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Merge branch 'develop' into feature/hyperliquid-hip3-support

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Matthias
2025-12-30 08:46:48 +01:00
parent 511b817e84 70834b6491
commit 0dad2b36ab
47 changed files with 775 additions and 551 deletions
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4
docs/commands/backtesting.md
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@@ -43,7 +43,9 @@ options:
separated.
--eps, --enable-position-stacking
Allow buying the same pair multiple times (position
stacking).
stacking). Only applicable to backtesting and
hyperopt. Results archived by this cannot be
reproduced in dry/live trading.
--enable-protections, --enableprotections
Enable protections for backtesting. Will slow
backtesting down by a considerable amount, but will

4
docs/commands/hyperopt.md
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@@ -41,7 +41,9 @@ options:
functions.
--eps, --enable-position-stacking
Allow buying the same pair multiple times (position
stacking).
stacking). Only applicable to backtesting and
hyperopt. Results archived by this cannot be
reproduced in dry/live trading.
--enable-protections, --enableprotections
Enable protections for backtesting. Will slow
backtesting down by a considerable amount, but will

10
docs/deprecated.md
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@@ -135,3 +135,13 @@ you can verify this with `freqtrade list-data --exchange <yourexchange> --show`.
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.
* [CatboostRegressor](https://github.com/freqtrade/freqtrade/blob/c6f3b0081927e161a16b116cc47fb663f7831d30/freqtrade/freqai/prediction_models/CatboostRegressor.py)
* [CatboostClassifier](https://github.com/freqtrade/freqtrade/blob/c6f3b0081927e161a16b116cc47fb663f7831d30/freqtrade/freqai/prediction_models/CatboostClassifier.py)
* [CatboostClassifierMultiTarget](https://github.com/freqtrade/freqtrade/blob/c6f3b0081927e161a16b116cc47fb663f7831d30/freqtrade/freqai/prediction_models/CatboostClassifierMultiTarget.py)

5
docs/developer.md
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@@ -432,7 +432,6 @@ freqtrade download-data --timerange 20250625-20250801 --config tests/testdata/co
freqtrade backtesting --config tests/testdata/config.tests.usdt.json -s SampleStrategy --userdir user_data_bttest/ --cache none --timerange 20250701-20250801
```
## Continuous integration
This documents some decisions taken for the CI Pipeline.
@@ -464,10 +463,10 @@ git checkout -b new_release <commitid>
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`.
### Create changelog from git commits

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docs/freqai-configuration.md
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@@ -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/
* LightGBM: https://lightgbm.readthedocs.io/en/v3.3.2/#
* XGBoost: https://xgboost.readthedocs.io/en/stable/#
* LightGBM: <https://lightgbm.readthedocs.io/en/v3.3.2/#>
* XGBoost: <https://xgboost.readthedocs.io/en/stable/#>
* 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
```python
df['&s-up_or_down'] = np.where( df["close"].shift(-100) > df["close"], 'up', 'down')

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docs/freqai-parameter-table.md
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@@ -107,7 +107,6 @@ Mandatory parameters are marked as **Required** and have to be set in one of the
| `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`.
### Additional parameters
| Parameter | Description |
@@ -116,3 +115,4 @@ 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`.

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docs/includes/release_template.md
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@@ -1,28 +1,28 @@
## Highlighted changes
# Highlighted changes
- ...
### How to update
## How to update
As always, you can update your bot using one of the following commands:
#### docker-compose
### docker-compose
```bash
docker-compose pull
docker-compose up -d
```
#### Installation via setup script
### Installation via setup script
```
``` bash
# Deactivate venv and run
./setup.sh --update
```
#### Plain native installation
### Plain native installation
```
``` bash
git pull
pip install -U -r requirements.txt
```

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docs/requirements-docs.txt
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@@ -1,7 +1,7 @@
markdown==3.10
mkdocs==1.6.1
mkdocs-material==9.7.0
mkdocs-material==9.7.1
mdx_truly_sane_lists==1.3
pymdown-extensions==10.18
pymdown-extensions==10.19.1
jinja2==3.1.6
mike==2.1.3