Update freqtrade/freqai/data_drawer.py

Co-authored-by: Matthias <xmatthias@outlook.com>

.github/workflows/ci.yml

@@ -14,7 +14,7 @@ on:
     - cron:  '0 5 * * 4'
 
 concurrency:
-  group: "${{ github.workflow }}-${{ github.ref }}-${{ github.event_name }}"
+  group: ${{ github.workflow }}-${{ github.ref }}
   cancel-in-progress: true
 permissions:
   repository-projects: read
@@ -77,17 +77,6 @@ jobs:
         # Allow failure for coveralls
         coveralls || true
 
-    - name: Check for repository changes
-      run: |
-        if [ -n "$(git status --porcelain)" ]; then
-          echo "Repository is dirty, changes detected:"
-          git status
-          git diff
-          exit 1
-        else
-          echo "Repository is clean, no changes detected."
-        fi
-
     - name: Backtesting (multi)
       run: |
         cp config_examples/config_bittrex.example.json config.json
@@ -185,17 +174,6 @@ jobs:
       run: |
         pytest --random-order
 
-    - name: Check for repository changes
-      run: |
-        if [ -n "$(git status --porcelain)" ]; then
-          echo "Repository is dirty, changes detected:"
-          git status
-          git diff
-          exit 1
-        else
-          echo "Repository is clean, no changes detected."
-        fi
-
     - name: Backtesting
       run: |
         cp config_examples/config_bittrex.example.json config.json
@@ -259,18 +237,6 @@ jobs:
       run: |
         pytest --random-order
 
-    - name: Check for repository changes
-      run: |
-        if (git status --porcelain) {
-          Write-Host "Repository is dirty, changes detected:"
-          git status
-          git diff
-          exit 1
-        }
-        else {
-          Write-Host "Repository is clean, no changes detected."
-        }
-
     - name: Backtesting
       run: |
         cp config_examples/config_bittrex.example.json config.json
@@ -336,7 +302,7 @@ jobs:
     - name: Set up Python
       uses: actions/setup-python@v4
       with:
-        python-version: "3.11"
+        python-version: "3.10"
 
     - name: Documentation build
       run: |
@@ -459,7 +425,7 @@ jobs:
         python setup.py sdist bdist_wheel
 
     - name: Publish to PyPI (Test)
-      uses: pypa/gh-action-pypi-publish@v1.8.6
+      uses: pypa/gh-action-pypi-publish@v1.8.1
       if: (github.event_name == 'release')
       with:
         user: __token__
@@ -467,7 +433,7 @@ jobs:
         repository_url: https://test.pypi.org/legacy/
 
     - name: Publish to PyPI
-      uses: pypa/gh-action-pypi-publish@v1.8.6
+      uses: pypa/gh-action-pypi-publish@v1.8.1
       if: (github.event_name == 'release')
       with:
         user: __token__

.pre-commit-config.yaml

@@ -13,12 +13,12 @@ repos:
       - id: mypy
         exclude: build_helpers
         additional_dependencies:
-          - types-cachetools==5.3.0.5
+          - types-cachetools==5.3.0.4
           - types-filelock==3.2.7
-          - types-requests==2.30.0.0
-          - types-tabulate==0.9.0.2
-          - types-python-dateutil==2.8.19.13
-          - SQLAlchemy==2.0.15
+          - types-requests==2.28.11.15
+          - types-tabulate==0.9.0.1
+          - types-python-dateutil==2.8.19.10
+          - SQLAlchemy==2.0.7
         # stages: [push]
 
   - repo: https://github.com/pycqa/isort
@@ -30,7 +30,7 @@ repos:
 
   - repo: https://github.com/charliermarsh/ruff-pre-commit
     # Ruff version.
-    rev: 'v0.0.263'
+    rev: 'v0.0.255'
     hooks:
       - id: ruff
 

Dockerfile

@@ -1,4 +1,4 @@
-FROM python:3.10.11-slim-bullseye as base
+FROM python:3.10.10-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 wheel
+  && pip install --upgrade pip
 
 # Install TA-lib
 COPY build_helpers/* /tmp/

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://ta-lib.github.io/ta-lib-python/)
+- [TA-Lib](https://mrjbq7.github.io/ta-lib/install.html)
 - [virtualenv](https://virtualenv.pypa.io/en/stable/installation.html) (Recommended)
 - [Docker](https://www.docker.com/products/docker) (Recommended)

build_helpers/install_windows.ps1

@@ -6,16 +6,16 @@ python -m pip install --upgrade pip wheel
 $pyv = python -c "import sys; print(f'{sys.version_info.major}.{sys.version_info.minor}')"
 
 if ($pyv -eq '3.8') {
-    pip install build_helpers\TA_Lib-0.4.26-cp38-cp38-win_amd64.whl
+    pip install build_helpers\TA_Lib-0.4.25-cp38-cp38-win_amd64.whl
 }
 if ($pyv -eq '3.9') {
-    pip install build_helpers\TA_Lib-0.4.26-cp39-cp39-win_amd64.whl
+    pip install build_helpers\TA_Lib-0.4.25-cp39-cp39-win_amd64.whl
 }
 if ($pyv -eq '3.10') {
-    pip install build_helpers\TA_Lib-0.4.26-cp310-cp310-win_amd64.whl
+    pip install build_helpers\TA_Lib-0.4.25-cp310-cp310-win_amd64.whl
 }
 if ($pyv -eq '3.11') {
-    pip install build_helpers\TA_Lib-0.4.26-cp311-cp311-win_amd64.whl
+    pip install build_helpers\TA_Lib-0.4.25-cp311-cp311-win_amd64.whl
 }
 pip install -r requirements-dev.txt
 pip install -e .

build_helpers/publish_docker_arm64.sh

@@ -12,7 +12,6 @@ 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
@@ -43,9 +42,9 @@ if [ $? -ne 0 ]; then
     return 1
 fi
 
-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 .
+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 .
 
 # Tag image for upload and next build step
 docker tag freqtrade:$TAG_ARM ${CACHE_IMAGE}:$TAG_ARM
@@ -85,10 +84,6 @@ 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 $(pwd)/.crane:/home/nonroot/.docker/ gcr.io/go-containerregistry/crane"
@@ -98,7 +93,6 @@ chmod a+rwx .crane
 echo "${GHCR_TOKEN}" | crane auth login ghcr.io -u "${GHCR_USERNAME}" --password-stdin
 
 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}

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 --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 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 tag freqtrade:$TAG_PLOT ${CACHE_IMAGE}:$TAG_PLOT
 docker tag freqtrade:$TAG_FREQAI ${CACHE_IMAGE}:$TAG_FREQAI

docker-compose.yml

@@ -6,15 +6,6 @@ services:
     # image: freqtradeorg/freqtrade:develop
     # Use plotting image
     # image: freqtradeorg/freqtrade:develop_plot
-    # # Enable GPU Image and GPU Resources (only relevant for freqAI)
-    # # Make sure to uncomment the whole deploy section
-    # deploy:
-    #   resources:
-    #     reservations:
-    #       devices:
-    #         - driver: nvidia
-    #           count: 1
-    #           capabilities: [gpu]
     # Build step - only needed when additional dependencies are needed
     # build:
     #   context: .
@@ -25,7 +16,7 @@ services:
       - "./user_data:/freqtrade/user_data"
     # Expose api on port 8080 (localhost only)
     # Please read the https://www.freqtrade.io/en/stable/rest-api/ documentation
-    # for more information.
+    # before enabling this.
     ports:
       - "127.0.0.1:8080:8080"
     # Default command used when running `docker compose up`

docker/docker-compose-freqai.yml

@@ -1,36 +0,0 @@
----
-version: '3'
-services:
-  freqtrade:
-    image: freqtradeorg/freqtrade:stable_freqaitorch
-    # # Enable GPU Image and GPU Resources
-    # # Make sure to uncomment the whole deploy section
-    # deploy:
-    #   resources:
-    #     reservations:
-    #       devices:
-    #         - driver: nvidia
-    #           count: 1
-    #           capabilities: [gpu]
-
-    # Build step - only needed when additional dependencies are needed
-    # build:
-    #   context: .
-    #   dockerfile: "./docker/Dockerfile.custom"
-    restart: unless-stopped
-    container_name: freqtrade
-    volumes:
-      - "./user_data:/freqtrade/user_data"
-    # Expose api on port 8080 (localhost only)
-    # Please read the https://www.freqtrade.io/en/stable/rest-api/ documentation
-    # for more information.
-    ports:
-      - "127.0.0.1:8080:8080"
-    # Default command used when running `docker compose up`
-    command: >
-      trade
-      --logfile /freqtrade/user_data/logs/freqtrade.log
-      --db-url sqlite:////freqtrade/user_data/tradesv3.sqlite
-      --config /freqtrade/user_data/config.json
-      --freqai-model XGBoostClassifier
-      --strategy SampleStrategy

docs/advanced-backtesting.md

@@ -29,7 +29,7 @@ If all goes well, you should now see a `backtest-result-{timestamp}_signals.pkl`
 `user_data/backtest_results` folder.
 
 To analyze the entry/exit tags, we now need to use the `freqtrade backtesting-analysis` command
-with `--analysis-groups` option provided with space-separated arguments:
+with `--analysis-groups` option provided with space-separated arguments (default `0 1 2`):
 
 ``` bash
 freqtrade backtesting-analysis -c <config.json> --analysis-groups 0 1 2 3 4 5
@@ -39,7 +39,6 @@ This command will read from the last backtesting results. The `--analysis-groups
 used to specify the various tabular outputs showing the profit fo each group or trade,
 ranging from the simplest (0) to the most detailed per pair, per buy and per sell tag (4):
 
-* 0: overall winrate and profit summary by enter_tag
 * 1: profit summaries grouped by enter_tag
 * 2: profit summaries grouped by enter_tag and exit_tag
 * 3: profit summaries grouped by pair and enter_tag
@@ -116,38 +115,3 @@ For example, if your backtest timerange was `20220101-20221231` but you only wan
 ```bash
 freqtrade backtesting-analysis -c <config.json> --timerange 20220101-20220201
 ```
-
-### Printing out rejected signals
-
-Use the `--rejected-signals` option to print out rejected signals.
-
-```bash
-freqtrade backtesting-analysis -c <config.json> --rejected-signals
-```
-
-### Writing tables to CSV
-
-Some of the tabular outputs can become large, so printing them out to the terminal is not preferable.
-Use the `--analysis-to-csv` option to disable printing out of tables to standard out and write them to CSV files.
-
-```bash
-freqtrade backtesting-analysis -c <config.json> --analysis-to-csv
-```
-
-By default this will write one file per output table you specified in the `backtesting-analysis` command, e.g.
-
-```bash
-freqtrade backtesting-analysis -c <config.json> --analysis-to-csv --rejected-signals --analysis-groups 0 1
-```
-
-This will write to `user_data/backtest_results`:
-
-* rejected_signals.csv
-* group_0.csv
-* group_1.csv
-
-To override where the files will be written, also specify the `--analysis-csv-path` option.
-
-```bash
-freqtrade backtesting-analysis -c <config.json> --analysis-to-csv --analysis-csv-path another/data/path/
-```

docs/backtesting.md

@@ -274,20 +274,19 @@ 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 |
-====================================================== 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 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 |
 ================== SUMMARY METRICS ==================
 | Metric                      | Value               |
 |-----------------------------+---------------------|

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).

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. <br> **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). <br> **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). <br>*Defaults to `0.99` 99%).*<br> **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). <br> **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). <br> **Datatype:** Positive float.
 | `amend_last_stake_amount` | Use reduced last stake amount if necessary. [More information below](#configuring-amount-per-trade). <br>*Defaults to `false`.* <br> **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). <br>*Defaults to `0.5`.* <br> **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. <br>*Defaults to `0.05` (5%).* <br> **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). <br>*Defaults to `0.0` (no offset).* <br> **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). <br>*Defaults to `false`.*  <br> **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. <br> **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) <br>*Defaults to `None`.*<br> **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) <br>*Defaults to None.*<br> **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). <br>*Defaults to `"spot"`.*  <br> **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). <br> **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). <br>*Defaults to `0.05`.*  <br> **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).<br> **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).<br> **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). <br> *Defaults to `"minutes"`.* <br> **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). <br> *Defaults to `minutes`.* <br> **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).<br>*Defaults to `0`.* <br> **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](#entry-price).<br> *Defaults to `"same"`.* <br> **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](#buy-price-side).<br> *Defaults to `same`.* <br> **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). <br> *Defaults to `true`.*<br> **Datatype:** Boolean
+| `entry_pricing.use_order_book` | Enable entering using the rates in [Order Book Entry](#entry-price-with-orderbook-enabled). <br> *Defaults to `True`.*<br> **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). <br>*Defaults to `1`.*  <br> **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). <br>*Defaults to `false`.* <br> **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) <br> *Defaults to `0`.*  <br> **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).<br> *Defaults to `"same"`.* <br> **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).<br> *Defaults to `same`.* <br> **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). <br> *Defaults to `true`.*<br> **Datatype:** Boolean
+| `exit_pricing.use_order_book` | Enable exiting of open trades using [Order Book Exit](#exit-price-with-orderbook-enabled). <br> *Defaults to `True`.*<br> **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)<br>*Defaults to `1`.* <br> **Datatype:** Positive Integer
 | `custom_price_max_distance_ratio` | Configure maximum distance ratio between current and custom entry or exit price. <br>*Defaults to `0.02` 2%).*<br> **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) <br> **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) <br> **Datatype:** Dict
 | `exchange.markets_refresh_interval` | The interval in minutes in which markets are reloaded. <br>*Defaults to `60` minutes.* <br> **Datatype:** Positive Integer
-| `exchange.skip_pair_validation` | Skip pairlist validation on startup.<br>*Defaults to `false`*<br> **Datatype:** Boolean
-| `exchange.skip_open_order_update` | Skips open order updates on startup should the exchange cause problems. Only relevant in live conditions.<br>*Defaults to `false`*<br> **Datatype:** Boolean
+| `exchange.skip_pair_validation` | Skip pairlist validation on startup.<br>*Defaults to `false`<br> **Datatype:** Boolean
+| `exchange.skip_open_order_update` | Skips open order updates on startup should the exchange cause problems. Only relevant in live conditions.<br>*Defaults to `false`<br> **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".<br>*Defaults to `None`<br> **Datatype:** float
-| `exchange.log_responses` | Log relevant exchange responses. For debug mode only - use with care.<br>*Defaults to `false`*<br> **Datatype:** Boolean
+| `exchange.log_responses` | Log relevant exchange responses. For debug mode only - use with care.<br>*Defaults to `false`<br> **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. <br>*Defaults to `true`.* <br> **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`. <br>**Keep it in secret, do not disclose publicly.** <br> **Datatype:** String
 | `telegram.chat_id` | Your personal Telegram account id. Only required if `telegram.enabled` is `true`. <br>**Keep it in secret, do not disclose publicly.** <br> **Datatype:** String
 | `telegram.balance_dust_level` | Dust-level (in stake currency) - currencies with a balance below this will not be shown by `/balance`. <br> **Datatype:** float
-| `telegram.reload` | Allow "reload" buttons on telegram messages. <br>*Defaults to `true`.<br> **Datatype:** boolean
+| `telegram.reload` | Allow "reload" buttons on telegram messages. <br>*Defaults to `True`.<br> **Datatype:** boolean
 | `telegram.notification_settings.*` | Detailed notification settings. Refer to the [telegram documentation](telegram-usage.md) for details.<br> **Datatype:** dictionary
 | `telegram.allow_custom_messages` | Enable the sending of Telegram messages from strategies via the dataprovider.send_msg() function. <br> **Datatype:** Boolean
 | | **Webhook**

docs/developer.md

@@ -327,18 +327,18 @@ To check how the new exchange behaves, you can use the following snippet:
 
 ``` python
 import ccxt
-from datetime import datetime, timezone
+from datetime import datetime
 from freqtrade.data.converter import ohlcv_to_dataframe
-ct = ccxt.binance()  # Use the exchange you're testing
+ct = ccxt.binance()
 timeframe = "1d"
-pair = "BTC/USDT"  # Make sure to use a pair that exists on that exchange!
+pair = "XLM/BTC"  # Make sure to use a pair that exists on that exchange!
 raw = ct.fetch_ohlcv(pair, timeframe=timeframe)
 
 # convert to dataframe
 df1 = ohlcv_to_dataframe(raw, timeframe, pair=pair, drop_incomplete=False)
 
 print(df1.tail(1))
-print(datetime.now(timezone.utc))
+print(datetime.utcnow())
 ```
 
 ``` output

docs/faq.md

@@ -142,13 +142,6 @@ 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.

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: DataFrame, period, **kwargs) -> DataFrame:
+    def feature_engineering_expand_all(self, dataframe, period, **kwargs):
         """
         *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: DataFrame, **kwargs) -> DataFrame:
+    def feature_engineering_expand_basic(self, dataframe, **kwargs):
         """
         *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: DataFrame, **kwargs) -> DataFrame:
+    def feature_engineering_standard(self, dataframe, **kwargs):
         """
         *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: DataFrame, **kwargs) -> DataFrame:
+    def set_freqai_targets(self, dataframe, **kwargs):
         """
         *Only functional with FreqAI enabled strategies*
         Required function to set the targets for the model.
@@ -139,7 +139,6 @@ 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`.
@@ -237,181 +236,3 @@ 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`.
-    We do provide an explicit docker-compose file for this in `docker/docker-compose-freqai.yml` - which can be used via `docker compose -f docker/docker-compose-freqai.yml run ...` - or can be copied to replace the original docker file.
-    This docker-compose file also contains a (disabled) section to enable GPU resources within docker containers. This obviously assumes the system has GPU resources available.
-
-### 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).
-
-
-#### Improving performance with `torch.compile()`
-
-Torch provides a `torch.compile()` method that can be used to improve performance for specific GPU hardware. More details can be found [here](https://pytorch.org/tutorials/intermediate/torch_compile_tutorial.html). In brief, you simply wrap your `model` in `torch.compile()`:
-
-
-```python
-        model = PyTorchMLPModel(
-            input_dim=n_features,
-            output_dim=1,
-            **self.model_kwargs
-        )
-        model.to(self.device)
-        model = torch.compile(model)
-```
-
-Then proceed to use the model as normal. Keep in mind that doing this will remove eager execution, which means errors and tracebacks will not be informative.

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: DataFrame, period, metadata, **kwargs) -> DataFrame:
+    def feature_engineering_expand_all(self, dataframe, period, metadata, **kwargs):
         """
         *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: DataFrame, metadata, **kwargs) -> DataFrame:
+    def feature_engineering_expand_basic(self, dataframe, metadata, **kwargs):
         """
         *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: DataFrame, metadata, **kwargs) -> DataFrame:
+    def feature_engineering_standard(self, dataframe, metadata, **kwargs):
         """
         *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: DataFrame, metadata, **kwargs) -> DataFrame:
+    def set_freqai_targets(self, dataframe, metadata, **kwargs):
         """
         *Only functional with FreqAI enabled strategies*
         Required function to set the targets for the model.
@@ -181,14 +181,15 @@ 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`
 
-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.
+ ### Gain finer control over `feature_engineering_*` functions with `metadata`
 
-```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)
+ 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)
 ```
 
 This will block `ta.ROC()` from being added to any timeframes other than `"1h"`.

docs/freqai-parameter-table.md

@@ -18,10 +18,9 @@ Mandatory parameters are marked as **Required** and have to be set in one of the
 | `purge_old_models` | Number of models to keep on disk (not relevant to backtesting). Default is 2, which means that dry/live runs will keep the latest 2 models on disk. Setting to 0 keeps all models. This parameter also accepts a boolean to maintain backwards compatibility. <br> **Datatype:** Integer. <br> Default: `2`.
 | `save_backtest_models` | Save models to disk when running backtesting. Backtesting operates most efficiently by saving the prediction data and reusing them directly for subsequent runs (when you wish to tune entry/exit parameters). Saving backtesting models to disk also allows to use the same model files for starting a dry/live instance with the same model `identifier`. <br> **Datatype:** Boolean. <br> Default: `False` (no models are saved).
 | `fit_live_predictions_candles` | Number of historical candles to use for computing target (label) statistics from prediction data, instead of from the training dataset (more information can be found [here](freqai-configuration.md#creating-a-dynamic-target-threshold)). <br> **Datatype:** Positive integer.
-| `continual_learning` | Use the final state of the most recently trained model as starting point for the new model, allowing for incremental learning (more information can be found [here](freqai-running.md#continual-learning)). Beware that this is currently a naive approach to incremental learning, and it has a high probability of overfitting/getting stuck in local minima while the market moves away from your model. We have the connections here primarily for experimental purposes and so that it is ready for more mature approaches to continual learning in chaotic systems like the crypto market. <br> **Datatype:** Boolean. <br> Default: `False`.
+| `continual_learning` | Use the final state of the most recently trained model as starting point for the new model, allowing for incremental learning (more information can be found [here](freqai-running.md#continual-learning)). <br> **Datatype:** Boolean. <br> Default: `False`.
 | `write_metrics_to_disk` | Collect train timings, inference timings and cpu usage in json file. <br> **Datatype:** Boolean. <br> Default: `False`
 | `data_kitchen_thread_count` | <br> Designate the number of threads you want to use for data processing (outlier methods, normalization, etc.). This has no impact on the number of threads used for training. If user does not set it (default), FreqAI will use max number of threads - 2 (leaving 1 physical core available for Freqtrade bot and FreqUI) <br> **Datatype:** Positive integer.
-| `activate_tensorboard` | <br> Indicate whether or not to activate tensorboard for the tensorboard enabled modules (currently Reinforcment Learning, XGBoost, Catboost, and PyTorch). Tensorboard needs Torch installed, which means you will need the torch/RL docker image or you need to answer "yes" to the install question about whether or not you wish to install Torch. <br> **Datatype:** Boolean. <br> Default: `True`.
 
 ### Feature parameters
 
@@ -47,7 +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. <br> **Datatype:** Float. <br> 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. <br> **Datatype:** Boolean. <br> Default: `False` (no reversal).
 | `shuffle_after_split` | Split the data into train and test sets, and then shuffle both sets individually. <br> **Datatype:** Boolean. <br> 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.<br> **Datatype:** Integer. <br> Default: `0`.
+| `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.<br> **Datatype:** Boolean. <br> Default: `False`.
 
 ### Data split parameters
 
@@ -86,28 +85,6 @@ 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: `[<shared layers>, dict(vf=[<non-shared value network layers>], pi=[<non-shared policy network layers>])]`. 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. <br> **Datatype:** bool. <br> 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) <br> **Datatype:** Boolean. <br> **Default:** `False`
-| `progress_bar` | Display a progress bar with the current progress, elapsed time and estimated remaining time. <br> **Datatype:** Boolean. <br> 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. <br> **Datatype:** float. <br> Default: `3e-4`.
-| `model_kwargs` | Parameters to be passed to the model class. <br> **Datatype:** dict. <br> Default: `{}`.
-| `trainer_kwargs` | Parameters to be passed to the trainer class. <br> **Datatype:** dict. <br> 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. <br> **Datatype:** int. <br> Default: `100`.
-| `batch_size` | The size of the batches to use during training.. <br> **Datatype:** int. <br> Default: `64`.
-| `max_n_eval_batches` | The maximum number batches to use for evaluation.. <br> **Datatype:** int, optional. <br> Default: `None`.
-
 
 ### Additional parameters
 
@@ -115,5 +92,5 @@ Mandatory parameters are marked as **Required** and have to be set in one of the
 |------------|-------------|
 |  |  **Extraneous parameters**
 | `freqai.keras` | If the selected model makes use of Keras (typical for TensorFlow-based prediction models), this flag needs to be activated so that the model save/loading follows Keras standards. <br> **Datatype:** Boolean. <br> Default: `False`.
-| `freqai.conv_width` | The width of a neural network input tensor. This replaces the need for shifting candles (`include_shifted_candles`) by feeding in historical data points as the second dimension of the tensor. Technically, this parameter can also be used for regressors, but it only adds computational overhead and does not change the model training/prediction. <br> **Datatype:** Integer. <br> Default: `2`.
+| `freqai.conv_width` | The width of a convolutional neural network input tensor. This replaces the need for shifting candles (`include_shifted_candles`) by feeding in historical data points as the second dimension of the tensor. Technically, this parameter can also be used for regressors, but it only adds computational overhead and does not change the model training/prediction. <br> **Datatype:** Integer. <br> Default: `2`.
 | `freqai.reduce_df_footprint` | Recast all numeric columns to float32/int32, with the objective of reducing ram/disk usage and decreasing train/inference timing. This parameter is set in the main level of the Freqtrade configuration file (not inside FreqAI). <br> **Datatype:** Boolean. <br> Default: `False`.

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) -> DataFrame:
+    def set_freqai_targets(self, dataframe, **kwargs):
         """
         *Only functional with FreqAI enabled strategies*
         Required function to set the targets for the model.
@@ -53,19 +53,17 @@ 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 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:
+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:
 
 ```python
-    def feature_engineering_standard(self, dataframe: DataFrame, **kwargs) -> DataFrame:
+    def feature_engineering_standard(self, dataframe, **kwargs):
         # 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.
@@ -135,104 +133,92 @@ Parameter details can be found [here](freqai-parameter-table.md), but in general
 
 ## Creating a custom reward function
 
-!!! danger "Not for production"
-    Warning!
-    The reward function provided with the Freqtrade source code is a showcase of functionality designed to show/test as many possible environment control features as possible. It is also designed to run quickly on small computers. This is a benchmark, it is *not* for live production. Please beware that you will need to create your own custom_reward() function or use a template built by other users outside of the Freqtrade source code.
-
-As you begin to modify the strategy and the prediction model, you will quickly realize some important differences between the Reinforcement Learner and the Regressors/Classifiers. Firstly, the strategy does not set a target value (no labels!). Instead, you set the `calculate_reward()` function inside the `MyRLEnv` class (see below). A default `calculate_reward()` is provided inside `prediction_models/ReinforcementLearner.py` to demonstrate the necessary building blocks for creating rewards, but this is *not* designed for production. Users *must* create their own custom reinforcement learning model class or use a pre-built one from outside the Freqtrade source code and save it to `user_data/freqaimodels`. It is inside the `calculate_reward()` where creative theories about the market can be expressed. For example, you can reward your agent when it makes a winning trade, and penalize the agent when it makes a losing trade. Or perhaps, you wish to reward the agent for entering trades, and penalize the agent for sitting in trades too long. Below we show examples of how these rewards are all calculated:
-
-!!! note "Hint"
-    The best reward functions are ones that are continuously differentiable, and well scaled. In other words, adding a single large negative penalty to a rare event is not a good idea, and the neural net will not be able to learn that function. Instead, it is better to add a small negative penalty to a common event. This will help the agent learn faster. Not only this, but you can help improve the continuity of your rewards/penalties by having them scale with severity according to some linear/exponential functions. In other words, you'd slowly scale the penalty as the duration of the trade increases. This is better than a single large penalty occuring at a single point in time.
+As you begin to modify the strategy and the prediction model, you will quickly realize some important differences between the Reinforcement Learner and the Regressors/Classifiers. Firstly, the strategy does not set a target value (no labels!). Instead, you set the `calculate_reward()` function inside the `MyRLEnv` class (see below). A default `calculate_reward()` is provided inside `prediction_models/ReinforcementLearner.py` to demonstrate the necessary building blocks for creating rewards, but users are encouraged to create their own custom reinforcement learning model class (see below) and save it to `user_data/freqaimodels`. It is inside the `calculate_reward()` where creative theories about the market can be expressed. For example, you can reward your agent when it makes a winning trade, and penalize the agent when it makes a losing trade. Or perhaps, you wish to reward the agent for entering trades, and penalize the agent for sitting in trades too long. Below we show examples of how these rewards are all calculated:
 
 ```python
-from freqtrade.freqai.prediction_models.ReinforcementLearner import ReinforcementLearner
-from freqtrade.freqai.RL.Base5ActionRLEnv import Actions, Base5ActionRLEnv, Positions
+    from freqtrade.freqai.prediction_models.ReinforcementLearner import ReinforcementLearner
+    from freqtrade.freqai.RL.Base5ActionRLEnv import Actions, Base5ActionRLEnv, Positions
 
 
-class MyCoolRLModel(ReinforcementLearner):
-    """
-    User created RL prediction model.
-
-    Save this file to `freqtrade/user_data/freqaimodels`
-
-    then use it with:
-
-    freqtrade trade --freqaimodel MyCoolRLModel --config config.json --strategy SomeCoolStrat
-
-    Here the users can override any of the functions
-    available in the `IFreqaiModel` inheritance tree. Most importantly for RL, this
-    is where the user overrides `MyRLEnv` (see below), to define custom
-    `calculate_reward()` function, or to override any other parts of the environment.
-
-    This class also allows users to override any other part of the IFreqaiModel tree.
-    For example, the user can override `def fit()` or `def train()` or `def predict()`
-    to take fine-tuned control over these processes.
-
-    Another common override may be `def data_cleaning_predict()` where the user can
-    take fine-tuned control over the data handling pipeline.
-    """
-    class MyRLEnv(Base5ActionRLEnv):
+    class MyCoolRLModel(ReinforcementLearner):
         """
-        User made custom environment. This class inherits from BaseEnvironment and gym.env.
-        Users can override any functions from those parent classes. Here is an example
-        of a user customized `calculate_reward()` function.
+        User created RL prediction model.
 
-        Warning!
-        This is function is a showcase of functionality designed to show as many possible
-        environment control features as possible. It is also designed to run quickly
-        on small computers. This is a benchmark, it is *not* for live production.
+        Save this file to `freqtrade/user_data/freqaimodels`
+
+        then use it with:
+
+        freqtrade trade --freqaimodel MyCoolRLModel --config config.json --strategy SomeCoolStrat
+
+        Here the users can override any of the functions
+        available in the `IFreqaiModel` inheritance tree. Most importantly for RL, this
+        is where the user overrides `MyRLEnv` (see below), to define custom
+        `calculate_reward()` function, or to override any other parts of the environment.
+
+        This class also allows users to override any other part of the IFreqaiModel tree.
+        For example, the user can override `def fit()` or `def train()` or `def predict()`
+        to take fine-tuned control over these processes.
+
+        Another common override may be `def data_cleaning_predict()` where the user can
+        take fine-tuned control over the data handling pipeline.
         """
-        def calculate_reward(self, action: int) -> float:
-            # first, penalize if the action is not valid
-            if not self._is_valid(action):
-                return -2
-            pnl = self.get_unrealized_profit()
+        class MyRLEnv(Base5ActionRLEnv):
+            """
+            User made custom environment. This class inherits from BaseEnvironment and gym.env.
+            Users can override any functions from those parent classes. Here is an example
+            of a user customized `calculate_reward()` function.
+            """
+            def calculate_reward(self, action: int) -> float:
+                # first, penalize if the action is not valid
+                if not self._is_valid(action):
+                    return -2
+                pnl = self.get_unrealized_profit()
 
-            factor = 100
+                factor = 100
 
-            pair = self.pair.replace(':', '')
+                pair = self.pair.replace(':', '')
 
-            # 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}_"
-                            f"{self.config['timeframe']}"].iloc[self._current_tick]
+                # 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}_"
+                                f"{self.config['timeframe']}"].iloc[self._current_tick]
 
-            # reward agent for entering trades
-            if (action in (Actions.Long_enter.value, Actions.Short_enter.value)
-                    and self._position == Positions.Neutral):
-                if rsi_now < 40:
-                    factor = 40 / rsi_now
-                else:
-                    factor = 1
-                return 25 * factor
+                # reward agent for entering trades
+                if (action in (Actions.Long_enter.value, Actions.Short_enter.value)
+                        and self._position == Positions.Neutral):
+                    if rsi_now < 40:
+                        factor = 40 / rsi_now
+                    else:
+                        factor = 1
+                    return 25 * factor
 
-            # discourage agent from not entering trades
-            if action == Actions.Neutral.value and self._position == Positions.Neutral:
-                return -1
-            max_trade_duration = self.rl_config.get('max_trade_duration_candles', 300)
-            trade_duration = self._current_tick - self._last_trade_tick
-            if trade_duration <= max_trade_duration:
-                factor *= 1.5
-            elif trade_duration > max_trade_duration:
-                factor *= 0.5
-            # discourage sitting in position
-            if self._position in (Positions.Short, Positions.Long) and \
-            action == Actions.Neutral.value:
-                return -1 * trade_duration / max_trade_duration
-            # close long
-            if action == Actions.Long_exit.value and self._position == Positions.Long:
-                if pnl > self.profit_aim * self.rr:
-                    factor *= self.rl_config['model_reward_parameters'].get('win_reward_factor', 2)
-                return float(pnl * factor)
-            # close short
-            if action == Actions.Short_exit.value and self._position == Positions.Short:
-                if pnl > self.profit_aim * self.rr:
-                    factor *= self.rl_config['model_reward_parameters'].get('win_reward_factor', 2)
-                return float(pnl * factor)
-            return 0.
+                # discourage agent from not entering trades
+                if action == Actions.Neutral.value and self._position == Positions.Neutral:
+                    return -1
+                max_trade_duration = self.rl_config.get('max_trade_duration_candles', 300)
+                trade_duration = self._current_tick - self._last_trade_tick
+                if trade_duration <= max_trade_duration:
+                    factor *= 1.5
+                elif trade_duration > max_trade_duration:
+                    factor *= 0.5
+                # discourage sitting in position
+                if self._position in (Positions.Short, Positions.Long) and \
+                action == Actions.Neutral.value:
+                    return -1 * trade_duration / max_trade_duration
+                # close long
+                if action == Actions.Long_exit.value and self._position == Positions.Long:
+                    if pnl > self.profit_aim * self.rr:
+                        factor *= self.rl_config['model_reward_parameters'].get('win_reward_factor', 2)
+                    return float(pnl * factor)
+                # close short
+                if action == Actions.Short_exit.value and self._position == Positions.Short:
+                    if pnl > self.profit_aim * self.rr:
+                        factor *= self.rl_config['model_reward_parameters'].get('win_reward_factor', 2)
+                    return float(pnl * factor)
+                return 0.
 ```
 
-## Using Tensorboard
+### Using Tensorboard
 
 Reinforcement Learning models benefit from tracking training metrics. FreqAI has integrated Tensorboard to allow users to track training and evaluation performance across all coins and across all retrainings. Tensorboard is activated via the following command:
 
@@ -245,30 +231,32 @@ where `unique-id` is the `identifier` set in the `freqai` configuration file. Th
 
 ![tensorboard](assets/tensorboard.jpg)
 
-## Custom logging
+
+### Custom logging
 
 FreqAI also provides a built in episodic summary logger called `self.tensorboard_log` for adding custom information to the Tensorboard log. By default, this function is already called once per step inside the environment to record the agent actions. All values accumulated for all steps in a single episode are reported at the conclusion of each episode, followed by a full reset of all metrics to 0 in preparation for the subsequent episode.
 
+
 `self.tensorboard_log` can also be used anywhere inside the environment, for example, it can be added to the `calculate_reward` function to collect more detailed information about how often various parts of the reward were called:
 
-```python
-    class MyRLEnv(Base5ActionRLEnv):
-        """
-        User made custom environment. This class inherits from BaseEnvironment and gym.env.
-        Users can override any functions from those parent classes. Here is an example
-        of a user customized `calculate_reward()` function.
-        """
-        def calculate_reward(self, action: int) -> float:
-            if not self._is_valid(action):
-                self.tensorboard_log("invalid")
-                return -2
+```py
+        class MyRLEnv(Base5ActionRLEnv):
+            """
+            User made custom environment. This class inherits from BaseEnvironment and gym.env.
+            Users can override any functions from those parent classes. Here is an example
+            of a user customized `calculate_reward()` function.
+            """
+            def calculate_reward(self, action: int) -> float:
+                if not self._is_valid(action):
+                    self.tensorboard_log("invalid")
+                    return -2
 
 ```
 
 !!! Note
     The `self.tensorboard_log()` function is designed for tracking incremented objects only i.e. events, actions inside the training environment. If the event of interest is a float, the float can be passed as the second argument e.g. `self.tensorboard_log("float_metric1", 0.23)`. In this case the metric values are not incremented.
 
-## Choosing a base environment
+### Choosing a base environment
 
 FreqAI provides three base environments, `Base3ActionRLEnvironment`, `Base4ActionEnvironment` and `Base5ActionEnvironment`. As the names imply, the environments are customized for agents that can select from 3, 4 or 5 actions. The `Base3ActionEnvironment` is the simplest, the agent can select from hold, long, or short. This environment can also be used for long-only bots (it automatically follows the `can_short` flag from the strategy), where long is the enter condition and short is the exit condition. Meanwhile, in the `Base4ActionEnvironment`, the agent can enter long, enter short, hold neutral, or exit position. Finally, in the `Base5ActionEnvironment`, the agent has the same actions as Base4, but instead of a single exit action, it separates exit long and exit short. The main changes stemming from the environment selection include:
 

docs/freqai-running.md

@@ -128,12 +128,6 @@ 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).
-
-???+ danger "Experimental functionality"
-    Beware that this is currently a naive approach to incremental learning, and it has a high probability of overfitting/getting stuck in local minima while the market moves away from your model. We have the mechanics available in FreqAI primarily for experimental purposes and so that it is ready for more mature approaches to continual learning in chaotic systems like the crypto market.
-
 ## Hyperopt
 
 You can hyperopt using the same command as for [typical Freqtrade hyperopt](hyperopt.md):
@@ -161,14 +155,7 @@ This specific hyperopt would help you understand the appropriate `DI_values` for
 
 ## Using Tensorboard
 
-!!! note "Availability"
-    FreqAI includes tensorboard for a variety of models, including XGBoost, all PyTorch models, Reinforcement Learning, and Catboost. If you would like to see Tensorboard integrated into another model type, please open an issue on the [Freqtrade GitHub](https://github.com/freqtrade/freqtrade/issues)
-
-!!! danger "Requirements"
-    Tensorboard logging requires the FreqAI torch installation/docker image.
-
-
-The easiest way to use tensorboard is to ensure `freqai.activate_tensorboard` is set to `True` (default setting) in your configuration file, run FreqAI, then open a separate shell and run:
+CatBoost models benefit from tracking training metrics via Tensorboard. You can take advantage of the FreqAI integration to track training and evaluation performance across all coins and across all retrainings. Tensorboard is activated via the following command:
 
 ```bash
 cd freqtrade
@@ -178,7 +165,3 @@ tensorboard --logdir user_data/models/unique-id
 where `unique-id` is the `identifier` set in the `freqai` configuration file. This command must be run in a separate shell if you wish to view the output in your browser at 127.0.0.1:6060 (6060 is the default port used by Tensorboard).
 
 ![tensorboard](assets/tensorboard.jpg)
-
-
-!!! note "Deactivate for improved performance"
-    Tensorboard logging can slow down training and should be deactivated for production use.

docs/freqai.md

@@ -32,10 +32,7 @@ The easiest way to quickly test FreqAI is to run it in dry mode with the followi
 freqtrade trade --config config_examples/config_freqai.example.json --strategy FreqaiExampleStrategy --freqaimodel LightGBMRegressor --strategy-path freqtrade/templates
 ```
 
-You will see the boot-up process of automatic data downloading, followed by simultaneous training and trading. 
-
-!!! danger "Not for production"
-    The example strategy provided with the Freqtrade source code is designed for showcasing/testing a wide variety of FreqAI features. It is also designed to run on small computers so that it can be used as a benchmark between developers and users. It is *not* designed to be run in production.
+You will see the boot-up process of automatic data downloading, followed by simultaneous training and trading.
 
 An example strategy, prediction model, and config to use as a starting points can be found in
 `freqtrade/templates/FreqaiExampleStrategy.py`, `freqtrade/freqai/prediction_models/LightGBMRegressor.py`, and
@@ -72,15 +69,16 @@ pip install -r requirements-freqai.txt
 ```
 
 !!! Note
-    Catboost will not be installed on low-powered arm devices (raspberry), since it does not provide wheels for this platform.
+    Catboost will not be installed on arm devices (raspberry, Mac M1, ARM based VPS, ...), since it does not provide wheels for this platform.
+
+!!! Note "python 3.11"
+    Some dependencies (Catboost, Torch) currently don't support python 3.11. Freqtrade therefore only supports python 3.10 for these models/dependencies.
+    Tests involving these dependencies are skipped on 3.11.
 
 ### Usage with docker
 
 If you are using docker, a dedicated tag with FreqAI dependencies is available as `:freqai`. As such - you can replace the image line in your docker compose file with `image: freqtradeorg/freqtrade:develop_freqai`. This image contains the regular FreqAI dependencies. Similar to native installs, Catboost will not be available on ARM based devices.
 
-!!! note "docker-compose-freqai.yml"
-   We do provide an explicit docker-compose file for this in `docker/docker-compose-freqai.yml` - which can be used via `docker compose -f docker/docker-compose-freqai.yml run ...` - or can be copied to replace the original docker file. This docker-compose file also contains a (disabled) section to enable GPU resources within docker containers. This obviously assumes the system has GPU resources available.
-
 ### FreqAI position in open-source machine learning landscape
 
 Forecasting chaotic time-series based systems, such as equity/cryptocurrency markets, requires a broad set of tools geared toward testing a wide range of hypotheses. Fortunately, a recent maturation of robust machine learning libraries (e.g. `scikit-learn`) has opened up a wide range of research possibilities. Scientists from a diverse range of fields can now easily prototype their studies on an abundance of established machine learning algorithms. Similarly, these user-friendly libraries enable "citzen scientists" to use their basic Python skills for data exploration. However, leveraging these machine learning libraries on historical and live chaotic data sources can be logistically difficult and expensive. Additionally, robust data collection, storage, and handling presents a disparate challenge. [`FreqAI`](#freqai) aims to provide a generalized and extensible open-sourced framework geared toward live deployments of adaptive modeling for market forecasting. The `FreqAI` framework is effectively a sandbox for the rich world of open-source machine learning libraries. Inside the `FreqAI` sandbox, users find they can combine a wide variety of third-party libraries to test creative hypotheses on a free live 24/7 chaotic data source - cryptocurrency exchange data. 

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 2 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 4 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

docs/installation.md

@@ -30,6 +30,12 @@ The easiest way to install and run Freqtrade is to clone the bot Github reposito
 !!! Warning "Up-to-date clock"
     The clock on the system running the bot must be accurate, synchronized to a NTP server frequently enough to avoid problems with communication to the exchanges.
 
+!!! Error "Running setup.py install for gym did not run successfully."
+    If you get an error related with gym we suggest you to downgrade setuptools it to version 65.5.0 you can do it with the following command:
+    ```bash
+    pip install setuptools==65.5.0
+    ```
+
 ------
 
 ## Requirements
@@ -46,7 +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://ta-lib.github.io/ta-lib-python/) (install instructions [below](#install-ta-lib))
+* [TA-Lib](https://mrjbq7.github.io/ta-lib/install.html) (install instructions [below](#install-ta-lib))
 
 ### Install code
 
@@ -204,7 +210,7 @@ sudo ./build_helpers/install_ta-lib.sh
 
 ##### TA-Lib manual installation
 
-[Official installation guide](https://ta-lib.github.io/ta-lib-python/install.html)
+Official webpage: https://mrjbq7.github.io/ta-lib/install.html
 
 ```bash
 wget http://prdownloads.sourceforge.net/ta-lib/ta-lib-0.4.0-src.tar.gz
@@ -236,7 +242,6 @@ source .env/bin/activate
 
 ```bash
 python3 -m pip install --upgrade pip
-python3 -m pip install -r requirements.txt
 python3 -m pip install -e .
 ```
 

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 <br> **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.<br> **Datatype:** string
 | `producers.host` | **Required.** The hostname or IP address from your producer.<br> **Datatype:** string
-| `producers.port` | **Required.** The port matching the above host.<br>*Defaults to `8080`.*<br> **Datatype:** Integer
+| `producers.port` | **Required.** The port matching the above host.<br> **Datatype:** string
 | `producers.secure` | **Optional.**  Use ssl in websockets connection. Default False.<br> **Datatype:** string
 | `producers.ws_token` | **Required.**  `ws_token` as configured on the producer.<br> **Datatype:** string
 | | **Optional settings**
 | `wait_timeout` | Timeout until we ping again if no message is received. <br>*Defaults to `300`.*<br> **Datatype:** Integer - in seconds.
-| `ping_timeout` | Ping timeout <br>*Defaults to `10`.*<br> **Datatype:** Integer - in seconds.
+| `wait_timeout` | Ping timeout <br>*Defaults to `10`.*<br> **Datatype:** Integer - in seconds.
 | `sleep_time` | Sleep time before retrying to connect.<br>*Defaults to `10`.*<br> **Datatype:** Integer - in seconds.
-| `remove_entry_exit_signals` | Remove signal columns from the dataframe (set them to 0) on dataframe receipt.<br>*Defaults to `false`.*<br> **Datatype:** Boolean.
+| `remove_entry_exit_signals` | Remove signal columns from the dataframe (set them to 0) on dataframe receipt.<br>*Defaults to `10`.*<br> **Datatype:** Integer - in seconds.
 | `message_size_limit` | Size limit per message<br>*Defaults to `8`.*<br> **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.

docs/requirements-docs.txt

@@ -1,6 +1,6 @@
 markdown==3.3.7
-mkdocs==1.4.3
-mkdocs-material==9.1.14
+mkdocs==1.4.2
+mkdocs-material==9.1.3
 mdx_truly_sane_lists==1.3
-pymdown-extensions==10.0.1
+pymdown-extensions==9.10
 jinja2==3.1.2

docs/rest-api.md

@@ -9,6 +9,9 @@ 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.
 
@@ -134,9 +137,7 @@ python3 scripts/rest_client.py --config rest_config.json <command> [optional par
 | `reload_config` | Reloads the configuration file.
 | `trades` | List last trades. Limited to 500 trades per call.
 | `trade/<tradeid>` | Get specific trade.
-| `trade/<tradeid>` | DELETE - Remove trade from the database. Tries to close open orders. Requires manual handling of this trade on the exchange.
-| `trade/<tradeid>/open-order` | DELETE - Cancel open order for this trade.
-| `trade/<tradeid>/reload` | GET - Reload a trade from the Exchange. Only works in live, and can potentially help recover a trade that was manually sold on the exchange.
+| `delete_trade <trade_id>` | Remove trade from the database. Tries to close open orders. Requires manual handling of this trade on the exchange.
 | `show_config` | Shows part of the current configuration with relevant settings to operation.
 | `logs` | Shows last log messages.
 | `status` | Lists all open trades.

docs/stoploss.md

@@ -23,22 +23,10 @@ These modes can be configured with these values:
     'stoploss_on_exchange_limit_ratio': 0.99
 ```
 
-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"
-    <ins>Do not set too low/tight stoploss value when using stop loss on exchange!</ins>  
-    If set to low/tight you will have greater risk of missing fill on the order and stoploss will not work.
+!!! 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.  
+    <ins>Do not set too low/tight stoploss value if using stop loss on exchange!</ins>  
+    If set to low/tight then you have greater risk of missing fill on the order and stoploss will not work.
 
 ### stoploss_on_exchange and stoploss_on_exchange_limit_ratio
 
@@ -209,6 +197,11 @@ 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

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 familiarize yourself with the [Freqtrade basics](bot-basics.md) and methods described in [Strategy Customization](strategy-customization.md) first.
+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.
 
-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.
+[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.
 
 !!! Note
-    Callback methods should *only* be implemented if a strategy uses them.
+    All callback methods described below should only be implemented in a strategy if they are actually used.
 
 !!! Tip
-    Start off with a strategy template containing all available callback methods by running `freqtrade new-strategy --strategy MyAwesomeStrategy --template advanced`
+    You can get a strategy template containing all below 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 `custom_` to avoid naming collisions with predefined strategy variables.
+The name of the variable can be chosen at will, but should be prefixed with `cust_` to avoid naming collisions with predefined strategy variables.
 
 ```python
 class AwesomeStrategy(IStrategy):
@@ -227,8 +227,8 @@ for val in self.buy_ema_short.range:
         f'ema_short_{val}': ta.EMA(dataframe, timeperiod=val)
     }))
 
-# Combine all dataframes, and reassign the original dataframe column
-dataframe = pd.concat(frames, axis=1)
+# Append columns to existing dataframe
+merged_frame = pd.concat(frames, axis=1)
 ```
 
 Freqtrade does however also counter this by running `dataframe.copy()` on the dataframe right after the `populate_indicators()` method - so performance implications of this should be low to non-existant.

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.custom_remote_data = requests.get('https://some_remote_source.example.com')
+            self.cust_remote_data = requests.get('https://some_remote_source.example.com')
 
 ```
 
@@ -51,8 +51,7 @@ 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 in dry/live mode (roughly every 5
-seconds, unless configured differently) or once per candle in backtest/hyperopt mode.
+A simple callback which is called once at the start of every bot throttling iteration (roughly every 5 seconds, unless configured differently).
 This can be used to perform calculations which are pair independent (apply to all pairs), loading of external data, etc.
 
 ``` python
@@ -62,12 +61,11 @@ class AwesomeStrategy(IStrategy):
 
     # ... populate_* methods
 
-    def bot_loop_start(self, current_time: datetime, **kwargs) -> None:
+    def bot_loop_start(self, **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'):
@@ -352,7 +350,7 @@ class AwesomeStrategy(IStrategy):
 
         # Convert absolute price to percentage relative to current_rate
         if stoploss_price < current_rate:
-            return stoploss_from_absolute(stoploss_price, current_rate, is_short=trade.is_short)
+            return (stoploss_price / current_rate) - 1
 
         # return maximum stoploss value, keeping current stoploss price unchanged
         return 1

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) -> DataFrame::
+    def feature_engineering_expand_all(self, dataframe, period, **kwargs):
         """
         *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: DataFrame, **kwargs) -> DataFrame::
+    def feature_engineering_expand_basic(self, dataframe, **kwargs):
         """
         *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: DataFrame, **kwargs) -> DataFrame:
+    def feature_engineering_standard(self, dataframe, **kwargs):
         """
         *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: DataFrame, **kwargs) -> DataFrame:
+    def set_freqai_targets(self, dataframe, **kwargs):
         """
         *Only functional with FreqAI enabled strategies*
         Required function to set the targets for the model.

docs/telegram-usage.md

@@ -187,13 +187,11 @@ official commands. You can ask at any moment for help with `/help`.
 | `/forcelong <pair> [rate]` | Instantly buys the given pair. Rate is optional and only applies to limit orders. (`force_entry_enable` must be set to True)
 | `/forceshort <pair> [rate]` | Instantly shorts the given pair. Rate is optional and only applies to limit orders. This will only work on non-spot markets. (`force_entry_enable` must be set to True)
 | `/delete <trade_id>` | Delete a specific trade from the Database. Tries to close open orders. Requires manual handling of this trade on the exchange.
-| `/reload_trade <trade_id>` | Reload a trade from the Exchange. Only works in live, and can potentially help recover a trade that was manually sold on the exchange.
 | `/cancel_open_order <trade_id> | /coo <trade_id>` | Cancel an open order for a trade.
 | **Metrics** |
 | `/profit [<n>]` | 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 bot managed balance per currency
-| `/balance full` | Show account balance per currency
+| `/balance` | Show account balance per currency
 | `/daily <n>` | Shows profit or loss per day, over the last n days (n defaults to 7)
 | `/weekly <n>` | Shows profit or loss per week, over the last n weeks (n defaults to 8)
 | `/monthly <n>` | Shows profit or loss per month, over the last n months (n defaults to 6)
@@ -204,6 +202,7 @@ 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.
@@ -280,7 +279,6 @@ 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`
@@ -294,7 +292,6 @@ 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 <trade_id>
 

docs/utils.md

@@ -723,9 +723,6 @@ usage: freqtrade backtesting-analysis [-h] [-v] [--logfile FILE] [-V]
                                       [--exit-reason-list EXIT_REASON_LIST [EXIT_REASON_LIST ...]]
                                       [--indicator-list INDICATOR_LIST [INDICATOR_LIST ...]]
                                       [--timerange YYYYMMDD-[YYYYMMDD]]
-                                      [--rejected]
-                                      [--analysis-to-csv]
-                                      [--analysis-csv-path PATH]
 
 optional arguments:
   -h, --help            show this help message and exit
@@ -739,27 +736,19 @@ optional arguments:
                         pair and enter_tag, 4: by pair, enter_ and exit_tag
                         (this can get quite large)
   --enter-reason-list ENTER_REASON_LIST [ENTER_REASON_LIST ...]
-                        Space separated list of entry signals to analyse.
-                        Default: all. e.g. 'entry_tag_a entry_tag_b'
+                        Comma separated list of entry signals to analyse.
+                        Default: all. e.g. 'entry_tag_a,entry_tag_b'
   --exit-reason-list EXIT_REASON_LIST [EXIT_REASON_LIST ...]
-                        Space separated list of exit signals to analyse.
+                        Comma separated list of exit signals to analyse.
                         Default: all. e.g.
-                        'exit_tag_a roi stop_loss trailing_stop_loss'
+                        'exit_tag_a,roi,stop_loss,trailing_stop_loss'
   --indicator-list INDICATOR_LIST [INDICATOR_LIST ...]
-                        Space separated list of indicators to analyse. e.g.
-                        'close rsi bb_lowerband profit_abs'
+                        Comma separated list of indicators to analyse. e.g.
+                        'close,rsi,bb_lowerband,profit_abs'
   --timerange YYYYMMDD-[YYYYMMDD]
                         Timerange to filter trades for analysis, 
                         start inclusive, end exclusive. e.g.
                         20220101-20220201
-  --rejected
-                        Print out rejected trades table
-  --analysis-to-csv
-                        Write out tables to individual CSVs, by default to 
-                        'user_data/backtest_results' unless '--analysis-csv-path' is given.
-  --analysis-csv-path [PATH]
-                        Optional path where individual CSVs will be written. If not used,
-                        CSVs will be written to 'user_data/backtest_results'.
 
 Common arguments:
   -v, --verbose         Verbose mode (-vv for more, -vvv to get all messages).

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), 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.
+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).
 
+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
@@ -45,6 +45,8 @@ 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

freqtrade/__init__.py

@@ -1,5 +1,5 @@
 """ Freqtrade bot """
-__version__ = '2023.5.1'
+__version__ = '2023.3.dev'
 
 if 'dev' in __version__:
     from pathlib import Path

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", "backtest_breakdown"]
+ARGS_BACKTEST_SHOW = ["exportfilename", "backtest_show_pair_list"]
 
 ARGS_LIST_EXCHANGES = ["print_one_column", "list_exchanges_all"]
 
@@ -106,8 +106,7 @@ ARGS_HYPEROPT_SHOW = ["hyperopt_list_best", "hyperopt_list_profitable", "hyperop
                       "disableparamexport", "backtest_breakdown"]
 
 ARGS_ANALYZE_ENTRIES_EXITS = ["exportfilename", "analysis_groups", "enter_reason_list",
-                              "exit_reason_list", "indicator_list", "timerange",
-                              "analysis_rejected", "analysis_to_csv", "analysis_csv_path"]
+                              "exit_reason_list", "indicator_list", "timerange"]
 
 NO_CONF_REQURIED = ["convert-data", "convert-trade-data", "download-data", "list-timeframes",
                     "list-markets", "list-pairs", "list-strategies", "list-freqaimodels",

freqtrade/commands/cli_options.py

@@ -636,45 +636,30 @@ AVAILABLE_CLI_OPTIONS = {
               "4: by pair, enter_ and exit_tag (this can get quite large), "
               "5: by exit_tag"),
         nargs='+',
-        default=[],
+        default=['0', '1', '2'],
         choices=['0', '1', '2', '3', '4', '5'],
     ),
     "enter_reason_list": Arg(
         "--enter-reason-list",
-        help=("Space separated list of entry signals to analyse. Default: all. "
-              "e.g. 'entry_tag_a entry_tag_b'"),
+        help=("Comma separated list of entry signals to analyse. Default: all. "
+              "e.g. 'entry_tag_a,entry_tag_b'"),
         nargs='+',
         default=['all'],
     ),
     "exit_reason_list": Arg(
         "--exit-reason-list",
-        help=("Space separated list of exit signals to analyse. Default: all. "
-              "e.g. 'exit_tag_a roi stop_loss trailing_stop_loss'"),
+        help=("Comma separated list of exit signals to analyse. Default: all. "
+              "e.g. 'exit_tag_a,roi,stop_loss,trailing_stop_loss'"),
         nargs='+',
         default=['all'],
     ),
     "indicator_list": Arg(
         "--indicator-list",
-        help=("Space separated list of indicators to analyse. "
-              "e.g. 'close rsi bb_lowerband profit_abs'"),
+        help=("Comma separated list of indicators to analyse. "
+              "e.g. 'close,rsi,bb_lowerband,profit_abs'"),
         nargs='+',
         default=[],
     ),
-    "analysis_rejected": Arg(
-        '--rejected-signals',
-        help='Analyse rejected signals',
-        action='store_true',
-    ),
-    "analysis_to_csv": Arg(
-        '--analysis-to-csv',
-        help='Save selected analysis tables to individual CSVs',
-        action='store_true',
-    ),
-    "analysis_csv_path": Arg(
-        '--analysis-csv-path',
-        help=("Specify a path to save the analysis CSVs "
-              "if --analysis-to-csv is enabled. Default: user_data/basktesting_results/"),
-    ),
     "freqaimodel": Arg(
         '--freqaimodel',
         help='Specify a custom freqaimodels.',

freqtrade/commands/data_commands.py

@@ -52,7 +52,7 @@ def start_download_data(args: Dict[str, Any]) -> None:
     pairs_not_available: List[str] = []
 
     # Init exchange
-    exchange = ExchangeResolver.load_exchange(config, validate=False)
+    exchange = ExchangeResolver.load_exchange(config['exchange']['name'], config, validate=False)
     markets = [p for p, m in exchange.markets.items() if market_is_active(m)
                or config.get('include_inactive')]
 
@@ -125,7 +125,7 @@ def start_convert_trades(args: Dict[str, Any]) -> None:
             "Please check the documentation on how to configure this.")
 
     # Init exchange
-    exchange = ExchangeResolver.load_exchange(config, validate=False)
+    exchange = ExchangeResolver.load_exchange(config['exchange']['name'], config, validate=False)
     # Manual validations of relevant settings
     if not config['exchange'].get('skip_pair_validation', False):
         exchange.validate_pairs(config['pairs'])
@@ -204,14 +204,11 @@ 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 sorted(
-                paircombs1,
-                key=lambda x: (x[0], timeframe_to_minutes(x[1]), x[2]))
+            for pair, timeframe, candle_type, start, end in paircombs1
             ],
             headers=("Pair", "Timeframe", "Type", 'From', 'To'),
             tablefmt='psql', stralign='right'))

freqtrade/commands/list_commands.py

@@ -114,7 +114,7 @@ def start_list_timeframes(args: Dict[str, Any]) -> None:
     config['timeframe'] = None
 
     # Init exchange
-    exchange = ExchangeResolver.load_exchange(config, validate=False)
+    exchange = ExchangeResolver.load_exchange(config['exchange']['name'], config, validate=False)
 
     if args['print_one_column']:
         print('\n'.join(exchange.timeframes))
@@ -133,7 +133,7 @@ def start_list_markets(args: Dict[str, Any], pairs_only: bool = False) -> None:
     config = setup_utils_configuration(args, RunMode.UTIL_EXCHANGE)
 
     # Init exchange
-    exchange = ExchangeResolver.load_exchange(config, validate=False)
+    exchange = ExchangeResolver.load_exchange(config['exchange']['name'], config, validate=False)
 
     # By default only active pairs/markets are to be shown
     active_only = not args.get('list_pairs_all', False)

freqtrade/commands/pairlist_commands.py

@@ -18,7 +18,7 @@ def start_test_pairlist(args: Dict[str, Any]) -> None:
     from freqtrade.plugins.pairlistmanager import PairListManager
     config = setup_utils_configuration(args, RunMode.UTIL_EXCHANGE)
 
-    exchange = ExchangeResolver.load_exchange(config, validate=False)
+    exchange = ExchangeResolver.load_exchange(config['exchange']['name'], config, validate=False)
 
     quote_currencies = args.get('quote_currencies')
     if not quote_currencies:

freqtrade/configuration/config_validation.py

@@ -174,7 +174,7 @@ def _validate_whitelist(conf: Dict[str, Any]) -> None:
         return
 
     for pl in conf.get('pairlists', [{'method': 'StaticPairList'}]):
-        if (isinstance(pl, dict) and pl.get('method') == 'StaticPairList'
+        if (pl.get('method') == 'StaticPairList'
                 and not conf.get('exchange', {}).get('pair_whitelist')):
             raise OperationalException("StaticPairList requires pair_whitelist to be set.")
 

freqtrade/configuration/configuration.py

@@ -465,15 +465,6 @@ class Configuration:
         self._args_to_config(config, argname='timerange',
                              logstring='Filter trades by timerange: {}')
 
-        self._args_to_config(config, argname='analysis_rejected',
-                             logstring='Analyse rejected signals: {}')
-
-        self._args_to_config(config, argname='analysis_to_csv',
-                             logstring='Store analysis tables to CSV: {}')
-
-        self._args_to_config(config, argname='analysis_csv_path',
-                             logstring='Path to store analysis CSVs: {}')
-
     def _process_runmode(self, config: Config) -> None:
 
         self._args_to_config(config, argname='dry_run',

freqtrade/configuration/timerange.py

@@ -6,6 +6,8 @@ import re
 from datetime import datetime, timezone
 from typing import Optional
 
+import arrow
+
 from freqtrade.constants import DATETIME_PRINT_FORMAT
 from freqtrade.exceptions import OperationalException
 
@@ -114,7 +116,7 @@ class TimeRange:
         :param text: value from --timerange
         :return: Start and End range period
         """
-        if not text:
+        if text is None:
             return TimeRange(None, None, 0, 0)
         syntax = [(r'^-(\d{8})$', (None, 'date')),
                   (r'^(\d{8})-$', ('date', None)),
@@ -137,8 +139,7 @@ class TimeRange:
                 if stype[0]:
                     starts = rvals[index]
                     if stype[0] == 'date' and len(starts) == 8:
-                        start = int(datetime.strptime(starts, '%Y%m%d').replace(
-                            tzinfo=timezone.utc).timestamp())
+                        start = arrow.get(starts, 'YYYYMMDD').int_timestamp
                     elif len(starts) == 13:
                         start = int(starts) // 1000
                     else:
@@ -147,8 +148,7 @@ class TimeRange:
                 if stype[1]:
                     stops = rvals[index]
                     if stype[1] == 'date' and len(stops) == 8:
-                        stop = int(datetime.strptime(stops, '%Y%m%d').replace(
-                            tzinfo=timezone.utc).timestamp())
+                        stop = arrow.get(stops, 'YYYYMMDD').int_timestamp
                     elif len(stops) == 13:
                         stop = int(stops) // 1000
                     else:

freqtrade/constants.py

@@ -36,10 +36,9 @@ 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', 'feather']
-AVAILABLE_DATAHANDLERS = AVAILABLE_DATAHANDLERS_TRADES + ['parquet']
+AVAILABLE_PROTECTIONS = ['CooldownPeriod', 'LowProfitPairs', 'MaxDrawdown', 'StoplossGuard']
+AVAILABLE_DATAHANDLERS_TRADES = ['json', 'jsongz', 'hdf5']
+AVAILABLE_DATAHANDLERS = AVAILABLE_DATAHANDLERS_TRADES + ['feather', 'parquet']
 BACKTEST_BREAKDOWNS = ['day', 'week', 'month']
 BACKTEST_CACHE_AGE = ['none', 'day', 'week', 'month']
 BACKTEST_CACHE_DEFAULT = 'day'
@@ -64,7 +63,6 @@ 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__'
 
@@ -599,8 +597,7 @@ CONF_SCHEMA = {
                         "model_type": {"type": "string", "default": "PPO"},
                         "policy_type": {"type": "string", "default": "MlpPolicy"},
                         "net_arch": {"type": "array", "default": [128, 128]},
-                        "randomize_starting_position": {"type": "boolean", "default": False},
-                        "progress_bar": {"type": "boolean", "default": True},
+                        "randomize_startinng_position": {"type": "boolean", "default": False},
                         "model_reward_parameters": {
                             "type": "object",
                             "properties": {
@@ -690,6 +687,4 @@ BidAsk = Literal['bid', 'ask']
 OBLiteral = Literal['asks', 'bids']
 
 Config = Dict[str, Any]
-# Exchange part of the configuration.
-ExchangeConfig = Dict[str, Any]
 IntOrInf = float

freqtrade/data/btanalysis.py

@@ -246,8 +246,14 @@ 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)
-    df['close_date'] = pd.to_datetime(df['close_date'], utc=True)
+    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
+                                      )
     # Compatibility support for pre short Columns
     if 'is_short' not in df.columns:
         df['is_short'] = False

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)
+    df['date'] = to_datetime(df['date'], unit='ms', utc=True, infer_datetime_format=True)
 
     # Some exchanges return int values for Volume and even for OHLC.
     # Convert them since TA-LIB indicators used in the strategy assume floats

freqtrade/data/dataprovider.py

@@ -21,7 +21,6 @@ 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
 
 
@@ -119,7 +118,8 @@ class DataProvider:
         :param new_candle: This is a new candle
         """
         if self.__rpc:
-            msg: RPCAnalyzedDFMsg = {
+            self.__rpc.send_msg(
+                {
                     '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,

freqtrade/data/entryexitanalysis.py

@@ -1,6 +1,5 @@
 import logging
 from pathlib import Path
-from typing import List
 
 import joblib
 import pandas as pd
@@ -16,31 +15,22 @@ from freqtrade.exceptions import OperationalException
 logger = logging.getLogger(__name__)
 
 
-def _load_backtest_analysis_data(backtest_dir: Path, name: str):
+def _load_signal_candles(backtest_dir: Path):
     if backtest_dir.is_dir():
         scpf = Path(backtest_dir,
-                    Path(get_latest_backtest_filename(backtest_dir)).stem + "_" + name + ".pkl"
+                    Path(get_latest_backtest_filename(backtest_dir)).stem + "_signals.pkl"
                     )
     else:
-        scpf = Path(backtest_dir.parent / f"{backtest_dir.stem}_{name}.pkl")
+        scpf = Path(backtest_dir.parent / f"{backtest_dir.stem}_signals.pkl")
 
     try:
         with scpf.open("rb") as scp:
-            loaded_data = joblib.load(scp)
-            logger.info(f"Loaded {name} candles: {str(scpf)}")
+            signal_candles = joblib.load(scp)
+            logger.info(f"Loaded signal candles: {str(scpf)}")
     except Exception as e:
-        logger.error(f"Cannot load {name} data from pickled results: ", e)
-        return None
+        logger.error("Cannot load signal candles from pickled results: ", e)
 
-    return loaded_data
-
-
-def _load_rejected_signals(backtest_dir: Path):
-    return _load_backtest_analysis_data(backtest_dir, "rejected")
-
-
-def _load_signal_candles(backtest_dir: Path):
-    return _load_backtest_analysis_data(backtest_dir, "signals")
+    return signal_candles
 
 
 def _process_candles_and_indicators(pairlist, strategy_name, trades, signal_candles):
@@ -53,7 +43,9 @@ def _process_candles_and_indicators(pairlist, strategy_name, trades, signal_cand
         for pair in pairlist:
             if pair in signal_candles[strategy_name]:
                 analysed_trades_dict[strategy_name][pair] = _analyze_candles_and_indicators(
-                    pair, trades, signal_candles[strategy_name][pair])
+                                                              pair,
+                                                              trades,
+                                                              signal_candles[strategy_name][pair])
     except Exception as e:
         print(f"Cannot process entry/exit reasons for {strategy_name}: ", e)
 
@@ -93,7 +85,7 @@ def _analyze_candles_and_indicators(pair, trades: pd.DataFrame, signal_candles:
         return pd.DataFrame()
 
 
-def _do_group_table_output(bigdf, glist, csv_path: Path, to_csv=False, ):
+def _do_group_table_output(bigdf, glist):
     for g in glist:
         # 0: summary wins/losses grouped by enter tag
         if g == "0":
@@ -124,8 +116,7 @@ def _do_group_table_output(bigdf, glist, csv_path: Path, to_csv=False, ):
 
             sortcols = ['total_num_buys']
 
-            _print_table(new, sortcols, show_index=True, name="Group 0:",
-                         to_csv=to_csv, csv_path=csv_path)
+            _print_table(new, sortcols, show_index=True)
 
         else:
             agg_mask = {'profit_abs': ['count', 'sum', 'median', 'mean'],
@@ -163,24 +154,11 @@ def _do_group_table_output(bigdf, glist, csv_path: Path, to_csv=False, ):
                 new['mean_profit_pct'] = new['mean_profit_pct'] * 100
                 new['total_profit_pct'] = new['total_profit_pct'] * 100
 
-                _print_table(new, sortcols, name=f"Group {g}:",
-                             to_csv=to_csv, csv_path=csv_path)
+                _print_table(new, sortcols)
             else:
                 logger.warning("Invalid group mask specified.")
 
 
-def _do_rejected_signals_output(rejected_signals_df: pd.DataFrame,
-                                to_csv: bool = False, csv_path=None) -> None:
-    cols = ['pair', 'date', 'enter_tag']
-    sortcols = ['date', 'pair', 'enter_tag']
-    _print_table(rejected_signals_df[cols],
-                 sortcols,
-                 show_index=False,
-                 name="Rejected Signals:",
-                 to_csv=to_csv,
-                 csv_path=csv_path)
-
-
 def _select_rows_within_dates(df, timerange=None, df_date_col: str = 'date'):
     if timerange:
         if timerange.starttype == 'date':
@@ -214,64 +192,38 @@ def prepare_results(analysed_trades, stratname,
     return res_df
 
 
-def print_results(res_df: pd.DataFrame, analysis_groups: List[str], indicator_list: List[str],
-                  csv_path: Path, rejected_signals=None, to_csv=False):
+def print_results(res_df, analysis_groups, indicator_list):
     if res_df.shape[0] > 0:
         if analysis_groups:
-            _do_group_table_output(res_df, analysis_groups, to_csv=to_csv, csv_path=csv_path)
+            _do_group_table_output(res_df, analysis_groups)
 
-        if rejected_signals is not None:
-            if rejected_signals.empty:
-                print("There were no rejected signals.")
-            else:
-                _do_rejected_signals_output(rejected_signals, to_csv=to_csv, csv_path=csv_path)
-
-        # NB this can be large for big dataframes!
         if "all" in indicator_list:
-            _print_table(res_df,
-                         show_index=False,
-                         name="Indicators:",
-                         to_csv=to_csv,
-                         csv_path=csv_path)
-        elif indicator_list is not None and indicator_list:
+            print(res_df)
+        elif indicator_list is not None:
             available_inds = []
             for ind in indicator_list:
                 if ind in res_df:
                     available_inds.append(ind)
             ilist = ["pair", "enter_reason", "exit_reason"] + available_inds
-            _print_table(res_df[ilist],
-                         sortcols=['exit_reason'],
-                         show_index=False,
-                         name="Indicators:",
-                         to_csv=to_csv,
-                         csv_path=csv_path)
+            _print_table(res_df[ilist], sortcols=['exit_reason'], show_index=False)
     else:
         print("\\No trades to show")
 
 
-def _print_table(df: pd.DataFrame, sortcols=None, *, show_index=False, name=None,
-                 to_csv=False, csv_path: Path):
+def _print_table(df, sortcols=None, show_index=False):
     if (sortcols is not None):
         data = df.sort_values(sortcols)
     else:
         data = df
 
-    if to_csv:
-        safe_name = Path(csv_path, name.lower().replace(" ", "_").replace(":", "") + ".csv")
-        data.to_csv(safe_name)
-        print(f"Saved {name} to {safe_name}")
-    else:
-        if name is not None:
-            print(name)
-
-        print(
-            tabulate(
-                data,
-                headers='keys',
-                tablefmt='psql',
-                showindex=show_index
-            )
+    print(
+        tabulate(
+            data,
+            headers='keys',
+            tablefmt='psql',
+            showindex=show_index
         )
+    )
 
 
 def process_entry_exit_reasons(config: Config):
@@ -280,11 +232,6 @@ def process_entry_exit_reasons(config: Config):
         enter_reason_list = config.get('enter_reason_list', ["all"])
         exit_reason_list = config.get('exit_reason_list', ["all"])
         indicator_list = config.get('indicator_list', [])
-        do_rejected = config.get('analysis_rejected', False)
-        to_csv = config.get('analysis_to_csv', False)
-        csv_path = Path(config.get('analysis_csv_path', config['exportfilename']))
-        if to_csv and not csv_path.is_dir():
-            raise OperationalException(f"Specified directory {csv_path} does not exist.")
 
         timerange = TimeRange.parse_timerange(None if config.get(
             'timerange') is None else str(config.get('timerange')))
@@ -294,16 +241,8 @@ def process_entry_exit_reasons(config: Config):
         for strategy_name, results in backtest_stats['strategy'].items():
             trades = load_backtest_data(config['exportfilename'], strategy_name)
 
-            if trades is not None and not trades.empty:
+            if not trades.empty:
                 signal_candles = _load_signal_candles(config['exportfilename'])
-
-                rej_df = None
-                if do_rejected:
-                    rejected_signals_dict = _load_rejected_signals(config['exportfilename'])
-                    rej_df = prepare_results(rejected_signals_dict, strategy_name,
-                                             enter_reason_list, exit_reason_list,
-                                             timerange=timerange)
-
                 analysed_trades_dict = _process_candles_and_indicators(
                                         config['exchange']['pair_whitelist'], strategy_name,
                                         trades, signal_candles)
@@ -314,10 +253,7 @@ def process_entry_exit_reasons(config: Config):
 
                 print_results(res_df,
                               analysis_groups,
-                              indicator_list,
-                              rejected_signals=rej_df,
-                              to_csv=to_csv,
-                              csv_path=csv_path)
+                              indicator_list)
 
     except ValueError as e:
         raise OperationalException(e) from e

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, DEFAULT_TRADES_COLUMNS, TradeList
+from freqtrade.constants import DEFAULT_DATAFRAME_COLUMNS, TradeList
 from freqtrade.enums import CandleType
 
 from .idatahandler import IDataHandler
@@ -63,7 +63,10 @@ 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)
+        pairdata['date'] = to_datetime(pairdata['date'],
+                                       unit='ms',
+                                       utc=True,
+                                       infer_datetime_format=True)
         return pairdata
 
     def ohlcv_append(
@@ -89,11 +92,12 @@ 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)
-        self.create_dir_if_needed(filename)
+        # filename = self._pair_trades_filename(self._datadir, pair)
 
-        tradesdata = DataFrame(data, columns=DEFAULT_TRADES_COLUMNS)
-        tradesdata.to_feather(filename, compression_level=9, compression='lz4')
+        raise NotImplementedError()
+        # array = pa.array(data)
+        # array
+        # feather.write_feather(data, filename)
 
     def trades_append(self, pair: str, data: TradeList):
         """
@@ -112,13 +116,14 @@ class FeatherDataHandler(IDataHandler):
         :param timerange: Timerange to load trades for - currently not implemented
         :return: List of trades
         """
-        filename = self._pair_trades_filename(self._datadir, pair)
-        if not filename.exists():
-            return []
+        raise NotImplementedError()
+        # filename = self._pair_trades_filename(self._datadir, pair)
+        # tradesdata = misc.file_load_json(filename)
 
-        tradesdata = read_feather(filename)
+        # if not tradesdata:
+        #     return []
 
-        return tradesdata.values.tolist()
+        # return tradesdata
 
     @classmethod
     def _get_file_extension(cls):

freqtrade/data/history/history_utils.py

@@ -1,9 +1,10 @@
 import logging
 import operator
-from datetime import datetime, timedelta
+from datetime import datetime
 from pathlib import Path
 from typing import Dict, List, Optional, Tuple
 
+import arrow
 from pandas import DataFrame, concat
 
 from freqtrade.configuration import TimeRange
@@ -235,8 +236,8 @@ def _download_pair_history(pair: str, *,
         new_data = exchange.get_historic_ohlcv(pair=pair,
                                                timeframe=timeframe,
                                                since_ms=since_ms if since_ms else
-                                               int((datetime.now() - timedelta(days=new_pairs_days)
-                                                    ).timestamp()) * 1000,
+                                               arrow.utcnow().shift(
+                                                   days=-new_pairs_days).int_timestamp * 1000,
                                                is_new_pair=data.empty,
                                                candle_type=candle_type,
                                                until_ms=until_ms if until_ms else None
@@ -348,7 +349,7 @@ def _download_trades_history(exchange: Exchange,
             trades = []
 
         if not since:
-            since = int((datetime.now() - timedelta(days=-new_pairs_days)).timestamp()) * 1000
+            since = arrow.utcnow().shift(days=-new_pairs_days).int_timestamp * 1000
 
         from_id = trades[-1][1] if trades else None
         if trades and since < trades[-1][0]:

freqtrade/data/history/jsondatahandler.py

@@ -75,7 +75,10 @@ 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)
+        pairdata['date'] = to_datetime(pairdata['date'],
+                                       unit='ms',
+                                       utc=True,
+                                       infer_datetime_format=True)
         return pairdata
 
     def ohlcv_append(

freqtrade/data/history/parquetdatahandler.py

@@ -62,7 +62,10 @@ 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)
+        pairdata['date'] = to_datetime(pairdata['date'],
+                                       unit='ms',
+                                       utc=True,
+                                       infer_datetime_format=True)
         return pairdata
 
     def ohlcv_append(

freqtrade/edge/edge_positioning.py

@@ -3,9 +3,9 @@
 import logging
 from collections import defaultdict
 from copy import deepcopy
-from datetime import timedelta
 from typing import Any, Dict, List, NamedTuple
 
+import arrow
 import numpy as np
 import utils_find_1st as utf1st
 from pandas import DataFrame
@@ -18,7 +18,6 @@ from freqtrade.exceptions import OperationalException
 from freqtrade.exchange import timeframe_to_seconds
 from freqtrade.plugins.pairlist.pairlist_helpers import expand_pairlist
 from freqtrade.strategy.interface import IStrategy
-from freqtrade.util import dt_now
 
 
 logger = logging.getLogger(__name__)
@@ -80,8 +79,8 @@ class Edge:
             self._stoploss_range_step
         )
 
-        self._timerange: TimeRange = TimeRange.parse_timerange(
-            f"{(dt_now() - timedelta(days=self._since_number_of_days)).strftime('%Y%m%d')}-")
+        self._timerange: TimeRange = TimeRange.parse_timerange("%s-" % arrow.now().shift(
+            days=-1 * self._since_number_of_days).format('YYYYMMDD'))
         if config.get('fee'):
             self.fee = config['fee']
         else:
@@ -98,7 +97,7 @@ class Edge:
         heartbeat = self.edge_config.get('process_throttle_secs')
 
         if (self._last_updated > 0) and (
-                self._last_updated + heartbeat > int(dt_now().timestamp())):
+                self._last_updated + heartbeat > arrow.utcnow().int_timestamp):
             return False
 
         data: Dict[str, Any] = {}
@@ -190,7 +189,7 @@ class Edge:
         # Fill missing, calculable columns, profit, duration , abs etc.
         trades_df = self._fill_calculable_fields(DataFrame(trades))
         self._cached_pairs = self._process_expectancy(trades_df)
-        self._last_updated = int(dt_now().timestamp())
+        self._last_updated = arrow.utcnow().int_timestamp
 
         return True
 

freqtrade/enums/exittype.py

@@ -15,7 +15,6 @@ class ExitType(Enum):
     EMERGENCY_EXIT = "emergency_exit"
     CUSTOM_EXIT = "custom_exit"
     PARTIAL_EXIT = "partial_exit"
-    SOLD_ON_EXCHANGE = "sold_on_exchange"
     NONE = ""
 
     def __str__(self):

freqtrade/exchange/__init__.py

@@ -1,23 +1,22 @@
 # flake8: noqa: F401
 # isort: off
-from freqtrade.exchange.common import remove_exchange_credentials, MAP_EXCHANGE_CHILDCLASS
+from freqtrade.exchange.common import remove_credentials, MAP_EXCHANGE_CHILDCLASS
 from freqtrade.exchange.exchange import Exchange
 # isort: on
 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 (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.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.gate import Gate
 from freqtrade.exchange.hitbtc import Hitbtc
 from freqtrade.exchange.huobi import Huobi

freqtrade/exchange/binance.py

@@ -1,11 +1,13 @@
 """ Binance exchange subclass """
 import logging
-from datetime import datetime, timezone
+from datetime import datetime
 from pathlib import Path
 from typing import Dict, List, Optional, Tuple
 
+import arrow
 import ccxt
 
+from freqtrade.constants import BuySell
 from freqtrade.enums import CandleType, MarginMode, PriceType, TradingMode
 from freqtrade.exceptions import DDosProtection, OperationalException, TemporaryError
 from freqtrade.exchange import Exchange
@@ -47,6 +49,26 @@ class Binance(Exchange):
         (TradingMode.FUTURES, MarginMode.ISOLATED)
     ]
 
+    def _get_params(
+        self,
+        side: BuySell,
+        ordertype: str,
+        leverage: float,
+        reduceOnly: bool,
+        time_in_force: str = 'GTC',
+    ) -> Dict:
+        params = super()._get_params(side, ordertype, leverage, reduceOnly, time_in_force)
+        if (
+            time_in_force == 'PO'
+            and ordertype != 'market'
+            and self.trading_mode == TradingMode.SPOT
+            # Only spot can do post only orders
+        ):
+            params.pop('timeInForce')
+            params['postOnly'] = True
+
+        return params
+
     def get_tickers(self, symbols: Optional[List[str]] = None, cached: bool = False) -> Tickers:
         tickers = super().get_tickers(symbols=symbols, cached=cached)
         if self.trading_mode == TradingMode.FUTURES:
@@ -65,7 +87,7 @@ class Binance(Exchange):
         """
         try:
             if self.trading_mode == TradingMode.FUTURES and not self._config['dry_run']:
-                position_side = self._api.fapiPrivateGetPositionSideDual()
+                position_side = self._api.fapiPrivateGetPositionsideDual()
                 self._log_exchange_response('position_side_setting', position_side)
                 assets_margin = self._api.fapiPrivateGetMultiAssetsMargin()
                 self._log_exchange_response('multi_asset_margin', assets_margin)
@@ -104,9 +126,8 @@ class Binance(Exchange):
             if x and x[3] and x[3][0] and x[3][0][0] > since_ms:
                 # Set starting date to first available candle.
                 since_ms = x[3][0][0]
-                logger.info(
-                    f"Candle-data for {pair} available starting with "
-                    f"{datetime.fromtimestamp(since_ms // 1000, tz=timezone.utc).isoformat()}.")
+                logger.info(f"Candle-data for {pair} available starting with "
+                            f"{arrow.get(since_ms // 1000).isoformat()}.")
 
         return await super()._async_get_historic_ohlcv(
             pair=pair,

freqtrade/exchange/bitvavo.py

@@ -1,23 +0,0 @@
-"""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,
-    }

freqtrade/exchange/common.py

@@ -4,7 +4,6 @@ import time
 from functools import wraps
 from typing import Any, Callable, Optional, TypeVar, cast, overload
 
-from freqtrade.constants import ExchangeConfig
 from freqtrade.exceptions import DDosProtection, RetryableOrderError, TemporaryError
 from freqtrade.mixins import LoggingMixin
 
@@ -85,22 +84,20 @@ EXCHANGE_HAS_OPTIONAL = [
     # 'fetchPositions',  # Futures trading
     # 'fetchLeverageTiers',  # Futures initialization
     # 'fetchMarketLeverageTiers',  # Futures initialization
-    # 'fetchOpenOrders', 'fetchClosedOrders',  # 'fetchOrders',  # Refinding balance...
 ]
 
 
-def remove_exchange_credentials(exchange_config: ExchangeConfig, dry_run: bool) -> None:
+def remove_credentials(config) -> None:
     """
     Removes exchange keys from the configuration and specifies dry-run
     Used for backtesting / hyperopt / edge and utils.
     Modifies the input dict!
     """
-    if dry_run:
-        exchange_config['key'] = ''
-        exchange_config['apiKey'] = ''
-        exchange_config['secret'] = ''
-        exchange_config['password'] = ''
-        exchange_config['uid'] = ''
+    if config.get('dry_run', False):
+        config['exchange']['key'] = ''
+        config['exchange']['secret'] = ''
+        config['exchange']['password'] = ''
+        config['exchange']['uid'] = ''
 
 
 def calculate_backoff(retrycount, max_retries):

freqtrade/exchange/exchange.py

@@ -11,6 +11,7 @@ from math import floor
 from threading import Lock
 from typing import Any, Coroutine, Dict, List, Literal, Optional, Tuple, Union
 
+import arrow
 import ccxt
 import ccxt.async_support as ccxt_async
 from cachetools import TTLCache
@@ -19,30 +20,27 @@ from dateutil import parser
 from pandas import DataFrame, concat
 
 from freqtrade.constants import (DEFAULT_AMOUNT_RESERVE_PERCENT, NON_OPEN_EXCHANGE_STATES, BidAsk,
-                                 BuySell, Config, EntryExit, ExchangeConfig,
-                                 ListPairsWithTimeframes, MakerTaker, OBLiteral, PairWithTimeframe)
+                                 BuySell, Config, EntryExit, ListPairsWithTimeframes, MakerTaker,
+                                 OBLiteral, PairWithTimeframe)
 from freqtrade.data.converter import clean_ohlcv_dataframe, ohlcv_to_dataframe, trades_dict_to_list
 from freqtrade.enums import OPTIMIZE_MODES, CandleType, MarginMode, TradingMode
 from freqtrade.enums.pricetype import PriceType
 from freqtrade.exceptions import (DDosProtection, ExchangeError, InsufficientFundsError,
                                   InvalidOrderException, OperationalException, PricingError,
                                   RetryableOrderError, TemporaryError)
-from freqtrade.exchange.common import (API_FETCH_ORDER_RETRY_COUNT, remove_exchange_credentials,
-                                       retrier, retrier_async)
-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.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.types import OHLCVResponse, OrderBook, Ticker, Tickers
 from freqtrade.misc import (chunks, deep_merge_dicts, file_dump_json, file_load_json,
                             safe_value_fallback2)
 from freqtrade.plugins.pairlist.pairlist_helpers import expand_pairlist
-from freqtrade.util import dt_from_ts, dt_now
-from freqtrade.util.datetime_helpers import dt_humanize, dt_ts
 
 
 logger = logging.getLogger(__name__)
@@ -61,7 +59,6 @@ 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"],
         "ohlcv_params": {},
         "ohlcv_candle_limit": 500,
@@ -83,8 +80,6 @@ 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 = {}
@@ -93,8 +88,8 @@ class Exchange:
         # TradingMode.SPOT always supported and not required in this list
     ]
 
-    def __init__(self, config: Config, *, exchange_config: Optional[ExchangeConfig] = None,
-                 validate: bool = True, load_leverage_tiers: bool = False) -> None:
+    def __init__(self, config: Config, validate: bool = True,
+                 load_leverage_tiers: bool = False) -> None:
         """
         Initializes this module with the given config,
         it does basic validation whether the specified exchange and pairs are valid.
@@ -108,7 +103,8 @@ 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 = self._init_async_loop()
+        self.loop = asyncio.new_event_loop()
+        asyncio.set_event_loop(self.loop)
         self._config: Config = {}
 
         self._config.update(config)
@@ -132,13 +128,13 @@ class Exchange:
 
         # Holds all open sell orders for dry_run
         self._dry_run_open_orders: Dict[str, Any] = {}
+        remove_credentials(config)
 
         if config['dry_run']:
             logger.info('Instance is running with dry_run enabled')
         logger.info(f"Using CCXT {ccxt.__version__}")
-        exchange_conf: Dict[str, Any] = exchange_config if exchange_config else config['exchange']
-        remove_exchange_credentials(exchange_conf, config.get('dry_run', False))
-        self.log_responses = exchange_conf.get('log_responses', False)
+        exchange_config = config['exchange']
+        self.log_responses = exchange_config.get('log_responses', False)
 
         # Leverage properties
         self.trading_mode: TradingMode = config.get('trading_mode', TradingMode.SPOT)
@@ -153,8 +149,8 @@ class Exchange:
         self._ft_has = deep_merge_dicts(self._ft_has, deepcopy(self._ft_has_default))
         if self.trading_mode == TradingMode.FUTURES:
             self._ft_has = deep_merge_dicts(self._ft_has_futures, self._ft_has)
-        if exchange_conf.get('_ft_has_params'):
-            self._ft_has = deep_merge_dicts(exchange_conf.get('_ft_has_params'),
+        if exchange_config.get('_ft_has_params'):
+            self._ft_has = deep_merge_dicts(exchange_config.get('_ft_has_params'),
                                             self._ft_has)
             logger.info("Overriding exchange._ft_has with config params, result: %s", self._ft_has)
 
@@ -166,18 +162,18 @@ class Exchange:
 
         # Initialize ccxt objects
         ccxt_config = self._ccxt_config
-        ccxt_config = deep_merge_dicts(exchange_conf.get('ccxt_config', {}), ccxt_config)
-        ccxt_config = deep_merge_dicts(exchange_conf.get('ccxt_sync_config', {}), ccxt_config)
+        ccxt_config = deep_merge_dicts(exchange_config.get('ccxt_config', {}), ccxt_config)
+        ccxt_config = deep_merge_dicts(exchange_config.get('ccxt_sync_config', {}), ccxt_config)
 
-        self._api = self._init_ccxt(exchange_conf, ccxt_kwargs=ccxt_config)
+        self._api = self._init_ccxt(exchange_config, ccxt_kwargs=ccxt_config)
 
         ccxt_async_config = self._ccxt_config
-        ccxt_async_config = deep_merge_dicts(exchange_conf.get('ccxt_config', {}),
+        ccxt_async_config = deep_merge_dicts(exchange_config.get('ccxt_config', {}),
                                              ccxt_async_config)
-        ccxt_async_config = deep_merge_dicts(exchange_conf.get('ccxt_async_config', {}),
+        ccxt_async_config = deep_merge_dicts(exchange_config.get('ccxt_async_config', {}),
                                              ccxt_async_config)
         self._api_async = self._init_ccxt(
-            exchange_conf, ccxt_async, ccxt_kwargs=ccxt_async_config)
+            exchange_config, ccxt_async, ccxt_kwargs=ccxt_async_config)
 
         logger.info(f'Using Exchange "{self.name}"')
         self.required_candle_call_count = 1
@@ -190,8 +186,8 @@ class Exchange:
                 self._startup_candle_count, config.get('timeframe', ''))
 
         # Converts the interval provided in minutes in config to seconds
-        self.markets_refresh_interval: int = exchange_conf.get(
-            "markets_refresh_interval", 60) * 60 * 1000
+        self.markets_refresh_interval: int = exchange_config.get(
+            "markets_refresh_interval", 60) * 60
 
         if self.trading_mode != TradingMode.SPOT and load_leverage_tiers:
             self.fill_leverage_tiers()
@@ -209,13 +205,6 @@ 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
@@ -491,7 +480,7 @@ class Exchange:
         try:
             self._markets = self._api.load_markets(params={})
             self._load_async_markets()
-            self._last_markets_refresh = dt_ts()
+            self._last_markets_refresh = arrow.utcnow().int_timestamp
             if self._ft_has['needs_trading_fees']:
                 self._trading_fees = self.fetch_trading_fees()
 
@@ -502,14 +491,15 @@ class Exchange:
         """Reload markets both sync and async if refresh interval has passed """
         # Check whether markets have to be reloaded
         if (self._last_markets_refresh > 0) and (
-                self._last_markets_refresh + self.markets_refresh_interval > dt_ts()):
+                self._last_markets_refresh + self.markets_refresh_interval
+                > arrow.utcnow().int_timestamp):
             return None
         logger.debug("Performing scheduled market reload..")
         try:
             self._markets = self._api.load_markets(reload=True, params={})
             # Also reload async markets to avoid issues with newly listed pairs
             self._load_async_markets(reload=True)
-            self._last_markets_refresh = dt_ts()
+            self._last_markets_refresh = arrow.utcnow().int_timestamp
             self.fill_leverage_tiers()
         except ccxt.BaseError:
             logger.exception("Could not reload markets.")
@@ -740,14 +730,12 @@ class Exchange:
         """
         return amount_to_precision(amount, self.get_precision_amount(pair), self.precisionMode)
 
-    def price_to_precision(self, pair: str, price: float, *, rounding_mode: int = ROUND) -> float:
+    def price_to_precision(self, pair: str, price: float) -> float:
         """
-        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.
+        Returns the price rounded up to the precision the Exchange accepts.
+        Rounds up
         """
-        return price_to_precision(price, self.get_precision_price(pair),
-                                  self.precisionMode, rounding_mode=rounding_mode)
+        return price_to_precision(price, self.get_precision_price(pair), self.precisionMode)
 
     def price_get_one_pip(self, pair: str, price: float) -> float:
         """
@@ -770,12 +758,12 @@ 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', leverage)
+        max_stake_amount = self._get_stake_amount_limit(pair, price, 0.0, 'max')
         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
+        return max_stake_amount / leverage
 
     def _get_stake_amount_limit(
         self,
@@ -793,41 +781,43 @@ 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]) * stoploss_reserve
+                self._contracts_to_amount(
+                    pair,
+                    limits['cost'][limit]
+                )
             )
 
         if (limits['amount'][limit] is not None):
             stake_limits.append(
-                self._contracts_to_amount(pair, limits['amount'][limit]) * price * margin_reserve
+                self._contracts_to_amount(
+                    pair,
+                    limits['amount'][limit] * price
+                )
             )
 
         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) if isMin else min(stake_limits),
+            max(stake_limits) * amount_reserve_percent,
             leverage or 1.0
-        )
+        ) if isMin else min(stake_limits)
 
     def _get_stake_amount_considering_leverage(self, stake_amount: float, leverage: float) -> float:
         """
@@ -843,8 +833,7 @@ class Exchange:
     def create_dry_run_order(self, pair: str, ordertype: str, side: str, amount: float,
                              rate: float, leverage: float, params: Dict = {},
                              stop_loss: bool = False) -> Dict[str, Any]:
-        now = dt_now()
-        order_id = f'dry_run_{side}_{now.timestamp()}'
+        order_id = f'dry_run_{side}_{datetime.now().timestamp()}'
         # Rounding here must respect to contract sizes
         _amount = self._contracts_to_amount(
             pair, self.amount_to_precision(pair, self._amount_to_contracts(pair, amount)))
@@ -859,8 +848,8 @@ class Exchange:
             'side': side,
             'filled': 0,
             'remaining': _amount,
-            'datetime': now.strftime('%Y-%m-%dT%H:%M:%S.%fZ'),
-            'timestamp': dt_ts(now),
+            'datetime': arrow.utcnow().strftime('%Y-%m-%dT%H:%M:%S.%fZ'),
+            'timestamp': arrow.utcnow().int_timestamp * 1000,
             'status': "open",
             'fee': None,
             'info': {},
@@ -868,7 +857,7 @@ class Exchange:
         }
         if stop_loss:
             dry_order["info"] = {"stopPrice": dry_order["price"]}
-            dry_order[self._ft_has['stop_price_param']] = dry_order["price"]
+            dry_order["stopPrice"] = dry_order["price"]
             # Workaround to avoid filling stoploss orders immediately
             dry_order["ft_order_type"] = "stoploss"
         orderbook: Optional[OrderBook] = None
@@ -891,7 +880,7 @@ class Exchange:
                 'filled': _amount,
                 'remaining': 0.0,
                 'status': "closed",
-                'cost': (dry_order['amount'] * average)
+                'cost': (dry_order['amount'] * average) / leverage
             })
             # market orders will always incurr taker fees
             dry_order = self.add_dry_order_fee(pair, dry_order, 'taker')
@@ -1020,7 +1009,7 @@ class Exchange:
             from freqtrade.persistence import Order
             order = Order.order_by_id(order_id)
             if order:
-                ccxt_order = order.to_ccxt_object(self._ft_has['stop_price_param'])
+                ccxt_order = order.to_ccxt_object()
                 self._dry_run_open_orders[order_id] = ccxt_order
                 return ccxt_order
             # Gracefully handle errors with dry-run orders.
@@ -1049,13 +1038,6 @@ class Exchange:
             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,
         *,
@@ -1078,7 +1060,8 @@ 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 = self._order_needs_price(ordertype)
+            needs_price = (ordertype != 'market'
+                           or self._api.options.get("createMarketBuyOrderRequiresPrice", False))
             rate_for_order = self.price_to_precision(pair, rate) if needs_price else None
 
             if not reduceOnly:
@@ -1121,11 +1104,11 @@ 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(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])))
+            order.get('stopPrice', None) is None
+            or ((side == "sell" and stop_loss > float(order['stopPrice'])) or
+                (side == "buy" and stop_loss < float(order['stopPrice'])))
         )
 
     def _get_stop_order_type(self, user_order_type) -> Tuple[str, str]:
@@ -1165,8 +1148,8 @@ 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, else configure stop_price_param
-        params.update({self._ft_has['stop_price_param']: stop_price})
+        # Verify if stopPrice works for your exchange!
+        params.update({'stopPrice': stop_price})
         return params
 
     @retrier(retries=0)
@@ -1192,12 +1175,12 @@ class Exchange:
 
         user_order_type = order_types.get('stoploss', 'market')
         ordertype, user_order_type = self._get_stop_order_type(user_order_type)
-        round_mode = ROUND_DOWN if side == 'buy' else ROUND_UP
-        stop_price_norm = self.price_to_precision(pair, stop_price, rounding_mode=round_mode)
+
+        stop_price_norm = self.price_to_precision(pair, stop_price)
         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, rounding_mode=round_mode)
+            limit_rate = self.price_to_precision(pair, limit_rate)
 
         if self._config['dry_run']:
             dry_order = self.create_dry_run_order(
@@ -1433,47 +1416,6 @@ class Exchange:
         except ccxt.BaseError as e:
             raise OperationalException(e) from e
 
-    @retrier(retries=0)
-    def fetch_orders(self, pair: str, since: datetime) -> List[Dict]:
-        """
-        Fetch all orders for a pair "since"
-        :param pair: Pair for the query
-        :param since: Starting time for the query
-        """
-        if self._config['dry_run']:
-            return []
-
-        def fetch_orders_emulate() -> List[Dict]:
-            orders = []
-            if self.exchange_has('fetchClosedOrders'):
-                orders = self._api.fetch_closed_orders(pair, since=since_ms)
-                if self.exchange_has('fetchOpenOrders'):
-                    orders_open = self._api.fetch_open_orders(pair, since=since_ms)
-                    orders.extend(orders_open)
-            return orders
-
-        try:
-            since_ms = int((since.timestamp() - 10) * 1000)
-            if self.exchange_has('fetchOrders'):
-                try:
-                    orders: List[Dict] = self._api.fetch_orders(pair, since=since_ms)
-                except ccxt.NotSupported:
-                    # Some exchanges don't support fetchOrders
-                    # attempt to fetch open and closed orders separately
-                    orders = fetch_orders_emulate()
-            else:
-                orders = fetch_orders_emulate()
-            self._log_exchange_response('fetch_orders', orders)
-            orders = [self._order_contracts_to_amount(o) for o in orders]
-            return orders
-        except ccxt.DDoSProtection as e:
-            raise DDosProtection(e) from e
-        except (ccxt.NetworkError, ccxt.ExchangeError) as e:
-            raise TemporaryError(
-                f'Could not fetch positions due to {e.__class__.__name__}. Message: {e}') from e
-        except ccxt.BaseError as e:
-            raise OperationalException(e) from e
-
     @retrier
     def fetch_trading_fees(self) -> Dict[str, Any]:
         """
@@ -1931,11 +1873,11 @@ class Exchange:
         logger.debug(
             "one_call: %s msecs (%s)",
             one_call,
-            dt_humanize(dt_now() - timedelta(milliseconds=one_call), only_distance=True)
+            arrow.utcnow().shift(seconds=one_call // 1000).humanize(only_distance=True)
         )
         input_coroutines = [self._async_get_candle_history(
             pair, timeframe, candle_type, since) for since in
-            range(since_ms, until_ms or dt_ts(), one_call)]
+            range(since_ms, until_ms or (arrow.utcnow().int_timestamp * 1000), one_call)]
 
         data: List = []
         # Chunk requests into batches of 100 to avoid overwelming ccxt Throttling
@@ -2118,7 +2060,7 @@ class Exchange:
         """
         try:
             # Fetch OHLCV asynchronously
-            s = '(' + dt_from_ts(since_ms).isoformat() + ') ' if since_ms is not None else ''
+            s = '(' + arrow.get(since_ms // 1000).isoformat() + ') ' if since_ms is not None else ''
             logger.debug(
                 "Fetching pair %s, %s, interval %s, since %s %s...",
                 pair, candle_type, timeframe, since_ms, s
@@ -2208,7 +2150,7 @@ class Exchange:
                 logger.debug(
                     "Fetching trades for pair %s, since %s %s...",
                     pair, since,
-                    '(' + dt_from_ts(since).isoformat() + ') ' if since is not None else ''
+                    '(' + arrow.get(since // 1000).isoformat() + ') ' if since is not None else ''
                 )
                 trades = await self._api_async.fetch_trades(pair, since=since, limit=1000)
             trades = self._trades_contracts_to_amount(trades)
@@ -2417,12 +2359,12 @@ 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 = []
 
-                symbols = [
-                    symbol for symbol, market in markets.items()
+                for symbol, market in markets.items():
                     if (self.market_is_future(market)
-                        and market['quote'] == self._config['stake_currency'])
-                ]
+                            and market['quote'] == self._config['stake_currency']):
+                        symbols.append(symbol)
 
                 tiers: Dict[str, List[Dict]] = {}
 
@@ -2442,26 +2384,25 @@ class Exchange:
                 else:
                     logger.info("Using cached leverage_tiers.")
 
-                async def gather_results(input_coro):
+                async def gather_results():
                     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(input_coro))
+                        results = self.loop.run_until_complete(gather_results())
 
-                    for res in results:
-                        if isinstance(res, Exception):
-                            logger.warning(f"Leverage tier exception: {repr(res)}")
-                            continue
-                        symbol, tier = res
-                        tiers[symbol] = tier
+                    for symbol, res in results:
+                        tiers[symbol] = res
                 if len(coros) > 0:
                     self.cache_leverage_tiers(tiers, self._config['stake_currency'])
                 logger.info(f"Done initializing {len(symbols)} markets.")
 
                 return tiers
-        return {}
+            else:
+                return {}
+        else:
+            return {}
 
     def cache_leverage_tiers(self, tiers: Dict[str, List[Dict]], stake_currency: str) -> None:
 
@@ -2477,17 +2418,14 @@ 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():
-            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.")
+            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']
         return None
 
     def fill_leverage_tiers(self) -> None:
@@ -2942,8 +2880,8 @@ class Exchange:
                 if nominal_value >= tier['minNotional']:
                     return (tier['maintenanceMarginRate'], tier['maintAmt'])
 
-            raise ExchangeError("nominal value can not be lower than 0")
+            raise OperationalException("nominal value can not be lower than 0")
             # The lowest notional_floor for any pair in fetch_leverage_tiers is always 0 because it
             # describes the min amt for a tier, and the lowest tier will always go down to 0
         else:
-            raise ExchangeError(f"Cannot get maintenance ratio using {self.name}")
+            raise OperationalException(f"Cannot get maintenance ratio using {self.name}")

freqtrade/exchange/exchange_utils.py

@@ -2,16 +2,14 @@
 Exchange support utils
 """
 from datetime import datetime, timedelta, timezone
-from math import ceil, floor
+from math import ceil
 from typing import Any, Dict, List, Optional, Tuple
 
 import ccxt
-from ccxt import (DECIMAL_PLACES, ROUND, ROUND_DOWN, ROUND_UP, SIGNIFICANT_DIGITS, TICK_SIZE,
-                  TRUNCATE, decimal_to_precision)
+from ccxt import ROUND_DOWN, ROUND_UP, TICK_SIZE, TRUNCATE, decimal_to_precision
 
 from freqtrade.exchange.common import BAD_EXCHANGES, EXCHANGE_HAS_OPTIONAL, EXCHANGE_HAS_REQUIRED
 from freqtrade.util import FtPrecise
-from freqtrade.util.datetime_helpers import dt_from_ts, dt_ts
 
 
 CcxtModuleType = Any
@@ -100,8 +98,9 @@ def timeframe_to_prev_date(timeframe: str, date: Optional[datetime] = None) -> d
     if not date:
         date = datetime.now(timezone.utc)
 
-    new_timestamp = ccxt.Exchange.round_timeframe(timeframe, dt_ts(date), ROUND_DOWN) // 1000
-    return dt_from_ts(new_timestamp)
+    new_timestamp = ccxt.Exchange.round_timeframe(timeframe, date.timestamp() * 1000,
+                                                  ROUND_DOWN) // 1000
+    return datetime.fromtimestamp(new_timestamp, tz=timezone.utc)
 
 
 def timeframe_to_next_date(timeframe: str, date: Optional[datetime] = None) -> datetime:
@@ -113,8 +112,9 @@ def timeframe_to_next_date(timeframe: str, date: Optional[datetime] = None) -> d
     """
     if not date:
         date = datetime.now(timezone.utc)
-    new_timestamp = ccxt.Exchange.round_timeframe(timeframe, dt_ts(date), ROUND_UP) // 1000
-    return dt_from_ts(new_timestamp)
+    new_timestamp = ccxt.Exchange.round_timeframe(timeframe, date.timestamp() * 1000,
+                                                  ROUND_UP) // 1000
+    return datetime.fromtimestamp(new_timestamp, tz=timezone.utc)
 
 
 def date_minus_candles(
@@ -219,51 +219,35 @@ def amount_to_contract_precision(
     return amount
 
 
-def price_to_precision(
-    price: float,
-    price_precision: Optional[float],
-    precisionMode: Optional[int],
-    *,
-    rounding_mode: int = ROUND,
-) -> float:
+def price_to_precision(price: float, price_precision: Optional[float],
+                       precisionMode: Optional[int]) -> float:
     """
-    Returns the price rounded to the precision the Exchange accepts.
+    Returns the price rounded up to the precision the Exchange accepts.
     Partial Re-implementation of ccxt internal method decimal_to_precision(),
-    which does not support rounding up.
-    For stoploss calculations, must use ROUND_UP for longs, and ROUND_DOWN for shorts.
-
+    which does not support rounding up
     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"):
-                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}")
+                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 price

freqtrade/exchange/gate.py

@@ -5,6 +5,7 @@ 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
 
@@ -27,12 +28,10 @@ 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'],
@@ -51,6 +50,14 @@ 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,

freqtrade/exchange/kraken.py

@@ -12,7 +12,6 @@ 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
 
 
@@ -110,7 +109,6 @@ 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)
@@ -118,11 +116,11 @@ 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, rounding_mode=round_mode)
+            params['price2'] = self.price_to_precision(pair, limit_rate)
         else:
             ordertype = "stop-loss"
 
-        stop_price = self.price_to_precision(pair, stop_price, rounding_mode=round_mode)
+        stop_price = self.price_to_precision(pair, stop_price)
 
         if self._config['dry_run']:
             dry_order = self.create_dry_run_order(

freqtrade/exchange/okx.py

@@ -28,7 +28,6 @@ class Okx(Exchange):
         "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,
@@ -163,27 +162,28 @@ class Okx(Exchange):
         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)
+
+        params = self._params.copy()
+        # Verify if stopPrice works for your exchange!
+        params.update({'stopLossPrice': 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 _convert_stop_order(self, pair: str, order_id: str, order: Dict) -> Dict:
-        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
+    def stoploss_adjust(self, stop_loss: float, order: Dict, side: str) -> bool:
+        """
+        OKX uses non-default stoploss price naming.
+        """
+        if not self._ft_has.get('stoploss_on_exchange'):
+            raise OperationalException(f"stoploss is not implemented for {self.name}.")
+
+        return (
+            order.get('stopLossPrice', None) is None
+            or ((side == "sell" and stop_loss > float(order['stopLossPrice'])) or
+                (side == "buy" and stop_loss < float(order['stopLossPrice'])))
+        )
 
     def fetch_stoploss_order(self, order_id: str, pair: str, params: Dict = {}) -> Dict:
         if self._config['dry_run']:
@@ -193,7 +193,7 @@ class Okx(Exchange):
             params1 = {'stop': True}
             order_reg = self._api.fetch_order(order_id, pair, params=params1)
             self._log_exchange_response('fetch_stoploss_order', order_reg)
-            return self._convert_stop_order(pair, order_id, order_reg)
+            return order_reg
         except ccxt.OrderNotFound:
             pass
         params2 = {'stop': True, 'ordType': 'conditional'}
@@ -204,7 +204,18 @@ class Okx(Exchange):
                 orders_f = [order for order in orders if order['id'] == order_id]
                 if orders_f:
                     order = orders_f[0]
-                    return self._convert_stop_order(pair, order_id, order)
+                    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(

freqtrade/freqai/RL/Base3ActionRLEnv.py

@@ -1,7 +1,7 @@
 import logging
 from enum import Enum
 
-from gymnasium import spaces
+from gym import spaces
 
 from freqtrade.freqai.RL.BaseEnvironment import BaseEnvironment, Positions
 
@@ -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 = "exit"
+                trade_type = "neutral"
                 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, 'profit': self.get_unrealized_profit()})
+                     'type': trade_type})
 
         if (self._total_profit < self.max_drawdown or
                 self._total_unrealized_profit < self.max_drawdown):
@@ -94,12 +94,9 @@ class Base3ActionRLEnv(BaseEnvironment):
 
         observation = self._get_observation()
 
-        # user can play with time if they want
-        truncated = False
-
         self._update_history(info)
 
-        return observation, step_reward, self._done, truncated, info
+        return observation, step_reward, self._done, info
 
     def is_tradesignal(self, action: int) -> bool:
         """

freqtrade/freqai/RL/Base4ActionRLEnv.py

@@ -1,7 +1,7 @@
 import logging
 from enum import Enum
 
-from gymnasium import spaces
+from gym import spaces
 
 from freqtrade.freqai.RL.BaseEnvironment import BaseEnvironment, Positions
 
@@ -52,6 +52,16 @@ 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
@@ -59,16 +69,16 @@ class Base4ActionRLEnv(BaseEnvironment):
                 self._last_trade_tick = None
             elif action == Actions.Long_enter.value:
                 self._position = Positions.Long
-                trade_type = "enter_long"
+                trade_type = "long"
                 self._last_trade_tick = self._current_tick
             elif action == Actions.Short_enter.value:
                 self._position = Positions.Short
-                trade_type = "enter_short"
+                trade_type = "short"
                 self._last_trade_tick = self._current_tick
             elif action == Actions.Exit.value:
                 self._update_total_profit()
                 self._position = Positions.Neutral
-                trade_type = "exit"
+                trade_type = "neutral"
                 self._last_trade_tick = None
             else:
                 print("case not defined")
@@ -76,7 +86,7 @@ class Base4ActionRLEnv(BaseEnvironment):
             if trade_type is not None:
                 self.trade_history.append(
                     {'price': self.current_price(), 'index': self._current_tick,
-                     'type': trade_type, 'profit': self.get_unrealized_profit()})
+                     'type': trade_type})
 
         if (self._total_profit < self.max_drawdown or
                 self._total_unrealized_profit < self.max_drawdown):
@@ -96,12 +106,9 @@ class Base4ActionRLEnv(BaseEnvironment):
 
         observation = self._get_observation()
 
-        # user can play with time if they want
-        truncated = False
-
         self._update_history(info)
 
-        return observation, step_reward, self._done, truncated, info
+        return observation, step_reward, self._done, info
 
     def is_tradesignal(self, action: int) -> bool:
         """

freqtrade/freqai/RL/Base5ActionRLEnv.py

@@ -1,7 +1,7 @@
 import logging
 from enum import Enum
 
-from gymnasium import spaces
+from gym import spaces
 
 from freqtrade.freqai.RL.BaseEnvironment import BaseEnvironment, Positions
 
@@ -53,6 +53,16 @@ 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
@@ -60,21 +70,21 @@ class Base5ActionRLEnv(BaseEnvironment):
                 self._last_trade_tick = None
             elif action == Actions.Long_enter.value:
                 self._position = Positions.Long
-                trade_type = "enter_long"
+                trade_type = "long"
                 self._last_trade_tick = self._current_tick
             elif action == Actions.Short_enter.value:
                 self._position = Positions.Short
-                trade_type = "enter_short"
+                trade_type = "short"
                 self._last_trade_tick = self._current_tick
             elif action == Actions.Long_exit.value:
                 self._update_total_profit()
                 self._position = Positions.Neutral
-                trade_type = "exit_long"
+                trade_type = "neutral"
                 self._last_trade_tick = None
             elif action == Actions.Short_exit.value:
                 self._update_total_profit()
                 self._position = Positions.Neutral
-                trade_type = "exit_short"
+                trade_type = "neutral"
                 self._last_trade_tick = None
             else:
                 print("case not defined")
@@ -82,7 +92,7 @@ class Base5ActionRLEnv(BaseEnvironment):
             if trade_type is not None:
                 self.trade_history.append(
                     {'price': self.current_price(), 'index': self._current_tick,
-                     'type': trade_type, 'profit': self.get_unrealized_profit()})
+                     'type': trade_type})
 
         if (self._total_profit < self.max_drawdown or
                 self._total_unrealized_profit < self.max_drawdown):
@@ -101,12 +111,10 @@ class Base5ActionRLEnv(BaseEnvironment):
         )
 
         observation = self._get_observation()
-        # user can play with time if they want
-        truncated = False
 
         self._update_history(info)
 
-        return observation, step_reward, self._done, truncated, info
+        return observation, step_reward, self._done, info
 
     def is_tradesignal(self, action: int) -> bool:
         """

freqtrade/freqai/RL/BaseEnvironment.py

@@ -4,11 +4,11 @@ from abc import abstractmethod
 from enum import Enum
 from typing import Optional, Type, Union
 
-import gymnasium as gym
+import gym
 import numpy as np
 import pandas as pd
-from gymnasium import spaces
-from gymnasium.utils import seeding
+from gym import spaces
+from gym.utils import seeding
 from pandas import DataFrame
 
 
@@ -127,14 +127,6 @@ class BaseEnvironment(gym.Env):
         self.history: dict = {}
         self.trade_history: list = []
 
-    def get_attr(self, attr: str):
-        """
-        Returns the attribute of the environment
-        :param attr: attribute to return
-        :return: attribute
-        """
-        return getattr(self, attr)
-
     @abstractmethod
     def set_action_space(self):
         """
@@ -180,7 +172,7 @@ class BaseEnvironment(gym.Env):
     def reset_tensorboard_log(self):
         self.tensorboard_metrics = {}
 
-    def reset(self, seed=None):
+    def reset(self):
         """
         Reset is called at the beginning of every episode
         """
@@ -211,7 +203,7 @@ class BaseEnvironment(gym.Env):
         self.close_trade_profit = []
         self._total_unrealized_profit = 1
 
-        return self._get_observation(), self.history
+        return self._get_observation()
 
     @abstractmethod
     def step(self, action: int):
@@ -306,12 +298,6 @@ class BaseEnvironment(gym.Env):
         """
         An example reward function. This is the one function that users will likely
         wish to inject their own creativity into.
-
-        Warning!
-        This is function is a showcase of functionality designed to show as many possible
-        environment control features as possible. It is also designed to run quickly
-        on small computers. This is a benchmark, it is *not* for live production.
-
         :param action: int = The action made by the agent for the current candle.
         :return:
         float = the reward to give to the agent for current step (used for optimization

freqtrade/freqai/RL/BaseReinforcementLearningModel.py

@@ -6,7 +6,7 @@ from datetime import datetime, timezone
 from pathlib import Path
 from typing import Any, Callable, Dict, Optional, Tuple, Type, Union
 
-import gymnasium as gym
+import gym
 import numpy as np
 import numpy.typing as npt
 import pandas as pd
@@ -16,14 +16,14 @@ from pandas import DataFrame
 from stable_baselines3.common.callbacks import EvalCallback
 from stable_baselines3.common.monitor import Monitor
 from stable_baselines3.common.utils import set_random_seed
-from stable_baselines3.common.vec_env import SubprocVecEnv, VecMonitor
+from stable_baselines3.common.vec_env import SubprocVecEnv
 
 from freqtrade.exceptions import OperationalException
 from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
 from freqtrade.freqai.freqai_interface import IFreqaiModel
 from freqtrade.freqai.RL.Base5ActionRLEnv import Actions, Base5ActionRLEnv
-from freqtrade.freqai.RL.BaseEnvironment import BaseActions, BaseEnvironment, Positions
-from freqtrade.freqai.tensorboard.TensorboardCallback import TensorboardCallback
+from freqtrade.freqai.RL.BaseEnvironment import BaseActions, Positions
+from freqtrade.freqai.RL.TensorboardCallback import TensorboardCallback
 from freqtrade.persistence import Trade
 
 
@@ -46,8 +46,8 @@ class BaseReinforcementLearningModel(IFreqaiModel):
             'cpu_count', 1), max(int(self.max_system_threads / 2), 1))
         th.set_num_threads(self.max_threads)
         self.reward_params = self.freqai_info['rl_config']['model_reward_parameters']
-        self.train_env: Union[VecMonitor, SubprocVecEnv, gym.Env] = gym.Env()
-        self.eval_env: Union[VecMonitor, SubprocVecEnv, gym.Env] = gym.Env()
+        self.train_env: Union[SubprocVecEnv, Type[gym.Env]] = gym.Env()
+        self.eval_env: Union[SubprocVecEnv, Type[gym.Env]] = gym.Env()
         self.eval_callback: Optional[EvalCallback] = None
         self.model_type = self.freqai_info['rl_config']['model_type']
         self.rl_config = self.freqai_info['rl_config']
@@ -371,12 +371,6 @@ class BaseReinforcementLearningModel(IFreqaiModel):
             """
             An example reward function. This is the one function that users will likely
             wish to inject their own creativity into.
-
-            Warning!
-            This is function is a showcase of functionality designed to show as many possible
-            environment control features as possible. It is also designed to run quickly
-            on small computers. This is a benchmark, it is *not* for live production.
-
             :param action: int = The action made by the agent for the current candle.
             :return:
             float = the reward to give to the agent for current step (used for optimization
@@ -437,8 +431,9 @@ class BaseReinforcementLearningModel(IFreqaiModel):
             return 0.
 
 
-def make_env(MyRLEnv: Type[BaseEnvironment], env_id: str, rank: int,
+def make_env(MyRLEnv: Type[gym.Env], env_id: str, rank: int,
              seed: int, train_df: DataFrame, price: DataFrame,
+             monitor: bool = False,
              env_info: Dict[str, Any] = {}) -> Callable:
     """
     Utility function for multiprocessed env.
@@ -455,7 +450,8 @@ def make_env(MyRLEnv: Type[BaseEnvironment], env_id: str, rank: int,
 
         env = MyRLEnv(df=train_df, prices=price, id=env_id, seed=seed + rank,
                       **env_info)
-
+        if monitor:
+            env = Monitor(env)
         return env
     set_random_seed(seed)
     return _init

freqtrade/freqai/RL/TensorboardCallback.py

@@ -3,9 +3,8 @@ from typing import Any, Dict, Type, Union
 
 from stable_baselines3.common.callbacks import BaseCallback
 from stable_baselines3.common.logger import HParam
-from stable_baselines3.common.vec_env import VecEnv
 
-from freqtrade.freqai.RL.BaseEnvironment import BaseActions
+from freqtrade.freqai.RL.BaseEnvironment import BaseActions, BaseEnvironment
 
 
 class TensorboardCallback(BaseCallback):
@@ -13,13 +12,11 @@ class TensorboardCallback(BaseCallback):
     Custom callback for plotting additional values in tensorboard and
     episodic summary reports.
     """
-    # Override training_env type to fix type errors
-    training_env: Union[VecEnv, None] = None
-
     def __init__(self, verbose=1, actions: Type[Enum] = BaseActions):
         super().__init__(verbose)
         self.model: Any = None
-        self.logger: Any = None
+        self.logger = None  # type: Any
+        self.training_env: BaseEnvironment = None  # type: ignore
         self.actions: Type[Enum] = actions
 
     def _on_training_start(self) -> None:
@@ -47,8 +44,6 @@ class TensorboardCallback(BaseCallback):
     def _on_step(self) -> bool:
 
         local_info = self.locals["infos"][0]
-        if self.training_env is None:
-            return True
         tensorboard_metrics = self.training_env.get_attr("tensorboard_metrics")[0]
 
         for metric in local_info:

freqtrade/freqai/base_models/BasePyTorchClassifier.py

@@ -1,151 +0,0 @@
-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 dk: dk: The datakitchen object
-        :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
-        )
-        self.model.model.eval()
-        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)
-        # used .tolist to convert probs into an iterable, in this way Tensors
-        # are automatically moved to the CPU first if necessary.
-        pred_df_prob = DataFrame(probs.detach().tolist(), 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

freqtrade/freqai/base_models/BasePyTorchModel.py

@@ -1,84 +0,0 @@
-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"]
-        self.window_size = self.freqai_info.get("conv_width", 1)
-
-    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")

freqtrade/freqai/base_models/BasePyTorchRegressor.py

@@ -1,51 +0,0 @@
-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
-        )
-        self.model.model.eval()
-        y = self.model.model(x)
-        pred_df = DataFrame(y.detach().tolist(), columns=[dk.label_list[0]])
-        pred_df = dk.denormalize_labels_from_metadata(pred_df)
-        return (pred_df, dk.do_predict)

freqtrade/freqai/data_drawer.py

@@ -74,8 +74,8 @@ class FreqaiDataDrawer:
         self.historic_predictions: Dict[str, DataFrame] = {}
         self.full_path = full_path
         self.historic_predictions_path = Path(self.full_path / "historic_predictions.pkl")
-        self.historic_predictions_bkp_path = Path(
-            self.full_path / "historic_predictions.backup.pkl")
+        self.historic_predictions_folder = Path(self.full_path / "historic_predictions")
+        self.historic_predictions_bkp_folder = Path(self.full_path / "historic_predictions_backup")
         self.pair_dictionary_path = Path(self.full_path / "pair_dictionary.json")
         self.global_metadata_path = Path(self.full_path / "global_metadata.json")
         self.metric_tracker_path = Path(self.full_path / "metric_tracker.json")
@@ -163,11 +163,12 @@ class FreqaiDataDrawer:
         Locate and load a previously saved historic predictions.
         :return: bool - whether or not the drawer was located
         """
-        exists = self.historic_predictions_path.is_file()
+        exists = self.historic_predictions_folder.exists()
+        convert = self.historic_predictions_path.is_file()
+
         if exists:
             try:
-                with self.historic_predictions_path.open("rb") as fp:
-                    self.historic_predictions = cloudpickle.load(fp)
+                self.load_historic_predictions_from_folder()
                 logger.info(
                     f"Found existing historic predictions at {self.full_path}, but beware "
                     "that statistics may be inaccurate if the bot has been offline for "
@@ -175,25 +176,54 @@ class FreqaiDataDrawer:
                 )
             except EOFError:
                 logger.warning(
-                    'Historical prediction file was corrupted. Trying to load backup file.')
-                with self.historic_predictions_bkp_path.open("rb") as fp:
-                    self.historic_predictions = cloudpickle.load(fp)
-                logger.warning('FreqAI successfully loaded the backup historical predictions file.')
+                    'Historical prediction files were corrupted. Trying to load backup files.')
+                self.load_historic_predictions_from_folder()
+                logger.warning('FreqAI successfully loaded the backup '
+                               'historical predictions files.')
+
+        elif not exists and convert:
+            logger.info("Converting your historic predictions pkl to parquet"
+                        "to improve performance.")
+            with Path.open(self.historic_predictions_path, "rb") as fp:
+                self.historic_predictions = cloudpickle.load(fp)
+            self.save_historic_predictions_to_disk()
+            exists = True
 
         else:
-            logger.info("Could not find existing historic_predictions, starting from scratch")
+            logger.warning(
+                f"Follower could not find historic predictions at {self.full_path} "
+                "sending null values back to strategy"
+            )
 
         return exists
 
+    def load_historic_predictions_from_folder(self):
+        """
+        Try to build the historic_predictions dictionary from parquet
+        files in the historic_predictions_folder
+        """
+        for file_path in self.historic_predictions_folder.glob("*.parquet"):
+            key = file_path.stem
+            key.replace("_", "/")
+            self.historic_predictions[key] = pd.read_parquet(file_path)
+
+        return
+
     def save_historic_predictions_to_disk(self):
         """
         Save historic predictions pickle to disk
         """
-        with self.historic_predictions_path.open("wb") as fp:
-            cloudpickle.dump(self.historic_predictions, fp, protocol=cloudpickle.DEFAULT_PROTOCOL)
+
+        self.historic_predictions_folder.mkdir(parents=True, exist_ok=True)
+        for key, value in self.historic_predictions.items():
+            key = key.replace("/", "_")
+            # pytest.set_trace()
+            filename = Path(self.historic_predictions_folder / f"{key}.parquet")
+            value.to_parquet(filename)
 
         # create a backup
-        shutil.copy(self.historic_predictions_path, self.historic_predictions_bkp_path)
+        shutil.copytree(self.historic_predictions_folder,
+                        self.historic_predictions_bkp_folder, dirs_exist_ok=True)
 
     def save_metric_tracker_to_disk(self):
         """
@@ -446,7 +476,7 @@ 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 self.model_type in ["stable_baselines3", "sb3_contrib", "pytorch"]:
+        elif 'stable_baselines' in self.model_type or 'sb3_contrib' == self.model_type:
             model.save(save_path / f"{dk.model_filename}_model.zip")
 
         if dk.svm_model is not None:
@@ -496,7 +526,7 @@ 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:  # noqa: C901
+    def load_data(self, coin: str, dk: FreqaiDataKitchen) -> Any:
         """
         loads all data required to make a prediction on a sub-train time range
         :returns:
@@ -537,11 +567,6 @@ 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")
@@ -680,7 +705,7 @@ class FreqaiDataDrawer:
         Returns timerange information based on historic predictions file
         :return: timerange calculated from saved live data
         """
-        if not self.historic_predictions_path.is_file():
+        if not self.historic_predictions_folder.exists():
             raise OperationalException(
                 'Historic predictions not found. Historic predictions data is required '
                 'to run backtest with the freqai-backtest-live-models option '

freqtrade/freqai/data_kitchen.py

@@ -1291,7 +1291,7 @@ class FreqaiDataKitchen:
 
         return dataframe
 
-    def use_strategy_to_populate_indicators(  # noqa: C901
+    def use_strategy_to_populate_indicators(
         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)
 
-        if self.live:
-            dataframe = strategy.set_freqai_targets(dataframe.copy(), metadata=metadata)
-            dataframe = self.remove_special_chars_from_feature_names(dataframe)
+        dataframe = strategy.set_freqai_targets(dataframe.copy(), metadata=metadata)
 
         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)
 

freqtrade/freqai/freqai_interface.py

@@ -21,7 +21,7 @@ from freqtrade.exceptions import OperationalException
 from freqtrade.exchange import timeframe_to_seconds
 from freqtrade.freqai.data_drawer import FreqaiDataDrawer
 from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
-from freqtrade.freqai.utils import get_tb_logger, plot_feature_importance, record_params
+from freqtrade.freqai.utils import plot_feature_importance, record_params
 from freqtrade.strategy.interface import IStrategy
 
 
@@ -80,11 +80,9 @@ class IFreqaiModel(ABC):
         if self.keras and self.ft_params.get("DI_threshold", 0):
             self.ft_params["DI_threshold"] = 0
             logger.warning("DI threshold is not configured for Keras models yet. Deactivating.")
-
         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"))
@@ -107,10 +105,6 @@ 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.')
-        self.activate_tensorboard: bool = self.freqai_info.get('activate_tensorboard', True)
 
         record_params(config, self.full_path)
 
@@ -160,7 +154,8 @@ 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
@@ -244,8 +239,8 @@ class IFreqaiModel(ABC):
                         new_trained_timerange, pair, strategy, dk, data_load_timerange
                     )
                 except Exception as msg:
-                    logger.exception(f"Training {pair} raised exception {msg.__class__.__name__}. "
-                                     f"Message: {msg}, skipping.")
+                    logger.warning(f"Training {pair} raised exception {msg.__class__.__name__}. "
+                                   f"Message: {msg}, skipping.")
 
                 self.train_timer('stop', pair)
 
@@ -308,11 +303,10 @@ class IFreqaiModel(ABC):
             if dk.check_if_backtest_prediction_is_valid(len_backtest_df):
                 if check_features:
                     self.dd.load_metadata(dk)
-                    df_fts = self.dk.use_strategy_to_populate_indicators(
-                        strategy, prediction_dataframe=dataframe.tail(1), pair=pair
+                    dataframe_dummy_features = self.dk.use_strategy_to_populate_indicators(
+                        strategy, prediction_dataframe=dataframe.tail(1), pair=metadata["pair"]
                     )
-                    df_fts = dk.remove_special_chars_from_feature_names(df_fts)
-                    dk.find_features(df_fts)
+                    dk.find_features(dataframe_dummy_features)
                     self.check_if_feature_list_matches_strategy(dk)
                     check_features = False
                 append_df = dk.get_backtesting_prediction()
@@ -320,7 +314,7 @@ class IFreqaiModel(ABC):
             else:
                 if populate_indicators:
                     dataframe = self.dk.use_strategy_to_populate_indicators(
-                        strategy, prediction_dataframe=dataframe, pair=pair
+                        strategy, prediction_dataframe=dataframe, pair=metadata["pair"]
                     )
                     populate_indicators = False
 
@@ -336,19 +330,12 @@ 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)
 
                     try:
-                        self.tb_logger = get_tb_logger(self.dd.model_type, dk.data_path,
-                                                       self.activate_tensorboard)
                         self.model = self.train(dataframe_train, pair, dk)
-                        self.tb_logger.close()
                     except Exception as msg:
                         logger.warning(
                             f"Training {pair} raised exception {msg.__class__.__name__}. "
@@ -495,9 +482,9 @@ 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"
@@ -578,9 +565,8 @@ class IFreqaiModel(ABC):
             file_type = ".joblib"
         elif self.dd.model_type == 'keras':
             file_type = ".h5"
-        elif self.dd.model_type in ["stable_baselines3", "sb3_contrib", "pytorch"]:
+        elif 'stable_baselines' in self.dd.model_type or 'sb3_contrib' == self.dd.model_type:
             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:
@@ -626,23 +612,18 @@ class IFreqaiModel(ABC):
             strategy, corr_dataframes, base_dataframes, pair
         )
 
-        trained_timestamp = new_trained_timerange.stopts
+        new_trained_timerange = dk.buffer_timerange(new_trained_timerange)
 
-        buffered_timerange = dk.buffer_timerange(new_trained_timerange)
-
-        unfiltered_dataframe = dk.slice_dataframe(buffered_timerange, unfiltered_dataframe)
+        unfiltered_dataframe = dk.slice_dataframe(new_trained_timerange, unfiltered_dataframe)
 
         # find the features indicated by strategy and store in datakitchen
         dk.find_features(unfiltered_dataframe)
         dk.find_labels(unfiltered_dataframe)
 
-        self.tb_logger = get_tb_logger(self.dd.model_type, dk.data_path,
-                                       self.activate_tensorboard)
         model = self.train(unfiltered_dataframe, pair, dk)
-        self.tb_logger.close()
 
-        self.dd.pair_dict[pair]["trained_timestamp"] = trained_timestamp
-        dk.set_new_model_names(pair, trained_timestamp)
+        self.dd.pair_dict[pair]["trained_timestamp"] = new_trained_timerange.stopts
+        dk.set_new_model_names(pair, new_trained_timerange.stopts)
         self.dd.save_data(model, pair, dk)
 
         if self.plot_features:

freqtrade/freqai/prediction_models/CatboostClassifier.py

@@ -14,20 +14,16 @@ logger = logging.getLogger(__name__)
 
 class CatboostClassifier(BaseClassifierModel):
     """
-    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.
+    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.
     """
 
     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
+        :param data_dictionary: the dictionary constructed by DataHandler to hold
+                                all the training and test data/labels.
         """
 
         train_data = Pool(

freqtrade/freqai/prediction_models/CatboostClassifierMultiTarget.py

@@ -15,20 +15,16 @@ logger = logging.getLogger(__name__)
 
 class CatboostClassifierMultiTarget(BaseClassifierModel):
     """
-    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.
+    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.
     """
 
     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
+        :param data_dictionary: the dictionary constructed by DataHandler to hold
+                                all the training and test data/labels.
         """
 
         cbc = CatBoostClassifier(

freqtrade/freqai/prediction_models/CatboostRegressor.py

@@ -14,20 +14,16 @@ logger = logging.getLogger(__name__)
 
 class CatboostRegressor(BaseRegressionModel):
     """
-    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.
+    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.
     """
 
     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
+        :param data_dictionary: the dictionary constructed by DataHandler to hold
+                                all the training and test data/labels.
         """
 
         train_data = Pool(

freqtrade/freqai/prediction_models/CatboostRegressorMultiTarget.py

@@ -15,20 +15,16 @@ logger = logging.getLogger(__name__)
 
 class CatboostRegressorMultiTarget(BaseRegressionModel):
     """
-    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.
+    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.
     """
 
     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
+        :param data_dictionary: the dictionary constructed by DataHandler to hold
+                                all the training and test data/labels.
         """
 
         cbr = CatBoostRegressor(

freqtrade/freqai/prediction_models/LightGBMClassifier.py

@@ -12,20 +12,16 @@ logger = logging.getLogger(__name__)
 
 class LightGBMClassifier(BaseClassifierModel):
     """
-    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.
+    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.
     """
 
     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
+        :param data_dictionary: the dictionary constructed by DataHandler to hold
+                                all the training and test data/labels.
         """
 
         if self.freqai_info.get('data_split_parameters', {}).get('test_size', 0.1) == 0:

freqtrade/freqai/prediction_models/LightGBMClassifierMultiTarget.py

@@ -13,20 +13,16 @@ logger = logging.getLogger(__name__)
 
 class LightGBMClassifierMultiTarget(BaseClassifierModel):
     """
-    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.
+    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.
     """
 
     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
+        :param data_dictionary: the dictionary constructed by DataHandler to hold
+                                all the training and test data/labels.
         """
 
         lgb = LGBMClassifier(**self.model_training_parameters)

freqtrade/freqai/prediction_models/LightGBMRegressor.py

@@ -12,20 +12,18 @@ logger = logging.getLogger(__name__)
 
 class LightGBMRegressor(BaseRegressionModel):
     """
-    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.
+    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.
     """
 
     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
+        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.
         """
 
         if self.freqai_info.get('data_split_parameters', {}).get('test_size', 0.1) == 0:

freqtrade/freqai/prediction_models/LightGBMRegressorMultiTarget.py

@@ -13,20 +13,16 @@ logger = logging.getLogger(__name__)
 
 class LightGBMRegressorMultiTarget(BaseRegressionModel):
     """
-    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.
+    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.
     """
 
     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
+        :param data_dictionary: the dictionary constructed by DataHandler to hold
+                                all the training and test data/labels.
         """
 
         lgb = LGBMRegressor(**self.model_training_parameters)

freqtrade/freqai/prediction_models/PyTorchMLPClassifier.py

@@ -1,91 +0,0 @@
-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()
-        # check if continual_learning is activated, and retreive the model to continue training
-        trainer = self.get_init_model(dk.pair)
-        if trainer is None:
-            trainer = PyTorchModelTrainer(
-                model=model,
-                optimizer=optimizer,
-                criterion=criterion,
-                model_meta_data={"class_names": class_names},
-                device=self.device,
-                data_convertor=self.data_convertor,
-                tb_logger=self.tb_logger,
-                **self.trainer_kwargs,
-            )
-        trainer.fit(data_dictionary, self.splits)
-        return trainer

freqtrade/freqai/prediction_models/PyTorchMLPRegressor.py

@@ -1,85 +0,0 @@
-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()
-        # check if continual_learning is activated, and retreive the model to continue training
-        trainer = self.get_init_model(dk.pair)
-        if trainer is None:
-            trainer = PyTorchModelTrainer(
-                model=model,
-                optimizer=optimizer,
-                criterion=criterion,
-                device=self.device,
-                data_convertor=self.data_convertor,
-                tb_logger=self.tb_logger,
-                **self.trainer_kwargs,
-            )
-        trainer.fit(data_dictionary, self.splits)
-        return trainer

freqtrade/freqai/prediction_models/PyTorchTransformerRegressor.py

@@ -1,140 +0,0 @@
-from typing import Any, Dict, Tuple
-
-import numpy as np
-import numpy.typing as npt
-import pandas as pd
-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.PyTorchModelTrainer import PyTorchTransformerTrainer
-from freqtrade.freqai.torch.PyTorchTransformerModel import PyTorchTransformerModel
-
-
-class PyTorchTransformerRegressor(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]
-        n_labels = data_dictionary["train_labels"].shape[-1]
-        model = PyTorchTransformerModel(
-            input_dim=n_features,
-            output_dim=n_labels,
-            time_window=self.window_size,
-            **self.model_kwargs
-        )
-        model.to(self.device)
-        optimizer = torch.optim.AdamW(model.parameters(), lr=self.learning_rate)
-        criterion = torch.nn.MSELoss()
-        # check if continual_learning is activated, and retreive the model to continue training
-        trainer = self.get_init_model(dk.pair)
-        if trainer is None:
-            trainer = PyTorchTransformerTrainer(
-                model=model,
-                optimizer=optimizer,
-                criterion=criterion,
-                device=self.device,
-                data_convertor=self.data_convertor,
-                window_size=self.window_size,
-                tb_logger=self.tb_logger,
-                **self.trainer_kwargs,
-            )
-        trainer.fit(data_dictionary, self.splits)
-        return trainer
-
-    def predict(
-        self, unfiltered_df: pd.DataFrame, dk: FreqaiDataKitchen, **kwargs
-    ) -> Tuple[pd.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
-        )
-        # if user is asking for multiple predictions, slide the window
-        # along the tensor
-        x = x.unsqueeze(0)
-        # create empty torch tensor
-        self.model.model.eval()
-        yb = torch.empty(0).to(self.device)
-        if x.shape[1] > 1:
-            ws = self.window_size
-            for i in range(0, x.shape[1] - ws):
-                xb = x[:, i:i + ws, :].to(self.device)
-                y = self.model.model(xb)
-                yb = torch.cat((yb, y), dim=0)
-        else:
-            yb = self.model.model(x)
-
-        yb = yb.cpu().squeeze()
-        pred_df = pd.DataFrame(yb.detach().numpy(), columns=dk.label_list)
-        pred_df = dk.denormalize_labels_from_metadata(pred_df)
-
-        if x.shape[1] > 1:
-            zeros_df = pd.DataFrame(np.zeros((x.shape[1] - len(pred_df), len(pred_df.columns))),
-                                    columns=pred_df.columns)
-            pred_df = pd.concat([zeros_df, pred_df], axis=0, ignore_index=True)
-        return (pred_df, dk.do_predict)

freqtrade/freqai/prediction_models/ReinforcementLearner.py

@@ -1,12 +1,11 @@
 import logging
 from pathlib import Path
-from typing import Any, Dict, Type
+from typing import Any, Dict
 
 import torch as th
 
 from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
 from freqtrade.freqai.RL.Base5ActionRLEnv import Actions, Base5ActionRLEnv, Positions
-from freqtrade.freqai.RL.BaseEnvironment import BaseEnvironment
 from freqtrade.freqai.RL.BaseReinforcementLearningModel import BaseReinforcementLearningModel
 
 
@@ -58,14 +57,10 @@ class ReinforcementLearner(BaseReinforcementLearningModel):
         policy_kwargs = dict(activation_fn=th.nn.ReLU,
                              net_arch=self.net_arch)
 
-        if self.activate_tensorboard:
-            tb_path = Path(dk.full_path / "tensorboard" / dk.pair.split('/')[0])
-        else:
-            tb_path = None
-
         if dk.pair not in self.dd.model_dictionary or not self.continual_learning:
             model = self.MODELCLASS(self.policy_type, self.train_env, policy_kwargs=policy_kwargs,
-                                    tensorboard_log=tb_path,
+                                    tensorboard_log=Path(
+                                        dk.full_path / "tensorboard" / dk.pair.split('/')[0]),
                                     **self.freqai_info.get('model_training_parameters', {})
                                     )
         else:
@@ -76,8 +71,7 @@ class ReinforcementLearner(BaseReinforcementLearningModel):
 
         model.learn(
             total_timesteps=int(total_timesteps),
-            callback=[self.eval_callback, self.tensorboard_callback],
-            progress_bar=self.rl_config.get('progress_bar', False)
+            callback=[self.eval_callback, self.tensorboard_callback]
         )
 
         if Path(dk.data_path / "best_model.zip").is_file():
@@ -89,9 +83,7 @@ class ReinforcementLearner(BaseReinforcementLearningModel):
 
         return model
 
-    MyRLEnv: Type[BaseEnvironment]
-
-    class MyRLEnv(Base5ActionRLEnv):  # type: ignore[no-redef]
+    class MyRLEnv(Base5ActionRLEnv):
         """
         User can override any function in BaseRLEnv and gym.Env. Here the user
         sets a custom reward based on profit and trade duration.
@@ -101,12 +93,6 @@ class ReinforcementLearner(BaseReinforcementLearningModel):
             """
             An example reward function. This is the one function that users will likely
             wish to inject their own creativity into.
-
-                        Warning!
-            This is function is a showcase of functionality designed to show as many possible
-            environment control features as possible. It is also designed to run quickly
-            on small computers. This is a benchmark, it is *not* for live production.
-
             :param action: int = The action made by the agent for the current candle.
             :return:
             float = the reward to give to the agent for current step (used for optimization

freqtrade/freqai/prediction_models/ReinforcementLearner_multiproc.py

@@ -3,12 +3,12 @@ from typing import Any, Dict
 
 from pandas import DataFrame
 from stable_baselines3.common.callbacks import EvalCallback
-from stable_baselines3.common.vec_env import SubprocVecEnv, VecMonitor
+from stable_baselines3.common.vec_env import SubprocVecEnv
 
 from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
 from freqtrade.freqai.prediction_models.ReinforcementLearner import ReinforcementLearner
 from freqtrade.freqai.RL.BaseReinforcementLearningModel import make_env
-from freqtrade.freqai.tensorboard.TensorboardCallback import TensorboardCallback
+from freqtrade.freqai.RL.TensorboardCallback import TensorboardCallback
 
 
 logger = logging.getLogger(__name__)
@@ -41,25 +41,22 @@ class ReinforcementLearner_multiproc(ReinforcementLearner):
 
         env_info = self.pack_env_dict(dk.pair)
 
-        eval_freq = len(train_df) // self.max_threads
-
         env_id = "train_env"
-        self.train_env = VecMonitor(SubprocVecEnv([make_env(self.MyRLEnv, env_id, i, 1,
-                                                            train_df, prices_train,
-                                                            env_info=env_info) for i
-                                                   in range(self.max_threads)]))
+        self.train_env = SubprocVecEnv([make_env(self.MyRLEnv, env_id, i, 1,
+                                        train_df, prices_train,
+                                        monitor=True,
+                                        env_info=env_info) for i
+                                        in range(self.max_threads)])
 
         eval_env_id = 'eval_env'
-        self.eval_env = VecMonitor(SubprocVecEnv([make_env(self.MyRLEnv, eval_env_id, i, 1,
-                                                           test_df, prices_test,
-                                                           env_info=env_info) for i
-                                                  in range(self.max_threads)]))
-
+        self.eval_env = SubprocVecEnv([make_env(self.MyRLEnv, eval_env_id, i, 1,
+                                                test_df, prices_test,
+                                                monitor=True,
+                                                env_info=env_info) for i
+                                       in range(self.max_threads)])
         self.eval_callback = EvalCallback(self.eval_env, deterministic=True,
-                                          render=False, eval_freq=eval_freq,
+                                          render=False, eval_freq=len(train_df),
                                           best_model_save_path=str(dk.data_path))
 
-        # TENSORBOARD CALLBACK DOES NOT RECOMMENDED TO USE WITH MULTIPLE ENVS,
-        # IT WILL RETURN FALSE INFORMATIONS, NEVERTHLESS NOT THREAD SAFE WITH SB3!!!
         actions = self.train_env.env_method("get_actions")[0]
         self.tensorboard_callback = TensorboardCallback(verbose=1, actions=actions)

freqtrade/freqai/prediction_models/XGBoostClassifier.py

@@ -18,20 +18,16 @@ logger = logging.getLogger(__name__)
 
 class XGBoostClassifier(BaseClassifierModel):
     """
-    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.
+    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.
     """
 
     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
+        :param data_dictionary: the dictionary constructed by DataHandler to hold
+                                all the training and test data/labels.
         """
 
         X = data_dictionary["train_features"].to_numpy()

freqtrade/freqai/prediction_models/XGBoostRFClassifier.py

@@ -18,20 +18,16 @@ logger = logging.getLogger(__name__)
 
 class XGBoostRFClassifier(BaseClassifierModel):
     """
-    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.
+    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.
     """
 
     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
+        :param data_dictionary: the dictionary constructed by DataHandler to hold
+            all the training and test data/labels.
         """
 
         X = data_dictionary["train_features"].to_numpy()