarginRatio": "0.025", @@ -20982,10 +22000,10 @@ "minNotional": 25000.0, "maxNotional": 100000.0, "maintenanceMarginRate": 0.05, - "maxLeverage": 8.0, + "maxLeverage": 6.0, "info": { "bracket": "3", - "initialLeverage": "8", + "initialLeverage": "6", "notionalCap": "100000", "notionalFloor": "25000", "maintMarginRatio": "0.05", @@ -21028,13 +22046,13 @@ "tier": 6.0, "currency": "BUSD", "minNotional": 1000000.0, - "maxNotional": 5000000.0, + "maxNotional": 1500000.0, "maintenanceMarginRate": 0.5, "maxLeverage": 1.0, "info": { "bracket": "6", "initialLeverage": "1", - "notionalCap": "5000000", + "notionalCap": "1500000", "notionalFloor": "1000000", "maintMarginRatio": "0.5", "cum": "386900.0" @@ -21042,104 +22060,6 @@ } ], "TLM/USDT:USDT": [ - { - "tier": 1.0, - "currency": "USDT", - "minNotional": 0.0, - "maxNotional": 5000.0, - "maintenanceMarginRate": 0.01, - "maxLeverage": 25.0, - "info": { - "bracket": "1", - "initialLeverage": "25", - "notionalCap": "5000", - "notionalFloor": "0", - "maintMarginRatio": "0.01", - "cum": "0.0" - } - }, - { - "tier": 2.0, - "currency": "USDT", - "minNotional": 5000.0, - "maxNotional": 25000.0, - "maintenanceMarginRate": 0.025, - "maxLeverage": 20.0, - "info": { - "bracket": "2", - "initialLeverage": "20", - "notionalCap": "25000", - "notionalFloor": "5000", - "maintMarginRatio": "0.025", - "cum": "75.0" - } - }, - { - "tier": 3.0, - "currency": "USDT", - "minNotional": 25000.0, - "maxNotional": 100000.0, - "maintenanceMarginRate": 0.05, - "maxLeverage": 10.0, - "info": { - "bracket": "3", - "initialLeverage": "10", - "notionalCap": "100000", - "notionalFloor": "25000", - "maintMarginRatio": "0.05", - "cum": "700.0" - } - }, - { - "tier": 4.0, - "currency": "USDT", - "minNotional": 100000.0, - "maxNotional": 250000.0, - "maintenanceMarginRate": 0.1, - "maxLeverage": 5.0, - "info": { - "bracket": "4", - "initialLeverage": "5", - "notionalCap": "250000", - "notionalFloor": "100000", - "maintMarginRatio": "0.1", - "cum": "5700.0" - } - }, - { - "tier": 5.0, - "currency": "USDT", - "minNotional": 250000.0, - "maxNotional": 1000000.0, - "maintenanceMarginRate": 0.125, - "maxLeverage": 2.0, - "info": { - "bracket": "5", - "initialLeverage": "2", - "notionalCap": "1000000", - "notionalFloor": "250000", - "maintMarginRatio": "0.125", - "cum": "11950.0" - } - }, - { - "tier": 6.0, - "currency": "USDT", - "minNotional": 1000000.0, - "maxNotional": 5000000.0, - "maintenanceMarginRate": 0.5, - "maxLeverage": 1.0, - "info": { - "bracket": "6", - "initialLeverage": "1", - "notionalCap": "5000000", - "notionalFloor": "1000000", - "maintMarginRatio": "0.5", - "cum": "386950.0" - } - } - ], - "TOMO/USDT:USDT": [ { "tier": 1.0, "currency": "USDT", @@ -21162,10 +22082,10 @@ "minNotional": 5000.0, "maxNotional": 25000.0, "maintenanceMarginRate": 0.025, - "maxLeverage": 10.0, + "maxLeverage": 15.0, "info": { "bracket": "2", - "initialLeverage": "10", + "initialLeverage": "15", "notionalCap": "25000", "notionalFloor": "5000", "maintMarginRatio": "0.025", @@ -21176,13 +22096,13 @@ "tier": 3.0, "currency": "USDT", "minNotional": 25000.0, - "maxNotional": 100000.0, + "maxNotional": 200000.0, "maintenanceMarginRate": 0.05, - "maxLeverage": 8.0, + "maxLeverage": 10.0, "info": { "bracket": "3", - "initialLeverage": "8", - "notionalCap": "100000", + "initialLeverage": "10", + "notionalCap": "200000", "notionalFloor": "25000", "maintMarginRatio": "0.05", "cum": "650.0" @@ -21191,33 +22111,33 @@ { "tier": 4.0, "currency": "USDT", - "minNotional": 100000.0, - "maxNotional": 250000.0, + "minNotional": 200000.0, + "maxNotional": 500000.0, "maintenanceMarginRate": 0.1, "maxLeverage": 5.0, "info": { "bracket": "4", "initialLeverage": "5", - "notionalCap": "250000", - "notionalFloor": "100000", + "notionalCap": "500000", + "notionalFloor": "200000", "maintMarginRatio": "0.1", - "cum": "5650.0" + "cum": "10650.0" } }, { "tier": 5.0, "currency": "USDT", - "minNotional": 250000.0, + "minNotional": 500000.0, "maxNotional": 1000000.0, "maintenanceMarginRate": 0.125, - "maxLeverage": 2.0, + "maxLeverage": 4.0, "info": { "bracket": "5", - "initialLeverage": "2", + "initialLeverage": "4", "notionalCap": "1000000", - "notionalFloor": "250000", + "notionalFloor": "500000", "maintMarginRatio": "0.125", - "cum": "11900.0" + "cum": "23150.0" } }, { @@ -21225,15 +22145,145 @@ "currency": "USDT", "minNotional": 1000000.0, "maxNotional": 3000000.0, + "maintenanceMarginRate": 0.25, + "maxLeverage": 2.0, + "info": { + "bracket": "6", + "initialLeverage": "2", + "notionalCap": "3000000", + "notionalFloor": "1000000", + "maintMarginRatio": "0.25", + "cum": "148150.0" + } + }, + { + "tier": 7.0, + "currency": "USDT", + "minNotional": 3000000.0, + "maxNotional": 5000000.0, "maintenanceMarginRate": 0.5, "maxLeverage": 1.0, "info": { - "bracket": "6", + "bracket": "7", "initialLeverage": "1", - "notionalCap": "3000000", - "notionalFloor": "1000000", + "notionalCap": "5000000", + "notionalFloor": "3000000", "maintMarginRatio": "0.5", - "cum": "386900.0" + "cum": "898150.0" + } + } + ], + "TOMO/USDT:USDT": [ + { + "tier": 1.0, + "currency": "USDT", + "minNotional": 0.0, + "maxNotional": 5000.0, + "maintenanceMarginRate": 0.02, + "maxLeverage": 25.0, + "info": { + "bracket": "1", + "initialLeverage": "25", + "notionalCap": "5000", + "notionalFloor": "0", + "maintMarginRatio": "0.02", + "cum": "0.0" + } + }, + { + "tier": 2.0, + "currency": "USDT", + "minNotional": 5000.0, + "maxNotional": 25000.0, + "maintenanceMarginRate": 0.025, + "maxLeverage": 20.0, + "info": { + "bracket": "2", + "initialLeverage": "20", + "notionalCap": "25000", + "notionalFloor": "5000", + "maintMarginRatio": "0.025", + "cum": "25.0" + } + }, + { + "tier": 3.0, + "currency": "USDT", + "minNotional": 25000.0, + "maxNotional": 480000.0, + "maintenanceMarginRate": 0.05, + "maxLeverage": 10.0, + "info": { + "bracket": "3", + "initialLeverage": "10", + "notionalCap": "480000", + "notionalFloor": "25000", + "maintMarginRatio": "0.05", + "cum": "650.0" + } + }, + { + "tier": 4.0, + "currency": "USDT", + "minNotional": 480000.0, + "maxNotional": 1280000.0, + "maintenanceMarginRate": 0.1, + "maxLeverage": 5.0, + "info": { + "bracket": "4", + "initialLeverage": "5", + "notionalCap": "1280000", + "notionalFloor": "480000", + "maintMarginRatio": "0.1", + "cum": "24650.0" + } + }, + { + "tier": 5.0, + "currency": "USDT", + "minNotional": 1280000.0, + "maxNotional": 1600000.0, + "maintenanceMarginRate": 0.125, + "maxLeverage": 4.0, + "info": { + "bracket": "5", + "initialLeverage": "4", + "notionalCap": "1600000", + "notionalFloor": "1280000", + "maintMarginRatio": "0.125", + "cum": "56650.0" + } + }, + { + "tier": 6.0, + "currency": "USDT", + "minNotional": 1600000.0, + "maxNotional": 4800000.0, + "maintenanceMarginRate": 0.25, + "maxLeverage": 2.0, + "info": { + "bracket": "6", + "initialLeverage": "2", + "notionalCap": "4800000", + "notionalFloor": "1600000", + "maintMarginRatio": "0.25", + "cum": "256650.0" + } + }, + { + "tier": 7.0, + "currency": "USDT", + "minNotional": 4800000.0, + "maxNotional": 8000000.0, + "maintenanceMarginRate": 0.5, + "maxLeverage": 1.0, + "info": { + "bracket": "7", + "initialLeverage": "1", + "notionalCap": "8000000", + "notionalFloor": "4800000", + "maintMarginRatio": "0.5", + "cum": "1456650.0" } } ], @@ -21814,10 +22864,10 @@ "minNotional": 0.0, "maxNotional": 5000.0, "maintenanceMarginRate": 0.02, - "maxLeverage": 20.0, + "maxLeverage": 8.0, "info": { "bracket": "1", - "initialLeverage": "20", + "initialLeverage": "8", "notionalCap": "5000", "notionalFloor": "0", "maintMarginRatio": "0.02", @@ -21830,10 +22880,10 @@ "minNotional": 5000.0, "maxNotional": 25000.0, "maintenanceMarginRate": 0.025, - "maxLeverage": 10.0, + "maxLeverage": 7.0, "info": { "bracket": "2", - "initialLeverage": "10", + "initialLeverage": "7", "notionalCap": "25000", "notionalFloor": "5000", "maintMarginRatio": "0.025", @@ -21846,10 +22896,10 @@ "minNotional": 25000.0, "maxNotional": 100000.0, "maintenanceMarginRate": 0.05, - "maxLeverage": 8.0, + "maxLeverage": 6.0, "info": { "bracket": "3", - "initialLeverage": "8", + "initialLeverage": "6", "notionalCap": "100000", "notionalFloor": "25000", "maintMarginRatio": "0.05", @@ -21892,13 +22942,13 @@ "tier": 6.0, "currency": "BUSD", "minNotional": 1000000.0, - "maxNotional": 5000000.0, + "maxNotional": 1500000.0, "maintenanceMarginRate": 0.5, "maxLeverage": 1.0, "info": { "bracket": "6", "initialLeverage": "1", - "notionalCap": "5000000", + "notionalCap": "1500000", "notionalFloor": "1000000", "maintMarginRatio": "0.5", "cum": "386900.0" @@ -21942,13 +22992,13 @@ "tier": 3.0, "currency": "USDT", "minNotional": 50000.0, - "maxNotional": 400000.0, + "maxNotional": 900000.0, "maintenanceMarginRate": 0.025, "maxLeverage": 20.0, "info": { "bracket": "3", "initialLeverage": "20", - "notionalCap": "400000", + "notionalCap": "900000", "notionalFloor": "50000", "maintMarginRatio": "0.025", "cum": "770.0" @@ -21957,81 +23007,81 @@ { "tier": 4.0, "currency": "USDT", - "minNotional": 400000.0, - "maxNotional": 800000.0, + "minNotional": 900000.0, + "maxNotional": 1800000.0, "maintenanceMarginRate": 0.05, "maxLeverage": 10.0, "info": { "bracket": "4", "initialLeverage": "10", - "notionalCap": "800000", - "notionalFloor": "400000", + "notionalCap": "1800000", + "notionalFloor": "900000", "maintMarginRatio": "0.05", - "cum": "10770.0" + "cum": "23270.0" } }, { "tier": 5.0, "currency": "USDT", - "minNotional": 800000.0, - "maxNotional": 2000000.0, + "minNotional": 1800000.0, + "maxNotional": 4800000.0, "maintenanceMarginRate": 0.1, "maxLeverage": 5.0, "info": { "bracket": "5", "initialLeverage": "5", - "notionalCap": "2000000", - "notionalFloor": "800000", + "notionalCap": "4800000", + "notionalFloor": "1800000", "maintMarginRatio": "0.1", - "cum": "50770.0" + "cum": "113270.0" } }, { "tier": 6.0, "currency": "USDT", - "minNotional": 2000000.0, - "maxNotional": 5000000.0, + "minNotional": 4800000.0, + "maxNotional": 6000000.0, "maintenanceMarginRate": 0.125, "maxLeverage": 4.0, "info": { "bracket": "6", "initialLeverage": "4", - "notionalCap": "5000000", - "notionalFloor": "2000000", + "notionalCap": "6000000", + "notionalFloor": "4800000", "maintMarginRatio": "0.125", - "cum": "100770.0" + "cum": "233270.0" } }, { "tier": 7.0, "currency": "USDT", - "minNotional": 5000000.0, - "maxNotional": 12000000.0, + "minNotional": 6000000.0, + "maxNotional": 18000000.0, "maintenanceMarginRate": 0.25, "maxLeverage": 2.0, "info": { "bracket": "7", "initialLeverage": "2", - "notionalCap": "12000000", - "notionalFloor": "5000000", + "notionalCap": "18000000", + "notionalFloor": "6000000", "maintMarginRatio": "0.25", - "cum": "725770.0" + "cum": "983270.0" } }, { "tier": 8.0, "currency": "USDT", - "minNotional": 12000000.0, - "maxNotional": 20000000.0, + "minNotional": 18000000.0, + "maxNotional": 30000000.0, "maintenanceMarginRate": 0.5, "maxLeverage": 1.0, "info": { "bracket": "8", "initialLeverage": "1", - "notionalCap": "20000000", - "notionalFloor": "12000000", + "notionalCap": "30000000", + "notionalFloor": "18000000", "maintMarginRatio": "0.5", - "cum": "3725770.0" + "cum": "5483270.0" } } ], @@ -22364,13 +23414,13 @@ "tier": 5.0, "currency": "BUSD", "minNotional": 250000.0, - "maxNotional": 1000000.0, + "maxNotional": 500000.0, "maintenanceMarginRate": 0.125, "maxLeverage": 2.0, "info": { "bracket": "5", "initialLeverage": "2", - "notionalCap": "1000000", + "notionalCap": "500000", "notionalFloor": "250000", "maintMarginRatio": "0.125", "cum": "11900.0" @@ -22379,17 +23429,17 @@ { "tier": 6.0, "currency": "BUSD", - "minNotional": 1000000.0, - "maxNotional": 3000000.0, + "minNotional": 500000.0, + "maxNotional": 1000000.0, "maintenanceMarginRate": 0.5, "maxLeverage": 1.0, "info": { "bracket": "6", "initialLeverage": "1", - "notionalCap": "3000000", - "notionalFloor": "1000000", + "notionalCap": "1000000", + "notionalFloor": "500000", "maintMarginRatio": "0.5", - "cum": "386900.0" + "cum": "199400.0" } } ], @@ -23067,15 +24117,15 @@ "currency": "USDT", "minNotional": 5000.0, "maxNotional": 10000.0, - "maintenanceMarginRate": 0.0065, + "maintenanceMarginRate": 0.006, "maxLeverage": 50.0, "info": { "bracket": "2", "initialLeverage": "50", "notionalCap": "10000", "notionalFloor": "5000", - "maintMarginRatio": "0.0065", - "cum": "7.5" + "maintMarginRatio": "0.006", + "cum": "5.0" } }, { @@ -23091,7 +24141,7 @@ "notionalCap": "50000", "notionalFloor": "10000", "maintMarginRatio": "0.01", - "cum": "42.5" + "cum": "45.0" } }, { @@ -23107,7 +24157,7 @@ "notionalCap": "250000", "notionalFloor": "50000", "maintMarginRatio": "0.02", - "cum": "542.5" + "cum": "545.0" } }, { @@ -23123,77 +24173,77 @@ "notionalCap": "1000000", "notionalFloor": "250000", "maintMarginRatio": "0.05", - "cum": "8042.5" + "cum": "8045.0" } }, { "tier": 6.0, "currency": "USDT", "minNotional": 1000000.0, - "maxNotional": 2000000.0, + "maxNotional": 5000000.0, "maintenanceMarginRate": 0.1, "maxLeverage": 5.0, "info": { "bracket": "6", "initialLeverage": "5", - "notionalCap": "2000000", + "notionalCap": "5000000", "notionalFloor": "1000000", "maintMarginRatio": "0.1", - "cum": "58042.5" + "cum": "58045.0" } }, { "tier": 7.0, "currency": "USDT", - "minNotional": 2000000.0, - "maxNotional": 5000000.0, + "minNotional": 5000000.0, + "maxNotional": 10000000.0, "maintenanceMarginRate": 0.125, "maxLeverage": 4.0, "info": { "bracket": "7", "initialLeverage": "4", - "notionalCap": "5000000", - "notionalFloor": "2000000", + "notionalCap": "10000000", + "notionalFloor": "5000000", "maintMarginRatio": "0.125", - "cum": "108042.5" + "cum": "183045.0" } }, { "tier": 8.0, "currency": "USDT", - "minNotional": 5000000.0, - "maxNotional": 10000000.0, + "minNotional": 10000000.0, + "maxNotional": 20000000.0, "maintenanceMarginRate": 0.15, "maxLeverage": 3.0, "info": { "bracket": "8", "initialLeverage": "3", - "notionalCap": "10000000", - "notionalFloor": "5000000", + "notionalCap": "20000000", + "notionalFloor": "10000000", "maintMarginRatio": "0.15", - "cum": "233042.5" + "cum": "433045.0" } }, { "tier": 9.0, "currency": "USDT", - "minNotional": 10000000.0, - "maxNotional": 20000000.0, + "minNotional": 20000000.0, + "maxNotional": 30000000.0, "maintenanceMarginRate": 0.25, "maxLeverage": 2.0, "info": { "bracket": "9", "initialLeverage": "2", - "notionalCap": "20000000", - "notionalFloor": "10000000", + "notionalCap": "30000000", + "notionalFloor": "20000000", "maintMarginRatio": "0.25", - "cum": "1233042.5" + "cum": "2433045.0" } }, { "tier": 10.0, "currency": "USDT", - "minNotional": 20000000.0, + "minNotional": 30000000.0, "maxNotional": 50000000.0, "maintenanceMarginRate": 0.5, "maxLeverage": 1.0, @@ -23201,9 +24251,9 @@ "bracket": "10", "initialLeverage": "1", "notionalCap": "50000000", - "notionalFloor": "20000000", + "notionalFloor": "30000000", "maintMarginRatio": "0.5", - "cum": "6233042.5" + "cum": "9933045.0" } } ], @@ -23321,6 +24371,120 @@ } } ], + "XVS/USDT:USDT": [ + { + "tier": 1.0, + "currency": "USDT", + "minNotional": 0.0, + "maxNotional": 5000.0, + "maintenanceMarginRate": 0.02, + "maxLeverage": 20.0, + "info": { + "bracket": "1", + "initialLeverage": "20", + "notionalCap": "5000", + "notionalFloor": "0", + "maintMarginRatio": "0.02", + "cum": "0.0" + } + }, + { + "tier": 2.0, + "currency": "USDT", + "minNotional": 5000.0, + "maxNotional": 25000.0, + "maintenanceMarginRate": 0.025, + "maxLeverage": 15.0, + "info": { + "bracket": "2", + "initialLeverage": "15", + "notionalCap": "25000", + "notionalFloor": "5000", + "maintMarginRatio": "0.025", + "cum": "25.0" + } + }, + { + "tier": 3.0, + "currency": "USDT", + "minNotional": 25000.0, + "maxNotional": 200000.0, + "maintenanceMarginRate": 0.05, + "maxLeverage": 10.0, + "info": { + "bracket": "3", + "initialLeverage": "10", + "notionalCap": "200000", + "notionalFloor": "25000", + "maintMarginRatio": "0.05", + "cum": "650.0" + } + }, + { + "tier": 4.0, + "currency": "USDT", + "minNotional": 200000.0, + "maxNotional": 500000.0, + "maintenanceMarginRate": 0.1, + "maxLeverage": 5.0, + "info": { + "bracket": "4", + "initialLeverage": "5", + "notionalCap": "500000", + "notionalFloor": "200000", + "maintMarginRatio": "0.1", + "cum": "10650.0" + } + }, + { + "tier": 5.0, + "currency": "USDT", + "minNotional": 500000.0, + "maxNotional": 1000000.0, + "maintenanceMarginRate": 0.125, + "maxLeverage": 4.0, + "info": { + "bracket": "5", + "initialLeverage": "4", + "notionalCap": "1000000", + "notionalFloor": "500000", + "maintMarginRatio": "0.125", + "cum": "23150.0" + } + }, + { + "tier": 6.0, + "currency": "USDT", + "minNotional": 1000000.0, + "maxNotional": 3000000.0, + "maintenanceMarginRate": 0.25, + "maxLeverage": 2.0, + "info": { + "bracket": "6", + "initialLeverage": "2", + "notionalCap": "3000000", + "notionalFloor": "1000000", + "maintMarginRatio": "0.25", + "cum": "148150.0" + } + }, + { + "tier": 7.0, + "currency": "USDT", + "minNotional": 3000000.0, + "maxNotional": 5000000.0, + "maintenanceMarginRate": 0.5, + "maxLeverage": 1.0, + "info": { + "bracket": "7", + "initialLeverage": "1", + "notionalCap": "5000000", + "notionalFloor": "3000000", + "maintMarginRatio": "0.5", + "cum": "898150.0" + } + } + ], "YFI/USDT:USDT": [ { "tier": 1.0, diff --git a/freqtrade/exchange/bitvavo.py b/freqtrade/exchange/bitvavo.py new file mode 100644 index 000000000..ba1d355cc --- /dev/null +++ b/freqtrade/exchange/bitvavo.py @@ -0,0 +1,23 @@ +"""Kucoin exchange subclass.""" +import logging +from typing import Dict + +from freqtrade.exchange import Exchange + + +logger = logging.getLogger(__name__) + + +class Bitvavo(Exchange): + """Bitvavo exchange class. + + Contains adjustments needed for Freqtrade to work with this exchange. + + Please note that this exchange is not included in the list of exchanges + officially supported by the Freqtrade development team. So some features + may still not work as expected. + """ + + _ft_has: Dict = { + "ohlcv_candle_limit": 1440, + } diff --git a/freqtrade/exchange/bybit.py b/freqtrade/exchange/bybit.py index 6f841b608..a4b070741 100644 --- a/freqtrade/exchange/bybit.py +++ b/freqtrade/exchange/bybit.py @@ -114,7 +114,7 @@ class Bybit(Exchange): data = [[x['timestamp'], x['fundingRate'], 0, 0, 0, 0] for x in data] return data - def _lev_prep(self, pair: str, leverage: float, side: BuySell): + def _lev_prep(self, pair: str, leverage: float, side: BuySell, accept_fail: bool = False): if self.trading_mode != TradingMode.SPOT: params = {'leverage': leverage} self.set_margin_mode(pair, self.margin_mode, accept_fail=True, params=params) diff --git a/freqtrade/exchange/exchange.py b/freqtrade/exchange/exchange.py index 489dc1b68..9a303426a 100644 --- a/freqtrade/exchange/exchange.py +++ b/freqtrade/exchange/exchange.py @@ -30,13 +30,14 @@ from freqtrade.exceptions import (DDosProtection, ExchangeError, InsufficientFun RetryableOrderError, TemporaryError) from freqtrade.exchange.common import (API_FETCH_ORDER_RETRY_COUNT, remove_credentials, retrier, retrier_async) -from freqtrade.exchange.exchange_utils import (CcxtModuleType, amount_to_contract_precision, - amount_to_contracts, amount_to_precision, - contracts_to_amount, date_minus_candles, - is_exchange_known_ccxt, market_is_active, - price_to_precision, timeframe_to_minutes, - timeframe_to_msecs, timeframe_to_next_date, - timeframe_to_prev_date, timeframe_to_seconds) +from freqtrade.exchange.exchange_utils import (ROUND, ROUND_DOWN, ROUND_UP, CcxtModuleType, + amount_to_contract_precision, amount_to_contracts, + amount_to_precision, contracts_to_amount, + date_minus_candles, is_exchange_known_ccxt, + market_is_active, price_to_precision, + timeframe_to_minutes, timeframe_to_msecs, + timeframe_to_next_date, timeframe_to_prev_date, + timeframe_to_seconds) from freqtrade.exchange.types import OHLCVResponse, OrderBook, Ticker, Tickers from freqtrade.misc import (chunks, deep_merge_dicts, file_dump_json, file_load_json, safe_value_fallback2) @@ -59,8 +60,8 @@ class Exchange: # or by specifying them in the configuration. _ft_has_default: Dict = { "stoploss_on_exchange": False, + "stop_price_param": "stopPrice", "order_time_in_force": ["GTC"], - "time_in_force_parameter": "timeInForce", "ohlcv_params": {}, "ohlcv_candle_limit": 500, "ohlcv_has_history": True, # Some exchanges (Kraken) don't provide history via ohlcv @@ -81,6 +82,8 @@ class Exchange: "fee_cost_in_contracts": False, # Fee cost needs contract conversion "needs_trading_fees": False, # use fetch_trading_fees to cache fees "order_props_in_contracts": ['amount', 'cost', 'filled', 'remaining'], + # Override createMarketBuyOrderRequiresPrice where ccxt has it wrong + "marketOrderRequiresPrice": False, } _ft_has: Dict = {} _ft_has_futures: Dict = {} @@ -104,8 +107,7 @@ class Exchange: # Lock event loop. This is necessary to avoid race-conditions when using force* commands # Due to funding fee fetching. self._loop_lock = Lock() - self.loop = asyncio.new_event_loop() - asyncio.set_event_loop(self.loop) + self.loop = self._init_async_loop() self._config: Config = {} self._config.update(config) @@ -206,6 +208,13 @@ class Exchange: and self._api_async.session): logger.debug("Closing async ccxt session.") self.loop.run_until_complete(self._api_async.close()) + if self.loop and not self.loop.is_closed(): + self.loop.close() + + def _init_async_loop(self) -> asyncio.AbstractEventLoop: + loop = asyncio.new_event_loop() + asyncio.set_event_loop(loop) + return loop def validate_config(self, config): # Check if timeframe is available @@ -731,12 +740,14 @@ class Exchange: """ return amount_to_precision(amount, self.get_precision_amount(pair), self.precisionMode) - def price_to_precision(self, pair: str, price: float) -> float: + def price_to_precision(self, pair: str, price: float, *, rounding_mode: int = ROUND) -> float: """ - Returns the price rounded up to the precision the Exchange accepts. - Rounds up + Returns the price rounded to the precision the Exchange accepts. + The default price_rounding_mode in conf is ROUND. + For stoploss calculations, must use ROUND_UP for longs, and ROUND_DOWN for shorts. """ - return price_to_precision(price, self.get_precision_price(pair), self.precisionMode) + return price_to_precision(price, self.get_precision_price(pair), + self.precisionMode, rounding_mode=rounding_mode) def price_get_one_pip(self, pair: str, price: float) -> float: """ @@ -759,12 +770,12 @@ class Exchange: return self._get_stake_amount_limit(pair, price, stoploss, 'min', leverage) def get_max_pair_stake_amount(self, pair: str, price: float, leverage: float = 1.0) -> float: - max_stake_amount = self._get_stake_amount_limit(pair, price, 0.0, 'max') + max_stake_amount = self._get_stake_amount_limit(pair, price, 0.0, 'max', leverage) if max_stake_amount is None: # * Should never be executed raise OperationalException(f'{self.name}.get_max_pair_stake_amount should' 'never set max_stake_amount to None') - return max_stake_amount / leverage + return max_stake_amount def _get_stake_amount_limit( self, @@ -782,43 +793,41 @@ class Exchange: except KeyError: raise ValueError(f"Can't get market information for symbol {pair}") + if isMin: + # reserve some percent defined in config (5% default) + stoploss + margin_reserve: float = 1.0 + self._config.get('amount_reserve_percent', + DEFAULT_AMOUNT_RESERVE_PERCENT) + stoploss_reserve = ( + margin_reserve / (1 - abs(stoploss)) if abs(stoploss) != 1 else 1.5 + ) + # it should not be more than 50% + stoploss_reserve = max(min(stoploss_reserve, 1.5), 1) + else: + margin_reserve = 1.0 + stoploss_reserve = 1.0 + stake_limits = [] limits = market['limits'] if (limits['cost'][limit] is not None): stake_limits.append( - self._contracts_to_amount( - pair, - limits['cost'][limit] - ) + self._contracts_to_amount(pair, limits['cost'][limit]) * stoploss_reserve ) if (limits['amount'][limit] is not None): stake_limits.append( - self._contracts_to_amount( - pair, - limits['amount'][limit] * price - ) + self._contracts_to_amount(pair, limits['amount'][limit]) * price * margin_reserve ) if not stake_limits: return None if isMin else float('inf') - # reserve some percent defined in config (5% default) + stoploss - amount_reserve_percent = 1.0 + self._config.get('amount_reserve_percent', - DEFAULT_AMOUNT_RESERVE_PERCENT) - amount_reserve_percent = ( - amount_reserve_percent / (1 - abs(stoploss)) if abs(stoploss) != 1 else 1.5 - ) - # it should not be more than 50% - amount_reserve_percent = max(min(amount_reserve_percent, 1.5), 1) - # The value returned should satisfy both limits: for amount (base currency) and # for cost (quote, stake currency), so max() is used here. # See also #2575 at github. return self._get_stake_amount_considering_leverage( - max(stake_limits) * amount_reserve_percent, + max(stake_limits) if isMin else min(stake_limits), leverage or 1.0 - ) if isMin else min(stake_limits) + ) def _get_stake_amount_considering_leverage(self, stake_amount: float, leverage: float) -> float: """ @@ -858,7 +867,7 @@ class Exchange: } if stop_loss: dry_order["info"] = {"stopPrice": dry_order["price"]} - dry_order["stopPrice"] = dry_order["price"] + dry_order[self._ft_has['stop_price_param']] = dry_order["price"] # Workaround to avoid filling stoploss orders immediately dry_order["ft_order_type"] = "stoploss" orderbook: Optional[OrderBook] = None @@ -881,7 +890,7 @@ class Exchange: 'filled': _amount, 'remaining': 0.0, 'status': "closed", - 'cost': (dry_order['amount'] * average) / leverage + 'cost': (dry_order['amount'] * average) }) # market orders will always incurr taker fees dry_order = self.add_dry_order_fee(pair, dry_order, 'taker') @@ -1010,7 +1019,7 @@ class Exchange: from freqtrade.persistence import Order order = Order.order_by_id(order_id) if order: - ccxt_order = order.to_ccxt_object() + ccxt_order = order.to_ccxt_object(self._ft_has['stop_price_param']) self._dry_run_open_orders[order_id] = ccxt_order return ccxt_order # Gracefully handle errors with dry-run orders. @@ -1019,10 +1028,10 @@ class Exchange: # Order handling - def _lev_prep(self, pair: str, leverage: float, side: BuySell): + def _lev_prep(self, pair: str, leverage: float, side: BuySell, accept_fail: bool = False): if self.trading_mode != TradingMode.SPOT: - self.set_margin_mode(pair, self.margin_mode) - self._set_leverage(leverage, pair) + self.set_margin_mode(pair, self.margin_mode, accept_fail) + self._set_leverage(leverage, pair, accept_fail) def _get_params( self, @@ -1034,12 +1043,18 @@ class Exchange: ) -> Dict: params = self._params.copy() if time_in_force != 'GTC' and ordertype != 'market': - param = self._ft_has.get('time_in_force_parameter', '') - params.update({param: time_in_force.upper()}) + params.update({'timeInForce': time_in_force.upper()}) if reduceOnly: params.update({'reduceOnly': True}) return params + def _order_needs_price(self, ordertype: str) -> bool: + return ( + ordertype != 'market' + or self._api.options.get("createMarketBuyOrderRequiresPrice", False) + or self._ft_has.get('marketOrderRequiresPrice', False) + ) + def create_order( self, *, @@ -1062,8 +1077,7 @@ class Exchange: try: # Set the precision for amount and price(rate) as accepted by the exchange amount = self.amount_to_precision(pair, self._amount_to_contracts(pair, amount)) - needs_price = (ordertype != 'market' - or self._api.options.get("createMarketBuyOrderRequiresPrice", False)) + needs_price = self._order_needs_price(ordertype) rate_for_order = self.price_to_precision(pair, rate) if needs_price else None if not reduceOnly: @@ -1106,11 +1120,11 @@ class Exchange: """ if not self._ft_has.get('stoploss_on_exchange'): raise OperationalException(f"stoploss is not implemented for {self.name}.") - + price_param = self._ft_has['stop_price_param'] return ( - order.get('stopPrice', None) is None - or ((side == "sell" and stop_loss > float(order['stopPrice'])) or - (side == "buy" and stop_loss < float(order['stopPrice']))) + order.get(price_param, None) is None + or ((side == "sell" and stop_loss > float(order[price_param])) or + (side == "buy" and stop_loss < float(order[price_param]))) ) def _get_stop_order_type(self, user_order_type) -> Tuple[str, str]: @@ -1137,7 +1151,11 @@ class Exchange: "sell" else (stop_price >= limit_rate)) # Ensure rate is less than stop price if bad_stop_price: - raise OperationalException( + # This can for example happen if the stop / liquidation price is set to 0 + # Which is possible if a market-order closes right away. + # The InvalidOrderException will bubble up to exit_positions, where it will be + # handled gracefully. + raise InvalidOrderException( "In stoploss limit order, stop price should be more than limit price. " f"Stop price: {stop_price}, Limit price: {limit_rate}, " f"Limit Price pct: {limit_price_pct}" @@ -1146,8 +1164,8 @@ class Exchange: def _get_stop_params(self, side: BuySell, ordertype: str, stop_price: float) -> Dict: params = self._params.copy() - # Verify if stopPrice works for your exchange! - params.update({'stopPrice': stop_price}) + # Verify if stopPrice works for your exchange, else configure stop_price_param + params.update({self._ft_has['stop_price_param']: stop_price}) return params @retrier(retries=0) @@ -1173,12 +1191,12 @@ class Exchange: user_order_type = order_types.get('stoploss', 'market') ordertype, user_order_type = self._get_stop_order_type(user_order_type) - - stop_price_norm = self.price_to_precision(pair, stop_price) + round_mode = ROUND_DOWN if side == 'buy' else ROUND_UP + stop_price_norm = self.price_to_precision(pair, stop_price, rounding_mode=round_mode) limit_rate = None if user_order_type == 'limit': limit_rate = self._get_stop_limit_rate(stop_price, order_types, side) - limit_rate = self.price_to_precision(pair, limit_rate) + limit_rate = self.price_to_precision(pair, limit_rate, rounding_mode=round_mode) if self._config['dry_run']: dry_order = self.create_dry_run_order( @@ -1204,7 +1222,7 @@ class Exchange: amount = self.amount_to_precision(pair, self._amount_to_contracts(pair, amount)) - self._lev_prep(pair, leverage, side) + self._lev_prep(pair, leverage, side, accept_fail=True) order = self._api.create_order(symbol=pair, type=ordertype, side=side, amount=amount, price=limit_rate, params=params) self._log_exchange_response('create_stoploss_order', order) @@ -2357,12 +2375,12 @@ class Exchange: # Must fetch the leverage tiers for each market separately # * This is slow(~45s) on Okx, makes ~90 api calls to load all linear swap markets markets = self.markets - symbols = [] - for symbol, market in markets.items(): + symbols = [ + symbol for symbol, market in markets.items() if (self.market_is_future(market) - and market['quote'] == self._config['stake_currency']): - symbols.append(symbol) + and market['quote'] == self._config['stake_currency']) + ] tiers: Dict[str, List[Dict]] = {} @@ -2382,25 +2400,26 @@ class Exchange: else: logger.info("Using cached leverage_tiers.") - async def gather_results(): + async def gather_results(input_coro): return await asyncio.gather(*input_coro, return_exceptions=True) for input_coro in chunks(coros, 100): with self._loop_lock: - results = self.loop.run_until_complete(gather_results()) + results = self.loop.run_until_complete(gather_results(input_coro)) - for symbol, res in results: - tiers[symbol] = res + for res in results: + if isinstance(res, Exception): + logger.warning(f"Leverage tier exception: {repr(res)}") + continue + symbol, tier = res + tiers[symbol] = tier if len(coros) > 0: self.cache_leverage_tiers(tiers, self._config['stake_currency']) logger.info(f"Done initializing {len(symbols)} markets.") return tiers - else: - return {} - else: - return {} + return {} def cache_leverage_tiers(self, tiers: Dict[str, List[Dict]], stake_currency: str) -> None: @@ -2416,14 +2435,17 @@ class Exchange: def load_cached_leverage_tiers(self, stake_currency: str) -> Optional[Dict[str, List[Dict]]]: filename = self._config['datadir'] / "futures" / f"leverage_tiers_{stake_currency}.json" if filename.is_file(): - tiers = file_load_json(filename) - updated = tiers.get('updated') - if updated: - updated_dt = parser.parse(updated) - if updated_dt < datetime.now(timezone.utc) - timedelta(weeks=4): - logger.info("Cached leverage tiers are outdated. Will update.") - return None - return tiers['data'] + try: + tiers = file_load_json(filename) + updated = tiers.get('updated') + if updated: + updated_dt = parser.parse(updated) + if updated_dt < datetime.now(timezone.utc) - timedelta(weeks=4): + logger.info("Cached leverage tiers are outdated. Will update.") + return None + return tiers['data'] + except Exception: + logger.exception("Error loading cached leverage tiers. Refreshing.") return None def fill_leverage_tiers(self) -> None: @@ -2529,7 +2551,6 @@ class Exchange: self, leverage: float, pair: Optional[str] = None, - trading_mode: Optional[TradingMode] = None, accept_fail: bool = False, ): """ @@ -2547,7 +2568,7 @@ class Exchange: self._log_exchange_response('set_leverage', res) except ccxt.DDoSProtection as e: raise DDosProtection(e) from e - except ccxt.BadRequest as e: + except (ccxt.BadRequest, ccxt.InsufficientFunds) as e: if not accept_fail: raise TemporaryError( f'Could not set leverage due to {e.__class__.__name__}. Message: {e}') from e diff --git a/freqtrade/exchange/exchange_utils.py b/freqtrade/exchange/exchange_utils.py index 6d3371a59..83d2a214d 100644 --- a/freqtrade/exchange/exchange_utils.py +++ b/freqtrade/exchange/exchange_utils.py @@ -2,11 +2,12 @@ Exchange support utils """ from datetime import datetime, timedelta, timezone -from math import ceil +from math import ceil, floor from typing import Any, Dict, List, Optional, Tuple import ccxt -from ccxt import ROUND_DOWN, ROUND_UP, TICK_SIZE, TRUNCATE, decimal_to_precision +from ccxt import (DECIMAL_PLACES, ROUND, ROUND_DOWN, ROUND_UP, SIGNIFICANT_DIGITS, TICK_SIZE, + TRUNCATE, decimal_to_precision) from freqtrade.exchange.common import BAD_EXCHANGES, EXCHANGE_HAS_OPTIONAL, EXCHANGE_HAS_REQUIRED from freqtrade.util import FtPrecise @@ -219,35 +220,51 @@ def amount_to_contract_precision( return amount -def price_to_precision(price: float, price_precision: Optional[float], - precisionMode: Optional[int]) -> float: +def price_to_precision( + price: float, + price_precision: Optional[float], + precisionMode: Optional[int], + *, + rounding_mode: int = ROUND, +) -> float: """ - Returns the price rounded up to the precision the Exchange accepts. + Returns the price rounded to the precision the Exchange accepts. Partial Re-implementation of ccxt internal method decimal_to_precision(), - which does not support rounding up + which does not support rounding up. + For stoploss calculations, must use ROUND_UP for longs, and ROUND_DOWN for shorts. + TODO: If ccxt supports ROUND_UP for decimal_to_precision(), we could remove this and align with amount_to_precision(). - !!! Rounds up :param price: price to convert :param price_precision: price precision to use. Used from markets[pair]['precision']['price'] :param precisionMode: precision mode to use. Should be used from precisionMode one of ccxt's DECIMAL_PLACES, SIGNIFICANT_DIGITS, or TICK_SIZE + :param rounding_mode: rounding mode to use. Defaults to ROUND :return: price rounded up to the precision the Exchange accepts - """ if price_precision is not None and precisionMode is not None: - # price = float(decimal_to_precision(price, rounding_mode=ROUND, - # precision=price_precision, - # counting_mode=self.precisionMode, - # )) if precisionMode == TICK_SIZE: + if rounding_mode == ROUND: + ticks = price / price_precision + rounded_ticks = round(ticks) + return rounded_ticks * price_precision precision = FtPrecise(price_precision) price_str = FtPrecise(price) missing = price_str % precision if not missing == FtPrecise("0"): - price = round(float(str(price_str - missing + precision)), 14) - else: - symbol_prec = price_precision - big_price = price * pow(10, symbol_prec) - price = ceil(big_price) / pow(10, symbol_prec) + return round(float(str(price_str - missing + precision)), 14) + return price + elif precisionMode in (SIGNIFICANT_DIGITS, DECIMAL_PLACES): + ndigits = round(price_precision) + if rounding_mode == ROUND: + return round(price, ndigits) + ticks = price * (10**ndigits) + if rounding_mode == ROUND_UP: + return ceil(ticks) / (10**ndigits) + if rounding_mode == TRUNCATE: + return int(ticks) / (10**ndigits) + if rounding_mode == ROUND_DOWN: + return floor(ticks) / (10**ndigits) + raise ValueError(f"Unknown rounding_mode {rounding_mode}") + raise ValueError(f"Unknown precisionMode {precisionMode}") return price diff --git a/freqtrade/exchange/gate.py b/freqtrade/exchange/gate.py index 03b568460..2ac135fc1 100644 --- a/freqtrade/exchange/gate.py +++ b/freqtrade/exchange/gate.py @@ -5,7 +5,6 @@ from typing import Any, Dict, List, Optional, Tuple from freqtrade.constants import BuySell from freqtrade.enums import MarginMode, PriceType, TradingMode -from freqtrade.exceptions import OperationalException from freqtrade.exchange import Exchange from freqtrade.misc import safe_value_fallback2 @@ -28,10 +27,12 @@ class Gate(Exchange): "order_time_in_force": ['GTC', 'IOC'], "stoploss_order_types": {"limit": "limit"}, "stoploss_on_exchange": True, + "marketOrderRequiresPrice": True, } _ft_has_futures: Dict = { "needs_trading_fees": True, + "marketOrderRequiresPrice": False, "tickers_have_bid_ask": False, "fee_cost_in_contracts": False, # Set explicitly to false for clarity "order_props_in_contracts": ['amount', 'filled', 'remaining'], @@ -50,14 +51,6 @@ class Gate(Exchange): (TradingMode.FUTURES, MarginMode.ISOLATED) ] - def validate_ordertypes(self, order_types: Dict) -> None: - - if self.trading_mode != TradingMode.FUTURES: - if any(v == 'market' for k, v in order_types.items()): - raise OperationalException( - f'Exchange {self.name} does not support market orders.') - super().validate_stop_ordertypes(order_types) - def _get_params( self, side: BuySell, @@ -75,8 +68,7 @@ class Gate(Exchange): ) if ordertype == 'market' and self.trading_mode == TradingMode.FUTURES: params['type'] = 'market' - param = self._ft_has.get('time_in_force_parameter', '') - params.update({param: 'IOC'}) + params.update({'timeInForce': 'IOC'}) return params def get_trades_for_order(self, order_id: str, pair: str, since: datetime, diff --git a/freqtrade/exchange/kraken.py b/freqtrade/exchange/kraken.py index 8a4f7f7e0..c41bb6d56 100644 --- a/freqtrade/exchange/kraken.py +++ b/freqtrade/exchange/kraken.py @@ -12,6 +12,7 @@ from freqtrade.exceptions import (DDosProtection, InsufficientFundsError, Invali OperationalException, TemporaryError) from freqtrade.exchange import Exchange from freqtrade.exchange.common import retrier +from freqtrade.exchange.exchange_utils import ROUND_DOWN, ROUND_UP from freqtrade.exchange.types import Tickers @@ -109,6 +110,7 @@ class Kraken(Exchange): if self.trading_mode == TradingMode.FUTURES: params.update({'reduceOnly': True}) + round_mode = ROUND_DOWN if side == 'buy' else ROUND_UP if order_types.get('stoploss', 'market') == 'limit': ordertype = "stop-loss-limit" limit_price_pct = order_types.get('stoploss_on_exchange_limit_ratio', 0.99) @@ -116,11 +118,11 @@ class Kraken(Exchange): limit_rate = stop_price * limit_price_pct else: limit_rate = stop_price * (2 - limit_price_pct) - params['price2'] = self.price_to_precision(pair, limit_rate) + params['price2'] = self.price_to_precision(pair, limit_rate, rounding_mode=round_mode) else: ordertype = "stop-loss" - stop_price = self.price_to_precision(pair, stop_price) + stop_price = self.price_to_precision(pair, stop_price, rounding_mode=round_mode) if self._config['dry_run']: dry_order = self.create_dry_run_order( @@ -158,7 +160,6 @@ class Kraken(Exchange): self, leverage: float, pair: Optional[str] = None, - trading_mode: Optional[TradingMode] = None, accept_fail: bool = False, ): """ diff --git a/freqtrade/exchange/okx.py b/freqtrade/exchange/okx.py index e7d658d24..84b7deb7a 100644 --- a/freqtrade/exchange/okx.py +++ b/freqtrade/exchange/okx.py @@ -1,14 +1,16 @@ import logging -from typing import Dict, List, Optional, Tuple +from typing import Any, Dict, List, Optional, Tuple import ccxt from freqtrade.constants import BuySell from freqtrade.enums import CandleType, MarginMode, TradingMode from freqtrade.enums.pricetype import PriceType -from freqtrade.exceptions import DDosProtection, OperationalException, TemporaryError +from freqtrade.exceptions import (DDosProtection, OperationalException, RetryableOrderError, + TemporaryError) from freqtrade.exchange import Exchange, date_minus_candles from freqtrade.exchange.common import retrier +from freqtrade.misc import safe_value_fallback2 logger = logging.getLogger(__name__) @@ -24,11 +26,14 @@ class Okx(Exchange): "ohlcv_candle_limit": 100, # Warning, special case with data prior to X months "mark_ohlcv_timeframe": "4h", "funding_fee_timeframe": "8h", + "stoploss_order_types": {"limit": "limit"}, + "stoploss_on_exchange": True, + "stop_price_param": "stopLossPrice", } _ft_has_futures: Dict = { "tickers_have_quoteVolume": False, "fee_cost_in_contracts": True, - "stop_price_type_field": "tpTriggerPxType", + "stop_price_type_field": "slTriggerPxType", "stop_price_type_value_mapping": { PriceType.LAST: "last", PriceType.MARK: "index", @@ -121,10 +126,9 @@ class Okx(Exchange): return params @retrier - def _lev_prep(self, pair: str, leverage: float, side: BuySell): + def _lev_prep(self, pair: str, leverage: float, side: BuySell, accept_fail: bool = False): if self.trading_mode != TradingMode.SPOT and self.margin_mode is not None: try: - # TODO-lev: Test me properly (check mgnMode passed) res = self._api.set_leverage( leverage=leverage, symbol=pair, @@ -157,3 +161,61 @@ class Okx(Exchange): pair_tiers = self._leverage_tiers[pair] return pair_tiers[-1]['maxNotional'] / leverage + + def _get_stop_params(self, side: BuySell, ordertype: str, stop_price: float) -> Dict: + params = super()._get_stop_params(side, ordertype, stop_price) + if self.trading_mode == TradingMode.FUTURES and self.margin_mode: + params['tdMode'] = self.margin_mode.value + params['posSide'] = self._get_posSide(side, True) + return params + + def fetch_stoploss_order(self, order_id: str, pair: str, params: Dict = {}) -> Dict: + if self._config['dry_run']: + return self.fetch_dry_run_order(order_id) + + try: + params1 = {'stop': True} + order_reg = self._api.fetch_order(order_id, pair, params=params1) + self._log_exchange_response('fetch_stoploss_order', order_reg) + return order_reg + except ccxt.OrderNotFound: + pass + params2 = {'stop': True, 'ordType': 'conditional'} + for method in (self._api.fetch_open_orders, self._api.fetch_closed_orders, + self._api.fetch_canceled_orders): + try: + orders = method(pair, params=params2) + orders_f = [order for order in orders if order['id'] == order_id] + if orders_f: + order = orders_f[0] + if (order['status'] == 'closed' + and (real_order_id := order.get('info', {}).get('ordId')) is not None): + # Once a order triggered, we fetch the regular followup order. + order_reg = self.fetch_order(real_order_id, pair) + self._log_exchange_response('fetch_stoploss_order1', order_reg) + order_reg['id_stop'] = order_reg['id'] + order_reg['id'] = order_id + order_reg['type'] = 'stoploss' + order_reg['status_stop'] = 'triggered' + return order_reg + order['type'] = 'stoploss' + return order + except ccxt.BaseError: + pass + raise RetryableOrderError( + f'StoplossOrder not found (pair: {pair} id: {order_id}).') + + def get_order_id_conditional(self, order: Dict[str, Any]) -> str: + if order['type'] == 'stop': + return safe_value_fallback2(order, order, 'id_stop', 'id') + return order['id'] + + def cancel_stoploss_order(self, order_id: str, pair: str, params: Dict = {}) -> Dict: + params1 = {'stop': True} + # 'ordType': 'conditional' + # + return self.cancel_order( + order_id=order_id, + pair=pair, + params=params1, + ) diff --git a/freqtrade/freqai/RL/Base3ActionRLEnv.py b/freqtrade/freqai/RL/Base3ActionRLEnv.py index a108d776e..c0a7eedaa 100644 --- a/freqtrade/freqai/RL/Base3ActionRLEnv.py +++ b/freqtrade/freqai/RL/Base3ActionRLEnv.py @@ -66,7 +66,7 @@ class Base3ActionRLEnv(BaseEnvironment): elif action == Actions.Sell.value and not self.can_short: self._update_total_profit() self._position = Positions.Neutral - trade_type = "neutral" + trade_type = "exit" self._last_trade_tick = None else: print("case not defined") @@ -74,7 +74,7 @@ class Base3ActionRLEnv(BaseEnvironment): if trade_type is not None: self.trade_history.append( {'price': self.current_price(), 'index': self._current_tick, - 'type': trade_type}) + 'type': trade_type, 'profit': self.get_unrealized_profit()}) if (self._total_profit < self.max_drawdown or self._total_unrealized_profit < self.max_drawdown): diff --git a/freqtrade/freqai/RL/Base4ActionRLEnv.py b/freqtrade/freqai/RL/Base4ActionRLEnv.py index 4f093f06c..e883136b2 100644 --- a/freqtrade/freqai/RL/Base4ActionRLEnv.py +++ b/freqtrade/freqai/RL/Base4ActionRLEnv.py @@ -52,16 +52,6 @@ class Base4ActionRLEnv(BaseEnvironment): trade_type = None if self.is_tradesignal(action): - """ - Action: Neutral, position: Long -> Close Long - Action: Neutral, position: Short -> Close Short - - Action: Long, position: Neutral -> Open Long - Action: Long, position: Short -> Close Short and Open Long - - Action: Short, position: Neutral -> Open Short - Action: Short, position: Long -> Close Long and Open Short - """ if action == Actions.Neutral.value: self._position = Positions.Neutral @@ -69,16 +59,16 @@ class Base4ActionRLEnv(BaseEnvironment): self._last_trade_tick = None elif action == Actions.Long_enter.value: self._position = Positions.Long - trade_type = "long" + trade_type = "enter_long" self._last_trade_tick = self._current_tick elif action == Actions.Short_enter.value: self._position = Positions.Short - trade_type = "short" + trade_type = "enter_short" self._last_trade_tick = self._current_tick elif action == Actions.Exit.value: self._update_total_profit() self._position = Positions.Neutral - trade_type = "neutral" + trade_type = "exit" self._last_trade_tick = None else: print("case not defined") @@ -86,7 +76,7 @@ class Base4ActionRLEnv(BaseEnvironment): if trade_type is not None: self.trade_history.append( {'price': self.current_price(), 'index': self._current_tick, - 'type': trade_type}) + 'type': trade_type, 'profit': self.get_unrealized_profit()}) if (self._total_profit < self.max_drawdown or self._total_unrealized_profit < self.max_drawdown): diff --git a/freqtrade/freqai/RL/Base5ActionRLEnv.py b/freqtrade/freqai/RL/Base5ActionRLEnv.py index 490ef3601..816211cc2 100644 --- a/freqtrade/freqai/RL/Base5ActionRLEnv.py +++ b/freqtrade/freqai/RL/Base5ActionRLEnv.py @@ -53,16 +53,6 @@ class Base5ActionRLEnv(BaseEnvironment): trade_type = None if self.is_tradesignal(action): - """ - Action: Neutral, position: Long -> Close Long - Action: Neutral, position: Short -> Close Short - - Action: Long, position: Neutral -> Open Long - Action: Long, position: Short -> Close Short and Open Long - - Action: Short, position: Neutral -> Open Short - Action: Short, position: Long -> Close Long and Open Short - """ if action == Actions.Neutral.value: self._position = Positions.Neutral @@ -70,21 +60,21 @@ class Base5ActionRLEnv(BaseEnvironment): self._last_trade_tick = None elif action == Actions.Long_enter.value: self._position = Positions.Long - trade_type = "long" + trade_type = "enter_long" self._last_trade_tick = self._current_tick elif action == Actions.Short_enter.value: self._position = Positions.Short - trade_type = "short" + trade_type = "enter_short" self._last_trade_tick = self._current_tick elif action == Actions.Long_exit.value: self._update_total_profit() self._position = Positions.Neutral - trade_type = "neutral" + trade_type = "exit_long" self._last_trade_tick = None elif action == Actions.Short_exit.value: self._update_total_profit() self._position = Positions.Neutral - trade_type = "neutral" + trade_type = "exit_short" self._last_trade_tick = None else: print("case not defined") @@ -92,7 +82,7 @@ class Base5ActionRLEnv(BaseEnvironment): if trade_type is not None: self.trade_history.append( {'price': self.current_price(), 'index': self._current_tick, - 'type': trade_type}) + 'type': trade_type, 'profit': self.get_unrealized_profit()}) if (self._total_profit < self.max_drawdown or self._total_unrealized_profit < self.max_drawdown): diff --git a/freqtrade/freqai/RL/TensorboardCallback.py b/freqtrade/freqai/RL/TensorboardCallback.py index 1828319cd..7f8c76956 100644 --- a/freqtrade/freqai/RL/TensorboardCallback.py +++ b/freqtrade/freqai/RL/TensorboardCallback.py @@ -13,7 +13,7 @@ class TensorboardCallback(BaseCallback): episodic summary reports. """ def __init__(self, verbose=1, actions: Type[Enum] = BaseActions): - super(TensorboardCallback, self).__init__(verbose) + super().__init__(verbose) self.model: Any = None self.logger = None # type: Any self.training_env: BaseEnvironment = None # type: ignore diff --git a/freqtrade/freqai/base_models/BasePyTorchClassifier.py b/freqtrade/freqai/base_models/BasePyTorchClassifier.py new file mode 100644 index 000000000..977152cc5 --- /dev/null +++ b/freqtrade/freqai/base_models/BasePyTorchClassifier.py @@ -0,0 +1,147 @@ +import logging +from typing import Dict, List, Tuple + +import numpy as np +import numpy.typing as npt +import pandas as pd +import torch +from pandas import DataFrame +from torch.nn import functional as F + +from freqtrade.exceptions import OperationalException +from freqtrade.freqai.base_models.BasePyTorchModel import BasePyTorchModel +from freqtrade.freqai.data_kitchen import FreqaiDataKitchen + + +logger = logging.getLogger(__name__) + + +class BasePyTorchClassifier(BasePyTorchModel): + """ + A PyTorch implementation of a classifier. + User must implement fit method + + Important! + + - User must declare the target class names in the strategy, + under IStrategy.set_freqai_targets method. + + for example, in your strategy: + ``` + def set_freqai_targets(self, dataframe: DataFrame, metadata: Dict, **kwargs): + self.freqai.class_names = ["down", "up"] + dataframe['&s-up_or_down'] = np.where(dataframe["close"].shift(-100) > + dataframe["close"], 'up', 'down') + + return dataframe + """ + def __init__(self, **kwargs): + super().__init__(**kwargs) + self.class_name_to_index = None + self.index_to_class_name = None + + def predict( + self, unfiltered_df: DataFrame, dk: FreqaiDataKitchen, **kwargs + ) -> Tuple[DataFrame, npt.NDArray[np.int_]]: + """ + Filter the prediction features data and predict with it. + :param unfiltered_df: Full dataframe for the current backtest period. + :return: + :pred_df: dataframe containing the predictions + :do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove + data (NaNs) or felt uncertain about data (PCA and DI index) + :raises ValueError: if 'class_names' doesn't exist in model meta_data. + """ + + class_names = self.model.model_meta_data.get("class_names", None) + if not class_names: + raise ValueError( + "Missing class names. " + "self.model.model_meta_data['class_names'] is None." + ) + + if not self.class_name_to_index: + self.init_class_names_to_index_mapping(class_names) + + dk.find_features(unfiltered_df) + filtered_df, _ = dk.filter_features( + unfiltered_df, dk.training_features_list, training_filter=False + ) + filtered_df = dk.normalize_data_from_metadata(filtered_df) + dk.data_dictionary["prediction_features"] = filtered_df + self.data_cleaning_predict(dk) + x = self.data_convertor.convert_x( + dk.data_dictionary["prediction_features"], + device=self.device + ) + logits = self.model.model(x) + probs = F.softmax(logits, dim=-1) + predicted_classes = torch.argmax(probs, dim=-1) + predicted_classes_str = self.decode_class_names(predicted_classes) + pred_df_prob = DataFrame(probs.detach().numpy(), columns=class_names) + pred_df = DataFrame(predicted_classes_str, columns=[dk.label_list[0]]) + pred_df = pd.concat([pred_df, pred_df_prob], axis=1) + return (pred_df, dk.do_predict) + + def encode_class_names( + self, + data_dictionary: Dict[str, pd.DataFrame], + dk: FreqaiDataKitchen, + class_names: List[str], + ): + """ + encode class name, str -> int + assuming first column of *_labels data frame to be the target column + containing the class names + """ + + target_column_name = dk.label_list[0] + for split in self.splits: + label_df = data_dictionary[f"{split}_labels"] + self.assert_valid_class_names(label_df[target_column_name], class_names) + label_df[target_column_name] = list( + map(lambda x: self.class_name_to_index[x], label_df[target_column_name]) + ) + + @staticmethod + def assert_valid_class_names( + target_column: pd.Series, + class_names: List[str] + ): + non_defined_labels = set(target_column) - set(class_names) + if len(non_defined_labels) != 0: + raise OperationalException( + f"Found non defined labels: {non_defined_labels}, ", + f"expecting labels: {class_names}" + ) + + def decode_class_names(self, class_ints: torch.Tensor) -> List[str]: + """ + decode class name, int -> str + """ + + return list(map(lambda x: self.index_to_class_name[x.item()], class_ints)) + + def init_class_names_to_index_mapping(self, class_names): + self.class_name_to_index = {s: i for i, s in enumerate(class_names)} + self.index_to_class_name = {i: s for i, s in enumerate(class_names)} + logger.info(f"encoded class name to index: {self.class_name_to_index}") + + def convert_label_column_to_int( + self, + data_dictionary: Dict[str, pd.DataFrame], + dk: FreqaiDataKitchen, + class_names: List[str] + ): + self.init_class_names_to_index_mapping(class_names) + self.encode_class_names(data_dictionary, dk, class_names) + + def get_class_names(self) -> List[str]: + if not self.class_names: + raise ValueError( + "self.class_names is empty, " + "set self.freqai.class_names = ['class a', 'class b', 'class c'] " + "inside IStrategy.set_freqai_targets method." + ) + + return self.class_names diff --git a/freqtrade/freqai/base_models/BasePyTorchModel.py b/freqtrade/freqai/base_models/BasePyTorchModel.py new file mode 100644 index 000000000..8177b8eb8 --- /dev/null +++ b/freqtrade/freqai/base_models/BasePyTorchModel.py @@ -0,0 +1,83 @@ +import logging +from abc import ABC, abstractmethod +from time import time +from typing import Any + +import torch +from pandas import DataFrame + +from freqtrade.freqai.data_kitchen import FreqaiDataKitchen +from freqtrade.freqai.freqai_interface import IFreqaiModel +from freqtrade.freqai.torch.PyTorchDataConvertor import PyTorchDataConvertor + + +logger = logging.getLogger(__name__) + + +class BasePyTorchModel(IFreqaiModel, ABC): + """ + Base class for PyTorch type models. + User *must* inherit from this class and set fit() and predict() and + data_convertor property. + """ + + def __init__(self, **kwargs): + super().__init__(config=kwargs["config"]) + self.dd.model_type = "pytorch" + self.device = "cuda" if torch.cuda.is_available() else "cpu" + test_size = self.freqai_info.get('data_split_parameters', {}).get('test_size') + self.splits = ["train", "test"] if test_size != 0 else ["train"] + + def train( + self, unfiltered_df: DataFrame, pair: str, dk: FreqaiDataKitchen, **kwargs + ) -> Any: + """ + Filter the training data and train a model to it. Train makes heavy use of the datakitchen + for storing, saving, loading, and analyzing the data. + :param unfiltered_df: Full dataframe for the current training period + :return: + :model: Trained model which can be used to inference (self.predict) + """ + + logger.info(f"-------------------- Starting training {pair} --------------------") + + start_time = time() + + features_filtered, labels_filtered = dk.filter_features( + unfiltered_df, + dk.training_features_list, + dk.label_list, + training_filter=True, + ) + + # split data into train/test data. + data_dictionary = dk.make_train_test_datasets(features_filtered, labels_filtered) + if not self.freqai_info.get("fit_live_predictions", 0) or not self.live: + dk.fit_labels() + # normalize all data based on train_dataset only + data_dictionary = dk.normalize_data(data_dictionary) + + # optional additional data cleaning/analysis + self.data_cleaning_train(dk) + + logger.info( + f"Training model on {len(dk.data_dictionary['train_features'].columns)} features" + ) + logger.info(f"Training model on {len(data_dictionary['train_features'])} data points") + + model = self.fit(data_dictionary, dk) + end_time = time() + + logger.info(f"-------------------- Done training {pair} " + f"({end_time - start_time:.2f} secs) --------------------") + + return model + + @property + @abstractmethod + def data_convertor(self) -> PyTorchDataConvertor: + """ + a class responsible for converting `*_features` & `*_labels` pandas dataframes + to pytorch tensors. + """ + raise NotImplementedError("Abstract property") diff --git a/freqtrade/freqai/base_models/BasePyTorchRegressor.py b/freqtrade/freqai/base_models/BasePyTorchRegressor.py new file mode 100644 index 000000000..ea6fabe49 --- /dev/null +++ b/freqtrade/freqai/base_models/BasePyTorchRegressor.py @@ -0,0 +1,50 @@ +import logging +from typing import Tuple + +import numpy as np +import numpy.typing as npt +from pandas import DataFrame + +from freqtrade.freqai.base_models.BasePyTorchModel import BasePyTorchModel +from freqtrade.freqai.data_kitchen import FreqaiDataKitchen + + +logger = logging.getLogger(__name__) + + +class BasePyTorchRegressor(BasePyTorchModel): + """ + A PyTorch implementation of a regressor. + User must implement fit method + """ + def __init__(self, **kwargs): + super().__init__(**kwargs) + + def predict( + self, unfiltered_df: DataFrame, dk: FreqaiDataKitchen, **kwargs + ) -> Tuple[DataFrame, npt.NDArray[np.int_]]: + """ + Filter the prediction features data and predict with it. + :param unfiltered_df: Full dataframe for the current backtest period. + :return: + :pred_df: dataframe containing the predictions + :do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove + data (NaNs) or felt uncertain about data (PCA and DI index) + """ + + dk.find_features(unfiltered_df) + filtered_df, _ = dk.filter_features( + unfiltered_df, dk.training_features_list, training_filter=False + ) + filtered_df = dk.normalize_data_from_metadata(filtered_df) + dk.data_dictionary["prediction_features"] = filtered_df + + self.data_cleaning_predict(dk) + x = self.data_convertor.convert_x( + dk.data_dictionary["prediction_features"], + device=self.device + ) + y = self.model.model(x) + y = y.cpu() + pred_df = DataFrame(y.detach().numpy(), columns=[dk.label_list[0]]) + return (pred_df, dk.do_predict) diff --git a/freqtrade/freqai/data_drawer.py b/freqtrade/freqai/data_drawer.py index 14986d854..b68a9dcad 100644 --- a/freqtrade/freqai/data_drawer.py +++ b/freqtrade/freqai/data_drawer.py @@ -446,7 +446,7 @@ class FreqaiDataDrawer: dump(model, save_path / f"{dk.model_filename}_model.joblib") elif self.model_type == 'keras': model.save(save_path / f"{dk.model_filename}_model.h5") - elif 'stable_baselines' in self.model_type or 'sb3_contrib' == self.model_type: + elif self.model_type in ["stable_baselines3", "sb3_contrib", "pytorch"]: model.save(save_path / f"{dk.model_filename}_model.zip") if dk.svm_model is not None: @@ -496,7 +496,7 @@ class FreqaiDataDrawer: dk.training_features_list = dk.data["training_features_list"] dk.label_list = dk.data["label_list"] - def load_data(self, coin: str, dk: FreqaiDataKitchen) -> Any: + def load_data(self, coin: str, dk: FreqaiDataKitchen) -> Any: # noqa: C901 """ loads all data required to make a prediction on a sub-train time range :returns: @@ -537,6 +537,11 @@ class FreqaiDataDrawer: self.model_type, self.freqai_info['rl_config']['model_type']) MODELCLASS = getattr(mod, self.freqai_info['rl_config']['model_type']) model = MODELCLASS.load(dk.data_path / f"{dk.model_filename}_model") + elif self.model_type == 'pytorch': + import torch + zip = torch.load(dk.data_path / f"{dk.model_filename}_model.zip") + model = zip["pytrainer"] + model = model.load_from_checkpoint(zip) if Path(dk.data_path / f"{dk.model_filename}_svm_model.joblib").is_file(): dk.svm_model = load(dk.data_path / f"{dk.model_filename}_svm_model.joblib") diff --git a/freqtrade/freqai/data_kitchen.py b/freqtrade/freqai/data_kitchen.py index 52d487b08..21b41db2d 100644 --- a/freqtrade/freqai/data_kitchen.py +++ b/freqtrade/freqai/data_kitchen.py @@ -1291,7 +1291,7 @@ class FreqaiDataKitchen: return dataframe - def use_strategy_to_populate_indicators( + def use_strategy_to_populate_indicators( # noqa: C901 self, strategy: IStrategy, corr_dataframes: dict = {}, @@ -1362,12 +1362,12 @@ class FreqaiDataKitchen: dataframe = self.populate_features(dataframe.copy(), corr_pair, strategy, corr_dataframes, base_dataframes, True) - dataframe = strategy.set_freqai_targets(dataframe.copy(), metadata=metadata) + if self.live: + dataframe = strategy.set_freqai_targets(dataframe.copy(), metadata=metadata) + dataframe = self.remove_special_chars_from_feature_names(dataframe) self.get_unique_classes_from_labels(dataframe) - dataframe = self.remove_special_chars_from_feature_names(dataframe) - if self.config.get('reduce_df_footprint', False): dataframe = reduce_dataframe_footprint(dataframe) diff --git a/freqtrade/freqai/freqai_interface.py b/freqtrade/freqai/freqai_interface.py index 07c357de3..039b6a175 100644 --- a/freqtrade/freqai/freqai_interface.py +++ b/freqtrade/freqai/freqai_interface.py @@ -83,6 +83,7 @@ class IFreqaiModel(ABC): self.CONV_WIDTH = self.freqai_info.get('conv_width', 1) if self.ft_params.get("inlier_metric_window", 0): self.CONV_WIDTH = self.ft_params.get("inlier_metric_window", 0) * 2 + self.class_names: List[str] = [] # used in classification subclasses self.pair_it = 0 self.pair_it_train = 0 self.total_pairs = len(self.config.get("exchange", {}).get("pair_whitelist")) @@ -105,6 +106,9 @@ class IFreqaiModel(ABC): self.max_system_threads = max(int(psutil.cpu_count() * 2 - 2), 1) self.can_short = True # overridden in start() with strategy.can_short self.model: Any = None + if self.ft_params.get('principal_component_analysis', False) and self.continual_learning: + self.ft_params.update({'principal_component_analysis': False}) + logger.warning('User tried to use PCA with continual learning. Deactivating PCA.') record_params(config, self.full_path) @@ -154,8 +158,7 @@ class IFreqaiModel(ABC): dk = self.start_backtesting(dataframe, metadata, self.dk, strategy) dataframe = dk.remove_features_from_df(dk.return_dataframe) else: - logger.info( - "Backtesting using historic predictions (live models)") + logger.info("Backtesting using historic predictions (live models)") dk = self.start_backtesting_from_historic_predictions( dataframe, metadata, self.dk) dataframe = dk.return_dataframe @@ -304,7 +307,7 @@ class IFreqaiModel(ABC): if check_features: self.dd.load_metadata(dk) dataframe_dummy_features = self.dk.use_strategy_to_populate_indicators( - strategy, prediction_dataframe=dataframe.tail(1), pair=metadata["pair"] + strategy, prediction_dataframe=dataframe.tail(1), pair=pair ) dk.find_features(dataframe_dummy_features) self.check_if_feature_list_matches_strategy(dk) @@ -314,7 +317,7 @@ class IFreqaiModel(ABC): else: if populate_indicators: dataframe = self.dk.use_strategy_to_populate_indicators( - strategy, prediction_dataframe=dataframe, pair=metadata["pair"] + strategy, prediction_dataframe=dataframe, pair=pair ) populate_indicators = False @@ -330,6 +333,10 @@ class IFreqaiModel(ABC): dataframe_train = dk.slice_dataframe(tr_train, dataframe_base_train) dataframe_backtest = dk.slice_dataframe(tr_backtest, dataframe_base_backtest) + dataframe_train = dk.remove_special_chars_from_feature_names(dataframe_train) + dataframe_backtest = dk.remove_special_chars_from_feature_names(dataframe_backtest) + dk.get_unique_classes_from_labels(dataframe_train) + if not self.model_exists(dk): dk.find_features(dataframe_train) dk.find_labels(dataframe_train) @@ -482,9 +489,9 @@ class IFreqaiModel(ABC): if dk.training_features_list != feature_list: raise OperationalException( "Trying to access pretrained model with `identifier` " - "but found different features furnished by current strategy." - "Change `identifier` to train from scratch, or ensure the" - "strategy is furnishing the same features as the pretrained" + "but found different features furnished by current strategy. " + "Change `identifier` to train from scratch, or ensure the " + "strategy is furnishing the same features as the pretrained " "model. In case of --strategy-list, please be aware that FreqAI " "requires all strategies to maintain identical " "feature_engineering_* functions" @@ -565,8 +572,9 @@ class IFreqaiModel(ABC): file_type = ".joblib" elif self.dd.model_type == 'keras': file_type = ".h5" - elif 'stable_baselines' in self.dd.model_type or 'sb3_contrib' == self.dd.model_type: + elif self.dd.model_type in ["stable_baselines3", "sb3_contrib", "pytorch"]: file_type = ".zip" + path_to_modelfile = Path(dk.data_path / f"{dk.model_filename}_model{file_type}") file_exists = path_to_modelfile.is_file() if file_exists: diff --git a/freqtrade/freqai/prediction_models/CatboostClassifier.py b/freqtrade/freqai/prediction_models/CatboostClassifier.py index ca1d8ece0..b9904e40d 100644 --- a/freqtrade/freqai/prediction_models/CatboostClassifier.py +++ b/freqtrade/freqai/prediction_models/CatboostClassifier.py @@ -14,16 +14,20 @@ logger = logging.getLogger(__name__) class CatboostClassifier(BaseClassifierModel): """ - User created prediction model. The class needs to override three necessary - functions, predict(), train(), fit(). The class inherits ModelHandler which - has its own DataHandler where data is held, saved, loaded, and managed. + User created prediction model. The class inherits IFreqaiModel, which + means it has full access to all Frequency AI functionality. Typically, + users would use this to override the common `fit()`, `train()`, or + `predict()` methods to add their custom data handling tools or change + various aspects of the training that cannot be configured via the + top level config.json file. """ def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any: """ User sets up the training and test data to fit their desired model here - :param data_dictionary: the dictionary constructed by DataHandler to hold - all the training and test data/labels. + :param data_dictionary: the dictionary holding all data for train, test, + labels, weights + :param dk: The datakitchen object for the current coin/model """ train_data = Pool( diff --git a/freqtrade/freqai/prediction_models/CatboostClassifierMultiTarget.py b/freqtrade/freqai/prediction_models/CatboostClassifierMultiTarget.py index c6f900fad..58c47566a 100644 --- a/freqtrade/freqai/prediction_models/CatboostClassifierMultiTarget.py +++ b/freqtrade/freqai/prediction_models/CatboostClassifierMultiTarget.py @@ -15,16 +15,20 @@ logger = logging.getLogger(__name__) class CatboostClassifierMultiTarget(BaseClassifierModel): """ - User created prediction model. The class needs to override three necessary - functions, predict(), train(), fit(). The class inherits ModelHandler which - has its own DataHandler where data is held, saved, loaded, and managed. + User created prediction model. The class inherits IFreqaiModel, which + means it has full access to all Frequency AI functionality. Typically, + users would use this to override the common `fit()`, `train()`, or + `predict()` methods to add their custom data handling tools or change + various aspects of the training that cannot be configured via the + top level config.json file. """ def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any: """ User sets up the training and test data to fit their desired model here - :param data_dictionary: the dictionary constructed by DataHandler to hold - all the training and test data/labels. + :param data_dictionary: the dictionary holding all data for train, test, + labels, weights + :param dk: The datakitchen object for the current coin/model """ cbc = CatBoostClassifier( diff --git a/freqtrade/freqai/prediction_models/CatboostRegressor.py b/freqtrade/freqai/prediction_models/CatboostRegressor.py index 4b17a703b..28b1b11cc 100644 --- a/freqtrade/freqai/prediction_models/CatboostRegressor.py +++ b/freqtrade/freqai/prediction_models/CatboostRegressor.py @@ -14,16 +14,20 @@ logger = logging.getLogger(__name__) class CatboostRegressor(BaseRegressionModel): """ - User created prediction model. The class needs to override three necessary - functions, predict(), train(), fit(). The class inherits ModelHandler which - has its own DataHandler where data is held, saved, loaded, and managed. + User created prediction model. The class inherits IFreqaiModel, which + means it has full access to all Frequency AI functionality. Typically, + users would use this to override the common `fit()`, `train()`, or + `predict()` methods to add their custom data handling tools or change + various aspects of the training that cannot be configured via the + top level config.json file. """ def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any: """ User sets up the training and test data to fit their desired model here - :param data_dictionary: the dictionary constructed by DataHandler to hold - all the training and test data/labels. + :param data_dictionary: the dictionary holding all data for train, test, + labels, weights + :param dk: The datakitchen object for the current coin/model """ train_data = Pool( diff --git a/freqtrade/freqai/prediction_models/CatboostRegressorMultiTarget.py b/freqtrade/freqai/prediction_models/CatboostRegressorMultiTarget.py index 976d0b29b..1562c2024 100644 --- a/freqtrade/freqai/prediction_models/CatboostRegressorMultiTarget.py +++ b/freqtrade/freqai/prediction_models/CatboostRegressorMultiTarget.py @@ -15,16 +15,20 @@ logger = logging.getLogger(__name__) class CatboostRegressorMultiTarget(BaseRegressionModel): """ - User created prediction model. The class needs to override three necessary - functions, predict(), train(), fit(). The class inherits ModelHandler which - has its own DataHandler where data is held, saved, loaded, and managed. + User created prediction model. The class inherits IFreqaiModel, which + means it has full access to all Frequency AI functionality. Typically, + users would use this to override the common `fit()`, `train()`, or + `predict()` methods to add their custom data handling tools or change + various aspects of the training that cannot be configured via the + top level config.json file. """ def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any: """ User sets up the training and test data to fit their desired model here - :param data_dictionary: the dictionary constructed by DataHandler to hold - all the training and test data/labels. + :param data_dictionary: the dictionary holding all data for train, test, + labels, weights + :param dk: The datakitchen object for the current coin/model """ cbr = CatBoostRegressor( diff --git a/freqtrade/freqai/prediction_models/LightGBMClassifier.py b/freqtrade/freqai/prediction_models/LightGBMClassifier.py index e467ad3c1..45f3a31d0 100644 --- a/freqtrade/freqai/prediction_models/LightGBMClassifier.py +++ b/freqtrade/freqai/prediction_models/LightGBMClassifier.py @@ -12,16 +12,20 @@ logger = logging.getLogger(__name__) class LightGBMClassifier(BaseClassifierModel): """ - User created prediction model. The class needs to override three necessary - functions, predict(), train(), fit(). The class inherits ModelHandler which - has its own DataHandler where data is held, saved, loaded, and managed. + User created prediction model. The class inherits IFreqaiModel, which + means it has full access to all Frequency AI functionality. Typically, + users would use this to override the common `fit()`, `train()`, or + `predict()` methods to add their custom data handling tools or change + various aspects of the training that cannot be configured via the + top level config.json file. """ def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any: """ User sets up the training and test data to fit their desired model here - :param data_dictionary: the dictionary constructed by DataHandler to hold - all the training and test data/labels. + :param data_dictionary: the dictionary holding all data for train, test, + labels, weights + :param dk: The datakitchen object for the current coin/model """ if self.freqai_info.get('data_split_parameters', {}).get('test_size', 0.1) == 0: diff --git a/freqtrade/freqai/prediction_models/LightGBMClassifierMultiTarget.py b/freqtrade/freqai/prediction_models/LightGBMClassifierMultiTarget.py index d1eb6daa2..72a8ee259 100644 --- a/freqtrade/freqai/prediction_models/LightGBMClassifierMultiTarget.py +++ b/freqtrade/freqai/prediction_models/LightGBMClassifierMultiTarget.py @@ -13,16 +13,20 @@ logger = logging.getLogger(__name__) class LightGBMClassifierMultiTarget(BaseClassifierModel): """ - User created prediction model. The class needs to override three necessary - functions, predict(), train(), fit(). The class inherits ModelHandler which - has its own DataHandler where data is held, saved, loaded, and managed. + User created prediction model. The class inherits IFreqaiModel, which + means it has full access to all Frequency AI functionality. Typically, + users would use this to override the common `fit()`, `train()`, or + `predict()` methods to add their custom data handling tools or change + various aspects of the training that cannot be configured via the + top level config.json file. """ def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any: """ User sets up the training and test data to fit their desired model here - :param data_dictionary: the dictionary constructed by DataHandler to hold - all the training and test data/labels. + :param data_dictionary: the dictionary holding all data for train, test, + labels, weights + :param dk: The datakitchen object for the current coin/model """ lgb = LGBMClassifier(**self.model_training_parameters) diff --git a/freqtrade/freqai/prediction_models/LightGBMRegressor.py b/freqtrade/freqai/prediction_models/LightGBMRegressor.py index 85c9b691c..3d1c30ed3 100644 --- a/freqtrade/freqai/prediction_models/LightGBMRegressor.py +++ b/freqtrade/freqai/prediction_models/LightGBMRegressor.py @@ -12,18 +12,20 @@ logger = logging.getLogger(__name__) class LightGBMRegressor(BaseRegressionModel): """ - User created prediction model. The class needs to override three necessary - functions, predict(), train(), fit(). The class inherits ModelHandler which - has its own DataHandler where data is held, saved, loaded, and managed. + User created prediction model. The class inherits IFreqaiModel, which + means it has full access to all Frequency AI functionality. Typically, + users would use this to override the common `fit()`, `train()`, or + `predict()` methods to add their custom data handling tools or change + various aspects of the training that cannot be configured via the + top level config.json file. """ def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any: """ - Most regressors use the same function names and arguments e.g. user - can drop in LGBMRegressor in place of CatBoostRegressor and all data - management will be properly handled by Freqai. - :param data_dictionary: the dictionary constructed by DataHandler to hold - all the training and test data/labels. + User sets up the training and test data to fit their desired model here + :param data_dictionary: the dictionary holding all data for train, test, + labels, weights + :param dk: The datakitchen object for the current coin/model """ if self.freqai_info.get('data_split_parameters', {}).get('test_size', 0.1) == 0: diff --git a/freqtrade/freqai/prediction_models/LightGBMRegressorMultiTarget.py b/freqtrade/freqai/prediction_models/LightGBMRegressorMultiTarget.py index 37c6bb186..663a611f0 100644 --- a/freqtrade/freqai/prediction_models/LightGBMRegressorMultiTarget.py +++ b/freqtrade/freqai/prediction_models/LightGBMRegressorMultiTarget.py @@ -13,16 +13,20 @@ logger = logging.getLogger(__name__) class LightGBMRegressorMultiTarget(BaseRegressionModel): """ - User created prediction model. The class needs to override three necessary - functions, predict(), train(), fit(). The class inherits ModelHandler which - has its own DataHandler where data is held, saved, loaded, and managed. + User created prediction model. The class inherits IFreqaiModel, which + means it has full access to all Frequency AI functionality. Typically, + users would use this to override the common `fit()`, `train()`, or + `predict()` methods to add their custom data handling tools or change + various aspects of the training that cannot be configured via the + top level config.json file. """ def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any: """ User sets up the training and test data to fit their desired model here - :param data_dictionary: the dictionary constructed by DataHandler to hold - all the training and test data/labels. + :param data_dictionary: the dictionary holding all data for train, test, + labels, weights + :param dk: The datakitchen object for the current coin/model """ lgb = LGBMRegressor(**self.model_training_parameters) diff --git a/freqtrade/freqai/prediction_models/PyTorchMLPClassifier.py b/freqtrade/freqai/prediction_models/PyTorchMLPClassifier.py new file mode 100644 index 000000000..ea7981405 --- /dev/null +++ b/freqtrade/freqai/prediction_models/PyTorchMLPClassifier.py @@ -0,0 +1,89 @@ +from typing import Any, Dict + +import torch + +from freqtrade.freqai.base_models.BasePyTorchClassifier import BasePyTorchClassifier +from freqtrade.freqai.data_kitchen import FreqaiDataKitchen +from freqtrade.freqai.torch.PyTorchDataConvertor import (DefaultPyTorchDataConvertor, + PyTorchDataConvertor) +from freqtrade.freqai.torch.PyTorchMLPModel import PyTorchMLPModel +from freqtrade.freqai.torch.PyTorchModelTrainer import PyTorchModelTrainer + + +class PyTorchMLPClassifier(BasePyTorchClassifier): + """ + This class implements the fit method of IFreqaiModel. + in the fit method we initialize the model and trainer objects. + the only requirement from the model is to be aligned to PyTorchClassifier + predict method that expects the model to predict a tensor of type long. + + parameters are passed via `model_training_parameters` under the freqai + section in the config file. e.g: + { + ... + "freqai": { + ... + "model_training_parameters" : { + "learning_rate": 3e-4, + "trainer_kwargs": { + "max_iters": 5000, + "batch_size": 64, + "max_n_eval_batches": null, + }, + "model_kwargs": { + "hidden_dim": 512, + "dropout_percent": 0.2, + "n_layer": 1, + }, + } + } + } + """ + + @property + def data_convertor(self) -> PyTorchDataConvertor: + return DefaultPyTorchDataConvertor( + target_tensor_type=torch.long, + squeeze_target_tensor=True + ) + + def __init__(self, **kwargs) -> None: + super().__init__(**kwargs) + config = self.freqai_info.get("model_training_parameters", {}) + self.learning_rate: float = config.get("learning_rate", 3e-4) + self.model_kwargs: Dict[str, Any] = config.get("model_kwargs", {}) + self.trainer_kwargs: Dict[str, Any] = config.get("trainer_kwargs", {}) + + def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any: + """ + User sets up the training and test data to fit their desired model here + :param data_dictionary: the dictionary holding all data for train, test, + labels, weights + :param dk: The datakitchen object for the current coin/model + :raises ValueError: If self.class_names is not defined in the parent class. + """ + + class_names = self.get_class_names() + self.convert_label_column_to_int(data_dictionary, dk, class_names) + n_features = data_dictionary["train_features"].shape[-1] + model = PyTorchMLPModel( + input_dim=n_features, + output_dim=len(class_names), + **self.model_kwargs + ) + model.to(self.device) + optimizer = torch.optim.AdamW(model.parameters(), lr=self.learning_rate) + criterion = torch.nn.CrossEntropyLoss() + init_model = self.get_init_model(dk.pair) + trainer = PyTorchModelTrainer( + model=model, + optimizer=optimizer, + criterion=criterion, + model_meta_data={"class_names": class_names}, + device=self.device, + init_model=init_model, + data_convertor=self.data_convertor, + **self.trainer_kwargs, + ) + trainer.fit(data_dictionary, self.splits) + return trainer diff --git a/freqtrade/freqai/prediction_models/PyTorchMLPRegressor.py b/freqtrade/freqai/prediction_models/PyTorchMLPRegressor.py new file mode 100644 index 000000000..64f0f4b03 --- /dev/null +++ b/freqtrade/freqai/prediction_models/PyTorchMLPRegressor.py @@ -0,0 +1,83 @@ +from typing import Any, Dict + +import torch + +from freqtrade.freqai.base_models.BasePyTorchRegressor import BasePyTorchRegressor +from freqtrade.freqai.data_kitchen import FreqaiDataKitchen +from freqtrade.freqai.torch.PyTorchDataConvertor import (DefaultPyTorchDataConvertor, + PyTorchDataConvertor) +from freqtrade.freqai.torch.PyTorchMLPModel import PyTorchMLPModel +from freqtrade.freqai.torch.PyTorchModelTrainer import PyTorchModelTrainer + + +class PyTorchMLPRegressor(BasePyTorchRegressor): + """ + This class implements the fit method of IFreqaiModel. + in the fit method we initialize the model and trainer objects. + the only requirement from the model is to be aligned to PyTorchRegressor + predict method that expects the model to predict tensor of type float. + the trainer defines the training loop. + + parameters are passed via `model_training_parameters` under the freqai + section in the config file. e.g: + { + ... + "freqai": { + ... + "model_training_parameters" : { + "learning_rate": 3e-4, + "trainer_kwargs": { + "max_iters": 5000, + "batch_size": 64, + "max_n_eval_batches": null, + }, + "model_kwargs": { + "hidden_dim": 512, + "dropout_percent": 0.2, + "n_layer": 1, + }, + } + } + } + """ + + @property + def data_convertor(self) -> PyTorchDataConvertor: + return DefaultPyTorchDataConvertor(target_tensor_type=torch.float) + + def __init__(self, **kwargs) -> None: + super().__init__(**kwargs) + config = self.freqai_info.get("model_training_parameters", {}) + self.learning_rate: float = config.get("learning_rate", 3e-4) + self.model_kwargs: Dict[str, Any] = config.get("model_kwargs", {}) + self.trainer_kwargs: Dict[str, Any] = config.get("trainer_kwargs", {}) + + def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any: + """ + User sets up the training and test data to fit their desired model here + :param data_dictionary: the dictionary holding all data for train, test, + labels, weights + :param dk: The datakitchen object for the current coin/model + """ + + n_features = data_dictionary["train_features"].shape[-1] + model = PyTorchMLPModel( + input_dim=n_features, + output_dim=1, + **self.model_kwargs + ) + model.to(self.device) + optimizer = torch.optim.AdamW(model.parameters(), lr=self.learning_rate) + criterion = torch.nn.MSELoss() + init_model = self.get_init_model(dk.pair) + trainer = PyTorchModelTrainer( + model=model, + optimizer=optimizer, + criterion=criterion, + device=self.device, + init_model=init_model, + data_convertor=self.data_convertor, + **self.trainer_kwargs, + ) + trainer.fit(data_dictionary, self.splits) + return trainer diff --git a/freqtrade/freqai/prediction_models/ReinforcementLearner.py b/freqtrade/freqai/prediction_models/ReinforcementLearner.py index e795703d4..65990da87 100644 --- a/freqtrade/freqai/prediction_models/ReinforcementLearner.py +++ b/freqtrade/freqai/prediction_models/ReinforcementLearner.py @@ -71,7 +71,8 @@ class ReinforcementLearner(BaseReinforcementLearningModel): model.learn( total_timesteps=int(total_timesteps), - callback=[self.eval_callback, self.tensorboard_callback] + callback=[self.eval_callback, self.tensorboard_callback], + progress_bar=self.rl_config.get('progress_bar', False) ) if Path(dk.data_path / "best_model.zip").is_file(): diff --git a/freqtrade/freqai/prediction_models/XGBoostClassifier.py b/freqtrade/freqai/prediction_models/XGBoostClassifier.py index 67c7c7783..b6f04b497 100644 --- a/freqtrade/freqai/prediction_models/XGBoostClassifier.py +++ b/freqtrade/freqai/prediction_models/XGBoostClassifier.py @@ -18,16 +18,20 @@ logger = logging.getLogger(__name__) class XGBoostClassifier(BaseClassifierModel): """ - User created prediction model. The class needs to override three necessary - functions, predict(), train(), fit(). The class inherits ModelHandler which - has its own DataHandler where data is held, saved, loaded, and managed. + User created prediction model. The class inherits IFreqaiModel, which + means it has full access to all Frequency AI functionality. Typically, + users would use this to override the common `fit()`, `train()`, or + `predict()` methods to add their custom data handling tools or change + various aspects of the training that cannot be configured via the + top level config.json file. """ def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any: """ User sets up the training and test data to fit their desired model here - :param data_dictionary: the dictionary constructed by DataHandler to hold - all the training and test data/labels. + :param data_dictionary: the dictionary holding all data for train, test, + labels, weights + :param dk: The datakitchen object for the current coin/model """ X = data_dictionary["train_features"].to_numpy() diff --git a/freqtrade/freqai/prediction_models/XGBoostRFClassifier.py b/freqtrade/freqai/prediction_models/XGBoostRFClassifier.py index 470c283ea..20156e9fd 100644 --- a/freqtrade/freqai/prediction_models/XGBoostRFClassifier.py +++ b/freqtrade/freqai/prediction_models/XGBoostRFClassifier.py @@ -18,16 +18,20 @@ logger = logging.getLogger(__name__) class XGBoostRFClassifier(BaseClassifierModel): """ - User created prediction model. The class needs to override three necessary - functions, predict(), train(), fit(). The class inherits ModelHandler which - has its own DataHandler where data is held, saved, loaded, and managed. + User created prediction model. The class inherits IFreqaiModel, which + means it has full access to all Frequency AI functionality. Typically, + users would use this to override the common `fit()`, `train()`, or + `predict()` methods to add their custom data handling tools or change + various aspects of the training that cannot be configured via the + top level config.json file. """ def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any: """ User sets up the training and test data to fit their desired model here - :param data_dictionary: the dictionary constructed by DataHandler to hold - all the training and test data/labels. + :param data_dictionary: the dictionary holding all data for train, test, + labels, weights + :param dk: The datakitchen object for the current coin/model """ X = data_dictionary["train_features"].to_numpy() diff --git a/freqtrade/freqai/prediction_models/XGBoostRFRegressor.py b/freqtrade/freqai/prediction_models/XGBoostRFRegressor.py index e7cc27f2e..1aefbf19a 100644 --- a/freqtrade/freqai/prediction_models/XGBoostRFRegressor.py +++ b/freqtrade/freqai/prediction_models/XGBoostRFRegressor.py @@ -12,16 +12,20 @@ logger = logging.getLogger(__name__) class XGBoostRFRegressor(BaseRegressionModel): """ - User created prediction model. The class needs to override three necessary - functions, predict(), train(), fit(). The class inherits ModelHandler which - has its own DataHandler where data is held, saved, loaded, and managed. + User created prediction model. The class inherits IFreqaiModel, which + means it has full access to all Frequency AI functionality. Typically, + users would use this to override the common `fit()`, `train()`, or + `predict()` methods to add their custom data handling tools or change + various aspects of the training that cannot be configured via the + top level config.json file. """ def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any: """ User sets up the training and test data to fit their desired model here - :param data_dictionary: the dictionary constructed by DataHandler to hold - all the training and test data/labels. + :param data_dictionary: the dictionary holding all data for train, test, + labels, weights + :param dk: The datakitchen object for the current coin/model """ X = data_dictionary["train_features"] diff --git a/freqtrade/freqai/prediction_models/XGBoostRegressor.py b/freqtrade/freqai/prediction_models/XGBoostRegressor.py index 9a280286b..93dfb319e 100644 --- a/freqtrade/freqai/prediction_models/XGBoostRegressor.py +++ b/freqtrade/freqai/prediction_models/XGBoostRegressor.py @@ -12,16 +12,20 @@ logger = logging.getLogger(__name__) class XGBoostRegressor(BaseRegressionModel): """ - User created prediction model. The class needs to override three necessary - functions, predict(), train(), fit(). The class inherits ModelHandler which - has its own DataHandler where data is held, saved, loaded, and managed. + User created prediction model. The class inherits IFreqaiModel, which + means it has full access to all Frequency AI functionality. Typically, + users would use this to override the common `fit()`, `train()`, or + `predict()` methods to add their custom data handling tools or change + various aspects of the training that cannot be configured via the + top level config.json file. """ def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any: """ User sets up the training and test data to fit their desired model here - :param data_dictionary: the dictionary constructed by DataHandler to hold - all the training and test data/labels. + :param data_dictionary: the dictionary holding all data for train, test, + labels, weights + :param dk: The datakitchen object for the current coin/model """ X = data_dictionary["train_features"] diff --git a/freqtrade/freqai/prediction_models/XGBoostRegressorMultiTarget.py b/freqtrade/freqai/prediction_models/XGBoostRegressorMultiTarget.py index 920745ec9..a0330485e 100644 --- a/freqtrade/freqai/prediction_models/XGBoostRegressorMultiTarget.py +++ b/freqtrade/freqai/prediction_models/XGBoostRegressorMultiTarget.py @@ -13,16 +13,20 @@ logger = logging.getLogger(__name__) class XGBoostRegressorMultiTarget(BaseRegressionModel): """ - User created prediction model. The class needs to override three necessary - functions, predict(), train(), fit(). The class inherits ModelHandler which - has its own DataHandler where data is held, saved, loaded, and managed. + User created prediction model. The class inherits IFreqaiModel, which + means it has full access to all Frequency AI functionality. Typically, + users would use this to override the common `fit()`, `train()`, or + `predict()` methods to add their custom data handling tools or change + various aspects of the training that cannot be configured via the + top level config.json file. """ def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any: """ User sets up the training and test data to fit their desired model here - :param data_dictionary: the dictionary constructed by DataHandler to hold - all the training and test data/labels. + :param data_dictionary: the dictionary holding all data for train, test, + labels, weights + :param dk: The datakitchen object for the current coin/model """ xgb = XGBRegressor(**self.model_training_parameters) diff --git a/freqtrade/freqai/torch/PyTorchDataConvertor.py b/freqtrade/freqai/torch/PyTorchDataConvertor.py new file mode 100644 index 000000000..a31ccdc79 --- /dev/null +++ b/freqtrade/freqai/torch/PyTorchDataConvertor.py @@ -0,0 +1,67 @@ +from abc import ABC, abstractmethod +from typing import List, Optional + +import pandas as pd +import torch + + +class PyTorchDataConvertor(ABC): + """ + This class is responsible for converting `*_features` & `*_labels` pandas dataframes + to pytorch tensors. + """ + + @abstractmethod + def convert_x(self, df: pd.DataFrame, device: Optional[str] = None) -> List[torch.Tensor]: + """ + :param df: "*_features" dataframe. + :param device: The device to use for training (e.g. 'cpu', 'cuda'). + """ + + @abstractmethod + def convert_y(self, df: pd.DataFrame, device: Optional[str] = None) -> List[torch.Tensor]: + """ + :param df: "*_labels" dataframe. + :param device: The device to use for training (e.g. 'cpu', 'cuda'). + """ + + +class DefaultPyTorchDataConvertor(PyTorchDataConvertor): + """ + A default conversion that keeps features dataframe shapes. + """ + + def __init__( + self, + target_tensor_type: Optional[torch.dtype] = None, + squeeze_target_tensor: bool = False + ): + """ + :param target_tensor_type: type of target tensor, for classification use + torch.long, for regressor use torch.float or torch.double. + :param squeeze_target_tensor: controls the target shape, used for loss functions + that requires 0D or 1D. + """ + self._target_tensor_type = target_tensor_type + self._squeeze_target_tensor = squeeze_target_tensor + + def convert_x(self, df: pd.DataFrame, device: Optional[str] = None) -> List[torch.Tensor]: + x = torch.from_numpy(df.values).float() + if device: + x = x.to(device) + + return [x] + + def convert_y(self, df: pd.DataFrame, device: Optional[str] = None) -> List[torch.Tensor]: + y = torch.from_numpy(df.values) + + if self._target_tensor_type: + y = y.to(self._target_tensor_type) + + if self._squeeze_target_tensor: + y = y.squeeze() + + if device: + y = y.to(device) + + return [y] diff --git a/freqtrade/freqai/torch/PyTorchMLPModel.py b/freqtrade/freqai/torch/PyTorchMLPModel.py new file mode 100644 index 000000000..62d3216df --- /dev/null +++ b/freqtrade/freqai/torch/PyTorchMLPModel.py @@ -0,0 +1,97 @@ +import logging +from typing import List + +import torch +from torch import nn + + +logger = logging.getLogger(__name__) + + +class PyTorchMLPModel(nn.Module): + """ + A multi-layer perceptron (MLP) model implemented using PyTorch. + + This class mainly serves as a simple example for the integration of PyTorch model's + to freqai. It is not optimized at all and should not be used for production purposes. + + :param input_dim: The number of input features. This parameter specifies the number + of features in the input data that the MLP will use to make predictions. + :param output_dim: The number of output classes. This parameter specifies the number + of classes that the MLP will predict. + :param hidden_dim: The number of hidden units in each layer. This parameter controls + the complexity of the MLP and determines how many nonlinear relationships the MLP + can represent. Increasing the number of hidden units can increase the capacity of + the MLP to model complex patterns, but it also increases the risk of overfitting + the training data. Default: 256 + :param dropout_percent: The dropout rate for regularization. This parameter specifies + the probability of dropping out a neuron during training to prevent overfitting. + The dropout rate should be tuned carefully to balance between underfitting and + overfitting. Default: 0.2 + :param n_layer: The number of layers in the MLP. This parameter specifies the number + of layers in the MLP architecture. Adding more layers to the MLP can increase its + capacity to model complex patterns, but it also increases the risk of overfitting + the training data. Default: 1 + + :returns: The output of the MLP, with shape (batch_size, output_dim) + """ + + def __init__(self, input_dim: int, output_dim: int, **kwargs): + super().__init__() + hidden_dim: int = kwargs.get("hidden_dim", 256) + dropout_percent: int = kwargs.get("dropout_percent", 0.2) + n_layer: int = kwargs.get("n_layer", 1) + self.input_layer = nn.Linear(input_dim, hidden_dim) + self.blocks = nn.Sequential(*[Block(hidden_dim, dropout_percent) for _ in range(n_layer)]) + self.output_layer = nn.Linear(hidden_dim, output_dim) + self.relu = nn.ReLU() + self.dropout = nn.Dropout(p=dropout_percent) + + def forward(self, tensors: List[torch.Tensor]) -> torch.Tensor: + x: torch.Tensor = tensors[0] + x = self.relu(self.input_layer(x)) + x = self.dropout(x) + x = self.blocks(x) + x = self.output_layer(x) + return x + + +class Block(nn.Module): + """ + A building block for a multi-layer perceptron (MLP). + + :param hidden_dim: The number of hidden units in the feedforward network. + :param dropout_percent: The dropout rate for regularization. + + :returns: torch.Tensor. with shape (batch_size, hidden_dim) + """ + + def __init__(self, hidden_dim: int, dropout_percent: int): + super().__init__() + self.ff = FeedForward(hidden_dim) + self.dropout = nn.Dropout(p=dropout_percent) + self.ln = nn.LayerNorm(hidden_dim) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + x = self.ff(self.ln(x)) + x = self.dropout(x) + return x + + +class FeedForward(nn.Module): + """ + A simple fully-connected feedforward neural network block. + + :param hidden_dim: The number of hidden units in the block. + :return: torch.Tensor. with shape (batch_size, hidden_dim) + """ + + def __init__(self, hidden_dim: int): + super().__init__() + self.net = nn.Sequential( + nn.Linear(hidden_dim, hidden_dim), + nn.ReLU(), + ) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + return self.net(x) diff --git a/freqtrade/freqai/torch/PyTorchModelTrainer.py b/freqtrade/freqai/torch/PyTorchModelTrainer.py new file mode 100644 index 000000000..8277ba937 --- /dev/null +++ b/freqtrade/freqai/torch/PyTorchModelTrainer.py @@ -0,0 +1,208 @@ +import logging +import math +from pathlib import Path +from typing import Any, Dict, List, Optional + +import pandas as pd +import torch +from torch import nn +from torch.optim import Optimizer +from torch.utils.data import DataLoader, TensorDataset + +from freqtrade.freqai.torch.PyTorchDataConvertor import PyTorchDataConvertor +from freqtrade.freqai.torch.PyTorchTrainerInterface import PyTorchTrainerInterface + + +logger = logging.getLogger(__name__) + + +class PyTorchModelTrainer(PyTorchTrainerInterface): + def __init__( + self, + model: nn.Module, + optimizer: Optimizer, + criterion: nn.Module, + device: str, + init_model: Dict, + data_convertor: PyTorchDataConvertor, + model_meta_data: Dict[str, Any] = {}, + **kwargs + ): + """ + :param model: The PyTorch model to be trained. + :param optimizer: The optimizer to use for training. + :param criterion: The loss function to use for training. + :param device: The device to use for training (e.g. 'cpu', 'cuda'). + :param init_model: A dictionary containing the initial model/optimizer + state_dict and model_meta_data saved by self.save() method. + :param model_meta_data: Additional metadata about the model (optional). + :param data_convertor: convertor from pd.DataFrame to torch.tensor. + :param max_iters: The number of training iterations to run. + iteration here refers to the number of times we call + self.optimizer.step(). used to calculate n_epochs. + :param batch_size: The size of the batches to use during training. + :param max_n_eval_batches: The maximum number batches to use for evaluation. + """ + self.model = model + self.optimizer = optimizer + self.criterion = criterion + self.model_meta_data = model_meta_data + self.device = device + self.max_iters: int = kwargs.get("max_iters", 100) + self.batch_size: int = kwargs.get("batch_size", 64) + self.max_n_eval_batches: Optional[int] = kwargs.get("max_n_eval_batches", None) + self.data_convertor = data_convertor + if init_model: + self.load_from_checkpoint(init_model) + + def fit(self, data_dictionary: Dict[str, pd.DataFrame], splits: List[str]): + """ + :param data_dictionary: the dictionary constructed by DataHandler to hold + all the training and test data/labels. + :param splits: splits to use in training, splits must contain "train", + optional "test" could be added by setting freqai.data_split_parameters.test_size > 0 + in the config file. + + - Calculates the predicted output for the batch using the PyTorch model. + - Calculates the loss between the predicted and actual output using a loss function. + - Computes the gradients of the loss with respect to the model's parameters using + backpropagation. + - Updates the model's parameters using an optimizer. + """ + data_loaders_dictionary = self.create_data_loaders_dictionary(data_dictionary, splits) + epochs = self.calc_n_epochs( + n_obs=len(data_dictionary["train_features"]), + batch_size=self.batch_size, + n_iters=self.max_iters + ) + for epoch in range(1, epochs + 1): + # training + losses = [] + for i, batch_data in enumerate(data_loaders_dictionary["train"]): + + for tensor in batch_data: + tensor.to(self.device) + + xb = batch_data[:-1] + yb = batch_data[-1] + yb_pred = self.model(xb) + loss = self.criterion(yb_pred, yb) + + self.optimizer.zero_grad(set_to_none=True) + loss.backward() + self.optimizer.step() + losses.append(loss.item()) + train_loss = sum(losses) / len(losses) + log_message = f"epoch {epoch}/{epochs}: train loss {train_loss:.4f}" + + # evaluation + if "test" in splits: + test_loss = self.estimate_loss( + data_loaders_dictionary, + self.max_n_eval_batches, + "test" + ) + log_message += f" ; test loss {test_loss:.4f}" + + logger.info(log_message) + + @torch.no_grad() + def estimate_loss( + self, + data_loader_dictionary: Dict[str, DataLoader], + max_n_eval_batches: Optional[int], + split: str, + ) -> float: + self.model.eval() + n_batches = 0 + losses = [] + for i, batch_data in enumerate(data_loader_dictionary[split]): + if max_n_eval_batches and i > max_n_eval_batches: + n_batches += 1 + break + + for tensor in batch_data: + tensor.to(self.device) + + xb = batch_data[:-1] + yb = batch_data[-1] + yb_pred = self.model(xb) + loss = self.criterion(yb_pred, yb) + losses.append(loss.item()) + + self.model.train() + return sum(losses) / len(losses) + + def create_data_loaders_dictionary( + self, + data_dictionary: Dict[str, pd.DataFrame], + splits: List[str] + ) -> Dict[str, DataLoader]: + """ + Converts the input data to PyTorch tensors using a data loader. + """ + data_loader_dictionary = {} + for split in splits: + x = self.data_convertor.convert_x(data_dictionary[f"{split}_features"], self.device) + y = self.data_convertor.convert_y(data_dictionary[f"{split}_labels"], self.device) + dataset = TensorDataset(*x, *y) + data_loader = DataLoader( + dataset, + batch_size=self.batch_size, + shuffle=True, + drop_last=True, + num_workers=0, + ) + data_loader_dictionary[split] = data_loader + + return data_loader_dictionary + + @staticmethod + def calc_n_epochs(n_obs: int, batch_size: int, n_iters: int) -> int: + """ + Calculates the number of epochs required to reach the maximum number + of iterations specified in the model training parameters. + + the motivation here is that `max_iters` is easier to optimize and keep stable, + across different n_obs - the number of data points. + """ + + n_batches = math.ceil(n_obs // batch_size) + epochs = math.ceil(n_iters // n_batches) + if epochs <= 10: + logger.warning("User set `max_iters` in such a way that the trainer will only perform " + f" {epochs} epochs. Please consider increasing this value accordingly") + if epochs <= 1: + logger.warning("Epochs set to 1. Please review your `max_iters` value") + epochs = 1 + return epochs + + def save(self, path: Path): + """ + - Saving any nn.Module state_dict + - Saving model_meta_data, this dict should contain any additional data that the + user needs to store. e.g class_names for classification models. + """ + + torch.save({ + "model_state_dict": self.model.state_dict(), + "optimizer_state_dict": self.optimizer.state_dict(), + "model_meta_data": self.model_meta_data, + "pytrainer": self + }, path) + + def load(self, path: Path): + checkpoint = torch.load(path) + return self.load_from_checkpoint(checkpoint) + + def load_from_checkpoint(self, checkpoint: Dict): + """ + when using continual_learning, DataDrawer will load the dictionary + (containing state dicts and model_meta_data) by calling torch.load(path). + you can access this dict from any class that inherits IFreqaiModel by calling + get_init_model method. + """ + self.model.load_state_dict(checkpoint["model_state_dict"]) + self.optimizer.load_state_dict(checkpoint["optimizer_state_dict"]) + self.model_meta_data = checkpoint["model_meta_data"] + return self diff --git a/freqtrade/freqai/torch/PyTorchTrainerInterface.py b/freqtrade/freqai/torch/PyTorchTrainerInterface.py new file mode 100644 index 000000000..840c145f7 --- /dev/null +++ b/freqtrade/freqai/torch/PyTorchTrainerInterface.py @@ -0,0 +1,53 @@ +from abc import ABC, abstractmethod +from pathlib import Path +from typing import Dict, List + +import pandas as pd +import torch +from torch import nn + + +class PyTorchTrainerInterface(ABC): + + @abstractmethod + def fit(self, data_dictionary: Dict[str, pd.DataFrame], splits: List[str]) -> None: + """ + :param data_dictionary: the dictionary constructed by DataHandler to hold + all the training and test data/labels. + :param splits: splits to use in training, splits must contain "train", + optional "test" could be added by setting freqai.data_split_parameters.test_size > 0 + in the config file. + + - Calculates the predicted output for the batch using the PyTorch model. + - Calculates the loss between the predicted and actual output using a loss function. + - Computes the gradients of the loss with respect to the model's parameters using + backpropagation. + - Updates the model's parameters using an optimizer. + """ + + @abstractmethod + def save(self, path: Path) -> None: + """ + - Saving any nn.Module state_dict + - Saving model_meta_data, this dict should contain any additional data that the + user needs to store. e.g class_names for classification models. + """ + + def load(self, path: Path) -> nn.Module: + """ + :param path: path to zip file. + :returns: pytorch model. + """ + checkpoint = torch.load(path) + return self.load_from_checkpoint(checkpoint) + + @abstractmethod + def load_from_checkpoint(self, checkpoint: Dict) -> nn.Module: + """ + when using continual_learning, DataDrawer will load the dictionary + (containing state dicts and model_meta_data) by calling torch.load(path). + you can access this dict from any class that inherits IFreqaiModel by calling + get_init_model method. + :checkpoint checkpoint: dict containing the model & optimizer state dicts, + model_meta_data, etc.. + """ diff --git a/freqtrade/freqai/torch/__init__.py b/freqtrade/freqai/torch/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/freqtrade/freqtradebot.py b/freqtrade/freqtradebot.py index 06c8831f5..89f0ac55d 100644 --- a/freqtrade/freqtradebot.py +++ b/freqtrade/freqtradebot.py @@ -21,15 +21,19 @@ from freqtrade.enums import (ExitCheckTuple, ExitType, RPCMessageType, RunMode, State, TradingMode) from freqtrade.exceptions import (DependencyException, ExchangeError, InsufficientFundsError, InvalidOrderException, PricingError) -from freqtrade.exchange import timeframe_to_minutes, timeframe_to_next_date, timeframe_to_seconds +from freqtrade.exchange import (ROUND_DOWN, ROUND_UP, timeframe_to_minutes, timeframe_to_next_date, + timeframe_to_seconds) from freqtrade.misc import safe_value_fallback, safe_value_fallback2 from freqtrade.mixins import LoggingMixin from freqtrade.persistence import Order, PairLocks, Trade, init_db +from freqtrade.persistence.key_value_store import set_startup_time from freqtrade.plugins.pairlistmanager import PairListManager from freqtrade.plugins.protectionmanager import ProtectionManager from freqtrade.resolvers import ExchangeResolver, StrategyResolver from freqtrade.rpc import RPCManager from freqtrade.rpc.external_message_consumer import ExternalMessageConsumer +from freqtrade.rpc.rpc_types import (RPCBuyMsg, RPCCancelMsg, RPCProtectionMsg, RPCSellCancelMsg, + RPCSellMsg) from freqtrade.strategy.interface import IStrategy from freqtrade.strategy.strategy_wrapper import strategy_safe_wrapper from freqtrade.util import FtPrecise @@ -179,6 +183,7 @@ class FreqtradeBot(LoggingMixin): performs startup tasks """ migrate_binance_futures_names(self.config) + set_startup_time() self.rpc.startup_messages(self.config, self.pairlists, self.protections) # Update older trades with precision and precision mode @@ -212,7 +217,8 @@ class FreqtradeBot(LoggingMixin): self.dataprovider.refresh(self.pairlists.create_pair_list(self.active_pair_whitelist), self.strategy.gather_informative_pairs()) - strategy_safe_wrapper(self.strategy.bot_loop_start, supress_error=True)() + strategy_safe_wrapper(self.strategy.bot_loop_start, supress_error=True)( + current_time=datetime.now(timezone.utc)) self.strategy.analyze(self.active_pair_whitelist) @@ -414,7 +420,7 @@ class FreqtradeBot(LoggingMixin): """ Try refinding a lost trade. Only used when InsufficientFunds appears on exit orders (stoploss or long sell/short buy). - Tries to walk the stored orders and sell them off eventually. + Tries to walk the stored orders and updates the trade state if necessary. """ logger.info(f"Trying to refind lost order for {trade}") for order in trade.orders: @@ -484,7 +490,8 @@ class FreqtradeBot(LoggingMixin): # Create entity and execute trade for each pair from whitelist for pair in whitelist: try: - trades_created += self.create_trade(pair) + with self._exit_lock: + trades_created += self.create_trade(pair) except DependencyException as exception: logger.warning('Unable to create trade for %s: %s', pair, exception) @@ -594,7 +601,7 @@ class FreqtradeBot(LoggingMixin): stake_available = self.wallets.get_available_stake_amount() logger.debug(f"Calling adjust_trade_position for pair {trade.pair}") stake_amount = strategy_safe_wrapper(self.strategy.adjust_trade_position, - default_retval=None)( + default_retval=None, supress_error=True)( trade=trade, current_time=datetime.now(timezone.utc), current_rate=current_entry_rate, current_profit=current_entry_profit, min_stake=min_entry_stake, @@ -810,6 +817,9 @@ class FreqtradeBot(LoggingMixin): precision_mode=self.exchange.precisionMode, contract_size=self.exchange.get_contract_size(pair), ) + stoploss = self.strategy.stoploss if not self.edge else self.edge.get_stoploss(pair) + trade.adjust_stop_loss(trade.open_rate, stoploss, initial=True) + else: # This is additional buy, we reset fee_open_currency so timeout checking can work trade.is_open = True @@ -847,11 +857,13 @@ class FreqtradeBot(LoggingMixin): logger.info(f"Canceling stoploss on exchange for {trade}") co = self.exchange.cancel_stoploss_order_with_result( trade.stoploss_order_id, trade.pair, trade.amount) - trade.update_order(co) + self.update_trade_state(trade, trade.stoploss_order_id, co, stoploss_order=True) + # Reset stoploss order id. trade.stoploss_order_id = None except InvalidOrderException: - logger.exception(f"Could not cancel stoploss order {trade.stoploss_order_id}") + logger.exception(f"Could not cancel stoploss order {trade.stoploss_order_id} " + f"for pair {trade.pair}") return trade def get_valid_enter_price_and_stake( @@ -938,12 +950,11 @@ class FreqtradeBot(LoggingMixin): return enter_limit_requested, stake_amount, leverage - def _notify_enter(self, trade: Trade, order: Order, order_type: Optional[str] = None, + def _notify_enter(self, trade: Trade, order: Order, order_type: str, fill: bool = False, sub_trade: bool = False) -> None: """ Sends rpc notification when a entry order occurred. """ - msg_type = RPCMessageType.ENTRY_FILL if fill else RPCMessageType.ENTRY open_rate = order.safe_price if open_rate is None: @@ -954,9 +965,9 @@ class FreqtradeBot(LoggingMixin): current_rate = self.exchange.get_rate( trade.pair, side='entry', is_short=trade.is_short, refresh=False) - msg = { + msg: RPCBuyMsg = { 'trade_id': trade.id, - 'type': msg_type, + 'type': RPCMessageType.ENTRY_FILL if fill else RPCMessageType.ENTRY, 'buy_tag': trade.enter_tag, 'enter_tag': trade.enter_tag, 'exchange': trade.exchange.capitalize(), @@ -968,6 +979,7 @@ class FreqtradeBot(LoggingMixin): 'order_type': order_type, 'stake_amount': trade.stake_amount, 'stake_currency': self.config['stake_currency'], + 'base_currency': self.exchange.get_pair_base_currency(trade.pair), 'fiat_currency': self.config.get('fiat_display_currency', None), 'amount': order.safe_amount_after_fee if fill else (order.amount or trade.amount), 'open_date': trade.open_date or datetime.utcnow(), @@ -986,7 +998,7 @@ class FreqtradeBot(LoggingMixin): current_rate = self.exchange.get_rate( trade.pair, side='entry', is_short=trade.is_short, refresh=False) - msg = { + msg: RPCCancelMsg = { 'trade_id': trade.id, 'type': RPCMessageType.ENTRY_CANCEL, 'buy_tag': trade.enter_tag, @@ -998,7 +1010,9 @@ class FreqtradeBot(LoggingMixin): 'limit': trade.open_rate, 'order_type': order_type, 'stake_amount': trade.stake_amount, + 'open_rate': trade.open_rate, 'stake_currency': self.config['stake_currency'], + 'base_currency': self.exchange.get_pair_base_currency(trade.pair), 'fiat_currency': self.config.get('fiat_display_currency', None), 'amount': trade.amount, 'open_date': trade.open_date, @@ -1021,12 +1035,16 @@ class FreqtradeBot(LoggingMixin): trades_closed = 0 for trade in trades: try: + try: + if (self.strategy.order_types.get('stoploss_on_exchange') and + self.handle_stoploss_on_exchange(trade)): + trades_closed += 1 + Trade.commit() + continue - if (self.strategy.order_types.get('stoploss_on_exchange') and - self.handle_stoploss_on_exchange(trade)): - trades_closed += 1 - Trade.commit() - continue + except InvalidOrderException as exception: + logger.warning( + f'Unable to handle stoploss on exchange for {trade.pair}: {exception}') # Check if we can sell our current pair if trade.open_order_id is None and trade.is_open and self.handle_trade(trade): trades_closed += 1 @@ -1158,7 +1176,8 @@ class FreqtradeBot(LoggingMixin): logger.warning('Unable to fetch stoploss order: %s', exception) if stoploss_order: - trade.update_order(stoploss_order) + self.update_trade_state(trade, trade.stoploss_order_id, stoploss_order, + stoploss_order=True) # We check if stoploss order is fulfilled if stoploss_order and stoploss_order['status'] in ('closed', 'triggered'): @@ -1222,7 +1241,9 @@ class FreqtradeBot(LoggingMixin): :param order: Current on exchange stoploss order :return: None """ - stoploss_norm = self.exchange.price_to_precision(trade.pair, trade.stoploss_or_liquidation) + stoploss_norm = self.exchange.price_to_precision( + trade.pair, trade.stoploss_or_liquidation, + rounding_mode=ROUND_DOWN if trade.is_short else ROUND_UP) if self.exchange.stoploss_adjust(stoploss_norm, order, side=trade.exit_side): # we check if the update is necessary @@ -1232,13 +1253,8 @@ class FreqtradeBot(LoggingMixin): # cancelling the current stoploss on exchange first logger.info(f"Cancelling current stoploss on exchange for pair {trade.pair} " f"(orderid:{order['id']}) in order to add another one ...") - try: - co = self.exchange.cancel_stoploss_order_with_result(order['id'], trade.pair, - trade.amount) - trade.update_order(co) - except InvalidOrderException: - logger.exception(f"Could not cancel stoploss order {order['id']} " - f"for pair {trade.pair}") + + self.cancel_stoploss_on_exchange(trade) # Create new stoploss order if not self.create_stoploss_order(trade=trade, stop_price=stoploss_norm): @@ -1410,7 +1426,7 @@ class FreqtradeBot(LoggingMixin): corder = order reason = constants.CANCEL_REASON['CANCELLED_ON_EXCHANGE'] - logger.info('%s order %s for %s.', side, reason, trade) + logger.info(f'{side} order {reason} for {trade}.') # Using filled to determine the filled amount filled_amount = safe_value_fallback2(corder, order, 'filled', 'filled') @@ -1470,8 +1486,8 @@ class FreqtradeBot(LoggingMixin): return False try: - order = self.exchange.cancel_order_with_result(order['id'], trade.pair, - trade.amount) + order = self.exchange.cancel_order_with_result( + order['id'], trade.pair, trade.amount) except InvalidOrderException: logger.exception( f"Could not cancel {trade.exit_side} order {trade.open_order_id}") @@ -1483,17 +1499,18 @@ class FreqtradeBot(LoggingMixin): # Order might be filled above in odd timing issues. if order.get('status') in ('canceled', 'cancelled'): trade.exit_reason = None + trade.open_order_id = None else: trade.exit_reason = exit_reason_prev cancelled = True else: reason = constants.CANCEL_REASON['CANCELLED_ON_EXCHANGE'] trade.exit_reason = None + trade.open_order_id = None - self.update_trade_state(trade, trade.open_order_id, order) + self.update_trade_state(trade, order['id'], order) logger.info(f'{trade.exit_side.capitalize()} order {reason} for {trade}.') - trade.open_order_id = None trade.close_rate = None trade.close_rate_requested = None @@ -1659,7 +1676,7 @@ class FreqtradeBot(LoggingMixin): amount = trade.amount gain = "profit" if profit_ratio > 0 else "loss" - msg = { + msg: RPCSellMsg = { 'type': (RPCMessageType.EXIT_FILL if fill else RPCMessageType.EXIT), 'trade_id': trade.id, @@ -1685,6 +1702,7 @@ class FreqtradeBot(LoggingMixin): 'close_date': trade.close_date or datetime.utcnow(), 'stake_amount': trade.stake_amount, 'stake_currency': self.config['stake_currency'], + 'base_currency': self.exchange.get_pair_base_currency(trade.pair), 'fiat_currency': self.config.get('fiat_display_currency'), 'sub_trade': sub_trade, 'cumulative_profit': trade.realized_profit, @@ -1715,7 +1733,7 @@ class FreqtradeBot(LoggingMixin): profit_ratio = trade.calc_profit_ratio(profit_rate) gain = "profit" if profit_ratio > 0 else "loss" - msg = { + msg: RPCSellCancelMsg = { 'type': RPCMessageType.EXIT_CANCEL, 'trade_id': trade.id, 'exchange': trade.exchange.capitalize(), @@ -1737,6 +1755,7 @@ class FreqtradeBot(LoggingMixin): 'open_date': trade.open_date, 'close_date': trade.close_date or datetime.now(timezone.utc), 'stake_currency': self.config['stake_currency'], + 'base_currency': self.exchange.get_pair_base_currency(trade.pair), 'fiat_currency': self.config.get('fiat_display_currency', None), 'reason': reason, 'sub_trade': sub_trade, @@ -1768,11 +1787,11 @@ class FreqtradeBot(LoggingMixin): return False # Update trade with order values - logger.info(f'Found open order for {trade}') + if not stoploss_order: + logger.info(f'Found open order for {trade}') try: - order = action_order or self.exchange.fetch_order_or_stoploss_order(order_id, - trade.pair, - stoploss_order) + order = action_order or self.exchange.fetch_order_or_stoploss_order( + order_id, trade.pair, stoploss_order) except InvalidOrderException as exception: logger.warning('Unable to fetch order %s: %s', order_id, exception) return False @@ -1801,7 +1820,7 @@ class FreqtradeBot(LoggingMixin): # TODO: should shorting/leverage be supported by Edge, # then this will need to be fixed. trade.adjust_stop_loss(trade.open_rate, self.strategy.stoploss, initial=True) - if order.get('side') == trade.entry_side or trade.amount > 0: + if order.get('side') == trade.entry_side or (trade.amount > 0 and trade.is_open): # Must also run for partial exits # TODO: Margin will need to use interest_rate as well. # interest_rate = self.exchange.get_interest_rate() @@ -1837,21 +1856,27 @@ class FreqtradeBot(LoggingMixin): self.handle_protections(trade.pair, trade.trade_direction) elif send_msg and not trade.open_order_id and not stoploss_order: # Enter fill - self._notify_enter(trade, order, fill=True, sub_trade=sub_trade) + self._notify_enter(trade, order, order.order_type, fill=True, sub_trade=sub_trade) def handle_protections(self, pair: str, side: LongShort) -> None: # Lock pair for one candle to prevent immediate rebuys self.strategy.lock_pair(pair, datetime.now(timezone.utc), reason='Auto lock') prot_trig = self.protections.stop_per_pair(pair, side=side) if prot_trig: - msg = {'type': RPCMessageType.PROTECTION_TRIGGER, } - msg.update(prot_trig.to_json()) + msg: RPCProtectionMsg = { + 'type': RPCMessageType.PROTECTION_TRIGGER, + 'base_currency': self.exchange.get_pair_base_currency(prot_trig.pair), + **prot_trig.to_json() # type: ignore + } self.rpc.send_msg(msg) prot_trig_glb = self.protections.global_stop(side=side) if prot_trig_glb: - msg = {'type': RPCMessageType.PROTECTION_TRIGGER_GLOBAL, } - msg.update(prot_trig_glb.to_json()) + msg = { + 'type': RPCMessageType.PROTECTION_TRIGGER_GLOBAL, + 'base_currency': self.exchange.get_pair_base_currency(prot_trig_glb.pair), + **prot_trig_glb.to_json() # type: ignore + } self.rpc.send_msg(msg) def apply_fee_conditional(self, trade: Trade, trade_base_currency: str, diff --git a/freqtrade/loggers.py b/freqtrade/loggers/__init__.py similarity index 88% rename from freqtrade/loggers.py rename to freqtrade/loggers/__init__.py index 823fa174e..528d274f2 100644 --- a/freqtrade/loggers.py +++ b/freqtrade/loggers/__init__.py @@ -1,24 +1,11 @@ import logging -import sys from logging import Formatter -from logging.handlers import BufferingHandler, RotatingFileHandler, SysLogHandler +from logging.handlers import RotatingFileHandler, SysLogHandler from freqtrade.constants import Config from freqtrade.exceptions import OperationalException - - -class FTBufferingHandler(BufferingHandler): - def flush(self): - """ - Override Flush behaviour - we keep half of the configured capacity - otherwise, we have moments with "empty" logs. - """ - self.acquire() - try: - # Keep half of the records in buffer. - self.buffer = self.buffer[-int(self.capacity / 2):] - finally: - self.release() +from freqtrade.loggers.buffering_handler import FTBufferingHandler +from freqtrade.loggers.std_err_stream_handler import FTStdErrStreamHandler logger = logging.getLogger(__name__) @@ -69,7 +56,7 @@ def setup_logging_pre() -> None: logging.basicConfig( level=logging.INFO, format=LOGFORMAT, - handlers=[logging.StreamHandler(sys.stderr), bufferHandler] + handlers=[FTStdErrStreamHandler(), bufferHandler] ) diff --git a/freqtrade/loggers/buffering_handler.py b/freqtrade/loggers/buffering_handler.py new file mode 100644 index 000000000..e4621fa79 --- /dev/null +++ b/freqtrade/loggers/buffering_handler.py @@ -0,0 +1,15 @@ +from logging.handlers import BufferingHandler + + +class FTBufferingHandler(BufferingHandler): + def flush(self): + """ + Override Flush behaviour - we keep half of the configured capacity + otherwise, we have moments with "empty" logs. + """ + self.acquire() + try: + # Keep half of the records in buffer. + self.buffer = self.buffer[-int(self.capacity / 2):] + finally: + self.release() diff --git a/freqtrade/loggers/std_err_stream_handler.py b/freqtrade/loggers/std_err_stream_handler.py new file mode 100644 index 000000000..487a7c100 --- /dev/null +++ b/freqtrade/loggers/std_err_stream_handler.py @@ -0,0 +1,26 @@ +import sys +from logging import Handler + + +class FTStdErrStreamHandler(Handler): + def flush(self): + """ + Override Flush behaviour - we keep half of the configured capacity + otherwise, we have moments with "empty" logs. + """ + self.acquire() + try: + sys.stderr.flush() + finally: + self.release() + + def emit(self, record): + try: + msg = self.format(record) + # Don't keep a reference to stderr - this can be problematic with progressbars. + sys.stderr.write(msg + '\n') + self.flush() + except RecursionError: + raise + except Exception: + self.handleError(record) diff --git a/freqtrade/optimize/backtesting.py b/freqtrade/optimize/backtesting.py index 8c2d0b016..5e121e8a2 100644 --- a/freqtrade/optimize/backtesting.py +++ b/freqtrade/optimize/backtesting.py @@ -205,9 +205,10 @@ class Backtesting: # since a "perfect" stoploss-exit is assumed anyway # And the regular "stoploss" function would not apply to that case self.strategy.order_types['stoploss_on_exchange'] = False + # Update can_short flag + self._can_short = self.trading_mode != TradingMode.SPOT and strategy.can_short self.strategy.ft_bot_start() - strategy_safe_wrapper(self.strategy.bot_loop_start, supress_error=True)() def _load_protections(self, strategy: IStrategy): if self.config.get('enable_protections', False): @@ -444,10 +445,6 @@ class Backtesting: # Worst case: price ticks tiny bit above open and dives down. stop_rate = row[OPEN_IDX] * (1 - side_1 * abs( (trade.stop_loss_pct or 0.0) / leverage)) - if is_short: - assert stop_rate > row[LOW_IDX] - else: - assert stop_rate < row[HIGH_IDX] # Limit lower-end to candle low to avoid exits below the low. # This still remains "worst case" - but "worst realistic case". @@ -528,7 +525,7 @@ class Backtesting: max_stake = self.exchange.get_max_pair_stake_amount(trade.pair, current_rate) stake_available = self.wallets.get_available_stake_amount() stake_amount = strategy_safe_wrapper(self.strategy.adjust_trade_position, - default_retval=None)( + default_retval=None, supress_error=True)( trade=trade, # type: ignore[arg-type] current_time=current_date, current_rate=current_rate, current_profit=current_profit, min_stake=min_stake, @@ -746,7 +743,7 @@ class Backtesting: proposed_leverage=1.0, max_leverage=max_leverage, side=direction, entry_tag=entry_tag, - ) if self._can_short else 1.0 + ) if self.trading_mode != TradingMode.SPOT else 1.0 # Cap leverage between 1.0 and max_leverage. leverage = min(max(leverage, 1.0), max_leverage) @@ -1036,6 +1033,9 @@ class Backtesting: requested_stake=( order.safe_remaining * order.ft_price / trade.leverage), direction='short' if trade.is_short else 'long') + # Delete trade if no successful entries happened (if placing the new order failed) + if trade.open_order_id is None and trade.nr_of_successful_entries == 0: + return True self.replaced_entry_orders += 1 else: # assumption: there can't be multiple open entry orders at any given time @@ -1175,6 +1175,8 @@ class Backtesting: while current_time <= end_date: open_trade_count_start = LocalTrade.bt_open_open_trade_count self.check_abort() + strategy_safe_wrapper(self.strategy.bot_loop_start, supress_error=True)( + current_time=current_time) for i, pair in enumerate(data): row_index = indexes[pair] row = self.validate_row(data, pair, row_index, current_time) diff --git a/freqtrade/optimize/hyperopt.py b/freqtrade/optimize/hyperopt.py index 96c95c4a2..fe590f0d2 100644 --- a/freqtrade/optimize/hyperopt.py +++ b/freqtrade/optimize/hyperopt.py @@ -13,13 +13,13 @@ from math import ceil from pathlib import Path from typing import Any, Dict, List, Optional, Tuple -import progressbar import rapidjson -from colorama import Fore, Style from colorama import init as colorama_init from joblib import Parallel, cpu_count, delayed, dump, load, wrap_non_picklable_objects from joblib.externals import cloudpickle from pandas import DataFrame +from rich.progress import (BarColumn, MofNCompleteColumn, Progress, TaskProgressColumn, TextColumn, + TimeElapsedColumn, TimeRemainingColumn) from freqtrade.constants import DATETIME_PRINT_FORMAT, FTHYPT_FILEVERSION, LAST_BT_RESULT_FN, Config from freqtrade.data.converter import trim_dataframes @@ -44,8 +44,6 @@ with warnings.catch_warnings(): from skopt import Optimizer from skopt.space import Dimension -progressbar.streams.wrap_stderr() -progressbar.streams.wrap_stdout() logger = logging.getLogger(__name__) @@ -381,7 +379,8 @@ class Hyperopt: strat_stats = generate_strategy_stats( self.pairlist, self.backtesting.strategy.get_strategy_name(), - backtesting_results, min_date, max_date, market_change=self.market_change + backtesting_results, min_date, max_date, market_change=self.market_change, + is_hyperopt=True, ) results_explanation = HyperoptTools.format_results_explanation_string( strat_stats, self.config['stake_currency']) @@ -520,29 +519,6 @@ class Hyperopt: else: return self.opt.ask(n_points=n_points), [False for _ in range(n_points)] - def get_progressbar_widgets(self): - if self.print_colorized: - widgets = [ - ' [Epoch ', progressbar.Counter(), ' of ', str(self.total_epochs), - ' (', progressbar.Percentage(), ')] ', - progressbar.Bar(marker=progressbar.AnimatedMarker( - fill='\N{FULL BLOCK}', - fill_wrap=Fore.GREEN + '{}' + Fore.RESET, - marker_wrap=Style.BRIGHT + '{}' + Style.RESET_ALL, - )), - ' [', progressbar.ETA(), ', ', progressbar.Timer(), ']', - ] - else: - widgets = [ - ' [Epoch ', progressbar.Counter(), ' of ', str(self.total_epochs), - ' (', progressbar.Percentage(), ')] ', - progressbar.Bar(marker=progressbar.AnimatedMarker( - fill='\N{FULL BLOCK}', - )), - ' [', progressbar.ETA(), ', ', progressbar.Timer(), ']', - ] - return widgets - def evaluate_result(self, val: Dict[str, Any], current: int, is_random: bool): """ Evaluate results returned from generate_optimizer @@ -602,11 +578,19 @@ class Hyperopt: logger.info(f'Effective number of parallel workers used: {jobs}') # Define progressbar - widgets = self.get_progressbar_widgets() - with progressbar.ProgressBar( - max_value=self.total_epochs, redirect_stdout=False, redirect_stderr=False, - widgets=widgets + with Progress( + TextColumn("[progress.description]{task.description}"), + BarColumn(bar_width=None), + MofNCompleteColumn(), + TaskProgressColumn(), + "•", + TimeElapsedColumn(), + "•", + TimeRemainingColumn(), + expand=True, ) as pbar: + task = pbar.add_task("Epochs", total=self.total_epochs) + start = 0 if self.analyze_per_epoch: @@ -616,7 +600,7 @@ class Hyperopt: f_val0 = self.generate_optimizer(asked[0]) self.opt.tell(asked, [f_val0['loss']]) self.evaluate_result(f_val0, 1, is_random[0]) - pbar.update(1) + pbar.update(task, advance=1) start += 1 evals = ceil((self.total_epochs - start) / jobs) @@ -630,14 +614,12 @@ class Hyperopt: f_val = self.run_optimizer_parallel(parallel, asked) self.opt.tell(asked, [v['loss'] for v in f_val]) - # Calculate progressbar outputs for j, val in enumerate(f_val): # Use human-friendly indexes here (starting from 1) current = i * jobs + j + 1 + start self.evaluate_result(val, current, is_random[j]) - - pbar.update(current) + pbar.update(task, advance=1) except KeyboardInterrupt: print('User interrupted..') diff --git a/freqtrade/optimize/hyperopt_tools.py b/freqtrade/optimize/hyperopt_tools.py index cf0650f7d..1e7befdf6 100644 --- a/freqtrade/optimize/hyperopt_tools.py +++ b/freqtrade/optimize/hyperopt_tools.py @@ -1,4 +1,3 @@ -import io import logging from copy import deepcopy from datetime import datetime, timezone @@ -24,6 +23,8 @@ logger = logging.getLogger(__name__) NON_OPT_PARAM_APPENDIX = " # value loaded from strategy" +HYPER_PARAMS_FILE_FORMAT = rapidjson.NM_NATIVE | rapidjson.NM_NAN + def hyperopt_serializer(x): if isinstance(x, np.integer): @@ -77,9 +78,18 @@ class HyperoptTools(): with filename.open('w') as f: rapidjson.dump(final_params, f, indent=2, default=hyperopt_serializer, - number_mode=rapidjson.NM_NATIVE | rapidjson.NM_NAN + number_mode=HYPER_PARAMS_FILE_FORMAT ) + @staticmethod + def load_params(filename: Path) -> Dict: + """ + Load parameters from file + """ + with filename.open('r') as f: + params = rapidjson.load(f, number_mode=HYPER_PARAMS_FILE_FORMAT) + return params + @staticmethod def try_export_params(config: Config, strategy_name: str, params: Dict): if params.get(FTHYPT_FILEVERSION, 1) >= 2 and not config.get('disableparamexport', False): @@ -190,7 +200,7 @@ class HyperoptTools(): for s in ['buy', 'sell', 'protection', 'roi', 'stoploss', 'trailing', 'max_open_trades']: HyperoptTools._params_update_for_json(result_dict, params, non_optimized, s) - print(rapidjson.dumps(result_dict, default=str, number_mode=rapidjson.NM_NATIVE)) + print(rapidjson.dumps(result_dict, default=str, number_mode=HYPER_PARAMS_FILE_FORMAT)) else: HyperoptTools._params_pretty_print(params, 'buy', "Buy hyperspace params:", @@ -464,8 +474,8 @@ class HyperoptTools(): return try: - io.open(csv_file, 'w+').close() - except IOError: + Path(csv_file).open('w+').close() + except OSError: logger.error(f"Failed to create CSV file: {csv_file}") return diff --git a/freqtrade/optimize/optimize_reports.py b/freqtrade/optimize/optimize_reports.py index c2425deb3..dc496e16f 100644 --- a/freqtrade/optimize/optimize_reports.py +++ b/freqtrade/optimize/optimize_reports.py @@ -7,8 +7,8 @@ from typing import Any, Dict, List, Union from pandas import DataFrame, to_datetime from tabulate import tabulate -from freqtrade.constants import (DATETIME_PRINT_FORMAT, LAST_BT_RESULT_FN, UNLIMITED_STAKE_AMOUNT, - Config, IntOrInf) +from freqtrade.constants import (BACKTEST_BREAKDOWNS, DATETIME_PRINT_FORMAT, LAST_BT_RESULT_FN, + UNLIMITED_STAKE_AMOUNT, Config, IntOrInf) from freqtrade.data.metrics import (calculate_cagr, calculate_calmar, calculate_csum, calculate_expectancy, calculate_market_change, calculate_max_drawdown, calculate_sharpe, calculate_sortino) @@ -282,7 +282,8 @@ def _get_resample_from_period(period: str) -> str: if period == 'day': return '1d' if period == 'week': - return '1w' + # Weekly defaulting to Monday. + return '1W-MON' if period == 'month': return '1M' raise ValueError(f"Period {period} is not supported.") @@ -304,6 +305,7 @@ def generate_periodic_breakdown_stats(trade_list: List, period: str) -> List[Dic stats.append( { 'date': name.strftime('%d/%m/%Y'), + 'date_ts': int(name.to_pydatetime().timestamp() * 1000), 'profit_abs': profit_abs, 'wins': wins, 'draws': draws, @@ -313,6 +315,13 @@ def generate_periodic_breakdown_stats(trade_list: List, period: str) -> List[Dic return stats +def generate_all_periodic_breakdown_stats(trade_list: List) -> Dict[str, List]: + result = {} + for period in BACKTEST_BREAKDOWNS: + result[period] = generate_periodic_breakdown_stats(trade_list, period) + return result + + def generate_trading_stats(results: DataFrame) -> Dict[str, Any]: """ Generate overall trade statistics """ if len(results) == 0: @@ -389,7 +398,8 @@ def generate_strategy_stats(pairlist: List[str], strategy: str, content: Dict[str, Any], min_date: datetime, max_date: datetime, - market_change: float + market_change: float, + is_hyperopt: bool = False, ) -> Dict[str, Any]: """ :param pairlist: List of pairs to backtest @@ -424,6 +434,11 @@ def generate_strategy_stats(pairlist: List[str], daily_stats = generate_daily_stats(results) trade_stats = generate_trading_stats(results) + + periodic_breakdown = {} + if not is_hyperopt: + periodic_breakdown = {'periodic_breakdown': generate_all_periodic_breakdown_stats(results)} + best_pair = max([pair for pair in pair_results if pair['key'] != 'TOTAL'], key=lambda x: x['profit_sum']) if len(pair_results) > 1 else None worst_pair = min([pair for pair in pair_results if pair['key'] != 'TOTAL'], @@ -442,7 +457,6 @@ def generate_strategy_stats(pairlist: List[str], 'results_per_enter_tag': enter_tag_results, 'exit_reason_summary': exit_reason_stats, 'left_open_trades': left_open_results, - # 'days_breakdown_stats': days_breakdown_stats, 'total_trades': len(results), 'trade_count_long': len(results.loc[~results['is_short']]), @@ -507,6 +521,7 @@ def generate_strategy_stats(pairlist: List[str], 'exit_profit_only': config['exit_profit_only'], 'exit_profit_offset': config['exit_profit_offset'], 'ignore_roi_if_entry_signal': config['ignore_roi_if_entry_signal'], + **periodic_breakdown, **daily_stats, **trade_stats } @@ -874,6 +889,11 @@ def show_backtest_result(strategy: str, results: Dict[str, Any], stake_currency: print(' BACKTESTING REPORT '.center(len(table.splitlines()[0]), '=')) print(table) + table = text_table_bt_results(results['left_open_trades'], stake_currency=stake_currency) + if isinstance(table, str) and len(table) > 0: + print(' LEFT OPEN TRADES REPORT '.center(len(table.splitlines()[0]), '=')) + print(table) + if (results.get('results_per_enter_tag') is not None or results.get('results_per_buy_tag') is not None): # results_per_buy_tag is deprecated and should be removed 2 versions after short golive. @@ -893,14 +913,12 @@ def show_backtest_result(strategy: str, results: Dict[str, Any], stake_currency: print(' EXIT REASON STATS '.center(len(table.splitlines()[0]), '=')) print(table) - table = text_table_bt_results(results['left_open_trades'], stake_currency=stake_currency) - if isinstance(table, str) and len(table) > 0: - print(' LEFT OPEN TRADES REPORT '.center(len(table.splitlines()[0]), '=')) - print(table) - for period in backtest_breakdown: - days_breakdown_stats = generate_periodic_breakdown_stats( - trade_list=results['trades'], period=period) + if period in results.get('periodic_breakdown', {}): + days_breakdown_stats = results['periodic_breakdown'][period] + else: + days_breakdown_stats = generate_periodic_breakdown_stats( + trade_list=results['trades'], period=period) table = text_table_periodic_breakdown(days_breakdown_stats=days_breakdown_stats, stake_currency=stake_currency, period=period) if isinstance(table, str) and len(table) > 0: @@ -926,11 +944,11 @@ def show_backtest_results(config: Config, backtest_stats: Dict): strategy, results, stake_currency, config.get('backtest_breakdown', [])) - if len(backtest_stats['strategy']) > 1: + if len(backtest_stats['strategy']) > 0: # Print Strategy summary table table = text_table_strategy(backtest_stats['strategy_comparison'], stake_currency) - print(f"{results['backtest_start']} -> {results['backtest_end']} |" + print(f"Backtested {results['backtest_start']} -> {results['backtest_end']} |" f" Max open trades : {results['max_open_trades']}") print(' STRATEGY SUMMARY '.center(len(table.splitlines()[0]), '=')) print(table) diff --git a/freqtrade/persistence/__init__.py b/freqtrade/persistence/__init__.py index 9e1a7e922..4cf7aa455 100644 --- a/freqtrade/persistence/__init__.py +++ b/freqtrade/persistence/__init__.py @@ -1,5 +1,6 @@ # flake8: noqa: F401 +from freqtrade.persistence.key_value_store import KeyStoreKeys, KeyValueStore from freqtrade.persistence.models import init_db from freqtrade.persistence.pairlock_middleware import PairLocks from freqtrade.persistence.trade_model import LocalTrade, Order, Trade diff --git a/freqtrade/persistence/key_value_store.py b/freqtrade/persistence/key_value_store.py new file mode 100644 index 000000000..110a23d6c --- /dev/null +++ b/freqtrade/persistence/key_value_store.py @@ -0,0 +1,179 @@ +from datetime import datetime, timezone +from enum import Enum +from typing import ClassVar, Optional, Union + +from sqlalchemy import String +from sqlalchemy.orm import Mapped, mapped_column + +from freqtrade.persistence.base import ModelBase, SessionType + + +ValueTypes = Union[str, datetime, float, int] + + +class ValueTypesEnum(str, Enum): + STRING = 'str' + DATETIME = 'datetime' + FLOAT = 'float' + INT = 'int' + + +class KeyStoreKeys(str, Enum): + BOT_START_TIME = 'bot_start_time' + STARTUP_TIME = 'startup_time' + + +class _KeyValueStoreModel(ModelBase): + """ + Pair Locks database model. + """ + __tablename__ = 'KeyValueStore' + session: ClassVar[SessionType] + + id: Mapped[int] = mapped_column(primary_key=True) + + key: Mapped[KeyStoreKeys] = mapped_column(String(25), nullable=False, index=True) + + value_type: Mapped[ValueTypesEnum] = mapped_column(String(20), nullable=False) + + string_value: Mapped[Optional[str]] = mapped_column(String(255), nullable=True) + datetime_value: Mapped[Optional[datetime]] + float_value: Mapped[Optional[float]] + int_value: Mapped[Optional[int]] + + +class KeyValueStore(): + """ + Generic bot-wide, persistent key-value store + Can be used to store generic values, e.g. very first bot startup time. + Supports the types str, datetime, float and int. + """ + + @staticmethod + def store_value(key: KeyStoreKeys, value: ValueTypes) -> None: + """ + Store the given value for the given key. + :param key: Key to store the value for - can be used in get-value to retrieve the key + :param value: Value to store - can be str, datetime, float or int + """ + kv = _KeyValueStoreModel.session.query(_KeyValueStoreModel).filter( + _KeyValueStoreModel.key == key).first() + if kv is None: + kv = _KeyValueStoreModel(key=key) + if isinstance(value, str): + kv.value_type = ValueTypesEnum.STRING + kv.string_value = value + elif isinstance(value, datetime): + kv.value_type = ValueTypesEnum.DATETIME + kv.datetime_value = value + elif isinstance(value, float): + kv.value_type = ValueTypesEnum.FLOAT + kv.float_value = value + elif isinstance(value, int): + kv.value_type = ValueTypesEnum.INT + kv.int_value = value + else: + raise ValueError(f'Unknown value type {kv.value_type}') + _KeyValueStoreModel.session.add(kv) + _KeyValueStoreModel.session.commit() + + @staticmethod + def delete_value(key: KeyStoreKeys) -> None: + """ + Delete the value for the given key. + :param key: Key to delete the value for + """ + kv = _KeyValueStoreModel.session.query(_KeyValueStoreModel).filter( + _KeyValueStoreModel.key == key).first() + if kv is not None: + _KeyValueStoreModel.session.delete(kv) + _KeyValueStoreModel.session.commit() + + @staticmethod + def get_value(key: KeyStoreKeys) -> Optional[ValueTypes]: + """ + Get the value for the given key. + :param key: Key to get the value for + """ + kv = _KeyValueStoreModel.session.query(_KeyValueStoreModel).filter( + _KeyValueStoreModel.key == key).first() + if kv is None: + return None + if kv.value_type == ValueTypesEnum.STRING: + return kv.string_value + if kv.value_type == ValueTypesEnum.DATETIME and kv.datetime_value is not None: + return kv.datetime_value.replace(tzinfo=timezone.utc) + if kv.value_type == ValueTypesEnum.FLOAT: + return kv.float_value + if kv.value_type == ValueTypesEnum.INT: + return kv.int_value + # This should never happen unless someone messed with the database manually + raise ValueError(f'Unknown value type {kv.value_type}') # pragma: no cover + + @staticmethod + def get_string_value(key: KeyStoreKeys) -> Optional[str]: + """ + Get the value for the given key. + :param key: Key to get the value for + """ + kv = _KeyValueStoreModel.session.query(_KeyValueStoreModel).filter( + _KeyValueStoreModel.key == key, + _KeyValueStoreModel.value_type == ValueTypesEnum.STRING).first() + if kv is None: + return None + return kv.string_value + + @staticmethod + def get_datetime_value(key: KeyStoreKeys) -> Optional[datetime]: + """ + Get the value for the given key. + :param key: Key to get the value for + """ + kv = _KeyValueStoreModel.session.query(_KeyValueStoreModel).filter( + _KeyValueStoreModel.key == key, + _KeyValueStoreModel.value_type == ValueTypesEnum.DATETIME).first() + if kv is None or kv.datetime_value is None: + return None + return kv.datetime_value.replace(tzinfo=timezone.utc) + + @staticmethod + def get_float_value(key: KeyStoreKeys) -> Optional[float]: + """ + Get the value for the given key. + :param key: Key to get the value for + """ + kv = _KeyValueStoreModel.session.query(_KeyValueStoreModel).filter( + _KeyValueStoreModel.key == key, + _KeyValueStoreModel.value_type == ValueTypesEnum.FLOAT).first() + if kv is None: + return None + return kv.float_value + + @staticmethod + def get_int_value(key: KeyStoreKeys) -> Optional[int]: + """ + Get the value for the given key. + :param key: Key to get the value for + """ + kv = _KeyValueStoreModel.session.query(_KeyValueStoreModel).filter( + _KeyValueStoreModel.key == key, + _KeyValueStoreModel.value_type == ValueTypesEnum.INT).first() + if kv is None: + return None + return kv.int_value + + +def set_startup_time(): + """ + sets bot_start_time to the first trade open date - or "now" on new databases. + sets startup_time to "now" + """ + st = KeyValueStore.get_value('bot_start_time') + if st is None: + from freqtrade.persistence import Trade + t = Trade.session.query(Trade).order_by(Trade.open_date.asc()).first() + if t is not None: + KeyValueStore.store_value('bot_start_time', t.open_date_utc) + else: + KeyValueStore.store_value('bot_start_time', datetime.now(timezone.utc)) + KeyValueStore.store_value('startup_time', datetime.now(timezone.utc)) diff --git a/freqtrade/persistence/models.py b/freqtrade/persistence/models.py index eee07e61c..e561e727b 100644 --- a/freqtrade/persistence/models.py +++ b/freqtrade/persistence/models.py @@ -2,7 +2,9 @@ This module contains the class to persist trades into SQLite """ import logging -from typing import Any, Dict +import threading +from contextvars import ContextVar +from typing import Any, Dict, Final, Optional from sqlalchemy import create_engine, inspect from sqlalchemy.exc import NoSuchModuleError @@ -11,6 +13,7 @@ from sqlalchemy.pool import StaticPool from freqtrade.exceptions import OperationalException from freqtrade.persistence.base import ModelBase +from freqtrade.persistence.key_value_store import _KeyValueStoreModel from freqtrade.persistence.migrations import check_migrate from freqtrade.persistence.pairlock import PairLock from freqtrade.persistence.trade_model import Order, Trade @@ -19,6 +22,22 @@ from freqtrade.persistence.trade_model import Order, Trade logger = logging.getLogger(__name__) +REQUEST_ID_CTX_KEY: Final[str] = 'request_id' +_request_id_ctx_var: ContextVar[Optional[str]] = ContextVar(REQUEST_ID_CTX_KEY, default=None) + + +def get_request_or_thread_id() -> Optional[str]: + """ + Helper method to get either async context (for fastapi requests), or thread id + """ + id = _request_id_ctx_var.get() + if id is None: + # when not in request context - use thread id + id = str(threading.current_thread().ident) + + return id + + _SQL_DOCS_URL = 'http://docs.sqlalchemy.org/en/latest/core/engines.html#database-urls' @@ -53,10 +72,12 @@ def init_db(db_url: str) -> None: # https://docs.sqlalchemy.org/en/13/orm/contextual.html#thread-local-scope # Scoped sessions proxy requests to the appropriate thread-local session. - # We should use the scoped_session object - not a seperately initialized version - Trade.session = scoped_session(sessionmaker(bind=engine, autoflush=False)) + # Since we also use fastAPI, we need to make it aware of the request id, too + Trade.session = scoped_session(sessionmaker( + bind=engine, autoflush=False), scopefunc=get_request_or_thread_id) Order.session = Trade.session PairLock.session = Trade.session + _KeyValueStoreModel.session = Trade.session previous_tables = inspect(engine).get_table_names() ModelBase.metadata.create_all(engine) diff --git a/freqtrade/persistence/trade_model.py b/freqtrade/persistence/trade_model.py index 27be0d726..cff2c37f0 100644 --- a/freqtrade/persistence/trade_model.py +++ b/freqtrade/persistence/trade_model.py @@ -9,13 +9,14 @@ from typing import Any, ClassVar, Dict, List, Optional, Sequence, cast from sqlalchemy import (Enum, Float, ForeignKey, Integer, ScalarResult, Select, String, UniqueConstraint, desc, func, select) -from sqlalchemy.orm import Mapped, lazyload, mapped_column, relationship +from sqlalchemy.orm import Mapped, lazyload, mapped_column, relationship, validates -from freqtrade.constants import (DATETIME_PRINT_FORMAT, MATH_CLOSE_PREC, NON_OPEN_EXCHANGE_STATES, - BuySell, LongShort) +from freqtrade.constants import (CUSTOM_TAG_MAX_LENGTH, DATETIME_PRINT_FORMAT, MATH_CLOSE_PREC, + NON_OPEN_EXCHANGE_STATES, BuySell, LongShort) from freqtrade.enums import ExitType, TradingMode from freqtrade.exceptions import DependencyException, OperationalException -from freqtrade.exchange import amount_to_contract_precision, price_to_precision +from freqtrade.exchange import (ROUND_DOWN, ROUND_UP, amount_to_contract_precision, + price_to_precision) from freqtrade.leverage import interest from freqtrade.persistence.base import ModelBase, SessionType from freqtrade.util import FtPrecise @@ -157,7 +158,7 @@ class Order(ModelBase): self.order_filled_date = datetime.now(timezone.utc) self.order_update_date = datetime.now(timezone.utc) - def to_ccxt_object(self) -> Dict[str, Any]: + def to_ccxt_object(self, stopPriceName: str = 'stopPrice') -> Dict[str, Any]: order: Dict[str, Any] = { 'id': self.order_id, 'symbol': self.ft_pair, @@ -169,7 +170,6 @@ class Order(ModelBase): 'side': self.ft_order_side, 'filled': self.filled, 'remaining': self.remaining, - 'stopPrice': self.stop_price, 'datetime': self.order_date_utc.strftime('%Y-%m-%dT%H:%M:%S.%f'), 'timestamp': int(self.order_date_utc.timestamp() * 1000), 'status': self.status, @@ -177,7 +177,11 @@ class Order(ModelBase): 'info': {}, } if self.ft_order_side == 'stoploss': - order['ft_order_type'] = 'stoploss' + order.update({ + stopPriceName: self.stop_price, + 'ft_order_type': 'stoploss', + }) + return order def to_json(self, entry_side: str, minified: bool = False) -> Dict[str, Any]: @@ -560,6 +564,9 @@ class LocalTrade(): 'trading_mode': self.trading_mode, 'funding_fees': self.funding_fees, 'open_order_id': self.open_order_id, + 'amount_precision': self.amount_precision, + 'price_precision': self.price_precision, + 'precision_mode': self.precision_mode, 'orders': orders, } @@ -594,7 +601,8 @@ class LocalTrade(): """ Method used internally to set self.stop_loss. """ - stop_loss_norm = price_to_precision(stop_loss, self.price_precision, self.precision_mode) + stop_loss_norm = price_to_precision(stop_loss, self.price_precision, self.precision_mode, + rounding_mode=ROUND_DOWN if self.is_short else ROUND_UP) if not self.stop_loss: self.initial_stop_loss = stop_loss_norm self.stop_loss = stop_loss_norm @@ -625,7 +633,8 @@ class LocalTrade(): if self.initial_stop_loss_pct is None or refresh: self.__set_stop_loss(new_loss, stoploss) self.initial_stop_loss = price_to_precision( - new_loss, self.price_precision, self.precision_mode) + new_loss, self.price_precision, self.precision_mode, + rounding_mode=ROUND_DOWN if self.is_short else ROUND_UP) self.initial_stop_loss_pct = -1 * abs(stoploss) # evaluate if the stop loss needs to be updated @@ -689,21 +698,27 @@ class LocalTrade(): else: logger.warning( f'Got different open_order_id {self.open_order_id} != {order.order_id}') - amount_tr = amount_to_contract_precision(self.amount, self.amount_precision, - self.precision_mode, self.contract_size) - if isclose(order.safe_amount_after_fee, amount_tr, abs_tol=MATH_CLOSE_PREC): - self.close(order.safe_price) - else: - self.recalc_trade_from_orders() - elif order.ft_order_side == 'stoploss' and order.status not in ('canceled', 'open'): + + elif order.ft_order_side == 'stoploss' and order.status not in ('open', ): self.stoploss_order_id = None self.close_rate_requested = self.stop_loss self.exit_reason = ExitType.STOPLOSS_ON_EXCHANGE.value if self.is_open: logger.info(f'{order.order_type.upper()} is hit for {self}.') - self.close(order.safe_price) else: raise ValueError(f'Unknown order type: {order.order_type}') + + if order.ft_order_side != self.entry_side: + amount_tr = amount_to_contract_precision(self.amount, self.amount_precision, + self.precision_mode, self.contract_size) + if ( + isclose(order.safe_amount_after_fee, amount_tr, abs_tol=MATH_CLOSE_PREC) + or order.safe_amount_after_fee > amount_tr + ): + self.close(order.safe_price) + else: + self.recalc_trade_from_orders() + Trade.commit() def close(self, rate: float, *, show_msg: bool = True) -> None: @@ -1250,11 +1265,13 @@ class Trade(ModelBase, LocalTrade): Float(), nullable=True, default=0.0) # type: ignore # Lowest price reached min_rate: Mapped[Optional[float]] = mapped_column(Float(), nullable=True) # type: ignore - exit_reason: Mapped[Optional[str]] = mapped_column(String(100), nullable=True) # type: ignore + exit_reason: Mapped[Optional[str]] = mapped_column( + String(CUSTOM_TAG_MAX_LENGTH), nullable=True) # type: ignore exit_order_status: Mapped[Optional[str]] = mapped_column( String(100), nullable=True) # type: ignore strategy: Mapped[Optional[str]] = mapped_column(String(100), nullable=True) # type: ignore - enter_tag: Mapped[Optional[str]] = mapped_column(String(100), nullable=True) # type: ignore + enter_tag: Mapped[Optional[str]] = mapped_column( + String(CUSTOM_TAG_MAX_LENGTH), nullable=True) # type: ignore timeframe: Mapped[Optional[int]] = mapped_column(Integer, nullable=True) # type: ignore trading_mode: Mapped[TradingMode] = mapped_column( @@ -1284,6 +1301,13 @@ class Trade(ModelBase, LocalTrade): self.realized_profit = 0 self.recalc_open_trade_value() + @validates('enter_tag', 'exit_reason') + def validate_string_len(self, key, value): + max_len = getattr(self.__class__, key).prop.columns[0].type.length + if value and len(value) > max_len: + return value[:max_len] + return value + def delete(self) -> None: for order in self.orders: @@ -1660,8 +1684,10 @@ class Trade(ModelBase, LocalTrade): stop_loss=data["stop_loss_abs"], stop_loss_pct=data["stop_loss_ratio"], stoploss_order_id=data["stoploss_order_id"], - stoploss_last_update=(datetime.fromtimestamp(data["stoploss_last_update"] // 1000, - tz=timezone.utc) if data["stoploss_last_update"] else None), + stoploss_last_update=( + datetime.fromtimestamp(data["stoploss_last_update_timestamp"] // 1000, + tz=timezone.utc) + if data["stoploss_last_update_timestamp"] else None), initial_stop_loss=data["initial_stop_loss_abs"], initial_stop_loss_pct=data["initial_stop_loss_ratio"], min_rate=data["min_rate"], diff --git a/freqtrade/plot/plotting.py b/freqtrade/plot/plotting.py index 1b2ee44da..e415c4911 100644 --- a/freqtrade/plot/plotting.py +++ b/freqtrade/plot/plotting.py @@ -1,4 +1,5 @@ import logging +from datetime import datetime, timezone from pathlib import Path from typing import Dict, List, Optional @@ -635,7 +636,7 @@ def load_and_plot_trades(config: Config): exchange = ExchangeResolver.load_exchange(config['exchange']['name'], config) IStrategy.dp = DataProvider(config, exchange) strategy.ft_bot_start() - strategy.bot_loop_start() + strategy.bot_loop_start(datetime.now(timezone.utc)) plot_elements = init_plotscript(config, list(exchange.markets), strategy.startup_candle_count) timerange = plot_elements['timerange'] trades = plot_elements['trades'] diff --git a/freqtrade/plugins/pairlist/PrecisionFilter.py b/freqtrade/plugins/pairlist/PrecisionFilter.py index 478eaec20..2e74aa293 100644 --- a/freqtrade/plugins/pairlist/PrecisionFilter.py +++ b/freqtrade/plugins/pairlist/PrecisionFilter.py @@ -6,6 +6,7 @@ from typing import Any, Dict, Optional from freqtrade.constants import Config from freqtrade.exceptions import OperationalException +from freqtrade.exchange import ROUND_UP from freqtrade.exchange.types import Ticker from freqtrade.plugins.pairlist.IPairList import IPairList @@ -61,9 +62,10 @@ class PrecisionFilter(IPairList): stop_price = ticker['last'] * self._stoploss # Adjust stop-prices to precision - sp = self._exchange.price_to_precision(pair, stop_price) + sp = self._exchange.price_to_precision(pair, stop_price, rounding_mode=ROUND_UP) - stop_gap_price = self._exchange.price_to_precision(pair, stop_price * 0.99) + stop_gap_price = self._exchange.price_to_precision(pair, stop_price * 0.99, + rounding_mode=ROUND_UP) logger.debug(f"{pair} - {sp} : {stop_gap_price}") if sp <= stop_gap_price: diff --git a/freqtrade/plugins/pairlist/RemotePairList.py b/freqtrade/plugins/pairlist/RemotePairList.py index 764c16f1a..d077330e0 100644 --- a/freqtrade/plugins/pairlist/RemotePairList.py +++ b/freqtrade/plugins/pairlist/RemotePairList.py @@ -143,6 +143,9 @@ class RemotePairList(IPairList): if self._init_done: pairlist = self._pair_cache.get('pairlist') + if pairlist == [None]: + # Valid but empty pairlist. + return [] else: pairlist = [] @@ -181,7 +184,11 @@ class RemotePairList(IPairList): pairlist = self._whitelist_for_active_markets(pairlist) pairlist = pairlist[:self._number_pairs] - self._pair_cache['pairlist'] = pairlist.copy() + if pairlist: + self._pair_cache['pairlist'] = pairlist.copy() + else: + # If pairlist is empty, set a dummy value to avoid fetching again + self._pair_cache['pairlist'] = [None] if time_elapsed != 0.0: self.log_once(f'Pairlist Fetched in {time_elapsed} seconds.', logger.info) diff --git a/freqtrade/rpc/api_server/api_schemas.py b/freqtrade/rpc/api_server/api_schemas.py index 18621ccbd..dd5ca3a62 100644 --- a/freqtrade/rpc/api_server/api_schemas.py +++ b/freqtrade/rpc/api_server/api_schemas.py @@ -36,20 +36,25 @@ class Balance(BaseModel): free: float balance: float used: float + bot_owned: Optional[float] est_stake: float + est_stake_bot: Optional[float] stake: str # Starting with 2.x side: str leverage: float is_position: bool position: float + is_bot_managed: bool class Balances(BaseModel): currencies: List[Balance] total: float + total_bot: float symbol: str value: float + value_bot: float stake: str note: str starting_capital: float @@ -108,6 +113,8 @@ class Profit(BaseModel): max_drawdown: float max_drawdown_abs: float trading_volume: Optional[float] + bot_start_timestamp: int + bot_start_date: str class SellReason(BaseModel): @@ -276,6 +283,10 @@ class TradeSchema(BaseModel): funding_fees: Optional[float] trading_mode: Optional[TradingMode] + amount_precision: Optional[float] + price_precision: Optional[float] + precision_mode: Optional[int] + class OpenTradeSchema(TradeSchema): stoploss_current_dist: Optional[float] diff --git a/freqtrade/rpc/api_server/api_v1.py b/freqtrade/rpc/api_server/api_v1.py index 8ea70bb69..5ee5e36c4 100644 --- a/freqtrade/rpc/api_server/api_v1.py +++ b/freqtrade/rpc/api_server/api_v1.py @@ -43,7 +43,8 @@ logger = logging.getLogger(__name__) # 2.23: Allow plot config request in webserver mode # 2.24: Add cancel_open_order endpoint # 2.25: Add several profit values to /status endpoint -API_VERSION = 2.25 +# 2.26: increase /balance output +API_VERSION = 2.26 # Public API, requires no auth. router_public = APIRouter() @@ -303,11 +304,11 @@ def get_strategy(strategy: str, config=Depends(get_config)): @router.get('/freqaimodels', response_model=FreqAIModelListResponse, tags=['freqai']) def list_freqaimodels(config=Depends(get_config)): from freqtrade.resolvers.freqaimodel_resolver import FreqaiModelResolver - strategies = FreqaiModelResolver.search_all_objects( + models = FreqaiModelResolver.search_all_objects( config, False) - strategies = sorted(strategies, key=lambda x: x['name']) + models = sorted(models, key=lambda x: x['name']) - return {'freqaimodels': [x['name'] for x in strategies]} + return {'freqaimodels': [x['name'] for x in models]} @router.get('/available_pairs', response_model=AvailablePairs, tags=['candle data']) diff --git a/freqtrade/rpc/api_server/deps.py b/freqtrade/rpc/api_server/deps.py index aed97367b..f5b1bcd74 100644 --- a/freqtrade/rpc/api_server/deps.py +++ b/freqtrade/rpc/api_server/deps.py @@ -1,9 +1,11 @@ -from typing import Any, Dict, Iterator, Optional +from typing import Any, AsyncIterator, Dict, Optional +from uuid import uuid4 from fastapi import Depends from freqtrade.enums import RunMode from freqtrade.persistence import Trade +from freqtrade.persistence.models import _request_id_ctx_var from freqtrade.rpc.rpc import RPC, RPCException from .webserver import ApiServer @@ -15,12 +17,19 @@ def get_rpc_optional() -> Optional[RPC]: return None -def get_rpc() -> Optional[Iterator[RPC]]: +async def get_rpc() -> Optional[AsyncIterator[RPC]]: + _rpc = get_rpc_optional() if _rpc: + request_id = str(uuid4()) + ctx_token = _request_id_ctx_var.set(request_id) Trade.rollback() - yield _rpc - Trade.rollback() + try: + yield _rpc + finally: + Trade.session.remove() + _request_id_ctx_var.reset(ctx_token) + else: raise RPCException('Bot is not in the correct state') diff --git a/freqtrade/rpc/api_server/uvicorn_threaded.py b/freqtrade/rpc/api_server/uvicorn_threaded.py index a79c1a5fc..48786bec2 100644 --- a/freqtrade/rpc/api_server/uvicorn_threaded.py +++ b/freqtrade/rpc/api_server/uvicorn_threaded.py @@ -55,7 +55,7 @@ class UvicornServer(uvicorn.Server): @contextlib.contextmanager def run_in_thread(self): - self.thread = threading.Thread(target=self.run) + self.thread = threading.Thread(target=self.run, name='FTUvicorn') self.thread.start() while not self.started: time.sleep(1e-3) diff --git a/freqtrade/rpc/api_server/webserver.py b/freqtrade/rpc/api_server/webserver.py index b53662451..8030e303b 100644 --- a/freqtrade/rpc/api_server/webserver.py +++ b/freqtrade/rpc/api_server/webserver.py @@ -13,6 +13,7 @@ from freqtrade.exceptions import OperationalException from freqtrade.rpc.api_server.uvicorn_threaded import UvicornServer from freqtrade.rpc.api_server.ws.message_stream import MessageStream from freqtrade.rpc.rpc import RPC, RPCException, RPCHandler +from freqtrade.rpc.rpc_types import RPCSendMsg logger = logging.getLogger(__name__) @@ -108,7 +109,7 @@ class ApiServer(RPCHandler): cls._has_rpc = False cls._rpc = None - def send_msg(self, msg: Dict[str, Any]) -> None: + def send_msg(self, msg: RPCSendMsg) -> None: """ Publish the message to the message stream """ diff --git a/freqtrade/rpc/rpc.py b/freqtrade/rpc/rpc.py index c6a6f5cae..35e08cbc0 100644 --- a/freqtrade/rpc/rpc.py +++ b/freqtrade/rpc/rpc.py @@ -24,12 +24,14 @@ from freqtrade.enums import (CandleType, ExitCheckTuple, ExitType, MarketDirecti State, TradingMode) from freqtrade.exceptions import ExchangeError, PricingError from freqtrade.exchange import timeframe_to_minutes, timeframe_to_msecs +from freqtrade.exchange.types import Tickers from freqtrade.loggers import bufferHandler from freqtrade.misc import decimals_per_coin, shorten_date -from freqtrade.persistence import Order, PairLocks, Trade +from freqtrade.persistence import KeyStoreKeys, KeyValueStore, Order, PairLocks, Trade from freqtrade.persistence.models import PairLock from freqtrade.plugins.pairlist.pairlist_helpers import expand_pairlist from freqtrade.rpc.fiat_convert import CryptoToFiatConverter +from freqtrade.rpc.rpc_types import RPCSendMsg from freqtrade.wallets import PositionWallet, Wallet @@ -79,7 +81,7 @@ class RPCHandler: """ Cleanup pending module resources """ @abstractmethod - def send_msg(self, msg: Dict[str, str]) -> None: + def send_msg(self, msg: RPCSendMsg) -> None: """ Sends a message to all registered rpc modules """ @@ -542,6 +544,7 @@ class RPC: first_date = trades[0].open_date if trades else None last_date = trades[-1].open_date if trades else None num = float(len(durations) or 1) + bot_start = KeyValueStore.get_datetime_value(KeyStoreKeys.BOT_START_TIME) return { 'profit_closed_coin': profit_closed_coin_sum, 'profit_closed_percent_mean': round(profit_closed_ratio_mean * 100, 2), @@ -575,17 +578,48 @@ class RPC: 'max_drawdown': max_drawdown, 'max_drawdown_abs': max_drawdown_abs, 'trading_volume': trading_volume, + 'bot_start_timestamp': int(bot_start.timestamp() * 1000) if bot_start else 0, + 'bot_start_date': bot_start.strftime(DATETIME_PRINT_FORMAT) if bot_start else '', } + def __balance_get_est_stake( + self, coin: str, stake_currency: str, amount: float, + balance: Wallet, tickers) -> Tuple[float, float]: + est_stake = 0.0 + est_bot_stake = 0.0 + if coin == stake_currency: + est_stake = balance.total + if self._config.get('trading_mode', TradingMode.SPOT) != TradingMode.SPOT: + # in Futures, "total" includes the locked stake, and therefore all positions + est_stake = balance.free + est_bot_stake = amount + else: + try: + pair = self._freqtrade.exchange.get_valid_pair_combination(coin, stake_currency) + rate: Optional[float] = tickers.get(pair, {}).get('last', None) + if rate: + if pair.startswith(stake_currency) and not pair.endswith(stake_currency): + rate = 1.0 / rate + est_stake = rate * balance.total + est_bot_stake = rate * amount + except (ExchangeError): + logger.warning(f"Could not get rate for pair {coin}.") + raise ValueError() + + return est_stake, est_bot_stake + def _rpc_balance(self, stake_currency: str, fiat_display_currency: str) -> Dict: """ Returns current account balance per crypto """ currencies: List[Dict] = [] total = 0.0 + total_bot = 0.0 try: - tickers = self._freqtrade.exchange.get_tickers(cached=True) + tickers: Tickers = self._freqtrade.exchange.get_tickers(cached=True) except (ExchangeError): raise RPCException('Error getting current tickers.') + open_trades: List[Trade] = Trade.get_open_trades() + open_assets: Dict[str, Trade] = {t.safe_base_currency: t for t in open_trades} self._freqtrade.wallets.update(require_update=False) starting_capital = self._freqtrade.wallets.get_starting_balance() starting_cap_fiat = self._fiat_converter.convert_amount( @@ -596,41 +630,42 @@ class RPC: if not balance.total: continue - est_stake: float = 0 + trade = open_assets.get(coin, None) + is_bot_managed = coin == stake_currency or trade is not None + trade_amount = trade.amount if trade else 0 if coin == stake_currency: - rate = 1.0 - est_stake = balance.total - if self._config.get('trading_mode', TradingMode.SPOT) != TradingMode.SPOT: - # in Futures, "total" includes the locked stake, and therefore all positions - est_stake = balance.free - else: - try: - pair = self._freqtrade.exchange.get_valid_pair_combination(coin, stake_currency) - rate = tickers.get(pair, {}).get('last') - if rate: - if pair.startswith(stake_currency) and not pair.endswith(stake_currency): - rate = 1.0 / rate - est_stake = rate * balance.total - except (ExchangeError): - logger.warning(f" Could not get rate for pair {coin}.") - continue - total = total + est_stake + trade_amount = self._freqtrade.wallets.get_available_stake_amount() + + try: + est_stake, est_stake_bot = self.__balance_get_est_stake( + coin, stake_currency, trade_amount, balance, tickers) + except ValueError: + continue + + total += est_stake + + if is_bot_managed: + total_bot += est_stake_bot currencies.append({ 'currency': coin, 'free': balance.free, 'balance': balance.total, 'used': balance.used, + 'bot_owned': trade_amount, 'est_stake': est_stake or 0, + 'est_stake_bot': est_stake_bot if is_bot_managed else 0, 'stake': stake_currency, 'side': 'long', 'leverage': 1, 'position': 0, + 'is_bot_managed': is_bot_managed, 'is_position': False, }) symbol: str position: PositionWallet for symbol, position in self._freqtrade.wallets.get_all_positions().items(): total += position.collateral + total_bot += position.collateral currencies.append({ 'currency': symbol, @@ -639,24 +674,30 @@ class RPC: 'used': 0, 'position': position.position, 'est_stake': position.collateral, + 'est_stake_bot': position.collateral, 'stake': stake_currency, 'leverage': position.leverage, 'side': position.side, + 'is_bot_managed': True, 'is_position': True }) value = self._fiat_converter.convert_amount( total, stake_currency, fiat_display_currency) if self._fiat_converter else 0 + value_bot = self._fiat_converter.convert_amount( + total_bot, stake_currency, fiat_display_currency) if self._fiat_converter else 0 trade_count = len(Trade.get_trades_proxy()) - starting_capital_ratio = (total / starting_capital) - 1 if starting_capital else 0.0 - starting_cap_fiat_ratio = (value / starting_cap_fiat) - 1 if starting_cap_fiat else 0.0 + starting_capital_ratio = (total_bot / starting_capital) - 1 if starting_capital else 0.0 + starting_cap_fiat_ratio = (value_bot / starting_cap_fiat) - 1 if starting_cap_fiat else 0.0 return { 'currencies': currencies, 'total': total, + 'total_bot': total_bot, 'symbol': fiat_display_currency, 'value': value, + 'value_bot': value_bot, 'stake': stake_currency, 'starting_capital': starting_capital, 'starting_capital_ratio': starting_capital_ratio, @@ -1192,6 +1233,7 @@ class RPC: from freqtrade.resolvers.strategy_resolver import StrategyResolver strategy = StrategyResolver.load_strategy(config) strategy.dp = DataProvider(config, exchange=exchange, pairlists=None) + strategy.ft_bot_start() df_analyzed = strategy.analyze_ticker(_data[pair], {'pair': pair}) diff --git a/freqtrade/rpc/rpc_manager.py b/freqtrade/rpc/rpc_manager.py index c4d4fa2dd..1972ad6e5 100644 --- a/freqtrade/rpc/rpc_manager.py +++ b/freqtrade/rpc/rpc_manager.py @@ -3,11 +3,12 @@ This module contains class to manage RPC communications (Telegram, API, ...) """ import logging from collections import deque -from typing import Any, Dict, List +from typing import List from freqtrade.constants import Config from freqtrade.enums import NO_ECHO_MESSAGES, RPCMessageType from freqtrade.rpc import RPC, RPCHandler +from freqtrade.rpc.rpc_types import RPCSendMsg logger = logging.getLogger(__name__) @@ -58,7 +59,7 @@ class RPCManager: mod.cleanup() del mod - def send_msg(self, msg: Dict[str, Any]) -> None: + def send_msg(self, msg: RPCSendMsg) -> None: """ Send given message to all registered rpc modules. A message consists of one or more key value pairs of strings. @@ -69,10 +70,6 @@ class RPCManager: """ if msg.get('type') not in NO_ECHO_MESSAGES: logger.info('Sending rpc message: %s', msg) - if 'pair' in msg: - msg.update({ - 'base_currency': self._rpc._freqtrade.exchange.get_pair_base_currency(msg['pair']) - }) for mod in self.registered_modules: logger.debug('Forwarding message to rpc.%s', mod.name) try: diff --git a/freqtrade/rpc/rpc_types.py b/freqtrade/rpc/rpc_types.py new file mode 100644 index 000000000..23f3ed5a9 --- /dev/null +++ b/freqtrade/rpc/rpc_types.py @@ -0,0 +1,128 @@ +from datetime import datetime +from typing import Any, List, Literal, Optional, TypedDict, Union + +from freqtrade.constants import PairWithTimeframe +from freqtrade.enums import RPCMessageType + + +class RPCSendMsgBase(TypedDict): + pass + # ty1pe: Literal[RPCMessageType] + + +class RPCStatusMsg(RPCSendMsgBase): + """Used for Status, Startup and Warning messages""" + type: Literal[RPCMessageType.STATUS, RPCMessageType.STARTUP, RPCMessageType.WARNING] + status: str + + +class RPCStrategyMsg(RPCSendMsgBase): + """Used for Status, Startup and Warning messages""" + type: Literal[RPCMessageType.STRATEGY_MSG] + msg: str + + +class RPCProtectionMsg(RPCSendMsgBase): + type: Literal[RPCMessageType.PROTECTION_TRIGGER, RPCMessageType.PROTECTION_TRIGGER_GLOBAL] + id: int + pair: str + base_currency: Optional[str] + lock_time: str + lock_timestamp: int + lock_end_time: str + lock_end_timestamp: int + reason: str + side: str + active: bool + + +class RPCWhitelistMsg(RPCSendMsgBase): + type: Literal[RPCMessageType.WHITELIST] + data: List[str] + + +class __RPCBuyMsgBase(RPCSendMsgBase): + trade_id: int + buy_tag: Optional[str] + enter_tag: Optional[str] + exchange: str + pair: str + base_currency: str + leverage: Optional[float] + direction: str + limit: float + open_rate: float + order_type: str + stake_amount: float + stake_currency: str + fiat_currency: Optional[str] + amount: float + open_date: datetime + current_rate: Optional[float] + sub_trade: bool + + +class RPCBuyMsg(__RPCBuyMsgBase): + type: Literal[RPCMessageType.ENTRY, RPCMessageType.ENTRY_FILL] + + +class RPCCancelMsg(__RPCBuyMsgBase): + type: Literal[RPCMessageType.ENTRY_CANCEL] + reason: str + + +class RPCSellMsg(__RPCBuyMsgBase): + type: Literal[RPCMessageType.EXIT, RPCMessageType.EXIT_FILL] + cumulative_profit: float + gain: str # Literal["profit", "loss"] + close_rate: float + profit_amount: float + profit_ratio: float + sell_reason: Optional[str] + exit_reason: Optional[str] + close_date: datetime + # current_rate: Optional[float] + order_rate: Optional[float] + + +class RPCSellCancelMsg(__RPCBuyMsgBase): + type: Literal[RPCMessageType.EXIT_CANCEL] + reason: str + gain: str # Literal["profit", "loss"] + profit_amount: float + profit_ratio: float + sell_reason: Optional[str] + exit_reason: Optional[str] + close_date: datetime + + +class _AnalyzedDFData(TypedDict): + key: PairWithTimeframe + df: Any + la: datetime + + +class RPCAnalyzedDFMsg(RPCSendMsgBase): + """New Analyzed dataframe message""" + type: Literal[RPCMessageType.ANALYZED_DF] + data: _AnalyzedDFData + + +class RPCNewCandleMsg(RPCSendMsgBase): + """New candle ping message, issued once per new candle/pair""" + type: Literal[RPCMessageType.NEW_CANDLE] + data: PairWithTimeframe + + +RPCSendMsg = Union[ + RPCStatusMsg, + RPCStrategyMsg, + RPCProtectionMsg, + RPCWhitelistMsg, + RPCBuyMsg, + RPCCancelMsg, + RPCSellMsg, + RPCSellCancelMsg, + RPCAnalyzedDFMsg, + RPCNewCandleMsg + ] diff --git a/freqtrade/rpc/telegram.py b/freqtrade/rpc/telegram.py index 0c0b24f00..caa8715ac 100644 --- a/freqtrade/rpc/telegram.py +++ b/freqtrade/rpc/telegram.py @@ -3,6 +3,7 @@ """ This module manage Telegram communication """ +import asyncio import json import logging import re @@ -13,15 +14,17 @@ from functools import partial from html import escape from itertools import chain from math import isnan -from typing import Any, Callable, Dict, List, Optional, Union +from threading import Thread +from typing import Any, Callable, Coroutine, Dict, List, Optional, Union import arrow from tabulate import tabulate -from telegram import (MAX_MESSAGE_LENGTH, CallbackQuery, InlineKeyboardButton, InlineKeyboardMarkup, - KeyboardButton, ParseMode, ReplyKeyboardMarkup, Update) +from telegram import (CallbackQuery, InlineKeyboardButton, InlineKeyboardMarkup, KeyboardButton, + ReplyKeyboardMarkup, Update) +from telegram.constants import MessageLimit, ParseMode from telegram.error import BadRequest, NetworkError, TelegramError -from telegram.ext import CallbackContext, CallbackQueryHandler, CommandHandler, Updater -from telegram.utils.helpers import escape_markdown +from telegram.ext import Application, CallbackContext, CallbackQueryHandler, CommandHandler +from telegram.helpers import escape_markdown from freqtrade.__init__ import __version__ from freqtrade.constants import DUST_PER_COIN, Config @@ -30,6 +33,10 @@ from freqtrade.exceptions import OperationalException from freqtrade.misc import chunks, plural, round_coin_value from freqtrade.persistence import Trade from freqtrade.rpc import RPC, RPCException, RPCHandler +from freqtrade.rpc.rpc_types import RPCSendMsg + + +MAX_MESSAGE_LENGTH = MessageLimit.MAX_TEXT_LENGTH logger = logging.getLogger(__name__) @@ -46,14 +53,14 @@ class TimeunitMappings: default: int -def authorized_only(command_handler: Callable[..., None]) -> Callable[..., Any]: +def authorized_only(command_handler: Callable[..., Coroutine[Any, Any, None]]): """ Decorator to check if the message comes from the correct chat_id :param command_handler: Telegram CommandHandler :return: decorated function """ - def wrapper(self, *args, **kwargs): + async def wrapper(self, *args, **kwargs): """ Decorator logic """ update = kwargs.get('update') or args[0] @@ -65,10 +72,7 @@ def authorized_only(command_handler: Callable[..., None]) -> Callable[..., Any]: chat_id = int(self._config['telegram']['chat_id']) if cchat_id != chat_id: - logger.info( - 'Rejected unauthorized message from: %s', - update.message.chat_id - ) + logger.info(f'Rejected unauthorized message from: {update.message.chat_id}') return wrapper # Rollback session to avoid getting data stored in a transaction. Trade.rollback() @@ -78,11 +82,13 @@ def authorized_only(command_handler: Callable[..., None]) -> Callable[..., Any]: chat_id ) try: - return command_handler(self, *args, **kwargs) + return await command_handler(self, *args, **kwargs) except RPCException as e: - self._send_msg(str(e)) + await self._send_msg(str(e)) except BaseException: logger.exception('Exception occurred within Telegram module') + finally: + Trade.session.remove() return wrapper @@ -99,9 +105,17 @@ class Telegram(RPCHandler): """ super().__init__(rpc, config) - self._updater: Updater + self._app: Application + self._loop: asyncio.AbstractEventLoop self._init_keyboard() - self._init() + self._start_thread() + + def _start_thread(self): + """ + Creates and starts the polling thread + """ + self._thread = Thread(target=self._init, name='FTTelegram') + self._thread.start() def _init_keyboard(self) -> None: """ @@ -152,14 +166,23 @@ class Telegram(RPCHandler): logger.info('using custom keyboard from ' f'config.json: {self._keyboard}') + def _init_telegram_app(self): + return Application.builder().token(self._config['telegram']['token']).build() + def _init(self) -> None: """ Initializes this module with the given config, registers all known command handlers and starts polling for message updates + Runs in a separate thread. """ - self._updater = Updater(token=self._config['telegram']['token'], workers=0, - use_context=True) + try: + self._loop = asyncio.get_running_loop() + except RuntimeError: + self._loop = asyncio.new_event_loop() + asyncio.set_event_loop(self._loop) + + self._app = self._init_telegram_app() # Register command handler and start telegram message polling handles = [ @@ -218,21 +241,38 @@ class Telegram(RPCHandler): CallbackQueryHandler(self._force_enter_inline, pattern=r"\S+\/\S+"), ] for handle in handles: - self._updater.dispatcher.add_handler(handle) + self._app.add_handler(handle) for callback in callbacks: - self._updater.dispatcher.add_handler(callback) + self._app.add_handler(callback) - self._updater.start_polling( - bootstrap_retries=-1, - timeout=20, - read_latency=60, # Assumed transmission latency - drop_pending_updates=True, - ) logger.info( 'rpc.telegram is listening for following commands: %s', - [h.command for h in handles] + [[x for x in sorted(h.commands)] for h in handles] ) + self._loop.run_until_complete(self._startup_telegram()) + + async def _startup_telegram(self) -> None: + await self._app.initialize() + await self._app.start() + if self._app.updater: + await self._app.updater.start_polling( + bootstrap_retries=-1, + timeout=20, + # read_latency=60, # Assumed transmission latency + drop_pending_updates=True, + # stop_signals=[], # Necessary as we don't run on the main thread + ) + while True: + await asyncio.sleep(10) + if not self._app.updater.running: + break + + async def _cleanup_telegram(self) -> None: + if self._app.updater: + await self._app.updater.stop() + await self._app.stop() + await self._app.shutdown() def cleanup(self) -> None: """ @@ -240,7 +280,8 @@ class Telegram(RPCHandler): :return: None """ # This can take up to `timeout` from the call to `start_polling`. - self._updater.stop() + asyncio.run_coroutine_threadsafe(self._cleanup_telegram(), self._loop) + self._thread.join() def _exchange_from_msg(self, msg: Dict[str, Any]) -> str: """ @@ -427,14 +468,14 @@ class Telegram(RPCHandler): return None return message - def send_msg(self, msg: Dict[str, Any]) -> None: + def send_msg(self, msg: RPCSendMsg) -> None: """ Send a message to telegram channel """ default_noti = 'on' msg_type = msg['type'] noti = '' - if msg_type == RPCMessageType.EXIT: + if msg['type'] == RPCMessageType.EXIT: sell_noti = self._config['telegram'] \ .get('notification_settings', {}).get(str(msg_type), {}) # For backward compatibility sell still can be string @@ -451,9 +492,11 @@ class Telegram(RPCHandler): # Notification disabled return - message = self.compose_message(deepcopy(msg), msg_type) + message = self.compose_message(deepcopy(msg), msg_type) # type: ignore if message: - self._send_msg(message, disable_notification=(noti == 'silent')) + asyncio.run_coroutine_threadsafe( + self._send_msg(message, disable_notification=(noti == 'silent')), + self._loop) def _get_sell_emoji(self, msg): """ @@ -536,7 +579,7 @@ class Telegram(RPCHandler): return lines_detail @authorized_only - def _status(self, update: Update, context: CallbackContext) -> None: + async def _status(self, update: Update, context: CallbackContext) -> None: """ Handler for /status. Returns the current TradeThread status @@ -546,12 +589,12 @@ class Telegram(RPCHandler): """ if context.args and 'table' in context.args: - self._status_table(update, context) + await self._status_table(update, context) return else: - self._status_msg(update, context) + await self._status_msg(update, context) - def _status_msg(self, update: Update, context: CallbackContext) -> None: + async def _status_msg(self, update: Update, context: CallbackContext) -> None: """ handler for `/status` and `/status `. @@ -635,9 +678,9 @@ class Telegram(RPCHandler): lines_detail = self._prepare_order_details( r['orders'], r['quote_currency'], r['is_open']) lines.extend(lines_detail if lines_detail else "") - self.__send_status_msg(lines, r) + await self.__send_status_msg(lines, r) - def __send_status_msg(self, lines: List[str], r: Dict[str, Any]) -> None: + async def __send_status_msg(self, lines: List[str], r: Dict[str, Any]) -> None: """ Send status message. """ @@ -648,13 +691,13 @@ class Telegram(RPCHandler): if (len(msg) + len(line) + 1) < MAX_MESSAGE_LENGTH: msg += line + '\n' else: - self._send_msg(msg.format(**r)) + await self._send_msg(msg.format(**r)) msg = "*Trade ID:* `{trade_id}` - continued\n" + line + '\n' - self._send_msg(msg.format(**r)) + await self._send_msg(msg.format(**r)) @authorized_only - def _status_table(self, update: Update, context: CallbackContext) -> None: + async def _status_table(self, update: Update, context: CallbackContext) -> None: """ Handler for /status table. Returns the current TradeThread status in table format @@ -687,12 +730,11 @@ class Telegram(RPCHandler): # insert separators line between Total lines = message.split("\n") message = "\n".join(lines[:-1] + [lines[1]] + [lines[-1]]) - self._send_msg(f"
{message}
", parse_mode=ParseMode.HTML, - reload_able=True, callback_path="update_status_table", - query=update.callback_query) + await self._send_msg(f"
{message}
", parse_mode=ParseMode.HTML, + reload_able=True, callback_path="update_status_table", + query=update.callback_query) - @authorized_only - def _timeunit_stats(self, update: Update, context: CallbackContext, unit: str) -> None: + async def _timeunit_stats(self, update: Update, context: CallbackContext, unit: str) -> None: """ Handler for /daily Returns a daily profit (in BTC) over the last n days. @@ -739,11 +781,11 @@ class Telegram(RPCHandler): f'{val.message} Profit over the last {timescale} {val.message2}:\n' f'
{stats_tab}
' ) - self._send_msg(message, parse_mode=ParseMode.HTML, reload_able=True, - callback_path=val.callback, query=update.callback_query) + await self._send_msg(message, parse_mode=ParseMode.HTML, reload_able=True, + callback_path=val.callback, query=update.callback_query) @authorized_only - def _daily(self, update: Update, context: CallbackContext) -> None: + async def _daily(self, update: Update, context: CallbackContext) -> None: """ Handler for /daily Returns a daily profit (in BTC) over the last n days. @@ -751,10 +793,10 @@ class Telegram(RPCHandler): :param update: message update :return: None """ - self._timeunit_stats(update, context, 'days') + await self._timeunit_stats(update, context, 'days') @authorized_only - def _weekly(self, update: Update, context: CallbackContext) -> None: + async def _weekly(self, update: Update, context: CallbackContext) -> None: """ Handler for /weekly Returns a weekly profit (in BTC) over the last n weeks. @@ -762,10 +804,10 @@ class Telegram(RPCHandler): :param update: message update :return: None """ - self._timeunit_stats(update, context, 'weeks') + await self._timeunit_stats(update, context, 'weeks') @authorized_only - def _monthly(self, update: Update, context: CallbackContext) -> None: + async def _monthly(self, update: Update, context: CallbackContext) -> None: """ Handler for /monthly Returns a monthly profit (in BTC) over the last n months. @@ -773,10 +815,10 @@ class Telegram(RPCHandler): :param update: message update :return: None """ - self._timeunit_stats(update, context, 'months') + await self._timeunit_stats(update, context, 'months') @authorized_only - def _profit(self, update: Update, context: CallbackContext) -> None: + async def _profit(self, update: Update, context: CallbackContext) -> None: """ Handler for /profit. Returns a cumulative profit statistics. @@ -816,7 +858,7 @@ class Telegram(RPCHandler): best_pair = stats['best_pair'] best_pair_profit_ratio = stats['best_pair_profit_ratio'] if stats['trade_count'] == 0: - markdown_msg = 'No trades yet.' + markdown_msg = f"No trades yet.\n*Bot started:* `{stats['bot_start_date']}`" else: # Message to display if stats['closed_trade_count'] > 0: @@ -835,6 +877,7 @@ class Telegram(RPCHandler): f"({profit_all_percent} \N{GREEK CAPITAL LETTER SIGMA}%)`\n" f"∙ `{round_coin_value(profit_all_fiat, fiat_disp_cur)}`\n" f"*Total Trade Count:* `{trade_count}`\n" + f"*Bot started:* `{stats['bot_start_date']}`\n" f"*{'First Trade opened' if not timescale else 'Showing Profit since'}:* " f"`{first_trade_date}`\n" f"*Latest Trade opened:* `{latest_trade_date}`\n" @@ -849,11 +892,11 @@ class Telegram(RPCHandler): f"*Max Drawdown:* `{stats['max_drawdown']:.2%} " f"({round_coin_value(stats['max_drawdown_abs'], stake_cur)})`" ) - self._send_msg(markdown_msg, reload_able=True, callback_path="update_profit", - query=update.callback_query) + await self._send_msg(markdown_msg, reload_able=True, callback_path="update_profit", + query=update.callback_query) @authorized_only - def _stats(self, update: Update, context: CallbackContext) -> None: + async def _stats(self, update: Update, context: CallbackContext) -> None: """ Handler for /stats Show stats of recent trades @@ -884,7 +927,7 @@ class Telegram(RPCHandler): headers=['Exit Reason', 'Exits', 'Wins', 'Losses'] ) if len(exit_reasons_tabulate) > 25: - self._send_msg(f"```\n{exit_reasons_msg}```", ParseMode.MARKDOWN) + await self._send_msg(f"```\n{exit_reasons_msg}```", ParseMode.MARKDOWN) exit_reasons_msg = '' durations = stats['durations'] @@ -899,11 +942,12 @@ class Telegram(RPCHandler): ) msg = (f"""```\n{exit_reasons_msg}```\n```\n{duration_msg}```""") - self._send_msg(msg, ParseMode.MARKDOWN) + await self._send_msg(msg, ParseMode.MARKDOWN) @authorized_only - def _balance(self, update: Update, context: CallbackContext) -> None: + async def _balance(self, update: Update, context: CallbackContext) -> None: """ Handler for /balance """ + full_result = context.args and 'full' in context.args result = self._rpc._rpc_balance(self._config['stake_currency'], self._config.get('fiat_display_currency', '')) @@ -914,8 +958,7 @@ class Telegram(RPCHandler): output = '' if self._config['dry_run']: output += "*Warning:* Simulated balances in Dry Mode.\n" - starting_cap = round_coin_value( - result['starting_capital'], self._config['stake_currency']) + starting_cap = round_coin_value(result['starting_capital'], self._config['stake_currency']) output += f"Starting capital: `{starting_cap}`" starting_cap_fiat = round_coin_value( result['starting_capital_fiat'], self._config['fiat_display_currency'] @@ -927,7 +970,10 @@ class Telegram(RPCHandler): total_dust_currencies = 0 for curr in result['currencies']: curr_output = '' - if curr['est_stake'] > balance_dust_level: + if ( + (curr['is_position'] or curr['est_stake'] > balance_dust_level) + and (full_result or curr['is_bot_managed']) + ): if curr['is_position']: curr_output = ( f"*{curr['currency']}:*\n" @@ -936,20 +982,24 @@ class Telegram(RPCHandler): f"\t`Est. {curr['stake']}: " f"{round_coin_value(curr['est_stake'], curr['stake'], False)}`\n") else: + est_stake = round_coin_value( + curr['est_stake' if full_result else 'est_stake_bot'], curr['stake'], False) + curr_output = ( f"*{curr['currency']}:*\n" f"\t`Available: {curr['free']:.8f}`\n" f"\t`Balance: {curr['balance']:.8f}`\n" f"\t`Pending: {curr['used']:.8f}`\n" - f"\t`Est. {curr['stake']}: " - f"{round_coin_value(curr['est_stake'], curr['stake'], False)}`\n") + f"\t`Bot Owned: {curr['bot_owned']:.8f}`\n" + f"\t`Est. {curr['stake']}: {est_stake}`\n") + elif curr['est_stake'] <= balance_dust_level: total_dust_balance += curr['est_stake'] total_dust_currencies += 1 # Handle overflowing message length if len(output + curr_output) >= MAX_MESSAGE_LENGTH: - self._send_msg(output) + await self._send_msg(output) output = curr_output else: output += curr_output @@ -964,19 +1014,20 @@ class Telegram(RPCHandler): tc = result['trade_count'] > 0 stake_improve = f" `({result['starting_capital_ratio']:.2%})`" if tc else '' fiat_val = f" `({result['starting_capital_fiat_ratio']:.2%})`" if tc else '' - - output += ("\n*Estimated Value*:\n" - f"\t`{result['stake']}: " - f"{round_coin_value(result['total'], result['stake'], False)}`" - f"{stake_improve}\n" - f"\t`{result['symbol']}: " - f"{round_coin_value(result['value'], result['symbol'], False)}`" - f"{fiat_val}\n") - self._send_msg(output, reload_able=True, callback_path="update_balance", - query=update.callback_query) + value = round_coin_value( + result['value' if full_result else 'value_bot'], result['symbol'], False) + total_stake = round_coin_value( + result['total' if full_result else 'total_bot'], result['stake'], False) + output += ( + f"\n*Estimated Value{' (Bot managed assets only)' if not full_result else ''}*:\n" + f"\t`{result['stake']}: {total_stake}`{stake_improve}\n" + f"\t`{result['symbol']}: {value}`{fiat_val}\n" + ) + await self._send_msg(output, reload_able=True, callback_path="update_balance", + query=update.callback_query) @authorized_only - def _start(self, update: Update, context: CallbackContext) -> None: + async def _start(self, update: Update, context: CallbackContext) -> None: """ Handler for /start. Starts TradeThread @@ -985,10 +1036,10 @@ class Telegram(RPCHandler): :return: None """ msg = self._rpc._rpc_start() - self._send_msg(f"Status: `{msg['status']}`") + await self._send_msg(f"Status: `{msg['status']}`") @authorized_only - def _stop(self, update: Update, context: CallbackContext) -> None: + async def _stop(self, update: Update, context: CallbackContext) -> None: """ Handler for /stop. Stops TradeThread @@ -997,10 +1048,10 @@ class Telegram(RPCHandler): :return: None """ msg = self._rpc._rpc_stop() - self._send_msg(f"Status: `{msg['status']}`") + await self._send_msg(f"Status: `{msg['status']}`") @authorized_only - def _reload_config(self, update: Update, context: CallbackContext) -> None: + async def _reload_config(self, update: Update, context: CallbackContext) -> None: """ Handler for /reload_config. Triggers a config file reload @@ -1009,10 +1060,10 @@ class Telegram(RPCHandler): :return: None """ msg = self._rpc._rpc_reload_config() - self._send_msg(f"Status: `{msg['status']}`") + await self._send_msg(f"Status: `{msg['status']}`") @authorized_only - def _stopentry(self, update: Update, context: CallbackContext) -> None: + async def _stopentry(self, update: Update, context: CallbackContext) -> None: """ Handler for /stop_buy. Sets max_open_trades to 0 and gracefully sells all open trades @@ -1021,10 +1072,10 @@ class Telegram(RPCHandler): :return: None """ msg = self._rpc._rpc_stopentry() - self._send_msg(f"Status: `{msg['status']}`") + await self._send_msg(f"Status: `{msg['status']}`") @authorized_only - def _force_exit(self, update: Update, context: CallbackContext) -> None: + async def _force_exit(self, update: Update, context: CallbackContext) -> None: """ Handler for /forceexit . Sells the given trade at current price @@ -1035,14 +1086,14 @@ class Telegram(RPCHandler): if context.args: trade_id = context.args[0] - self._force_exit_action(trade_id) + await self._force_exit_action(trade_id) else: fiat_currency = self._config.get('fiat_display_currency', '') try: statlist, _, _ = self._rpc._rpc_status_table( self._config['stake_currency'], fiat_currency) except RPCException: - self._send_msg(msg='No open trade found.') + await self._send_msg(msg='No open trade found.') return trades = [] for trade in statlist: @@ -1055,51 +1106,51 @@ class Telegram(RPCHandler): buttons_aligned.append([InlineKeyboardButton( text='Cancel', callback_data='force_exit__cancel')]) - self._send_msg(msg="Which trade?", keyboard=buttons_aligned) + await self._send_msg(msg="Which trade?", keyboard=buttons_aligned) - def _force_exit_action(self, trade_id): + async def _force_exit_action(self, trade_id): if trade_id != 'cancel': try: self._rpc._rpc_force_exit(trade_id) except RPCException as e: - self._send_msg(str(e)) + await self._send_msg(str(e)) - def _force_exit_inline(self, update: Update, _: CallbackContext) -> None: + async def _force_exit_inline(self, update: Update, _: CallbackContext) -> None: if update.callback_query: query = update.callback_query if query.data and '__' in query.data: # Input data is "force_exit__" trade_id = query.data.split("__")[1].split(' ')[0] if trade_id == 'cancel': - query.answer() - query.edit_message_text(text="Force exit canceled.") + await query.answer() + await query.edit_message_text(text="Force exit canceled.") return trade: Optional[Trade] = Trade.get_trades(trade_filter=Trade.id == trade_id).first() - query.answer() + await query.answer() if trade: - query.edit_message_text( + await query.edit_message_text( text=f"Manually exiting Trade #{trade_id}, {trade.pair}") - self._force_exit_action(trade_id) + await self._force_exit_action(trade_id) else: - query.edit_message_text(text=f"Trade {trade_id} not found.") + await query.edit_message_text(text=f"Trade {trade_id} not found.") - def _force_enter_action(self, pair, price: Optional[float], order_side: SignalDirection): + async def _force_enter_action(self, pair, price: Optional[float], order_side: SignalDirection): if pair != 'cancel': try: self._rpc._rpc_force_entry(pair, price, order_side=order_side) except RPCException as e: logger.exception("Forcebuy error!") - self._send_msg(str(e), ParseMode.HTML) + await self._send_msg(str(e), ParseMode.HTML) - def _force_enter_inline(self, update: Update, _: CallbackContext) -> None: + async def _force_enter_inline(self, update: Update, _: CallbackContext) -> None: if update.callback_query: query = update.callback_query if query.data and '_||_' in query.data: pair, side = query.data.split('_||_') order_side = SignalDirection(side) - query.answer() - query.edit_message_text(text=f"Manually entering {order_side} for {pair}") - self._force_enter_action(pair, None, order_side) + await query.answer() + await query.edit_message_text(text=f"Manually entering {order_side} for {pair}") + await self._force_enter_action(pair, None, order_side) @staticmethod def _layout_inline_keyboard( @@ -1112,7 +1163,7 @@ class Telegram(RPCHandler): return [buttons[i:i + cols] for i in range(0, len(buttons), cols)] @authorized_only - def _force_enter( + async def _force_enter( self, update: Update, context: CallbackContext, order_side: SignalDirection) -> None: """ Handler for /forcelong and `/forceshort @@ -1124,7 +1175,7 @@ class Telegram(RPCHandler): if context.args: pair = context.args[0] price = float(context.args[1]) if len(context.args) > 1 else None - self._force_enter_action(pair, price, order_side) + await self._force_enter_action(pair, price, order_side) else: whitelist = self._rpc._rpc_whitelist()['whitelist'] pair_buttons = [ @@ -1134,12 +1185,12 @@ class Telegram(RPCHandler): buttons_aligned = self._layout_inline_keyboard(pair_buttons) buttons_aligned.append([InlineKeyboardButton(text='Cancel', callback_data='cancel')]) - self._send_msg(msg="Which pair?", - keyboard=buttons_aligned, - query=update.callback_query) + await self._send_msg(msg="Which pair?", + keyboard=buttons_aligned, + query=update.callback_query) @authorized_only - def _trades(self, update: Update, context: CallbackContext) -> None: + async def _trades(self, update: Update, context: CallbackContext) -> None: """ Handler for /trades Returns last n recent trades. @@ -1168,10 +1219,10 @@ class Telegram(RPCHandler): tablefmt='simple') message = (f"{min(trades['trades_count'], nrecent)} recent trades:\n" + (f"
{trades_tab}
" if trades['trades_count'] > 0 else '')) - self._send_msg(message, parse_mode=ParseMode.HTML) + await self._send_msg(message, parse_mode=ParseMode.HTML) @authorized_only - def _delete_trade(self, update: Update, context: CallbackContext) -> None: + async def _delete_trade(self, update: Update, context: CallbackContext) -> None: """ Handler for /delete . Delete the given trade @@ -1183,13 +1234,13 @@ class Telegram(RPCHandler): raise RPCException("Trade-id not set.") trade_id = int(context.args[0]) msg = self._rpc._rpc_delete(trade_id) - self._send_msg( + await self._send_msg( f"`{msg['result_msg']}`\n" 'Please make sure to take care of this asset on the exchange manually.' ) @authorized_only - def _cancel_open_order(self, update: Update, context: CallbackContext) -> None: + async def _cancel_open_order(self, update: Update, context: CallbackContext) -> None: """ Handler for /cancel_open_order . Cancel open order for tradeid @@ -1201,10 +1252,10 @@ class Telegram(RPCHandler): raise RPCException("Trade-id not set.") trade_id = int(context.args[0]) self._rpc._rpc_cancel_open_order(trade_id) - self._send_msg('Open order canceled.') + await self._send_msg('Open order canceled.') @authorized_only - def _performance(self, update: Update, context: CallbackContext) -> None: + async def _performance(self, update: Update, context: CallbackContext) -> None: """ Handler for /performance. Shows a performance statistic from finished trades @@ -1222,17 +1273,17 @@ class Telegram(RPCHandler): f"({trade['count']})\n") if len(output + stat_line) >= MAX_MESSAGE_LENGTH: - self._send_msg(output, parse_mode=ParseMode.HTML) + await self._send_msg(output, parse_mode=ParseMode.HTML) output = stat_line else: output += stat_line - self._send_msg(output, parse_mode=ParseMode.HTML, - reload_able=True, callback_path="update_performance", - query=update.callback_query) + await self._send_msg(output, parse_mode=ParseMode.HTML, + reload_able=True, callback_path="update_performance", + query=update.callback_query) @authorized_only - def _enter_tag_performance(self, update: Update, context: CallbackContext) -> None: + async def _enter_tag_performance(self, update: Update, context: CallbackContext) -> None: """ Handler for /buys PAIR . Shows a performance statistic from finished trades @@ -1254,17 +1305,17 @@ class Telegram(RPCHandler): f"({trade['count']})\n") if len(output + stat_line) >= MAX_MESSAGE_LENGTH: - self._send_msg(output, parse_mode=ParseMode.HTML) + await self._send_msg(output, parse_mode=ParseMode.HTML) output = stat_line else: output += stat_line - self._send_msg(output, parse_mode=ParseMode.HTML, - reload_able=True, callback_path="update_enter_tag_performance", - query=update.callback_query) + await self._send_msg(output, parse_mode=ParseMode.HTML, + reload_able=True, callback_path="update_enter_tag_performance", + query=update.callback_query) @authorized_only - def _exit_reason_performance(self, update: Update, context: CallbackContext) -> None: + async def _exit_reason_performance(self, update: Update, context: CallbackContext) -> None: """ Handler for /sells. Shows a performance statistic from finished trades @@ -1286,17 +1337,17 @@ class Telegram(RPCHandler): f"({trade['count']})\n") if len(output + stat_line) >= MAX_MESSAGE_LENGTH: - self._send_msg(output, parse_mode=ParseMode.HTML) + await self._send_msg(output, parse_mode=ParseMode.HTML) output = stat_line else: output += stat_line - self._send_msg(output, parse_mode=ParseMode.HTML, - reload_able=True, callback_path="update_exit_reason_performance", - query=update.callback_query) + await self._send_msg(output, parse_mode=ParseMode.HTML, + reload_able=True, callback_path="update_exit_reason_performance", + query=update.callback_query) @authorized_only - def _mix_tag_performance(self, update: Update, context: CallbackContext) -> None: + async def _mix_tag_performance(self, update: Update, context: CallbackContext) -> None: """ Handler for /mix_tags. Shows a performance statistic from finished trades @@ -1318,17 +1369,17 @@ class Telegram(RPCHandler): f"({trade['count']})\n") if len(output + stat_line) >= MAX_MESSAGE_LENGTH: - self._send_msg(output, parse_mode=ParseMode.HTML) + await self._send_msg(output, parse_mode=ParseMode.HTML) output = stat_line else: output += stat_line - self._send_msg(output, parse_mode=ParseMode.HTML, - reload_able=True, callback_path="update_mix_tag_performance", - query=update.callback_query) + await self._send_msg(output, parse_mode=ParseMode.HTML, + reload_able=True, callback_path="update_mix_tag_performance", + query=update.callback_query) @authorized_only - def _count(self, update: Update, context: CallbackContext) -> None: + async def _count(self, update: Update, context: CallbackContext) -> None: """ Handler for /count. Returns the number of trades running @@ -1340,21 +1391,21 @@ class Telegram(RPCHandler): message = tabulate({k: [v] for k, v in counts.items()}, headers=['current', 'max', 'total stake'], tablefmt='simple') - message = "
{}
".format(message) + message = f"
{message}
" logger.debug(message) - self._send_msg(message, parse_mode=ParseMode.HTML, - reload_able=True, callback_path="update_count", - query=update.callback_query) + await self._send_msg(message, parse_mode=ParseMode.HTML, + reload_able=True, callback_path="update_count", + query=update.callback_query) @authorized_only - def _locks(self, update: Update, context: CallbackContext) -> None: + async def _locks(self, update: Update, context: CallbackContext) -> None: """ Handler for /locks. Returns the currently active locks """ rpc_locks = self._rpc._rpc_locks() if not rpc_locks['locks']: - self._send_msg('No active locks.', parse_mode=ParseMode.HTML) + await self._send_msg('No active locks.', parse_mode=ParseMode.HTML) for locks in chunks(rpc_locks['locks'], 25): message = tabulate([[ @@ -1366,10 +1417,10 @@ class Telegram(RPCHandler): tablefmt='simple') message = f"
{escape(message)}
" logger.debug(message) - self._send_msg(message, parse_mode=ParseMode.HTML) + await self._send_msg(message, parse_mode=ParseMode.HTML) @authorized_only - def _delete_locks(self, update: Update, context: CallbackContext) -> None: + async def _delete_locks(self, update: Update, context: CallbackContext) -> None: """ Handler for /delete_locks. Returns the currently active locks @@ -1384,10 +1435,10 @@ class Telegram(RPCHandler): pair = arg self._rpc._rpc_delete_lock(lockid=lockid, pair=pair) - self._locks(update, context) + await self._locks(update, context) @authorized_only - def _whitelist(self, update: Update, context: CallbackContext) -> None: + async def _whitelist(self, update: Update, context: CallbackContext) -> None: """ Handler for /whitelist Shows the currently active whitelist @@ -1404,39 +1455,39 @@ class Telegram(RPCHandler): message += f"`{', '.join(whitelist['whitelist'])}`" logger.debug(message) - self._send_msg(message) + await self._send_msg(message) @authorized_only - def _blacklist(self, update: Update, context: CallbackContext) -> None: + async def _blacklist(self, update: Update, context: CallbackContext) -> None: """ Handler for /blacklist Shows the currently active blacklist """ - self.send_blacklist_msg(self._rpc._rpc_blacklist(context.args)) + await self.send_blacklist_msg(self._rpc._rpc_blacklist(context.args)) - def send_blacklist_msg(self, blacklist: Dict): + async def send_blacklist_msg(self, blacklist: Dict): errmsgs = [] for pair, error in blacklist['errors'].items(): - errmsgs.append(f"Error adding `{pair}` to blacklist: `{error['error_msg']}`") + errmsgs.append(f"Error: {error['error_msg']}") if errmsgs: - self._send_msg('\n'.join(errmsgs)) + await self._send_msg('\n'.join(errmsgs)) message = f"Blacklist contains {blacklist['length']} pairs\n" message += f"`{', '.join(blacklist['blacklist'])}`" logger.debug(message) - self._send_msg(message) + await self._send_msg(message) @authorized_only - def _blacklist_delete(self, update: Update, context: CallbackContext) -> None: + async def _blacklist_delete(self, update: Update, context: CallbackContext) -> None: """ Handler for /bl_delete Deletes pair(s) from current blacklist """ - self.send_blacklist_msg(self._rpc._rpc_blacklist_delete(context.args or [])) + await self.send_blacklist_msg(self._rpc._rpc_blacklist_delete(context.args or [])) @authorized_only - def _logs(self, update: Update, context: CallbackContext) -> None: + async def _logs(self, update: Update, context: CallbackContext) -> None: """ Handler for /logs Shows the latest logs @@ -1455,17 +1506,17 @@ class Telegram(RPCHandler): escape_markdown(logrec[4], version=2)) if len(msgs + msg) + 10 >= MAX_MESSAGE_LENGTH: # Send message immediately if it would become too long - self._send_msg(msgs, parse_mode=ParseMode.MARKDOWN_V2) + await self._send_msg(msgs, parse_mode=ParseMode.MARKDOWN_V2) msgs = msg + '\n' else: # Append message to messages to send msgs += msg + '\n' if msgs: - self._send_msg(msgs, parse_mode=ParseMode.MARKDOWN_V2) + await self._send_msg(msgs, parse_mode=ParseMode.MARKDOWN_V2) @authorized_only - def _edge(self, update: Update, context: CallbackContext) -> None: + async def _edge(self, update: Update, context: CallbackContext) -> None: """ Handler for /edge Shows information related to Edge @@ -1473,17 +1524,17 @@ class Telegram(RPCHandler): edge_pairs = self._rpc._rpc_edge() if not edge_pairs: message = 'Edge only validated following pairs:' - self._send_msg(message, parse_mode=ParseMode.HTML) + await self._send_msg(message, parse_mode=ParseMode.HTML) for chunk in chunks(edge_pairs, 25): edge_pairs_tab = tabulate(chunk, headers='keys', tablefmt='simple') message = (f'Edge only validated following pairs:\n' f'
{edge_pairs_tab}
') - self._send_msg(message, parse_mode=ParseMode.HTML) + await self._send_msg(message, parse_mode=ParseMode.HTML) @authorized_only - def _help(self, update: Update, context: CallbackContext) -> None: + async def _help(self, update: Update, context: CallbackContext) -> None: """ Handler for /help. Show commands of the bot @@ -1527,7 +1578,8 @@ class Telegram(RPCHandler): "------------\n" "*/show_config:* `Show running configuration` \n" "*/locks:* `Show currently locked pairs`\n" - "*/balance:* `Show account balance per currency`\n" + "*/balance:* `Show bot managed balance per currency`\n" + "*/balance total:* `Show account balance per currency`\n" "*/logs [limit]:* `Show latest logs - defaults to 10` \n" "*/count:* `Show number of active trades compared to allowed number of trades`\n" "*/edge:* `Shows validated pairs by Edge if it is enabled` \n" @@ -1560,20 +1612,20 @@ class Telegram(RPCHandler): "*/version:* `Show version`" ) - self._send_msg(message, parse_mode=ParseMode.MARKDOWN) + await self._send_msg(message, parse_mode=ParseMode.MARKDOWN) @authorized_only - def _health(self, update: Update, context: CallbackContext) -> None: + async def _health(self, update: Update, context: CallbackContext) -> None: """ Handler for /health Shows the last process timestamp """ health = self._rpc.health() message = f"Last process: `{health['last_process_loc']}`" - self._send_msg(message) + await self._send_msg(message) @authorized_only - def _version(self, update: Update, context: CallbackContext) -> None: + async def _version(self, update: Update, context: CallbackContext) -> None: """ Handler for /version. Show version information @@ -1584,12 +1636,12 @@ class Telegram(RPCHandler): strategy_version = self._rpc._freqtrade.strategy.version() version_string = f'*Version:* `{__version__}`' if strategy_version is not None: - version_string += f', *Strategy version: * `{strategy_version}`' + version_string += f'\n*Strategy version: * `{strategy_version}`' - self._send_msg(version_string) + await self._send_msg(version_string) @authorized_only - def _show_config(self, update: Update, context: CallbackContext) -> None: + async def _show_config(self, update: Update, context: CallbackContext) -> None: """ Handler for /show_config. Show config information information @@ -1618,7 +1670,7 @@ class Telegram(RPCHandler): else: pa_info = "*Position adjustment:* Off\n" - self._send_msg( + await self._send_msg( f"*Mode:* `{'Dry-run' if val['dry_run'] else 'Live'}`\n" f"*Exchange:* `{val['exchange']}`\n" f"*Market: * `{val['trading_mode']}`\n" @@ -1634,22 +1686,22 @@ class Telegram(RPCHandler): f"*Current state:* `{val['state']}`" ) - def _update_msg(self, query: CallbackQuery, msg: str, callback_path: str = "", - reload_able: bool = False, parse_mode: str = ParseMode.MARKDOWN) -> None: + async def _update_msg(self, query: CallbackQuery, msg: str, callback_path: str = "", + reload_able: bool = False, parse_mode: str = ParseMode.MARKDOWN) -> None: if reload_able: reply_markup = InlineKeyboardMarkup([ [InlineKeyboardButton("Refresh", callback_data=callback_path)], ]) else: reply_markup = InlineKeyboardMarkup([[]]) - msg += "\nUpdated: {}".format(datetime.now().ctime()) + msg += f"\nUpdated: {datetime.now().ctime()}" if not query.message: return chat_id = query.message.chat_id message_id = query.message.message_id try: - self._updater.bot.edit_message_text( + await self._app.bot.edit_message_text( chat_id=chat_id, message_id=message_id, text=msg, @@ -1664,12 +1716,12 @@ class Telegram(RPCHandler): except TelegramError as telegram_err: logger.warning('TelegramError: %s! Giving up on that message.', telegram_err.message) - def _send_msg(self, msg: str, parse_mode: str = ParseMode.MARKDOWN, - disable_notification: bool = False, - keyboard: Optional[List[List[InlineKeyboardButton]]] = None, - callback_path: str = "", - reload_able: bool = False, - query: Optional[CallbackQuery] = None) -> None: + async def _send_msg(self, msg: str, parse_mode: str = ParseMode.MARKDOWN, + disable_notification: bool = False, + keyboard: Optional[List[List[InlineKeyboardButton]]] = None, + callback_path: str = "", + reload_able: bool = False, + query: Optional[CallbackQuery] = None) -> None: """ Send given markdown message :param msg: message @@ -1679,20 +1731,20 @@ class Telegram(RPCHandler): """ reply_markup: Union[InlineKeyboardMarkup, ReplyKeyboardMarkup] if query: - self._update_msg(query=query, msg=msg, parse_mode=parse_mode, - callback_path=callback_path, reload_able=reload_able) + await self._update_msg(query=query, msg=msg, parse_mode=parse_mode, + callback_path=callback_path, reload_able=reload_able) return if reload_able and self._config['telegram'].get('reload', True): reply_markup = InlineKeyboardMarkup([ [InlineKeyboardButton("Refresh", callback_data=callback_path)]]) else: if keyboard is not None: - reply_markup = InlineKeyboardMarkup(keyboard, resize_keyboard=True) + reply_markup = InlineKeyboardMarkup(keyboard) else: reply_markup = ReplyKeyboardMarkup(self._keyboard, resize_keyboard=True) try: try: - self._updater.bot.send_message( + await self._app.bot.send_message( self._config['telegram']['chat_id'], text=msg, parse_mode=parse_mode, @@ -1706,7 +1758,7 @@ class Telegram(RPCHandler): 'Telegram NetworkError: %s! Trying one more time.', network_err.message ) - self._updater.bot.send_message( + await self._app.bot.send_message( self._config['telegram']['chat_id'], text=msg, parse_mode=parse_mode, @@ -1720,7 +1772,7 @@ class Telegram(RPCHandler): ) @authorized_only - def _changemarketdir(self, update: Update, context: CallbackContext) -> None: + async def _changemarketdir(self, update: Update, context: CallbackContext) -> None: """ Handler for /marketdir. Updates the bot's market_direction @@ -1743,14 +1795,14 @@ class Telegram(RPCHandler): if new_market_dir is not None: self._rpc._update_market_direction(new_market_dir) - self._send_msg("Successfully updated market direction" - f" from *{old_market_dir}* to *{new_market_dir}*.") + await self._send_msg("Successfully updated market direction" + f" from *{old_market_dir}* to *{new_market_dir}*.") else: raise RPCException("Invalid market direction provided. \n" "Valid market directions: *long, short, even, none*") elif context.args is not None and len(context.args) == 0: old_market_dir = self._rpc._get_market_direction() - self._send_msg(f"Currently set market direction: *{old_market_dir}*") + await self._send_msg(f"Currently set market direction: *{old_market_dir}*") else: raise RPCException("Invalid usage of command /marketdir. \n" "Usage: */marketdir [short | long | even | none]*") diff --git a/freqtrade/rpc/webhook.py b/freqtrade/rpc/webhook.py index 0967de70d..80690ec0c 100644 --- a/freqtrade/rpc/webhook.py +++ b/freqtrade/rpc/webhook.py @@ -10,6 +10,7 @@ from requests import RequestException, post from freqtrade.constants import Config from freqtrade.enums import RPCMessageType from freqtrade.rpc import RPC, RPCHandler +from freqtrade.rpc.rpc_types import RPCSendMsg logger = logging.getLogger(__name__) @@ -41,10 +42,13 @@ class Webhook(RPCHandler): """ pass - def _get_value_dict(self, msg: Dict[str, Any]) -> Optional[Dict[str, Any]]: + def _get_value_dict(self, msg: RPCSendMsg) -> Optional[Dict[str, Any]]: whconfig = self._config['webhook'] + if msg['type'].value in whconfig: + # Explicit types should have priority + valuedict = whconfig.get(msg['type'].value) # Deprecated 2022.10 - only keep generic method. - if msg['type'] in [RPCMessageType.ENTRY]: + elif msg['type'] in [RPCMessageType.ENTRY]: valuedict = whconfig.get('webhookentry') elif msg['type'] in [RPCMessageType.ENTRY_CANCEL]: valuedict = whconfig.get('webhookentrycancel') @@ -61,9 +65,6 @@ class Webhook(RPCHandler): RPCMessageType.EXCEPTION, RPCMessageType.WARNING): valuedict = whconfig.get('webhookstatus') - elif msg['type'].value in whconfig: - # Allow all types ... - valuedict = whconfig.get(msg['type'].value) elif msg['type'] in ( RPCMessageType.PROTECTION_TRIGGER, RPCMessageType.PROTECTION_TRIGGER_GLOBAL, @@ -75,7 +76,7 @@ class Webhook(RPCHandler): return None return valuedict - def send_msg(self, msg: Dict[str, Any]) -> None: + def send_msg(self, msg: RPCSendMsg) -> None: """ Send a message to telegram channel """ try: @@ -113,7 +114,7 @@ class Webhook(RPCHandler): response = post(self._url, data=payload['data'], headers={'Content-Type': 'text/plain'}) else: - raise NotImplementedError('Unknown format: {}'.format(self._format)) + raise NotImplementedError(f'Unknown format: {self._format}') # Throw a RequestException if the post was not successful response.raise_for_status() diff --git a/freqtrade/strategy/hyper.py b/freqtrade/strategy/hyper.py index 52ba22951..d38110a2a 100644 --- a/freqtrade/strategy/hyper.py +++ b/freqtrade/strategy/hyper.py @@ -8,7 +8,7 @@ from typing import Any, Dict, Iterator, List, Optional, Tuple, Type, Union from freqtrade.constants import Config from freqtrade.exceptions import OperationalException -from freqtrade.misc import deep_merge_dicts, json_load +from freqtrade.misc import deep_merge_dicts from freqtrade.optimize.hyperopt_tools import HyperoptTools from freqtrade.strategy.parameters import BaseParameter @@ -124,8 +124,7 @@ class HyperStrategyMixin: if filename.is_file(): logger.info(f"Loading parameters from file {filename}") try: - with filename.open('r') as f: - params = json_load(f) + params = HyperoptTools.load_params(filename) if params.get('strategy_name') != self.__class__.__name__: raise OperationalException('Invalid parameter file provided.') return params diff --git a/freqtrade/strategy/interface.py b/freqtrade/strategy/interface.py index 96b2ac8ce..7adb7a154 100644 --- a/freqtrade/strategy/interface.py +++ b/freqtrade/strategy/interface.py @@ -10,7 +10,7 @@ from typing import Dict, List, Optional, Tuple, Union import arrow from pandas import DataFrame -from freqtrade.constants import Config, IntOrInf, ListPairsWithTimeframes +from freqtrade.constants import CUSTOM_TAG_MAX_LENGTH, Config, IntOrInf, ListPairsWithTimeframes from freqtrade.data.dataprovider import DataProvider from freqtrade.enums import (CandleType, ExitCheckTuple, ExitType, MarketDirection, RunMode, SignalDirection, SignalTagType, SignalType, TradingMode) @@ -27,7 +27,6 @@ from freqtrade.wallets import Wallets logger = logging.getLogger(__name__) -CUSTOM_EXIT_MAX_LENGTH = 64 class IStrategy(ABC, HyperStrategyMixin): @@ -251,11 +250,12 @@ class IStrategy(ABC, HyperStrategyMixin): """ pass - def bot_loop_start(self, **kwargs) -> None: + def bot_loop_start(self, current_time: datetime, **kwargs) -> None: """ Called at the start of the bot iteration (one loop). Might be used to perform pair-independent tasks (e.g. gather some remote resource for comparison) + :param current_time: datetime object, containing the current datetime :param **kwargs: Ensure to keep this here so updates to this won't break your strategy. """ pass @@ -618,7 +618,7 @@ class IStrategy(ABC, HyperStrategyMixin): return df def feature_engineering_expand_all(self, dataframe: DataFrame, period: int, - metadata: Dict, **kwargs): + metadata: Dict, **kwargs) -> DataFrame: """ *Only functional with FreqAI enabled strategies* This function will automatically expand the defined features on the config defined @@ -644,7 +644,8 @@ class IStrategy(ABC, HyperStrategyMixin): """ return dataframe - def feature_engineering_expand_basic(self, dataframe: DataFrame, metadata: Dict, **kwargs): + def feature_engineering_expand_basic( + self, dataframe: DataFrame, metadata: Dict, **kwargs) -> DataFrame: """ *Only functional with FreqAI enabled strategies* This function will automatically expand the defined features on the config defined @@ -673,7 +674,8 @@ class IStrategy(ABC, HyperStrategyMixin): """ return dataframe - def feature_engineering_standard(self, dataframe: DataFrame, metadata: Dict, **kwargs): + def feature_engineering_standard( + self, dataframe: DataFrame, metadata: Dict, **kwargs) -> DataFrame: """ *Only functional with FreqAI enabled strategies* This optional function will be called once with the dataframe of the base timeframe. @@ -697,7 +699,7 @@ class IStrategy(ABC, HyperStrategyMixin): """ return dataframe - def set_freqai_targets(self, dataframe: DataFrame, metadata: Dict, **kwargs): + def set_freqai_targets(self, dataframe: DataFrame, metadata: Dict, **kwargs) -> DataFrame: """ *Only functional with FreqAI enabled strategies* Required function to set the targets for the model. @@ -1117,11 +1119,11 @@ class IStrategy(ABC, HyperStrategyMixin): exit_signal = ExitType.CUSTOM_EXIT if isinstance(reason_cust, str): custom_reason = reason_cust - if len(reason_cust) > CUSTOM_EXIT_MAX_LENGTH: + if len(reason_cust) > CUSTOM_TAG_MAX_LENGTH: logger.warning(f'Custom exit reason returned from ' f'custom_exit is too long and was trimmed' - f'to {CUSTOM_EXIT_MAX_LENGTH} characters.') - custom_reason = reason_cust[:CUSTOM_EXIT_MAX_LENGTH] + f'to {CUSTOM_TAG_MAX_LENGTH} characters.') + custom_reason = reason_cust[:CUSTOM_TAG_MAX_LENGTH] else: custom_reason = '' if ( diff --git a/freqtrade/templates/FreqaiExampleHybridStrategy.py b/freqtrade/templates/FreqaiExampleHybridStrategy.py index 0e7113f8c..03446d76e 100644 --- a/freqtrade/templates/FreqaiExampleHybridStrategy.py +++ b/freqtrade/templates/FreqaiExampleHybridStrategy.py @@ -97,7 +97,7 @@ class FreqaiExampleHybridStrategy(IStrategy): exit_short_rsi = IntParameter(low=1, high=50, default=30, space='buy', optimize=True, load=True) def feature_engineering_expand_all(self, dataframe: DataFrame, period: int, - metadata: Dict, **kwargs): + metadata: Dict, **kwargs) -> DataFrame: """ *Only functional with FreqAI enabled strategies* This function will automatically expand the defined features on the config defined @@ -151,7 +151,8 @@ class FreqaiExampleHybridStrategy(IStrategy): return dataframe - def feature_engineering_expand_basic(self, dataframe: DataFrame, metadata: Dict, **kwargs): + def feature_engineering_expand_basic( + self, dataframe: DataFrame, metadata: Dict, **kwargs) -> DataFrame: """ *Only functional with FreqAI enabled strategies* This function will automatically expand the defined features on the config defined @@ -183,7 +184,8 @@ class FreqaiExampleHybridStrategy(IStrategy): dataframe["%-raw_price"] = dataframe["close"] return dataframe - def feature_engineering_standard(self, dataframe: DataFrame, metadata: Dict, **kwargs): + def feature_engineering_standard( + self, dataframe: DataFrame, metadata: Dict, **kwargs) -> DataFrame: """ *Only functional with FreqAI enabled strategies* This optional function will be called once with the dataframe of the base timeframe. @@ -209,7 +211,7 @@ class FreqaiExampleHybridStrategy(IStrategy): dataframe["%-hour_of_day"] = dataframe["date"].dt.hour return dataframe - def set_freqai_targets(self, dataframe: DataFrame, metadata: Dict, **kwargs): + def set_freqai_targets(self, dataframe: DataFrame, metadata: Dict, **kwargs) -> DataFrame: """ *Only functional with FreqAI enabled strategies* Required function to set the targets for the model. @@ -223,6 +225,7 @@ class FreqaiExampleHybridStrategy(IStrategy): :param metadata: metadata of current pair usage example: dataframe["&-target"] = dataframe["close"].shift(-1) / dataframe["close"] """ + self.freqai.class_names = ["down", "up"] dataframe['&s-up_or_down'] = np.where(dataframe["close"].shift(-50) > dataframe["close"], 'up', 'down') diff --git a/freqtrade/templates/FreqaiExampleStrategy.py b/freqtrade/templates/FreqaiExampleStrategy.py index 0093c7f7a..493ea17f3 100644 --- a/freqtrade/templates/FreqaiExampleStrategy.py +++ b/freqtrade/templates/FreqaiExampleStrategy.py @@ -48,7 +48,7 @@ class FreqaiExampleStrategy(IStrategy): [0.75, 1, 1.25, 1.5, 1.75], space="sell", default=1.25, optimize=True) def feature_engineering_expand_all(self, dataframe: DataFrame, period: int, - metadata: Dict, **kwargs): + metadata: Dict, **kwargs) -> DataFrame: """ *Only functional with FreqAI enabled strategies* This function will automatically expand the defined features on the config defined @@ -106,7 +106,8 @@ class FreqaiExampleStrategy(IStrategy): return dataframe - def feature_engineering_expand_basic(self, dataframe: DataFrame, metadata: Dict, **kwargs): + def feature_engineering_expand_basic( + self, dataframe: DataFrame, metadata: Dict, **kwargs) -> DataFrame: """ *Only functional with FreqAI enabled strategies* This function will automatically expand the defined features on the config defined @@ -142,7 +143,8 @@ class FreqaiExampleStrategy(IStrategy): dataframe["%-raw_price"] = dataframe["close"] return dataframe - def feature_engineering_standard(self, dataframe: DataFrame, metadata: Dict, **kwargs): + def feature_engineering_standard( + self, dataframe: DataFrame, metadata: Dict, **kwargs) -> DataFrame: """ *Only functional with FreqAI enabled strategies* This optional function will be called once with the dataframe of the base timeframe. @@ -172,7 +174,7 @@ class FreqaiExampleStrategy(IStrategy): dataframe["%-hour_of_day"] = dataframe["date"].dt.hour return dataframe - def set_freqai_targets(self, dataframe: DataFrame, metadata: Dict, **kwargs): + def set_freqai_targets(self, dataframe: DataFrame, metadata: Dict, **kwargs) -> DataFrame: """ *Only functional with FreqAI enabled strategies* Required function to set the targets for the model. diff --git a/freqtrade/templates/strategy_subtemplates/strategy_methods_advanced.j2 b/freqtrade/templates/strategy_subtemplates/strategy_methods_advanced.j2 index 488ca2fd7..bfbb20ec1 100644 --- a/freqtrade/templates/strategy_subtemplates/strategy_methods_advanced.j2 +++ b/freqtrade/templates/strategy_subtemplates/strategy_methods_advanced.j2 @@ -1,5 +1,5 @@ -def bot_loop_start(self, **kwargs) -> None: +def bot_loop_start(self, current_time: datetime, **kwargs) -> None: """ Called at the start of the bot iteration (one loop). Might be used to perform pair-independent tasks @@ -8,6 +8,7 @@ def bot_loop_start(self, **kwargs) -> None: For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/ When not implemented by a strategy, this simply does nothing. + :param current_time: datetime object, containing the current datetime :param **kwargs: Ensure to keep this here so updates to this won't break your strategy. """ pass diff --git a/freqtrade/vendor/qtpylib/indicators.py b/freqtrade/vendor/qtpylib/indicators.py index 3da4f038d..63797d462 100644 --- a/freqtrade/vendor/qtpylib/indicators.py +++ b/freqtrade/vendor/qtpylib/indicators.py @@ -1,5 +1,3 @@ -# -*- coding: utf-8 -*- -# # QTPyLib: Quantitative Trading Python Library # https://github.com/ranaroussi/qtpylib # @@ -18,7 +16,6 @@ # limitations under the License. # -import sys import warnings from datetime import datetime, timedelta @@ -27,11 +24,6 @@ import pandas as pd from pandas.core.base import PandasObject -# ============================================= -# check min, python version -if sys.version_info < (3, 4): - raise SystemError("QTPyLib requires Python version >= 3.4") - # ============================================= warnings.simplefilter(action="ignore", category=RuntimeWarning) diff --git a/freqtrade/wallets.py b/freqtrade/wallets.py index 8dcc92af4..6f86398f3 100644 --- a/freqtrade/wallets.py +++ b/freqtrade/wallets.py @@ -11,6 +11,7 @@ from freqtrade.constants import UNLIMITED_STAKE_AMOUNT, Config from freqtrade.enums import RunMode, TradingMode from freqtrade.exceptions import DependencyException from freqtrade.exchange import Exchange +from freqtrade.misc import safe_value_fallback from freqtrade.persistence import LocalTrade, Trade @@ -148,7 +149,7 @@ class Wallets: # Position is not open ... continue size = self._exchange._contracts_to_amount(symbol, position['contracts']) - collateral = position['collateral'] or 0.0 + collateral = safe_value_fallback(position, 'collateral', 'initialMargin', 0.0) leverage = position['leverage'] self._positions[symbol] = PositionWallet( symbol, position=size, diff --git a/pyproject.toml b/pyproject.toml index 71687961d..28de6a1d8 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,3 +1,7 @@ +[build-system] +requires = ["setuptools >= 46.4.0", "wheel"] +build-backend = "setuptools.build_meta" + [tool.black] line-length = 100 exclude = ''' @@ -48,10 +52,6 @@ ignore_errors = true module = "telegram.*" implicit_optional = true -[build-system] -requires = ["setuptools >= 46.4.0", "wheel"] -build-backend = "setuptools.build_meta" - [tool.pyright] include = ["freqtrade"] exclude = [ @@ -68,15 +68,22 @@ target-version = "py38" extend-select = [ "C90", # mccabe # "N", # pep8-naming - # "UP", # pyupgrade + "F", # pyflakes + "E", # pycodestyle + "W", # pycodestyle + "UP", # pyupgrade "TID", # flake8-tidy-imports # "EXE", # flake8-executable "YTT", # flake8-2020 + # "S", # flake8-bandit # "DTZ", # flake8-datetimez # "RSE", # flake8-raise # "TCH", # flake8-type-checking - "PTH", # flake8-use-pathlib + "PTH", # flake8-use-pathlib ] [tool.ruff.mccabe] max-complexity = 12 + +[tool.ruff.per-file-ignores] +"tests/*" = ["S"] diff --git a/requirements-dev.txt b/requirements-dev.txt index 6d076777f..ea75bb8f2 100644 --- a/requirements-dev.txt +++ b/requirements-dev.txt @@ -7,26 +7,24 @@ -r docs/requirements-docs.txt coveralls==3.3.1 -ruff==0.0.255 -mypy==1.1.1 -pre-commit==3.1.1 -pytest==7.2.2 -pytest-asyncio==0.20.3 +ruff==0.0.262 +mypy==1.2.0 +pre-commit==3.2.2 +pytest==7.3.1 +pytest-asyncio==0.21.0 pytest-cov==4.0.0 pytest-mock==3.10.0 pytest-random-order==1.1.0 isort==5.12.0 # For datetime mocking time-machine==2.9.0 -# fastapi testing -httpx==0.23.3 # Convert jupyter notebooks to markdown documents -nbconvert==7.2.9 +nbconvert==7.3.1 # mypy types -types-cachetools==5.3.0.4 +types-cachetools==5.3.0.5 types-filelock==3.2.7 -types-requests==2.28.11.15 -types-tabulate==0.9.0.1 -types-python-dateutil==2.8.19.10 +types-requests==2.28.11.17 +types-tabulate==0.9.0.2 +types-python-dateutil==2.8.19.12 diff --git a/requirements-freqai-rl.txt b/requirements-freqai-rl.txt index 4de7d8fab..f4e1e557b 100644 --- a/requirements-freqai-rl.txt +++ b/requirements-freqai-rl.txt @@ -8,3 +8,5 @@ sb3-contrib==1.7.0; python_version < '3.11' # Gym is forced to this version by stable-baselines3. setuptools==65.5.1 # Should be removed when gym is fixed. gym==0.21; python_version < '3.11' +# Progress bar for stable-baselines3 and sb3-contrib +tqdm==4.65.0; python_version < '3.11' diff --git a/requirements-freqai.txt b/requirements-freqai.txt index bc0be85e5..51396ab91 100644 --- a/requirements-freqai.txt +++ b/requirements-freqai.txt @@ -5,7 +5,7 @@ # Required for freqai scikit-learn==1.1.3 joblib==1.2.0 -catboost==1.1.1; platform_machine != 'aarch64' and python_version < '3.11' +catboost==1.1.1; platform_machine != 'aarch64' and 'arm' not in platform_machine and python_version < '3.11' lightgbm==3.3.5 -xgboost==1.7.4 -tensorboard==2.12.0 +xgboost==1.7.5 +tensorboard==2.12.2 diff --git a/requirements-hyperopt.txt b/requirements-hyperopt.txt index 904b5d661..87b1fd3c8 100644 --- a/requirements-hyperopt.txt +++ b/requirements-hyperopt.txt @@ -5,5 +5,4 @@ scipy==1.10.1 scikit-learn==1.1.3 scikit-optimize==0.9.0 -filelock==3.9.0 -progressbar2==4.2.0 +filelock==3.12.0 diff --git a/requirements-plot.txt b/requirements-plot.txt index ad7bade95..8b9ad5bc4 100644 --- a/requirements-plot.txt +++ b/requirements-plot.txt @@ -1,4 +1,4 @@ # Include all requirements to run the bot. -r requirements.txt -plotly==5.13.1 +plotly==5.14.1 diff --git a/requirements.txt b/requirements.txt index 9e17424f5..7c646aec6 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,18 +1,20 @@ -numpy==1.24.2 -pandas==1.5.3 +numpy==1.24.3 +pandas==2.0.1 pandas-ta==0.3.14b -ccxt==2.9.12 -cryptography==39.0.2 +ccxt==3.0.75 +cryptography==40.0.2 aiohttp==3.8.4 -SQLAlchemy==2.0.5.post1 -python-telegram-bot==13.15 +SQLAlchemy==2.0.10 +python-telegram-bot==20.2 +# can't be hard-pinned due to telegram-bot pinning httpx with ~ +httpx>=0.23.3 arrow==1.2.3 cachetools==4.2.2 requests==2.28.2 urllib3==1.26.15 jsonschema==4.17.3 -TA-Lib==0.4.25 +TA-Lib==0.4.26 technical==1.4.0 tabulate==0.9.0 pycoingecko==3.1.0 @@ -20,26 +22,27 @@ jinja2==3.1.2 tables==3.8.0 blosc==1.11.1 joblib==1.2.0 +rich==13.3.4 pyarrow==11.0.0; platform_machine != 'armv7l' # find first, C search in arrays py_find_1st==1.1.5 # Load ticker files 30% faster -python-rapidjson==1.9 +python-rapidjson==1.10 # Properly format api responses -orjson==3.8.7 +orjson==3.8.10 # Notify systemd sdnotify==0.3.2 # API Server -fastapi==0.94.0 -pydantic==1.10.6 -uvicorn==0.21.0 +fastapi==0.95.1 +pydantic==1.10.7 +uvicorn==0.21.1 pyjwt==2.6.0 aiofiles==23.1.0 -psutil==5.9.4 +psutil==5.9.5 # Support for colorized terminal output colorama==0.4.6 @@ -50,10 +53,10 @@ prompt-toolkit==3.0.38 python-dateutil==2.8.2 #Futures -schedule==1.1.0 +schedule==1.2.0 #WS Messages -websockets==10.4 +websockets==11.0.2 janus==1.0.0 -ast-comments==1.0.0 +ast-comments==1.0.1 diff --git a/setup.py b/setup.py index edd7b243b..048dc066d 100644 --- a/setup.py +++ b/setup.py @@ -8,7 +8,6 @@ hyperopt = [ 'scikit-learn', 'scikit-optimize>=0.7.0', 'filelock', - 'progressbar2', ] freqai = [ @@ -59,7 +58,7 @@ setup( install_requires=[ # from requirements.txt 'ccxt>=2.6.26', - 'SQLAlchemy', + 'SQLAlchemy>=2.0.6', 'python-telegram-bot>=13.4', 'arrow>=0.17.0', 'cachetools', @@ -82,6 +81,7 @@ setup( 'numpy', 'pandas', 'joblib>=1.2.0', + 'rich', 'pyarrow; platform_machine != "armv7l"', 'fastapi', 'pydantic>=1.8.0', diff --git a/setup.sh b/setup.sh index a9ff36536..d46569a53 100755 --- a/setup.sh +++ b/setup.sh @@ -50,7 +50,7 @@ function updateenv() { SYS_ARCH=$(uname -m) echo "pip install in-progress. Please wait..." # Setuptools 65.5.0 is the last version that can install gym==0.21.0 - ${PYTHON} -m pip install --upgrade pip wheel setuptools==65.5.1 + ${PYTHON} -m pip install --upgrade pip==23.0.1 wheel==0.38.4 setuptools==65.5.1 REQUIREMENTS_HYPEROPT="" REQUIREMENTS_PLOT="" REQUIREMENTS_FREQAI="" @@ -85,7 +85,7 @@ function updateenv() { if [[ $REPLY =~ ^[Yy]$ ]] then REQUIREMENTS_FREQAI="-r requirements-freqai.txt --use-pep517" - read -p "Do you also want dependencies for freqai-rl (~700mb additional space required) [y/N]? " + read -p "Do you also want dependencies for freqai-rl or PyTorch (~700mb additional space required) [y/N]? " if [[ $REPLY =~ ^[Yy]$ ]] then REQUIREMENTS_FREQAI="-r requirements-freqai-rl.txt" diff --git a/tests/conftest.py b/tests/conftest.py index 0aa6e70a8..1c737b3aa 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -3,7 +3,7 @@ import json import logging import re from copy import deepcopy -from datetime import datetime, timedelta +from datetime import timedelta from pathlib import Path from typing import Optional from unittest.mock import MagicMock, Mock, PropertyMock @@ -12,7 +12,6 @@ import arrow import numpy as np import pandas as pd import pytest -from telegram import Chat, Message, Update from freqtrade import constants from freqtrade.commands import Arguments @@ -504,7 +503,7 @@ def get_default_conf(testdatadir): {"method": "StaticPairList"} ], "telegram": { - "enabled": True, + "enabled": False, "token": "token", "chat_id": "0", "notification_settings": {}, @@ -550,13 +549,6 @@ def get_default_conf_usdt(testdatadir): return configuration -@pytest.fixture -def update(): - _update = Update(0) - _update.message = Message(0, datetime.utcnow(), Chat(0, 0)) - return _update - - @pytest.fixture def fee(): return MagicMock(return_value=0.0025) diff --git a/tests/data/test_btanalysis.py b/tests/data/test_btanalysis.py index 345e3c299..2c5515f7c 100644 --- a/tests/data/test_btanalysis.py +++ b/tests/data/test_btanalysis.py @@ -98,7 +98,7 @@ def test_load_backtest_data_new_format(testdatadir): assert bt_data.equals(bt_data3) with pytest.raises(ValueError, match=r"File .* does not exist\."): - load_backtest_data(str("filename") + "nofile") + load_backtest_data("filename" + "nofile") with pytest.raises(ValueError, match=r"Unknown dataformat."): load_backtest_data(testdatadir / "backtest_results" / LAST_BT_RESULT_FN) diff --git a/tests/data/test_datahandler.py b/tests/data/test_datahandler.py index f673ede6e..f19b15455 100644 --- a/tests/data/test_datahandler.py +++ b/tests/data/test_datahandler.py @@ -252,7 +252,7 @@ def test_datahandler__check_empty_df(testdatadir, caplog): assert log_has_re(expected_text, caplog) -@pytest.mark.parametrize('datahandler', ['feather', 'parquet']) +@pytest.mark.parametrize('datahandler', ['parquet']) def test_datahandler_trades_not_supported(datahandler, testdatadir, ): dh = get_datahandler(testdatadir, datahandler) with pytest.raises(NotImplementedError): @@ -496,6 +496,58 @@ def test_hdf5datahandler_ohlcv_purge(mocker, testdatadir): assert unlinkmock.call_count == 2 +def test_featherdatahandler_trades_load(testdatadir): + dh = get_datahandler(testdatadir, 'feather') + trades = dh.trades_load('XRP/ETH') + assert isinstance(trades, list) + assert trades[0][0] == 1570752011620 + assert trades[-1][-1] == 0.1986231 + + trades1 = dh.trades_load('UNITTEST/NONEXIST') + assert trades1 == [] + + +def test_featherdatahandler_trades_store(testdatadir, tmpdir): + tmpdir1 = Path(tmpdir) + dh = get_datahandler(testdatadir, 'feather') + trades = dh.trades_load('XRP/ETH') + + dh1 = get_datahandler(tmpdir1, 'feather') + dh1.trades_store('XRP/NEW', trades) + file = tmpdir1 / 'XRP_NEW-trades.feather' + assert file.is_file() + # Load trades back + trades_new = dh1.trades_load('XRP/NEW') + + assert len(trades_new) == len(trades) + assert trades[0][0] == trades_new[0][0] + assert trades[0][1] == trades_new[0][1] + # assert trades[0][2] == trades_new[0][2] # This is nan - so comparison does not make sense + assert trades[0][3] == trades_new[0][3] + assert trades[0][4] == trades_new[0][4] + assert trades[0][5] == trades_new[0][5] + assert trades[0][6] == trades_new[0][6] + assert trades[-1][0] == trades_new[-1][0] + assert trades[-1][1] == trades_new[-1][1] + # assert trades[-1][2] == trades_new[-1][2] # This is nan - so comparison does not make sense + assert trades[-1][3] == trades_new[-1][3] + assert trades[-1][4] == trades_new[-1][4] + assert trades[-1][5] == trades_new[-1][5] + assert trades[-1][6] == trades_new[-1][6] + + +def test_featherdatahandler_trades_purge(mocker, testdatadir): + mocker.patch.object(Path, "exists", MagicMock(return_value=False)) + unlinkmock = mocker.patch.object(Path, "unlink", MagicMock()) + dh = get_datahandler(testdatadir, 'feather') + assert not dh.trades_purge('UNITTEST/NONEXIST') + assert unlinkmock.call_count == 0 + + mocker.patch.object(Path, "exists", MagicMock(return_value=True)) + assert dh.trades_purge('UNITTEST/NONEXIST') + assert unlinkmock.call_count == 1 + + def test_gethandlerclass(): cl = get_datahandlerclass('json') assert cl == JsonDataHandler diff --git a/tests/data/test_history.py b/tests/data/test_history.py index c967f0c89..24ad8bcc9 100644 --- a/tests/data/test_history.py +++ b/tests/data/test_history.py @@ -409,7 +409,7 @@ def test_init_with_refresh(default_conf, mocker) -> None: def test_file_dump_json_tofile(testdatadir) -> None: - file = testdatadir / 'test_{id}.json'.format(id=str(uuid.uuid4())) + file = testdatadir / f'test_{uuid.uuid4()}.json' data = {'bar': 'foo'} # check the file we will create does not exist diff --git a/tests/exchange/test_binance.py b/tests/exchange/test_binance.py index 616910682..d44dae00d 100644 --- a/tests/exchange/test_binance.py +++ b/tests/exchange/test_binance.py @@ -11,6 +11,19 @@ from tests.conftest import EXMS, get_mock_coro, get_patched_exchange, log_has_re from tests.exchange.test_exchange import ccxt_exceptionhandlers +@pytest.mark.parametrize('side,type,time_in_force,expected', [ + ('buy', 'limit', 'gtc', {'timeInForce': 'GTC'}), + ('buy', 'limit', 'IOC', {'timeInForce': 'IOC'}), + ('buy', 'market', 'IOC', {}), + ('buy', 'limit', 'PO', {'timeInForce': 'PO'}), + ('sell', 'limit', 'PO', {'timeInForce': 'PO'}), + ('sell', 'market', 'PO', {}), + ]) +def test__get_params_binance(default_conf, mocker, side, type, time_in_force, expected): + exchange = get_patched_exchange(mocker, default_conf, id='binance') + assert exchange._get_params(side, type, 1, False, time_in_force) == expected + + @pytest.mark.parametrize('trademode', [TradingMode.FUTURES, TradingMode.SPOT]) @pytest.mark.parametrize('limitratio,expected,side', [ (None, 220 * 0.99, "sell"), @@ -35,11 +48,11 @@ def test_create_stoploss_order_binance(default_conf, mocker, limitratio, expecte default_conf['margin_mode'] = MarginMode.ISOLATED default_conf['trading_mode'] = trademode mocker.patch(f'{EXMS}.amount_to_precision', lambda s, x, y: y) - mocker.patch(f'{EXMS}.price_to_precision', lambda s, x, y: y) + mocker.patch(f'{EXMS}.price_to_precision', lambda s, x, y, **kwargs: y) exchange = get_patched_exchange(mocker, default_conf, api_mock, 'binance') - with pytest.raises(OperationalException): + with pytest.raises(InvalidOrderException): order = exchange.create_stoploss( pair='ETH/BTC', amount=1, @@ -114,11 +127,11 @@ def test_create_stoploss_order_dry_run_binance(default_conf, mocker): order_type = 'stop_loss_limit' default_conf['dry_run'] = True mocker.patch(f'{EXMS}.amount_to_precision', lambda s, x, y: y) - mocker.patch(f'{EXMS}.price_to_precision', lambda s, x, y: y) + mocker.patch(f'{EXMS}.price_to_precision', lambda s, x, y, **kwargs: y) exchange = get_patched_exchange(mocker, default_conf, api_mock, 'binance') - with pytest.raises(OperationalException): + with pytest.raises(InvalidOrderException): order = exchange.create_stoploss( pair='ETH/BTC', amount=1, @@ -542,7 +555,6 @@ def test__set_leverage_binance(mocker, default_conf): "set_leverage", pair="XRP/USDT", leverage=5.0, - trading_mode=TradingMode.FUTURES ) diff --git a/tests/exchange/test_ccxt_compat.py b/tests/exchange/test_ccxt_compat.py index 872cf5059..60855ca54 100644 --- a/tests/exchange/test_ccxt_compat.py +++ b/tests/exchange/test_ccxt_compat.py @@ -37,7 +37,7 @@ EXCHANGES = { 'stake_currency': 'USDT', 'use_ci_proxy': True, 'hasQuoteVolume': True, - 'timeframe': '5m', + 'timeframe': '1h', 'futures': True, 'futures_pair': 'BTC/USDT:USDT', 'hasQuoteVolumeFutures': True, @@ -66,7 +66,7 @@ EXCHANGES = { 'pair': 'BTC/USDT', 'stake_currency': 'USDT', 'hasQuoteVolume': True, - 'timeframe': '5m', + 'timeframe': '1h', 'futures': False, 'sample_order': [{ "symbol": "SOLUSDT", @@ -91,7 +91,7 @@ EXCHANGES = { 'pair': 'BTC/USDT', 'stake_currency': 'USDT', 'hasQuoteVolume': True, - 'timeframe': '5m', + 'timeframe': '1h', 'leverage_tiers_public': False, 'leverage_in_spot_market': True, }, @@ -99,7 +99,7 @@ EXCHANGES = { 'pair': 'XRP/USDT', 'stake_currency': 'USDT', 'hasQuoteVolume': True, - 'timeframe': '5m', + 'timeframe': '1h', 'leverage_tiers_public': False, 'leverage_in_spot_market': True, 'sample_order': [ @@ -141,7 +141,7 @@ EXCHANGES = { 'pair': 'BTC/USDT', 'stake_currency': 'USDT', 'hasQuoteVolume': True, - 'timeframe': '5m', + 'timeframe': '1h', 'futures': True, 'futures_pair': 'BTC/USDT:USDT', 'hasQuoteVolumeFutures': True, @@ -215,7 +215,7 @@ EXCHANGES = { 'pair': 'BTC/USDT', 'stake_currency': 'USDT', 'hasQuoteVolume': True, - 'timeframe': '5m', + 'timeframe': '1h', 'futures': True, 'futures_pair': 'BTC/USDT:USDT', 'hasQuoteVolumeFutures': False, @@ -226,7 +226,7 @@ EXCHANGES = { 'pair': 'BTC/USDT', 'stake_currency': 'USDT', 'hasQuoteVolume': True, - 'timeframe': '5m', + 'timeframe': '1h', 'futures_pair': 'BTC/USDT:USDT', 'futures': True, 'leverage_tiers_public': True, @@ -253,14 +253,14 @@ EXCHANGES = { 'pair': 'ETH/BTC', 'stake_currency': 'BTC', 'hasQuoteVolume': True, - 'timeframe': '5m', + 'timeframe': '1h', 'futures': False, }, 'bitvavo': { 'pair': 'BTC/EUR', 'stake_currency': 'EUR', 'hasQuoteVolume': True, - 'timeframe': '5m', + 'timeframe': '1h', 'leverage_tiers_public': False, 'leverage_in_spot_market': False, }, @@ -528,9 +528,11 @@ class TestCCXTExchange(): assert res[1] == timeframe assert res[2] == candle_type candles = res[3] - candle_count = exchange.ohlcv_candle_limit(timeframe, candle_type, since_ms) * 0.9 - candle_count1 = (now.timestamp() * 1000 - since_ms) // timeframe_ms - assert len(candles) >= min(candle_count, candle_count1) + factor = 0.9 + candle_count = exchange.ohlcv_candle_limit(timeframe, candle_type, since_ms) * factor + candle_count1 = (now.timestamp() * 1000 - since_ms) // timeframe_ms * factor + assert len(candles) >= min(candle_count, candle_count1), \ + f"{len(candles)} < {candle_count} in {timeframe}, Offset: {offset} {factor}" assert candles[0][0] == since_ms or (since_ms + timeframe_ms) def test_ccxt__async_get_candle_history(self, exchange: EXCHANGE_FIXTURE_TYPE): diff --git a/tests/exchange/test_exchange.py b/tests/exchange/test_exchange.py index d7f6a8b90..b0760944a 100644 --- a/tests/exchange/test_exchange.py +++ b/tests/exchange/test_exchange.py @@ -8,6 +8,7 @@ from unittest.mock import MagicMock, Mock, PropertyMock, patch import arrow import ccxt import pytest +from ccxt import DECIMAL_PLACES, ROUND, ROUND_UP, TICK_SIZE, TRUNCATE from pandas import DataFrame from freqtrade.enums import CandleType, MarginMode, TradingMode @@ -113,18 +114,21 @@ async def async_ccxt_exception(mocker, default_conf, api_mock, fun, mock_ccxt_fu exchange = get_patched_exchange(mocker, default_conf, api_mock) await getattr(exchange, fun)(**kwargs) assert api_mock.__dict__[mock_ccxt_fun].call_count == retries + exchange.close() with pytest.raises(TemporaryError): api_mock.__dict__[mock_ccxt_fun] = MagicMock(side_effect=ccxt.NetworkError("DeadBeef")) exchange = get_patched_exchange(mocker, default_conf, api_mock) await getattr(exchange, fun)(**kwargs) assert api_mock.__dict__[mock_ccxt_fun].call_count == retries + exchange.close() with pytest.raises(OperationalException): api_mock.__dict__[mock_ccxt_fun] = MagicMock(side_effect=ccxt.BaseError("DeadBeef")) exchange = get_patched_exchange(mocker, default_conf, api_mock) await getattr(exchange, fun)(**kwargs) assert api_mock.__dict__[mock_ccxt_fun].call_count == 1 + exchange.close() def test_init(default_conf, mocker, caplog): @@ -312,35 +316,54 @@ def test_amount_to_precision(amount, precision_mode, precision, expected,): assert amount_to_precision(amount, precision, precision_mode) == expected -@pytest.mark.parametrize("price,precision_mode,precision,expected", [ - (2.34559, 2, 4, 2.3456), - (2.34559, 2, 5, 2.34559), - (2.34559, 2, 3, 2.346), - (2.9999, 2, 3, 3.000), - (2.9909, 2, 3, 2.991), - # Tests for Tick_size - (2.34559, 4, 0.0001, 2.3456), - (2.34559, 4, 0.00001, 2.34559), - (2.34559, 4, 0.001, 2.346), - (2.9999, 4, 0.001, 3.000), - (2.9909, 4, 0.001, 2.991), - (2.9909, 4, 0.005, 2.995), - (2.9973, 4, 0.005, 3.0), - (2.9977, 4, 0.005, 3.0), - (234.43, 4, 0.5, 234.5), - (234.53, 4, 0.5, 235.0), - (0.891534, 4, 0.0001, 0.8916), - (64968.89, 4, 0.01, 64968.89), - (0.000000003483, 4, 1e-12, 0.000000003483), - +@pytest.mark.parametrize("price,precision_mode,precision,expected,rounding_mode", [ + # Tests for DECIMAL_PLACES, ROUND_UP + (2.34559, 2, 4, 2.3456, ROUND_UP), + (2.34559, 2, 5, 2.34559, ROUND_UP), + (2.34559, 2, 3, 2.346, ROUND_UP), + (2.9999, 2, 3, 3.000, ROUND_UP), + (2.9909, 2, 3, 2.991, ROUND_UP), + # Tests for DECIMAL_PLACES, ROUND + (2.345600000000001, DECIMAL_PLACES, 4, 2.3456, ROUND), + (2.345551, DECIMAL_PLACES, 4, 2.3456, ROUND), + (2.49, DECIMAL_PLACES, 0, 2., ROUND), + (2.51, DECIMAL_PLACES, 0, 3., ROUND), + (5.1, DECIMAL_PLACES, -1, 10., ROUND), + (4.9, DECIMAL_PLACES, -1, 0., ROUND), + # Tests for TICK_SIZE, ROUND_UP + (2.34559, TICK_SIZE, 0.0001, 2.3456, ROUND_UP), + (2.34559, TICK_SIZE, 0.00001, 2.34559, ROUND_UP), + (2.34559, TICK_SIZE, 0.001, 2.346, ROUND_UP), + (2.9999, TICK_SIZE, 0.001, 3.000, ROUND_UP), + (2.9909, TICK_SIZE, 0.001, 2.991, ROUND_UP), + (2.9909, TICK_SIZE, 0.005, 2.995, ROUND_UP), + (2.9973, TICK_SIZE, 0.005, 3.0, ROUND_UP), + (2.9977, TICK_SIZE, 0.005, 3.0, ROUND_UP), + (234.43, TICK_SIZE, 0.5, 234.5, ROUND_UP), + (234.53, TICK_SIZE, 0.5, 235.0, ROUND_UP), + (0.891534, TICK_SIZE, 0.0001, 0.8916, ROUND_UP), + (64968.89, TICK_SIZE, 0.01, 64968.89, ROUND_UP), + (0.000000003483, TICK_SIZE, 1e-12, 0.000000003483, ROUND_UP), + # Tests for TICK_SIZE, ROUND + (2.49, TICK_SIZE, 1., 2., ROUND), + (2.51, TICK_SIZE, 1., 3., ROUND), + (2.000000051, TICK_SIZE, 0.0000001, 2.0000001, ROUND), + (2.000000049, TICK_SIZE, 0.0000001, 2., ROUND), + (2.9909, TICK_SIZE, 0.005, 2.990, ROUND), + (2.9973, TICK_SIZE, 0.005, 2.995, ROUND), + (2.9977, TICK_SIZE, 0.005, 3.0, ROUND), + (234.24, TICK_SIZE, 0.5, 234., ROUND), + (234.26, TICK_SIZE, 0.5, 234.5, ROUND), + # Tests for TRUNCATTE + (2.34559, 2, 4, 2.3455, TRUNCATE), + (2.34559, 2, 5, 2.34559, TRUNCATE), + (2.34559, 2, 3, 2.345, TRUNCATE), + (2.9999, 2, 3, 2.999, TRUNCATE), + (2.9909, 2, 3, 2.990, TRUNCATE), ]) -def test_price_to_precision(price, precision_mode, precision, expected): - # digits counting mode - # DECIMAL_PLACES = 2 - # SIGNIFICANT_DIGITS = 3 - # TICK_SIZE = 4 - - assert price_to_precision(price, precision, precision_mode) == expected +def test_price_to_precision(price, precision_mode, precision, expected, rounding_mode): + assert price_to_precision( + price, precision, precision_mode, rounding_mode=rounding_mode) == expected @pytest.mark.parametrize("price,precision_mode,precision,expected", [ @@ -414,7 +437,7 @@ def test__get_stake_amount_limit(mocker, default_conf) -> None: } mocker.patch(f'{EXMS}.markets', PropertyMock(return_value=markets)) result = exchange.get_min_pair_stake_amount('ETH/BTC', 2, stoploss) - expected_result = 2 * 2 * (1 + 0.05) / (1 - abs(stoploss)) + expected_result = 2 * 2 * (1 + 0.05) assert pytest.approx(result) == expected_result # With Leverage result = exchange.get_min_pair_stake_amount('ETH/BTC', 2, stoploss, 5.0) @@ -423,14 +446,14 @@ def test__get_stake_amount_limit(mocker, default_conf) -> None: result = exchange.get_max_pair_stake_amount('ETH/BTC', 2) assert result == 20000 - # min amount and cost are set (cost is minimal) + # min amount and cost are set (cost is minimal and therefore ignored) markets["ETH/BTC"]["limits"] = { 'cost': {'min': 2, 'max': None}, 'amount': {'min': 2, 'max': None}, } mocker.patch(f'{EXMS}.markets', PropertyMock(return_value=markets)) result = exchange.get_min_pair_stake_amount('ETH/BTC', 2, stoploss) - expected_result = max(2, 2 * 2) * (1 + 0.05) / (1 - abs(stoploss)) + expected_result = max(2, 2 * 2) * (1 + 0.05) assert pytest.approx(result) == expected_result # With Leverage result = exchange.get_min_pair_stake_amount('ETH/BTC', 2, stoploss, 10) @@ -473,6 +496,9 @@ def test__get_stake_amount_limit(mocker, default_conf) -> None: result = exchange.get_max_pair_stake_amount('ETH/BTC', 2) assert result == 1000 + result = exchange.get_max_pair_stake_amount('ETH/BTC', 2, 12.0) + assert result == 1000 / 12 + markets["ETH/BTC"]["contractSize"] = '0.01' default_conf['trading_mode'] = 'futures' default_conf['margin_mode'] = 'isolated' @@ -1039,9 +1065,9 @@ def test_validate_ordertypes(default_conf, mocker): ('bybit', 'last', True), ('bybit', 'mark', True), ('bybit', 'index', True), - # ('okx', 'last', True), - # ('okx', 'mark', True), - # ('okx', 'index', True), + ('okx', 'last', True), + ('okx', 'mark', True), + ('okx', 'index', True), ('gate', 'last', True), ('gate', 'mark', True), ('gate', 'index', True), @@ -1229,9 +1255,10 @@ def test_create_dry_run_order_fees( ("buy", 29.563, True, True), ("sell", 21.563, True, True), ]) +@pytest.mark.parametrize("leverage", [1, 2, 5]) @pytest.mark.parametrize("exchange_name", EXCHANGES) def test_create_dry_run_order_limit_fill(default_conf, mocker, side, price, filled, caplog, - exchange_name, order_book_l2_usd, converted): + exchange_name, order_book_l2_usd, converted, leverage): default_conf['dry_run'] = True exchange = get_patched_exchange(mocker, default_conf, id=exchange_name) mocker.patch.multiple(EXMS, @@ -1245,7 +1272,7 @@ def test_create_dry_run_order_limit_fill(default_conf, mocker, side, price, fill side=side, amount=1, rate=price, - leverage=1.0 + leverage=leverage, ) assert order_book_l2_usd.call_count == 1 assert 'id' in order @@ -1269,6 +1296,7 @@ def test_create_dry_run_order_limit_fill(default_conf, mocker, side, price, fill assert order_book_l2_usd.call_count == (1 if not filled else 0) assert order_closed['status'] == ('open' if not filled else 'closed') assert order_closed['filled'] == (0 if not filled else 1) + assert order_closed['cost'] == 1 * order_closed['average'] order_book_l2_usd.reset_mock() @@ -1291,9 +1319,10 @@ def test_create_dry_run_order_limit_fill(default_conf, mocker, side, price, fill ("sell", 25.564, 1000, 25.5555), # More than orderbook return ("sell", 27, 10000, 25.65), # max-slippage 5% ]) +@pytest.mark.parametrize("leverage", [1, 2, 5]) @pytest.mark.parametrize("exchange_name", EXCHANGES) def test_create_dry_run_order_market_fill(default_conf, mocker, side, rate, amount, endprice, - exchange_name, order_book_l2_usd): + exchange_name, order_book_l2_usd, leverage): default_conf['dry_run'] = True exchange = get_patched_exchange(mocker, default_conf, id=exchange_name) mocker.patch.multiple(EXMS, @@ -1307,7 +1336,7 @@ def test_create_dry_run_order_market_fill(default_conf, mocker, side, rate, amou side=side, amount=amount, rate=rate, - leverage=1.0 + leverage=leverage, ) assert 'id' in order assert f'dry_run_{side}_' in order["id"] @@ -1316,6 +1345,8 @@ def test_create_dry_run_order_market_fill(default_conf, mocker, side, rate, amou assert order["symbol"] == "LTC/USDT" assert order['status'] == 'closed' assert order['filled'] == amount + assert order['amount'] == amount + assert pytest.approx(order['cost']) == amount * order['average'] assert round(order["average"], 4) == round(endprice, 4) @@ -1436,7 +1467,10 @@ def test_buy_prod(default_conf, mocker, exchange_name): assert api_mock.create_order.call_args[0][1] == order_type assert api_mock.create_order.call_args[0][2] == 'buy' assert api_mock.create_order.call_args[0][3] == 1 - assert api_mock.create_order.call_args[0][4] is None + if exchange._order_needs_price(order_type): + assert api_mock.create_order.call_args[0][4] == 200 + else: + assert api_mock.create_order.call_args[0][4] is None api_mock.create_order.reset_mock() order_type = 'limit' @@ -1541,7 +1575,10 @@ def test_buy_considers_time_in_force(default_conf, mocker, exchange_name): assert api_mock.create_order.call_args[0][1] == order_type assert api_mock.create_order.call_args[0][2] == 'buy' assert api_mock.create_order.call_args[0][3] == 1 - assert api_mock.create_order.call_args[0][4] is None + if exchange._order_needs_price(order_type): + assert api_mock.create_order.call_args[0][4] == 200 + else: + assert api_mock.create_order.call_args[0][4] is None # Market orders should not send timeInForce!! assert "timeInForce" not in api_mock.create_order.call_args[0][5] @@ -1585,7 +1622,10 @@ def test_sell_prod(default_conf, mocker, exchange_name): assert api_mock.create_order.call_args[0][1] == order_type assert api_mock.create_order.call_args[0][2] == 'sell' assert api_mock.create_order.call_args[0][3] == 1 - assert api_mock.create_order.call_args[0][4] is None + if exchange._order_needs_price(order_type): + assert api_mock.create_order.call_args[0][4] == 200 + else: + assert api_mock.create_order.call_args[0][4] is None api_mock.create_order.reset_mock() order_type = 'limit' @@ -1679,7 +1719,10 @@ def test_sell_considers_time_in_force(default_conf, mocker, exchange_name): assert api_mock.create_order.call_args[0][1] == order_type assert api_mock.create_order.call_args[0][2] == 'sell' assert api_mock.create_order.call_args[0][3] == 1 - assert api_mock.create_order.call_args[0][4] is None + if exchange._order_needs_price(order_type): + assert api_mock.create_order.call_args[0][4] == 200 + else: + assert api_mock.create_order.call_args[0][4] is None # Market orders should not send timeInForce!! assert "timeInForce" not in api_mock.create_order.call_args[0][5] @@ -2248,7 +2291,6 @@ def test_refresh_latest_ohlcv_cache(mocker, default_conf, candle_type, time_mach assert res[pair2].at[0, 'open'] -@pytest.mark.asyncio @pytest.mark.parametrize("exchange_name", EXCHANGES) async def test__async_get_candle_history(default_conf, mocker, caplog, exchange_name): ohlcv = [ @@ -2277,7 +2319,7 @@ async def test__async_get_candle_history(default_conf, mocker, caplog, exchange_ assert res[3] == ohlcv assert exchange._api_async.fetch_ohlcv.call_count == 1 assert not log_has(f"Using cached candle (OHLCV) data for {pair} ...", caplog) - + exchange.close() # exchange = Exchange(default_conf) await async_ccxt_exception(mocker, default_conf, MagicMock(), "_async_get_candle_history", "fetch_ohlcv", @@ -2292,15 +2334,17 @@ async def test__async_get_candle_history(default_conf, mocker, caplog, exchange_ await exchange._async_get_candle_history(pair, "5m", CandleType.SPOT, (arrow.utcnow().int_timestamp - 2000) * 1000) + exchange.close() + with pytest.raises(OperationalException, match=r'Exchange.* does not support fetching ' r'historical candle \(OHLCV\) data\..*'): api_mock.fetch_ohlcv = MagicMock(side_effect=ccxt.NotSupported("Not supported")) exchange = get_patched_exchange(mocker, default_conf, api_mock, id=exchange_name) await exchange._async_get_candle_history(pair, "5m", CandleType.SPOT, (arrow.utcnow().int_timestamp - 2000) * 1000) + exchange.close() -@pytest.mark.asyncio async def test__async_kucoin_get_candle_history(default_conf, mocker, caplog): from freqtrade.exchange.common import _reset_logging_mixin _reset_logging_mixin() @@ -2341,9 +2385,9 @@ async def test__async_kucoin_get_candle_history(default_conf, mocker, caplog): # Expect the "returned exception" message 12 times (4 retries * 3 (loop)) assert num_log_has_re(msg, caplog) == 12 assert num_log_has_re(msg2, caplog) == 9 + exchange.close() -@pytest.mark.asyncio async def test__async_get_candle_history_empty(default_conf, mocker, caplog): """ Test empty exchange result """ ohlcv = [] @@ -2363,6 +2407,7 @@ async def test__async_get_candle_history_empty(default_conf, mocker, caplog): assert res[2] == CandleType.SPOT assert res[3] == ohlcv assert exchange._api_async.fetch_ohlcv.call_count == 1 + exchange.close() def test_refresh_latest_ohlcv_inv_result(default_conf, mocker, caplog): @@ -2757,7 +2802,6 @@ async def test___async_get_candle_history_sort(default_conf, mocker, exchange_na assert res_ohlcv[9][5] == 2.31452783 -@pytest.mark.asyncio @pytest.mark.parametrize("exchange_name", EXCHANGES) async def test__async_fetch_trades(default_conf, mocker, caplog, exchange_name, fetch_trades_result): @@ -2785,8 +2829,8 @@ async def test__async_fetch_trades(default_conf, mocker, caplog, exchange_name, assert exchange._api_async.fetch_trades.call_args[1]['limit'] == 1000 assert exchange._api_async.fetch_trades.call_args[1]['params'] == {'from': '123'} assert log_has_re(f"Fetching trades for pair {pair}, params: .*", caplog) + exchange.close() - exchange = Exchange(default_conf) await async_ccxt_exception(mocker, default_conf, MagicMock(), "_async_fetch_trades", "fetch_trades", pair='ABCD/BTC', since=None) @@ -2796,15 +2840,16 @@ async def test__async_fetch_trades(default_conf, mocker, caplog, exchange_name, api_mock.fetch_trades = MagicMock(side_effect=ccxt.BaseError("Unknown error")) exchange = get_patched_exchange(mocker, default_conf, api_mock, id=exchange_name) await exchange._async_fetch_trades(pair, since=(arrow.utcnow().int_timestamp - 2000) * 1000) + exchange.close() with pytest.raises(OperationalException, match=r'Exchange.* does not support fetching ' r'historical trade data\..*'): api_mock.fetch_trades = MagicMock(side_effect=ccxt.NotSupported("Not supported")) exchange = get_patched_exchange(mocker, default_conf, api_mock, id=exchange_name) await exchange._async_fetch_trades(pair, since=(arrow.utcnow().int_timestamp - 2000) * 1000) + exchange.close() -@pytest.mark.asyncio @pytest.mark.parametrize("exchange_name", EXCHANGES) async def test__async_fetch_trades_contract_size(default_conf, mocker, caplog, exchange_name, fetch_trades_result): @@ -2839,6 +2884,7 @@ async def test__async_fetch_trades_contract_size(default_conf, mocker, caplog, e pair = 'ETH/USDT:USDT' res = await exchange._async_fetch_trades(pair, since=None, params=None) assert res[0][5] == 300 + exchange.close() @pytest.mark.asyncio @@ -3387,7 +3433,7 @@ def test_merge_ft_has_dict(default_conf, mocker): ex = Binance(default_conf) assert ex._ft_has != Exchange._ft_has_default assert ex.get_option('stoploss_on_exchange') - assert ex.get_option('order_time_in_force') == ['GTC', 'FOK', 'IOC'] + assert ex.get_option('order_time_in_force') == ['GTC', 'FOK', 'IOC', 'PO'] assert ex.get_option('trades_pagination') == 'id' assert ex.get_option('trades_pagination_arg') == 'fromId' @@ -3868,29 +3914,6 @@ def test_get_stake_amount_considering_leverage( stake_amount, leverage) == min_stake_with_lev -@pytest.mark.parametrize("exchange_name,trading_mode", [ - ("binance", TradingMode.FUTURES), -]) -def test__set_leverage(mocker, default_conf, exchange_name, trading_mode): - - api_mock = MagicMock() - api_mock.set_leverage = MagicMock() - type(api_mock).has = PropertyMock(return_value={'setLeverage': True}) - default_conf['dry_run'] = False - - ccxt_exceptionhandlers( - mocker, - default_conf, - api_mock, - exchange_name, - "_set_leverage", - "set_leverage", - pair="XRP/USDT", - leverage=5.0, - trading_mode=trading_mode - ) - - @pytest.mark.parametrize("margin_mode", [ (MarginMode.CROSS), (MarginMode.ISOLATED) @@ -4830,7 +4853,6 @@ def test_load_leverage_tiers(mocker, default_conf, leverage_tiers, exchange_name ) -@pytest.mark.asyncio @pytest.mark.parametrize('exchange_name', EXCHANGES) async def test_get_market_leverage_tiers(mocker, default_conf, exchange_name): default_conf['exchange']['name'] = exchange_name @@ -5287,7 +5309,7 @@ def test_stoploss_contract_size(mocker, default_conf, contract_size, order_amoun }) default_conf['dry_run'] = False mocker.patch(f'{EXMS}.amount_to_precision', lambda s, x, y: y) - mocker.patch(f'{EXMS}.price_to_precision', lambda s, x, y: y) + mocker.patch(f'{EXMS}.price_to_precision', lambda s, x, y, **kwargs: y) exchange = get_patched_exchange(mocker, default_conf, api_mock) exchange.get_contract_size = MagicMock(return_value=contract_size) @@ -5307,3 +5329,10 @@ def test_stoploss_contract_size(mocker, default_conf, contract_size, order_amoun assert order['cost'] == 100 assert order['filled'] == 100 assert order['remaining'] == 100 + + +def test_price_to_precision_with_default_conf(default_conf, mocker): + conf = copy.deepcopy(default_conf) + patched_ex = get_patched_exchange(mocker, conf) + prec_price = patched_ex.price_to_precision("XRP/USDT", 1.0000000101) + assert prec_price == 1.00000001 diff --git a/tests/exchange/test_gate.py b/tests/exchange/test_gate.py index db7591a40..3cb5a9a3e 100644 --- a/tests/exchange/test_gate.py +++ b/tests/exchange/test_gate.py @@ -4,42 +4,9 @@ from unittest.mock import MagicMock import pytest from freqtrade.enums import MarginMode, TradingMode -from freqtrade.exceptions import OperationalException -from freqtrade.exchange import Gate -from freqtrade.resolvers.exchange_resolver import ExchangeResolver from tests.conftest import EXMS, get_patched_exchange -def test_validate_order_types_gate(default_conf, mocker): - default_conf['exchange']['name'] = 'gate' - mocker.patch(f'{EXMS}._init_ccxt') - mocker.patch(f'{EXMS}._load_markets', return_value={}) - mocker.patch(f'{EXMS}.validate_pairs') - mocker.patch(f'{EXMS}.validate_timeframes') - mocker.patch(f'{EXMS}.validate_stakecurrency') - mocker.patch(f'{EXMS}.validate_pricing') - mocker.patch(f'{EXMS}.name', 'Gate') - exch = ExchangeResolver.load_exchange('gate', default_conf, True) - assert isinstance(exch, Gate) - - default_conf['order_types'] = { - 'entry': 'market', - 'exit': 'limit', - 'stoploss': 'market', - 'stoploss_on_exchange': False - } - - with pytest.raises(OperationalException, - match=r'Exchange .* does not support market orders.'): - ExchangeResolver.load_exchange('gate', default_conf, True) - - # market-orders supported on futures markets. - default_conf['trading_mode'] = 'futures' - default_conf['margin_mode'] = 'isolated' - ex = ExchangeResolver.load_exchange('gate', default_conf, True) - assert ex - - @pytest.mark.usefixtures("init_persistence") def test_fetch_stoploss_order_gate(default_conf, mocker): exchange = get_patched_exchange(mocker, default_conf, id='gate') diff --git a/tests/exchange/test_huobi.py b/tests/exchange/test_huobi.py index 5e4fd7316..8be8ef8b3 100644 --- a/tests/exchange/test_huobi.py +++ b/tests/exchange/test_huobi.py @@ -4,7 +4,7 @@ from unittest.mock import MagicMock import ccxt import pytest -from freqtrade.exceptions import DependencyException, InvalidOrderException, OperationalException +from freqtrade.exceptions import DependencyException, InvalidOrderException from tests.conftest import EXMS, get_patched_exchange from tests.exchange.test_exchange import ccxt_exceptionhandlers @@ -27,11 +27,11 @@ def test_create_stoploss_order_huobi(default_conf, mocker, limitratio, expected, }) default_conf['dry_run'] = False mocker.patch(f'{EXMS}.amount_to_precision', lambda s, x, y: y) - mocker.patch(f'{EXMS}.price_to_precision', lambda s, x, y: y) + mocker.patch(f'{EXMS}.price_to_precision', lambda s, x, y, **kwargs: y) exchange = get_patched_exchange(mocker, default_conf, api_mock, 'huobi') - with pytest.raises(OperationalException): + with pytest.raises(InvalidOrderException): order = exchange.create_stoploss(pair='ETH/BTC', amount=1, stop_price=190, order_types={'stoploss_on_exchange_limit_ratio': 1.05}, side=side, @@ -80,11 +80,11 @@ def test_create_stoploss_order_dry_run_huobi(default_conf, mocker): order_type = 'stop-limit' default_conf['dry_run'] = True mocker.patch(f'{EXMS}.amount_to_precision', lambda s, x, y: y) - mocker.patch(f'{EXMS}.price_to_precision', lambda s, x, y: y) + mocker.patch(f'{EXMS}.price_to_precision', lambda s, x, y, **kwargs: y) exchange = get_patched_exchange(mocker, default_conf, api_mock, 'huobi') - with pytest.raises(OperationalException): + with pytest.raises(InvalidOrderException): order = exchange.create_stoploss(pair='ETH/BTC', amount=1, stop_price=190, order_types={'stoploss_on_exchange_limit_ratio': 1.05}, side='sell', leverage=1.0) diff --git a/tests/exchange/test_kraken.py b/tests/exchange/test_kraken.py index 40a5a5b38..8fc23b94e 100644 --- a/tests/exchange/test_kraken.py +++ b/tests/exchange/test_kraken.py @@ -29,7 +29,7 @@ def test_buy_kraken_trading_agreement(default_conf, mocker): default_conf['dry_run'] = False mocker.patch(f'{EXMS}.amount_to_precision', lambda s, x, y: y) - mocker.patch(f'{EXMS}.price_to_precision', lambda s, x, y: y) + mocker.patch(f'{EXMS}.price_to_precision', lambda s, x, y, **kwargs: y) exchange = get_patched_exchange(mocker, default_conf, api_mock, id="kraken") order = exchange.create_order( @@ -192,7 +192,7 @@ def test_create_stoploss_order_kraken(default_conf, mocker, ordertype, side, adj default_conf['dry_run'] = False mocker.patch(f'{EXMS}.amount_to_precision', lambda s, x, y: y) - mocker.patch(f'{EXMS}.price_to_precision', lambda s, x, y: y) + mocker.patch(f'{EXMS}.price_to_precision', lambda s, x, y, **kwargs: y) exchange = get_patched_exchange(mocker, default_conf, api_mock, 'kraken') @@ -263,7 +263,7 @@ def test_create_stoploss_order_dry_run_kraken(default_conf, mocker, side): api_mock = MagicMock() default_conf['dry_run'] = True mocker.patch(f'{EXMS}.amount_to_precision', lambda s, x, y: y) - mocker.patch(f'{EXMS}.price_to_precision', lambda s, x, y: y) + mocker.patch(f'{EXMS}.price_to_precision', lambda s, x, y, **kwargs: y) exchange = get_patched_exchange(mocker, default_conf, api_mock, 'kraken') diff --git a/tests/exchange/test_kucoin.py b/tests/exchange/test_kucoin.py index e0bb32b7c..741ee27be 100644 --- a/tests/exchange/test_kucoin.py +++ b/tests/exchange/test_kucoin.py @@ -4,7 +4,7 @@ from unittest.mock import MagicMock import ccxt import pytest -from freqtrade.exceptions import DependencyException, InvalidOrderException, OperationalException +from freqtrade.exceptions import DependencyException, InvalidOrderException from tests.conftest import EXMS, get_patched_exchange from tests.exchange.test_exchange import ccxt_exceptionhandlers @@ -27,11 +27,11 @@ def test_create_stoploss_order_kucoin(default_conf, mocker, limitratio, expected }) default_conf['dry_run'] = False mocker.patch(f'{EXMS}.amount_to_precision', lambda s, x, y: y) - mocker.patch(f'{EXMS}.price_to_precision', lambda s, x, y: y) + mocker.patch(f'{EXMS}.price_to_precision', lambda s, x, y, **kwargs: y) exchange = get_patched_exchange(mocker, default_conf, api_mock, 'kucoin') if order_type == 'limit': - with pytest.raises(OperationalException): + with pytest.raises(InvalidOrderException): order = exchange.create_stoploss(pair='ETH/BTC', amount=1, stop_price=190, order_types={ 'stoploss': order_type, @@ -88,11 +88,11 @@ def test_stoploss_order_dry_run_kucoin(default_conf, mocker): order_type = 'market' default_conf['dry_run'] = True mocker.patch(f'{EXMS}.amount_to_precision', lambda s, x, y: y) - mocker.patch(f'{EXMS}.price_to_precision', lambda s, x, y: y) + mocker.patch(f'{EXMS}.price_to_precision', lambda s, x, y, **kwargs: y) exchange = get_patched_exchange(mocker, default_conf, api_mock, 'kucoin') - with pytest.raises(OperationalException): + with pytest.raises(InvalidOrderException): order = exchange.create_stoploss(pair='ETH/BTC', amount=1, stop_price=190, order_types={'stoploss': 'limit', 'stoploss_on_exchange_limit_ratio': 1.05}, diff --git a/tests/exchange/test_okx.py b/tests/exchange/test_okx.py index fce77f4c7..3824eddb7 100644 --- a/tests/exchange/test_okx.py +++ b/tests/exchange/test_okx.py @@ -1,12 +1,14 @@ from datetime import datetime, timedelta, timezone from pathlib import Path -from unittest.mock import MagicMock, PropertyMock +from unittest.mock import AsyncMock, MagicMock, PropertyMock +import ccxt import pytest from freqtrade.enums import CandleType, MarginMode, TradingMode +from freqtrade.exceptions import RetryableOrderError, TemporaryError from freqtrade.exchange.exchange import timeframe_to_minutes -from tests.conftest import get_mock_coro, get_patched_exchange, log_has +from tests.conftest import EXMS, get_patched_exchange, log_has from tests.exchange.test_exchange import ccxt_exceptionhandlers @@ -276,7 +278,7 @@ def test_load_leverage_tiers_okx(default_conf, mocker, markets, tmpdir, caplog, 'fetchLeverageTiers': False, 'fetchMarketLeverageTiers': True, }) - api_mock.fetch_market_leverage_tiers = get_mock_coro(side_effect=[ + api_mock.fetch_market_leverage_tiers = AsyncMock(side_effect=[ [ { 'tier': 1, @@ -339,6 +341,7 @@ def test_load_leverage_tiers_okx(default_conf, mocker, markets, tmpdir, caplog, } }, ], + TemporaryError("this Failed"), [ { 'tier': 1, @@ -476,3 +479,116 @@ def test_load_leverage_tiers_okx(default_conf, mocker, markets, tmpdir, caplog, exchange.load_leverage_tiers() assert log_has(logmsg, caplog) + + +def test__set_leverage_okx(mocker, default_conf): + + api_mock = MagicMock() + api_mock.set_leverage = MagicMock() + type(api_mock).has = PropertyMock(return_value={'setLeverage': True}) + default_conf['dry_run'] = False + default_conf['trading_mode'] = TradingMode.FUTURES + default_conf['margin_mode'] = MarginMode.ISOLATED + + exchange = get_patched_exchange(mocker, default_conf, api_mock, id="okx") + exchange._lev_prep('BTC/USDT:USDT', 3.2, 'buy') + assert api_mock.set_leverage.call_count == 1 + # Leverage is rounded to 3. + assert api_mock.set_leverage.call_args_list[0][1]['leverage'] == 3.2 + assert api_mock.set_leverage.call_args_list[0][1]['symbol'] == 'BTC/USDT:USDT' + assert api_mock.set_leverage.call_args_list[0][1]['params'] == { + 'mgnMode': 'isolated', + 'posSide': 'net'} + + ccxt_exceptionhandlers( + mocker, + default_conf, + api_mock, + "okx", + "_lev_prep", + "set_leverage", + pair="XRP/USDT:USDT", + leverage=5.0, + side='buy' + ) + + +@pytest.mark.usefixtures("init_persistence") +def test_fetch_stoploss_order_okx(default_conf, mocker): + default_conf['dry_run'] = False + api_mock = MagicMock() + api_mock.fetch_order = MagicMock() + + exchange = get_patched_exchange(mocker, default_conf, api_mock, id='okx') + + exchange.fetch_stoploss_order('1234', 'ETH/BTC') + assert api_mock.fetch_order.call_count == 1 + assert api_mock.fetch_order.call_args_list[0][0][0] == '1234' + assert api_mock.fetch_order.call_args_list[0][0][1] == 'ETH/BTC' + assert api_mock.fetch_order.call_args_list[0][1]['params'] == {'stop': True} + + api_mock.fetch_order = MagicMock(side_effect=ccxt.OrderNotFound) + api_mock.fetch_open_orders = MagicMock(return_value=[]) + api_mock.fetch_closed_orders = MagicMock(return_value=[]) + api_mock.fetch_canceled_orders = MagicMock(creturn_value=[]) + + with pytest.raises(RetryableOrderError): + exchange.fetch_stoploss_order('1234', 'ETH/BTC') + assert api_mock.fetch_order.call_count == 1 + assert api_mock.fetch_open_orders.call_count == 1 + assert api_mock.fetch_closed_orders.call_count == 1 + assert api_mock.fetch_canceled_orders.call_count == 1 + + api_mock.fetch_order.reset_mock() + api_mock.fetch_open_orders.reset_mock() + api_mock.fetch_closed_orders.reset_mock() + api_mock.fetch_canceled_orders.reset_mock() + + api_mock.fetch_closed_orders = MagicMock(return_value=[ + { + 'id': '1234', + 'status': 'closed', + 'info': {'ordId': '123455'} + } + ]) + mocker.patch(f"{EXMS}.fetch_order", MagicMock(return_value={'id': '123455'})) + resp = exchange.fetch_stoploss_order('1234', 'ETH/BTC') + assert api_mock.fetch_order.call_count == 1 + assert api_mock.fetch_open_orders.call_count == 1 + assert api_mock.fetch_closed_orders.call_count == 1 + assert api_mock.fetch_canceled_orders.call_count == 0 + + assert resp['id'] == '1234' + assert resp['id_stop'] == '123455' + assert resp['type'] == 'stoploss' + + default_conf['dry_run'] = True + exchange = get_patched_exchange(mocker, default_conf, api_mock, id='okx') + dro_mock = mocker.patch(f"{EXMS}.fetch_dry_run_order", MagicMock(return_value={'id': '123455'})) + + api_mock.fetch_order.reset_mock() + api_mock.fetch_open_orders.reset_mock() + api_mock.fetch_closed_orders.reset_mock() + api_mock.fetch_canceled_orders.reset_mock() + resp = exchange.fetch_stoploss_order('1234', 'ETH/BTC') + + assert api_mock.fetch_order.call_count == 0 + assert api_mock.fetch_open_orders.call_count == 0 + assert api_mock.fetch_closed_orders.call_count == 0 + assert api_mock.fetch_canceled_orders.call_count == 0 + assert dro_mock.call_count == 1 + + +@pytest.mark.parametrize('sl1,sl2,sl3,side', [ + (1501, 1499, 1501, "sell"), + (1499, 1501, 1499, "buy") +]) +def test_stoploss_adjust_okx(mocker, default_conf, sl1, sl2, sl3, side): + exchange = get_patched_exchange(mocker, default_conf, id='okx') + order = { + 'type': 'stoploss', + 'price': 1500, + 'stopLossPrice': 1500, + } + assert exchange.stoploss_adjust(sl1, order, side=side) + assert not exchange.stoploss_adjust(sl2, order, side=side) diff --git a/tests/freqai/conftest.py b/tests/freqai/conftest.py index e140ee80b..ab4a62a9e 100644 --- a/tests/freqai/conftest.py +++ b/tests/freqai/conftest.py @@ -1,5 +1,6 @@ from copy import deepcopy from pathlib import Path +from typing import Any, Dict from unittest.mock import MagicMock import pytest @@ -85,6 +86,22 @@ def make_rl_config(conf): return conf +def mock_pytorch_mlp_model_training_parameters() -> Dict[str, Any]: + return { + "learning_rate": 3e-4, + "trainer_kwargs": { + "max_iters": 1, + "batch_size": 64, + "max_n_eval_batches": 1, + }, + "model_kwargs": { + "hidden_dim": 32, + "dropout_percent": 0.2, + "n_layer": 1, + } + } + + def get_patched_data_kitchen(mocker, freqaiconf): dk = FreqaiDataKitchen(freqaiconf) return dk @@ -119,6 +136,7 @@ def make_unfiltered_dataframe(mocker, freqai_conf): freqai = strategy.freqai freqai.live = True freqai.dk = FreqaiDataKitchen(freqai_conf) + freqai.dk.live = True freqai.dk.pair = "ADA/BTC" data_load_timerange = TimeRange.parse_timerange("20180110-20180130") freqai.dd.load_all_pair_histories(data_load_timerange, freqai.dk) @@ -152,6 +170,7 @@ def make_data_dictionary(mocker, freqai_conf): freqai = strategy.freqai freqai.live = True freqai.dk = FreqaiDataKitchen(freqai_conf) + freqai.dk.live = True freqai.dk.pair = "ADA/BTC" data_load_timerange = TimeRange.parse_timerange("20180110-20180130") freqai.dd.load_all_pair_histories(data_load_timerange, freqai.dk) diff --git a/tests/freqai/test_freqai_datadrawer.py b/tests/freqai/test_freqai_datadrawer.py index da3b8f9c1..8ab2c75da 100644 --- a/tests/freqai/test_freqai_datadrawer.py +++ b/tests/freqai/test_freqai_datadrawer.py @@ -19,6 +19,7 @@ def test_update_historic_data(mocker, freqai_conf): freqai = strategy.freqai freqai.live = True freqai.dk = FreqaiDataKitchen(freqai_conf) + freqai.dk.live = True timerange = TimeRange.parse_timerange("20180110-20180114") freqai.dd.load_all_pair_histories(timerange, freqai.dk) @@ -41,6 +42,7 @@ def test_load_all_pairs_histories(mocker, freqai_conf): freqai = strategy.freqai freqai.live = True freqai.dk = FreqaiDataKitchen(freqai_conf) + freqai.dk.live = True timerange = TimeRange.parse_timerange("20180110-20180114") freqai.dd.load_all_pair_histories(timerange, freqai.dk) @@ -60,6 +62,7 @@ def test_get_base_and_corr_dataframes(mocker, freqai_conf): freqai = strategy.freqai freqai.live = True freqai.dk = FreqaiDataKitchen(freqai_conf) + freqai.dk.live = True timerange = TimeRange.parse_timerange("20180110-20180114") freqai.dd.load_all_pair_histories(timerange, freqai.dk) sub_timerange = TimeRange.parse_timerange("20180111-20180114") @@ -87,6 +90,7 @@ def test_use_strategy_to_populate_indicators(mocker, freqai_conf): freqai = strategy.freqai freqai.live = True freqai.dk = FreqaiDataKitchen(freqai_conf) + freqai.dk.live = True timerange = TimeRange.parse_timerange("20180110-20180114") freqai.dd.load_all_pair_histories(timerange, freqai.dk) sub_timerange = TimeRange.parse_timerange("20180111-20180114") @@ -103,8 +107,9 @@ def test_get_timerange_from_live_historic_predictions(mocker, freqai_conf): exchange = get_patched_exchange(mocker, freqai_conf) strategy.dp = DataProvider(freqai_conf, exchange) freqai = strategy.freqai - freqai.live = True + freqai.live = False freqai.dk = FreqaiDataKitchen(freqai_conf) + freqai.dk.live = False timerange = TimeRange.parse_timerange("20180126-20180130") freqai.dd.load_all_pair_histories(timerange, freqai.dk) sub_timerange = TimeRange.parse_timerange("20180128-20180130") diff --git a/tests/freqai/test_freqai_datakitchen.py b/tests/freqai/test_freqai_datakitchen.py index 95665a775..3f0fc697d 100644 --- a/tests/freqai/test_freqai_datakitchen.py +++ b/tests/freqai/test_freqai_datakitchen.py @@ -180,6 +180,7 @@ def test_get_full_model_path(mocker, freqai_conf, model): freqai = strategy.freqai freqai.live = True freqai.dk = FreqaiDataKitchen(freqai_conf) + freqai.dk.live = True timerange = TimeRange.parse_timerange("20180110-20180130") freqai.dd.load_all_pair_histories(timerange, freqai.dk) diff --git a/tests/freqai/test_freqai_interface.py b/tests/freqai/test_freqai_interface.py index 3b370aea4..7346191db 100644 --- a/tests/freqai/test_freqai_interface.py +++ b/tests/freqai/test_freqai_interface.py @@ -15,7 +15,8 @@ from freqtrade.optimize.backtesting import Backtesting from freqtrade.persistence import Trade from freqtrade.plugins.pairlistmanager import PairListManager from tests.conftest import EXMS, create_mock_trades, get_patched_exchange, log_has_re -from tests.freqai.conftest import get_patched_freqai_strategy, make_rl_config +from tests.freqai.conftest import (get_patched_freqai_strategy, make_rl_config, + mock_pytorch_mlp_model_training_parameters) def is_py11() -> bool: @@ -34,13 +35,14 @@ def is_mac() -> bool: def can_run_model(model: str) -> None: if (is_arm() or is_py11()) and "Catboost" in model: - pytest.skip("CatBoost is not supported on ARM") + pytest.skip("CatBoost is not supported on ARM.") - if is_mac() and not is_arm() and 'Reinforcement' in model: - pytest.skip("Reinforcement learning module not available on intel based Mac OS") + is_pytorch_model = 'Reinforcement' in model or 'PyTorch' in model + if is_pytorch_model and is_mac() and not is_arm(): + pytest.skip("Reinforcement learning / PyTorch module not available on intel based Mac OS.") - if is_py11() and 'Reinforcement' in model: - pytest.skip("Reinforcement learning currently not available on python 3.11.") + if is_pytorch_model and is_py11(): + pytest.skip("Reinforcement learning / PyTorch currently not available on python 3.11.") @pytest.mark.parametrize('model, pca, dbscan, float32, can_short, shuffle, buffer', [ @@ -48,11 +50,12 @@ def can_run_model(model: str) -> None: ('XGBoostRegressor', False, True, False, True, False, 10), ('XGBoostRFRegressor', False, False, False, True, False, 0), ('CatboostRegressor', False, False, False, True, True, 0), + ('PyTorchMLPRegressor', False, False, False, True, False, 0), ('ReinforcementLearner', False, True, False, True, False, 0), ('ReinforcementLearner_multiproc', False, False, False, True, False, 0), ('ReinforcementLearner_test_3ac', False, False, False, False, False, 0), ('ReinforcementLearner_test_3ac', False, False, False, True, False, 0), - ('ReinforcementLearner_test_4ac', False, False, False, True, False, 0) + ('ReinforcementLearner_test_4ac', False, False, False, True, False, 0), ]) def test_extract_data_and_train_model_Standard(mocker, freqai_conf, model, pca, dbscan, float32, can_short, shuffle, buffer): @@ -79,6 +82,11 @@ def test_extract_data_and_train_model_Standard(mocker, freqai_conf, model, pca, freqai_conf["freqaimodel_path"] = str(Path(__file__).parents[1] / "freqai" / "test_models") freqai_conf["freqai"]["rl_config"]["drop_ohlc_from_features"] = True + if 'PyTorchMLPRegressor' in model: + model_save_ext = 'zip' + pytorch_mlp_mtp = mock_pytorch_mlp_model_training_parameters() + freqai_conf['freqai']['model_training_parameters'].update(pytorch_mlp_mtp) + strategy = get_patched_freqai_strategy(mocker, freqai_conf) exchange = get_patched_exchange(mocker, freqai_conf) strategy.dp = DataProvider(freqai_conf, exchange) @@ -87,6 +95,7 @@ def test_extract_data_and_train_model_Standard(mocker, freqai_conf, model, pca, freqai.live = True freqai.can_short = can_short freqai.dk = FreqaiDataKitchen(freqai_conf) + freqai.dk.live = True freqai.dk.set_paths('ADA/BTC', 10000) timerange = TimeRange.parse_timerange("20180110-20180130") freqai.dd.load_all_pair_histories(timerange, freqai.dk) @@ -122,8 +131,7 @@ def test_extract_data_and_train_model_Standard(mocker, freqai_conf, model, pca, ('CatboostClassifierMultiTarget', "freqai_test_multimodel_classifier_strat") ]) def test_extract_data_and_train_model_MultiTargets(mocker, freqai_conf, model, strat): - if (is_arm() or is_py11()) and 'Catboost' in model: - pytest.skip("CatBoost is not supported on ARM") + can_run_model(model) freqai_conf.update({"timerange": "20180110-20180130"}) freqai_conf.update({"strategy": strat}) @@ -135,6 +143,7 @@ def test_extract_data_and_train_model_MultiTargets(mocker, freqai_conf, model, s freqai = strategy.freqai freqai.live = True freqai.dk = FreqaiDataKitchen(freqai_conf) + freqai.dk.live = True timerange = TimeRange.parse_timerange("20180110-20180130") freqai.dd.load_all_pair_histories(timerange, freqai.dk) @@ -162,10 +171,10 @@ def test_extract_data_and_train_model_MultiTargets(mocker, freqai_conf, model, s 'CatboostClassifier', 'XGBoostClassifier', 'XGBoostRFClassifier', + 'PyTorchMLPClassifier', ]) def test_extract_data_and_train_model_Classifiers(mocker, freqai_conf, model): - if (is_arm() or is_py11()) and model == 'CatboostClassifier': - pytest.skip("CatBoost is not supported on ARM") + can_run_model(model) freqai_conf.update({"freqaimodel": model}) freqai_conf.update({"strategy": "freqai_test_classifier"}) @@ -178,6 +187,7 @@ def test_extract_data_and_train_model_Classifiers(mocker, freqai_conf, model): freqai = strategy.freqai freqai.live = True freqai.dk = FreqaiDataKitchen(freqai_conf) + freqai.dk.live = True timerange = TimeRange.parse_timerange("20180110-20180130") freqai.dd.load_all_pair_histories(timerange, freqai.dk) @@ -190,7 +200,20 @@ def test_extract_data_and_train_model_Classifiers(mocker, freqai_conf, model): freqai.extract_data_and_train_model(new_timerange, "ADA/BTC", strategy, freqai.dk, data_load_timerange) - assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_model.joblib").exists() + if 'PyTorchMLPClassifier': + pytorch_mlp_mtp = mock_pytorch_mlp_model_training_parameters() + freqai_conf['freqai']['model_training_parameters'].update(pytorch_mlp_mtp) + + if freqai.dd.model_type == 'joblib': + model_file_extension = ".joblib" + elif freqai.dd.model_type == "pytorch": + model_file_extension = ".zip" + else: + raise Exception(f"Unsupported model type: {freqai.dd.model_type}," + f" can't assign model_file_extension") + + assert Path(freqai.dk.data_path / + f"{freqai.dk.model_filename}_model{model_file_extension}").exists() assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_metadata.json").exists() assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_trained_df.pkl").exists() assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_svm_model.joblib").exists() @@ -204,10 +227,12 @@ def test_extract_data_and_train_model_Classifiers(mocker, freqai_conf, model): ("LightGBMRegressor", 2, "freqai_test_strat"), ("XGBoostRegressor", 2, "freqai_test_strat"), ("CatboostRegressor", 2, "freqai_test_strat"), + ("PyTorchMLPRegressor", 2, "freqai_test_strat"), ("ReinforcementLearner", 3, "freqai_rl_test_strat"), ("XGBoostClassifier", 2, "freqai_test_classifier"), ("LightGBMClassifier", 2, "freqai_test_classifier"), - ("CatboostClassifier", 2, "freqai_test_classifier") + ("CatboostClassifier", 2, "freqai_test_classifier"), + ("PyTorchMLPClassifier", 2, "freqai_test_classifier") ], ) def test_start_backtesting(mocker, freqai_conf, model, num_files, strat, caplog): @@ -228,6 +253,10 @@ def test_start_backtesting(mocker, freqai_conf, model, num_files, strat, caplog) if 'test_4ac' in model: freqai_conf["freqaimodel_path"] = str(Path(__file__).parents[1] / "freqai" / "test_models") + if 'PyTorchMLP' in model: + pytorch_mlp_mtp = mock_pytorch_mlp_model_training_parameters() + freqai_conf['freqai']['model_training_parameters'].update(pytorch_mlp_mtp) + freqai_conf.get("freqai", {}).get("feature_parameters", {}).update( {"indicator_periods_candles": [2]}) @@ -371,6 +400,9 @@ def test_backtesting_fit_live_predictions(mocker, freqai_conf, caplog): sub_timerange = TimeRange.parse_timerange("20180129-20180130") corr_df, base_df = freqai.dd.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC", freqai.dk) df = freqai.dk.use_strategy_to_populate_indicators(strategy, corr_df, base_df, "LTC/BTC") + df = strategy.set_freqai_targets(df.copy(), metadata={"pair": "LTC/BTC"}) + df = freqai.dk.remove_special_chars_from_feature_names(df) + freqai.dk.get_unique_classes_from_labels(df) freqai.dk.pair = "ADA/BTC" freqai.dk.full_df = df.fillna(0) freqai.dk.full_df @@ -394,6 +426,7 @@ def test_principal_component_analysis(mocker, freqai_conf): freqai = strategy.freqai freqai.live = True freqai.dk = FreqaiDataKitchen(freqai_conf) + freqai.dk.live = True timerange = TimeRange.parse_timerange("20180110-20180130") freqai.dd.load_all_pair_histories(timerange, freqai.dk) @@ -425,10 +458,12 @@ def test_plot_feature_importance(mocker, freqai_conf): freqai = strategy.freqai freqai.live = True freqai.dk = FreqaiDataKitchen(freqai_conf) + freqai.dk.live = True timerange = TimeRange.parse_timerange("20180110-20180130") freqai.dd.load_all_pair_histories(timerange, freqai.dk) - freqai.dd.pair_dict = MagicMock() + freqai.dd.pair_dict = {"ADA/BTC": {"model_filename": "fake_name", + "trained_timestamp": 1, "data_path": "", "extras": {}}} data_load_timerange = TimeRange.parse_timerange("20180110-20180130") new_timerange = TimeRange.parse_timerange("20180120-20180130") diff --git a/tests/optimize/test_backtest_detail.py b/tests/optimize/test_backtest_detail.py index 2cb42c003..158dd04dc 100644 --- a/tests/optimize/test_backtest_detail.py +++ b/tests/optimize/test_backtest_detail.py @@ -5,7 +5,7 @@ from unittest.mock import MagicMock import pytest from freqtrade.data.history import get_timerange -from freqtrade.enums import ExitType +from freqtrade.enums import ExitType, TradingMode from freqtrade.optimize.backtesting import Backtesting from freqtrade.persistence.trade_model import LocalTrade from tests.conftest import EXMS, patch_exchange @@ -925,12 +925,14 @@ def test_backtest_results(default_conf, fee, mocker, caplog, data: BTContainer) mocker.patch(f"{EXMS}.get_min_pair_stake_amount", return_value=0.00001) mocker.patch(f"{EXMS}.get_max_pair_stake_amount", return_value=float('inf')) mocker.patch(f"{EXMS}.get_max_leverage", return_value=100) + mocker.patch(f"{EXMS}.calculate_funding_fees", return_value=0) patch_exchange(mocker) frame = _build_backtest_dataframe(data.data) backtesting = Backtesting(default_conf) # TODO: Should we initialize this properly?? - backtesting._can_short = True + backtesting.trading_mode = TradingMode.MARGIN backtesting._set_strategy(backtesting.strategylist[0]) + backtesting._can_short = True backtesting.required_startup = 0 backtesting.strategy.advise_entry = lambda a, m: frame backtesting.strategy.advise_exit = lambda a, m: frame diff --git a/tests/optimize/test_backtesting.py b/tests/optimize/test_backtesting.py index a8ff4aa69..a9e87347c 100644 --- a/tests/optimize/test_backtesting.py +++ b/tests/optimize/test_backtesting.py @@ -344,7 +344,7 @@ def test_backtest_abort(default_conf, mocker, testdatadir) -> None: assert backtesting.progress.progress == 0 -def test_backtesting_start(default_conf, mocker, testdatadir, caplog) -> None: +def test_backtesting_start(default_conf, mocker, caplog) -> None: def get_timerange(input1): return Arrow(2017, 11, 14, 21, 17), Arrow(2017, 11, 14, 22, 59) @@ -367,6 +367,7 @@ def test_backtesting_start(default_conf, mocker, testdatadir, caplog) -> None: backtesting = Backtesting(default_conf) backtesting._set_strategy(backtesting.strategylist[0]) backtesting.strategy.bot_loop_start = MagicMock() + backtesting.strategy.bot_start = MagicMock() backtesting.start() # check the logs, that will contain the backtest result exists = [ @@ -376,7 +377,8 @@ def test_backtesting_start(default_conf, mocker, testdatadir, caplog) -> None: for line in exists: assert log_has(line, caplog) assert backtesting.strategy.dp._pairlists is not None - assert backtesting.strategy.bot_loop_start.call_count == 1 + assert backtesting.strategy.bot_start.call_count == 1 + assert backtesting.strategy.bot_loop_start.call_count == 0 assert sbs.call_count == 1 assert sbc.call_count == 1 diff --git a/tests/optimize/test_backtesting_adjust_position.py b/tests/optimize/test_backtesting_adjust_position.py index 9fc726bd1..0d57ff89a 100644 --- a/tests/optimize/test_backtesting_adjust_position.py +++ b/tests/optimize/test_backtesting_adjust_position.py @@ -10,7 +10,7 @@ from arrow import Arrow from freqtrade.configuration import TimeRange from freqtrade.data import history from freqtrade.data.history import get_timerange -from freqtrade.enums import ExitType +from freqtrade.enums import ExitType, TradingMode from freqtrade.optimize.backtesting import Backtesting from tests.conftest import EXMS, patch_exchange @@ -108,9 +108,10 @@ def test_backtest_position_adjustment_detailed(default_conf, fee, mocker, levera default_conf.update({ "stake_amount": 100.0, "dry_run_wallet": 1000.0, - "strategy": "StrategyTestV3" + "strategy": "StrategyTestV3", }) backtesting = Backtesting(default_conf) + backtesting.trading_mode = TradingMode.FUTURES backtesting._can_short = True backtesting._set_strategy(backtesting.strategylist[0]) pair = 'XRP/USDT' diff --git a/tests/optimize/test_hyperopt.py b/tests/optimize/test_hyperopt.py index 998798580..786720030 100644 --- a/tests/optimize/test_hyperopt.py +++ b/tests/optimize/test_hyperopt.py @@ -872,7 +872,8 @@ def test_in_strategy_auto_hyperopt(mocker, hyperopt_conf, tmpdir, fee) -> None: hyperopt.backtesting.exchange.get_max_leverage = MagicMock(return_value=1.0) assert isinstance(hyperopt.custom_hyperopt, HyperOptAuto) assert isinstance(hyperopt.backtesting.strategy.buy_rsi, IntParameter) - assert hyperopt.backtesting.strategy.bot_loop_started is True + assert hyperopt.backtesting.strategy.bot_started is True + assert hyperopt.backtesting.strategy.bot_loop_started is False assert hyperopt.backtesting.strategy.buy_rsi.in_space is True assert hyperopt.backtesting.strategy.buy_rsi.value == 35 @@ -922,7 +923,8 @@ def test_in_strategy_auto_hyperopt_with_parallel(mocker, hyperopt_conf, tmpdir, assert isinstance(hyperopt.custom_hyperopt, HyperOptAuto) assert isinstance(hyperopt.backtesting.strategy.buy_rsi, IntParameter) - assert hyperopt.backtesting.strategy.bot_loop_started is True + assert hyperopt.backtesting.strategy.bot_started is True + assert hyperopt.backtesting.strategy.bot_loop_started is False assert hyperopt.backtesting.strategy.buy_rsi.in_space is True assert hyperopt.backtesting.strategy.buy_rsi.value == 35 @@ -959,7 +961,8 @@ def test_in_strategy_auto_hyperopt_per_epoch(mocker, hyperopt_conf, tmpdir, fee) hyperopt.backtesting.exchange.get_max_leverage = MagicMock(return_value=1.0) assert isinstance(hyperopt.custom_hyperopt, HyperOptAuto) assert isinstance(hyperopt.backtesting.strategy.buy_rsi, IntParameter) - assert hyperopt.backtesting.strategy.bot_loop_started is True + assert hyperopt.backtesting.strategy.bot_loop_started is False + assert hyperopt.backtesting.strategy.bot_started is True assert hyperopt.backtesting.strategy.buy_rsi.in_space is True assert hyperopt.backtesting.strategy.buy_rsi.value == 35 diff --git a/tests/optimize/test_optimize_reports.py b/tests/optimize/test_optimize_reports.py index 955fcee4f..4e3803f17 100644 --- a/tests/optimize/test_optimize_reports.py +++ b/tests/optimize/test_optimize_reports.py @@ -9,7 +9,7 @@ import pytest from arrow import Arrow from freqtrade.configuration import TimeRange -from freqtrade.constants import DATETIME_PRINT_FORMAT, LAST_BT_RESULT_FN +from freqtrade.constants import BACKTEST_BREAKDOWNS, DATETIME_PRINT_FORMAT, LAST_BT_RESULT_FN from freqtrade.data import history from freqtrade.data.btanalysis import (get_latest_backtest_filename, load_backtest_data, load_backtest_stats) @@ -236,7 +236,7 @@ def test_store_backtest_candles(testdatadir, mocker): assert dump_mock.call_count == 2 assert isinstance(dump_mock.call_args_list[0][0][0], Path) - assert str(dump_mock.call_args_list[0][0][0]).endswith(str('_signals.pkl')) + assert str(dump_mock.call_args_list[0][0][0]).endswith('_signals.pkl') dump_mock.reset_mock() # mock file exporting @@ -245,7 +245,7 @@ def test_store_backtest_candles(testdatadir, mocker): assert dump_mock.call_count == 2 assert isinstance(dump_mock.call_args_list[0][0][0], Path) # result will be testdatadir / testresult-_signals.pkl - assert str(dump_mock.call_args_list[0][0][0]).endswith(str('_signals.pkl')) + assert str(dump_mock.call_args_list[0][0][0]).endswith('_signals.pkl') dump_mock.reset_mock() @@ -466,11 +466,14 @@ def test_generate_periodic_breakdown_stats(testdatadir): def test__get_resample_from_period(): assert _get_resample_from_period('day') == '1d' - assert _get_resample_from_period('week') == '1w' + assert _get_resample_from_period('week') == '1W-MON' assert _get_resample_from_period('month') == '1M' with pytest.raises(ValueError, match=r"Period noooo is not supported."): _get_resample_from_period('noooo') + for period in BACKTEST_BREAKDOWNS: + assert isinstance(_get_resample_from_period(period), str) + def test_show_sorted_pairlist(testdatadir, default_conf, capsys): filename = testdatadir / "backtest_results/backtest-result.json" diff --git a/tests/persistence/test_key_value_store.py b/tests/persistence/test_key_value_store.py new file mode 100644 index 000000000..1dab8764a --- /dev/null +++ b/tests/persistence/test_key_value_store.py @@ -0,0 +1,69 @@ +from datetime import datetime, timedelta, timezone + +import pytest + +from freqtrade.persistence.key_value_store import KeyValueStore, set_startup_time +from tests.conftest import create_mock_trades_usdt + + +@pytest.mark.usefixtures("init_persistence") +def test_key_value_store(time_machine): + start = datetime(2023, 1, 1, 4, tzinfo=timezone.utc) + time_machine.move_to(start, tick=False) + + KeyValueStore.store_value("test", "testStringValue") + KeyValueStore.store_value("test_dt", datetime.now(timezone.utc)) + KeyValueStore.store_value("test_float", 22.51) + KeyValueStore.store_value("test_int", 15) + + assert KeyValueStore.get_value("test") == "testStringValue" + assert KeyValueStore.get_value("test") == "testStringValue" + assert KeyValueStore.get_string_value("test") == "testStringValue" + assert KeyValueStore.get_value("test_dt") == datetime.now(timezone.utc) + assert KeyValueStore.get_datetime_value("test_dt") == datetime.now(timezone.utc) + assert KeyValueStore.get_string_value("test_dt") is None + assert KeyValueStore.get_float_value("test_dt") is None + assert KeyValueStore.get_int_value("test_dt") is None + assert KeyValueStore.get_value("test_float") == 22.51 + assert KeyValueStore.get_float_value("test_float") == 22.51 + assert KeyValueStore.get_value("test_int") == 15 + assert KeyValueStore.get_int_value("test_int") == 15 + assert KeyValueStore.get_datetime_value("test_int") is None + + time_machine.move_to(start + timedelta(days=20, hours=5), tick=False) + assert KeyValueStore.get_value("test_dt") != datetime.now(timezone.utc) + assert KeyValueStore.get_value("test_dt") == start + # Test update works + KeyValueStore.store_value("test_dt", datetime.now(timezone.utc)) + assert KeyValueStore.get_value("test_dt") == datetime.now(timezone.utc) + + KeyValueStore.store_value("test_float", 23.51) + assert KeyValueStore.get_value("test_float") == 23.51 + # test deleting + KeyValueStore.delete_value("test_float") + assert KeyValueStore.get_value("test_float") is None + # Delete same value again (should not fail) + KeyValueStore.delete_value("test_float") + + with pytest.raises(ValueError, match=r"Unknown value type"): + KeyValueStore.store_value("test_float", {'some': 'dict'}) + + +@pytest.mark.usefixtures("init_persistence") +def test_set_startup_time(fee, time_machine): + create_mock_trades_usdt(fee) + start = datetime.now(timezone.utc) + time_machine.move_to(start, tick=False) + set_startup_time() + + assert KeyValueStore.get_value("startup_time") == start + initial_time = KeyValueStore.get_value("bot_start_time") + assert initial_time <= start + + # Simulate bot restart + new_start = start + timedelta(days=5) + time_machine.move_to(new_start, tick=False) + set_startup_time() + + assert KeyValueStore.get_value("startup_time") == new_start + assert KeyValueStore.get_value("bot_start_time") == initial_time diff --git a/tests/persistence/test_migrations.py b/tests/persistence/test_migrations.py index 854d39994..13b3f89bf 100644 --- a/tests/persistence/test_migrations.py +++ b/tests/persistence/test_migrations.py @@ -1,15 +1,18 @@ # pragma pylint: disable=missing-docstring, C0103 import logging +from importlib import import_module from pathlib import Path from unittest.mock import MagicMock import pytest from sqlalchemy import create_engine, select, text +from sqlalchemy.schema import CreateTable from freqtrade.constants import DEFAULT_DB_PROD_URL from freqtrade.enums import TradingMode from freqtrade.exceptions import OperationalException from freqtrade.persistence import Trade, init_db +from freqtrade.persistence.base import ModelBase from freqtrade.persistence.migrations import get_last_sequence_ids, set_sequence_ids from freqtrade.persistence.models import PairLock from tests.conftest import log_has @@ -411,3 +414,14 @@ def test_migrate_pairlocks(mocker, default_conf, fee, caplog): assert len(pairlocks) == 1 pairlocks[0].pair == 'ETH/BTC' pairlocks[0].side == '*' + + +@pytest.mark.parametrize('dialect', [ + 'sqlite', 'postgresql', 'mysql', 'oracle', 'mssql', + ]) +def test_create_table_compiles(dialect): + + dialect_mod = import_module(f"sqlalchemy.dialects.{dialect}") + for table in ModelBase.metadata.tables.values(): + create_sql = str(CreateTable(table).compile(dialect=dialect_mod.dialect())) + assert 'CREATE TABLE' in create_sql diff --git a/tests/persistence/test_persistence.py b/tests/persistence/test_persistence.py index db882d56d..1a7d84eca 100644 --- a/tests/persistence/test_persistence.py +++ b/tests/persistence/test_persistence.py @@ -6,7 +6,7 @@ import arrow import pytest from sqlalchemy import select -from freqtrade.constants import DATETIME_PRINT_FORMAT +from freqtrade.constants import CUSTOM_TAG_MAX_LENGTH, DATETIME_PRINT_FORMAT from freqtrade.enums import TradingMode from freqtrade.exceptions import DependencyException from freqtrade.persistence import LocalTrade, Order, Trade, init_db @@ -1330,71 +1330,78 @@ def test_to_json(fee): open_rate=0.123, exchange='binance', enter_tag=None, - open_order_id='dry_run_buy_12345' + open_order_id='dry_run_buy_12345', + precision_mode=1, + amount_precision=8.0, + price_precision=7.0, ) result = trade.to_json() assert isinstance(result, dict) - assert result == {'trade_id': None, - 'pair': 'ADA/USDT', - 'base_currency': 'ADA', - 'quote_currency': 'USDT', - 'is_open': None, - 'open_date': trade.open_date.strftime(DATETIME_PRINT_FORMAT), - 'open_timestamp': int(trade.open_date.timestamp() * 1000), - 'open_order_id': 'dry_run_buy_12345', - 'close_date': None, - 'close_timestamp': None, - 'open_rate': 0.123, - 'open_rate_requested': None, - 'open_trade_value': 15.1668225, - 'fee_close': 0.0025, - 'fee_close_cost': None, - 'fee_close_currency': None, - 'fee_open': 0.0025, - 'fee_open_cost': None, - 'fee_open_currency': None, - 'close_rate': None, - 'close_rate_requested': None, - 'amount': 123.0, - 'amount_requested': 123.0, - 'stake_amount': 0.001, - 'max_stake_amount': None, - 'trade_duration': None, - 'trade_duration_s': None, - 'realized_profit': 0.0, - 'realized_profit_ratio': None, - 'close_profit': None, - 'close_profit_pct': None, - 'close_profit_abs': None, - 'profit_ratio': None, - 'profit_pct': None, - 'profit_abs': None, - 'exit_reason': None, - 'exit_order_status': None, - 'stop_loss_abs': None, - 'stop_loss_ratio': None, - 'stop_loss_pct': None, - 'stoploss_order_id': None, - 'stoploss_last_update': None, - 'stoploss_last_update_timestamp': None, - 'initial_stop_loss_abs': None, - 'initial_stop_loss_pct': None, - 'initial_stop_loss_ratio': None, - 'min_rate': None, - 'max_rate': None, - 'strategy': None, - 'enter_tag': None, - 'timeframe': None, - 'exchange': 'binance', - 'leverage': None, - 'interest_rate': None, - 'liquidation_price': None, - 'is_short': None, - 'trading_mode': None, - 'funding_fees': None, - 'orders': [], - } + assert result == { + 'trade_id': None, + 'pair': 'ADA/USDT', + 'base_currency': 'ADA', + 'quote_currency': 'USDT', + 'is_open': None, + 'open_date': trade.open_date.strftime(DATETIME_PRINT_FORMAT), + 'open_timestamp': int(trade.open_date.timestamp() * 1000), + 'open_order_id': 'dry_run_buy_12345', + 'close_date': None, + 'close_timestamp': None, + 'open_rate': 0.123, + 'open_rate_requested': None, + 'open_trade_value': 15.1668225, + 'fee_close': 0.0025, + 'fee_close_cost': None, + 'fee_close_currency': None, + 'fee_open': 0.0025, + 'fee_open_cost': None, + 'fee_open_currency': None, + 'close_rate': None, + 'close_rate_requested': None, + 'amount': 123.0, + 'amount_requested': 123.0, + 'stake_amount': 0.001, + 'max_stake_amount': None, + 'trade_duration': None, + 'trade_duration_s': None, + 'realized_profit': 0.0, + 'realized_profit_ratio': None, + 'close_profit': None, + 'close_profit_pct': None, + 'close_profit_abs': None, + 'profit_ratio': None, + 'profit_pct': None, + 'profit_abs': None, + 'exit_reason': None, + 'exit_order_status': None, + 'stop_loss_abs': None, + 'stop_loss_ratio': None, + 'stop_loss_pct': None, + 'stoploss_order_id': None, + 'stoploss_last_update': None, + 'stoploss_last_update_timestamp': None, + 'initial_stop_loss_abs': None, + 'initial_stop_loss_pct': None, + 'initial_stop_loss_ratio': None, + 'min_rate': None, + 'max_rate': None, + 'strategy': None, + 'enter_tag': None, + 'timeframe': None, + 'exchange': 'binance', + 'leverage': None, + 'interest_rate': None, + 'liquidation_price': None, + 'is_short': None, + 'trading_mode': None, + 'funding_fees': None, + 'amount_precision': 8.0, + 'price_precision': 7.0, + 'precision_mode': 1, + 'orders': [], + } # Simulate dry_run entries trade = Trade( @@ -1410,70 +1417,77 @@ def test_to_json(fee): close_rate=0.125, enter_tag='buys_signal_001', exchange='binance', + precision_mode=2, + amount_precision=7.0, + price_precision=8.0, ) result = trade.to_json() assert isinstance(result, dict) - assert result == {'trade_id': None, - 'pair': 'XRP/BTC', - 'base_currency': 'XRP', - 'quote_currency': 'BTC', - 'open_date': trade.open_date.strftime(DATETIME_PRINT_FORMAT), - 'open_timestamp': int(trade.open_date.timestamp() * 1000), - 'close_date': trade.close_date.strftime(DATETIME_PRINT_FORMAT), - 'close_timestamp': int(trade.close_date.timestamp() * 1000), - 'open_rate': 0.123, - 'close_rate': 0.125, - 'amount': 100.0, - 'amount_requested': 101.0, - 'stake_amount': 0.001, - 'max_stake_amount': None, - 'trade_duration': 60, - 'trade_duration_s': 3600, - 'stop_loss_abs': None, - 'stop_loss_pct': None, - 'stop_loss_ratio': None, - 'stoploss_order_id': None, - 'stoploss_last_update': None, - 'stoploss_last_update_timestamp': None, - 'initial_stop_loss_abs': None, - 'initial_stop_loss_pct': None, - 'initial_stop_loss_ratio': None, - 'realized_profit': 0.0, - 'realized_profit_ratio': None, - 'close_profit': None, - 'close_profit_pct': None, - 'close_profit_abs': None, - 'profit_ratio': None, - 'profit_pct': None, - 'profit_abs': None, - 'close_rate_requested': None, - 'fee_close': 0.0025, - 'fee_close_cost': None, - 'fee_close_currency': None, - 'fee_open': 0.0025, - 'fee_open_cost': None, - 'fee_open_currency': None, - 'is_open': None, - 'max_rate': None, - 'min_rate': None, - 'open_order_id': None, - 'open_rate_requested': None, - 'open_trade_value': 12.33075, - 'exit_reason': None, - 'exit_order_status': None, - 'strategy': None, - 'enter_tag': 'buys_signal_001', - 'timeframe': None, - 'exchange': 'binance', - 'leverage': None, - 'interest_rate': None, - 'liquidation_price': None, - 'is_short': None, - 'trading_mode': None, - 'funding_fees': None, - 'orders': [], - } + assert result == { + 'trade_id': None, + 'pair': 'XRP/BTC', + 'base_currency': 'XRP', + 'quote_currency': 'BTC', + 'open_date': trade.open_date.strftime(DATETIME_PRINT_FORMAT), + 'open_timestamp': int(trade.open_date.timestamp() * 1000), + 'close_date': trade.close_date.strftime(DATETIME_PRINT_FORMAT), + 'close_timestamp': int(trade.close_date.timestamp() * 1000), + 'open_rate': 0.123, + 'close_rate': 0.125, + 'amount': 100.0, + 'amount_requested': 101.0, + 'stake_amount': 0.001, + 'max_stake_amount': None, + 'trade_duration': 60, + 'trade_duration_s': 3600, + 'stop_loss_abs': None, + 'stop_loss_pct': None, + 'stop_loss_ratio': None, + 'stoploss_order_id': None, + 'stoploss_last_update': None, + 'stoploss_last_update_timestamp': None, + 'initial_stop_loss_abs': None, + 'initial_stop_loss_pct': None, + 'initial_stop_loss_ratio': None, + 'realized_profit': 0.0, + 'realized_profit_ratio': None, + 'close_profit': None, + 'close_profit_pct': None, + 'close_profit_abs': None, + 'profit_ratio': None, + 'profit_pct': None, + 'profit_abs': None, + 'close_rate_requested': None, + 'fee_close': 0.0025, + 'fee_close_cost': None, + 'fee_close_currency': None, + 'fee_open': 0.0025, + 'fee_open_cost': None, + 'fee_open_currency': None, + 'is_open': None, + 'max_rate': None, + 'min_rate': None, + 'open_order_id': None, + 'open_rate_requested': None, + 'open_trade_value': 12.33075, + 'exit_reason': None, + 'exit_order_status': None, + 'strategy': None, + 'enter_tag': 'buys_signal_001', + 'timeframe': None, + 'exchange': 'binance', + 'leverage': None, + 'interest_rate': None, + 'liquidation_price': None, + 'is_short': None, + 'trading_mode': None, + 'funding_fees': None, + 'amount_precision': 7.0, + 'price_precision': 8.0, + 'precision_mode': 2, + 'orders': [], + } def test_stoploss_reinitialization(default_conf, fee): @@ -2023,6 +2037,7 @@ def test_Trade_object_idem(): 'get_mix_tag_performance', 'get_trading_volume', 'from_json', + 'validate_string_len', ) EXCLUDES2 = ('trades', 'trades_open', 'bt_trades_open_pp', 'bt_open_open_trade_count', 'total_profit') @@ -2041,6 +2056,31 @@ def test_Trade_object_idem(): assert item in trade +@pytest.mark.usefixtures("init_persistence") +def test_trade_truncates_string_fields(): + trade = Trade( + pair='ADA/USDT', + stake_amount=20.0, + amount=30.0, + open_rate=2.0, + open_date=datetime.utcnow() - timedelta(minutes=20), + fee_open=0.001, + fee_close=0.001, + exchange='binance', + leverage=1.0, + trading_mode='futures', + enter_tag='a' * CUSTOM_TAG_MAX_LENGTH * 2, + exit_reason='b' * CUSTOM_TAG_MAX_LENGTH * 2, + ) + Trade.session.add(trade) + Trade.commit() + + trade1 = Trade.session.scalars(select(Trade)).first() + + assert trade1.enter_tag == 'a' * CUSTOM_TAG_MAX_LENGTH + assert trade1.exit_reason == 'b' * CUSTOM_TAG_MAX_LENGTH + + def test_recalc_trade_from_orders(fee): o1_amount = 100 @@ -2441,7 +2481,7 @@ def test_select_filled_orders(fee): @pytest.mark.usefixtures("init_persistence") -def test_order_to_ccxt(limit_buy_order_open): +def test_order_to_ccxt(limit_buy_order_open, limit_sell_order_usdt_open): order = Order.parse_from_ccxt_object(limit_buy_order_open, 'mocked', 'buy') order.ft_trade_id = 1 @@ -2455,11 +2495,23 @@ def test_order_to_ccxt(limit_buy_order_open): del raw_order['fee'] del raw_order['datetime'] del raw_order['info'] - assert raw_order['stopPrice'] is None - del raw_order['stopPrice'] + assert raw_order.get('stopPrice') is None + raw_order.pop('stopPrice', None) del limit_buy_order_open['datetime'] assert raw_order == limit_buy_order_open + order1 = Order.parse_from_ccxt_object(limit_sell_order_usdt_open, 'mocked', 'sell') + order1.ft_order_side = 'stoploss' + order1.stop_price = order1.price * 0.9 + order1.ft_trade_id = 1 + order1.session.add(order1) + Order.session.commit() + + order_resp1 = Order.order_by_id(limit_sell_order_usdt_open['id']) + raw_order1 = order_resp1.to_ccxt_object() + + assert raw_order1.get('stopPrice') is not None + @pytest.mark.usefixtures("init_persistence") @pytest.mark.parametrize('data', [ diff --git a/tests/persistence/test_trade_fromjson.py b/tests/persistence/test_trade_fromjson.py index 529008e02..22053463d 100644 --- a/tests/persistence/test_trade_fromjson.py +++ b/tests/persistence/test_trade_fromjson.py @@ -50,8 +50,8 @@ def test_trade_fromjson(): "stop_loss_ratio": -0.216, "stop_loss_pct": -21.6, "stoploss_order_id": null, - "stoploss_last_update": null, - "stoploss_last_update_timestamp": null, + "stoploss_last_update": "2022-10-18 09:13:42", + "stoploss_last_update_timestamp": 1666077222000, "initial_stop_loss_abs": 0.1981, "initial_stop_loss_ratio": -0.216, "initial_stop_loss_pct": -21.6, diff --git a/tests/rpc/test_rpc.py b/tests/rpc/test_rpc.py index 7d829bdb6..bb84ff8e9 100644 --- a/tests/rpc/test_rpc.py +++ b/tests/rpc/test_rpc.py @@ -88,6 +88,9 @@ def test_rpc_trade_status(default_conf, ticker, fee, mocker) -> None: 'is_short': False, 'funding_fees': 0.0, 'trading_mode': TradingMode.SPOT, + 'amount_precision': 8.0, + 'price_precision': 8.0, + 'precision_mode': 2, 'orders': [{ 'amount': 91.07468123, 'average': 1.098e-05, 'safe_price': 1.098e-05, 'cost': 0.0009999999999054, 'filled': 91.07468123, 'ft_order_side': 'buy', @@ -125,17 +128,6 @@ def test_rpc_trade_status(default_conf, ticker, fee, mocker) -> None: 'profit_pct': 0.0, 'profit_abs': 0.0, 'total_profit_abs': 0.0, - 'stop_loss_abs': 0.0, - 'stop_loss_pct': None, - 'stop_loss_ratio': None, - 'stoploss_current_dist': -1.099e-05, - 'stoploss_current_dist_ratio': -1.0, - 'stoploss_current_dist_pct': pytest.approx(-100.0), - 'stoploss_entry_dist': -0.0010025, - 'stoploss_entry_dist_ratio': -1.0, - 'initial_stop_loss_abs': 0.0, - 'initial_stop_loss_pct': None, - 'initial_stop_loss_ratio': None, 'open_order': '(limit buy rem=91.07468123)', }) response_unfilled['orders'][0].update({ @@ -554,51 +546,67 @@ def test_rpc_balance_handle(default_conf, mocker, tickers): 'free': 10.0, 'balance': 12.0, 'used': 2.0, + 'bot_owned': 9.9, # available stake - reducing by reserved amount 'est_stake': 10.0, # In futures mode, "free" is used here. + 'est_stake_bot': 9.9, 'stake': 'BTC', 'is_position': False, 'leverage': 1.0, 'position': 0.0, 'side': 'long', + 'is_bot_managed': True, }, { 'free': 1.0, 'balance': 5.0, 'currency': 'ETH', + 'bot_owned': 0, 'est_stake': 0.30794, + 'est_stake_bot': 0, 'used': 4.0, 'stake': 'BTC', 'is_position': False, 'leverage': 1.0, 'position': 0.0, 'side': 'long', - + 'is_bot_managed': False, }, { 'free': 5.0, 'balance': 10.0, 'currency': 'USDT', + 'bot_owned': 0, 'est_stake': 0.0011562404610161968, + 'est_stake_bot': 0, 'used': 5.0, 'stake': 'BTC', 'is_position': False, 'leverage': 1.0, 'position': 0.0, 'side': 'long', + 'is_bot_managed': False, }, { 'free': 0.0, 'balance': 0.0, 'currency': 'ETH/USDT:USDT', 'est_stake': 20, + 'est_stake_bot': 20, 'used': 0, 'stake': 'BTC', 'is_position': True, 'leverage': 5.0, 'position': 1000.0, 'side': 'short', + 'is_bot_managed': True, } ] + assert pytest.approx(result['total_bot']) == 29.9 + assert pytest.approx(result['total']) == 30.309096 + assert result['starting_capital'] == 10 + # Very high starting capital ratio, because the futures position really has the wrong unit. + # TODO: improve this test (see comment above) + assert result['starting_capital_ratio'] == pytest.approx(1.98999999) def test_rpc_start(mocker, default_conf) -> None: diff --git a/tests/rpc/test_rpc_apiserver.py b/tests/rpc/test_rpc_apiserver.py index 97319b78b..8123e4689 100644 --- a/tests/rpc/test_rpc_apiserver.py +++ b/tests/rpc/test_rpc_apiserver.py @@ -1,6 +1,7 @@ """ Unit test file for rpc/api_server.py """ +import asyncio import logging import time from datetime import datetime, timedelta, timezone @@ -282,7 +283,7 @@ def test_api__init__(default_conf, mocker): "username": "TestUser", "password": "testPass", }}) - mocker.patch('freqtrade.rpc.telegram.Updater', MagicMock()) + mocker.patch('freqtrade.rpc.telegram.Telegram._init') mocker.patch('freqtrade.rpc.api_server.webserver.ApiServer.start_api', MagicMock()) apiserver = ApiServer(default_conf) apiserver.add_rpc_handler(RPC(get_patched_freqtradebot(mocker, default_conf))) @@ -299,10 +300,6 @@ def test_api_UvicornServer(mocker): s = UvicornServer(uvicorn.Config(MagicMock(), port=8080, host='127.0.0.1')) assert thread_mock.call_count == 0 - s.install_signal_handlers() - # Original implementation starts a thread - make sure that's not the case - assert thread_mock.call_count == 0 - # Fake started to avoid sleeping forever s.started = True s.run_in_thread() @@ -318,10 +315,6 @@ def test_api_UvicornServer_run(mocker): s = UvicornServer(uvicorn.Config(MagicMock(), port=8080, host='127.0.0.1')) assert serve_mock.call_count == 0 - s.install_signal_handlers() - # Original implementation starts a thread - make sure that's not the case - assert serve_mock.call_count == 0 - # Fake started to avoid sleeping forever s.started = True s.run() @@ -331,13 +324,10 @@ def test_api_UvicornServer_run(mocker): def test_api_UvicornServer_run_no_uvloop(mocker, import_fails): serve_mock = mocker.patch('freqtrade.rpc.api_server.uvicorn_threaded.UvicornServer.serve', get_mock_coro(None)) + asyncio.set_event_loop(asyncio.new_event_loop()) s = UvicornServer(uvicorn.Config(MagicMock(), port=8080, host='127.0.0.1')) assert serve_mock.call_count == 0 - s.install_signal_handlers() - # Original implementation starts a thread - make sure that's not the case - assert serve_mock.call_count == 0 - # Fake started to avoid sleeping forever s.started = True s.run() @@ -351,7 +341,7 @@ def test_api_run(default_conf, mocker, caplog): "username": "TestUser", "password": "testPass", }}) - mocker.patch('freqtrade.rpc.telegram.Updater', MagicMock()) + mocker.patch('freqtrade.rpc.telegram.Telegram._init') server_inst_mock = MagicMock() server_inst_mock.run_in_thread = MagicMock() @@ -429,7 +419,7 @@ def test_api_cleanup(default_conf, mocker, caplog): "username": "TestUser", "password": "testPass", }}) - mocker.patch('freqtrade.rpc.telegram.Updater', MagicMock()) + mocker.patch('freqtrade.rpc.telegram.Telegram._init') server_mock = MagicMock() server_mock.cleanup = MagicMock() @@ -490,13 +480,18 @@ def test_api_balance(botclient, mocker, rpc_balance, tickers): 'free': 12.0, 'balance': 12.0, 'used': 0.0, + 'bot_owned': pytest.approx(11.879999), 'est_stake': 12.0, + 'est_stake_bot': pytest.approx(11.879999), 'stake': 'BTC', 'is_position': False, 'leverage': 1.0, 'position': 0.0, 'side': 'long', + 'is_bot_managed': True, } + assert response['total'] == 12.159513094 + assert response['total_bot'] == pytest.approx(11.879999) assert 'starting_capital' in response assert 'starting_capital_fiat' in response assert 'starting_capital_pct' in response @@ -893,6 +888,8 @@ def test_api_profit(botclient, mocker, ticker, fee, markets, is_short, expected) 'max_drawdown': ANY, 'max_drawdown_abs': ANY, 'trading_volume': expected['trading_volume'], + 'bot_start_timestamp': 0, + 'bot_start_date': '', } @@ -1066,6 +1063,9 @@ def test_api_status(botclient, mocker, ticker, fee, markets, is_short, 'liquidation_price': None, 'funding_fees': None, 'trading_mode': ANY, + 'amount_precision': None, + 'price_precision': None, + 'precision_mode': None, 'orders': [ANY], } @@ -1271,6 +1271,9 @@ def test_api_force_entry(botclient, mocker, fee, endpoint): 'liquidation_price': None, 'funding_fees': None, 'trading_mode': 'spot', + 'amount_precision': None, + 'price_precision': None, + 'precision_mode': None, 'orders': [], } @@ -1407,10 +1410,10 @@ def test_api_pair_candles(botclient, ohlcv_history): ]) -def test_api_pair_history(botclient, ohlcv_history): +def test_api_pair_history(botclient, mocker): ftbot, client = botclient timeframe = '5m' - + lfm = mocker.patch('freqtrade.strategy.interface.IStrategy.load_freqAI_model') # No pair rc = client_get(client, f"{BASE_URI}/pair_history?timeframe={timeframe}" @@ -1444,6 +1447,7 @@ def test_api_pair_history(botclient, ohlcv_history): assert len(rc.json()['data']) == rc.json()['length'] assert 'columns' in rc.json() assert 'data' in rc.json() + assert lfm.call_count == 1 assert rc.json()['pair'] == 'UNITTEST/BTC' assert rc.json()['strategy'] == CURRENT_TEST_STRATEGY assert rc.json()['data_start'] == '2018-01-11 00:00:00+00:00' @@ -1873,7 +1877,7 @@ def test_api_ws_send_msg(default_conf, mocker, caplog): "password": _TEST_PASS, "ws_token": _TEST_WS_TOKEN }}) - mocker.patch('freqtrade.rpc.telegram.Updater') + mocker.patch('freqtrade.rpc.telegram.Telegram._init') mocker.patch('freqtrade.rpc.api_server.ApiServer.start_api') apiserver = ApiServer(default_conf) apiserver.add_rpc_handler(RPC(get_patched_freqtradebot(mocker, default_conf))) diff --git a/tests/rpc/test_rpc_manager.py b/tests/rpc/test_rpc_manager.py index 21c8b0813..f0bb72fc9 100644 --- a/tests/rpc/test_rpc_manager.py +++ b/tests/rpc/test_rpc_manager.py @@ -28,6 +28,7 @@ def test_init_telegram_disabled(mocker, default_conf, caplog) -> None: def test_init_telegram_enabled(mocker, default_conf, caplog) -> None: caplog.set_level(logging.DEBUG) + default_conf['telegram']['enabled'] = True mocker.patch('freqtrade.rpc.telegram.Telegram._init', MagicMock()) rpc_manager = RPCManager(get_patched_freqtradebot(mocker, default_conf)) @@ -52,6 +53,7 @@ def test_cleanup_telegram_disabled(mocker, default_conf, caplog) -> None: def test_cleanup_telegram_enabled(mocker, default_conf, caplog) -> None: caplog.set_level(logging.DEBUG) + default_conf['telegram']['enabled'] = True mocker.patch('freqtrade.rpc.telegram.Telegram._init', MagicMock()) telegram_mock = mocker.patch('freqtrade.rpc.telegram.Telegram.cleanup', MagicMock()) @@ -85,7 +87,7 @@ def test_send_msg_telegram_disabled(mocker, default_conf, caplog) -> None: def test_send_msg_telegram_error(mocker, default_conf, caplog) -> None: mocker.patch('freqtrade.rpc.telegram.Telegram._init', MagicMock()) mocker.patch('freqtrade.rpc.telegram.Telegram.send_msg', side_effect=ValueError()) - + default_conf['telegram']['enabled'] = True freqtradebot = get_patched_freqtradebot(mocker, default_conf) rpc_manager = RPCManager(freqtradebot) rpc_manager.send_msg({ @@ -99,6 +101,7 @@ def test_send_msg_telegram_error(mocker, default_conf, caplog) -> None: def test_process_msg_queue(mocker, default_conf, caplog) -> None: telegram_mock = mocker.patch('freqtrade.rpc.telegram.Telegram.send_msg') + default_conf['telegram']['enabled'] = True default_conf['telegram']['allow_custom_messages'] = True mocker.patch('freqtrade.rpc.telegram.Telegram._init') @@ -115,9 +118,9 @@ def test_process_msg_queue(mocker, default_conf, caplog) -> None: def test_send_msg_telegram_enabled(mocker, default_conf, caplog) -> None: + default_conf['telegram']['enabled'] = True telegram_mock = mocker.patch('freqtrade.rpc.telegram.Telegram.send_msg') mocker.patch('freqtrade.rpc.telegram.Telegram._init') - freqtradebot = get_patched_freqtradebot(mocker, default_conf) rpc_manager = RPCManager(freqtradebot) rpc_manager.send_msg({ @@ -166,7 +169,8 @@ def test_send_msg_webhook_CustomMessagetype(mocker, default_conf, caplog) -> Non caplog) -def test_startupmessages_telegram_enabled(mocker, default_conf, caplog) -> None: +def test_startupmessages_telegram_enabled(mocker, default_conf) -> None: + default_conf['telegram']['enabled'] = True telegram_mock = mocker.patch('freqtrade.rpc.telegram.Telegram.send_msg', MagicMock()) mocker.patch('freqtrade.rpc.telegram.Telegram._init', MagicMock()) diff --git a/tests/rpc/test_rpc_telegram.py b/tests/rpc/test_rpc_telegram.py index b1859f581..4b4c2b028 100644 --- a/tests/rpc/test_rpc_telegram.py +++ b/tests/rpc/test_rpc_telegram.py @@ -2,13 +2,15 @@ # pragma pylint: disable=protected-access, unused-argument, invalid-name # pragma pylint: disable=too-many-lines, too-many-arguments +import asyncio import logging import re +import threading from datetime import datetime, timedelta, timezone from functools import reduce from random import choice, randint from string import ascii_uppercase -from unittest.mock import ANY, MagicMock +from unittest.mock import ANY, AsyncMock, MagicMock import arrow import pytest @@ -36,6 +38,40 @@ from tests.conftest import (CURRENT_TEST_STRATEGY, EXMS, create_mock_trades, patch_exchange, patch_get_signal, patch_whitelist) +@pytest.fixture(autouse=True) +def mock_exchange_loop(mocker): + mocker.patch('freqtrade.exchange.exchange.Exchange._init_async_loop') + + +@pytest.fixture +def default_conf(default_conf) -> dict: + # Telegram is enabled by default + default_conf['telegram']['enabled'] = True + return default_conf + + +@pytest.fixture +def update(): + message = Message(0, datetime.utcnow(), Chat(0, 0)) + _update = Update(0, message=message) + + return _update + + +def patch_eventloop_threading(telegrambot): + is_init = False + + def thread_fuck(): + nonlocal is_init + telegrambot._loop = asyncio.new_event_loop() + is_init = True + telegrambot._loop.run_forever() + x = threading.Thread(target=thread_fuck, daemon=True) + x.start() + while not is_init: + pass + + class DummyCls(Telegram): """ Dummy class for testing the Telegram @authorized_only decorator @@ -49,14 +85,14 @@ class DummyCls(Telegram): pass @authorized_only - def dummy_handler(self, *args, **kwargs) -> None: + async def dummy_handler(self, *args, **kwargs) -> None: """ Fake method that only change the state of the object """ self.state['called'] = True @authorized_only - def dummy_exception(self, *args, **kwargs) -> None: + async def dummy_exception(self, *args, **kwargs) -> None: """ Fake method that throw an exception """ @@ -64,23 +100,26 @@ class DummyCls(Telegram): def get_telegram_testobject(mocker, default_conf, mock=True, ftbot=None): - msg_mock = MagicMock() + msg_mock = AsyncMock() if mock: mocker.patch.multiple( 'freqtrade.rpc.telegram.Telegram', _init=MagicMock(), - _send_msg=msg_mock + _send_msg=msg_mock, + _start_thread=MagicMock(), ) if not ftbot: + mocker.patch('freqtrade.exchange.exchange.Exchange._init_async_loop') ftbot = get_patched_freqtradebot(mocker, default_conf) rpc = RPC(ftbot) telegram = Telegram(rpc, default_conf) + telegram._loop = MagicMock() + patch_eventloop_threading(telegram) return telegram, ftbot, msg_mock def test_telegram__init__(default_conf, mocker) -> None: - mocker.patch('freqtrade.rpc.telegram.Updater', MagicMock()) mocker.patch('freqtrade.rpc.telegram.Telegram._init', MagicMock()) telegram, _, _ = get_telegram_testobject(mocker, default_conf) @@ -88,43 +127,73 @@ def test_telegram__init__(default_conf, mocker) -> None: def test_telegram_init(default_conf, mocker, caplog) -> None: - start_polling = MagicMock() - mocker.patch('freqtrade.rpc.telegram.Updater', MagicMock(return_value=start_polling)) + app_mock = MagicMock() + mocker.patch('freqtrade.rpc.telegram.Telegram._start_thread', MagicMock()) + mocker.patch('freqtrade.rpc.telegram.Telegram._init_telegram_app', return_value=app_mock) + mocker.patch('freqtrade.rpc.telegram.Telegram._startup_telegram', AsyncMock()) - get_telegram_testobject(mocker, default_conf, mock=False) - assert start_polling.call_count == 0 + telegram, _, _ = get_telegram_testobject(mocker, default_conf, mock=False) + telegram._init() + assert app_mock.call_count == 0 # number of handles registered - assert start_polling.dispatcher.add_handler.call_count > 0 - assert start_polling.start_polling.call_count == 1 + assert app_mock.add_handler.call_count > 0 + # assert start_polling.start_polling.call_count == 1 message_str = ("rpc.telegram is listening for following commands: [['status'], ['profit'], " "['balance'], ['start'], ['stop'], " - "['forcesell', 'forceexit', 'fx'], ['forcebuy', 'forcelong'], ['forceshort'], " - "['trades'], ['delete'], ['coo', 'cancel_open_order'], ['performance'], " - "['buys', 'entries'], ['sells', 'exits'], ['mix_tags'], " + "['forceexit', 'forcesell', 'fx'], ['forcebuy', 'forcelong'], ['forceshort'], " + "['trades'], ['delete'], ['cancel_open_order', 'coo'], ['performance'], " + "['buys', 'entries'], ['exits', 'sells'], ['mix_tags'], " "['stats'], ['daily'], ['weekly'], ['monthly'], " - "['count'], ['locks'], ['unlock', 'delete_locks'], " - "['reload_config', 'reload_conf'], ['show_config', 'show_conf'], " + "['count'], ['locks'], ['delete_locks', 'unlock'], " + "['reload_conf', 'reload_config'], ['show_conf', 'show_config'], " "['stopbuy', 'stopentry'], ['whitelist'], ['blacklist'], " - "['blacklist_delete', 'bl_delete'], " + "['bl_delete', 'blacklist_delete'], " "['logs'], ['edge'], ['health'], ['help'], ['version'], ['marketdir']" "]") assert log_has(message_str, caplog) -def test_cleanup(default_conf, mocker, ) -> None: +async def test_telegram_startup(default_conf, mocker) -> None: + app_mock = MagicMock() + app_mock.initialize = AsyncMock() + app_mock.start = AsyncMock() + app_mock.updater.start_polling = AsyncMock() + app_mock.updater.running = False + sleep_mock = mocker.patch('freqtrade.rpc.telegram.asyncio.sleep', AsyncMock()) + + telegram, _, _ = get_telegram_testobject(mocker, default_conf) + telegram._app = app_mock + await telegram._startup_telegram() + assert app_mock.initialize.call_count == 1 + assert app_mock.start.call_count == 1 + assert app_mock.updater.start_polling.call_count == 1 + assert sleep_mock.call_count == 1 + + +async def test_telegram_cleanup(default_conf, mocker, ) -> None: + app_mock = MagicMock() + app_mock.stop = AsyncMock() + app_mock.initialize = AsyncMock() + updater_mock = MagicMock() - updater_mock.stop = MagicMock() - mocker.patch('freqtrade.rpc.telegram.Updater', updater_mock) + updater_mock.stop = AsyncMock() + app_mock.updater = updater_mock + # mocker.patch('freqtrade.rpc.telegram.Application', app_mock) - telegram, _, _ = get_telegram_testobject(mocker, default_conf, mock=False) + telegram, _, _ = get_telegram_testobject(mocker, default_conf) + telegram._app = app_mock + telegram._loop = asyncio.get_running_loop() + telegram._thread = MagicMock() telegram.cleanup() - assert telegram._updater.stop.call_count == 1 + await asyncio.sleep(0.1) + assert app_mock.stop.call_count == 1 + assert telegram._thread.join.call_count == 1 -def test_authorized_only(default_conf, mocker, caplog, update) -> None: +async def test_authorized_only(default_conf, mocker, caplog, update) -> None: patch_exchange(mocker) caplog.set_level(logging.DEBUG) default_conf['telegram']['enabled'] = False @@ -133,19 +202,19 @@ def test_authorized_only(default_conf, mocker, caplog, update) -> None: dummy = DummyCls(rpc, default_conf) patch_get_signal(bot) - dummy.dummy_handler(update=update, context=MagicMock()) + await dummy.dummy_handler(update=update, context=MagicMock()) assert dummy.state['called'] is True assert log_has('Executing handler: dummy_handler for chat_id: 0', caplog) assert not log_has('Rejected unauthorized message from: 0', caplog) assert not log_has('Exception occurred within Telegram module', caplog) -def test_authorized_only_unauthorized(default_conf, mocker, caplog) -> None: +async def test_authorized_only_unauthorized(default_conf, mocker, caplog) -> None: patch_exchange(mocker) caplog.set_level(logging.DEBUG) chat = Chat(0xdeadbeef, 0) - update = Update(randint(1, 100)) - update.message = Message(randint(1, 100), datetime.utcnow(), chat) + message = Message(randint(1, 100), datetime.utcnow(), chat) + update = Update(randint(1, 100), message=message) default_conf['telegram']['enabled'] = False bot = FreqtradeBot(default_conf) @@ -153,14 +222,14 @@ def test_authorized_only_unauthorized(default_conf, mocker, caplog) -> None: dummy = DummyCls(rpc, default_conf) patch_get_signal(bot) - dummy.dummy_handler(update=update, context=MagicMock()) + await dummy.dummy_handler(update=update, context=MagicMock()) assert dummy.state['called'] is False assert not log_has('Executing handler: dummy_handler for chat_id: 3735928559', caplog) assert log_has('Rejected unauthorized message from: 3735928559', caplog) assert not log_has('Exception occurred within Telegram module', caplog) -def test_authorized_only_exception(default_conf, mocker, caplog, update) -> None: +async def test_authorized_only_exception(default_conf, mocker, caplog, update) -> None: patch_exchange(mocker) default_conf['telegram']['enabled'] = False @@ -170,17 +239,15 @@ def test_authorized_only_exception(default_conf, mocker, caplog, update) -> None dummy = DummyCls(rpc, default_conf) patch_get_signal(bot) - dummy.dummy_exception(update=update, context=MagicMock()) + await dummy.dummy_exception(update=update, context=MagicMock()) assert dummy.state['called'] is False assert not log_has('Executing handler: dummy_handler for chat_id: 0', caplog) assert not log_has('Rejected unauthorized message from: 0', caplog) assert log_has('Exception occurred within Telegram module', caplog) -def test_telegram_status(default_conf, update, mocker) -> None: - update.message.chat.id = "123" +async def test_telegram_status(default_conf, update, mocker) -> None: default_conf['telegram']['enabled'] = False - default_conf['telegram']['chat_id'] = "123" status_table = MagicMock() mocker.patch('freqtrade.rpc.telegram.Telegram._status_table', status_table) @@ -225,21 +292,19 @@ def test_telegram_status(default_conf, update, mocker) -> None: telegram, _, msg_mock = get_telegram_testobject(mocker, default_conf) - telegram._status(update=update, context=MagicMock()) + await telegram._status(update=update, context=MagicMock()) assert msg_mock.call_count == 1 context = MagicMock() # /status table context.args = ["table"] - telegram._status(update=update, context=context) + await telegram._status(update=update, context=context) assert status_table.call_count == 1 @pytest.mark.usefixtures("init_persistence") -def test_telegram_status_multi_entry(default_conf, update, mocker, fee) -> None: - update.message.chat.id = "123" +async def test_telegram_status_multi_entry(default_conf, update, mocker, fee) -> None: default_conf['telegram']['enabled'] = False - default_conf['telegram']['chat_id'] = "123" default_conf['position_adjustment_enable'] = True mocker.patch.multiple( EXMS, @@ -277,7 +342,7 @@ def test_telegram_status_multi_entry(default_conf, update, mocker, fee) -> None: trade.recalc_trade_from_orders() Trade.commit() - telegram._status(update=update, context=MagicMock()) + await telegram._status(update=update, context=MagicMock()) assert msg_mock.call_count == 4 msg = msg_mock.call_args_list[0][0][0] assert re.search(r'Number of Entries.*2', msg) @@ -289,10 +354,7 @@ def test_telegram_status_multi_entry(default_conf, update, mocker, fee) -> None: @pytest.mark.usefixtures("init_persistence") -def test_telegram_status_closed_trade(default_conf, update, mocker, fee) -> None: - update.message.chat.id = "123" - default_conf['telegram']['enabled'] = False - default_conf['telegram']['chat_id'] = "123" +async def test_telegram_status_closed_trade(default_conf, update, mocker, fee) -> None: default_conf['position_adjustment_enable'] = True mocker.patch.multiple( EXMS, @@ -306,14 +368,14 @@ def test_telegram_status_closed_trade(default_conf, update, mocker, fee) -> None trade = Trade.get_trades([Trade.is_open.is_(False)]).first() context = MagicMock() context.args = [str(trade.id)] - telegram._status(update=update, context=context) + await telegram._status(update=update, context=context) assert msg_mock.call_count == 1 msg = msg_mock.call_args_list[0][0][0] assert re.search(r'Close Date:', msg) assert re.search(r'Close Profit:', msg) -def test_status_handle(default_conf, update, ticker, fee, mocker) -> None: +async def test_status_handle(default_conf, update, ticker, fee, mocker) -> None: default_conf['max_open_trades'] = 3 mocker.patch.multiple( EXMS, @@ -333,13 +395,13 @@ def test_status_handle(default_conf, update, ticker, fee, mocker) -> None: freqtradebot.state = State.STOPPED # Status is also enabled when stopped - telegram._status(update=update, context=MagicMock()) + await telegram._status(update=update, context=MagicMock()) assert msg_mock.call_count == 1 assert 'no active trade' in msg_mock.call_args_list[0][0][0] msg_mock.reset_mock() freqtradebot.state = State.RUNNING - telegram._status(update=update, context=MagicMock()) + await telegram._status(update=update, context=MagicMock()) assert msg_mock.call_count == 1 assert 'no active trade' in msg_mock.call_args_list[0][0][0] msg_mock.reset_mock() @@ -347,7 +409,7 @@ def test_status_handle(default_conf, update, ticker, fee, mocker) -> None: # Create some test data freqtradebot.enter_positions() # Trigger status while we have a fulfilled order for the open trade - telegram._status(update=update, context=MagicMock()) + await telegram._status(update=update, context=MagicMock()) # close_rate should not be included in the message as the trade is not closed # and no line should be empty @@ -364,7 +426,7 @@ def test_status_handle(default_conf, update, ticker, fee, mocker) -> None: context = MagicMock() context.args = ["2", "3"] - telegram._status(update=update, context=context) + await telegram._status(update=update, context=context) lines = msg_mock.call_args_list[0][0][0].split('\n') assert '' not in lines[:-1] @@ -379,7 +441,7 @@ def test_status_handle(default_conf, update, ticker, fee, mocker) -> None: msg_mock.reset_mock() context = MagicMock() context.args = ["2"] - telegram._status(update=update, context=context) + await telegram._status(update=update, context=context) assert msg_mock.call_count == 2 @@ -391,7 +453,7 @@ def test_status_handle(default_conf, update, ticker, fee, mocker) -> None: assert 'Trade ID:* `2` - continued' in msg2 -def test_status_table_handle(default_conf, update, ticker, fee, mocker) -> None: +async def test_status_table_handle(default_conf, update, ticker, fee, mocker) -> None: mocker.patch.multiple( EXMS, fetch_ticker=ticker, @@ -406,13 +468,13 @@ def test_status_table_handle(default_conf, update, ticker, fee, mocker) -> None: freqtradebot.state = State.STOPPED # Status table is also enabled when stopped - telegram._status_table(update=update, context=MagicMock()) + await telegram._status_table(update=update, context=MagicMock()) assert msg_mock.call_count == 1 assert 'no active trade' in msg_mock.call_args_list[0][0][0] msg_mock.reset_mock() freqtradebot.state = State.RUNNING - telegram._status_table(update=update, context=MagicMock()) + await telegram._status_table(update=update, context=MagicMock()) assert msg_mock.call_count == 1 assert 'no active trade' in msg_mock.call_args_list[0][0][0] msg_mock.reset_mock() @@ -420,7 +482,7 @@ def test_status_table_handle(default_conf, update, ticker, fee, mocker) -> None: # Create some test data freqtradebot.enter_positions() - telegram._status_table(update=update, context=MagicMock()) + await telegram._status_table(update=update, context=MagicMock()) text = re.sub('', '', msg_mock.call_args_list[-1][0][0]) line = text.split("\n") @@ -432,7 +494,7 @@ def test_status_table_handle(default_conf, update, ticker, fee, mocker) -> None: assert msg_mock.call_count == 1 -def test_daily_handle(default_conf_usdt, update, ticker, fee, mocker, time_machine) -> None: +async def test_daily_handle(default_conf_usdt, update, ticker, fee, mocker, time_machine) -> None: mocker.patch( 'freqtrade.rpc.rpc.CryptoToFiatConverter._find_price', return_value=1.1 @@ -454,7 +516,7 @@ def test_daily_handle(default_conf_usdt, update, ticker, fee, mocker, time_machi # /daily 2 context = MagicMock() context.args = ["2"] - telegram._daily(update=update, context=context) + await telegram._daily(update=update, context=context) assert msg_mock.call_count == 1 assert "Daily Profit over the last 2 days:" in msg_mock.call_args_list[0][0][0] assert 'Day ' in msg_mock.call_args_list[0][0][0] @@ -468,7 +530,7 @@ def test_daily_handle(default_conf_usdt, update, ticker, fee, mocker, time_machi # Reset msg_mock msg_mock.reset_mock() context.args = [] - telegram._daily(update=update, context=context) + await telegram._daily(update=update, context=context) assert msg_mock.call_count == 1 assert "Daily Profit over the last 7 days:" in msg_mock.call_args_list[0][0][0] assert str(datetime.utcnow().date()) in msg_mock.call_args_list[0][0][0] @@ -485,13 +547,13 @@ def test_daily_handle(default_conf_usdt, update, ticker, fee, mocker, time_machi # /daily 1 context = MagicMock() context.args = ["1"] - telegram._daily(update=update, context=context) + await telegram._daily(update=update, context=context) assert ' 6.83 USDT' in msg_mock.call_args_list[0][0][0] assert ' 7.51 USD' in msg_mock.call_args_list[0][0][0] assert '(2)' in msg_mock.call_args_list[0][0][0] -def test_daily_wrong_input(default_conf, update, ticker, mocker) -> None: +async def test_daily_wrong_input(default_conf, update, ticker, mocker) -> None: mocker.patch.multiple( EXMS, fetch_ticker=ticker @@ -506,7 +568,7 @@ def test_daily_wrong_input(default_conf, update, ticker, mocker) -> None: # /daily -2 context = MagicMock() context.args = ["-2"] - telegram._daily(update=update, context=context) + await telegram._daily(update=update, context=context) assert msg_mock.call_count == 1 assert 'must be an integer greater than 0' in msg_mock.call_args_list[0][0][0] @@ -516,11 +578,11 @@ def test_daily_wrong_input(default_conf, update, ticker, mocker) -> None: # /daily today context = MagicMock() context.args = ["today"] - telegram._daily(update=update, context=context) + await telegram._daily(update=update, context=context) assert 'Daily Profit over the last 7 days:' in msg_mock.call_args_list[0][0][0] -def test_weekly_handle(default_conf_usdt, update, ticker, fee, mocker, time_machine) -> None: +async def test_weekly_handle(default_conf_usdt, update, ticker, fee, mocker, time_machine) -> None: default_conf_usdt['max_open_trades'] = 1 mocker.patch( 'freqtrade.rpc.rpc.CryptoToFiatConverter._find_price', @@ -541,7 +603,7 @@ def test_weekly_handle(default_conf_usdt, update, ticker, fee, mocker, time_mach # /weekly 2 context = MagicMock() context.args = ["2"] - telegram._weekly(update=update, context=context) + await telegram._weekly(update=update, context=context) assert msg_mock.call_count == 1 assert "Weekly Profit over the last 2 weeks (starting from Monday):" \ in msg_mock.call_args_list[0][0][0] @@ -557,7 +619,7 @@ def test_weekly_handle(default_conf_usdt, update, ticker, fee, mocker, time_mach # Reset msg_mock msg_mock.reset_mock() context.args = [] - telegram._weekly(update=update, context=context) + await telegram._weekly(update=update, context=context) assert msg_mock.call_count == 1 assert "Weekly Profit over the last 8 weeks (starting from Monday):" \ in msg_mock.call_args_list[0][0][0] @@ -573,7 +635,7 @@ def test_weekly_handle(default_conf_usdt, update, ticker, fee, mocker, time_mach # /weekly -3 context = MagicMock() context.args = ["-3"] - telegram._weekly(update=update, context=context) + await telegram._weekly(update=update, context=context) assert msg_mock.call_count == 1 assert 'must be an integer greater than 0' in msg_mock.call_args_list[0][0][0] @@ -583,14 +645,14 @@ def test_weekly_handle(default_conf_usdt, update, ticker, fee, mocker, time_mach # /weekly this week context = MagicMock() context.args = ["this week"] - telegram._weekly(update=update, context=context) + await telegram._weekly(update=update, context=context) assert ( 'Weekly Profit over the last 8 weeks (starting from Monday):' in msg_mock.call_args_list[0][0][0] ) -def test_monthly_handle(default_conf_usdt, update, ticker, fee, mocker, time_machine) -> None: +async def test_monthly_handle(default_conf_usdt, update, ticker, fee, mocker, time_machine) -> None: default_conf_usdt['max_open_trades'] = 1 mocker.patch( 'freqtrade.rpc.rpc.CryptoToFiatConverter._find_price', @@ -611,7 +673,7 @@ def test_monthly_handle(default_conf_usdt, update, ticker, fee, mocker, time_mac # /monthly 2 context = MagicMock() context.args = ["2"] - telegram._monthly(update=update, context=context) + await telegram._monthly(update=update, context=context) assert msg_mock.call_count == 1 assert 'Monthly Profit over the last 2 months:' in msg_mock.call_args_list[0][0][0] assert 'Month ' in msg_mock.call_args_list[0][0][0] @@ -626,7 +688,7 @@ def test_monthly_handle(default_conf_usdt, update, ticker, fee, mocker, time_mac # Reset msg_mock msg_mock.reset_mock() context.args = [] - telegram._monthly(update=update, context=context) + await telegram._monthly(update=update, context=context) assert msg_mock.call_count == 1 # Default to 6 months assert 'Monthly Profit over the last 6 months:' in msg_mock.call_args_list[0][0][0] @@ -643,7 +705,7 @@ def test_monthly_handle(default_conf_usdt, update, ticker, fee, mocker, time_mac # /monthly 12 context = MagicMock() context.args = ["12"] - telegram._monthly(update=update, context=context) + await telegram._monthly(update=update, context=context) assert msg_mock.call_count == 1 assert 'Monthly Profit over the last 12 months:' in msg_mock.call_args_list[0][0][0] assert ' 2.74 USDT' in msg_mock.call_args_list[0][0][0] @@ -652,7 +714,7 @@ def test_monthly_handle(default_conf_usdt, update, ticker, fee, mocker, time_mac # The one-digit months should contain a zero, Eg: September 2021 = "2021-09" # Since we loaded the last 12 months, any month should appear - assert str('-09') in msg_mock.call_args_list[0][0][0] + assert '-09' in msg_mock.call_args_list[0][0][0] # Try invalid data msg_mock.reset_mock() @@ -660,7 +722,7 @@ def test_monthly_handle(default_conf_usdt, update, ticker, fee, mocker, time_mac # /monthly -3 context = MagicMock() context.args = ["-3"] - telegram._monthly(update=update, context=context) + await telegram._monthly(update=update, context=context) assert msg_mock.call_count == 1 assert 'must be an integer greater than 0' in msg_mock.call_args_list[0][0][0] @@ -670,12 +732,13 @@ def test_monthly_handle(default_conf_usdt, update, ticker, fee, mocker, time_mac # /monthly february context = MagicMock() context.args = ["february"] - telegram._monthly(update=update, context=context) - assert str('Monthly Profit over the last 6 months:') in msg_mock.call_args_list[0][0][0] + await telegram._monthly(update=update, context=context) + assert 'Monthly Profit over the last 6 months:' in msg_mock.call_args_list[0][0][0] -def test_profit_handle(default_conf_usdt, update, ticker_usdt, ticker_sell_up, fee, - limit_sell_order_usdt, mocker) -> None: +async def test_telegram_profit_handle( + default_conf_usdt, update, ticker_usdt, ticker_sell_up, fee, + limit_sell_order_usdt, mocker) -> None: mocker.patch('freqtrade.rpc.rpc.CryptoToFiatConverter._find_price', return_value=1.1) mocker.patch.multiple( EXMS, @@ -686,7 +749,7 @@ def test_profit_handle(default_conf_usdt, update, ticker_usdt, ticker_sell_up, f telegram, freqtradebot, msg_mock = get_telegram_testobject(mocker, default_conf_usdt) patch_get_signal(freqtradebot) - telegram._profit(update=update, context=MagicMock()) + await telegram._profit(update=update, context=MagicMock()) assert msg_mock.call_count == 1 assert 'No trades yet.' in msg_mock.call_args_list[0][0][0] msg_mock.reset_mock() @@ -698,7 +761,7 @@ def test_profit_handle(default_conf_usdt, update, ticker_usdt, ticker_sell_up, f context = MagicMock() # Test with invalid 2nd argument (should silently pass) context.args = ["aaa"] - telegram._profit(update=update, context=context) + await telegram._profit(update=update, context=context) assert msg_mock.call_count == 1 assert 'No closed trade' in msg_mock.call_args_list[-1][0][0] assert '*ROI:* All trades' in msg_mock.call_args_list[-1][0][0] @@ -710,6 +773,7 @@ def test_profit_handle(default_conf_usdt, update, ticker_usdt, ticker_sell_up, f # Update the ticker with a market going up mocker.patch(f'{EXMS}.fetch_ticker', ticker_sell_up) # Simulate fulfilled LIMIT_SELL order for trade + trade = Trade.session.scalars(select(Trade)).first() oobj = Order.parse_from_ccxt_object( limit_sell_order_usdt, limit_sell_order_usdt['symbol'], 'sell') trade.orders.append(oobj) @@ -720,7 +784,7 @@ def test_profit_handle(default_conf_usdt, update, ticker_usdt, ticker_sell_up, f Trade.commit() context.args = [3] - telegram._profit(update=update, context=context) + await telegram._profit(update=update, context=context) assert msg_mock.call_count == 1 assert '*ROI:* Closed trades' in msg_mock.call_args_list[-1][0][0] assert ('∙ `5.685 USDT (9.45%) (0.57 \N{GREEK CAPITAL LETTER SIGMA}%)`' @@ -738,7 +802,7 @@ def test_profit_handle(default_conf_usdt, update, ticker_usdt, ticker_sell_up, f @pytest.mark.parametrize('is_short', [True, False]) -def test_telegram_stats(default_conf, update, ticker, fee, mocker, is_short) -> None: +async def test_telegram_stats(default_conf, update, ticker, fee, mocker, is_short) -> None: mocker.patch('freqtrade.rpc.rpc.CryptoToFiatConverter._find_price', return_value=15000.0) mocker.patch.multiple( EXMS, @@ -748,7 +812,7 @@ def test_telegram_stats(default_conf, update, ticker, fee, mocker, is_short) -> telegram, freqtradebot, msg_mock = get_telegram_testobject(mocker, default_conf) patch_get_signal(freqtradebot) - telegram._stats(update=update, context=MagicMock()) + await telegram._stats(update=update, context=MagicMock()) assert msg_mock.call_count == 1 assert 'No trades yet.' in msg_mock.call_args_list[0][0][0] msg_mock.reset_mock() @@ -756,7 +820,7 @@ def test_telegram_stats(default_conf, update, ticker, fee, mocker, is_short) -> # Create some test data create_mock_trades(fee, is_short=is_short) - telegram._stats(update=update, context=MagicMock()) + await telegram._stats(update=update, context=MagicMock()) assert msg_mock.call_count == 1 assert 'Exit Reason' in msg_mock.call_args_list[-1][0][0] assert 'ROI' in msg_mock.call_args_list[-1][0][0] @@ -764,7 +828,7 @@ def test_telegram_stats(default_conf, update, ticker, fee, mocker, is_short) -> msg_mock.reset_mock() -def test_telegram_balance_handle(default_conf, update, mocker, rpc_balance, tickers) -> None: +async def test_telegram_balance_handle(default_conf, update, mocker, rpc_balance, tickers) -> None: default_conf['dry_run'] = False mocker.patch(f'{EXMS}.get_balances', return_value=rpc_balance) mocker.patch(f'{EXMS}.get_tickers', tickers) @@ -773,23 +837,32 @@ def test_telegram_balance_handle(default_conf, update, mocker, rpc_balance, tick telegram, freqtradebot, msg_mock = get_telegram_testobject(mocker, default_conf) patch_get_signal(freqtradebot) - telegram._balance(update=update, context=MagicMock()) + await telegram._balance(update=update, context=MagicMock()) + context = MagicMock() + context.args = ["full"] + await telegram._balance(update=update, context=context) result = msg_mock.call_args_list[0][0][0] - assert msg_mock.call_count == 1 + result_full = msg_mock.call_args_list[1][0][0] + assert msg_mock.call_count == 2 assert '*BTC:*' in result assert '*ETH:*' not in result assert '*USDT:*' not in result assert '*EUR:*' not in result - assert '*LTC:*' in result + assert '*LTC:*' not in result + + assert '*LTC:*' in result_full assert '*XRP:*' not in result assert 'Balance:' in result assert 'Est. BTC:' in result - assert 'BTC: 12' in result + assert 'BTC: 11' in result + assert 'BTC: 12' in result_full assert "*3 Other Currencies (< 0.0001 BTC):*" in result assert 'BTC: 0.00000309' in result + assert '*Estimated Value*:' in result_full + assert '*Estimated Value (Bot managed assets only)*:' in result -def test_balance_handle_empty_response(default_conf, update, mocker) -> None: +async def test_balance_handle_empty_response(default_conf, update, mocker) -> None: default_conf['dry_run'] = False mocker.patch(f'{EXMS}.get_balances', return_value={}) @@ -797,26 +870,26 @@ def test_balance_handle_empty_response(default_conf, update, mocker) -> None: patch_get_signal(freqtradebot) freqtradebot.config['dry_run'] = False - telegram._balance(update=update, context=MagicMock()) + await telegram._balance(update=update, context=MagicMock()) result = msg_mock.call_args_list[0][0][0] assert msg_mock.call_count == 1 assert 'Starting capital: `0 BTC' in result -def test_balance_handle_empty_response_dry(default_conf, update, mocker) -> None: +async def test_balance_handle_empty_response_dry(default_conf, update, mocker) -> None: mocker.patch(f'{EXMS}.get_balances', return_value={}) telegram, freqtradebot, msg_mock = get_telegram_testobject(mocker, default_conf) patch_get_signal(freqtradebot) - telegram._balance(update=update, context=MagicMock()) + await telegram._balance(update=update, context=MagicMock()) result = msg_mock.call_args_list[0][0][0] assert msg_mock.call_count == 1 assert "*Warning:* Simulated balances in Dry Mode." in result assert "Starting capital: `1000 BTC`" in result -def test_balance_handle_too_large_response(default_conf, update, mocker) -> None: +async def test_balance_handle_too_large_response(default_conf, update, mocker) -> None: balances = [] for i in range(100): curr = choice(ascii_uppercase) + choice(ascii_uppercase) + choice(ascii_uppercase) @@ -825,18 +898,23 @@ def test_balance_handle_too_large_response(default_conf, update, mocker) -> None 'free': 1.0, 'used': 0.5, 'balance': i, + 'bot_owned': 0.5, 'est_stake': 1, + 'est_stake_bot': 1, 'stake': 'BTC', 'is_position': False, 'leverage': 1.0, 'position': 0.0, 'side': 'long', + 'is_bot_managed': True, }) mocker.patch('freqtrade.rpc.rpc.RPC._rpc_balance', return_value={ 'currencies': balances, 'total': 100.0, + 'total_bot': 100.0, 'symbol': 100.0, 'value': 1000.0, + 'value_bot': 1000.0, 'starting_capital': 1000, 'starting_capital_fiat': 1000, }) @@ -844,7 +922,7 @@ def test_balance_handle_too_large_response(default_conf, update, mocker) -> None telegram, freqtradebot, msg_mock = get_telegram_testobject(mocker, default_conf) patch_get_signal(freqtradebot) - telegram._balance(update=update, context=MagicMock()) + await telegram._balance(update=update, context=MagicMock()) assert msg_mock.call_count > 1 # Test if wrap happens around 4000 - # and each single currency-output is around 120 characters long so we need @@ -853,79 +931,79 @@ def test_balance_handle_too_large_response(default_conf, update, mocker) -> None assert len(msg_mock.call_args_list[0][0][0]) > (4096 - 120) -def test_start_handle(default_conf, update, mocker) -> None: +async def test_start_handle(default_conf, update, mocker) -> None: telegram, freqtradebot, msg_mock = get_telegram_testobject(mocker, default_conf) freqtradebot.state = State.STOPPED assert freqtradebot.state == State.STOPPED - telegram._start(update=update, context=MagicMock()) + await telegram._start(update=update, context=MagicMock()) assert freqtradebot.state == State.RUNNING assert msg_mock.call_count == 1 -def test_start_handle_already_running(default_conf, update, mocker) -> None: +async def test_start_handle_already_running(default_conf, update, mocker) -> None: telegram, freqtradebot, msg_mock = get_telegram_testobject(mocker, default_conf) freqtradebot.state = State.RUNNING assert freqtradebot.state == State.RUNNING - telegram._start(update=update, context=MagicMock()) + await telegram._start(update=update, context=MagicMock()) assert freqtradebot.state == State.RUNNING assert msg_mock.call_count == 1 assert 'already running' in msg_mock.call_args_list[0][0][0] -def test_stop_handle(default_conf, update, mocker) -> None: +async def test_stop_handle(default_conf, update, mocker) -> None: telegram, freqtradebot, msg_mock = get_telegram_testobject(mocker, default_conf) freqtradebot.state = State.RUNNING assert freqtradebot.state == State.RUNNING - telegram._stop(update=update, context=MagicMock()) + await telegram._stop(update=update, context=MagicMock()) assert freqtradebot.state == State.STOPPED assert msg_mock.call_count == 1 assert 'stopping trader' in msg_mock.call_args_list[0][0][0] -def test_stop_handle_already_stopped(default_conf, update, mocker) -> None: +async def test_stop_handle_already_stopped(default_conf, update, mocker) -> None: telegram, freqtradebot, msg_mock = get_telegram_testobject(mocker, default_conf) freqtradebot.state = State.STOPPED assert freqtradebot.state == State.STOPPED - telegram._stop(update=update, context=MagicMock()) + await telegram._stop(update=update, context=MagicMock()) assert freqtradebot.state == State.STOPPED assert msg_mock.call_count == 1 assert 'already stopped' in msg_mock.call_args_list[0][0][0] -def test_stopbuy_handle(default_conf, update, mocker) -> None: +async def test_stopbuy_handle(default_conf, update, mocker) -> None: telegram, freqtradebot, msg_mock = get_telegram_testobject(mocker, default_conf) assert freqtradebot.config['max_open_trades'] != 0 - telegram._stopentry(update=update, context=MagicMock()) + await telegram._stopentry(update=update, context=MagicMock()) assert freqtradebot.config['max_open_trades'] == 0 assert msg_mock.call_count == 1 assert 'No more entries will occur from now. Run /reload_config to reset.' \ in msg_mock.call_args_list[0][0][0] -def test_reload_config_handle(default_conf, update, mocker) -> None: +async def test_reload_config_handle(default_conf, update, mocker) -> None: telegram, freqtradebot, msg_mock = get_telegram_testobject(mocker, default_conf) freqtradebot.state = State.RUNNING assert freqtradebot.state == State.RUNNING - telegram._reload_config(update=update, context=MagicMock()) + await telegram._reload_config(update=update, context=MagicMock()) assert freqtradebot.state == State.RELOAD_CONFIG assert msg_mock.call_count == 1 assert 'Reloading config' in msg_mock.call_args_list[0][0][0] -def test_telegram_forceexit_handle(default_conf, update, ticker, fee, - ticker_sell_up, mocker) -> None: +async def test_telegram_forceexit_handle(default_conf, update, ticker, fee, + ticker_sell_up, mocker) -> None: mocker.patch('freqtrade.rpc.rpc.CryptoToFiatConverter._find_price', return_value=15000.0) msg_mock = mocker.patch('freqtrade.rpc.telegram.Telegram.send_msg', MagicMock()) mocker.patch('freqtrade.rpc.telegram.Telegram._init', MagicMock()) @@ -955,7 +1033,7 @@ def test_telegram_forceexit_handle(default_conf, update, ticker, fee, # /forceexit 1 context = MagicMock() context.args = ["1"] - telegram._force_exit(update=update, context=context) + await telegram._force_exit(update=update, context=context) assert msg_mock.call_count == 4 last_msg = msg_mock.call_args_list[-2][0][0] @@ -991,8 +1069,8 @@ def test_telegram_forceexit_handle(default_conf, update, ticker, fee, } == last_msg -def test_telegram_force_exit_down_handle(default_conf, update, ticker, fee, - ticker_sell_down, mocker) -> None: +async def test_telegram_force_exit_down_handle(default_conf, update, ticker, fee, + ticker_sell_down, mocker) -> None: mocker.patch('freqtrade.rpc.fiat_convert.CryptoToFiatConverter._find_price', return_value=15000.0) msg_mock = mocker.patch('freqtrade.rpc.telegram.Telegram.send_msg', MagicMock()) @@ -1027,7 +1105,7 @@ def test_telegram_force_exit_down_handle(default_conf, update, ticker, fee, # /forceexit 1 context = MagicMock() context.args = ["1"] - telegram._force_exit(update=update, context=context) + await telegram._force_exit(update=update, context=context) assert msg_mock.call_count == 4 @@ -1064,7 +1142,7 @@ def test_telegram_force_exit_down_handle(default_conf, update, ticker, fee, } == last_msg -def test_forceexit_all_handle(default_conf, update, ticker, fee, mocker) -> None: +async def test_forceexit_all_handle(default_conf, update, ticker, fee, mocker) -> None: patch_exchange(mocker) mocker.patch('freqtrade.rpc.fiat_convert.CryptoToFiatConverter._find_price', return_value=15000.0) @@ -1090,7 +1168,7 @@ def test_forceexit_all_handle(default_conf, update, ticker, fee, mocker) -> None # /forceexit all context = MagicMock() context.args = ["all"] - telegram._force_exit(update=update, context=context) + await telegram._force_exit(update=update, context=context) # Called for each trade 2 times assert msg_mock.call_count == 8 @@ -1127,7 +1205,7 @@ def test_forceexit_all_handle(default_conf, update, ticker, fee, mocker) -> None } == msg -def test_forceexit_handle_invalid(default_conf, update, mocker) -> None: +async def test_forceexit_handle_invalid(default_conf, update, mocker) -> None: mocker.patch('freqtrade.rpc.fiat_convert.CryptoToFiatConverter._find_price', return_value=15000.0) @@ -1139,7 +1217,7 @@ def test_forceexit_handle_invalid(default_conf, update, mocker) -> None: # /forceexit 1 context = MagicMock() context.args = ["1"] - telegram._force_exit(update=update, context=context) + await telegram._force_exit(update=update, context=context) assert msg_mock.call_count == 1 assert 'not running' in msg_mock.call_args_list[0][0][0] @@ -1149,12 +1227,12 @@ def test_forceexit_handle_invalid(default_conf, update, mocker) -> None: # /forceexit 123456 context = MagicMock() context.args = ["123456"] - telegram._force_exit(update=update, context=context) + await telegram._force_exit(update=update, context=context) assert msg_mock.call_count == 1 assert 'invalid argument' in msg_mock.call_args_list[0][0][0] -def test_force_exit_no_pair(default_conf, update, ticker, fee, mocker) -> None: +async def test_force_exit_no_pair(default_conf, update, ticker, fee, mocker) -> None: default_conf['max_open_trades'] = 4 mocker.patch.multiple( EXMS, @@ -1170,7 +1248,7 @@ def test_force_exit_no_pair(default_conf, update, ticker, fee, mocker) -> None: # /forceexit context = MagicMock() context.args = [] - telegram._force_exit(update=update, context=context) + await telegram._force_exit(update=update, context=context) # No pair assert msg_mock.call_args_list[0][1]['msg'] == 'No open trade found.' @@ -1179,7 +1257,7 @@ def test_force_exit_no_pair(default_conf, update, ticker, fee, mocker) -> None: msg_mock.reset_mock() # /forceexit - telegram._force_exit(update=update, context=context) + await telegram._force_exit(update=update, context=context) keyboard = msg_mock.call_args_list[0][1]['keyboard'] # 4 pairs + cancel assert reduce(lambda acc, x: acc + len(x), keyboard, 0) == 5 @@ -1187,9 +1265,9 @@ def test_force_exit_no_pair(default_conf, update, ticker, fee, mocker) -> None: assert keyboard[1][0].callback_data == 'force_exit__2 ' update = MagicMock() - update.callback_query = MagicMock() + update.callback_query = AsyncMock() update.callback_query.data = keyboard[1][0].callback_data - telegram._force_exit_inline(update, None) + await telegram._force_exit_inline(update, None) assert update.callback_query.answer.call_count == 1 assert update.callback_query.edit_message_text.call_count == 1 assert femock.call_count == 1 @@ -1197,17 +1275,17 @@ def test_force_exit_no_pair(default_conf, update, ticker, fee, mocker) -> None: # Retry exiting - but cancel instead update.callback_query.reset_mock() - telegram._force_exit(update=update, context=context) + await telegram._force_exit(update=update, context=context) # Use cancel button update.callback_query.data = keyboard[-1][0].callback_data - telegram._force_exit_inline(update, None) + await telegram._force_exit_inline(update, None) query = update.callback_query assert query.answer.call_count == 1 assert query.edit_message_text.call_count == 1 assert query.edit_message_text.call_args_list[-1][1]['text'] == "Force exit canceled." -def test_force_enter_handle(default_conf, update, mocker) -> None: +async def test_force_enter_handle(default_conf, update, mocker) -> None: mocker.patch('freqtrade.rpc.rpc.CryptoToFiatConverter._find_price', return_value=15000.0) fbuy_mock = MagicMock(return_value=None) @@ -1219,7 +1297,7 @@ def test_force_enter_handle(default_conf, update, mocker) -> None: # /forcelong ETH/BTC context = MagicMock() context.args = ["ETH/BTC"] - telegram._force_enter(update=update, context=context, order_side=SignalDirection.LONG) + await telegram._force_enter(update=update, context=context, order_side=SignalDirection.LONG) assert fbuy_mock.call_count == 1 assert fbuy_mock.call_args_list[0][0][0] == 'ETH/BTC' @@ -1232,7 +1310,7 @@ def test_force_enter_handle(default_conf, update, mocker) -> None: # /forcelong ETH/BTC 0.055 context = MagicMock() context.args = ["ETH/BTC", "0.055"] - telegram._force_enter(update=update, context=context, order_side=SignalDirection.LONG) + await telegram._force_enter(update=update, context=context, order_side=SignalDirection.LONG) assert fbuy_mock.call_count == 1 assert fbuy_mock.call_args_list[0][0][0] == 'ETH/BTC' @@ -1240,20 +1318,19 @@ def test_force_enter_handle(default_conf, update, mocker) -> None: assert fbuy_mock.call_args_list[0][0][1] == 0.055 -def test_force_enter_handle_exception(default_conf, update, mocker) -> None: +async def test_force_enter_handle_exception(default_conf, update, mocker) -> None: mocker.patch('freqtrade.rpc.rpc.CryptoToFiatConverter._find_price', return_value=15000.0) telegram, freqtradebot, msg_mock = get_telegram_testobject(mocker, default_conf) patch_get_signal(freqtradebot) - update.message.text = '/forcebuy ETH/Nonepair' - telegram._force_enter(update=update, context=MagicMock(), order_side=SignalDirection.LONG) + await telegram._force_enter(update=update, context=MagicMock(), order_side=SignalDirection.LONG) assert msg_mock.call_count == 1 assert msg_mock.call_args_list[0][0][0] == 'Force_entry not enabled.' -def test_force_enter_no_pair(default_conf, update, mocker) -> None: +async def test_force_enter_no_pair(default_conf, update, mocker) -> None: mocker.patch('freqtrade.rpc.rpc.CryptoToFiatConverter._find_price', return_value=15000.0) fbuy_mock = MagicMock(return_value=None) @@ -1265,7 +1342,7 @@ def test_force_enter_no_pair(default_conf, update, mocker) -> None: context = MagicMock() context.args = [] - telegram._force_enter(update=update, context=context, order_side=SignalDirection.LONG) + await telegram._force_enter(update=update, context=context, order_side=SignalDirection.LONG) assert fbuy_mock.call_count == 0 assert msg_mock.call_count == 1 @@ -1275,13 +1352,13 @@ def test_force_enter_no_pair(default_conf, update, mocker) -> None: # One additional button - cancel assert reduce(lambda acc, x: acc + len(x), keyboard, 0) == 5 update = MagicMock() - update.callback_query = MagicMock() + update.callback_query = AsyncMock() update.callback_query.data = 'XRP/USDT_||_long' - telegram._force_enter_inline(update, None) + await telegram._force_enter_inline(update, None) assert fbuy_mock.call_count == 1 -def test_telegram_performance_handle(default_conf_usdt, update, ticker, fee, mocker) -> None: +async def test_telegram_performance_handle(default_conf_usdt, update, ticker, fee, mocker) -> None: mocker.patch.multiple( EXMS, @@ -1293,13 +1370,13 @@ def test_telegram_performance_handle(default_conf_usdt, update, ticker, fee, moc # Create some test data create_mock_trades_usdt(fee) - telegram._performance(update=update, context=MagicMock()) + await telegram._performance(update=update, context=MagicMock()) assert msg_mock.call_count == 1 assert 'Performance' in msg_mock.call_args_list[0][0][0] assert 'XRP/USDT\t2.842 USDT (10.00%) (1)' in msg_mock.call_args_list[0][0][0] -def test_telegram_entry_tag_performance_handle( +async def test_telegram_entry_tag_performance_handle( default_conf_usdt, update, ticker, fee, mocker) -> None: mocker.patch.multiple( EXMS, @@ -1312,26 +1389,26 @@ def test_telegram_entry_tag_performance_handle( create_mock_trades_usdt(fee) context = MagicMock() - telegram._enter_tag_performance(update=update, context=context) + await telegram._enter_tag_performance(update=update, context=context) assert msg_mock.call_count == 1 assert 'Entry Tag Performance' in msg_mock.call_args_list[0][0][0] assert 'TEST1\t3.987 USDT (5.00%) (1)' in msg_mock.call_args_list[0][0][0] context.args = ['XRP/USDT'] - telegram._enter_tag_performance(update=update, context=context) + await telegram._enter_tag_performance(update=update, context=context) assert msg_mock.call_count == 2 msg_mock.reset_mock() mocker.patch('freqtrade.rpc.rpc.RPC._rpc_enter_tag_performance', side_effect=RPCException('Error')) - telegram._enter_tag_performance(update=update, context=MagicMock()) + await telegram._enter_tag_performance(update=update, context=MagicMock()) assert msg_mock.call_count == 1 assert "Error" in msg_mock.call_args_list[0][0][0] -def test_telegram_exit_reason_performance_handle(default_conf_usdt, update, ticker, fee, - mocker) -> None: +async def test_telegram_exit_reason_performance_handle( + default_conf_usdt, update, ticker, fee, mocker) -> None: mocker.patch.multiple( EXMS, fetch_ticker=ticker, @@ -1343,26 +1420,26 @@ def test_telegram_exit_reason_performance_handle(default_conf_usdt, update, tick create_mock_trades_usdt(fee) context = MagicMock() - telegram._exit_reason_performance(update=update, context=context) + await telegram._exit_reason_performance(update=update, context=context) assert msg_mock.call_count == 1 assert 'Exit Reason Performance' in msg_mock.call_args_list[0][0][0] assert 'roi\t2.842 USDT (10.00%) (1)' in msg_mock.call_args_list[0][0][0] context.args = ['XRP/USDT'] - telegram._exit_reason_performance(update=update, context=context) + await telegram._exit_reason_performance(update=update, context=context) assert msg_mock.call_count == 2 msg_mock.reset_mock() mocker.patch('freqtrade.rpc.rpc.RPC._rpc_exit_reason_performance', side_effect=RPCException('Error')) - telegram._exit_reason_performance(update=update, context=MagicMock()) + await telegram._exit_reason_performance(update=update, context=MagicMock()) assert msg_mock.call_count == 1 assert "Error" in msg_mock.call_args_list[0][0][0] -def test_telegram_mix_tag_performance_handle(default_conf_usdt, update, ticker, fee, - mocker) -> None: +async def test_telegram_mix_tag_performance_handle(default_conf_usdt, update, ticker, fee, + mocker) -> None: mocker.patch.multiple( EXMS, fetch_ticker=ticker, @@ -1375,26 +1452,26 @@ def test_telegram_mix_tag_performance_handle(default_conf_usdt, update, ticker, create_mock_trades_usdt(fee) context = MagicMock() - telegram._mix_tag_performance(update=update, context=context) + await telegram._mix_tag_performance(update=update, context=context) assert msg_mock.call_count == 1 assert 'Mix Tag Performance' in msg_mock.call_args_list[0][0][0] assert ('TEST3 roi\t2.842 USDT (10.00%) (1)' in msg_mock.call_args_list[0][0][0]) context.args = ['XRP/USDT'] - telegram._mix_tag_performance(update=update, context=context) + await telegram._mix_tag_performance(update=update, context=context) assert msg_mock.call_count == 2 msg_mock.reset_mock() mocker.patch('freqtrade.rpc.rpc.RPC._rpc_mix_tag_performance', side_effect=RPCException('Error')) - telegram._mix_tag_performance(update=update, context=MagicMock()) + await telegram._mix_tag_performance(update=update, context=MagicMock()) assert msg_mock.call_count == 1 assert "Error" in msg_mock.call_args_list[0][0][0] -def test_count_handle(default_conf, update, ticker, fee, mocker) -> None: +async def test_count_handle(default_conf, update, ticker, fee, mocker) -> None: mocker.patch.multiple( EXMS, fetch_ticker=ticker, @@ -1404,7 +1481,7 @@ def test_count_handle(default_conf, update, ticker, fee, mocker) -> None: patch_get_signal(freqtradebot) freqtradebot.state = State.STOPPED - telegram._count(update=update, context=MagicMock()) + await telegram._count(update=update, context=MagicMock()) assert msg_mock.call_count == 1 assert 'not running' in msg_mock.call_args_list[0][0][0] msg_mock.reset_mock() @@ -1413,7 +1490,7 @@ def test_count_handle(default_conf, update, ticker, fee, mocker) -> None: # Create some test data freqtradebot.enter_positions() msg_mock.reset_mock() - telegram._count(update=update, context=MagicMock()) + await telegram._count(update=update, context=MagicMock()) msg = ('
  current    max    total stake\n---------  -----  -------------\n'
            '        1      {}          {}
').format( @@ -1423,7 +1500,7 @@ def test_count_handle(default_conf, update, ticker, fee, mocker) -> None: assert msg in msg_mock.call_args_list[0][0][0] -def test_telegram_lock_handle(default_conf, update, ticker, fee, mocker) -> None: +async def test_telegram_lock_handle(default_conf, update, ticker, fee, mocker) -> None: mocker.patch.multiple( EXMS, fetch_ticker=ticker, @@ -1431,7 +1508,7 @@ def test_telegram_lock_handle(default_conf, update, ticker, fee, mocker) -> None ) telegram, freqtradebot, msg_mock = get_telegram_testobject(mocker, default_conf) patch_get_signal(freqtradebot) - telegram._locks(update=update, context=MagicMock()) + await telegram._locks(update=update, context=MagicMock()) assert msg_mock.call_count == 1 assert 'No active locks.' in msg_mock.call_args_list[0][0][0] @@ -1440,7 +1517,7 @@ def test_telegram_lock_handle(default_conf, update, ticker, fee, mocker) -> None PairLocks.lock_pair('ETH/BTC', arrow.utcnow().shift(minutes=4).datetime, 'randreason') PairLocks.lock_pair('XRP/BTC', arrow.utcnow().shift(minutes=20).datetime, 'deadbeef') - telegram._locks(update=update, context=MagicMock()) + await telegram._locks(update=update, context=MagicMock()) assert 'Pair' in msg_mock.call_args_list[0][0][0] assert 'Until' in msg_mock.call_args_list[0][0][0] @@ -1453,7 +1530,7 @@ def test_telegram_lock_handle(default_conf, update, ticker, fee, mocker) -> None context = MagicMock() context.args = ['XRP/BTC'] msg_mock.reset_mock() - telegram._delete_locks(update=update, context=context) + await telegram._delete_locks(update=update, context=context) assert 'ETH/BTC' in msg_mock.call_args_list[0][0][0] assert 'randreason' in msg_mock.call_args_list[0][0][0] @@ -1461,11 +1538,11 @@ def test_telegram_lock_handle(default_conf, update, ticker, fee, mocker) -> None assert 'deadbeef' not in msg_mock.call_args_list[0][0][0] -def test_whitelist_static(default_conf, update, mocker) -> None: +async def test_whitelist_static(default_conf, update, mocker) -> None: telegram, freqtradebot, msg_mock = get_telegram_testobject(mocker, default_conf) - telegram._whitelist(update=update, context=MagicMock()) + await telegram._whitelist(update=update, context=MagicMock()) assert msg_mock.call_count == 1 assert ("Using whitelist `['StaticPairList']` with 4 pairs\n" "`ETH/BTC, LTC/BTC, XRP/BTC, NEO/BTC`" in msg_mock.call_args_list[0][0][0]) @@ -1473,33 +1550,33 @@ def test_whitelist_static(default_conf, update, mocker) -> None: context = MagicMock() context.args = ['sorted'] msg_mock.reset_mock() - telegram._whitelist(update=update, context=context) + await telegram._whitelist(update=update, context=context) assert ("Using whitelist `['StaticPairList']` with 4 pairs\n" "`ETH/BTC, LTC/BTC, NEO/BTC, XRP/BTC`" in msg_mock.call_args_list[0][0][0]) context = MagicMock() context.args = ['baseonly'] msg_mock.reset_mock() - telegram._whitelist(update=update, context=context) + await telegram._whitelist(update=update, context=context) assert ("Using whitelist `['StaticPairList']` with 4 pairs\n" "`ETH, LTC, XRP, NEO`" in msg_mock.call_args_list[0][0][0]) context = MagicMock() context.args = ['baseonly', 'sorted'] msg_mock.reset_mock() - telegram._whitelist(update=update, context=context) + await telegram._whitelist(update=update, context=context) assert ("Using whitelist `['StaticPairList']` with 4 pairs\n" "`ETH, LTC, NEO, XRP`" in msg_mock.call_args_list[0][0][0]) -def test_whitelist_dynamic(default_conf, update, mocker) -> None: +async def test_whitelist_dynamic(default_conf, update, mocker) -> None: mocker.patch(f'{EXMS}.exchange_has', return_value=True) default_conf['pairlists'] = [{'method': 'VolumePairList', 'number_assets': 4 }] telegram, _, msg_mock = get_telegram_testobject(mocker, default_conf) - telegram._whitelist(update=update, context=MagicMock()) + await telegram._whitelist(update=update, context=MagicMock()) assert msg_mock.call_count == 1 assert ("Using whitelist `['VolumePairList']` with 4 pairs\n" "`ETH/BTC, LTC/BTC, XRP/BTC, NEO/BTC`" in msg_mock.call_args_list[0][0][0]) @@ -1507,30 +1584,30 @@ def test_whitelist_dynamic(default_conf, update, mocker) -> None: context = MagicMock() context.args = ['sorted'] msg_mock.reset_mock() - telegram._whitelist(update=update, context=context) + await telegram._whitelist(update=update, context=context) assert ("Using whitelist `['VolumePairList']` with 4 pairs\n" "`ETH/BTC, LTC/BTC, NEO/BTC, XRP/BTC`" in msg_mock.call_args_list[0][0][0]) context = MagicMock() context.args = ['baseonly'] msg_mock.reset_mock() - telegram._whitelist(update=update, context=context) + await telegram._whitelist(update=update, context=context) assert ("Using whitelist `['VolumePairList']` with 4 pairs\n" "`ETH, LTC, XRP, NEO`" in msg_mock.call_args_list[0][0][0]) context = MagicMock() context.args = ['baseonly', 'sorted'] msg_mock.reset_mock() - telegram._whitelist(update=update, context=context) + await telegram._whitelist(update=update, context=context) assert ("Using whitelist `['VolumePairList']` with 4 pairs\n" "`ETH, LTC, NEO, XRP`" in msg_mock.call_args_list[0][0][0]) -def test_blacklist_static(default_conf, update, mocker) -> None: +async def test_blacklist_static(default_conf, update, mocker) -> None: telegram, freqtradebot, msg_mock = get_telegram_testobject(mocker, default_conf) - telegram._blacklist(update=update, context=MagicMock()) + await telegram._blacklist(update=update, context=MagicMock()) assert msg_mock.call_count == 1 assert ("Blacklist contains 2 pairs\n`DOGE/BTC, HOT/BTC`" in msg_mock.call_args_list[0][0][0]) @@ -1540,7 +1617,7 @@ def test_blacklist_static(default_conf, update, mocker) -> None: # /blacklist ETH/BTC context = MagicMock() context.args = ["ETH/BTC"] - telegram._blacklist(update=update, context=context) + await telegram._blacklist(update=update, context=context) assert msg_mock.call_count == 1 assert ("Blacklist contains 3 pairs\n`DOGE/BTC, HOT/BTC, ETH/BTC`" in msg_mock.call_args_list[0][0][0]) @@ -1549,7 +1626,7 @@ def test_blacklist_static(default_conf, update, mocker) -> None: msg_mock.reset_mock() context = MagicMock() context.args = ["XRP/.*"] - telegram._blacklist(update=update, context=context) + await telegram._blacklist(update=update, context=context) assert msg_mock.call_count == 1 assert ("Blacklist contains 4 pairs\n`DOGE/BTC, HOT/BTC, ETH/BTC, XRP/.*`" @@ -1558,13 +1635,13 @@ def test_blacklist_static(default_conf, update, mocker) -> None: msg_mock.reset_mock() context.args = ["DOGE/BTC"] - telegram._blacklist_delete(update=update, context=context) + await telegram._blacklist_delete(update=update, context=context) assert msg_mock.call_count == 1 assert ("Blacklist contains 3 pairs\n`HOT/BTC, ETH/BTC, XRP/.*`" in msg_mock.call_args_list[0][0][0]) -def test_telegram_logs(default_conf, update, mocker) -> None: +async def test_telegram_logs(default_conf, update, mocker) -> None: mocker.patch.multiple( 'freqtrade.rpc.telegram.Telegram', _init=MagicMock(), @@ -1575,13 +1652,13 @@ def test_telegram_logs(default_conf, update, mocker) -> None: context = MagicMock() context.args = [] - telegram._logs(update=update, context=context) + await telegram._logs(update=update, context=context) assert msg_mock.call_count == 1 assert "freqtrade\\.rpc\\.telegram" in msg_mock.call_args_list[0][0][0] msg_mock.reset_mock() context.args = ["1"] - telegram._logs(update=update, context=context) + await telegram._logs(update=update, context=context) assert msg_mock.call_count == 1 msg_mock.reset_mock() @@ -1589,22 +1666,22 @@ def test_telegram_logs(default_conf, update, mocker) -> None: mocker.patch('freqtrade.rpc.telegram.MAX_MESSAGE_LENGTH', 200) context = MagicMock() context.args = [] - telegram._logs(update=update, context=context) + await telegram._logs(update=update, context=context) # Called at least 2 times. Exact times will change with unrelated changes to setup messages # Therefore we don't test for this explicitly. assert msg_mock.call_count >= 2 -def test_edge_disabled(default_conf, update, mocker) -> None: +async def test_edge_disabled(default_conf, update, mocker) -> None: telegram, _, msg_mock = get_telegram_testobject(mocker, default_conf) - telegram._edge(update=update, context=MagicMock()) + await telegram._edge(update=update, context=MagicMock()) assert msg_mock.call_count == 1 assert "Edge is not enabled." in msg_mock.call_args_list[0][0][0] -def test_edge_enabled(edge_conf, update, mocker) -> None: +async def test_edge_enabled(edge_conf, update, mocker) -> None: mocker.patch('freqtrade.edge.Edge._cached_pairs', mocker.PropertyMock( return_value={ 'E/F': PairInfo(-0.01, 0.66, 3.71, 0.50, 1.71, 10, 60), @@ -1613,7 +1690,7 @@ def test_edge_enabled(edge_conf, update, mocker) -> None: telegram, _, msg_mock = get_telegram_testobject(mocker, edge_conf) - telegram._edge(update=update, context=MagicMock()) + await telegram._edge(update=update, context=MagicMock()) assert msg_mock.call_count == 1 assert 'Edge only validated following pairs:\n
' in msg_mock.call_args_list[0][0][0]
     assert 'Pair      Winrate    Expectancy    Stoploss' in msg_mock.call_args_list[0][0][0]
@@ -1622,7 +1699,7 @@ def test_edge_enabled(edge_conf, update, mocker) -> None:
 
     mocker.patch('freqtrade.edge.Edge._cached_pairs', mocker.PropertyMock(
         return_value={}))
-    telegram._edge(update=update, context=MagicMock())
+    await telegram._edge(update=update, context=MagicMock())
     assert msg_mock.call_count == 1
     assert 'Edge only validated following pairs:' in msg_mock.call_args_list[0][0][0]
     assert 'Winrate' not in msg_mock.call_args_list[0][0][0]
@@ -1631,20 +1708,20 @@ def test_edge_enabled(edge_conf, update, mocker) -> None:
 @pytest.mark.parametrize('is_short,regex_pattern',
                          [(True, r"just now[ ]*XRP\/BTC \(#3\)  -1.00% \("),
                           (False, r"just now[ ]*XRP\/BTC \(#3\)  1.00% \(")])
-def test_telegram_trades(mocker, update, default_conf, fee, is_short, regex_pattern):
+async def test_telegram_trades(mocker, update, default_conf, fee, is_short, regex_pattern):
 
     telegram, _, msg_mock = get_telegram_testobject(mocker, default_conf)
 
     context = MagicMock()
     context.args = []
 
-    telegram._trades(update=update, context=context)
+    await telegram._trades(update=update, context=context)
     assert "0 recent trades:" in msg_mock.call_args_list[0][0][0]
     assert "
" not in msg_mock.call_args_list[0][0][0]
     msg_mock.reset_mock()
 
     context.args = ['hello']
-    telegram._trades(update=update, context=context)
+    await telegram._trades(update=update, context=context)
     assert "0 recent trades:" in msg_mock.call_args_list[0][0][0]
     assert "
" not in msg_mock.call_args_list[0][0][0]
     msg_mock.reset_mock()
@@ -1653,7 +1730,7 @@ def test_telegram_trades(mocker, update, default_conf, fee, is_short, regex_patt
 
     context = MagicMock()
     context.args = [5]
-    telegram._trades(update=update, context=context)
+    await telegram._trades(update=update, context=context)
     msg_mock.call_count == 1
     assert "2 recent trades:" in msg_mock.call_args_list[0][0][0]
     assert "Profit (" in msg_mock.call_args_list[0][0][0]
@@ -1663,13 +1740,13 @@ def test_telegram_trades(mocker, update, default_conf, fee, is_short, regex_patt
 
 
 @pytest.mark.parametrize('is_short', [True, False])
-def test_telegram_delete_trade(mocker, update, default_conf, fee, is_short):
+async def test_telegram_delete_trade(mocker, update, default_conf, fee, is_short):
 
     telegram, _, msg_mock = get_telegram_testobject(mocker, default_conf)
     context = MagicMock()
     context.args = []
 
-    telegram._delete_trade(update=update, context=context)
+    await telegram._delete_trade(update=update, context=context)
     assert "Trade-id not set." in msg_mock.call_args_list[0][0][0]
 
     msg_mock.reset_mock()
@@ -1677,14 +1754,14 @@ def test_telegram_delete_trade(mocker, update, default_conf, fee, is_short):
 
     context = MagicMock()
     context.args = [1]
-    telegram._delete_trade(update=update, context=context)
+    await telegram._delete_trade(update=update, context=context)
     msg_mock.call_count == 1
     assert "Deleted trade 1." in msg_mock.call_args_list[0][0][0]
     assert "Please make sure to take care of this asset" in msg_mock.call_args_list[0][0][0]
 
 
 @pytest.mark.parametrize('is_short', [True, False])
-def test_telegram_delete_open_order(mocker, update, default_conf, fee, is_short, ticker):
+async def test_telegram_delete_open_order(mocker, update, default_conf, fee, is_short, ticker):
 
     mocker.patch.multiple(
         EXMS,
@@ -1694,7 +1771,7 @@ def test_telegram_delete_open_order(mocker, update, default_conf, fee, is_short,
     context = MagicMock()
     context.args = []
 
-    telegram._cancel_open_order(update=update, context=context)
+    await telegram._cancel_open_order(update=update, context=context)
     assert "Trade-id not set." in msg_mock.call_args_list[0][0][0]
 
     msg_mock.reset_mock()
@@ -1702,7 +1779,7 @@ def test_telegram_delete_open_order(mocker, update, default_conf, fee, is_short,
 
     context = MagicMock()
     context.args = [5]
-    telegram._cancel_open_order(update=update, context=context)
+    await telegram._cancel_open_order(update=update, context=context)
     assert "No open order for trade_id" in msg_mock.call_args_list[0][0][0]
 
     msg_mock.reset_mock()
@@ -1711,43 +1788,43 @@ def test_telegram_delete_open_order(mocker, update, default_conf, fee, is_short,
     mocker.patch(f'{EXMS}.fetch_order', return_value=trade.orders[-1].to_ccxt_object())
     context = MagicMock()
     context.args = [6]
-    telegram._cancel_open_order(update=update, context=context)
+    await telegram._cancel_open_order(update=update, context=context)
     assert msg_mock.call_count == 1
     assert "Open order canceled." in msg_mock.call_args_list[0][0][0]
 
 
-def test_help_handle(default_conf, update, mocker) -> None:
+async def test_help_handle(default_conf, update, mocker) -> None:
     telegram, _, msg_mock = get_telegram_testobject(mocker, default_conf)
 
-    telegram._help(update=update, context=MagicMock())
+    await telegram._help(update=update, context=MagicMock())
     assert msg_mock.call_count == 1
     assert '*/help:* `This help message`' in msg_mock.call_args_list[0][0][0]
 
 
-def test_version_handle(default_conf, update, mocker) -> None:
+async def test_version_handle(default_conf, update, mocker) -> None:
 
     telegram, freqtradebot, msg_mock = get_telegram_testobject(mocker, default_conf)
 
-    telegram._version(update=update, context=MagicMock())
+    await telegram._version(update=update, context=MagicMock())
     assert msg_mock.call_count == 1
-    assert '*Version:* `{}`'.format(__version__) in msg_mock.call_args_list[0][0][0]
+    assert f'*Version:* `{__version__}`' in msg_mock.call_args_list[0][0][0]
 
     msg_mock.reset_mock()
     freqtradebot.strategy.version = lambda: '1.1.1'
 
-    telegram._version(update=update, context=MagicMock())
+    await telegram._version(update=update, context=MagicMock())
     assert msg_mock.call_count == 1
-    assert '*Version:* `{}`'.format(__version__) in msg_mock.call_args_list[0][0][0]
+    assert f'*Version:* `{__version__}`' in msg_mock.call_args_list[0][0][0]
     assert '*Strategy version: * `1.1.1`' in msg_mock.call_args_list[0][0][0]
 
 
-def test_show_config_handle(default_conf, update, mocker) -> None:
+async def test_show_config_handle(default_conf, update, mocker) -> None:
 
     default_conf['runmode'] = RunMode.DRY_RUN
 
     telegram, freqtradebot, msg_mock = get_telegram_testobject(mocker, default_conf)
 
-    telegram._show_config(update=update, context=MagicMock())
+    await telegram._show_config(update=update, context=MagicMock())
     assert msg_mock.call_count == 1
     assert '*Mode:* `{}`'.format('Dry-run') in msg_mock.call_args_list[0][0][0]
     assert '*Exchange:* `binance`' in msg_mock.call_args_list[0][0][0]
@@ -1756,7 +1833,7 @@ def test_show_config_handle(default_conf, update, mocker) -> None:
 
     msg_mock.reset_mock()
     freqtradebot.config['trailing_stop'] = True
-    telegram._show_config(update=update, context=MagicMock())
+    await telegram._show_config(update=update, context=MagicMock())
     assert msg_mock.call_count == 1
     assert '*Mode:* `{}`'.format('Dry-run') in msg_mock.call_args_list[0][0][0]
     assert '*Exchange:* `binance`' in msg_mock.call_args_list[0][0][0]
@@ -2063,7 +2140,7 @@ def test_send_msg_sell_notification(default_conf, mocker) -> None:
         telegram._rpc._fiat_converter.convert_amount = old_convamount
 
 
-def test_send_msg_sell_cancel_notification(default_conf, mocker) -> None:
+async def test_send_msg_sell_cancel_notification(default_conf, mocker) -> None:
 
     telegram, _, msg_mock = get_telegram_testobject(mocker, default_conf)
 
@@ -2155,7 +2232,7 @@ def test_send_msg_status_notification(default_conf, mocker) -> None:
     assert msg_mock.call_args[0][0] == '*Status:* `running`'
 
 
-def test_warning_notification(default_conf, mocker) -> None:
+async def test_warning_notification(default_conf, mocker) -> None:
     telegram, _, msg_mock = get_telegram_testobject(mocker, default_conf)
     telegram.send_msg({
         'type': RPCMessageType.WARNING,
@@ -2239,8 +2316,9 @@ def test_send_msg_buy_notification_no_fiat(
     ('Short', 'short_signal_01', 2.0),
 ])
 def test_send_msg_sell_notification_no_fiat(
-        default_conf, mocker, direction, enter_signal, leverage) -> None:
+        default_conf, mocker, direction, enter_signal, leverage, time_machine) -> None:
     del default_conf['fiat_display_currency']
+    time_machine.move_to('2022-05-02 00:00:00 +00:00', tick=False)
     telegram, _, msg_mock = get_telegram_testobject(mocker, default_conf)
 
     telegram.send_msg({
@@ -2299,60 +2377,62 @@ def test__sell_emoji(default_conf, mocker, msg, expected):
     assert telegram._get_sell_emoji(msg) == expected
 
 
-def test_telegram__send_msg(default_conf, mocker, caplog) -> None:
+async def test_telegram__send_msg(default_conf, mocker, caplog) -> None:
     mocker.patch('freqtrade.rpc.telegram.Telegram._init', MagicMock())
     bot = MagicMock()
+    bot.send_message = AsyncMock()
+    bot.edit_message_text = AsyncMock()
     telegram, _, _ = get_telegram_testobject(mocker, default_conf, mock=False)
-    telegram._updater = MagicMock()
-    telegram._updater.bot = bot
+    telegram._app = MagicMock()
+    telegram._app.bot = bot
 
-    telegram._config['telegram']['enabled'] = True
-    telegram._send_msg('test')
+    await telegram._send_msg('test')
     assert len(bot.method_calls) == 1
 
     # Test update
     query = MagicMock()
-    telegram._send_msg('test', callback_path="DeadBeef", query=query, reload_able=True)
-    edit_message_text = telegram._updater.bot.edit_message_text
+    await telegram._send_msg('test', callback_path="DeadBeef", query=query, reload_able=True)
+    edit_message_text = telegram._app.bot.edit_message_text
     assert edit_message_text.call_count == 1
     assert "Updated: " in edit_message_text.call_args_list[0][1]['text']
 
-    telegram._updater.bot.edit_message_text = MagicMock(side_effect=BadRequest("not modified"))
-    telegram._send_msg('test', callback_path="DeadBeef", query=query)
-    assert telegram._updater.bot.edit_message_text.call_count == 1
+    telegram._app.bot.edit_message_text = AsyncMock(side_effect=BadRequest("not modified"))
+    await telegram._send_msg('test', callback_path="DeadBeef", query=query)
+    assert telegram._app.bot.edit_message_text.call_count == 1
     assert not log_has_re(r"TelegramError: .*", caplog)
 
-    telegram._updater.bot.edit_message_text = MagicMock(side_effect=BadRequest(""))
-    telegram._send_msg('test2', callback_path="DeadBeef", query=query)
-    assert telegram._updater.bot.edit_message_text.call_count == 1
+    telegram._app.bot.edit_message_text = AsyncMock(side_effect=BadRequest(""))
+    await telegram._send_msg('test2', callback_path="DeadBeef", query=query)
+    assert telegram._app.bot.edit_message_text.call_count == 1
     assert log_has_re(r"TelegramError: .*", caplog)
 
-    telegram._updater.bot.edit_message_text = MagicMock(side_effect=TelegramError("DeadBEEF"))
-    telegram._send_msg('test3', callback_path="DeadBeef", query=query)
+    telegram._app.bot.edit_message_text = AsyncMock(side_effect=TelegramError("DeadBEEF"))
+    await telegram._send_msg('test3', callback_path="DeadBeef", query=query)
 
     assert log_has_re(r"TelegramError: DeadBEEF! Giving up.*", caplog)
 
 
-def test__send_msg_network_error(default_conf, mocker, caplog) -> None:
+async def test__send_msg_network_error(default_conf, mocker, caplog) -> None:
     mocker.patch('freqtrade.rpc.telegram.Telegram._init', MagicMock())
     bot = MagicMock()
     bot.send_message = MagicMock(side_effect=NetworkError('Oh snap'))
     telegram, _, _ = get_telegram_testobject(mocker, default_conf, mock=False)
-    telegram._updater = MagicMock()
-    telegram._updater.bot = bot
+    telegram._app = MagicMock()
+    telegram._app.bot = bot
 
     telegram._config['telegram']['enabled'] = True
-    telegram._send_msg('test')
+    await telegram._send_msg('test')
 
     # Bot should've tried to send it twice
     assert len(bot.method_calls) == 2
     assert log_has('Telegram NetworkError: Oh snap! Trying one more time.', caplog)
 
 
-def test__send_msg_keyboard(default_conf, mocker, caplog) -> None:
+@pytest.mark.filterwarnings("ignore:.*ChatPermissions")
+async def test__send_msg_keyboard(default_conf, mocker, caplog) -> None:
     mocker.patch('freqtrade.rpc.telegram.Telegram._init', MagicMock())
     bot = MagicMock()
-    bot.send_message = MagicMock()
+    bot.send_message = AsyncMock()
     freqtradebot = get_patched_freqtradebot(mocker, default_conf)
     rpc = RPC(freqtradebot)
 
@@ -2368,14 +2448,14 @@ def test__send_msg_keyboard(default_conf, mocker, caplog) -> None:
 
     def init_telegram(freqtradebot):
         telegram = Telegram(rpc, default_conf)
-        telegram._updater = MagicMock()
-        telegram._updater.bot = bot
+        telegram._app = MagicMock()
+        telegram._app.bot = bot
         return telegram
 
     # no keyboard in config -> default keyboard
     freqtradebot.config['telegram']['enabled'] = True
     telegram = init_telegram(freqtradebot)
-    telegram._send_msg('test')
+    await telegram._send_msg('test')
     used_keyboard = bot.send_message.call_args[1]['reply_markup']
     assert used_keyboard == default_keyboard
 
@@ -2392,7 +2472,7 @@ def test__send_msg_keyboard(default_conf, mocker, caplog) -> None:
     freqtradebot.config['telegram']['enabled'] = True
     freqtradebot.config['telegram']['keyboard'] = custom_keys_list
     telegram = init_telegram(freqtradebot)
-    telegram._send_msg('test')
+    await telegram._send_msg('test')
     used_keyboard = bot.send_message.call_args[1]['reply_markup']
     assert used_keyboard == custom_keyboard
     assert log_has("using custom keyboard from config.json: "
@@ -2400,13 +2480,14 @@ def test__send_msg_keyboard(default_conf, mocker, caplog) -> None:
                    "'/start', '/reload_config', '/help']]", caplog)
 
 
-def test_change_market_direction(default_conf, mocker, update) -> None:
+async def test_change_market_direction(default_conf, mocker, update) -> None:
     telegram, _, msg_mock = get_telegram_testobject(mocker, default_conf)
     assert telegram._rpc._freqtrade.strategy.market_direction == MarketDirection.NONE
     context = MagicMock()
     context.args = ["long"]
-    telegram._changemarketdir(update, context)
+    await telegram._changemarketdir(update, context)
     assert telegram._rpc._freqtrade.strategy.market_direction == MarketDirection.LONG
     context = MagicMock()
     context.args = ["invalid"]
+    await telegram._changemarketdir(update, context)
     assert telegram._rpc._freqtrade.strategy.market_direction == MarketDirection.LONG
diff --git a/tests/rpc/test_rpc_webhook.py b/tests/rpc/test_rpc_webhook.py
index f55582107..d0a0f5b1e 100644
--- a/tests/rpc/test_rpc_webhook.py
+++ b/tests/rpc/test_rpc_webhook.py
@@ -17,6 +17,10 @@ def get_webhook_dict() -> dict:
         "enabled": True,
         "url": "https://maker.ifttt.com/trigger/freqtrade_test/with/key/c764udvJ5jfSlswVRukZZ2/",
         "webhookentry": {
+            # Intentionally broken, as "entry" should have priority.
+            "value1": "Buying {pair55555}",
+        },
+        "entry": {
             "value1": "Buying {pair}",
             "value2": "limit {limit:8f}",
             "value3": "{stake_amount:8f} {stake_currency}",
@@ -89,15 +93,15 @@ def test_send_msg_webhook(default_conf, mocker):
     webhook.send_msg(msg=msg)
     assert msg_mock.call_count == 1
     assert (msg_mock.call_args[0][0]["value1"] ==
-            default_conf["webhook"]["webhookentry"]["value1"].format(**msg))
+            default_conf["webhook"]["entry"]["value1"].format(**msg))
     assert (msg_mock.call_args[0][0]["value2"] ==
-            default_conf["webhook"]["webhookentry"]["value2"].format(**msg))
+            default_conf["webhook"]["entry"]["value2"].format(**msg))
     assert (msg_mock.call_args[0][0]["value3"] ==
-            default_conf["webhook"]["webhookentry"]["value3"].format(**msg))
+            default_conf["webhook"]["entry"]["value3"].format(**msg))
     assert (msg_mock.call_args[0][0]["value4"] ==
-            default_conf["webhook"]["webhookentry"]["value4"].format(**msg))
+            default_conf["webhook"]["entry"]["value4"].format(**msg))
     assert (msg_mock.call_args[0][0]["value5"] ==
-            default_conf["webhook"]["webhookentry"]["value5"].format(**msg))
+            default_conf["webhook"]["entry"]["value5"].format(**msg))
     # Test short
     msg_mock.reset_mock()
 
@@ -116,15 +120,15 @@ def test_send_msg_webhook(default_conf, mocker):
     webhook.send_msg(msg=msg)
     assert msg_mock.call_count == 1
     assert (msg_mock.call_args[0][0]["value1"] ==
-            default_conf["webhook"]["webhookentry"]["value1"].format(**msg))
+            default_conf["webhook"]["entry"]["value1"].format(**msg))
     assert (msg_mock.call_args[0][0]["value2"] ==
-            default_conf["webhook"]["webhookentry"]["value2"].format(**msg))
+            default_conf["webhook"]["entry"]["value2"].format(**msg))
     assert (msg_mock.call_args[0][0]["value3"] ==
-            default_conf["webhook"]["webhookentry"]["value3"].format(**msg))
+            default_conf["webhook"]["entry"]["value3"].format(**msg))
     assert (msg_mock.call_args[0][0]["value4"] ==
-            default_conf["webhook"]["webhookentry"]["value4"].format(**msg))
+            default_conf["webhook"]["entry"]["value4"].format(**msg))
     assert (msg_mock.call_args[0][0]["value5"] ==
-            default_conf["webhook"]["webhookentry"]["value5"].format(**msg))
+            default_conf["webhook"]["entry"]["value5"].format(**msg))
     # Test buy cancel
     msg_mock.reset_mock()
 
@@ -328,6 +332,7 @@ def test_send_msg_webhook(default_conf, mocker):
 
 def test_exception_send_msg(default_conf, mocker, caplog):
     default_conf["webhook"] = get_webhook_dict()
+    del default_conf["webhook"]["entry"]
     del default_conf["webhook"]["webhookentry"]
 
     webhook = Webhook(RPC(get_patched_freqtradebot(mocker, default_conf)), default_conf)
diff --git a/tests/strategy/strats/freqai_test_classifier.py b/tests/strategy/strats/freqai_test_classifier.py
index 61b9f0c37..a68a87b2a 100644
--- a/tests/strategy/strats/freqai_test_classifier.py
+++ b/tests/strategy/strats/freqai_test_classifier.py
@@ -82,7 +82,7 @@ class freqai_test_classifier(IStrategy):
         return dataframe
 
     def set_freqai_targets(self, dataframe: DataFrame, metadata: Dict, **kwargs):
-
+        self.freqai.class_names = ["down", "up"]
         dataframe['&s-up_or_down'] = np.where(dataframe["close"].shift(-100) >
                                               dataframe["close"], 'up', 'down')
 
diff --git a/tests/strategy/strats/hyperoptable_strategy.py b/tests/strategy/strats/hyperoptable_strategy.py
index eadbc533f..d05e8ead2 100644
--- a/tests/strategy/strats/hyperoptable_strategy.py
+++ b/tests/strategy/strats/hyperoptable_strategy.py
@@ -50,6 +50,7 @@ class HyperoptableStrategy(StrategyTestV3):
         return prot
 
     bot_loop_started = False
+    bot_started = False
 
     def bot_loop_start(self):
         self.bot_loop_started = True
@@ -58,6 +59,7 @@ class HyperoptableStrategy(StrategyTestV3):
         """
         Parameters can also be defined here ...
         """
+        self.bot_started = True
         self.buy_rsi = IntParameter([0, 50], default=30, space='buy')
 
     def informative_pairs(self):
diff --git a/tests/strategy/test_interface.py b/tests/strategy/test_interface.py
index 7b1399507..204fa996d 100644
--- a/tests/strategy/test_interface.py
+++ b/tests/strategy/test_interface.py
@@ -9,6 +9,7 @@ import pytest
 from pandas import DataFrame
 
 from freqtrade.configuration import TimeRange
+from freqtrade.constants import CUSTOM_TAG_MAX_LENGTH
 from freqtrade.data.dataprovider import DataProvider
 from freqtrade.data.history import load_data
 from freqtrade.enums import ExitCheckTuple, ExitType, HyperoptState, SignalDirection
@@ -529,13 +530,13 @@ def test_custom_exit(default_conf, fee, caplog) -> None:
     assert res[0].exit_reason == 'hello world'
 
     caplog.clear()
-    strategy.custom_exit = MagicMock(return_value='h' * 100)
+    strategy.custom_exit = MagicMock(return_value='h' * CUSTOM_TAG_MAX_LENGTH * 2)
     res = strategy.should_exit(trade, 1, now,
                                enter=False, exit_=False,
                                low=None, high=None)
     assert res[0].exit_type == ExitType.CUSTOM_EXIT
     assert res[0].exit_flag is True
-    assert res[0].exit_reason == 'h' * 64
+    assert res[0].exit_reason == 'h' * (CUSTOM_TAG_MAX_LENGTH)
     assert log_has_re('Custom exit reason returned from custom_exit is too long.*', caplog)
 
 
@@ -986,7 +987,8 @@ def test_auto_hyperopt_interface_loadparams(default_conf, mocker, caplog):
             }
         }
     }
-    mocker.patch('freqtrade.strategy.hyper.json_load', return_value=expected_result)
+    mocker.patch('freqtrade.strategy.hyper.HyperoptTools.load_params',
+                 return_value=expected_result)
     PairLocks.timeframe = default_conf['timeframe']
     strategy = StrategyResolver.load_strategy(default_conf)
     assert strategy.stoploss == -0.05
@@ -1005,11 +1007,13 @@ def test_auto_hyperopt_interface_loadparams(default_conf, mocker, caplog):
         }
     }
 
-    mocker.patch('freqtrade.strategy.hyper.json_load', return_value=expected_result)
+    mocker.patch('freqtrade.strategy.hyper.HyperoptTools.load_params',
+                 return_value=expected_result)
     with pytest.raises(OperationalException, match="Invalid parameter file provided."):
         StrategyResolver.load_strategy(default_conf)
 
-    mocker.patch('freqtrade.strategy.hyper.json_load', MagicMock(side_effect=ValueError()))
+    mocker.patch('freqtrade.strategy.hyper.HyperoptTools.load_params',
+                 MagicMock(side_effect=ValueError()))
 
     StrategyResolver.load_strategy(default_conf)
     assert log_has("Invalid parameter file format.", caplog)
diff --git a/tests/strategy/test_strategy_loading.py b/tests/strategy/test_strategy_loading.py
index 98185e152..4cdb35936 100644
--- a/tests/strategy/test_strategy_loading.py
+++ b/tests/strategy/test_strategy_loading.py
@@ -69,7 +69,7 @@ def test_load_strategy(default_conf, dataframe_1m):
 def test_load_strategy_base64(dataframe_1m, caplog, default_conf):
     filepath = Path(__file__).parents[2] / 'freqtrade/templates/sample_strategy.py'
     encoded_string = urlsafe_b64encode(filepath.read_bytes()).decode("utf-8")
-    default_conf.update({'strategy': 'SampleStrategy:{}'.format(encoded_string)})
+    default_conf.update({'strategy': f'SampleStrategy:{encoded_string}'})
 
     strategy = StrategyResolver.load_strategy(default_conf)
     assert 'rsi' in strategy.advise_indicators(dataframe_1m, {'pair': 'ETH/BTC'})
diff --git a/tests/test_configuration.py b/tests/test_configuration.py
index aab868bec..c445b989d 100644
--- a/tests/test_configuration.py
+++ b/tests/test_configuration.py
@@ -23,7 +23,8 @@ from freqtrade.configuration.load_config import (load_config_file, load_file, lo
 from freqtrade.constants import DEFAULT_DB_DRYRUN_URL, DEFAULT_DB_PROD_URL, ENV_VAR_PREFIX
 from freqtrade.enums import RunMode
 from freqtrade.exceptions import OperationalException
-from freqtrade.loggers import FTBufferingHandler, _set_loggers, setup_logging, setup_logging_pre
+from freqtrade.loggers import (FTBufferingHandler, FTStdErrStreamHandler, _set_loggers,
+                               setup_logging, setup_logging_pre)
 from tests.conftest import (CURRENT_TEST_STRATEGY, log_has, log_has_re,
                             patched_configuration_load_config_file)
 
@@ -658,7 +659,7 @@ def test_set_loggers_syslog():
     setup_logging(config)
     assert len(logger.handlers) == 3
     assert [x for x in logger.handlers if type(x) == logging.handlers.SysLogHandler]
-    assert [x for x in logger.handlers if type(x) == logging.StreamHandler]
+    assert [x for x in logger.handlers if type(x) == FTStdErrStreamHandler]
     assert [x for x in logger.handlers if type(x) == FTBufferingHandler]
     # setting up logging again should NOT cause the loggers to be added a second time.
     setup_logging(config)
@@ -681,7 +682,7 @@ def test_set_loggers_Filehandler(tmpdir):
     setup_logging(config)
     assert len(logger.handlers) == 3
     assert [x for x in logger.handlers if type(x) == logging.handlers.RotatingFileHandler]
-    assert [x for x in logger.handlers if type(x) == logging.StreamHandler]
+    assert [x for x in logger.handlers if type(x) == FTStdErrStreamHandler]
     assert [x for x in logger.handlers if type(x) == FTBufferingHandler]
     # setting up logging again should NOT cause the loggers to be added a second time.
     setup_logging(config)
@@ -706,7 +707,7 @@ def test_set_loggers_journald(mocker):
     setup_logging(config)
     assert len(logger.handlers) == 3
     assert [x for x in logger.handlers if type(x).__name__ == "JournaldLogHandler"]
-    assert [x for x in logger.handlers if type(x) == logging.StreamHandler]
+    assert [x for x in logger.handlers if type(x) == FTStdErrStreamHandler]
     # reset handlers to not break pytest
     logger.handlers = orig_handlers
 
diff --git a/tests/test_freqtradebot.py b/tests/test_freqtradebot.py
index cea70ec48..ea99061b8 100644
--- a/tests/test_freqtradebot.py
+++ b/tests/test_freqtradebot.py
@@ -356,7 +356,7 @@ def test_create_trade_no_stake_amount(default_conf_usdt, ticker_usdt, fee, mocke
 @pytest.mark.parametrize("is_short", [False, True])
 @pytest.mark.parametrize('stake_amount,create,amount_enough,max_open_trades', [
     (5.0, True, True, 99),
-    (0.049, True, False, 99),  # Amount will be adjusted to min - which is 0.051
+    (0.042, True, False, 99),  # Amount will be adjusted to min - which is 0.051
     (0, False, True, 99),
     (UNLIMITED_STAKE_AMOUNT, False, True, 0),
 ])
@@ -1060,9 +1060,19 @@ def test_execute_entry_min_leverage(mocker, default_conf_usdt, fee, limit_order,
 
 
 @pytest.mark.parametrize("is_short", [False, True])
-def test_add_stoploss_on_exchange(mocker, default_conf_usdt, limit_order, is_short) -> None:
+def test_add_stoploss_on_exchange(mocker, default_conf_usdt, limit_order, is_short, fee) -> None:
     patch_RPCManager(mocker)
     patch_exchange(mocker)
+    mocker.patch.multiple(
+        EXMS,
+        fetch_ticker=MagicMock(return_value={
+            'bid': 1.9,
+            'ask': 2.2,
+            'last': 1.9
+        }),
+        create_order=MagicMock(return_value=limit_order[entry_side(is_short)]),
+        get_fee=fee,
+    )
     order = limit_order[entry_side(is_short)]
     mocker.patch('freqtrade.freqtradebot.FreqtradeBot.handle_trade', MagicMock(return_value=True))
     mocker.patch(f'{EXMS}.fetch_order', return_value=order)
@@ -1074,8 +1084,10 @@ def test_add_stoploss_on_exchange(mocker, default_conf_usdt, limit_order, is_sho
     freqtrade = FreqtradeBot(default_conf_usdt)
     freqtrade.strategy.order_types['stoploss_on_exchange'] = True
 
-    # TODO: should not be magicmock
-    trade = MagicMock()
+    patch_get_signal(freqtrade, enter_short=is_short, enter_long=not is_short)
+
+    freqtrade.enter_positions()
+    trade = Trade.session.scalars(select(Trade)).first()
     trade.is_short = is_short
     trade.open_order_id = None
     trade.stoploss_order_id = None
@@ -1091,7 +1103,8 @@ def test_add_stoploss_on_exchange(mocker, default_conf_usdt, limit_order, is_sho
 @pytest.mark.parametrize("is_short", [False, True])
 def test_handle_stoploss_on_exchange(mocker, default_conf_usdt, fee, caplog, is_short,
                                      limit_order) -> None:
-    stoploss = MagicMock(return_value={'id': 13434334})
+    stop_order_dict = {'id': "13434334"}
+    stoploss = MagicMock(return_value=stop_order_dict)
     enter_order = limit_order[entry_side(is_short)]
     exit_order = limit_order[exit_side(is_short)]
     patch_RPCManager(mocker)
@@ -1116,8 +1129,9 @@ def test_handle_stoploss_on_exchange(mocker, default_conf_usdt, fee, caplog, is_
     # First case: when stoploss is not yet set but the order is open
     # should get the stoploss order id immediately
     # and should return false as no trade actually happened
-    # TODO: should not be magicmock
-    trade = MagicMock()
+
+    freqtrade.enter_positions()
+    trade = Trade.session.scalars(select(Trade)).first()
     trade.is_short = is_short
     trade.is_open = True
     trade.open_order_id = None
@@ -1129,44 +1143,62 @@ def test_handle_stoploss_on_exchange(mocker, default_conf_usdt, fee, caplog, is_
 
     # Second case: when stoploss is set but it is not yet hit
     # should do nothing and return false
+    stop_order_dict.update({'id': "102"})
     trade.is_open = True
     trade.open_order_id = None
-    trade.stoploss_order_id = "100"
+    trade.stoploss_order_id = "102"
+    trade.orders.append(
+        Order(
+            ft_order_side='stoploss',
+            ft_pair=trade.pair,
+            ft_is_open=True,
+            ft_amount=trade.amount,
+            ft_price=trade.stop_loss,
+            order_id='102',
+            status='open',
+        )
+    )
 
     hanging_stoploss_order = MagicMock(return_value={'status': 'open'})
     mocker.patch(f'{EXMS}.fetch_stoploss_order', hanging_stoploss_order)
 
     assert freqtrade.handle_stoploss_on_exchange(trade) is False
-    assert trade.stoploss_order_id == "100"
+    assert trade.stoploss_order_id == "102"
 
     # Third case: when stoploss was set but it was canceled for some reason
     # should set a stoploss immediately and return False
     caplog.clear()
     trade.is_open = True
     trade.open_order_id = None
-    trade.stoploss_order_id = "100"
+    trade.stoploss_order_id = "102"
 
-    canceled_stoploss_order = MagicMock(return_value={'status': 'canceled'})
+    canceled_stoploss_order = MagicMock(return_value={'id': '103_1', 'status': 'canceled'})
     mocker.patch(f'{EXMS}.fetch_stoploss_order', canceled_stoploss_order)
     stoploss.reset_mock()
+    amount_before = trade.amount
+
+    stop_order_dict.update({'id': "103_1"})
 
     assert freqtrade.handle_stoploss_on_exchange(trade) is False
     assert stoploss.call_count == 1
-    assert trade.stoploss_order_id == "13434334"
+    assert trade.stoploss_order_id == "103_1"
+    assert trade.amount == amount_before
 
     # Fourth case: when stoploss is set and it is hit
     # should unset stoploss_order_id and return true
     # as a trade actually happened
     caplog.clear()
     freqtrade.enter_positions()
+    stop_order_dict.update({'id': "104"})
+
     trade = Trade.session.scalars(select(Trade)).first()
     trade.is_short = is_short
     trade.is_open = True
     trade.open_order_id = None
-    trade.stoploss_order_id = "100"
+    trade.stoploss_order_id = "104"
     trade.orders.append(Order(
         ft_order_side='stoploss',
-        order_id='100',
+        order_id='104',
         ft_pair=trade.pair,
         ft_is_open=True,
         ft_amount=trade.amount,
@@ -1175,7 +1207,7 @@ def test_handle_stoploss_on_exchange(mocker, default_conf_usdt, fee, caplog, is_
     assert trade
 
     stoploss_order_hit = MagicMock(return_value={
-        'id': "100",
+        'id': "104",
         'status': 'closed',
         'type': 'stop_loss_limit',
         'price': 3,
@@ -1197,7 +1229,8 @@ def test_handle_stoploss_on_exchange(mocker, default_conf_usdt, fee, caplog, is_
 
     # Fifth case: fetch_order returns InvalidOrder
     # It should try to add stoploss order
-    trade.stoploss_order_id = 100
+    stop_order_dict.update({'id': "105"})
+    trade.stoploss_order_id = "105"
     stoploss.reset_mock()
     mocker.patch(f'{EXMS}.fetch_stoploss_order', side_effect=InvalidOrderException())
     mocker.patch(f'{EXMS}.create_stoploss', stoploss)
@@ -1217,21 +1250,36 @@ def test_handle_stoploss_on_exchange(mocker, default_conf_usdt, fee, caplog, is_
     # Seventh case: emergency exit triggered
     # Trailing stop should not act anymore
     stoploss_order_cancelled = MagicMock(side_effect=[{
-        'id': "100",
+        'id': "107",
         'status': 'canceled',
         'type': 'stop_loss_limit',
         'price': 3,
         'average': 2,
         'amount': enter_order['amount'],
+        'filled': 0,
+        'remaining': enter_order['amount'],
         'info': {'stopPrice': 22},
     }])
-    trade.stoploss_order_id = 100
+    trade.stoploss_order_id = "107"
     trade.is_open = True
     trade.stoploss_last_update = arrow.utcnow().shift(hours=-1).datetime
     trade.stop_loss = 24
+    trade.exit_reason = None
+    trade.orders.append(
+        Order(
+            ft_order_side='stoploss',
+            ft_pair=trade.pair,
+            ft_is_open=True,
+            ft_amount=trade.amount,
+            ft_price=trade.stop_loss,
+            order_id='107',
+            status='open',
+        )
+    )
     freqtrade.config['trailing_stop'] = True
     stoploss = MagicMock(side_effect=InvalidOrderException())
 
+    Trade.commit()
     mocker.patch(f'{EXMS}.cancel_stoploss_order_with_result',
                  side_effect=InvalidOrderException())
     mocker.patch(f'{EXMS}.fetch_stoploss_order', stoploss_order_cancelled)
@@ -1242,6 +1290,137 @@ def test_handle_stoploss_on_exchange(mocker, default_conf_usdt, fee, caplog, is_
     assert trade.exit_reason == str(ExitType.EMERGENCY_EXIT)
 
 
+@pytest.mark.parametrize("is_short", [False, True])
+def test_handle_stoploss_on_exchange_partial(
+        mocker, default_conf_usdt, fee, is_short, limit_order) -> None:
+    stop_order_dict = {'id': "101", "status": "open"}
+    stoploss = MagicMock(return_value=stop_order_dict)
+    enter_order = limit_order[entry_side(is_short)]
+    exit_order = limit_order[exit_side(is_short)]
+    patch_RPCManager(mocker)
+    patch_exchange(mocker)
+    mocker.patch.multiple(
+        EXMS,
+        fetch_ticker=MagicMock(return_value={
+            'bid': 1.9,
+            'ask': 2.2,
+            'last': 1.9
+        }),
+        create_order=MagicMock(side_effect=[
+            enter_order,
+            exit_order,
+        ]),
+        get_fee=fee,
+        create_stoploss=stoploss
+    )
+    freqtrade = FreqtradeBot(default_conf_usdt)
+    patch_get_signal(freqtrade, enter_short=is_short, enter_long=not is_short)
+
+    freqtrade.enter_positions()
+    trade = Trade.session.scalars(select(Trade)).first()
+    trade.is_short = is_short
+    trade.is_open = True
+    trade.open_order_id = None
+    trade.stoploss_order_id = None
+
+    assert freqtrade.handle_stoploss_on_exchange(trade) is False
+    assert stoploss.call_count == 1
+    assert trade.stoploss_order_id == "101"
+    assert trade.amount == 30
+    stop_order_dict.update({'id': "102"})
+    # Stoploss on exchange is cancelled on exchange, but filled partially.
+    # Must update trade amount to guarantee successful exit.
+    stoploss_order_hit = MagicMock(return_value={
+        'id': "101",
+        'status': 'canceled',
+        'type': 'stop_loss_limit',
+        'price': 3,
+        'average': 2,
+        'filled': trade.amount / 2,
+        'remaining': trade.amount / 2,
+        'amount': enter_order['amount'],
+    })
+    mocker.patch(f'{EXMS}.fetch_stoploss_order', stoploss_order_hit)
+    assert freqtrade.handle_stoploss_on_exchange(trade) is False
+    # Stoploss filled partially ...
+    assert trade.amount == 15
+
+    assert trade.stoploss_order_id == "102"
+
+
+@pytest.mark.parametrize("is_short", [False, True])
+def test_handle_stoploss_on_exchange_partial_cancel_here(
+        mocker, default_conf_usdt, fee, is_short, limit_order, caplog) -> None:
+    stop_order_dict = {'id': "101", "status": "open"}
+    default_conf_usdt['trailing_stop'] = True
+    stoploss = MagicMock(return_value=stop_order_dict)
+    enter_order = limit_order[entry_side(is_short)]
+    exit_order = limit_order[exit_side(is_short)]
+    patch_RPCManager(mocker)
+    patch_exchange(mocker)
+    mocker.patch.multiple(
+        EXMS,
+        fetch_ticker=MagicMock(return_value={
+            'bid': 1.9,
+            'ask': 2.2,
+            'last': 1.9
+        }),
+        create_order=MagicMock(side_effect=[
+            enter_order,
+            exit_order,
+        ]),
+        get_fee=fee,
+        create_stoploss=stoploss
+    )
+    freqtrade = FreqtradeBot(default_conf_usdt)
+    patch_get_signal(freqtrade, enter_short=is_short, enter_long=not is_short)
+
+    freqtrade.enter_positions()
+    trade = Trade.session.scalars(select(Trade)).first()
+    trade.is_short = is_short
+    trade.is_open = True
+    trade.open_order_id = None
+    trade.stoploss_order_id = None
+
+    assert freqtrade.handle_stoploss_on_exchange(trade) is False
+    assert stoploss.call_count == 1
+    assert trade.stoploss_order_id == "101"
+    assert trade.amount == 30
+    stop_order_dict.update({'id': "102"})
+    # Stoploss on exchange is open.
+    # Freqtrade cancels the stop - but cancel returns a partial filled order.
+    stoploss_order_hit = MagicMock(return_value={
+        'id': "101",
+        'status': 'open',
+        'type': 'stop_loss_limit',
+        'price': 3,
+        'average': 2,
+        'filled': 0,
+        'remaining': trade.amount,
+        'amount': enter_order['amount'],
+    })
+    stoploss_order_cancel = MagicMock(return_value={
+        'id': "101",
+        'status': 'canceled',
+        'type': 'stop_loss_limit',
+        'price': 3,
+        'average': 2,
+        'filled': trade.amount / 2,
+        'remaining': trade.amount / 2,
+        'amount': enter_order['amount'],
+    })
+    mocker.patch(f'{EXMS}.fetch_stoploss_order', stoploss_order_hit)
+    mocker.patch(f'{EXMS}.cancel_stoploss_order_with_result', stoploss_order_cancel)
+    trade.stoploss_last_update = arrow.utcnow().shift(minutes=-10).datetime
+
+    assert freqtrade.handle_stoploss_on_exchange(trade) is False
+    # Canceled Stoploss filled partially ...
+    assert log_has_re('Cancelling current stoploss on exchange.*', caplog)
+
+    assert trade.stoploss_order_id == "102"
+    assert trade.amount == 15
+
+
 @pytest.mark.parametrize("is_short", [False, True])
 def test_handle_sle_cancel_cant_recreate(mocker, default_conf_usdt, fee, caplog, is_short,
                                          limit_order) -> None:
@@ -1273,10 +1452,21 @@ def test_handle_sle_cancel_cant_recreate(mocker, default_conf_usdt, fee, caplog,
 
     freqtrade.enter_positions()
     trade = Trade.session.scalars(select(Trade)).first()
-    trade.is_short = is_short
+    assert trade.is_short == is_short
     trade.is_open = True
     trade.open_order_id = None
-    trade.stoploss_order_id = 100
+    trade.stoploss_order_id = "100"
+    trade.orders.append(
+        Order(
+            ft_order_side='stoploss',
+            ft_pair=trade.pair,
+            ft_is_open=True,
+            ft_amount=trade.amount,
+            ft_price=trade.stop_loss,
+            order_id='100',
+            status='open',
+        )
+    )
     assert trade
 
     assert freqtrade.handle_stoploss_on_exchange(trade) is False
@@ -1395,7 +1585,7 @@ def test_handle_stoploss_on_exchange_trailing(
     # When trailing stoploss is set
     enter_order = limit_order[entry_side(is_short)]
     exit_order = limit_order[exit_side(is_short)]
-    stoploss = MagicMock(return_value={'id': 13434334})
+    stoploss = MagicMock(return_value={'id': 13434334, 'status': 'open'})
     patch_RPCManager(mocker)
     mocker.patch.multiple(
         EXMS,
@@ -1440,11 +1630,21 @@ def test_handle_stoploss_on_exchange_trailing(
     trade.is_short = is_short
     trade.is_open = True
     trade.open_order_id = None
-    trade.stoploss_order_id = 100
+    trade.stoploss_order_id = '100'
     trade.stoploss_last_update = arrow.utcnow().shift(minutes=-20).datetime
+    trade.orders.append(
+        Order(
+            ft_order_side='stoploss',
+            ft_pair=trade.pair,
+            ft_is_open=True,
+            ft_amount=trade.amount,
+            ft_price=trade.stop_loss,
+            order_id='100',
+        )
+    )
 
     stoploss_order_hanging = MagicMock(return_value={
-        'id': 100,
+        'id': '100',
         'status': 'open',
         'type': 'stop_loss_limit',
         'price': hang_price,
@@ -1471,7 +1671,7 @@ def test_handle_stoploss_on_exchange_trailing(
     )
 
     cancel_order_mock = MagicMock()
-    stoploss_order_mock = MagicMock(return_value={'id': 'so1'})
+    stoploss_order_mock = MagicMock(return_value={'id': 'so1', 'status': 'open'})
     mocker.patch(f'{EXMS}.cancel_stoploss_order', cancel_order_mock)
     mocker.patch(f'{EXMS}.create_stoploss', stoploss_order_mock)
 
@@ -1483,13 +1683,14 @@ def test_handle_stoploss_on_exchange_trailing(
 
     assert freqtrade.handle_trade(trade) is False
     assert trade.stop_loss == stop_price[1]
+    trade.stoploss_order_id = '100'
 
     # setting stoploss_on_exchange_interval to 0 seconds
     freqtrade.strategy.order_types['stoploss_on_exchange_interval'] = 0
 
     assert freqtrade.handle_stoploss_on_exchange(trade) is False
 
-    cancel_order_mock.assert_called_once_with(100, 'ETH/USDT')
+    cancel_order_mock.assert_called_once_with('100', 'ETH/USDT')
     stoploss_order_mock.assert_called_once_with(
         amount=pytest.approx(amt),
         pair='ETH/USDT',
@@ -1519,7 +1720,7 @@ def test_handle_stoploss_on_exchange_trailing_error(
     enter_order = limit_order[entry_side(is_short)]
     exit_order = limit_order[exit_side(is_short)]
     # When trailing stoploss is set
-    stoploss = MagicMock(return_value={'id': 13434334})
+    stoploss = MagicMock(return_value={'id': '13434334', 'status': 'open'})
     patch_exchange(mocker)
 
     mocker.patch.multiple(
@@ -1601,7 +1802,7 @@ def test_stoploss_on_exchange_price_rounding(
         EXMS,
         get_fee=fee,
     )
-    price_mock = MagicMock(side_effect=lambda p, s: int(s))
+    price_mock = MagicMock(side_effect=lambda p, s, **kwargs: int(s))
     stoploss_mock = MagicMock(return_value={'id': '13434334'})
     adjust_mock = MagicMock(return_value=False)
     mocker.patch.multiple(
@@ -1628,7 +1829,7 @@ def test_handle_stoploss_on_exchange_custom_stop(
     enter_order = limit_order[entry_side(is_short)]
     exit_order = limit_order[exit_side(is_short)]
     # When trailing stoploss is set
-    stoploss = MagicMock(return_value={'id': 13434334})
+    stoploss = MagicMock(return_value={'id': 13434334, 'status': 'open'})
     patch_RPCManager(mocker)
     mocker.patch.multiple(
         EXMS,
@@ -1673,11 +1874,21 @@ def test_handle_stoploss_on_exchange_custom_stop(
     trade.is_short = is_short
     trade.is_open = True
     trade.open_order_id = None
-    trade.stoploss_order_id = 100
+    trade.stoploss_order_id = '100'
     trade.stoploss_last_update = arrow.utcnow().shift(minutes=-601).datetime
+    trade.orders.append(
+        Order(
+            ft_order_side='stoploss',
+            ft_pair=trade.pair,
+            ft_is_open=True,
+            ft_amount=trade.amount,
+            ft_price=trade.stop_loss,
+            order_id='100',
+        )
+    )
 
     stoploss_order_hanging = MagicMock(return_value={
-        'id': 100,
+        'id': '100',
         'status': 'open',
         'type': 'stop_loss_limit',
         'price': 3,
@@ -1703,9 +1914,10 @@ def test_handle_stoploss_on_exchange_custom_stop(
     )
 
     cancel_order_mock = MagicMock()
-    stoploss_order_mock = MagicMock(return_value={'id': 'so1'})
+    stoploss_order_mock = MagicMock(return_value={'id': 'so1', 'status': 'open'})
     mocker.patch(f'{EXMS}.cancel_stoploss_order', cancel_order_mock)
     mocker.patch(f'{EXMS}.create_stoploss', stoploss_order_mock)
+    trade.stoploss_order_id = '100'
 
     # stoploss should not be updated as the interval is 60 seconds
     assert freqtrade.handle_trade(trade) is False
@@ -1722,7 +1934,7 @@ def test_handle_stoploss_on_exchange_custom_stop(
 
     assert freqtrade.handle_stoploss_on_exchange(trade) is False
 
-    cancel_order_mock.assert_called_once_with(100, 'ETH/USDT')
+    cancel_order_mock.assert_called_once_with('100', 'ETH/USDT')
     # Long uses modified ask - offset, short modified bid + offset
     stoploss_order_mock.assert_called_once_with(
         amount=pytest.approx(trade.amount),
@@ -1751,7 +1963,7 @@ def test_tsl_on_exchange_compatible_with_edge(mocker, edge_conf, fee, limit_orde
     exit_order = limit_order['sell']
 
     # When trailing stoploss is set
-    stoploss = MagicMock(return_value={'id': 13434334})
+    stoploss = MagicMock(return_value={'id': '13434334', 'status': 'open'})
     patch_RPCManager(mocker)
     patch_exchange(mocker)
     patch_edge(mocker)
@@ -1800,11 +2012,21 @@ def test_tsl_on_exchange_compatible_with_edge(mocker, edge_conf, fee, limit_orde
     trade = Trade.session.scalars(select(Trade)).first()
     trade.is_open = True
     trade.open_order_id = None
-    trade.stoploss_order_id = 100
-    trade.stoploss_last_update = arrow.utcnow()
+    trade.stoploss_order_id = '100'
+    trade.stoploss_last_update = arrow.utcnow().datetime
+    trade.orders.append(
+        Order(
+            ft_order_side='stoploss',
+            ft_pair=trade.pair,
+            ft_is_open=True,
+            ft_amount=trade.amount,
+            ft_price=trade.stop_loss,
+            order_id='100',
+        )
+    )
 
     stoploss_order_hanging = MagicMock(return_value={
-        'id': 100,
+        'id': '100',
         'status': 'open',
         'type': 'stop_loss_limit',
         'price': 3,
@@ -1851,7 +2073,7 @@ def test_tsl_on_exchange_compatible_with_edge(mocker, edge_conf, fee, limit_orde
 
     # stoploss should be set to 1% as trailing is on
     assert trade.stop_loss == 4.4 * 0.99
-    cancel_order_mock.assert_called_once_with(100, 'NEO/BTC')
+    cancel_order_mock.assert_called_once_with('100', 'NEO/BTC')
     stoploss_order_mock.assert_called_once_with(
         amount=pytest.approx(11.41438356),
         pair='NEO/BTC',
@@ -1885,6 +2107,7 @@ def test_enter_positions(mocker, default_conf_usdt, return_value, side_effect,
     assert mock_ct.call_count == len(default_conf_usdt['exchange']['pair_whitelist'])
 
 
+@pytest.mark.usefixtures("init_persistence")
 @pytest.mark.parametrize("is_short", [False, True])
 def test_exit_positions(mocker, default_conf_usdt, limit_order, is_short, caplog) -> None:
     freqtrade = get_patched_freqtradebot(mocker, default_conf_usdt)
@@ -1893,12 +2116,33 @@ def test_exit_positions(mocker, default_conf_usdt, limit_order, is_short, caplog
     mocker.patch(f'{EXMS}.fetch_order', return_value=limit_order[entry_side(is_short)])
     mocker.patch(f'{EXMS}.get_trades_for_order', return_value=[])
 
-    # TODO: should not be magicmock
-    trade = MagicMock()
-    trade.is_short = is_short
-    trade.open_order_id = '123'
-    trade.open_fee = 0.001
+    order_id = '123'
+    trade = Trade(
+            open_order_id=order_id,
+            pair='ETH/USDT',
+            fee_open=0.001,
+            fee_close=0.001,
+            open_rate=0.01,
+            open_date=arrow.utcnow().datetime,
+            stake_amount=0.01,
+            amount=11,
+            exchange="binance",
+            is_short=is_short,
+            leverage=1,
+            )
+    trade.orders.append(Order(
+        ft_order_side=entry_side(is_short),
+        price=0.01,
+        ft_pair=trade.pair,
+        ft_amount=trade.amount,
+        ft_price=trade.open_rate,
+        order_id=order_id,
+
+    ))
+    Trade.session.add(trade)
+    Trade.commit()
     trades = [trade]
+    freqtrade.wallets.update()
     n = freqtrade.exit_positions(trades)
     assert n == 0
     # Test amount not modified by fee-logic
@@ -1911,17 +2155,40 @@ def test_exit_positions(mocker, default_conf_usdt, limit_order, is_short, caplog
     assert gra.call_count == 0
 
 
+@pytest.mark.usefixtures("init_persistence")
 @pytest.mark.parametrize("is_short", [False, True])
 def test_exit_positions_exception(mocker, default_conf_usdt, limit_order, caplog, is_short) -> None:
     freqtrade = get_patched_freqtradebot(mocker, default_conf_usdt)
     order = limit_order[entry_side(is_short)]
     mocker.patch(f'{EXMS}.fetch_order', return_value=order)
 
-    # TODO: should not be magicmock
-    trade = MagicMock()
-    trade.is_short = is_short
+    order_id = '123'
+    trade = Trade(
+        open_order_id=order_id,
+        pair='ETH/USDT',
+        fee_open=0.001,
+        fee_close=0.001,
+        open_rate=0.01,
+        open_date=arrow.utcnow().datetime,
+        stake_amount=0.01,
+        amount=11,
+        exchange="binance",
+        is_short=is_short,
+        leverage=1,
+    )
+    trade.orders.append(Order(
+        ft_order_side=entry_side(is_short),
+        price=0.01,
+        ft_pair=trade.pair,
+        ft_amount=trade.amount,
+        ft_price=trade.open_rate,
+        order_id=order_id,
+
+    ))
     trade.open_order_id = None
-    trade.pair = 'ETH/USDT'
+    Trade.session.add(trade)
+    Trade.commit()
+    freqtrade.wallets.update()
     trades = [trade]
 
     # Test raise of DependencyException exception
@@ -2733,6 +3000,9 @@ def test_manage_open_orders_exit_usercustom(
     assert rpc_mock.call_count == 2
     assert freqtrade.strategy.check_exit_timeout.call_count == 1
     assert freqtrade.strategy.check_entry_timeout.call_count == 0
+    trade = Trade.session.scalars(select(Trade)).first()
+    # cancelling didn't succeed - order-id remains open.
+    assert trade.open_order_id is not None
 
     # 2nd canceled trade - Fail execute exit
     caplog.clear()
@@ -3243,6 +3513,7 @@ def test_handle_cancel_exit_cancel_exception(mocker, default_conf_usdt) -> None:
 
     # TODO: should not be magicmock
     trade = MagicMock()
+    trade.open_order_id = '125'
     reason = CANCEL_REASON['TIMEOUT']
     order = {'remaining': 1,
              'id': '125',
@@ -3250,6 +3521,10 @@ def test_handle_cancel_exit_cancel_exception(mocker, default_conf_usdt) -> None:
              'status': "open"}
     assert not freqtrade.handle_cancel_exit(trade, order, reason)
 
+    # mocker.patch(f'{EXMS}.cancel_order_with_result', return_value=order)
+    # assert not freqtrade.handle_cancel_exit(trade, order, reason)
+    # assert trade.open_order_id == '125'
+
 
 @pytest.mark.parametrize("is_short, open_rate, amt", [
     (False, 2.0, 30.0),
@@ -3326,6 +3601,7 @@ def test_execute_trade_exit_up(default_conf_usdt, ticker_usdt, fee, ticker_usdt_
         'profit_ratio': 0.00493809 if is_short else 0.09451372,
         'stake_currency': 'USDT',
         'fiat_currency': 'USD',
+        'base_currency': 'ETH',
         'sell_reason': ExitType.ROI.value,
         'exit_reason': ExitType.ROI.value,
         'open_date': ANY,
@@ -3389,6 +3665,7 @@ def test_execute_trade_exit_down(default_conf_usdt, ticker_usdt, fee, ticker_usd
         'profit_amount': -5.65990099 if is_short else -0.00075,
         'profit_ratio': -0.0945681 if is_short else -1.247e-05,
         'stake_currency': 'USDT',
+        'base_currency': 'ETH',
         'fiat_currency': 'USD',
         'sell_reason': ExitType.STOP_LOSS.value,
         'exit_reason': ExitType.STOP_LOSS.value,
@@ -3474,6 +3751,7 @@ def test_execute_trade_exit_custom_exit_price(
         'profit_amount': pytest.approx(profit_amount),
         'profit_ratio': profit_ratio,
         'stake_currency': 'USDT',
+        'base_currency': 'ETH',
         'fiat_currency': 'USD',
         'sell_reason': 'foo',
         'exit_reason': 'foo',
@@ -3547,6 +3825,7 @@ def test_execute_trade_exit_down_stoploss_on_exchange_dry_run(
         'profit_ratio': -0.00501253 if is_short else -0.01493766,
         'stake_currency': 'USDT',
         'fiat_currency': 'USD',
+        'base_currency': 'ETH',
         'sell_reason': ExitType.STOP_LOSS.value,
         'exit_reason': ExitType.STOP_LOSS.value,
         'open_date': ANY,
@@ -3588,7 +3867,7 @@ def test_execute_trade_exit_sloe_cancel_exception(
     freqtrade.execute_trade_exit(trade=trade, limit=1234,
                                  exit_check=ExitCheckTuple(exit_type=ExitType.STOP_LOSS))
     assert create_order_mock.call_count == 2
-    assert log_has('Could not cancel stoploss order abcd', caplog)
+    assert log_has('Could not cancel stoploss order abcd for pair ETH/USDT', caplog)
 
 
 @pytest.mark.parametrize("is_short", [False, True])
@@ -3600,10 +3879,12 @@ def test_execute_trade_exit_with_stoploss_on_exchange(
     patch_exchange(mocker)
     stoploss = MagicMock(return_value={
         'id': 123,
+        'status': 'open',
         'info': {
             'foo': 'bar'
         }
     })
+    mocker.patch('freqtrade.freqtradebot.FreqtradeBot.handle_order_fee')
 
     cancel_order = MagicMock(return_value=True)
     mocker.patch.multiple(
@@ -3701,12 +3982,12 @@ def test_may_execute_trade_exit_after_stoploss_on_exchange_hit(
         "lastTradeTimestamp": None,
         "symbol": "BTC/USDT",
         "type": "stop_loss_limit",
-        "side": "sell",
+        "side": "buy" if is_short else "sell",
         "price": 1.08801,
-        "amount": 90.99181074,
-        "cost": 99.0000000032274,
+        "amount": trade.amount,
+        "cost": 1.08801 * trade.amount,
         "average": 1.08801,
-        "filled": 90.99181074,
+        "filled": trade.amount,
         "remaining": 0.0,
         "status": "closed",
         "fee": None,
@@ -3811,6 +4092,7 @@ def test_execute_trade_exit_market_order(
         'profit_amount': pytest.approx(profit_amount),
         'profit_ratio': profit_ratio,
         'stake_currency': 'USDT',
+        'base_currency': 'ETH',
         'fiat_currency': 'USD',
         'sell_reason': ExitType.ROI.value,
         'exit_reason': ExitType.ROI.value,
diff --git a/tests/test_integration.py b/tests/test_integration.py
index 922285309..9fb9fd8b3 100644
--- a/tests/test_integration.py
+++ b/tests/test_integration.py
@@ -35,7 +35,7 @@ def test_may_execute_exit_stoploss_on_exchange_multi(default_conf, ticker, fee,
         "type": "stop_loss_limit",
         "side": "sell",
         "price": 1.08801,
-        "amount": 90.99181074,
+        "amount": 91.07468123,
         "cost": 0.0,
         "average": 0.0,
         "filled": 0.0,
@@ -49,8 +49,9 @@ def test_may_execute_exit_stoploss_on_exchange_multi(default_conf, ticker, fee,
     stoploss_order_closed['filled'] = stoploss_order_closed['amount']
 
     # Sell first trade based on stoploss, keep 2nd and 3rd trade open
+    stop_orders = [stoploss_order_closed, stoploss_order_open, stoploss_order_open]
     stoploss_order_mock = MagicMock(
-        side_effect=[stoploss_order_closed, stoploss_order_open, stoploss_order_open])
+        side_effect=stop_orders)
     # Sell 3rd trade (not called for the first trade)
     should_sell_mock = MagicMock(side_effect=[
         [],
@@ -93,13 +94,14 @@ def test_may_execute_exit_stoploss_on_exchange_multi(default_conf, ticker, fee,
     wallets_mock.reset_mock()
 
     trades = Trade.session.scalars(select(Trade)).all()
-    # Make sure stoploss-order is open and trade is bought (since we mock update_trade_state)
-    for trade in trades:
-        stoploss_order_closed['id'] = '3'
-        oobj = Order.parse_from_ccxt_object(stoploss_order_closed, trade.pair, 'stoploss')
+    # Make sure stoploss-order is open and trade is bought
+    for idx, trade in enumerate(trades):
+        stop_order = stop_orders[idx]
+        stop_order['id'] = f"stop{idx}"
+        oobj = Order.parse_from_ccxt_object(stop_order, trade.pair, 'stoploss')
 
         trade.orders.append(oobj)
-        trade.stoploss_order_id = '3'
+        trade.stoploss_order_id = f"stop{idx}"
         trade.open_order_id = None
 
     n = freqtrade.exit_positions(trades)
@@ -386,12 +388,12 @@ def test_dca_order_adjust(default_conf_usdt, ticker_usdt, leverage, fee, mocker)
     assert trade.open_order_id is not None
     assert pytest.approx(trade.stake_amount) == 60
     assert trade.open_rate == 1.96
-    assert trade.stop_loss_pct is None
-    assert trade.stop_loss == 0.0
+    assert trade.stop_loss_pct == -0.1
+    assert pytest.approx(trade.stop_loss) == trade.open_rate * (1 - 0.1 / leverage)
+    assert pytest.approx(trade.initial_stop_loss) == trade.open_rate * (1 - 0.1 / leverage)
+    assert trade.initial_stop_loss_pct == -0.1
     assert trade.leverage == leverage
     assert trade.stake_amount == 60
-    assert trade.initial_stop_loss == 0.0
-    assert trade.initial_stop_loss_pct is None
     # No adjustment
     freqtrade.process()
     trade = Trade.get_trades().first()
@@ -407,11 +409,11 @@ def test_dca_order_adjust(default_conf_usdt, ticker_usdt, leverage, fee, mocker)
     assert trade.open_order_id is not None
     # Open rate is not adjusted yet
     assert trade.open_rate == 1.96
-    assert trade.stop_loss_pct is None
-    assert trade.stop_loss == 0.0
+    assert trade.stop_loss_pct == -0.1
+    assert pytest.approx(trade.stop_loss) == trade.open_rate * (1 - 0.1 / leverage)
+    assert pytest.approx(trade.initial_stop_loss) == trade.open_rate * (1 - 0.1 / leverage)
     assert trade.stake_amount == 60
-    assert trade.initial_stop_loss == 0.0
-    assert trade.initial_stop_loss_pct is None
+    assert trade.initial_stop_loss_pct == -0.1
 
     # Fill order
     mocker.patch(f'{EXMS}._dry_is_price_crossed', return_value=True)
@@ -424,7 +426,7 @@ def test_dca_order_adjust(default_conf_usdt, ticker_usdt, leverage, fee, mocker)
     assert pytest.approx(trade.stake_amount) == 60
     assert trade.stop_loss_pct == -0.1
     assert pytest.approx(trade.stop_loss) == 1.99 * (1 - 0.1 / leverage)
-    assert pytest.approx(trade.initial_stop_loss) == 1.99 * (1 - 0.1 / leverage)
+    assert pytest.approx(trade.initial_stop_loss) == 1.96 * (1 - 0.1 / leverage)
     assert trade.initial_stop_loss_pct == -0.1
 
     # 2nd order - not filling
diff --git a/tests/test_timerange.py b/tests/test_timerange.py
index 06ff1983a..993b24d95 100644
--- a/tests/test_timerange.py
+++ b/tests/test_timerange.py
@@ -10,6 +10,8 @@ from freqtrade.exceptions import OperationalException
 
 def test_parse_timerange_incorrect():
 
+    timerange = TimeRange.parse_timerange('')
+    assert timerange == TimeRange(None, None, 0, 0)
     timerange = TimeRange.parse_timerange('20100522-')
     assert TimeRange('date', None, 1274486400, 0) == timerange
     assert timerange.timerange_str == '20100522-'
diff --git a/tests/testdata/XRP_ETH-trades.feather b/tests/testdata/XRP_ETH-trades.feather
new file mode 100644
index 000000000..68e1c8467
Binary files /dev/null and b/tests/testdata/XRP_ETH-trades.feather differ