read_sql may return the wallet_history `bot_managed` Boolean column as an
integer dtype (e.g. MySQL/MariaDB TINYINT). results.loc[results["bot_managed"]]
then does label-based indexing instead of boolean masking, raising a KeyError
on GET /api/v1/historic_balance (HTTP 500). Coerce with .astype(bool).
Document the new early_stopping_patience trainer_kwargs parameter
in the FreqAI parameter table, including description, datatype,
default value, and usage notes.
Add optional early stopping to prevent overfitting in PyTorch-based
FreqAI models. When `early_stopping_patience` is set in
model_training_parameters, training will stop if validation loss
does not improve for the specified number of epochs.
Changes:
- Add `early_stopping_patience` parameter (default 0 = disabled)
- `estimate_loss()` now returns average loss (float | None) instead
of None, enabling downstream use for schedulers and early stopping
- Track best validation loss and patience counter across epochs
Usage in config:
```json
{
"model_training_parameters": {
"n_epochs": 100,
"early_stopping_patience": 10
}
}
```
The change is fully backward compatible - early stopping is disabled
by default, and the return value of estimate_loss() can be safely
ignored by existing subclasses.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Kraken Futures now provides 1 year of hourly funding rate history.
The previous limit of 700 candles (29 days) was a workaround for
limited API retention. Freqtrade paginates funding rate fetches
automatically, so the CCXT default (2000) works correctly.
Kraken Futures' /orders/status returns limitPrice (not fill price) and
/fills omits fee amounts (only fillType). This adds:
- _adjust_krakenfutures_order: fetches trades for closed/filled orders
with average=None and computes VWAP average price.
- get_trades_for_order override: enriches trades with calculated fees
from the market's maker/taker fee schedule.
Tests: 7 new tests covering VWAP computation, fee enrichment with
maker/taker rates, and preservation of existing values.
CCXT's krakenfutures parse_order misses triggerPrice when the
/orders/status endpoint nests it inside priceTriggerOptions.
Override _order_contracts_to_amount to populate triggerPrice and
stopPrice from info.order.priceTriggerOptions.triggerPrice.
The futures_only ternary incorrectly used CandleType.SPOT for exchanges
that support both spot and futures (binance, gate), causing candle limit
mismatch against candle_count_futures.
Kraken Futures retains only ~29 days of hourly funding rate history.
Set funding_fee_candle_limit to 700 so ohlcv_candle_limit returns the
correct cap for CandleType.FUNDING_RATE instead of the general OHLCV
limit.
- Add test for stoploss_query_requires_stop_flag in _ft_has.
- Replace _fix_trigger_order_id tests with base class integration tests
(verify stop=True is passed by fetch_stoploss_order/cancel_stoploss_order).
- Add tests for stop param flow: passed to history endpoints, stripped
from open orders query.
- Remove obsolete cancel_stoploss_order/fetch_stoploss_order override tests.
- Add stoploss_query_requires_stop_flag to _ft_has so the base class
handles fetch_stoploss_order and cancel_stoploss_order (adds stop=True
which CCXT maps to trigger history endpoints).
- Remove _fix_trigger_order_id — ccxt 4.5.38 now parses order IDs
correctly in all response formats.
- Remove cancel_stoploss_order override — base class handles it.
- Remove fetch_stoploss_order override — base class handles it.
- Simplify _fetch_order_fallback — stop=True flows from base class,
no need to auto-retry with trigger=True.
Addresses review comments from freqtrade/freqtrade#12706.
Address review feedback: do_predict and DI_values were still assigned
after DataFrame creation, partially defeating the fragmentation fix.
Now all columns are collected in the dict before the single DataFrame()
call, ensuring zero post-construction column assignments.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Replace column-by-column DataFrame assignment with dict-based
construction. The previous approach triggered pandas
PerformanceWarning about DataFrame fragmentation when many
prediction columns and their corresponding mean/std columns
were added one at a time.
Also add defensive key checks (label in self.data["labels_mean"])
to prevent KeyError when custom models produce prediction columns
that don't have corresponding entries in labels_mean/labels_std.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Allow services like Uptimerobot to monitor the WebUI which uses `HEAD` request method because it is lighter whereas in order to use `GET` method, payment is required.
There's no significant impact for this changes.
if required_candle_call_count > 5:
as per matthias suggestion.
adjusted the elif part too, since this would have to be worded similarly.
If a native speaker thinks there is a better wording, be our guest.
## Summary
Fix IndexError crash in fetch_positions() when initializing wallets on Hyperliquid exchange.
## Quick changelog
- Changed fetch_positions to pass None instead of empty list when no specific pair is requested
- Fixes compatibility with Hyperliquid CCXT implementation that expects None for all positions
## What's new?
When fetch_positions() is called without a specific pair parameter, the code was passing an empty list [] to the CCXT API.
For Hyperliquid exchange, this causes an IndexError because the exchange's implementation attempts to access symbols[0]
without checking if the list is empty.
The CCXT standard is to pass None (not an empty list) when requesting all positions. This change aligns the code with
the CCXT API convention and prevents the crash on Hyperliquid during wallet initialization.
Error that was occurring:
```
IndexError: list index out of range
at /root/freqtrade/.venv/lib/python3.11/site-packages/ccxt/hyperliquid.py:3051
market = self.market(symbols[0])
```
This change does not use AI-generated code.
closes#12590
Backtesting assumes round dates, so a timeout at "candle length" needs
to timeout at the hour - not after the hour.
otherwise the timeout becomes either double (60 instead of 30) -
or longer by one "timeframe detail" (31 instead of 30).
- Stick to english in both commit messages, PR descriptions and code comments and variable names.
- New features need to contain unit tests, must pass CI (run pre-commit and pytest to get an early feedback) and should be documented with the introduction PR.
- PR's can be declared as draft - signaling Work in Progress for Pull Requests (which are not finished). We'll still aim to provide feedback on draft PR's in a timely manner.
- If you're using AI for your PR, please both mention it in the PR description and do a thorough review of the generated code. The final responsibility for the code with the PR author, not with the AI.
- If you're using AI for your PR, please both mention it in the PR description and do a thorough review of the generated code yourself.
The final responsibility for the code with the PR author, not with the AI, which also means that commits must be linked to your (human) account, not some generic AI account.
If you are unsure, discuss the feature on our [discord server](https://discord.gg/p7nuUNVfP7) or in a [issue](https://github.com/freqtrade/freqtrade/issues) before a Pull Request.
@@ -24,8 +25,7 @@ Best start by reading the [documentation](https://www.freqtrade.io/) to get a fe
### 1. Run unit tests
All unit tests must pass. If a unit test is broken, change your code to
make it pass. It means you have introduced a regression.
All unit tests must pass. If a unit test is broken, change your code to make it pass. It means you have introduced a regression.
Freqtrade is a free and open source crypto trading bot written in Python. It is designed to support all major exchanges and be controlled via Telegram or webUI. It contains backtesting, plotting and money management tools as well as strategy optimization by machine learning.
@@ -15,7 +16,7 @@ This software is for educational purposes only. Do not risk money which
you are afraid to lose. USE THE SOFTWARE AT YOUR OWN RISK. THE AUTHORS
AND ALL AFFILIATES ASSUME NO RESPONSIBILITY FOR YOUR TRADING RESULTS.
Always start by running a trading bot in Dry-run and do not engage money
Always start by running a trading bot in Dry-Run and do not engage money
before you understand how it works and what profit/loss you should
expect.
@@ -24,7 +25,9 @@ hesitate to read the source code and understand the mechanism of this bot.
## Supported Exchange marketplaces
Please read the [exchangespecific notes](docs/exchanges.md) to learn about eventual, special configurations needed for each exchange.
Please read the [exchange-specific notes](https://www.freqtrade.io/en/stable/exchanges/) to learn about special configurations that maybe needed for each exchange.
### Supported Spot Exchanges
- [X] [Binance](https://www.binance.com/)
- [X] [BingX](https://bingx.com/invite/0EM9RX)
@@ -39,7 +42,7 @@ Please read the [exchange specific notes](docs/exchanges.md) to learn about even
- [X] [MyOKX](https://okx.com/) (OKX EEA)
- [ ] [potentially many others](https://github.com/ccxt/ccxt/). _(We cannot guarantee they will work)_
### Supported Futures Exchanges (experimental)
### Supported Futures Exchanges
- [X] [Binance](https://www.binance.com/)
- [X] [Bitget](https://www.bitget.com/)
@@ -47,8 +50,9 @@ Please read the [exchange specific notes](docs/exchanges.md) to learn about even
- [X] [Hyperliquid](https://hyperliquid.xyz/) (A decentralized exchange, or DEX)
Please make sure to read the [exchange specific notes](docs/exchanges.md), as well as the [trading with leverage](docs/leverage.md) documentation before diving in.
Please make sure to read the [exchange specific notes](https://www.freqtrade.io/en/stable/exchanges/), as well as the [trading with leverage](https://www.freqtrade.io/en/stable/leverage/) documentation before diving in.
### Community tested
@@ -140,7 +144,7 @@ options:
### Telegram RPC commands
Telegram is not mandatory. However, this is a great way to control your bot. More details and the full command list on the [documentation](https://www.freqtrade.io/en/latest/telegram-usage/)
Telegram is not mandatory. However, this is a great way to control your bot. More details and the full command list on the [documentation](https://www.freqtrade.io/en/stable/telegram-usage/)
@@ -41,7 +41,7 @@ ranging from the simplest (0) to the most detailed per pair, per buy and per sel
* 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
* 4: profit summaries grouped by pair, enter_ and exit_tag (this can get quite large)
* 4: profit summaries grouped by pair, enter_tag and exit_tag (this can get quite large)
* 5: profit summaries grouped by exit_tag
More options are available by running with the `-h` option.
@@ -52,11 +52,10 @@ By default, `backtesting-analysis` processes the most recent backtest results in
If you want to analyze results from an earlier backtest, use the `--backtest-filename` option to specify the desired file. This lets you revisit and re-analyze historical backtest outputs at any time by providing the filename of the relevant backtest result:
@@ -142,7 +142,7 @@ class MyAwesomeStrategy(IStrategy):
!!! Note
All overrides are optional and can be mixed/matched as necessary.
### Dynamic parameters
## Dynamic parameters
Parameters can also be defined dynamically, but must be available to the instance once the [`bot_start()` callback](strategy-callbacks.md#bot-start) has been called.
@@ -159,7 +159,7 @@ class MyAwesomeStrategy(IStrategy):
!!! Warning
Parameters created this way will not show up in the `list-strategies` parameter count.
### Overriding Base estimator
## Overriding Base estimator
You can define your own optuna sampler for Hyperopt by implementing `generate_estimator()` in the Hyperopt subclass.
@@ -208,7 +208,6 @@ Some research will be necessary to find additional Samplers (from optunahub) for
Obviously the same approach will work for all other Samplers optuna supports.
## Space options
For the additional spaces, scikit-optimize (in combination with Freqtrade) provides the following space types:
-`Backtesting from` / `Backtesting to`: Backtesting range (usually defined with the `--timerange` option).
@@ -388,14 +420,15 @@ It contains key metrics about the performance of your strategy on backtesting da
-`Absolute profit`: Profit made in stake currency.
-`Total profit %`: Total profit. Aligned to the `TOTAL` row's `Tot Profit %` from the first table. Calculated as `(End capital − Starting capital) / Starting capital`.
-`CAGR %`: Compound annual growth rate.
-`Sortino`: Annualized Sortino ratio.
-`Sharpe`: Annualized Sharpe ratio.
-`Calmar`: Annualized Calmar ratio.
-`Sharpe (closed trades)`: Annualized Sharpe ratio including only closed trades (ignoring open trades with profits or losses).
-`Sortino (closed trades)`: Annualized Sortino ratio including only closed trades (ignoring open trades with profits or losses).
-`Calmar (closed trades)`: Annualized Calmar ratio including only closed trades (ignoring open trades with profits or losses).
-`SQN`: System Quality Number (SQN) - by Van Tharp.
-`Profit factor`: Sum of the profits of all winning trades divided by the sum of the losses of all losing trades.
-`Expectancy (Ratio)`: Expectancy ratio, which is the average profit or loss per trade. A negative expectancy ratio means that your strategy is not profitable.
-`Avg. daily profit`: Average profit per day, calculated as `(Total Profit / Backtest Days)`.
-`Avg. stake amount`: Average stake amount, either `stake_amount` or the average when using dynamic stake amount.
-`Market change`: Change of the market during the backtest period. Calculated as the average of all pairs' changes from the first to the last candle using the "close" column.
-`Total trade volume`: Volume generated on the exchange to reach the above profit.
-`Long / Short trades`: Split long/short trade counts (only shown when short trades were made).
-`Long / Short profit %`: Profit percentage for long and short trades (only shown when short trades were made).
@@ -409,13 +442,21 @@ It contains key metrics about the performance of your strategy on backtesting da
-`Max Consecutive Wins / Loss`: Maximum consecutive wins/losses in a row.
-`Rejected Entry signals`: Trade entry signals that could not be acted upon due to `max_open_trades` being reached.
-`Entry/Exit Timeouts`: Entry/exit orders which did not fill (only applicable if custom pricing is used).
-`Min balance` / `Max balance`: Lowest and Highest Wallet balance during the backtest period.
-`Min/Max balance (closed trades)`: Lowest and Highest Wallet balance during the backtest period based on closed trades trades.
-`Max % of account underwater`: Maximum percentage your account has decreased from the top since the simulation started. Calculated as the maximum of `(Max Balance - Current Balance) / (Max Balance)`.
-`Absolute drawdown`: Maximum absolute drawdown experienced, including percentage relative to the account calculated as `(Absolute Drawdown) / (DrawdownHigh + startingBalance)`..
-`Absolute drawdown (wallet balance)`: Maximum absolute drawdown experienced based on the unrealized balance, including percentage relative to the account calculated as `(Absolute Drawdown) / (DrawdownHigh + startingBalance)`.
-`Drawdown duration`: Duration of the largest drawdown period.
-`Profit at drawdown start` / `Profit at drawdown end`: Profit at the beginning and end of the largest drawdown period.
-`Drawdown start` / `Drawdown end`: Start and end datetime for the largest drawdown (can also be visualized via the `plot-dataframe` sub-command).
-`Market change`: Change of the market during the backtest period. Calculated as the average of all pairs' changes from the first to the last candle using the "close" column.
-`Min/Max balance (wallet balance)`: Lowest and Highest Wallet balance during the backtest period - including capital tied in open trades.
-`Min/Max balance dates (wallet balance)`: Dates when the minimum and maximum unrealized balance occurred.
-`Sharpe (wallet balance)` Annualized Sharpe ratio calculation including unrealized profits.
-`Sortino (wallet balance)` Annualized Sortino ratio calculation including unrealized profits.
-`Calmar (wallet balance)` Annualized Calmar ratio calculation including unrealized profits.
!!! Tip "Wallet based Metrics"
The metrics under the "Wallet based Metrics" section are calculated based on the unrealized balance, which includes the capital tied in open trades. This provides a more comprehensive view of the strategy's performance, as it accounts for both realized and unrealized profits and losses.
@@ -191,7 +191,7 @@ Mandatory parameters are marked as **Required**, which means that they are requi
| | **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. [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`).
@@ -229,7 +229,7 @@ Mandatory parameters are marked as **Required**, which means that they are requi
| `exchange.enable_ws` | Enable the usage of Websockets for the exchange. <br>[More information](#consuming-exchange-websockets).<br>*Defaults to `true`.* <br> **Datatype:** Boolean
| `exchange.markets_refresh_interval` | The interval in minutes in which markets are reloaded. <br>*Defaults to `60` minutes.* <br> **Datatype:** Positive Integer
| `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.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.only_from_ccxt` | Prevent data-download from data.binance.vision. Leaving this as false can greatly speed up downloads, but may be problematic if the site is not available.<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
@@ -240,7 +240,7 @@ 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**
@@ -280,8 +280,8 @@ Mandatory parameters are marked as **Required**, which means that they are requi
| `add_config_files` | Additional config files. These files will be loaded and merged with the current config file. The files are resolved relative to the initial file.<br> *Defaults to `[]`*. <br> **Datatype:** List of strings
| `dataformat_ohlcv` | Data format to use to store historical candle (OHLCV) data. <br> *Defaults to `feather`*. <br> **Datatype:** String
| `dataformat_trades` | Data format to use to store historical trades data. <br> *Defaults to `feather`*. <br> **Datatype:** String
| `reduce_df_footprint` | Recast all numeric columns to float32/int32, with the objective of reducing ram/disk usage (and decreasing train/inference timing backtesting/hyperopt and in FreqAI). <br> **Datatype:** Boolean. <br> Default: `False`.
| `log_config` | Dictionary containing the log config for python logging. [more info](advanced-setup.md#advanced-logging) <br> **Datatype:** dict. <br> Default: `FtRichHandler`
| `reduce_df_footprint` | Recast all numeric columns to float32/int32, with the objective of reducing ram/disk usage (and decreasing train/inference timing backtesting/hyperopt and in FreqAI). <br> Default: `False`. <br> **Datatype:** Boolean.
| `log_config` | Dictionary containing the log config for python logging. [more info](advanced-setup.md#advanced-logging) <br> Default: `FtRichHandler` <br> **Datatype:** dict.
* Given starting points are ignored if data is already available, downloading only missing data up to today.
* Use `--timeframes` to specify what timeframe download the historical candle (OHLCV) data for. Default is `--timeframes 1m 5m` which will download 1-minute and 5-minute data.
* To use exchange, timeframe and list of pairs as defined in your configuration file, use the `-c/--config` option. With this, the script uses the whitelist defined in the config as the list of currency pairs to download data for and does not require the pairs.json file. You can combine `-c/--config` with most other options.
* When downloading futures data (`--trading-mode futures` or a configuration specifying futures mode), freqtrade will automatically download the necessary candle types (e.g. `mark` and `funding_rate` candles) unless specified otherwise via `--candle-types`.
??? Note "Permission denied errors"
If your configuration directory `user_data` was made by docker, you may get the following error:
@@ -268,6 +269,8 @@ If `--convert` is also provided, the resample step will happen automatically and
!!! Note "Kraken user"
Kraken users should read [this](exchanges.md#historic-kraken-data) before starting to download data.
Kraken Futures uses standard OHLCV downloads and does not require `--dl-trades`.
@@ -98,3 +98,50 @@ Please use configuration based [log setup](advanced-setup.md#advanced-logging) i
The edge module has been deprecated in 2023.9 and removed in 2025.6.
All functionalities of edge have been removed, and having edge configured will result in an error.
## Adjustment to dynamic funding rate handling
With version 2025.12, the handling of dynamic funding rates has been adjusted to also support dynamic funding rates down to 1h funding intervals.
As a consequence, the mark and funding rate timeframes have been changed to 1h for every supported futures exchange.
As the timeframe for both mark and funding_fee candles has changed (usually from 8h to 1h) - already downloaded data will have to be adjusted or partially re-downloaded.
You can either re-download everything (`freqtrade download-data [...] --erase` - :warning: can take a long time) - or download the updated data selectively.
### Strategy
Most strategies should not need adjustments to continue to work as expected - however, strategies using `@informative("8h", candle_type="funding_rate")` or similar will have to switch the timeframe to 1h.
The same is true for `dp.get_pair_dataframe(metadata["pair"], "8h", candle_type="funding_rate")` - which will need to be switched to 1h.
freqtrade will auto-adjust the timeframe and return `funding_rates` despite the wrongly given timeframe. It'll issue a warning - and may still break your strategy.
### Selective data re-download
The script below should serve as an example - you may need to adjust the timeframe and exchange to your needs!
# download new data (only required once to fix the mark and funding fee data)
freqtrade download-data -t 1h --trading-mode futures --candle-types funding_rate mark [...] --timerange <full timerange you've got other data for>
```
The result of the above will be that your funding_rates and mark data will have the 1h timeframe.
you can verify this with `freqtrade list-data --exchange <yourexchange> --show`.
!!! Note "Additional arguments"
Additional arguments to the above commands may be necessary, like configuration files or explicit user_data if they deviate from the default.
**Hyperliquid** is a special case now - which will no longer require 1h mark data - but will use regular candles instead (this data never existed and is identical to 1h futures candles). As we don't support download-data for hyperliquid (they don't provide historic data) - there won't be actions necessary for hyperliquid users.
## Catboost models in freqAI
CatBoost models have been removed with version 2025.12 and are no longer actively supported.
If you have existing bots using CatBoost models, you can still use them in your custom models by copy/pasting them from the git history (as linked below) and installing the Catboost library manually.
We do however recommend switching to other supported model libraries like LightGBM or XGBoost for better support and future compatibility.
Determine if crucial bugfixes have been made between this commit and the current state, and eventually cherry-pick these.
* Merge the release branch (stable) into this branch.
* Edit `freqtrade/__init__.py` and add the version matching the current date (for example `2019.7` for July 2019). Minor versions can be `2019.7.1` should we need to do a second release that month. Version numbers must follow allowed versions from PEP0440 to avoid failures pushing to pypi.
* Edit `freqtrade/__init__.py` and add the version matching the current date (for example `2025.7` for July 2025). Minor versions can be `2025.7.1` should we need to do a second release that month. Version numbers must follow allowed versions from PEP0440 to avoid failures pushing to pypi.
* Commit this part.
* Push that branch to the remote and create a PR against the **stable branch**.
* Update develop version to next version following the pattern `2019.8-dev`.
* Update develop version to next version following the pattern `2025.8-dev`.
Please pay attention that rateLimit configuration entry holds delay in milliseconds between requests, NOT requests/sec rate.
So, in order to mitigate Kraken API "Rate limit exceeded" exception, this configuration should be increased, NOT decreased.
## Kraken Futures
Kraken Futures uses the exchange id `krakenfutures` and supports isolated futures mode.
```jsonc
"exchange": {
"name": "krakenfutures",
"key": "your_exchange_key",
"secret": "your_exchange_secret"
},
"trading_mode": "futures",
"margin_mode": "isolated",
"stake_currency": "USD"
```
!!! Tip "Stoploss on Exchange"
Kraken Futures supports `stoploss_on_exchange` with both `limit` and `market` stop orders.
Use `order_types.stoploss_price_type` to select the trigger price source (`mark`, `last`, or `index`).
!!! Note "Collateral"
Kraken Futures is USD-settled. Use USD as your stake currency.
!!! Note "Flex (Multi-collateral) Accounts"
Kraken Futures flex accounts allow collateral in multiple currencies, while trading remains USD-settled.
Freqtrade derives the `USD` balance from Kraken margin fields, so keep `stake_currency` set to `USD`.
## Kucoin
Kucoin requires a passphrase for each api key, you will therefore need to add this key into the configuration so your exchange section looks as follows:
For Kucoin, it is suggested to add `"KCS/<STAKE>"` to your blacklist to avoid issues, unless you are willing to maintain enough extra `KCS` on the account or unless you're willing to disable using `KCS` for fees.
For Kucoin, it is suggested to add `"KCS/<STAKE>"` to your blacklist to avoid issues, unless you are willing to maintain enough extra `KCS` on the account or unless you're willing to disable using `KCS` for fees.
Kucoin accounts may use `KCS` for fees, and if a trade happens to be on `KCS`, further trades may consume this position and make the initial `KCS` trade unsellable as the expected amount is not there anymore.
## HTX
@@ -319,6 +345,14 @@ API Keys for live futures trading must have the following permissions:
We do strongly recommend to limit all API keys to the IP you're going to use it from.
### Bybit Demo Mode
Bybit has a [demo mode](https://learn.bybit.com/en/bybit-guide/how-to-use-bybit-demo-trading) - which can be activated by setting `exchange.demo_trading` to `true` in the configuration.
Bybit uses live markets to simulate your trades (without market impact) - making it work very similar to freqtrade's dry-run mode.
You'll need to use separate API keys for demo trading, which you can create on bybit's demo page.
Demo mode is incompatible with dry-run.
## Bitmart
@@ -369,6 +403,11 @@ On startup, freqtrade will set the position mode to "One-way Mode" for the whole
!!! Tip "Stoploss on Exchange"
Hyperliquid supports `stoploss_on_exchange` and uses `stop-loss-limit` orders. It provides great advantages, so we recommend to benefit from it.
!!! Warning "Unified accounts"
Hyperliquid unified accounts are supported - though this relies freqtrade's assumption of "owning" the account, and being the only one trading on it (in this case, extended to both spot and futures).
We hence recommend the usage of subaccounts where possible, and to avoid manual trading on the same account while the bot is running.
Freqtrade will attempt to detect the account type on startup - changing the account type mid-trading is not supported and may lead to exceptions and errors.
Hyperliquid is a Decentralized Exchange (DEX). Decentralized exchanges work a bit different compared to normal exchanges. Instead of authenticating private API calls using an API key, private API calls need to be signed with the private key of your wallet (We recommend using an api Wallet for this, generated either on Hyperliquid or in your wallet of choice).
This needs to be configured like this:
@@ -399,35 +438,87 @@ Hyperliquid handles deposits and withdrawals on the Arbitrum One chain, a Layer
* Create a different software wallet, only transfer the funds you want to trade with to that wallet, and use that wallet to trade on Hyperliquid.
* If you have funds you don't want to use for trading (after making a profit for example), transfer them back to your hardware wallet.
### Hyperliquid Vault / Subaccount
Hyperliquid allows you to create either a vault or a subaccount.
To use these with Freqtrade, you will need to use the following configuration pattern:
!!! Warning "Vaults and Subaccounts"
You can only use either a vault or a subaccount - not both at the same time.
### Hyperliquid Subaccount
Hyperliquid allows you to create subaccounts with sufficient previous trading volume.
To use subaccounts with Freqtrade, you will need to use the following configuration pattern:
``` json
"exchange": {
"name": "hyperliquid",
"walletAddress": "your_master_wallet_address", // Your master wallet address (not the API wallet address and not the vault/subaccount address).
"walletAddress": "your_master_wallet_address", // Your master wallet address (not the API wallet or vault address - but not subaccount address).
"privateKey": "your_api_private_key", // API wallet private key (see https://app.hyperliquid.xyz/API). You'll only need the private key.
"ccxt_config": {
"options": {
"vaultAddress": "your_vault_address", // Optional, only if you want to use a vault ...
"subAccountAddress": "your_subaccount_address" // OR optional, only if you want to use a subaccount
"subAccountAddress": "your_subaccount_address" // Required if you want to use a subaccount.
}
},
// ...
}
```
Your balance and trades will now be used from your vault / subaccount - and no longer from your main account.
Your balance and trades will now be used from your subaccount - and no longer from your main account.
!!! Note
You can only use either a vault or a subaccount - not both at the same time.
### Hyperliquid Vault
Hyperliquid allows you to create vaults. To use vaults with Freqtrade, you will need to use the following configuration pattern:
``` json
"exchange": {
"name": "hyperliquid",
"walletAddress": "your_vault_address", // Your vault wallet address (Must also be added below in the ccxt_config.options.vaultAddress field)
"privateKey": "your_api_private_key", // API wallet private key (see https://app.hyperliquid.xyz/API). You'll only need the private key.
"ccxt_config": {
"options": {
"vaultAddress": "your_vault_address", // Optional, only if you want to use a vault ... (vault address must also be added to walletAdress)
}
},
// ...
}
```
Your balance and trades will now be used from your vault - and no longer from your main account.
### Historic Hyperliquid data
The Hyperliquid API does not provide historic data beyond the single call to fetch current data, so downloading data is not possible, as the downloaded data would not constitute proper historic data.
### HIP-3 DEXes
Hyperliquid supports HIP-3 decentralized exchanges (DEXes), which are independent exchanges built on top of the Hyperliquid infrastructure.
These DEXes operate similarly to the main Hyperliquid exchange but are community-created and managed.
To trade on HIP-3 DEXes with Freqtrade, you need to add them to your configuration using the `hip3_dexes` parameter:
```json
"exchange": {
"name": "hyperliquid",
"walletAddress": "your_master_wallet_address",
"privateKey": "your_api_private_key",
"hip3_dexes": ["dex_name_1", "dex_name_2"]
}
```
Replace `"dex_name_1"` and `"dex_name_2"` with the actual names of the HIP-3 DEXes you want to trade on (e.g. `vntl` and `xyz`).
!!! Warning "Performance and Rate Limit Impact"
Each HIP-3 DEX you add significantly impacts bot performance and rate limits.
* **Additional API Calls**: For each HIP-3 DEX configured, Freqtrade needs to make additional API calls.
* **Rate Limit Pressure**: Additional API calls contribute to Hyperliquid's strict rate limits. With multiple DEXes, you may hit rate limits faster, or rather, slow down bot operations due to enforced delays.
Please only add HIP-3 DEXes that you actively trade on. Monitor your logs for rate limit warnings or signs of slowed operations, and adjust your configuration accordingly.
Different HIP-3 DEXes may also use different quote currencies - so make sure to only add DEXes that are compatible with your stake currency to avoid unnecessary delays.
!!! Note
HIP-3 DEXes share the same wallet and free amount of collateral as your main Hyperliquid account. Trades on different DEXes will affect your overall account balance and margin.
The pair name for HIP-3 pairs will be slightly different than non HIP-3 pairs. Please use `list-pairs` subcommand to get the correct pair naming for all pairs for the specified dexes.
## Bitvavo
If your account is required to use an operatorId, you can set it in the configuration file as follows:
@@ -491,5 +582,5 @@ For example, to test the order type `FOK` with Kraken, and modify candle limit t
!!! Warning
Please make sure to fully understand the impacts of these settings before modifying them.
Using `_ft_has_params` overrides may lead to unexpected behavior, and may even break your bot.
Using `_ft_has_params` overrides may lead to unexpected behavior, and may even break your bot.
We will not be able to provide support for issues caused by custom settings in `_ft_has_params`.
Freqtrade supports spot trading, as well as (isolated) futures trading for some selected exchanges. Please refer to the [documentation start page](index.md#supported-futures-exchanges-experimental) for an up-to-date list of supported exchanges.
Freqtrade supports spot trading, as well as futures trading for some selected exchanges. Please refer to the [documentation start page](index.md#supported-futures-exchanges) for an up-to-date list of supported exchanges.
### Can my bot open short positions?
@@ -14,7 +14,7 @@ In spot markets, you can in some cases use leveraged spot tokens, which reflect
### Can my bot trade options or futures?
Futures trading is supported for selected exchanges. Please refer to the [documentation start page](index.md#supported-futures-exchanges-experimental) for an up-to-date list of supported exchanges.
Futures trading is supported for selected exchanges. Please refer to the [documentation start page](index.md#supported-futures-exchanges) for an up-to-date list of supported exchanges.
## Beginner Tips & Tricks
@@ -29,6 +29,13 @@ You can however use the [`adjust_trade_position()` callback](strategy-callbacks.
Backtesting provides an option for this in `--eps` - however this is only there to highlight "hidden" signals, and will not work in live.
### Does freqtrade support sandbox accounts?
No, but you can use dry-run mode to simulate trading without risking real funds.
Sandbox markets are separate, simulated markets - which are not suitable to test your strategy in a realistic environment.
These markets usually have different order books, liquidity and trading behaviour (usually with very few participants) - which makes them unsuitable for realistic tests of your strategy.
### The bot does not start
Running the bot with `freqtrade trade --config config.json` shows the output `freqtrade: command not found`.
The dashboard view provides an overview of the bot's performance and status.
If multiple bots are connected, the dashboard will show an overview of all connected bots, allowing you to easily switch between them or show just a subset of available bots.
#### Wallet Balance
New in freqtrade 2026.4: This shows the balance of the bot over time.
Compared to the "cumulative Profit" chart, this chart will show the actual balance of the bot over time, including unrealized profit and losses, as well as deposits and withdrawals.
Historic data has re-populated based on available exchange data - however is assumed to be best-effort and may not be 100% accurate.
More specifically, it won't cover deposits and withdrawals, and will assume a starting balance of current balance - profit/losses.
For clarity - a "Capture start" marker line is shown on the chart, which indicates the point at which the migration to the new wallet balance tracking system happened.
Only beyond this point, the wallet balance is expected to be accurate.
### Plot Configurator
FreqUI Plots can be configured either via a `plot_config` configuration object in the strategy (which can be loaded via "from strategy" button) or via the UI.
@@ -166,7 +166,7 @@ Below are the values you can expect to include/use inside a typical strategy dat
| `df['do_predict']` | Indication of an outlier data point. The return value is integer between -2 and 2, which lets you know if the prediction is trustworthy or not. `do_predict==1` means that the prediction is trustworthy. If the Dissimilarity Index (DI, see details [here](freqai-feature-engineering.md#identifying-outliers-with-the-dissimilarity-index-di)) of the input data point is above the threshold defined in the config, FreqAI will subtract 1 from `do_predict`, resulting in `do_predict==0`. If `use_SVM_to_remove_outliers` is active, the Support Vector Machine (SVM, see details [here](freqai-feature-engineering.md#identifying-outliers-using-a-support-vector-machine-svm)) may also detect outliers in training and prediction data. In this case, the SVM will also subtract 1 from `do_predict`. If the input data point was considered an outlier by the SVM but not by the DI, or vice versa, the result will be `do_predict==0`. If both the DI and the SVM considers the input data point to be an outlier, the result will be `do_predict==-1`. As with the SVM, if `use_DBSCAN_to_remove_outliers` is active, DBSCAN (see details [here](freqai-feature-engineering.md#identifying-outliers-with-dbscan)) may also detect outliers and subtract 1 from `do_predict`. Hence, if both the SVM and DBSCAN are active and identify a datapoint that was above the DI threshold as an outlier, the result will be `do_predict==-2`. A particular case is when `do_predict == 2`, which means that the model has expired due to exceeding `expired_hours`. <br> **Datatype:** Integer between -2 and 2.
| `df['DI_values']` | Dissimilarity Index (DI) values are proxies for the level of confidence FreqAI has in the prediction. A lower DI means the prediction is close to the training data, i.e., higher prediction confidence. See details about the DI [here](freqai-feature-engineering.md#identifying-outliers-with-the-dissimilarity-index-di). <br> **Datatype:** Float.
| `df['%*']` | Any dataframe column prepended with `%` in `feature_engineering_*()` is treated as a training feature. For example, you can include the RSI in the training feature set (similar to in `templates/FreqaiExampleStrategy.py`) by setting `df['%-rsi']`. See more details on how this is done [here](freqai-feature-engineering.md). <br> **Note:** Since the number of features prepended with `%` can multiply very quickly (10s of thousands of features are easily engineered using the multiplictative functionality of, e.g., `include_shifted_candles` and `include_timeframes` as described in the [parameter table](freqai-parameter-table.md)), these features are removed from the dataframe that is returned from FreqAI to the strategy. To keep a particular type of feature for plotting purposes, you would prepend it with `%%` (see details below). <br> **Datatype:** Depends on the feature created by the user.
| `df['%%*']` | Any dataframe column prepended with `%%` in `feature_engineering_*()` is treated as a training feature, just the same as the above `%` prepend. However, in this case, the features are returned back to the strategy for FreqUI/plot-dataframe plotting and monitoring in Dry/Live/Backtesting <br> **Datatype:** Depends on the feature created by the user. Please note that features created in `feature_engineering_expand()` will have automatic FreqAI naming schemas depending on the expansions that you configured (i.e. `include_timeframes`, `include_corr_pairlist`, `indicators_periods_candles`, `include_shifted_candles`). So if you want to plot `%%-rsi` from `feature_engineering_expand_all()`, the final naming scheme for your plotting config would be: `%%-rsi-period_10_ETH/USDT:USDT_1h` for the `rsi` feature with `period=10`, `timeframe=1h`, and `pair=ETH/USDT:USDT` (the `:USDT` is added if you are using futures pairs). It is useful to simply add `print(dataframe.columns)` in your `populate_indicators()` after `self.freqai.start()` to see the full list of available features that are returned to the strategy for plotting purposes.
| `df['%%*']` | Any dataframe column prepended with `%%` in `feature_engineering_*()` is treated as a training feature, just the same as the above `%` prepend. However, in this case, the features are returned back to the strategy for FreqUI/plot-dataframe plotting and monitoring in Dry/Live/Backtesting <br> **Datatype:** Depends on the feature created by the user. <br>*Please note* that features created in `feature_engineering_expand()` will have automatic FreqAI naming schemas depending on the expansions that you configured (i.e. `include_timeframes`, `include_corr_pairlist`, `indicators_periods_candles`, `include_shifted_candles`). So if you want to plot `%%-rsi` from `feature_engineering_expand_all()`, the final naming scheme for your plotting config would be: `%%-rsi-period_10_ETH/USDT:USDT_1h` for the `rsi` feature with `period=10`, `timeframe=1h`, and `pair=ETH/USDT:USDT` (the `:USDT` is added if you are using futures pairs). It is useful to simply add `print(dataframe.columns)` in your `populate_indicators()` after `self.freqai.start()` to see the full list of available features that are returned to the strategy for plotting purposes.
## Setting the `startup_candle_count`
@@ -200,15 +200,15 @@ If this value is set, FreqAI will initially use the predictions from the trainin
## Using different prediction models
FreqAI has multiple example prediction model libraries that are ready to be used as is via the flag `--freqaimodel`. These libraries include `CatBoost`, `LightGBM`, and `XGBoost` regression, classification, and multi-target models, and can be found in `freqai/prediction_models/`.
FreqAI has multiple example prediction model libraries that are ready to be used as is via the flag `--freqaimodel`. These libraries include `LightGBM`, and `XGBoost` regression, classification, and multi-target models, and can be found in `freqai/prediction_models/`.
Regression and classification models differ in what targets they predict - a regression model will predict a target of continuous values, for example what price BTC will be at tomorrow, whilst a classifier will predict a target of discrete values, for example if the price of BTC will go up tomorrow or not. This means that you have to specify your targets differently depending on which model type you are using (see details [below](#setting-model-targets)).
All of the aforementioned model libraries implement gradient boosted decision tree algorithms. They all work on the principle of ensemble learning, where predictions from multiple simple learners are combined to get a final prediction that is more stable and generalized. The simple learners in this case are decision trees. Gradient boosting refers to the method of learning, where each simple learner is built in sequence - the subsequent learner is used to improve on the error from the previous learner. If you want to learn more about the different model libraries you can find the information in their respective docs:
* CatBoost: <https://catboost.ai/en/docs/> (No longer actively supported since 2025.12)
There are also numerous online articles describing and comparing the algorithms. Some relatively lightweight examples would be [CatBoost vs. LightGBM vs. XGBoost — Which is the best algorithm?](https://towardsdatascience.com/catboost-vs-lightgbm-vs-xgboost-c80f40662924#:~:text=In%20CatBoost%2C%20symmetric%20trees%2C%20or,the%20same%20depth%20can%20differ.) and [XGBoost, LightGBM or CatBoost — which boosting algorithm should I use?](https://medium.com/riskified-technology/xgboost-lightgbm-or-catboost-which-boosting-algorithm-should-i-use-e7fda7bb36bc). Keep in mind that the performance of each model is highly dependent on the application and so any reported metrics might not be true for your particular use of the model.
@@ -219,7 +219,7 @@ Make sure to use unique names to avoid overriding built-in models.
#### Regressors
If you are using a regressor, you need to specify a target that has continuous values. FreqAI includes a variety of regressors, such as the `CatboostRegressor`via the flag `--freqaimodel CatboostRegressor`. An example of how you could set a regression target for predicting the price 100 candles into the future would be
If you are using a regressor, you need to specify a target that has continuous values. FreqAI includes a variety of regressors, such as the `LightGBMRegressor`via the flag `--freqaimodel LightGBMRegressor`. An example of how you could set a regression target for predicting the price 100 candles into the future would be
```python
df['&s-close_price'] = df['close'].shift(-100)
@@ -229,7 +229,7 @@ If you want to predict multiple targets, you need to define multiple labels usin
#### Classifiers
If you are using a classifier, you need to specify a target that has discrete values. FreqAI includes a variety of classifiers, such as the `CatboostClassifier` via the flag `--freqaimodel CatboostClassifier`. If you elects to use a classifier, the classes need to be set using strings. For example, if you want to predict if the price 100 candles into the future goes up or down you would set
If you are using a classifier, you need to specify a target that has discrete values. FreqAI includes a variety of classifiers, such as the `LightGBMClassifier` via the flag `--freqaimodel LightGBMClassifier`. If you elects to use a classifier, the classes need to be set using strings. For example, if you want to predict if the price 100 candles into the future goes up or down you would set
@@ -260,6 +260,10 @@ freqtrade trade --config config_examples/config_freqai.example.json --strategy F
PyTorch dropped support for macOS x64 (intel based Apple devices) in version 2.3. Subsequently, freqtrade also dropped support for PyTorch on this platform.
!!! Danger "Security notice"
Loading saved models from disk can cause security issues if using remote model files (files you downloaded from the internet or received from an untrusted source) due to having the necessity to have `weights_only=False`, which can cause security problems.
As long as you only load models that you have trained yourself, there is no risk.
@@ -106,7 +106,7 @@ Mandatory parameters are marked as **Required** and have to be set in one of the
| `n_epochs` | The `n_epochs` parameter is a crucial setting in the PyTorch training loop that determines the number of times the entire training dataset will be used to update the model's parameters. An epoch represents one full pass through the entire training dataset. Overrides `n_steps`. Either `n_epochs` or `n_steps` must be set. <br><br> **Datatype:** int. optional. <br> Default: `10`.
| `n_steps` | An alternative way of setting `n_epochs` - the number of training iterations to run. Iteration here refer to the number of times we call `optimizer.step()`. Ignored if `n_epochs` is set. A simplified version of the function: <br><br> n_epochs = n_steps / (n_obs / batch_size) <br><br> The motivation here is that `n_steps` is easier to optimize and keep stable across different n_obs - the number of data points. <br> <br> **Datatype:** int. optional. <br> Default: `None`.
| `batch_size` | The size of the batches to use during training. <br><br> **Datatype:** int. <br> Default: `64`.
| `early_stopping_patience` | Number of epochs with no improvement in validation loss before training is stopped early. This helps prevent overfitting by halting training when the model stops improving. Set to `0` to disable early stopping. Requires a test/validation split (`test_size > 0`). <br><br> **Datatype:** int. <br> Default: `0` (disabled).
### Additional parameters
@@ -116,3 +116,4 @@ Mandatory parameters are marked as **Required** and have to be set in one of the
| `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.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`.
| `freqai.override_exchange_check` | Override the exchange check to force FreqAI to use exchanges that may not have enough historic data. Turn this to True if you know your FreqAI model and strategy do not require historical data. <br> **Datatype:** Boolean. <br> Default: `False`.
@@ -87,6 +87,10 @@ To save the models generated during a particular backtest so that you can start
To ensure that the model can be reused, freqAI will call your strategy with a dataframe of length 1.
If your strategy requires more data than this to generate the same features, you can't reuse backtest predictions for live deployment and need to update your `identifier` for each new backtest.
!!! Danger "Security notice"
Loading saved models from disk can cause security issues if using remote model files (files you downloaded from the internet or received from an untrusted source) due to having the necessity to have `weights_only=False`, which can cause security problems.
As long as you only load models that you have trained yourself, there is no risk.
### Backtest live collected predictions
FreqAI allow you to reuse live historic predictions through the backtest parameter `--freqai-backtest-live-models`. This can be useful when you want to reuse predictions generated in dry/run for comparison or other study.
FreqAI is a software designed to automate a variety of tasks associated with training a predictive machine learning model to generate market forecasts given a set of input signals. In general, FreqAI aims to be a sandbox for easily deploying robust machine learning libraries on real-time data ([details](#freqai-position-in-open-source-machine-learning-landscape)).
!!! Note
FreqAI is, and always will be, a not-for-profit, open source project. FreqAI does *not* have a crypto token, FreqAI does *not* sell signals, and FreqAI does not have a domain besides the present [freqtrade documentation](https://www.freqtrade.io/en/latest/freqai/).
FreqAI is, and always will be, a not-for-profit, open source project. FreqAI does *not* have a crypto token, FreqAI does *not* sell signals, and FreqAI does not have a domain besides the present [freqtrade documentation](https://www.freqtrade.io/en/stable/freqai/).
Pairlist Handlers define the list of pairs (pairlist) that the bot should trade. They are configured in the `pairlists` section of the configuration settings.
In your configuration, you can use Static Pairlist (defined by the [`StaticPairList`](#static-pair-list) Pairlist Handler) and Dynamic Pairlist (defined by the [`VolumePairList`](#volume-pair-list) and [`PercentChangePairList`](#percent-change-pair-list) Pairlist Handlers).
In your configuration, you can use Static Pairlist (defined by the [`StaticPairList`](#static-pair-list) Pairlist Handler) and Dynamic Pairlist (defined by the [`VolumePairList`](#volume-pair-list), [`CrossMarketPairList`](#crossmarketpairlist), [`MarketCapPairlist`](#marketcappairlist) and [`PercentChangePairList`](#percent-change-pair-list) Pairlist Handlers).
Additionally, [`AgeFilter`](#agefilter), [`DelistFilter`](#delistfilter), [`PrecisionFilter`](#precisionfilter), [`PriceFilter`](#pricefilter), [`ShuffleFilter`](#shufflefilter), [`SpreadFilter`](#spreadfilter) and [`VolatilityFilter`](#volatilityfilter) act as Pairlist Filters, removing certain pairs and/or moving their positions in the pairlist.
If multiple Pairlist Handlers are used, they are chained and a combination of all Pairlist Handlers forms the resulting pairlist the bot uses for trading and backtesting. Pairlist Handlers are executed in the sequence they are configured. You can define either `StaticPairList`, `VolumePairList`, `ProducerPairList`, `RemotePairList`, `MarketCapPairList` or`PercentChangePairList` as the starting Pairlist Handler.
If multiple Pairlist Handlers are used, they are chained and a combination of all Pairlist Handlers forms the resulting pairlist the bot uses for trading and backtesting. Pairlist Handlers are executed in the sequence they are configured. You can define either `StaticPairList`, `VolumePairList`, `ProducerPairList`, `RemotePairList`, `MarketCapPairList`,`PercentChangePairList` or `CrossMarketPairList` as the starting Pairlist Handler.
Inactive markets are always removed from the resulting pairlist. Explicitly blacklisted pairs (those in the `pair_blacklist` configuration setting) are also always removed from the resulting pairlist.
@@ -26,6 +26,7 @@ You may also use something like `.*DOWN/BTC` or `.*UP/BTC` to exclude leveraged
* [`ProducerPairList`](#producerpairlist)
* [`RemotePairList`](#remotepairlist)
* [`MarketCapPairList`](#marketcappairlist)
* [`CrossMarketPairList`](#crossmarketpairlist)
* [`AgeFilter`](#agefilter)
* [`DelistFilter`](#delistfilter)
* [`FullTradesFilter`](#fulltradesfilter)
@@ -303,6 +304,8 @@ The optional `mode` option specifies if the pairlist should be used as a `blackl
The optional `processing_mode` option in the RemotePairList configuration determines how the retrieved pairlist is processed. It can have two values: "filter" or "append". The default value is "filter".
The optional `number_assets` option in the RemotePairList configuration determines how many pairs will be returned if used in whitelist `mode`. By default, all pairs will be returned. In blacklist `mode`, this option will be ignored.
In "filter" mode, the retrieved pairlist is used as a filter. Only the pairs present in both the original pairlist and the retrieved pairlist are included in the final pairlist. Other pairs are filtered out.
In "append" mode, the retrieved pairlist is added to the original pairlist. All pairs from both lists are included in the final pairlist without any filtering.
@@ -402,6 +405,12 @@ Coins like 1000PEPE/USDT or KPEPE/USDT:USDT are detected on a best effort basis,
!!! Danger "Duplicate symbols in coingecko"
Coingecko often has duplicate symbols, where the same symbol is used for different coins. Freqtrade will use the symbol as is and try to search for it on the exchange. If the symbol exists - it will be used. Freqtrade will however not check if the _intended_ symbol is the one coingecko meant. This can sometimes lead to unexpected results, especially on low volume coins or with meme coin categories.
#### CrossMarketPairList
Generate or filter pairs based of their availability on the opposite market.
The `pairs_exist_on` setting defines whether the pairs should exists on both spot and futures market (`both_markets`) or only exist on the specified trading mode (`current_market_only`). By default, the plugin will be in `both_markets` setting, which means whitelisted pairs have to exists on both spot and futures markets.
#### AgeFilter
Removes pairs that have been listed on the exchange for less than `min_days_listed` days (defaults to `10`) or more than `max_days_listed` days (defaults `None` mean infinity).
@@ -20,15 +20,15 @@ All protection end times are rounded up to the next candle to avoid sudden, unex
### Common settings to all Protections
| Parameter| Description |
|------------|-------------|
| `method` | Protection name to use. <br> **Datatype:** String, selected from [available Protections](#available-protections)
| `stop_duration_candles` | For how many candles should the lock be set? <br> **Datatype:** Positive integer (in candles)
| `stop_duration` | how many minutes should protections be locked. <br>Cannot be used together with `stop_duration_candles`. <br> **Datatype:** Float (in minutes)
| `lookback_period_candles` | Only trades that completed within the last `lookback_period_candles` candles will be considered. This setting may be ignored by some Protections. <br> **Datatype:** Positive integer (in candles).
| `lookback_period` | Only trades that completed after `current_time - lookback_period` will be considered. <br>Cannot be used together with `lookback_period_candles`. <br>This setting may be ignored by some Protections. <br> **Datatype:** Float (in minutes)
| `trade_limit` | Number of trades required at minimum (not used by all Protections). <br> **Datatype:** Positive integer
| `unlock_at` | Time when trading will be unlocked regularly (not used by all Protections). <br> **Datatype:** string <br>**Input Format:** "HH:MM" (24-hours)
| Parameter| Description |
|--------- | ----------|
| `method` | Protection name to use. <br> **Datatype:** String, selected from [available Protections](#available-protections) |
| `stop_duration_candles` | For how many candles should the lock be set? <br> **Datatype:** Positive integer (in candles) |
| `stop_duration` | how many minutes should protections be locked. <br>Cannot be used together with `stop_duration_candles`. <br> **Datatype:** Float (in minutes) |
| `lookback_period_candles` | Only trades that completed within the last `lookback_period_candles` candles will be considered. This setting may be ignored by some Protections. <br> **Datatype:** Positive integer (in candles). |
| `lookback_period` | Only trades that completed after `current_time - lookback_period` will be considered. <br>Cannot be used together with `lookback_period_candles`. <br>This setting may be ignored by some Protections. <br> **Datatype:** Float (in minutes) |
| `trade_limit` | Number of trades required at minimum (not used by all Protections). <br> **Datatype:** Positive integer |
| `unlock_at` | Time when trading will be unlocked regularly (not used by all Protections). <br> **Datatype:** string <br>**Input Format:** "HH:MM" (24-hours) |
!!! Note "Durations"
Durations (`stop_duration*` and `lookback_period*` can be defined in either minutes or candles).
@@ -69,7 +69,17 @@ def protections(self):
#### MaxDrawdown
`MaxDrawdown` uses all trades within `lookback_period` in minutes (or in candles when using `lookback_period_candles`) to determine the maximum drawdown. If the drawdown is below `max_allowed_drawdown`, trading will stop for `stop_duration` in minutes (or in candles when using `stop_duration_candles`) after the last trade - assuming that the bot needs some time to let markets recover.
The `MaxDrawdown` protection evaluates trades that closed within the current `lookback_period` (or `lookback_period_candles`).
It supports 2 calculation modes:
- `calculation_mode: "ratios"` (default): Legacy approximation based on cumulative profit ratios.
- `calculation_mode: "equity"`: Standard peak-to-trough drawdown on the account equity curve, using starting balance and cumulative absolute profit.
With `calculation_mode: "ratios"`, drawdown is derived from cumulative trade profit ratios, not from the account equity curve. This is kept for backward compatibility and can differ from account-level drawdown when position sizing changes over time.
For new setups, `calculation_mode: "equity"` is recommended. Prefer `calculation_mode: "ratios"` only when you intentionally rely on legacy behavior, especially with fixed stake amount configurations where ratio-based behavior is easier to reason about.
If the observed drawdown exceeds `max_allowed_drawdown`, trading will stop for `stop_duration` after the last trade - assuming that the bot needs some time to let markets recover.
The below sample stops trading for 12 candles if max-drawdown is > 20% considering all pairs - with a minimum of `trade_limit` trades - within the last 48 candles. If desired, `lookback_period` and/or `stop_duration` can be used.
@@ -79,6 +89,7 @@ def protections(self):
return [
{
"method": "MaxDrawdown",
"calculation_mode": "equity",
"lookback_period_candles": 48,
"trade_limit": 20,
"stop_duration_candles": 12,
@@ -160,6 +171,7 @@ class AwesomeStrategy(IStrategy)
@@ -37,6 +39,8 @@ Freqtrade is a free and open source crypto trading bot written in Python. It is
Please read the [exchange specific notes](exchanges.md) to learn about eventual, special configurations needed for each exchange.
### Supported Spot Exchanges
- [X] [Binance](https://www.binance.com/)
- [X] [BingX](https://bingx.com/invite/0EM9RX)
- [X] [Bitget](https://www.bitget.com/)
@@ -50,7 +54,7 @@ Please read the [exchange specific notes](exchanges.md) to learn about eventual,
- [X] [MyOKX](https://okx.com/) (OKX EEA)
- [ ] [potentially many others through <img alt="ccxt" width="30px" src="assets/ccxt-logo.svg" />](https://github.com/ccxt/ccxt/). _(We cannot guarantee they will work)_
### Supported Futures Exchanges (experimental)
### Supported Futures Exchanges
- [X] [Binance](https://www.binance.com/)
- [X] [Bitget](https://www.bitget.com/)
@@ -58,6 +62,7 @@ Please read the [exchange specific notes](exchanges.md) to learn about eventual,
- [X] [Gate.io](https://www.gate.io/ref/6266643)
- [X] [Hyperliquid](https://hyperliquid.xyz/) (A decentralized exchange, or DEX)
Please make sure to read the [exchange specific notes](exchanges.md), as well as the [trading with leverage](leverage.md) documentation before diving in.
@@ -9,15 +9,25 @@ The freqtrade documentation describes various ways to install freqtrade
* [Manual Installation](#manual-installation)
* [Installation with Conda](#installation-with-conda)
Please consider using the prebuilt [docker images](docker_quickstart.md) to get started quickly while evaluating how freqtrade works.
Please consider using the prebuilt [docker images](docker_quickstart.md) to get started quickly.
!!! Note "Updating"
Keeping freqtrade updated is important to [ensure ongoing compatibility](updating.md#why-update) with exchange API's.
Please refer to the [updating guide](updating.md) for details on how to update your installation.
!!! Note "Windows users"
We **strongly** recommend that Windows users use [Docker](docker_quickstart.md) as this will work much easier and smoother (also more secure).
If that is not possible, try using the Windows Linux subsystem (WSL) - for which the Ubuntu/Linux instructions will work.
If you really want to install freqtrade natively on Windows, best use the [`./setup.ps1` installation script](#use-setupps1-windows).
Please also make sure to use the 64bit version of Python, as 32bit versions have severe memory limitations, which can negatively impact your experience with backtesting/hyperopt.
------
## Information
For Windows installation, please use the [windows installation guide](windows_installation.md).
The easiest way to install and run Freqtrade is to clone the bot Github repository and then run the `./setup.sh` script, if it's available for your platform.
The easiest way to install and run Freqtrade is to clone the bot Github repository and then run the `./setup.sh` (`./setup.ps1` for Windows) script, if it's available for your platform.
!!! Note "Version considerations"
When cloning the repository the default working branch has the name `develop`. This branch contains all last features (can be considered as relatively stable, thanks to automated tests).
@@ -152,20 +162,9 @@ If you are on Debian, Ubuntu or MacOS, freqtrade provides the script to install
./setup.sh -i
```
### Activate your virtual environment
#### Other options of /setup.sh script
Each time you open a new terminal, you must run `source .venv/bin/activate` to activate your virtual environment.
```bash
# activate virtual environment
source ./.venv/bin/activate
```
[You are now ready](#you-are-ready) to run the bot.
### Other options of /setup.sh script
You can as well update, configure and reset the codebase of your bot with `./script.sh`
You can also update, configure and reset the codebase of your bot with `./setup.sh`
```bash
# --update, Command git pull to update.
@@ -194,6 +193,34 @@ This option will pull the last version of your current branch and update your vi
This option will hard reset your branch (only if you are on either `stable` or `develop`) and recreate your virtualenv.
```
#### Activate your virtual environment
Each time you open a new terminal, you must run `source .venv/bin/activate` to activate your virtual environment.
```bash
# activate virtual environment
source ./.venv/bin/activate
```
### Use ./setup.ps1 (Windows)
The script will ask you a few questions to determine which parts should be installed.
```powershell
Set-ExecutionPolicy -ExecutionPolicy Bypass
cd freqtrade
. .\setup.ps1
```
#### Activate your virtual environment (Windows)
```powershell
# activate virtual environment
. .\.venv\Scripts\Activate.ps1
```
[You are now ready](#you-are-ready) to run the bot.
-----
## Manual Installation
@@ -337,7 +364,7 @@ conda deactivate
Happy trading!
-----
------
## You are ready
@@ -394,3 +421,15 @@ open /Library/Developer/CommandLineTools/Packages/macOS_SDK_headers_for_macOS_10
```
If this file is inexistent, then you're probably on a different version of MacOS, so you may need to consult the internet for specific resolution details.
### Windows Installation error
```bash
error: Microsoft Visual C++ 14.0 is required. Get it with "Microsoft Visual C++ Build Tools": http://landinghub.visualstudio.com/visual-cpp-build-tools
```
Unfortunately, many packages requiring compilation don't provide a pre-built wheel. It is therefore mandatory to have a C/C++ compiler installed and available for your python environment to use.
You can download the Visual C++ build tools from [the Visual Studio website](https://visualstudio.microsoft.com/visual-cpp-build-tools/) and install "Desktop development with C++" in it's default configuration. Unfortunately, this is a heavy download / dependency so you might want to consider WSL2 or [docker compose](docker_quickstart.md) first.
This feature is still in it's testing phase. Should you notice something you think is wrong please let us know via Discord or via Github Issue.
!!! Note "Multiple bots on one account"
You can't run 2 bots on the same account with leverage. For leveraged / margin trading, freqtrade assumes it's the only user of the account, and all liquidation levels are calculated based on this assumption.
@@ -55,7 +52,7 @@ Perpetual swaps (also known as Perpetual Futures) are contracts traded at a pric
In addition to the gains/losses from the change in price of the futures contract, traders also exchange _funding fees_, which are gains/losses worth an amount that is derived from the difference in price between the futures contract and the underlying asset. The difference in price between a futures contract and the underlying asset varies between exchanges.
To trade in futures markets, you'll have to set `trading_mode` to "futures".
You will also have to pick a "margin mode" (explanation below) - with freqtrade currently only supporting isolated margin.
You will also have to pick a "margin mode" (explanation below).
@@ -111,10 +111,10 @@ It also allows multiple subplots to display both MACD and RSI at the same time.
Plot type can be configured using `type` key. Possible types are:
* `scatter` corresponding to `plotly.graph_objects.Scatter` class (default).
* `bar` corresponding to `plotly.graph_objects.Bar` class.
* `scatter` corresponding a scatter plot.
* `bar` corresponding to a bar plot.
Extra parameters to `plotly.graph_objects.*` constructor can be specified in `plotly` dict.
Extra parameters to `plotly.graph_objects.*` constructor can be specified in `plotly` dict - these are only supported when using plotly as plotting library and will be ignored when using freq-ui.
Sample configuration with inline comments explaining the process:
@@ -163,7 +163,7 @@ def plot_config(self):
```
??? Note "As attribute (former method)"
Assigning plot_config is also possible as Attribute (this used to be the default way).
Assigning `plot_config` is also possible as Attribute (this used to be the default way).
This has the disadvantage that strategy parameters are not available, preventing certain configurations from working.
Please make sure to select a very strong, unique password to protect your bot from unauthorized access.
Also change `jwt_secret_key` to something random (no need to remember this, but it'll be used to encrypt your session, so it better be something unique!).
Also change `jwt_secret_key` to something random (no need to remember this, but it'll be used to encrypt your session, so it better be something unique!). This value should also be 32 characters or longer to be safe.
### Configuration with docker
@@ -202,20 +202,20 @@ All endpoints in the below table need to be prefixed with the base URL of the AP
| `/blacklist` | GET | Show the current blacklist.
| `/blacklist` | POST | Adds the specified pair to the blacklist.<br/>*Params:*<br/>- `blacklist` (`str`)
| `/blacklist` | DELETE | Deletes the specified list of pairs from the blacklist.<br/>*Params:*<br/>- `[pair,pair]` (`list[str]`)
| `/pair_candles` | GET | Returns dataframe for a pair / timeframe combination while the bot is running. **Alpha**
| `/pair_candles` | POST | Returns dataframe for a pair / timeframe combination while the bot is running, filtered by a provided list of columns to return.**Alpha**<br/>*Params:*<br/>- `<column_list>` (`list[str]`)
| `/pair_history` | GET | Returns an analyzed dataframe for a given timerange, analyzed by a given strategy. **Alpha**
| `/pair_history` | POST | Returns an analyzed dataframe for a given timerange, analyzed by a given strategy, filtered by a provided list of columns to return.**Alpha**<br/>*Params:*<br/>- `<column_list>` (`list[str]`)
| `/plot_config` | GET | Get plot config from the strategy (or nothing if not configured). **Alpha**
| `/strategies` | GET | List strategies in strategy directory. **Alpha**
| `/strategy/<strategy>` | GET | Get specific Strategy content by strategy class name.**Alpha**<br/>*Params:*<br/>- `<strategy>` (`str`)
| `/available_pairs` | GET | List available backtest data. **Alpha**
| `/pair_candles` | GET | Returns dataframe for a pair / timeframe combination while the bot is running.
| `/pair_candles` | POST | Returns dataframe for a pair / timeframe combination while the bot is running, filtered by a provided list of columns to return.<br/>*Params:*<br/>- `<column_list>` (`list[str]`)
| `/pair_history` | GET | Returns an analyzed dataframe for a given timerange, analyzed by a given strategy.
| `/pair_history` | POST | Returns an analyzed dataframe for a given timerange, analyzed by a given strategy, filtered by a provided list of columns to return.<br/>*Params:*<br/>- `<column_list>` (`list[str]`)
| `/plot_config` | GET | Get plot config from the strategy (or nothing if not configured).
| `/strategies` | GET | List strategies in strategy directory.
| `/strategy/<strategy>` | GET | Get specific Strategy content by strategy class name.<br/>*Params:*<br/>- `<strategy>` (`str`)
| `/available_pairs` | GET | List available backtest data.
| `/version` | GET | Show version.
| `/sysinfo` | GET | Show information about the system load.
| `/health` | GET | Show bot health (last bot loop).
!!! Warning "Alpha status"
Endpoints labeled with *Alpha status* above may change at any time without notice.
Endpoints labeled with *Alpha status* or *Beta status* above may change at any time without notice.
### Message WebSocket
@@ -245,7 +245,7 @@ You would then add that token under `ws_token` in your `api_server` config. Like
Consider using `/delete <tradeid>` via telegram or rest API. That's the recommended way to deleting trades.
Consider using `/delete <tradeid>` via telegram or rest API. That's the recommended way to deleting trades, as it will also remove the corresponding orders and custom data, and it will also trigger the necessary events in the bot to keep everything in sync.
If you'd still like to remove a trade from the database directly, you can use the below query.
@@ -113,9 +113,14 @@ If you'd still like to remove a trade from the database directly, you can use th
```sql
DELETE FROM trades WHERE id = <tradeid>;
DELETE FROM orders WHERE ft_trade_id = <tradeid>;
DELETE FROM trade_custom_data WHERE ft_trade_id = <tradeid>;
DELETE FROM trades WHERE id = 31;
DELETE FROM orders WHERE ft_trade_id = 31;
DELETE FROM trade_custom_data WHERE ft_trade_id = 31;
```
!!! Warning
This will remove this trade from the database. Please make sure you got the correct id and **NEVER** run this query without the `where` clause.
This will remove the specified trade from the database. Please make sure you got the correct id and **NEVER** run this query without the `where` clause.
@@ -31,9 +31,30 @@ The Order-type will be ignored if only one mode is available.
--8<-- "includes/exchange-features.md"
!!! Note "Tight stoploss"
<ins>Do not set too low/tight stoploss value when using stop loss on exchange!</ins>
Do not set too low/tight stoploss value when using stop loss on exchange!
If set to low/tight you will have greater risk of missing fill on the order and stoploss will not work.
!!! Warning "Loose stoploss"
Using stoploss on exchange with a very wide stoploss (e.g. -1) may fail to place the stoploss order on exchange due to exchange limitations.
In that case, the bot will fallback to using the `emergency_exit` order type to place a market order as placing the stoploss order failed.
Freqtrade currently does not implement a limitation to avoid this situation, so please ensure your stoploss values are within reasonable limits for your exchange or disable stoploss on exchange.
### Which order type is used for stoploss on exchange?
The order type used for stoploss on exchange is determined by the `stoploss` value and the exchange capabilities.
If your selected exchange supports both stop-limit and stop-market orders, then the `stoploss` value will determine which order type is used for stoploss on exchange.
If your exchange only supports one of the two order types, you must configure your `stoploss` value accordingly, otherwise the bot will fail to start.
### Which order type should i use for stoploss on exchange?
If we translate the two stoploss order types into human words - they would be something like this:
* **stoploss-market** -> "when stop triggers, get me the hell out of here at whatever price".
* **stoploss-limit** -> "when stop triggers, place a limit order x% below the stoploss price. I accept a loss of "stoploss + 1%" at worst - but if price jumps further - i accept to wait for price to get back down to me, potentially resulting in a much bigger loss than "stoploss + 1%".
As a consequence, we recommend using stoploss-market orders whenever possible, as the main point of a stoploss is to get you out of a position when the market is crashing, and in such situations, you'll want to exit the position immediately at the best available price, rather than risking a limit order not getting filled and potentially incurring even greater losses.
The choice is ultimately up to you, but please be aware of the risk of using stoploss-limit orders, especially in volatile markets.
### stoploss_on_exchange and stoploss_on_exchange_limit_ratio
Enable or Disable stop loss on exchange.
@@ -61,9 +82,10 @@ This same logic will reapply a stoploss order on the exchange should you cancel
### stoploss_price_type
!!! Warning "Only applies to futures"
`stoploss_price_type` only applies to futures markets (on exchanges where it's available).
`stoploss_price_type` only applies to futures markets (on exchanges where it's available).
Freqtrade will perform a validation of this setting on startup, failing to start if an invalid setting for your exchange has been selected.
Supported price types are gonna differs between each exchanges. Please check with your exchange on which price types it supports.
Supported price types are gonna differs between each exchanges. Please check with your exchange on which price types it supports.
In spot markets, this setting is ignored and not validated, as most exchanges only support one price type for stoploss orders on spot markets.
Stoploss on exchange on futures markets can trigger on different price types.
The naming for these prices in exchange terminology often varies, but is usually something around "last" (or "contract price" ), "mark" and "index".
@@ -225,7 +225,7 @@ class AwesomeStrategy(IStrategy):
e.g. returning -0.05 would create a stoploss 5% below current_rate.
The custom stoploss can never be below self.stoploss, which serves as a hard maximum loss.
For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/
For full documentation please go to https://www.freqtrade.io/en/stable/strategy-advanced/
When not implemented by a strategy, returns the initial stoploss value.
Only called when use_custom_stoploss is set to True.
@@ -634,7 +634,7 @@ class AwesomeStrategy(IStrategy):
## Custom order price rules
By default, freqtrade use the orderbook to automatically set an order price([Relevant documentation](configuration.md#prices-used-for-orders)), you also have the option to create custom order prices based on your strategy.
By default, freqtrade use the orderbook to automatically set an order price([Relevant documentation](configuration.md#prices-used-for-orders)), you also have the option to create custom order prices based on your strategy.
You can use this feature by creating a `custom_entry_price()` function in your strategy file to customize entry prices and `custom_exit_price()` for exits.
@@ -644,7 +644,7 @@ Each of these methods are called right before placing an order on the exchange.
If your custom pricing function return None or an invalid value, price will fall back to `proposed_rate`, which is based on the regular pricing configuration.
!!! Note
Using custom_entry_price, the Trade object will be available as soon as the first entry order associated with the trade is created, for the first entry, `trade` parameter value will be `None`.
When using `custom_entry_price()`, the Trade object will be available as soon as the first entry order associated with the trade is created, for the first entry, `trade` parameter value will be `None`.
### Custom order entry and exit price example
@@ -696,6 +696,9 @@ However, freqtrade also offers a custom callback for both order types, which all
Backtesting fills orders if their price falls within the candle's low/high range.
The below callbacks will be called once per (detail) candle for orders that don't fill immediately (which use custom pricing).
!!! Tip "Replacing orders"
If you'd like to replace an order with a different price instead of just cancelling it, you might want to look at [`adjust_order_price()`](#adjust-order-price) instead, which will allow you to both cancel the order, as well as replace it with a new price.
### Custom order timeout example
Called for every open order until that order is either filled or cancelled.
@@ -805,7 +808,7 @@ class AwesomeStrategy(IStrategy):
Timing for this function is critical, so avoid doing heavy computations or
network requests in this method.
For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/
For full documentation please go to https://www.freqtrade.io/en/stable/strategy-advanced/
When not implemented by a strategy, returns True (always confirming).
@@ -853,7 +856,7 @@ class AwesomeStrategy(IStrategy):
Timing for this function is critical, so avoid doing heavy computations or
network requests in this method.
For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/
For full documentation please go to https://www.freqtrade.io/en/stable/strategy-advanced/
When not implemented by a strategy, returns True (always confirming).
@@ -991,7 +994,7 @@ class DigDeeperStrategy(IStrategy):
This means extra entry or exit orders with additional fees.
Only called when `position_adjustment_enable` is set to True.
For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/
For full documentation please go to https://www.freqtrade.io/en/stable/strategy-advanced/
When not implemented by a strategy, returns None
@@ -1118,7 +1121,7 @@ class AwesomeStrategy(IStrategy):
This only executes when a order was already placed, still open (unfilled fully or partially)
and not timed out on subsequent candles after entry trigger.
For full documentation please go to https://www.freqtrade.io/en/latest/strategy-callbacks/
For full documentation please go to https://www.freqtrade.io/en/stable/strategy-callbacks/
When not implemented by a strategy, returns current_order_rate as default.
If current_order_rate is returned then the existing order is maintained.
@@ -1292,6 +1295,23 @@ Currently two types of annotations are supported, `area` and `line`.
}
```
#### Point
``` json
{
"type": "point", // Type of the annotation, currently only "point" is supported
"x": "2024-01-01 15:00:00", // Start date of the point
"y": 94000.2, // Price / y axis value
"color": "",
"z_level": 5, // z-level, higher values are drawn on top of lower values. Positions relative to the Chart elements need to be set in freqUI.
"label": "some label",
"size": 2, // Optional, line width in pixels. Defaults to 10
"shape": "circle", // Optional, can be "circle", "rect", "roundRect", "triangle", "pin", "arrow", "none".
"rotate": 0, // Optional, rotation of the shape/symbol in degrees. Defaults to 0
}
```
The below example will mark the chart with areas for the hours 8 and 15, with a grey color, highlighting the market open and close hours.
This is obviously a very basic example.
@@ -1369,7 +1389,7 @@ Entries will be validated, and won't be passed to the UI if they don't correspon
@@ -6,6 +6,12 @@ To update your freqtrade installation, please use one of the below methods, corr
Breaking changes / changed behavior will be documented in the changelog that is posted alongside every release.
For the develop branch, please follow PR's to avoid being surprised by changes.
## Why update?
Keeping your bot updated not only ensures that you have the latest features and improvements, but is a requirement to keep your bot running smoothly.
Freqtrade is heavily dependent on the underlying exchange API's, which change pretty frequently if considered across exchanges.
To ensure ongoing compatibility, please make sure to update your bot regularly.
## Docker
!!! Note "Legacy installations using the `master` image"
@@ -38,7 +44,12 @@ pip install -e .
freqtrade install-ui
```
### Problems updating
## Problems updating
Update-problems usually come missing dependencies (you didn't follow the above instructions) - or from updated dependencies, which fail to install (for example TA-lib).
Please refer to the corresponding installation sections (common problems linked below)
Update-problems usually come missing dependencies (you didn't follow the above instructions) - or from dependencies which fail to install.
We try to make sure that heavy dependencies have wheels available for major platforms, but sometimes this is not possible.
Please refer to the corresponding installation sections (common problem sections linked below).
Now, choose your installation method, either automatically via script (recommended) or manually following the corresponding instructions.
## Install freqtrade automatically
### Run the installation script
The script will ask you a few questions to determine which parts should be installed.
```powershell
Set-ExecutionPolicy -ExecutionPolicy Bypass
cd freqtrade
. .\setup.ps1
```
## Install freqtrade manually
!!! Note "64bit Python version"
Please make sure to use 64bit Windows and 64bit Python to avoid problems with backtesting or hyperopt due to the memory constraints 32bit applications have under Windows.
32bit python versions are no longer supported under Windows.
!!! Hint
Using the [Anaconda Distribution](https://www.anaconda.com/distribution/) under Windows can greatly help with installation problems. Check out the [Anaconda installation section](installation.md#installation-with-conda) in the documentation for more information.
### Error during installation on Windows
``` bash
error: Microsoft Visual C++ 14.0 is required. Get it with "Microsoft Visual C++ Build Tools": http://landinghub.visualstudio.com/visual-cpp-build-tools
```
Unfortunately, many packages requiring compilation don't provide a pre-built wheel. It is therefore mandatory to have a C/C++ compiler installed and available for your python environment to use.
You can download the Visual C++ build tools from [here](https://visualstudio.microsoft.com/visual-cpp-build-tools/) and install "Desktop development with C++" in it's default configuration. Unfortunately, this is a heavy download / dependency so you might want to consider WSL2 or [docker compose](docker_quickstart.md) first.
File diff suppressed because it is too large
Load Diff
Some files were not shown because too many files have changed in this diff
Show More
Reference in New Issue
Block a user
Blocking a user prevents them from interacting with repositories, such as opening or commenting on pull requests or issues. Learn more about blocking a user.
dependabot[bot]