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.github/workflows/ci.yml vendored
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@ -24,7 +24,7 @@ jobs:
strategy:
matrix:
os: [ ubuntu-18.04, ubuntu-20.04, ubuntu-22.04 ]
python-version: ["3.8", "3.9", "3.10.6"]
python-version: ["3.8", "3.9", "3.10"]
steps:
- uses: actions/checkout@v3
@ -74,7 +74,7 @@ jobs:
if: matrix.python-version == '3.9' && matrix.os == 'ubuntu-22.04'
- name: Coveralls
if: (runner.os == 'Linux' && matrix.python-version == '3.9')
if: (runner.os == 'Linux' && matrix.python-version == '3.10' && matrix.os == 'ubuntu-22.04')
env:
# Coveralls token. Not used as secret due to github not providing secrets to forked repositories
COVERALLS_REPO_TOKEN: 6D1m0xupS3FgutfuGao8keFf9Hc0FpIXu
@ -121,7 +121,7 @@ jobs:
strategy:
matrix:
os: [ macos-latest ]
python-version: ["3.8", "3.9", "3.10.6"]
python-version: ["3.8", "3.9", "3.10"]
steps:
- uses: actions/checkout@v3
@ -205,7 +205,7 @@ jobs:
strategy:
matrix:
os: [ windows-latest ]
python-version: ["3.8", "3.9", "3.10.6"]
python-version: ["3.8", "3.9", "3.10"]
steps:
- uses: actions/checkout@v3

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.pre-commit-config.yaml
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@ -15,8 +15,8 @@ repos:
additional_dependencies:
- types-cachetools==5.2.1
- types-filelock==3.2.7
- types-requests==2.28.11
- types-tabulate==0.8.11
- types-requests==2.28.11.2
- types-tabulate==0.9.0.0
- types-python-dateutil==2.8.19
# stages: [push]

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docs/exchanges.md
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@ -60,11 +60,18 @@ Binance supports [time_in_force](configuration.md#understand-order_time_in_force
Binance supports `stoploss_on_exchange` and uses `stop-loss-limit` orders. It provides great advantages, so we recommend to benefit from it by enabling stoploss on exchange.
On futures, Binance supports both `stop-limit` as well as `stop-market` orders. You can use either `"limit"` or `"market"` in the `order_types.stoploss` configuration setting to decide which type to use.
### Binance Blacklist
### Binance Blacklist recommendation
For Binance, it is suggested to add `"BNB/<STAKE>"` to your blacklist to avoid issues, unless you are willing to maintain enough extra `BNB` on the account or unless you're willing to disable using `BNB` for fees.
Binance accounts may use `BNB` for fees, and if a trade happens to be on `BNB`, further trades may consume this position and make the initial BNB trade unsellable as the expected amount is not there anymore.
### Binance sites
Binance has been split into 2, and users must use the correct ccxt exchange ID for their exchange, otherwise API keys are not recognized.
* [binance.com](https://www.binance.com/) - International users. Use exchange id: `binance`.
* [binance.us](https://www.binance.us/) - US based users. Use exchange id: `binanceus`.
### Binance Futures
Binance has specific (unfortunately complex) [Futures Trading Quantitative Rules](https://www.binance.com/en/support/faq/4f462ebe6ff445d4a170be7d9e897272) which need to be followed, and which prohibit a too low stake-amount (among others) for too many orders.
@ -87,12 +94,14 @@ When trading on Binance Futures market, orderbook must be used because there is
},
```
### Binance sites
#### Binance futures settings
Binance has been split into 2, and users must use the correct ccxt exchange ID for their exchange, otherwise API keys are not recognized.
Users will also have to have the futures-setting "Position Mode" set to "One-way Mode", and "Asset Mode" set to "Single-Asset Mode".
These settings will be checked on startup, and freqtrade will show an error if this setting is wrong.
* [binance.com](https://www.binance.com/) - International users. Use exchange id: `binance`.
* [binance.us](https://www.binance.us/) - US based users. Use exchange id: `binanceus`.
![Binance futures settings](assets/binance_futures_settings.png)
Freqtrade will not attempt to change these settings.
## Kraken

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docs/freqai-configuration.md
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@ -1,10 +1,10 @@
# Configuration
`FreqAI` is configured through the typical [Freqtrade config file](configuration.md) and the standard [Freqtrade strategy](strategy-customization.md). Examples of `FreqAI` config and strategy files can be found in `config_examples/config_freqai.example.json` and `freqtrade/templates/FreqaiExampleStrategy.py`, respectively.
FreqAI is configured through the typical [Freqtrade config file](configuration.md) and the standard [Freqtrade strategy](strategy-customization.md). Examples of FreqAI config and strategy files can be found in `config_examples/config_freqai.example.json` and `freqtrade/templates/FreqaiExampleStrategy.py`, respectively.
## Setting up the configuration file
Although there are plenty of additional parameters to choose from, as highlighted in the [parameter table](freqai-parameter-table.md#parameter-table), a `FreqAI` config must at minimum include the following parameters (the parameter values are only examples):
Although there are plenty of additional parameters to choose from, as highlighted in the [parameter table](freqai-parameter-table.md#parameter-table), a FreqAI config must at minimum include the following parameters (the parameter values are only examples):
```json
"freqai": {
@ -35,9 +35,9 @@
A full example config is available in `config_examples/config_freqai.example.json`.
## Building a `FreqAI` strategy
## Building a FreqAI strategy
The `FreqAI` strategy requires including the following lines of code in the standard [Freqtrade strategy](strategy-customization.md):
The FreqAI strategy requires including the following lines of code in the standard [Freqtrade strategy](strategy-customization.md):
```python
# user should define the maximum startup candle count (the largest number of candles
@ -129,7 +129,7 @@ Notice also the location of the labels under `if set_generalized_indicators:` at
The `self.freqai.start()` function cannot be called outside the `populate_indicators()`.
!!! Note
Features **must** be defined in `populate_any_indicators()`. Defining `FreqAI` features in `populate_indicators()`
Features **must** be defined in `populate_any_indicators()`. Defining FreqAI features in `populate_indicators()`
will cause the algorithm to fail in live/dry mode. In order to add generalized features that are not associated with a specific pair or timeframe, the following structure inside `populate_any_indicators()` should be used
(as exemplified in `freqtrade/templates/FreqaiExampleStrategy.py`):
@ -166,15 +166,15 @@ Below are the values you can expect to include/use inside a typical strategy dat
| DataFrame Key | Description |
|------------|-------------|
| `df['&*']` | Any dataframe column prepended with `&` in `populate_any_indicators()` is treated as a training target (label) inside `FreqAI` (typically following the naming convention `&-s*`). The names of these dataframe columns are fed back as the predictions. For example, to predict the price change in the next 40 candles (similar to `templates/FreqaiExampleStrategy.py`), you would set `df['&-s_close']`. `FreqAI` makes the predictions and gives them back under the same key (`df['&-s_close']`) to be used in `populate_entry/exit_trend()`. <br> **Datatype:** Depends on the output of the model.
| `df['&*']` | Any dataframe column prepended with `&` in `populate_any_indicators()` is treated as a training target (label) inside FreqAI (typically following the naming convention `&-s*`). For example, to predict the close price 40 candles into the future, you would set `df['&-s_close'] = df['close'].shift(-self.freqai_info["feature_parameters"]["label_period_candles"])` with `"label_period_candles": 40` in the config. FreqAI makes the predictions and gives them back under the same key (`df['&-s_close']`) to be used in `populate_entry/exit_trend()`. <br> **Datatype:** Depends on the output of the model.
| `df['&*_std/mean']` | Standard deviation and mean values of the defined labels during training (or live tracking with `fit_live_predictions_candles`). Commonly used to understand the rarity of a prediction (use the z-score as shown in `templates/FreqaiExampleStrategy.py` and explained [here](#creating-a-dynamic-target-threshold) to evaluate how often a particular prediction was observed during training or historically with `fit_live_predictions_candles`). <br> **Datatype:** Float.
| `df['do_predict']` | Indication of an outlier data point. The return value is integer between -1 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`. A particular case is when `do_predict == 2`, which means that the model has expired due to exceeding `expired_hours`. <br> **Datatype:** Integer between -1 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 `populate_any_indicators()` 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 is easily engineered using the multiplictative functionality described in the `feature_parameters` table shown above), these features are removed from the dataframe upon return from `FreqAI`. To keep a particular type of feature for plotting purposes, you would prepend it with `%%`. <br> **Datatype:** Depends on the output of the model.
| `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 `populate_any_indicators()` 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 `%%`. <br> **Datatype:** Depends on the output of the model.
## Setting the `startup_candle_count`
The `startup_candle_count` in the `FreqAI` strategy needs to be set up in the same way as in the standard Freqtrade strategy (see details [here](strategy-customization.md#strategy-startup-period)). This value is used by Freqtrade to ensure that a sufficient amount of data is provided when calling the `dataprovider`, to avoid any NaNs at the beginning of the first training. You can easily set this value by identifying the longest period (in candle units) which is passed to the indicator creation functions (e.g., Ta-Lib functions). In the presented example, `startup_candle_count` is 20 since this is the maximum value in `indicators_periods_candles`.
The `startup_candle_count` in the FreqAI strategy needs to be set up in the same way as in the standard Freqtrade strategy (see details [here](strategy-customization.md#strategy-startup-period)). This value is used by Freqtrade to ensure that a sufficient amount of data is provided when calling the `dataprovider`, to avoid any NaNs at the beginning of the first training. You can easily set this value by identifying the longest period (in candle units) which is passed to the indicator creation functions (e.g., Ta-Lib functions). In the presented example, `startup_candle_count` is 20 since this is the maximum value in `indicators_periods_candles`.
!!! Note
There are instances where the Ta-Lib functions actually require more data than just the passed `period` or else the feature dataset gets populated with NaNs. Anecdotally, multiplying the `startup_candle_count` by 2 always leads to a fully NaN free training dataset. Hence, it is typically safest to multiply the expected `startup_candle_count` by 2. Look out for this log message to confirm that the data is clean:
@ -185,7 +185,7 @@ The `startup_candle_count` in the `FreqAI` strategy needs to be set up in the sa
## Creating a dynamic target threshold
Deciding when to enter or exit a trade can be done in a dynamic way to reflect current market conditions. `FreqAI` allows you to return additional information from the training of a model (more info [here](freqai-feature-engineering.md#returning-additional-info-from-training)). For example, the `&*_std/mean` return values describe the statistical distribution of the target/label *during the most recent training*. Comparing a given prediction to these values allows you to know the rarity of the prediction. In `templates/FreqaiExampleStrategy.py`, the `target_roi` and `sell_roi` are defined to be 1.25 z-scores away from the mean which causes predictions that are closer to the mean to be filtered out.
Deciding when to enter or exit a trade can be done in a dynamic way to reflect current market conditions. FreqAI allows you to return additional information from the training of a model (more info [here](freqai-feature-engineering.md#returning-additional-info-from-training)). For example, the `&*_std/mean` return values describe the statistical distribution of the target/label *during the most recent training*. Comparing a given prediction to these values allows you to know the rarity of the prediction. In `templates/FreqaiExampleStrategy.py`, the `target_roi` and `sell_roi` are defined to be 1.25 z-scores away from the mean which causes predictions that are closer to the mean to be filtered out.
```python
dataframe["target_roi"] = dataframe["&-s_close_mean"] + dataframe["&-s_close_std"] * 1.25
@ -200,15 +200,15 @@ To consider the population of *historical predictions* for creating the dynamic
}
```
If this value is set, `FreqAI` will initially use the predictions from the training data and subsequently begin introducing real prediction data as it is generated. `FreqAI` will save this historical data to be reloaded if you stop and restart a model with the same `identifier`.
If this value is set, FreqAI will initially use the predictions from the training data and subsequently begin introducing real prediction data as it is generated. FreqAI will save this historical data to be reloaded if you stop and restart a model with the same `identifier`.
## 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/`. However, it is possible to customize and create your own prediction models using the `IFreqaiModel` class. You are encouraged to inherit `fit()`, `train()`, and `predict()` to let these customize various aspects of the training procedures.
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/`. However, it is possible to customize and create your own prediction models using the `IFreqaiModel` class. You are encouraged to inherit `fit()`, `train()`, and `predict()` to let these customize various aspects of the training procedures.
### Setting classifier targets
`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:
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:
```python
df['&s-up_or_down'] = np.where( df["close"].shift(-100) > df["close"], 'up', 'down')

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@ -2,13 +2,13 @@
## Project architecture
The architecture and functions of `FreqAI` are generalized to encourages development of unique features, functions, models, etc.
The architecture and functions of FreqAI are generalized to encourages development of unique features, functions, models, etc.
The class structure and a detailed algorithmic overview is depicted in the following diagram:
![image](assets/freqai_algorithm-diagram.jpg)
As shown, there are three distinct objects comprising `FreqAI`:
As shown, there are three distinct objects comprising FreqAI:
* **IFreqaiModel** - A singular persistent object containing all the necessary logic to collect, store, and process data, engineer features, run training, and inference models.
* **FreqaiDataKitchen** - A non-persistent object which is created uniquely for each unique asset/model. Beyond metadata, it also contains a variety of data processing tools.
@ -18,7 +18,7 @@ There are a variety of built-in [prediction models](freqai-configuration.md#usin
## Data handling
`FreqAI` aims to organize model files, prediction data, and meta data in a way that simplifies post-processing and enhances crash resilience by automatic data reloading. The data is saved in a file structure,`user_data_dir/models/`, which contains all the data associated with the trainings and backtests. The `FreqaiDataKitchen()` relies heavily on the file structure for proper training and inferencing and should therefore not be manually modified.
FreqAI aims to organize model files, prediction data, and meta data in a way that simplifies post-processing and enhances crash resilience by automatic data reloading. The data is saved in a file structure,`user_data_dir/models/`, which contains all the data associated with the trainings and backtests. The `FreqaiDataKitchen()` relies heavily on the file structure for proper training and inferencing and should therefore not be manually modified.
### File structure
@ -27,13 +27,13 @@ The file structure is automatically generated based on the model `identifier` se
| Structure | Description |
|-----------|-------------|
| `config_*.json` | A copy of the model specific configuration file. |
| `historic_predictions.pkl` | A file containing all historic predictions generated during the lifetime of the `identifier` model during live deployment. `historic_predictions.pkl` is used to reload the model after a crash or a config change. A backup file is always held incase of corruption on the main file. **`FreqAI` automatically detects corruption and replaces the corrupted file with the backup**. |
| `historic_predictions.pkl` | A file containing all historic predictions generated during the lifetime of the `identifier` model during live deployment. `historic_predictions.pkl` is used to reload the model after a crash or a config change. A backup file is always held in case of corruption on the main file. FreqAI **automatically** detects corruption and replaces the corrupted file with the backup. |
| `pair_dictionary.json` | A file containing the training queue as well as the on disk location of the most recently trained model. |
| `sub-train-*_TIMESTAMP` | A folder containing all the files associated with a single model, such as: <br>
|| `*_metadata.json` - Metadata for the model, such as normalization max/mins, expected training feature list, etc. <br>
|| `*_metadata.json` - Metadata for the model, such as normalization max/min, expected training feature list, etc. <br>
|| `*_model.*` - The model file saved to disk for reloading from a crash. Can be `joblib` (typical boosting libs), `zip` (stable_baselines), `hd5` (keras type), etc. <br>
|| `*_pca_object.pkl` - The [Principal component analysis (PCA)](freqai-feature-engineering.md#data-dimensionality-reduction-with-principal-component-analysis) transform (if `principal_component_analysis: true` is set in the config) which will be used to transform unseen prediction features. <br>
|| `*_svm_model.pkl` - The [Support Vector Machine (SVM)](freqai-feature-engineering.md#identifying-outliers-using-a-support-vector-machine-svm) model which is used to detect outliers in unseen prediction features. <br>
|| `*_pca_object.pkl` - The [Principal component analysis (PCA)](freqai-feature-engineering.md#data-dimensionality-reduction-with-principal-component-analysis) transform (if `principal_component_analysis: True` is set in the config) which will be used to transform unseen prediction features. <br>
|| `*_svm_model.pkl` - The [Support Vector Machine (SVM)](freqai-feature-engineering.md#identifying-outliers-using-a-support-vector-machine-svm) model (if `use_SVM_to_remove_outliers: True` is set in the config) which is used to detect outliers in unseen prediction features. <br>
|| `*_trained_df.pkl` - The dataframe containing all the training features used to train the `identifier` model. This is used for computing the [Dissimilarity Index (DI)](freqai-feature-engineering.md#identifying-outliers-with-the-dissimilarity-index-di) and can also be used for post-processing. <br>
|| `*_trained_dates.df.pkl` - The dates associated with the `trained_df.pkl`, which is useful for post-processing. |

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@ -4,7 +4,7 @@
Low level feature engineering is performed in the user strategy within a function called `populate_any_indicators()`. That function sets the `base features` such as, `RSI`, `MFI`, `EMA`, `SMA`, time of day, volume, etc. The `base features` can be custom indicators or they can be imported from any technical-analysis library that you can find. One important syntax rule is that all `base features` string names are prepended with `%`, while labels/targets are prepended with `&`.
Meanwhile, high level feature engineering is handled within `"feature_parameters":{}` in the `FreqAI` config. Within this file, it is possible to decide large scale feature expansions on top of the `base_features` such as "including correlated pairs" or "including informative timeframes" or even "including recent candles."
Meanwhile, high level feature engineering is handled within `"feature_parameters":{}` in the FreqAI config. Within this file, it is possible to decide large scale feature expansions on top of the `base_features` such as "including correlated pairs" or "including informative timeframes" or even "including recent candles."
It is advisable to start from the template `populate_any_indicators()` in the source provided example strategy (found in `templates/FreqaiExampleStrategy.py`) to ensure that the feature definitions are following the correct conventions. Here is an example of how to set the indicators and labels in the strategy:
@ -122,7 +122,7 @@ The `include_timeframes` in the config above are the timeframes (`tf`) of each c
You can ask for each of the defined features to be included also for informative pairs using the `include_corr_pairlist`. This means that the feature set will include all the features from `populate_any_indicators` on all the `include_timeframes` for each of the correlated pairs defined in the config (`ETH/USD`, `LINK/USD`, and `BNB/USD` in the presented example).
`include_shifted_candles` indicates the number of previous candles to include in the feature set. For example, `include_shifted_candles: 2` tells `FreqAI` to include the past 2 candles for each of the features in the feature set.
`include_shifted_candles` indicates the number of previous candles to include in the feature set. For example, `include_shifted_candles: 2` tells FreqAI to include the past 2 candles for each of the features in the feature set.
In total, the number of features the user of the presented example strat has created is: length of `include_timeframes` * no. features in `populate_any_indicators()` * length of `include_corr_pairlist` * no. `include_shifted_candles` * length of `indicator_periods_candles`
$= 3 * 3 * 3 * 2 * 2 = 108$.
@ -131,7 +131,7 @@ In total, the number of features the user of the presented example strat has cre
Important metrics can be returned to the strategy at the end of each model training by assigning them to `dk.data['extra_returns_per_train']['my_new_value'] = XYZ` inside the custom prediction model class.
`FreqAI` takes the `my_new_value` assigned in this dictionary and expands it to fit the dataframe that is returned to the strategy. You can then use the returned metrics in your strategy through `dataframe['my_new_value']`. An example of how return values can be used in `FreqAI` are the `&*_mean` and `&*_std` values that are used to [created a dynamic target threshold](freqai-configuration.md#creating-a-dynamic-target-threshold).
FreqAI takes the `my_new_value` assigned in this dictionary and expands it to fit the dataframe that is returned to the strategy. You can then use the returned metrics in your strategy through `dataframe['my_new_value']`. An example of how return values can be used in FreqAI are the `&*_mean` and `&*_std` values that are used to [created a dynamic target threshold](freqai-configuration.md#creating-a-dynamic-target-threshold).
Another example, where the user wants to use live metrics from the trade database, is shown below:
@ -141,15 +141,15 @@ Another example, where the user wants to use live metrics from the trade databas
}
```
You need to set the standard dictionary in the config so that `FreqAI` can return proper dataframe shapes. These values will likely be overridden by the prediction model, but in the case where the model has yet to set them, or needs a default initial value, the preset values are what will be returned.
You need to set the standard dictionary in the config so that FreqAI can return proper dataframe shapes. These values will likely be overridden by the prediction model, but in the case where the model has yet to set them, or needs a default initial value, the pre-set values are what will be returned.
## Feature normalization
`FreqAI` is strict when it comes to data normalization. The train features, $X^{train}$, are always normalized to [-1, 1] using a shifted min-max normalization:
FreqAI is strict when it comes to data normalization. The train features, $X^{train}$, are always normalized to [-1, 1] using a shifted min-max normalization:
$$X^{train}_{norm} = 2 * \frac{X^{train} - X^{train}.min()}{X^{train}.max() - X^{train}.min()} - 1$$
All other data (test data and unseen prediction data in dry/live/backtest) is always automatically normalized to the training feature space according to industry standards. `FreqAI` stores all the metadata required to ensure that test and prediction features will be properly normalized and that predictions are properly denormalized. For this reason, it is not recommended to eschew industry standards and modify `FreqAI` internals - however - advanced users can do so by inheriting `train()` in their custom `IFreqaiModel` and using their own normalization functions.
All other data (test data and unseen prediction data in dry/live/backtest) is always automatically normalized to the training feature space according to industry standards. FreqAI stores all the metadata required to ensure that test and prediction features will be properly normalized and that predictions are properly denormalized. For this reason, it is not recommended to eschew industry standards and modify FreqAI internals - however - advanced users can do so by inheriting `train()` in their custom `IFreqaiModel` and using their own normalization functions.
## Data dimensionality reduction with Principal Component Analysis
@ -169,17 +169,17 @@ This will perform PCA on the features and reduce their dimensionality so that th
The `inlier_metric` is a metric aimed at quantifying how similar a the features of a data point are to the most recent historic data points.
You define the lookback window by setting `inlier_metric_window` and `FreqAI` computes the distance between the present time point and each of the previous `inlier_metric_window` lookback points. A Weibull function is fit to each of the lookback distributions and its cumulative distribution function (CDF) is used to produce a quantile for each lookback point. The `inlier_metric` is then computed for each time point as the average of the corresponding lookback quantiles. The figure below explains the concept for an `inlier_metric_window` of 5.
You define the lookback window by setting `inlier_metric_window` and FreqAI computes the distance between the present time point and each of the previous `inlier_metric_window` lookback points. A Weibull function is fit to each of the lookback distributions and its cumulative distribution function (CDF) is used to produce a quantile for each lookback point. The `inlier_metric` is then computed for each time point as the average of the corresponding lookback quantiles. The figure below explains the concept for an `inlier_metric_window` of 5.
![inlier-metric](assets/freqai_inlier-metric.jpg)
`FreqAI` adds the `inlier_metric` to the training features and hence gives the model access to a novel type of temporal information.
FreqAI adds the `inlier_metric` to the training features and hence gives the model access to a novel type of temporal information.
This function does **not** remove outliers from the data set.
## Weighting features for temporal importance
`FreqAI` allows you to set a `weight_factor` to weight recent data more strongly than past data via an exponential function:
FreqAI allows you to set a `weight_factor` to weight recent data more strongly than past data via an exponential function:
$$ W_i = \exp(\frac{-i}{\alpha*n}) $$
@ -189,13 +189,13 @@ where $W_i$ is the weight of data point $i$ in a total set of $n$ data points. B
## Outlier detection
Equity and crypto markets suffer from a high level of non-patterned noise in the form of outlier data points. `FreqAI` implements a variety of methods to identify such outliers and hence mitigate risk.
Equity and crypto markets suffer from a high level of non-patterned noise in the form of outlier data points. FreqAI implements a variety of methods to identify such outliers and hence mitigate risk.
### Identifying outliers with the Dissimilarity Index (DI)
The Dissimilarity Index (DI) aims to quantify the uncertainty associated with each prediction made by the model.
You can tell `FreqAI` to remove outlier data points from the training/test data sets using the DI by including the following statement in the config:
You can tell FreqAI to remove outlier data points from the training/test data sets using the DI by including the following statement in the config:
```json
"freqai": {
@ -205,7 +205,7 @@ You can tell `FreqAI` to remove outlier data points from the training/test data
}
```
The DI allows predictions which are outliers (not existent in the model feature space) to be thrown out due to low levels of certainty. To do so, `FreqAI` measures the distance between each training data point (feature vector), $X_{a}$, and all other training data points:
The DI allows predictions which are outliers (not existent in the model feature space) to be thrown out due to low levels of certainty. To do so, FreqAI measures the distance between each training data point (feature vector), $X_{a}$, and all other training data points:
$$ d_{ab} = \sqrt{\sum_{j=1}^p(X_{a,j}-X_{b,j})^2} $$
@ -229,7 +229,7 @@ Below is a figure that describes the DI for a 3D data set.
### Identifying outliers using a Support Vector Machine (SVM)
You can tell `FreqAI` to remove outlier data points from the training/test data sets using a Support Vector Machine (SVM) by including the following statement in the config:
You can tell FreqAI to remove outlier data points from the training/test data sets using a Support Vector Machine (SVM) by including the following statement in the config:
```json
"freqai": {
@ -241,7 +241,7 @@ You can tell `FreqAI` to remove outlier data points from the training/test data
The SVM will be trained on the training data and any data point that the SVM deems to be beyond the feature space will be removed.
`FreqAI` uses `sklearn.linear_model.SGDOneClassSVM` (details are available on scikit-learn's webpage [here](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDOneClassSVM.html) (external website)) and you can elect to provide additional parameters for the SVM, such as `shuffle`, and `nu`.
FreqAI uses `sklearn.linear_model.SGDOneClassSVM` (details are available on scikit-learn's webpage [here](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDOneClassSVM.html) (external website)) and you can elect to provide additional parameters for the SVM, such as `shuffle`, and `nu`.
The parameter `shuffle` is by default set to `False` to ensure consistent results. If it is set to `True`, running the SVM multiple times on the same data set might result in different outcomes due to `max_iter` being to low for the algorithm to reach the demanded `tol`. Increasing `max_iter` solves this issue but causes the procedure to take longer time.
@ -249,7 +249,7 @@ The parameter `nu`, *very* broadly, is the amount of data points that should be
### Identifying outliers with DBSCAN
You can configure `FreqAI` to use DBSCAN to cluster and remove outliers from the training/test data set or incoming outliers from predictions, by activating `use_DBSCAN_to_remove_outliers` in the config:
You can configure FreqAI to use DBSCAN to cluster and remove outliers from the training/test data set or incoming outliers from predictions, by activating `use_DBSCAN_to_remove_outliers` in the config:
```json
"freqai": {
@ -265,4 +265,4 @@ Given a number of data points $N$, and a distance $\varepsilon$, DBSCAN clusters
![dbscan](assets/freqai_dbscan.jpg)
`FreqAI` uses `sklearn.cluster.DBSCAN` (details are available on scikit-learn's webpage [here](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.DBSCAN.html) (external website)) with `min_samples` ($N$) taken as 1/4 of the no. of time points in the feature set. `eps` ($\varepsilon$) is computed automatically as the elbow point in the *k-distance graph* computed from the nearest neighbors in the pairwise distances of all data points in the feature set.
FreqAI uses `sklearn.cluster.DBSCAN` (details are available on scikit-learn's webpage [here](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.DBSCAN.html) (external website)) with `min_samples` ($N$) taken as 1/4 of the no. of time points (candles) in the feature set. `eps` ($\varepsilon$) is computed automatically as the elbow point in the *k-distance graph* computed from the nearest neighbors in the pairwise distances of all data points in the feature set.

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@ -1,18 +1,18 @@
# Parameter table
The table below will list all configuration parameters available for `FreqAI`. Some of the parameters are exemplified in `config_examples/config_freqai.example.json`.
The table below will list all configuration parameters available for FreqAI. Some of the parameters are exemplified in `config_examples/config_freqai.example.json`.
Mandatory parameters are marked as **Required** and have to be set in one of the suggested ways.
| Parameter | Description |
|------------|-------------|
| | **General configuration parameters**
| `freqai` | **Required.** <br> The parent dictionary containing all the parameters for controlling `FreqAI`. <br> **Datatype:** Dictionary.
| `freqai` | **Required.** <br> The parent dictionary containing all the parameters for controlling FreqAI. <br> **Datatype:** Dictionary.
| `train_period_days` | **Required.** <br> Number of days to use for the training data (width of the sliding window). <br> **Datatype:** Positive integer.
| `backtest_period_days` | **Required.** <br> Number of days to inference from the trained model before sliding the `train_period_days` window defined above, and retraining the model during backtesting (more info [here](freqai-running.md#backtesting)). This can be fractional days, but beware that the provided `timerange` will be divided by this number to yield the number of trainings necessary to complete the backtest. <br> **Datatype:** Float.
| `identifier` | **Required.** <br> A unique ID for the current model. If models are saved to disk, the `identifier` allows for reloading specific pre-trained models/data. <br> **Datatype:** String.
| `live_retrain_hours` | Frequency of retraining during dry/live runs. <br> **Datatype:** Float > 0. <br> Default: 0 (models retrain as often as possible).
| `expiration_hours` | Avoid making predictions if a model is more than `expiration_hours` old. <br> **Datatype:** Positive integer. <br> Default: 0 (models never expire).
| `live_retrain_hours` | Frequency of retraining during dry/live runs. <br> **Datatype:** Float > 0. <br> Default: `0` (models retrain as often as possible).
| `expiration_hours` | Avoid making predictions if a model is more than `expiration_hours` old. <br> **Datatype:** Positive integer. <br> Default: `0` (models never expire).
| `purge_old_models` | Delete obsolete models. <br> **Datatype:** Boolean. <br> Default: `False` (all historic models remain on disk).
| `save_backtest_models` | Save models to disk when running backtesting. Backtesting operates most efficiently by saving the prediction data and reusing them directly for subsequent runs (when you wish to tune entry/exit parameters). Saving backtesting models to disk also allows to use the same model files for starting a dry/live instance with the same model `identifier`. <br> **Datatype:** Boolean. <br> Default: `False` (no models are saved).
| `fit_live_predictions_candles` | Number of historical candles to use for computing target (label) statistics from prediction data, instead of from the training dataset (more information can be found [here](freqai-configuration.md#creating-a-dynamic-target-threshold)). <br> **Datatype:** Positive integer.
@ -21,32 +21,31 @@ Mandatory parameters are marked as **Required** and have to be set in one of the
| | **Feature parameters**
| `feature_parameters` | A dictionary containing the parameters used to engineer the feature set. Details and examples are shown [here](freqai-feature-engineering.md). <br> **Datatype:** Dictionary.
| `include_timeframes` | A list of timeframes that all indicators in `populate_any_indicators` will be created for. The list is added as features to the base indicators dataset. <br> **Datatype:** List of timeframes (strings).
| `include_corr_pairlist` | A list of correlated coins that `FreqAI` will add as additional features to all `pair_whitelist` coins. All indicators set in `populate_any_indicators` during feature engineering (see details [here](freqai-feature-engineering.md)) will be created for each correlated coin. The correlated coins features are added to the base indicators dataset. <br> **Datatype:** List of assets (strings).
| `include_corr_pairlist` | A list of correlated coins that FreqAI will add as additional features to all `pair_whitelist` coins. All indicators set in `populate_any_indicators` during feature engineering (see details [here](freqai-feature-engineering.md)) will be created for each correlated coin. The correlated coins features are added to the base indicators dataset. <br> **Datatype:** List of assets (strings).
| `label_period_candles` | Number of candles into the future that the labels are created for. This is used in `populate_any_indicators` (see `templates/FreqaiExampleStrategy.py` for detailed usage). You can create custom labels and choose whether to make use of this parameter or not. <br> **Datatype:** Positive integer.
| `include_shifted_candles` | Add features from previous candles to subsequent candles with the intent of adding historical information. If used, `FreqAI` will duplicate and shift all features from the `include_shifted_candles` previous candles so that the information is available for the subsequent candle. <br> **Datatype:** Positive integer.
| `include_shifted_candles` | Add features from previous candles to subsequent candles with the intent of adding historical information. If used, FreqAI will duplicate and shift all features from the `include_shifted_candles` previous candles so that the information is available for the subsequent candle. <br> **Datatype:** Positive integer.
| `weight_factor` | Weight training data points according to their recency (see details [here](freqai-feature-engineering.md#weighting-features-for-temporal-importance)). <br> **Datatype:** Positive float (typically < 1).
| `indicator_max_period_candles` | **No longer used (#7325)**. Replaced by `startup_candle_count` which is set in the [strategy](freqai-configuration.md#building-a-freqai-strategy). `startup_candle_count` is timeframe independent and defines the maximum *period* used in `populate_any_indicators()` for indicator creation. `FreqAI` uses this parameter together with the maximum timeframe in `include_time_frames` to calculate how many data points to download such that the first data point does not include a NaN <br> **Datatype:** Positive integer.
| `indicator_max_period_candles` | **No longer used (#7325)**. Replaced by `startup_candle_count` which is set in the [strategy](freqai-configuration.md#building-a-freqai-strategy). `startup_candle_count` is timeframe independent and defines the maximum *period* used in `populate_any_indicators()` for indicator creation. FreqAI uses this parameter together with the maximum timeframe in `include_time_frames` to calculate how many data points to download such that the first data point does not include a NaN. <br> **Datatype:** Positive integer.
| `indicator_periods_candles` | Time periods to calculate indicators for. The indicators are added to the base indicator dataset. <br> **Datatype:** List of positive integers.
| `stratify_training_data` | Split the feature set into training and testing datasets. For example, `stratify_training_data: 2` would set every 2nd data point into a separate dataset to be pulled from during training/testing. See details about how it works [here](freqai-running.md#data-stratification-for-training-and-testing-the-model). <br> **Datatype:** Positive integer.
| `principal_component_analysis` | Automatically reduce the dimensionality of the data set using Principal Component Analysis. See details about how it works [here](#reducing-data-dimensionality-with-principal-component-analysis) <br> **Datatype:** Boolean. defaults to `false`.
| `plot_feature_importances` | Create a feature importance plot for each model for the top/bottom `plot_feature_importances` number of features.<br> **Datatype:** Integer, defaults to `0`.
| `principal_component_analysis` | Automatically reduce the dimensionality of the data set using Principal Component Analysis. See details about how it works [here](#reducing-data-dimensionality-with-principal-component-analysis) <br> **Datatype:** Boolean. <br> Default: `False`.
| `plot_feature_importances` | Create a feature importance plot for each model for the top/bottom `plot_feature_importances` number of features. <br> **Datatype:** Integer. <br> Default: `0`.
| `DI_threshold` | Activates the use of the Dissimilarity Index for outlier detection when set to > 0. See details about how it works [here](freqai-feature-engineering.md#identifying-outliers-with-the-dissimilarity-index-di). <br> **Datatype:** Positive float (typically < 1).
| `use_SVM_to_remove_outliers` | Train a support vector machine to detect and remove outliers from the training dataset, as well as from incoming data points. See details about how it works [here](freqai-feature-engineering.md#identifying-outliers-using-a-support-vector-machine-svm). <br> **Datatype:** Boolean.
| `svm_params` | All parameters available in Sklearn's `SGDOneClassSVM()`. See details about some select parameters [here](freqai-feature-engineering.md#identifying-outliers-using-a-support-vector-machine-svm). <br> **Datatype:** Dictionary.
| `use_DBSCAN_to_remove_outliers` | Cluster data using the DBSCAN algorithm to identify and remove outliers from training and prediction data. See details about how it works [here](freqai-feature-engineering.md#identifying-outliers-with-dbscan). <br> **Datatype:** Boolean.
| `inlier_metric_window` | If set, `FreqAI` adds an `inlier_metric` to the training feature set and set the lookback to be the `inlier_metric_window`, i.e., the number of previous time points to compare the current candle to. Details of how the `inlier_metric` is computed can be found [here](freqai-feature-engineering.md#inlier-metric). <br> **Datatype:** Integer. <br> Default: 0.
| `noise_standard_deviation` | If set, `FreqAI` adds noise to the training features with the aim of preventing overfitting. `FreqAI` generates random deviates from a gaussian distribution with a standard deviation of `noise_standard_deviation` and adds them to all data points. `noise_standard_deviation` should be kept relative to the normalized space, i.e., between -1 and 1. In other words, since data in `FreqAI` is always normalized to be between -1 and 1, `noise_standard_deviation: 0.05` would result in 32% of the data being randomly increased/decreased by more than 2.5% (i.e., the percent of data falling within the first standard deviation). <br> **Datatype:** Integer. <br> Default: 0.
| `outlier_protection_percentage` | Enable to prevent outlier detection methods from discarding too much data. If more than `outlier_protection_percentage` % of points are detected as outliers by the SVM or DBSCAN, `FreqAI` will log a warning message and ignore outlier detection, i.e., the original dataset will be kept intact. If the outlier protection is triggered, no predictions will be made based on the training dataset. <br> **Datatype:** Float. <br> Default: `30`.
| `inlier_metric_window` | If set, FreqAI adds an `inlier_metric` to the training feature set and set the lookback to be the `inlier_metric_window`, i.e., the number of previous time points to compare the current candle to. Details of how the `inlier_metric` is computed can be found [here](freqai-feature-engineering.md#inlier-metric). <br> **Datatype:** Integer. <br> Default: `0`.
| `noise_standard_deviation` | If set, FreqAI adds noise to the training features with the aim of preventing overfitting. FreqAI generates random deviates from a gaussian distribution with a standard deviation of `noise_standard_deviation` and adds them to all data points. `noise_standard_deviation` should be kept relative to the normalized space, i.e., between -1 and 1. In other words, since data in FreqAI is always normalized to be between -1 and 1, `noise_standard_deviation: 0.05` would result in 32% of the data being randomly increased/decreased by more than 2.5% (i.e., the percent of data falling within the first standard deviation). <br> **Datatype:** Integer. <br> Default: `0`.
| `outlier_protection_percentage` | Enable to prevent outlier detection methods from discarding too much data. If more than `outlier_protection_percentage` % of points are detected as outliers by the SVM or DBSCAN, FreqAI will log a warning message and ignore outlier detection, i.e., the original dataset will be kept intact. If the outlier protection is triggered, no predictions will be made based on the training dataset. <br> **Datatype:** Float. <br> Default: `30`.
| `reverse_train_test_order` | Split the feature dataset (see below) and use the latest data split for training and test on historical split of the data. This allows the model to be trained up to the most recent data point, while avoiding overfitting. However, you should be careful to understand the unorthodox nature of this parameter before employing it. <br> **Datatype:** Boolean. <br> Default: `False` (no reversal).
| | **Data split parameters**
| `data_split_parameters` | Include any additional parameters available from Scikit-learn `test_train_split()`, which are shown [here](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html) (external website). <br> **Datatype:** Dictionary.
| `test_size` | The fraction of data that should be used for testing instead of training. <br> **Datatype:** Positive float < 1.
| `shuffle` | Shuffle the training data points during training. Typically, for time-series forecasting, this is set to `False`. <br> **Datatype:** Boolean.
| `shuffle` | Shuffle the training data points during training. Typically, to not remove the chronological order of data in time-series forecasting, this is set to `False`. <br> **Datatype:** Boolean. <br> Defaut: `False`.
| | **Model training parameters**
| `model_training_parameters` | A flexible dictionary that includes all parameters available by the selected model library. For example, if you use `LightGBMRegressor`, this dictionary can contain any parameter available by the `LightGBMRegressor` [here](https://lightgbm.readthedocs.io/en/latest/pythonapi/lightgbm.LGBMRegressor.html) (external website). If you select a different model, this dictionary can contain any parameter from that model. <br> **Datatype:** Dictionary.
| `n_estimators` | The number of boosted trees to fit in regression. <br> **Datatype:** Integer.
| `learning_rate` | Boosting learning rate during regression. <br> **Datatype:** Float.
| `n_estimators` | The number of boosted trees to fit in the training of the model. <br> **Datatype:** Integer.
| `learning_rate` | Boosting learning rate during training of the model. <br> **Datatype:** Float.
| `n_jobs`, `thread_count`, `task_type` | Set the number of threads for parallel processing and the `task_type` (`gpu` or `cpu`). Different model libraries use different parameter names. <br> **Datatype:** Float.
| | **Extraneous parameters**
| `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`.
| `conv_width` | The width of a convolutional neural network input tensor. This replaces the need for shifting candles (`include_shifted_candles`) by feeding in historical data points as the second dimension of the tensor. Technically, this parameter can also be used for regressors, but it only adds computational overhead and does not change the model training/prediction. <br> **Datatype:** Integer. <br> Default: 2.
| `conv_width` | The width of a convolutional neural network input tensor. This replaces the need for shifting candles (`include_shifted_candles`) by feeding in historical data points as the second dimension of the tensor. Technically, this parameter can also be used for regressors, but it only adds computational overhead and does not change the model training/prediction. <br> **Datatype:** Integer. <br> Default: `2`.

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@ -1,6 +1,6 @@
# Running FreqAI
There are two ways to train and deploy an adaptive machine learning model - live deployment and historical backtesting. In both cases, `FreqAI` runs/simulates periodic retraining of models as shown in the following figure:
There are two ways to train and deploy an adaptive machine learning model - live deployment and historical backtesting. In both cases, FreqAI runs/simulates periodic retraining of models as shown in the following figure:
![freqai-window](assets/freqai_moving-window.jpg)
@ -33,7 +33,7 @@ FreqAI automatically downloads the proper amount of data needed to ensure traini
### Saving prediction data
All predictions made during the lifetime of a specific `identifier` model are stored in `historical_predictions.pkl` to allow for reloading after a crash or changes made to the config.
All predictions made during the lifetime of a specific `identifier` model are stored in `historic_predictions.pkl` to allow for reloading after a crash or changes made to the config.
### Purging old model data
@ -75,19 +75,19 @@ To allow for tweaking your strategy (**not** the features!), FreqAI will automat
An additional directory called `predictions`, which contains all the predictions stored in `hdf` format, will be created in the `unique-id` folder.
To change your **features**, you **must** set a new `identifier` in the config to signal to `FreqAI` to train new models.
To change your **features**, you **must** set a new `identifier` in the config to signal to FreqAI to train new models.
To save the models generated during a particular backtest so that you can start a live deployment from one of them instead of training a new model, you must set `save_backtest_models` to `True` in the config.
### Downloading data to cover the full backtest period
For live/dry deployments, FreqAI will download the necessary data automatically. However, to use backtesting functionality, you need to download the necessary data using `download-data` (details [here](data-download.md#data-downloading)). You need to pay careful attention to understanding how much *additional* data needs to be downloaded to ensure that there is a sufficient amount of training data *before* the start of the backtesting timerange. The amount of additional data can be roughly estimated by moving the start date of the timerange backwards by `train_period_days` and the `startup_candle_count` (see the [parameter table](freqai-parameter-table.md) for detailed descriptions of these parameters) from the beginning of the desired backtesting timerange.
For live/dry deployments, FreqAI will download the necessary data automatically. However, to use backtesting functionality, you need to download the necessary data using `download-data` (details [here](data-download.md#data-downloading)). You need to pay careful attention to understanding how much *additional* data needs to be downloaded to ensure that there is a sufficient amount of training data *before* the start of the backtesting time range. The amount of additional data can be roughly estimated by moving the start date of the time range backwards by `train_period_days` and the `startup_candle_count` (see the [parameter table](freqai-parameter-table.md) for detailed descriptions of these parameters) from the beginning of the desired backtesting time range.
As an example, to backtest the `--timerange 20210501-20210701` using the [example config](freqai-configuration.md#setting-up-the-configuration-file) which sets `train_period_days` to 30, together with `startup_candle_count: 40` on a maximum `include_timeframes` of 1h, the start date for the downloaded data needs to be `20210501` - 30 days - 40 * 1h / 24 hours = 20210330 (31.7 days earlier than the start of the desired training timerange).
As an example, to backtest the `--timerange 20210501-20210701` using the [example config](freqai-configuration.md#setting-up-the-configuration-file) which sets `train_period_days` to 30, together with `startup_candle_count: 40` on a maximum `include_timeframes` of 1h, the start date for the downloaded data needs to be `20210501` - 30 days - 40 * 1h / 24 hours = 20210330 (31.7 days earlier than the start of the desired training time range).
### Deciding the size of the sliding training window and backtesting duration
The backtesting timerange is defined with the typical `--timerange` parameter in the configuration file. The duration of the sliding training window is set by `train_period_days`, whilst `backtest_period_days` is the sliding backtesting window, both in number of days (`backtest_period_days` can be
The backtesting time range is defined with the typical `--timerange` parameter in the configuration file. The duration of the sliding training window is set by `train_period_days`, whilst `backtest_period_days` is the sliding backtesting window, both in number of days (`backtest_period_days` can be
a float to indicate sub-daily retraining in live/dry mode). In the presented [example config](freqai-configuration.md#setting-up-the-configuration-file) (found in `config_examples/config_freqai.example.json`), the user is asking FreqAI to use a training period of 30 days and backtest on the subsequent 7 days. After the training of the model, FreqAI will backtest the subsequent 7 days. The "sliding window" then moves one week forward (emulating FreqAI retraining once per week in live mode) and the new model uses the previous 30 days (including the 7 days used for backtesting by the previous model) to train. This is repeated until the end of `--timerange`. This means that if you set `--timerange 20210501-20210701`, FreqAI will have trained 8 separate models at the end of `--timerange` (because the full range comprises 8 weeks).
!!! Note
@ -105,23 +105,6 @@ During dry/live mode, FreqAI trains each coin pair sequentially (on separate thr
In the presented example config, the user will only allow predictions on models that are less than 1/2 hours old.
## Data stratification for training and testing the model
You can stratify (group) the training/testing data using:
```json
"freqai": {
"feature_parameters" : {
"stratify_training_data": 3
}
}
```
This will split the data chronologically so that every Xth data point is used to test the model after training. In the example above, the user is asking for every third data point in the dataframe to be used for
testing; the other points are used for training.
The test data is used to evaluate the performance of the model after training. If the test score is high, the model is able to capture the behavior of the data well. If the test score is low, either the model does not capture the complexity of the data, the test data is significantly different from the train data, or a different type of model should be used.
## Controlling the model learning process
Model training parameters are unique to the selected machine learning library. FreqAI allows you to set any parameter for any library using the `model_training_parameters` dictionary in the config. The example config (found in `config_examples/config_freqai.example.json`) shows some of the example parameters associated with `Catboost` and `LightGBM`, but you can add any parameters available in those libraries or any other machine learning library you choose to implement.
@ -132,7 +115,7 @@ The FreqAI specific parameter `label_period_candles` defines the offset (number
## Continual learning
You can choose to adopt a continual learning scheme by setting `"continual_learning": true` in the config. By enabling `continual_learning`, after training an initial model from scratch, subsequent trainings will start from the final model state of the preceding training. This gives the new model a "memory" of the previous state. By default, this is set to `false` which means that all new models are trained from scratch, without input from previous models.
You can choose to adopt a continual learning scheme by setting `"continual_learning": true` in the config. By enabling `continual_learning`, after training an initial model from scratch, subsequent trainings will start from the final model state of the preceding training. This gives the new model a "memory" of the previous state. By default, this is set to `False` which means that all new models are trained from scratch, without input from previous models.
## Hyperopt

35
docs/freqai.md
View File

@ -1,10 +1,10 @@
![freqai-logo](assets/freqai_doc_logo.svg)
# `FreqAI`
# FreqAI
## Introduction
`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 features.
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 features.
Features include:
@ -23,7 +23,7 @@ Features include:
## Quick start
The easiest way to quickly test `FreqAI` is to run it in dry mode with the following command:
The easiest way to quickly test FreqAI is to run it in dry mode with the following command:
```bash
freqtrade trade --config config_examples/config_freqai.example.json --strategy FreqaiExampleStrategy --freqaimodel LightGBMRegressor --strategy-path freqtrade/templates
@ -37,7 +37,7 @@ An example strategy, prediction model, and config to use as a starting points ca
## General approach
You provide `FreqAI` with a set of custom *base indicators* (the same way as in a [typical Freqtrade strategy](strategy-customization.md)) as well as target values (*labels*). For each pair in the whitelist, `FreqAI` trains a model to predict the target values based on the input of custom indicators. The models are then consistently retrained, with a predetermined frequency, to adapt to market conditions. `FreqAI` offers the ability to both backtest strategies (emulating reality with periodic retraining on historic data) and deploy dry/live runs. In dry/live conditions, `FreqAI` can be set to constant retraining in a background thread to keep models as up to date as possible.
You provide FreqAI with a set of custom *base indicators* (the same way as in a [typical Freqtrade strategy](strategy-customization.md)) as well as target values (*labels*). For each pair in the whitelist, FreqAI trains a model to predict the target values based on the input of custom indicators. The models are then consistently retrained, with a predetermined frequency, to adapt to market conditions. FreqAI offers the ability to both backtest strategies (emulating reality with periodic retraining on historic data) and deploy dry/live runs. In dry/live conditions, FreqAI can be set to constant retraining in a background thread to keep models as up to date as possible.
An overview of the algorithm, explaining the data processing pipeline and model usage, is shown below.
@ -45,21 +45,21 @@ An overview of the algorithm, explaining the data processing pipeline and model
### Important machine learning vocabulary
**Features** - the parameters, based on historic data, on which a model is trained. All features for a single candle is stored as a vector. In `FreqAI`, you build a feature data sets from anything you can construct in the strategy.
**Features** - the parameters, based on historic data, on which a model is trained. All features for a single candle are stored as a vector. In FreqAI, you build a feature data set from anything you can construct in the strategy.
**Labels** - the target values that a model is trained toward. Each feature vector is associated with a single label that is defined by you within the strategy. These labels intentionally look into the future, and are not available to the model during dry/live/backtesting.
**Labels** - the target values that the model is trained toward. Each feature vector is associated with a single label that is defined by you within the strategy. These labels intentionally look into the future and are what you are training the model to be able to predict.
**Training** - the process of "teaching" the model to match the feature sets to the associated labels. Different types of models "learn" in different ways. More information about the different models can be found [here](freqai-configuration.md#using-different-prediction-models).
**Training** - the process of "teaching" the model to match the feature sets to the associated labels. Different types of models "learn" in different ways which means that one might be better than another for a specific application. More information about the different models that are already implemented in FreqAI can be found [here](freqai-configuration.md#using-different-prediction-models).
**Train data** - a subset of the feature data set that is fed to the model during training. This data directly influences weight connections in the model.
**Train data** - a subset of the feature data set that is fed to the model during training to "teach" the model how to predict the targets. This data directly influences weight connections in the model.
**Test data** - a subset of the feature data set that is used to evaluate the performance of the model after training. This data does not influence nodal weights within the model.
**Inferencing** - the process of feeding a trained model new data on which it will make a prediction.
**Inferencing** - the process of feeding a trained model new unseen data on which it will make a prediction.
## Install prerequisites
The normal Freqtrade install process will ask if you wish to install `FreqAI` dependencies. You should reply "yes" to this question if you wish to use `FreqAI`. If you did not reply yes, you can manually install these dependencies after the install with:
The normal Freqtrade install process will ask if you wish to install FreqAI dependencies. You should reply "yes" to this question if you wish to use FreqAI. If you did not reply yes, you can manually install these dependencies after the install with:
``` bash
pip install -r requirements-freqai.txt
@ -70,18 +70,18 @@ pip install -r requirements-freqai.txt
### Usage with docker
If you are using docker, a dedicated tag with `FreqAI` dependencies is available as `:freqai`. As such - you can replace the image line in your docker-compose file with `image: freqtradeorg/freqtrade:develop_freqai`. This image contains the regular `FreqAI` dependencies. Similar to native installs, Catboost will not be available on ARM based devices.
If you are using docker, a dedicated tag with FreqAI dependencies is available as `:freqai`. As such - you can replace the image line in your docker-compose file with `image: freqtradeorg/freqtrade:develop_freqai`. This image contains the regular FreqAI dependencies. Similar to native installs, Catboost will not be available on ARM based devices.
## Common pitfalls
`FreqAI` cannot be combined with dynamic `VolumePairlists` (or any pairlist filter that adds and removes pairs dynamically).
This is for performance reasons - `FreqAI` relies on making quick predictions/retrains. To do this effectively,
it needs to download all the training data at the beginning of a dry/live instance. `FreqAI` stores and appends
new candles automatically for future retrains. This means that if new pairs arrive later in the dry run due to a volume pairlist, it will not have the data ready. However, `FreqAI` does work with the `ShufflePairlist` or a `VolumePairlist` which keeps the total pairlist constant (but reorders the pairs according to volume).
FreqAI cannot be combined with dynamic `VolumePairlists` (or any pairlist filter that adds and removes pairs dynamically).
This is for performance reasons - FreqAI relies on making quick predictions/retrains. To do this effectively,
it needs to download all the training data at the beginning of a dry/live instance. FreqAI stores and appends
new candles automatically for future retrains. This means that if new pairs arrive later in the dry run due to a volume pairlist, it will not have the data ready. However, FreqAI does work with the `ShufflePairlist` or a `VolumePairlist` which keeps the total pairlist constant (but reorders the pairs according to volume).
## Credits
`FreqAI` is developed by a group of individuals who all contribute specific skillsets to the project.
FreqAI is developed by a group of individuals who all contribute specific skillsets to the project.
Conception and software development:
Robert Caulk @robcaulk
@ -96,5 +96,4 @@ Software development:
Wagner Costa @wagnercosta
Beta testing and bug reporting:
Stefan Gehring @bloodhunter4rc, @longyu, Andrew Robert Lawless @paranoidandy, Pascal Schmidt @smidelis, Ryan McMullan @smarmau,
Juha Nykänen @suikula, Johan van der Vlugt @jooopiert, Richárd Józsa @richardjosza
Stefan Gehring @bloodhunter4rc, @longyu, Andrew Lawless @paranoidandy, Pascal Schmidt @smidelis, Ryan McMullan @smarmau, Juha Nykänen @suikula, Johan van der Vlugt @jooopiert, Richárd Józsa @richardjosza, Timothy Pogue @wizrds

29
docs/includes/pairlists.md
View File

@ -22,6 +22,7 @@ You may also use something like `.*DOWN/BTC` or `.*UP/BTC` to exclude leveraged
* [`StaticPairList`](#static-pair-list) (default, if not configured differently)
* [`VolumePairList`](#volume-pair-list)
* [`ProducerPairList`](#producerpairlist)
* [`AgeFilter`](#agefilter)
* [`OffsetFilter`](#offsetfilter)
* [`PerformanceFilter`](#performancefilter)
@ -84,7 +85,7 @@ Filtering instances (not the first position in the list) will not apply any cach
You can define a minimum volume with `min_value` - which will filter out pairs with a volume lower than the specified value in the specified timerange.
### VolumePairList Advanced mode
##### VolumePairList Advanced mode
`VolumePairList` can also operate in an advanced mode to build volume over a given timerange of specified candle size. It utilizes exchange historical candle data, builds a typical price (calculated by (open+high+low)/3) and multiplies the typical price with every candle's volume. The sum is the `quoteVolume` over the given range. This allows different scenarios, for a more smoothened volume, when using longer ranges with larger candle sizes, or the opposite when using a short range with small candles.
@ -146,6 +147,32 @@ More sophisticated approach can be used, by using `lookback_timeframe` for candl
!!! Note
`VolumePairList` does not support backtesting mode.
#### ProducerPairList
With `ProducerPairList`, you can reuse the pairlist from a [Producer](producer-consumer.md) without explicitly defining the pairlist on each consumer.
[Consumer mode](producer-consumer.md) is required for this pairlist to work.
The pairlist will perform a check on active pairs against the current exchange configuration to avoid attempting to trade on invalid markets.
You can limit the length of the pairlist with the optional parameter `number_assets`. Using `"number_assets"=0` or omitting this key will result in the reuse of all producer pairs valid for the current setup.
```json
"pairlists": [
{
"method": "ProducerPairList",
"number_assets": 5,
"producer_name": "default",
}
],
```
!!! Tip "Combining pairlists"
This pairlist can be combined with all other pairlists and filters for further pairlist reduction, and can also act as an "additional" pairlist, on top of already defined pairs.
`ProducerPairList` can also be used multiple times in sequence, combining the pairs from multiple producers.
Obviously in complex such configurations, the Producer may not provide data for all pairs, so the strategy must be fit for this.
#### 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).

6
docs/requirements-docs.txt
View File

@ -1,6 +1,6 @@
markdown==3.3.7
mkdocs==1.3.1
mkdocs-material==8.5.3
mkdocs==1.4.0
mkdocs-material==8.5.6
mdx_truly_sane_lists==1.3
pymdown-extensions==9.5
pymdown-extensions==9.6
jinja2==3.1.2

2
docs/strategy-callbacks.md
View File

@ -643,7 +643,7 @@ This callback is **not** called when there is an open order (either buy or sell)
Additional Buys are ignored once you have reached the maximum amount of extra buys that you have set on `max_entry_position_adjustment`, but the callback is called anyway looking for partial exits.
Position adjustments will always be applied in the direction of the trade, so a positive value will always increase your position (negative values will decrease your position), no matter if it's a long or short trade. Modifications to leverage are not possible.
Position adjustments will always be applied in the direction of the trade, so a positive value will always increase your position (negative values will decrease your position), no matter if it's a long or short trade. Modifications to leverage are not possible, and the stake-amount is assumed to be before applying leverage.
!!! Note "About stake size"
Using fixed stake size means it will be the amount used for the first order, just like without position adjustment.

9
docs/updating.md
View File

@ -37,3 +37,12 @@ pip install -e .
# Ensure freqUI is at the latest version
freqtrade install-ui
```
### 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)
Common problems and their solutions:
* [ta-lib update on windows](windows_installation.md#2-install-ta-lib)

2
docs/windows_installation.md
View File

@ -34,7 +34,7 @@ python -m venv .env
.env\Scripts\activate.ps1
# optionally install ta-lib from wheel
# Eventually adjust the below filename to match the downloaded wheel
pip install --find-links build_helpers\ TA-Lib
pip install --find-links build_helpers\ TA-Lib -U
pip install -r requirements.txt
pip install -e .
freqtrade

2
freqtrade/__init__.py
View File

@ -1,5 +1,5 @@
""" Freqtrade bot """
__version__ = '2022.9.dev'
__version__ = '2022.10.dev'
if 'dev' in __version__:
try:

1
freqtrade/configuration/__init__.py
View File

@ -1,6 +1,5 @@
# flake8: noqa: F401
from freqtrade.configuration.check_exchange import check_exchange
from freqtrade.configuration.config_setup import setup_utils_configuration
from freqtrade.configuration.config_validation import validate_config_consistency
from freqtrade.configuration.configuration import Configuration

21
freqtrade/configuration/config_validation.py
View File

@ -87,6 +87,7 @@ def validate_config_consistency(conf: Dict[str, Any], preliminary: bool = False)
_validate_ask_orderbook(conf)
_validate_freqai_hyperopt(conf)
_validate_freqai_backtest(conf)
_validate_freqai_include_timeframes(conf)
_validate_consumers(conf)
validate_migrated_strategy_settings(conf)
@ -335,6 +336,26 @@ def _validate_freqai_hyperopt(conf: Dict[str, Any]) -> None:
'Using analyze-per-epoch parameter is not supported with a FreqAI strategy.')
def _validate_freqai_include_timeframes(conf: Dict[str, Any]) -> None:
freqai_enabled = conf.get('freqai', {}).get('enabled', False)
if freqai_enabled:
main_tf = conf.get('timeframe', '5m')
freqai_include_timeframes = conf.get('freqai', {}).get('feature_parameters', {}
).get('include_timeframes', [])
from freqtrade.exchange import timeframe_to_seconds
main_tf_s = timeframe_to_seconds(main_tf)
offending_lines = []
for tf in freqai_include_timeframes:
tf_s = timeframe_to_seconds(tf)
if tf_s < main_tf_s:
offending_lines.append(tf)
if offending_lines:
raise OperationalException(
f"Main timeframe of {main_tf} must be smaller or equal to FreqAI "
f"`include_timeframes`.Offending include-timeframes: {', '.join(offending_lines)}")
def _validate_freqai_backtest(conf: Dict[str, Any]) -> None:
if conf.get('runmode', RunMode.OTHER) == RunMode.BACKTEST:
freqai_enabled = conf.get('freqai', {}).get('enabled', False)

4
freqtrade/configuration/configuration.py
View File

@ -8,7 +8,6 @@ from pathlib import Path
from typing import Any, Callable, Dict, List, Optional
from freqtrade import constants
from freqtrade.configuration.check_exchange import check_exchange
from freqtrade.configuration.deprecated_settings import process_temporary_deprecated_settings
from freqtrade.configuration.directory_operations import create_datadir, create_userdata_dir
from freqtrade.configuration.environment_vars import enironment_vars_to_dict
@ -100,6 +99,9 @@ class Configuration:
self._process_freqai_options(config)
# Import check_exchange here to avoid import cycle problems
from freqtrade.exchange.check_exchange import check_exchange
# Check if the exchange set by the user is supported
check_exchange(config, config.get('experimental', {}).get('block_bad_exchanges', True))

3
freqtrade/constants.py
View File

@ -31,7 +31,7 @@ HYPEROPT_LOSS_BUILTIN = ['ShortTradeDurHyperOptLoss', 'OnlyProfitHyperOptLoss',
'CalmarHyperOptLoss',
'MaxDrawDownHyperOptLoss', 'MaxDrawDownRelativeHyperOptLoss',
'ProfitDrawDownHyperOptLoss']
AVAILABLE_PAIRLISTS = ['StaticPairList', 'VolumePairList',
AVAILABLE_PAIRLISTS = ['StaticPairList', 'VolumePairList', 'ProducerPairList',
'AgeFilter', 'OffsetFilter', 'PerformanceFilter',
'PrecisionFilter', 'PriceFilter', 'RangeStabilityFilter',
'ShuffleFilter', 'SpreadFilter', 'VolatilityFilter']
@ -567,6 +567,7 @@ CONF_SCHEMA = {
"properties": {
"test_size": {"type": "number"},
"random_state": {"type": "integer"},
"shuffle": {"type": "boolean", "default": False}
},
},
"model_training_parameters": {

3
freqtrade/data/converter.py
View File

@ -47,8 +47,7 @@ def ohlcv_to_dataframe(ohlcv: list, timeframe: str, pair: str, *,
def clean_ohlcv_dataframe(data: DataFrame, timeframe: str, pair: str, *,
fill_missing: bool = True,
drop_incomplete: bool = True) -> DataFrame:
fill_missing: bool, drop_incomplete: bool) -> DataFrame:
"""
Cleanse a OHLCV dataframe by
* Grouping it by date (removes duplicate tics)

2
freqtrade/data/history/history_utils.py
View File

@ -26,7 +26,7 @@ def load_pair_history(pair: str,
datadir: Path, *,
timerange: Optional[TimeRange] = None,
fill_up_missing: bool = True,
drop_incomplete: bool = True,
drop_incomplete: bool = False,
startup_candles: int = 0,
data_format: str = None,
data_handler: IDataHandler = None,

2
freqtrade/data/history/idatahandler.py
View File

@ -275,7 +275,7 @@ class IDataHandler(ABC):
candle_type: CandleType, *,
timerange: Optional[TimeRange] = None,
fill_missing: bool = True,
drop_incomplete: bool = True,
drop_incomplete: bool = False,
startup_candles: int = 0,
warn_no_data: bool = True,
) -> DataFrame:

4
freqtrade/exchange/__init__.py
View File

@ -12,8 +12,8 @@ from freqtrade.exchange.coinbasepro import Coinbasepro
from freqtrade.exchange.exchange import (amount_to_contract_precision, amount_to_contracts,
amount_to_precision, available_exchanges, ccxt_exchanges,
contracts_to_amount, date_minus_candles,
is_exchange_known_ccxt, is_exchange_officially_supported,
market_is_active, price_to_precision, timeframe_to_minutes,
is_exchange_known_ccxt, market_is_active,
price_to_precision, timeframe_to_minutes,
timeframe_to_msecs, timeframe_to_next_date,
timeframe_to_prev_date, timeframe_to_seconds,
validate_exchange, validate_exchanges)

31
freqtrade/exchange/binance.py
View File

@ -68,6 +68,37 @@ class Binance(Exchange):
tickers = deep_merge_dicts(bidsasks, tickers, allow_null_overrides=False)
return tickers
@retrier
def additional_exchange_init(self) -> None:
"""
Additional exchange initialization logic.
.api will be available at this point.
Must be overridden in child methods if required.
"""
try:
if self.trading_mode == TradingMode.FUTURES and not self._config['dry_run']:
position_side = self._api.fapiPrivateGetPositionsideDual()
self._log_exchange_response('position_side_setting', position_side)
assets_margin = self._api.fapiPrivateGetMultiAssetsMargin()
self._log_exchange_response('multi_asset_margin', assets_margin)
msg = ""
if position_side.get('dualSidePosition') is True:
msg += (
"\nHedge Mode is not supported by freqtrade. "
"Please change 'Position Mode' on your binance futures account.")
if assets_margin.get('multiAssetsMargin') is True:
msg += ("\nMulti-Asset Mode is not supported by freqtrade. "
"Please change 'Asset Mode' on your binance futures account.")
if msg:
raise OperationalException(msg)
except ccxt.DDoSProtection as e:
raise DDosProtection(e) from e
except (ccxt.NetworkError, ccxt.ExchangeError) as e:
raise TemporaryError(
f'Could not set leverage due to {e.__class__.__name__}. Message: {e}') from e
except ccxt.BaseError as e:
raise OperationalException(e) from e
@retrier
def _set_leverage(
self,

6
freqtrade/configuration/check_exchange.py → freqtrade/exchange/check_exchange.py
View File

@ -3,8 +3,8 @@ import logging
from freqtrade.constants import Config
from freqtrade.enums import RunMode
from freqtrade.exceptions import OperationalException
from freqtrade.exchange import (available_exchanges, is_exchange_known_ccxt,
is_exchange_officially_supported, validate_exchange)
from freqtrade.exchange import available_exchanges, is_exchange_known_ccxt, validate_exchange
from freqtrade.exchange.common import MAP_EXCHANGE_CHILDCLASS, SUPPORTED_EXCHANGES
logger = logging.getLogger(__name__)
@ -52,7 +52,7 @@ def check_exchange(config: Config, check_for_bad: bool = True) -> bool:
else:
logger.warning(f'Exchange "{exchange}" will not work with Freqtrade. Reason: {reason}')
if is_exchange_officially_supported(exchange):
if MAP_EXCHANGE_CHILDCLASS.get(exchange, exchange) in SUPPORTED_EXCHANGES:
logger.info(f'Exchange "{exchange}" is officially supported '
f'by the Freqtrade development team.')
else:

141
freqtrade/exchange/exchange.py
View File

@ -18,20 +18,19 @@ import ccxt.async_support as ccxt_async
from cachetools import TTLCache
from ccxt import ROUND_DOWN, ROUND_UP, TICK_SIZE, TRUNCATE, decimal_to_precision
from dateutil import parser
from pandas import DataFrame
from pandas import DataFrame, concat
from freqtrade.constants import (DEFAULT_AMOUNT_RESERVE_PERCENT, NON_OPEN_EXCHANGE_STATES, BuySell,
Config, EntryExit, ListPairsWithTimeframes, MakerTaker,
PairWithTimeframe)
from freqtrade.data.converter import ohlcv_to_dataframe, trades_dict_to_list
from freqtrade.data.converter import clean_ohlcv_dataframe, ohlcv_to_dataframe, trades_dict_to_list
from freqtrade.enums import OPTIMIZE_MODES, CandleType, MarginMode, TradingMode
from freqtrade.exceptions import (DDosProtection, ExchangeError, InsufficientFundsError,
InvalidOrderException, OperationalException, PricingError,
RetryableOrderError, TemporaryError)
from freqtrade.exchange.common import (API_FETCH_ORDER_RETRY_COUNT, BAD_EXCHANGES,
EXCHANGE_HAS_OPTIONAL, EXCHANGE_HAS_REQUIRED,
SUPPORTED_EXCHANGES, remove_credentials, retrier,
retrier_async)
remove_credentials, retrier, retrier_async)
from freqtrade.misc import (chunks, deep_merge_dicts, file_dump_json, file_load_json,
safe_value_fallback2)
from freqtrade.plugins.pairlist.pairlist_helpers import expand_pairlist
@ -180,13 +179,14 @@ class Exchange:
exchange_config, ccxt_async, ccxt_kwargs=ccxt_async_config)
logger.info(f'Using Exchange "{self.name}"')
self.required_candle_call_count = 1
if validate:
# Initial markets load
self._load_markets()
self.validate_config(config)
self._startup_candle_count: int = config.get('startup_candle_count', 0)
self.required_candle_call_count = self.validate_required_startup_candles(
config.get('startup_candle_count', 0), config.get('timeframe', ''))
self._startup_candle_count, config.get('timeframe', ''))
# Converts the interval provided in minutes in config to seconds
self.markets_refresh_interval: int = exchange_config.get(
@ -1292,7 +1292,14 @@ class Exchange:
order = self.fetch_order(order_id, pair)
except InvalidOrderException:
logger.warning(f"Could not fetch cancelled order {order_id}.")
order = {'fee': {}, 'status': 'canceled', 'amount': amount, 'info': {}}
order = {
'id': order_id,
'status': 'canceled',
'amount': amount,
'filled': 0.0,
'fee': {},
'info': {}
}
return order
@ -1844,10 +1851,22 @@ class Exchange:
return pair, timeframe, candle_type, data
def _build_coroutine(self, pair: str, timeframe: str, candle_type: CandleType,
since_ms: Optional[int]) -> Coroutine:
since_ms: Optional[int], cache: bool) -> Coroutine:
not_all_data = cache and self.required_candle_call_count > 1
if cache and (pair, timeframe, candle_type) in self._klines:
candle_limit = self.ohlcv_candle_limit(timeframe, candle_type)
min_date = date_minus_candles(timeframe, candle_limit - 5).timestamp()
# Check if 1 call can get us updated candles without hole in the data.
if min_date < self._pairs_last_refresh_time.get((pair, timeframe, candle_type), 0):
# Cache can be used - do one-off call.
not_all_data = False
else:
# Time jump detected, evict cache
logger.info(
f"Time jump detected. Evicting cache for {pair}, {timeframe}, {candle_type}")
del self._klines[(pair, timeframe, candle_type)]
if (not since_ms
and (self._ft_has["ohlcv_require_since"] or self.required_candle_call_count > 1)):
if (not since_ms and (self._ft_has["ohlcv_require_since"] or not_all_data)):
# Multiple calls for one pair - to get more history
one_call = timeframe_to_msecs(timeframe) * self.ohlcv_candle_limit(
timeframe, candle_type, since_ms)
@ -1863,6 +1882,59 @@ class Exchange:
return self._async_get_candle_history(
pair, timeframe, since_ms=since_ms, candle_type=candle_type)
def _build_ohlcv_dl_jobs(
self, pair_list: ListPairsWithTimeframes, since_ms: Optional[int],
cache: bool) -> Tuple[List[Coroutine], List[Tuple[str, str, CandleType]]]:
"""
Build Coroutines to execute as part of refresh_latest_ohlcv
"""
input_coroutines = []
cached_pairs = []
for pair, timeframe, candle_type in set(pair_list):
if (timeframe not in self.timeframes
and candle_type in (CandleType.SPOT, CandleType.FUTURES)):
logger.warning(
f"Cannot download ({pair}, {timeframe}) combination as this timeframe is "
f"not available on {self.name}. Available timeframes are "
f"{', '.join(self.timeframes)}.")
continue
if ((pair, timeframe, candle_type) not in self._klines or not cache
or self._now_is_time_to_refresh(pair, timeframe, candle_type)):
input_coroutines.append(
self._build_coroutine(pair, timeframe, candle_type, since_ms, cache))
else:
logger.debug(
f"Using cached candle (OHLCV) data for {pair}, {timeframe}, {candle_type} ..."
)
cached_pairs.append((pair, timeframe, candle_type))
return input_coroutines, cached_pairs
def _process_ohlcv_df(self, pair: str, timeframe: str, c_type: CandleType, ticks: List[List],
cache: bool, drop_incomplete: bool) -> DataFrame:
# keeping last candle time as last refreshed time of the pair
if ticks and cache:
self._pairs_last_refresh_time[(pair, timeframe, c_type)] = ticks[-1][0] // 1000
# keeping parsed dataframe in cache
ohlcv_df = ohlcv_to_dataframe(ticks, timeframe, pair=pair, fill_missing=True,
drop_incomplete=drop_incomplete)
if cache:
if (pair, timeframe, c_type) in self._klines:
old = self._klines[(pair, timeframe, c_type)]
# Reassign so we return the updated, combined df
ohlcv_df = clean_ohlcv_dataframe(concat([old, ohlcv_df], axis=0), timeframe, pair,
fill_missing=True, drop_incomplete=False)
candle_limit = self.ohlcv_candle_limit(timeframe, self._config['candle_type_def'])
# Age out old candles
ohlcv_df = ohlcv_df.tail(candle_limit + self._startup_candle_count)
self._klines[(pair, timeframe, c_type)] = ohlcv_df
else:
self._klines[(pair, timeframe, c_type)] = ohlcv_df
return ohlcv_df
def refresh_latest_ohlcv(self, pair_list: ListPairsWithTimeframes, *,
since_ms: Optional[int] = None, cache: bool = True,
drop_incomplete: Optional[bool] = None
@ -1880,27 +1952,9 @@ class Exchange:
"""
logger.debug("Refreshing candle (OHLCV) data for %d pairs", len(pair_list))
drop_incomplete = self._ohlcv_partial_candle if drop_incomplete is None else drop_incomplete
input_coroutines = []
cached_pairs = []
# Gather coroutines to run
for pair, timeframe, candle_type in set(pair_list):
if (timeframe not in self.timeframes
and candle_type in (CandleType.SPOT, CandleType.FUTURES)):
logger.warning(
f"Cannot download ({pair}, {timeframe}) combination as this timeframe is "
f"not available on {self.name}. Available timeframes are "
f"{', '.join(self.timeframes)}.")
continue
if ((pair, timeframe, candle_type) not in self._klines or not cache
or self._now_is_time_to_refresh(pair, timeframe, candle_type)):
input_coroutines.append(self._build_coroutine(
pair, timeframe, candle_type=candle_type, since_ms=since_ms))
else:
logger.debug(
f"Using cached candle (OHLCV) data for {pair}, {timeframe}, {candle_type} ..."
)
cached_pairs.append((pair, timeframe, candle_type))
# Gather coroutines to run
input_coroutines, cached_pairs = self._build_ohlcv_dl_jobs(pair_list, since_ms, cache)
results_df = {}
# Chunk requests into batches of 100 to avoid overwelming ccxt Throttling
@ -1917,16 +1971,11 @@ class Exchange:
continue
# Deconstruct tuple (has 4 elements)
pair, timeframe, c_type, ticks = res
# keeping last candle time as last refreshed time of the pair
if ticks:
self._pairs_last_refresh_time[(pair, timeframe, c_type)] = ticks[-1][0] // 1000
# keeping parsed dataframe in cache
ohlcv_df = ohlcv_to_dataframe(
ticks, timeframe, pair=pair, fill_missing=True,
drop_incomplete=drop_incomplete)
ohlcv_df = self._process_ohlcv_df(
pair, timeframe, c_type, ticks, cache, drop_incomplete)
results_df[(pair, timeframe, c_type)] = ohlcv_df
if cache:
self._klines[(pair, timeframe, c_type)] = ohlcv_df
# Return cached klines
for pair, timeframe, c_type in cached_pairs:
results_df[(pair, timeframe, c_type)] = self.klines(
@ -1941,10 +1990,8 @@ class Exchange:
interval_in_sec = timeframe_to_seconds(timeframe)
return not (
(self._pairs_last_refresh_time.get(
(pair, timeframe, candle_type),
0
) + interval_in_sec) >= arrow.utcnow().int_timestamp
(self._pairs_last_refresh_time.get((pair, timeframe, candle_type), 0)
+ interval_in_sec) >= arrow.utcnow().int_timestamp
)
@retrier_async
@ -1971,8 +2018,8 @@ class Exchange:
candle_limit = self.ohlcv_candle_limit(
timeframe, candle_type=candle_type, since_ms=since_ms)
if candle_type != CandleType.SPOT:
params.update({'price': candle_type})
if candle_type and candle_type != CandleType.SPOT:
params.update({'price': candle_type.value})
if candle_type != CandleType.FUNDING_RATE:
data = await self._api_async.fetch_ohlcv(
pair, timeframe=timeframe, since=since_ms,
@ -2754,10 +2801,6 @@ def is_exchange_known_ccxt(exchange_name: str, ccxt_module: CcxtModuleType = Non
return exchange_name in ccxt_exchanges(ccxt_module)
def is_exchange_officially_supported(exchange_name: str) -> bool:
return exchange_name in SUPPORTED_EXCHANGES
def ccxt_exchanges(ccxt_module: CcxtModuleType = None) -> List[str]:
"""
Return the list of all exchanges known to ccxt

3
freqtrade/exchange/okx.py
View File

@ -78,7 +78,8 @@ class Okx(Exchange):
raise DDosProtection(e) from e
except (ccxt.NetworkError, ccxt.ExchangeError) as e:
raise TemporaryError(
f'Could not set leverage due to {e.__class__.__name__}. Message: {e}') from e
f'Error in additional_exchange_init due to {e.__class__.__name__}. Message: {e}'
) from e
except ccxt.BaseError as e:
raise OperationalException(e) from e

4
freqtrade/freqai/base_models/BaseClassifierModel.py
View File

@ -78,7 +78,7 @@ class BaseClassifierModel(IFreqaiModel):
) -> Tuple[DataFrame, npt.NDArray[np.int_]]:
"""
Filter the prediction features data and predict with it.
:param: unfiltered_df: Full dataframe for the current backtest period.
:param unfiltered_df: Full dataframe for the current backtest period.
:return:
:pred_df: dataframe containing the predictions
:do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove
@ -92,7 +92,7 @@ class BaseClassifierModel(IFreqaiModel):
filtered_df = dk.normalize_data_from_metadata(filtered_df)
dk.data_dictionary["prediction_features"] = filtered_df
self.data_cleaning_predict(dk, filtered_df)
self.data_cleaning_predict(dk)
predictions = self.model.predict(dk.data_dictionary["prediction_features"])
pred_df = DataFrame(predictions, columns=dk.label_list)

4
freqtrade/freqai/base_models/BaseRegressionModel.py
View File

@ -77,7 +77,7 @@ class BaseRegressionModel(IFreqaiModel):
) -> Tuple[DataFrame, npt.NDArray[np.int_]]:
"""
Filter the prediction features data and predict with it.
:param: unfiltered_df: Full dataframe for the current backtest period.
:param unfiltered_df: Full dataframe for the current backtest period.
:return:
:pred_df: dataframe containing the predictions
:do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove
@ -92,7 +92,7 @@ class BaseRegressionModel(IFreqaiModel):
dk.data_dictionary["prediction_features"] = filtered_df
# optional additional data cleaning/analysis
self.data_cleaning_predict(dk, filtered_df)
self.data_cleaning_predict(dk)
predictions = self.model.predict(dk.data_dictionary["prediction_features"])
pred_df = DataFrame(predictions, columns=dk.label_list)

39
freqtrade/freqai/data_drawer.py
View File

@ -257,7 +257,7 @@ class FreqaiDataDrawer:
def append_model_predictions(self, pair: str, predictions: DataFrame,
do_preds: NDArray[np.int_],
dk: FreqaiDataKitchen, len_df: int) -> None:
dk: FreqaiDataKitchen, strat_df: DataFrame) -> None:
"""
Append model predictions to historic predictions dataframe, then set the
strategy return dataframe to the tail of the historic predictions. The length of
@ -266,6 +266,7 @@ class FreqaiDataDrawer:
historic predictions.
"""
len_df = len(strat_df)
index = self.historic_predictions[pair].index[-1:]
columns = self.historic_predictions[pair].columns
@ -293,6 +294,15 @@ class FreqaiDataDrawer:
for return_str in rets:
df[return_str].iloc[-1] = rets[return_str]
# this logic carries users between version without needing to
# change their identifier
if 'close_price' not in df.columns:
df['close_price'] = np.nan
df['date_pred'] = np.nan
df['close_price'].iloc[-1] = strat_df['close'].iloc[-1]
df['date_pred'].iloc[-1] = strat_df['date'].iloc[-1]
self.model_return_values[pair] = df.tail(len_df).reset_index(drop=True)
def attach_return_values_to_return_dataframe(
@ -402,9 +412,8 @@ class FreqaiDataDrawer:
def save_data(self, model: Any, coin: str, dk: FreqaiDataKitchen) -> None:
"""
Saves all data associated with a model for a single sub-train time range
:params:
:model: User trained model which can be reused for inferencing to generate
predictions
:param model: User trained model which can be reused for inferencing to generate
predictions
"""
if not dk.data_path.is_dir():
@ -423,7 +432,7 @@ class FreqaiDataDrawer:
dk.data["data_path"] = str(dk.data_path)
dk.data["model_filename"] = str(dk.model_filename)
dk.data["training_features_list"] = list(dk.data_dictionary["train_features"].columns)
dk.data["training_features_list"] = dk.training_features_list
dk.data["label_list"] = dk.label_list
# store the metadata
with open(save_path / f"{dk.model_filename}_metadata.json", "w") as fp:
@ -522,8 +531,7 @@ class FreqaiDataDrawer:
Append new candles to our stores historic data (in memory) so that
we do not need to load candle history from disk and we dont need to
pinging exchange multiple times for the same candle.
:params:
dataframe: DataFrame = strategy provided dataframe
:param dataframe: DataFrame = strategy provided dataframe
"""
feat_params = self.freqai_info["feature_parameters"]
with self.history_lock:
@ -569,9 +577,8 @@ class FreqaiDataDrawer:
"""
Load pair histories for all whitelist and corr_pairlist pairs.
Only called once upon startup of bot.
:params:
timerange: TimeRange = full timerange required to populate all indicators
for training according to user defined train_period_days
:param timerange: TimeRange = full timerange required to populate all indicators
for training according to user defined train_period_days
"""
history_data = self.historic_data
@ -594,10 +601,9 @@ class FreqaiDataDrawer:
"""
Searches through our historic_data in memory and returns the dataframes relevant
to the present pair.
:params:
timerange: TimeRange = full timerange required to populate all indicators
for training according to user defined train_period_days
metadata: dict = strategy furnished pair metadata
:param timerange: TimeRange = full timerange required to populate all indicators
for training according to user defined train_period_days
:param metadata: dict = strategy furnished pair metadata
"""
with self.history_lock:
corr_dataframes: Dict[Any, Any] = {}
@ -608,7 +614,8 @@ class FreqaiDataDrawer:
)
for tf in self.freqai_info["feature_parameters"].get("include_timeframes"):
base_dataframes[tf] = dk.slice_dataframe(timerange, historic_data[pair][tf])
base_dataframes[tf] = dk.slice_dataframe(
timerange, historic_data[pair][tf]).reset_index(drop=True)
if pairs:
for p in pairs:
if pair in p:
@ -617,7 +624,7 @@ class FreqaiDataDrawer:
corr_dataframes[p] = {}
corr_dataframes[p][tf] = dk.slice_dataframe(
timerange, historic_data[p][tf]
)
).reset_index(drop=True)
return corr_dataframes, base_dataframes

81
freqtrade/freqai/data_kitchen.py
View File

@ -114,9 +114,8 @@ class FreqaiDataKitchen:
) -> None:
"""
Set the paths to the data for the present coin/botloop
:params:
metadata: dict = strategy furnished pair metadata
trained_timestamp: int = timestamp of most recent training
:param metadata: dict = strategy furnished pair metadata
:param trained_timestamp: int = timestamp of most recent training
"""
self.full_path = freqai_util.get_full_models_path(self.config)
self.data_path = Path(
@ -133,25 +132,20 @@ class FreqaiDataKitchen:
Given the dataframe for the full history for training, split the data into
training and test data according to user specified parameters in configuration
file.
:filtered_dataframe: cleaned dataframe ready to be split.
:labels: cleaned labels ready to be split.
:param filtered_dataframe: cleaned dataframe ready to be split.
:param labels: cleaned labels ready to be split.
"""
feat_dict = self.freqai_config["feature_parameters"]
if 'shuffle' not in self.freqai_config['data_split_parameters']:
self.freqai_config["data_split_parameters"].update({'shuffle': False})
weights: npt.ArrayLike
if feat_dict.get("weight_factor", 0) > 0:
weights = self.set_weights_higher_recent(len(filtered_dataframe))
else:
weights = np.ones(len(filtered_dataframe))
if feat_dict.get("stratify_training_data", 0) > 0:
stratification = np.zeros(len(filtered_dataframe))
for i in range(1, len(stratification)):
if i % feat_dict.get("stratify_training_data", 0) == 0:
stratification[i] = 1
else:
stratification = None
if self.freqai_config.get('data_split_parameters', {}).get('test_size', 0.1) != 0:
(
train_features,
@ -164,7 +158,6 @@ class FreqaiDataKitchen:
filtered_dataframe[: filtered_dataframe.shape[0]],
labels,
weights,
stratify=stratification,
**self.config["freqai"]["data_split_parameters"],
)
else:
@ -199,13 +192,14 @@ class FreqaiDataKitchen:
remove all NaNs. Any row with a NaN is removed from training dataset or replaced with
0s in the prediction dataset. However, prediction dataset do_predict will reflect any
row that had a NaN and will shield user from that prediction.
:params:
:unfiltered_df: the full dataframe for the present training period
:training_feature_list: list, the training feature list constructed by
self.build_feature_list() according to user specified parameters in the configuration file.
:labels: the labels for the dataset
:training_filter: boolean which lets the function know if it is training data or
prediction data to be filtered.
:param unfiltered_df: the full dataframe for the present training period
:param training_feature_list: list, the training feature list constructed by
self.build_feature_list() according to user specified
parameters in the configuration file.
:param labels: the labels for the dataset
:param training_filter: boolean which lets the function know if it is training data or
prediction data to be filtered.
:returns:
:filtered_df: dataframe cleaned of NaNs and only containing the user
requested feature set.
@ -214,7 +208,7 @@ class FreqaiDataKitchen:
filtered_df = unfiltered_df.filter(training_feature_list, axis=1)
filtered_df = filtered_df.replace([np.inf, -np.inf], np.nan)
drop_index = pd.isnull(filtered_df).any(1) # get the rows that have NaNs,
drop_index = pd.isnull(filtered_df).any(axis=1) # get the rows that have NaNs,
drop_index = drop_index.replace(True, 1).replace(False, 0) # pep8 requirement.
if (training_filter):
const_cols = list((filtered_df.nunique() == 1).loc[lambda x: x].index)
@ -225,7 +219,7 @@ class FreqaiDataKitchen:
# about removing any row with NaNs
# if labels has multiple columns (user wants to train multiple modelEs), we detect here
labels = unfiltered_df.filter(label_list, axis=1)
drop_index_labels = pd.isnull(labels).any(1)
drop_index_labels = pd.isnull(labels).any(axis=1)
drop_index_labels = drop_index_labels.replace(True, 1).replace(False, 0)
dates = unfiltered_df['date']
filtered_df = filtered_df[
@ -253,7 +247,7 @@ class FreqaiDataKitchen:
else:
# we are backtesting so we need to preserve row number to send back to strategy,
# so now we use do_predict to avoid any prediction based on a NaN
drop_index = pd.isnull(filtered_df).any(1)
drop_index = pd.isnull(filtered_df).any(axis=1)
self.data["filter_drop_index_prediction"] = drop_index
filtered_df.fillna(0, inplace=True)
# replacing all NaNs with zeros to avoid issues in 'prediction', but any prediction
@ -295,8 +289,8 @@ class FreqaiDataKitchen:
def normalize_data(self, data_dictionary: Dict) -> Dict[Any, Any]:
"""
Normalize all data in the data_dictionary according to the training dataset
:params:
:data_dictionary: dictionary containing the cleaned and split training/test data/labels
:param data_dictionary: dictionary containing the cleaned and
split training/test data/labels
:returns:
:data_dictionary: updated dictionary with standardized values.
"""
@ -549,8 +543,7 @@ class FreqaiDataKitchen:
def pca_transform(self, filtered_dataframe: DataFrame) -> None:
"""
Use an existing pca transform to transform data into components
:params:
filtered_dataframe: DataFrame = the cleaned dataframe
:param filtered_dataframe: DataFrame = the cleaned dataframe
"""
pca_components = self.pca.transform(filtered_dataframe)
self.data_dictionary["prediction_features"] = pd.DataFrame(
@ -594,8 +587,7 @@ class FreqaiDataKitchen:
"""
Build/inference a Support Vector Machine to detect outliers
in training data and prediction
:params:
predict: bool = If true, inference an existing SVM model, else construct one
:param predict: bool = If true, inference an existing SVM model, else construct one
"""
if self.keras:
@ -680,11 +672,11 @@ class FreqaiDataKitchen:
Use DBSCAN to cluster training data and remove "noisy" data (read outliers).
User controls this via the config param `DBSCAN_outlier_pct` which indicates the
pct of training data that they want to be considered outliers.
:params:
predict: bool = If False (training), iterate to find the best hyper parameters to match
user requested outlier percent target. If True (prediction), use the parameters
determined from the previous training to estimate if the current prediction point
is an outlier.
:param predict: bool = If False (training), iterate to find the best hyper parameters
to match user requested outlier percent target.
If True (prediction), use the parameters determined from
the previous training to estimate if the current prediction point
is an outlier.
"""
if predict:
@ -835,7 +827,7 @@ class FreqaiDataKitchen:
:, :no_prev_pts
]
distances = distances.replace([np.inf, -np.inf], np.nan)
drop_index = pd.isnull(distances).any(1)
drop_index = pd.isnull(distances).any(axis=1)
distances = distances[drop_index == 0]
inliers = pd.DataFrame(index=distances.index)
@ -908,6 +900,7 @@ class FreqaiDataKitchen:
"""
column_names = dataframe.columns
features = [c for c in column_names if "%" in c]
if not features:
raise OperationalException("Could not find any features!")
@ -1145,15 +1138,13 @@ class FreqaiDataKitchen:
prediction_dataframe: DataFrame = pd.DataFrame(),
) -> DataFrame:
"""
Use the user defined strategy for populating indicators during
retrain
:params:
strategy: IStrategy = user defined strategy object
corr_dataframes: dict = dict containing the informative pair dataframes
(for user defined timeframes)
base_dataframes: dict = dict containing the current pair dataframes
(for user defined timeframes)
metadata: dict = strategy furnished pair metadata
Use the user defined strategy for populating indicators during retrain
:param strategy: IStrategy = user defined strategy object
:param corr_dataframes: dict = dict containing the informative pair dataframes
(for user defined timeframes)
:param base_dataframes: dict = dict containing the current pair dataframes
(for user defined timeframes)
:param metadata: dict = strategy furnished pair metadata
:returns:
dataframe: DataFrame = dataframe containing populated indicators
"""

39
freqtrade/freqai/freqai_interface.py
View File

@ -215,7 +215,8 @@ class IFreqaiModel(ABC):
new_trained_timerange, pair, strategy, dk, data_load_timerange
)
except Exception as msg:
logger.warning(f'Training {pair} raised exception {msg}, skipping.')
logger.warning(f"Training {pair} raised exception {msg.__class__.__name__}. "
f"Message: {msg}, skipping.")
self.train_timer('stop')
@ -297,7 +298,8 @@ class IFreqaiModel(ABC):
if dk.check_if_backtest_prediction_exists():
self.dd.load_metadata(dk)
self.check_if_feature_list_matches_strategy(dataframe_train, dk)
dk.find_features(dataframe_train)
self.check_if_feature_list_matches_strategy(dk)
append_df = dk.get_backtesting_prediction()
dk.append_predictions(append_df)
else:
@ -318,7 +320,6 @@ class IFreqaiModel(ABC):
else:
self.model = self.dd.load_data(pair, dk)
# self.check_if_feature_list_matches_strategy(dataframe_train, dk)
pred_df, do_preds = self.predict(dataframe_backtest, dk)
append_df = dk.get_predictions_to_append(pred_df, do_preds)
dk.append_predictions(append_df)
@ -415,7 +416,7 @@ class IFreqaiModel(ABC):
# allows FreqUI to show full return values.
pred_df, do_preds = self.predict(dataframe, dk)
if pair not in self.dd.historic_predictions:
self.set_initial_historic_predictions(pred_df, dk, pair)
self.set_initial_historic_predictions(pred_df, dk, pair, dataframe)
self.dd.set_initial_return_values(pair, pred_df)
dk.return_dataframe = self.dd.attach_return_values_to_return_dataframe(pair, dataframe)
@ -436,13 +437,13 @@ class IFreqaiModel(ABC):
if self.freqai_info.get('fit_live_predictions_candles', 0) and self.live:
self.fit_live_predictions(dk, pair)
self.dd.append_model_predictions(pair, pred_df, do_preds, dk, len(dataframe))
self.dd.append_model_predictions(pair, pred_df, do_preds, dk, dataframe)
dk.return_dataframe = self.dd.attach_return_values_to_return_dataframe(pair, dataframe)
return
def check_if_feature_list_matches_strategy(
self, dataframe: DataFrame, dk: FreqaiDataKitchen
self, dk: FreqaiDataKitchen
) -> None:
"""
Ensure user is passing the proper feature set if they are reusing an `identifier` pointing
@ -451,11 +452,12 @@ class IFreqaiModel(ABC):
:param dk: FreqaiDataKitchen = non-persistent data container/analyzer for
current coin/bot loop
"""
dk.find_features(dataframe)
if "training_features_list_raw" in dk.data:
feature_list = dk.data["training_features_list_raw"]
else:
feature_list = dk.data['training_features_list']
if dk.training_features_list != feature_list:
raise OperationalException(
"Trying to access pretrained model with `identifier` "
@ -503,13 +505,16 @@ class IFreqaiModel(ABC):
if self.freqai_info["feature_parameters"].get('noise_standard_deviation', 0):
dk.add_noise_to_training_features()
def data_cleaning_predict(self, dk: FreqaiDataKitchen, dataframe: DataFrame) -> None:
def data_cleaning_predict(self, dk: FreqaiDataKitchen) -> None:
"""
Base data cleaning method for predict.
Functions here are complementary to the functions of data_cleaning_train.
"""
ft_params = self.freqai_info["feature_parameters"]
# ensure user is feeding the correct indicators to the model
self.check_if_feature_list_matches_strategy(dk)
if ft_params.get('inlier_metric_window', 0):
dk.compute_inlier_metric(set_='predict')
@ -527,9 +532,6 @@ class IFreqaiModel(ABC):
if ft_params.get("use_DBSCAN_to_remove_outliers", False):
dk.use_DBSCAN_to_remove_outliers(predict=True)
# ensure user is feeding the correct indicators to the model
self.check_if_feature_list_matches_strategy(dk.data_dictionary['prediction_features'], dk)
def model_exists(self, dk: FreqaiDataKitchen) -> bool:
"""
Given a pair and path, check if a model already exists
@ -604,7 +606,7 @@ class IFreqaiModel(ABC):
self.dd.purge_old_models()
def set_initial_historic_predictions(
self, pred_df: DataFrame, dk: FreqaiDataKitchen, pair: str
self, pred_df: DataFrame, dk: FreqaiDataKitchen, pair: str, strat_df: DataFrame
) -> None:
"""
This function is called only if the datadrawer failed to load an
@ -623,11 +625,11 @@ class IFreqaiModel(ABC):
If the user reuses an identifier on a subsequent instance,
this function will not be called. In that case, "real" predictions
will be appended to the loaded set of historic predictions.
:param: df: DataFrame = the dataframe containing the training feature data
:param: model: Any = A model which was `fit` using a common library such as
catboost or lightgbm
:param: dk: FreqaiDataKitchen = object containing methods for data analysis
:param: pair: str = current pair
:param df: DataFrame = the dataframe containing the training feature data
:param model: Any = A model which was `fit` using a common library such as
catboost or lightgbm
:param dk: FreqaiDataKitchen = object containing methods for data analysis
:param pair: str = current pair
"""
self.dd.historic_predictions[pair] = pred_df
@ -647,6 +649,9 @@ class IFreqaiModel(ABC):
for return_str in dk.data['extra_returns_per_train']:
hist_preds_df[return_str] = 0
hist_preds_df['close_price'] = strat_df['close']
hist_preds_df['date_pred'] = strat_df['date']
# # for keras type models, the conv_window needs to be prepended so
# # viewing is correct in frequi
if self.freqai_info.get('keras', False) or self.ft_params.get('inlier_metric_window', 0):

5
freqtrade/freqai/prediction_models/CatboostClassifier.py
View File

@ -20,9 +20,8 @@ class CatboostClassifier(BaseClassifierModel):
def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
"""
User sets up the training and test data to fit their desired model here
:params:
:data_dictionary: the dictionary constructed by DataHandler to hold
all the training and test data/labels.
:param data_dictionary: the dictionary constructed by DataHandler to hold
all the training and test data/labels.
"""
train_data = Pool(

5
freqtrade/freqai/prediction_models/LightGBMClassifier.py
View File

@ -20,9 +20,8 @@ class LightGBMClassifier(BaseClassifierModel):
def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
"""
User sets up the training and test data to fit their desired model here
:params:
:data_dictionary: the dictionary constructed by DataHandler to hold
all the training and test data/labels.
:param data_dictionary: the dictionary constructed by DataHandler to hold
all the training and test data/labels.
"""
if self.freqai_info.get('data_split_parameters', {}).get('test_size', 0.1) == 0:

7
freqtrade/freqai/prediction_models/XGBoostClassifier.py
View File

@ -26,9 +26,8 @@ class XGBoostClassifier(BaseClassifierModel):
def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
"""
User sets up the training and test data to fit their desired model here
:params:
:data_dictionary: the dictionary constructed by DataHandler to hold
all the training and test data/labels.
:param data_dictionary: the dictionary constructed by DataHandler to hold
all the training and test data/labels.
"""
X = data_dictionary["train_features"].to_numpy()
@ -65,7 +64,7 @@ class XGBoostClassifier(BaseClassifierModel):
) -> Tuple[DataFrame, npt.NDArray[np.int_]]:
"""
Filter the prediction features data and predict with it.
:param: unfiltered_df: Full dataframe for the current backtest period.
:param unfiltered_df: Full dataframe for the current backtest period.
:return:
:pred_df: dataframe containing the predictions
:do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove

120
freqtrade/freqtradebot.py
View File

@ -82,7 +82,10 @@ class FreqtradeBot(LoggingMixin):
# Keep this at the end of this initialization method.
self.rpc: RPCManager = RPCManager(self)
self.dataprovider = DataProvider(self.config, self.exchange, self.pairlists, self.rpc)
self.dataprovider = DataProvider(self.config, self.exchange, rpc=self.rpc)
self.pairlists = PairListManager(self.exchange, self.config, self.dataprovider)
self.dataprovider.add_pairlisthandler(self.pairlists)
# Attach Dataprovider to strategy instance
self.strategy.dp = self.dataprovider
@ -597,7 +600,7 @@ class FreqtradeBot(LoggingMixin):
# We should decrease our position
amount = self.exchange.amount_to_contract_precision(
trade.pair,
abs(float(FtPrecise(stake_amount) / FtPrecise(current_exit_rate))))
abs(float(FtPrecise(stake_amount * trade.leverage) / FtPrecise(current_exit_rate))))
if amount > trade.amount:
# This is currently ineffective as remaining would become < min tradable
# Fixing this would require checking for 0.0 there -
@ -1308,7 +1311,7 @@ class FreqtradeBot(LoggingMixin):
# place new order only if new price is supplied
self.execute_entry(
pair=trade.pair,
stake_amount=(order_obj.remaining * order_obj.price),
stake_amount=(order_obj.remaining * order_obj.price / trade.leverage),
price=adjusted_entry_price,
trade=trade,
is_short=trade.is_short,
@ -1340,11 +1343,12 @@ class FreqtradeBot(LoggingMixin):
replacing: Optional[bool] = False
) -> bool:
"""
Buy cancel - cancel order
entry cancel - cancel order
:param replacing: Replacing order - prevent trade deletion.
:return: True if order was fully cancelled
:return: True if trade was fully cancelled
"""
was_trade_fully_canceled = False
side = trade.entry_side.capitalize()
# Cancelled orders may have the status of 'canceled' or 'closed'
if order['status'] not in constants.NON_OPEN_EXCHANGE_STATES:
@ -1371,7 +1375,6 @@ class FreqtradeBot(LoggingMixin):
corder = order
reason = constants.CANCEL_REASON['CANCELLED_ON_EXCHANGE']
side = trade.entry_side.capitalize()
logger.info('%s order %s for %s.', side, reason, trade)
# Using filled to determine the filled amount
@ -1385,24 +1388,15 @@ class FreqtradeBot(LoggingMixin):
was_trade_fully_canceled = True
reason += f", {constants.CANCEL_REASON['FULLY_CANCELLED']}"
else:
# FIXME TODO: This could possibly reworked to not duplicate the code 15 lines below.
self.update_trade_state(trade, trade.open_order_id, corder)
trade.open_order_id = None
logger.info(f'{side} Order timeout for {trade}.')
else:
# if trade is partially complete, edit the stake details for the trade
# and close the order
# cancel_order may not contain the full order dict, so we need to fallback
# to the order dict acquired before cancelling.
# we need to fall back to the values from order if corder does not contain these keys.
trade.amount = filled_amount
# * Check edge cases, we don't want to make leverage > 1.0 if we don't have to
# * (for leverage modes which aren't isolated futures)
trade.stake_amount = trade.amount * trade.open_rate / trade.leverage
# update_trade_state (and subsequently recalc_trade_from_orders) will handle updates
# to the trade object
self.update_trade_state(trade, trade.open_order_id, corder)
trade.open_order_id = None
logger.info(f'Partial {trade.entry_side} order timeout for {trade}.')
reason += f", {constants.CANCEL_REASON['PARTIALLY_FILLED']}"
@ -1417,49 +1411,63 @@ class FreqtradeBot(LoggingMixin):
:return: True if exit order was cancelled, false otherwise
"""
cancelled = False
# if trade is not partially completed, just cancel the order
if order['remaining'] == order['amount'] or order.get('filled') == 0.0:
if not self.exchange.check_order_canceled_empty(order):
try:
# if trade is not partially completed, just delete the order
co = self.exchange.cancel_order_with_result(trade.open_order_id, trade.pair,
trade.amount)
trade.update_order(co)
except InvalidOrderException:
logger.exception(
f"Could not cancel {trade.exit_side} order {trade.open_order_id}")
return False
logger.info('%s order %s for %s.', trade.exit_side.capitalize(), reason, trade)
else:
reason = constants.CANCEL_REASON['CANCELLED_ON_EXCHANGE']
logger.info('%s order %s for %s.', trade.exit_side.capitalize(), reason, trade)
trade.update_order(order)
# Cancelled orders may have the status of 'canceled' or 'closed'
if order['status'] not in constants.NON_OPEN_EXCHANGE_STATES:
filled_val: float = order.get('filled', 0.0) or 0.0
filled_rem_stake = trade.stake_amount - filled_val * trade.open_rate
minstake = self.exchange.get_min_pair_stake_amount(
trade.pair, trade.open_rate, self.strategy.stoploss)
# Double-check remaining amount
if filled_val > 0:
reason = constants.CANCEL_REASON['PARTIALLY_FILLED']
if minstake and filled_rem_stake < minstake:
logger.warning(
f"Order {trade.open_order_id} for {trade.pair} not cancelled, as "
f"the filled amount of {filled_val} would result in an unexitable trade.")
reason = constants.CANCEL_REASON['PARTIALLY_FILLED_KEEP_OPEN']
self._notify_exit_cancel(
trade,
order_type=self.strategy.order_types['exit'],
reason=reason, order_id=order['id'],
sub_trade=trade.amount != order['amount']
)
return False
try:
co = self.exchange.cancel_order_with_result(trade.open_order_id, trade.pair,
trade.amount)
except InvalidOrderException:
logger.exception(
f"Could not cancel {trade.exit_side} order {trade.open_order_id}")
return False
trade.close_rate = None
trade.close_rate_requested = None
trade.close_profit = None
trade.close_profit_abs = None
trade.close_date = None
trade.is_open = True
trade.open_order_id = None
trade.exit_reason = None
# Set exit_reason for fill message
exit_reason_prev = trade.exit_reason
trade.exit_reason = trade.exit_reason + f", {reason}" if trade.exit_reason else reason
self.update_trade_state(trade, trade.open_order_id, co)
# Order might be filled above in odd timing issues.
if co.get('status') in ('canceled', 'cancelled'):
trade.exit_reason = None
trade.open_order_id = None
else:
trade.exit_reason = exit_reason_prev
logger.info(f'{trade.exit_side.capitalize()} order {reason} for {trade}.')
cancelled = True
self.wallets.update()
else:
# TODO: figure out how to handle partially complete sell orders
reason = constants.CANCEL_REASON['PARTIALLY_FILLED_KEEP_OPEN']
cancelled = False
reason = constants.CANCEL_REASON['CANCELLED_ON_EXCHANGE']
logger.info(f'{trade.exit_side.capitalize()} order {reason} for {trade}.')
self.update_trade_state(trade, trade.open_order_id, order)
trade.open_order_id = None
order_obj = trade.select_order_by_order_id(order['id'])
if not order_obj:
raise DependencyException(
f"Order_obj not found for {order['id']}. This should not have happened.")
sub_trade = order_obj.amount != trade.amount
self._notify_exit_cancel(
trade,
order_type=self.strategy.order_types['exit'],
reason=reason, order=order_obj, sub_trade=sub_trade
reason=reason, order_id=order['id'], sub_trade=trade.amount != order['amount']
)
return cancelled
@ -1656,7 +1664,7 @@ class FreqtradeBot(LoggingMixin):
self.rpc.send_msg(msg)
def _notify_exit_cancel(self, trade: Trade, order_type: str, reason: str,
order: Order, sub_trade: bool = False) -> None:
order_id: str, sub_trade: bool = False) -> None:
"""
Sends rpc notification when a sell cancel occurred.
"""
@ -1665,6 +1673,11 @@ class FreqtradeBot(LoggingMixin):
else:
trade.exit_order_status = reason
order = trade.select_order_by_order_id(order_id)
if not order:
raise DependencyException(
f"Order_obj not found for {order_id}. This should not have happened.")
profit_rate = trade.close_rate if trade.close_rate else trade.close_rate_requested
profit_trade = trade.calc_profit(rate=profit_rate)
current_rate = self.exchange.get_rate(
@ -1700,11 +1713,6 @@ class FreqtradeBot(LoggingMixin):
'stake_amount': trade.stake_amount,
}
if 'fiat_display_currency' in self.config:
msg.update({
'fiat_currency': self.config['fiat_display_currency'],
})
# Send the message
self.rpc.send_msg(msg)

6
freqtrade/optimize/backtesting.py
View File

@ -110,7 +110,7 @@ class Backtesting:
self.timeframe = str(self.config.get('timeframe'))
self.timeframe_min = timeframe_to_minutes(self.timeframe)
self.init_backtest_detail()
self.pairlists = PairListManager(self.exchange, self.config)
self.pairlists = PairListManager(self.exchange, self.config, self.dataprovider)
if 'VolumePairList' in self.pairlists.name_list:
raise OperationalException("VolumePairList not allowed for backtesting. "
"Please use StaticPairList instead.")
@ -544,7 +544,7 @@ class Backtesting:
if stake_amount is not None and stake_amount < 0.0:
amount = amount_to_contract_precision(
abs(stake_amount) / current_rate, trade.amount_precision,
abs(stake_amount * trade.leverage) / current_rate, trade.amount_precision,
self.precision_mode, trade.contract_size)
if amount == 0.0:
return trade
@ -1049,7 +1049,7 @@ class Backtesting:
if requested_rate:
self._enter_trade(pair=trade.pair, row=row, trade=trade,
requested_rate=requested_rate,
requested_stake=(order.remaining * order.price),
requested_stake=(order.remaining * order.price / trade.leverage),
direction='short' if trade.is_short else 'long')
self.replaced_entry_orders += 1
else:

7
freqtrade/optimize/hyperopt.py
View File

@ -24,6 +24,7 @@ from pandas import DataFrame
from freqtrade.constants import DATETIME_PRINT_FORMAT, FTHYPT_FILEVERSION, LAST_BT_RESULT_FN, Config
from freqtrade.data.converter import trim_dataframes
from freqtrade.data.history import get_timerange
from freqtrade.data.metrics import calculate_market_change
from freqtrade.enums import HyperoptState
from freqtrade.exceptions import OperationalException
from freqtrade.misc import deep_merge_dicts, file_dump_json, plural
@ -111,6 +112,7 @@ class Hyperopt:
self.clean_hyperopt()
self.market_change = 0.0
self.num_epochs_saved = 0
self.current_best_epoch: Optional[Dict[str, Any]] = None
@ -357,7 +359,7 @@ class Hyperopt:
strat_stats = generate_strategy_stats(
self.pairlist, self.backtesting.strategy.get_strategy_name(),
backtesting_results, min_date, max_date, market_change=0
backtesting_results, min_date, max_date, market_change=self.market_change
)
results_explanation = HyperoptTools.format_results_explanation_string(
strat_stats, self.config['stake_currency'])
@ -425,6 +427,9 @@ class Hyperopt:
# Trim startup period from analyzed dataframe to get correct dates for output.
trimmed = trim_dataframes(preprocessed, self.timerange, self.backtesting.required_startup)
self.min_date, self.max_date = get_timerange(trimmed)
if not self.market_change:
self.market_change = calculate_market_change(trimmed, 'close')
# Real trimming will happen as part of backtesting.
return preprocessed

2
freqtrade/plugins/pairlist/AgeFilter.py
View File

@ -70,7 +70,7 @@ class AgeFilter(IPairList):
def filter_pairlist(self, pairlist: List[str], tickers: Dict) -> List[str]:
"""
:param pairlist: pairlist to filter or sort
:param tickers: Tickers (from exchange.get_tickers()). May be cached.
:param tickers: Tickers (from exchange.get_tickers). May be cached.
:return: new allowlist
"""
needed_pairs: ListPairsWithTimeframes = [

6
freqtrade/plugins/pairlist/IPairList.py
View File

@ -69,7 +69,7 @@ class IPairList(LoggingMixin, ABC):
filter_pairlist() method.
:param pair: Pair that's currently validated
:param ticker: ticker dict as returned from ccxt.fetch_tickers()
:param ticker: ticker dict as returned from ccxt.fetch_ticker
:return: True if the pair can stay, false if it should be removed
"""
raise NotImplementedError()
@ -85,7 +85,7 @@ class IPairList(LoggingMixin, ABC):
it will raise the exception if a Pairlist Handler is used at the first
position in the chain.
:param tickers: Tickers (from exchange.get_tickers()). May be cached.
:param tickers: Tickers (from exchange.get_tickers). May be cached.
:return: List of pairs
"""
raise OperationalException("This Pairlist Handler should not be used "
@ -103,7 +103,7 @@ class IPairList(LoggingMixin, ABC):
own filtration.
:param pairlist: pairlist to filter or sort
:param tickers: Tickers (from exchange.get_tickers()). May be cached.
:param tickers: Tickers (from exchange.get_tickers). May be cached.
:return: new whitelist
"""
if self._enabled:

2
freqtrade/plugins/pairlist/OffsetFilter.py
View File

@ -47,7 +47,7 @@ class OffsetFilter(IPairList):
Filters and sorts pairlist and returns the whitelist again.
Called on each bot iteration - please use internal caching if necessary
:param pairlist: pairlist to filter or sort
:param tickers: Tickers (from exchange.get_tickers()). May be cached.
:param tickers: Tickers (from exchange.get_tickers). May be cached.
:return: new whitelist
"""
if self._offset > len(pairlist):

2
freqtrade/plugins/pairlist/PerformanceFilter.py
View File

@ -44,7 +44,7 @@ class PerformanceFilter(IPairList):
Filters and sorts pairlist and returns the allowlist again.
Called on each bot iteration - please use internal caching if necessary
:param pairlist: pairlist to filter or sort
:param tickers: Tickers (from exchange.get_tickers()). May be cached.
:param tickers: Tickers (from exchange.get_tickers). May be cached.
:return: new allowlist
"""
# Get the trading performance for pairs from database

2
freqtrade/plugins/pairlist/PrecisionFilter.py
View File

@ -49,7 +49,7 @@ class PrecisionFilter(IPairList):
Check if pair has enough room to add a stoploss to avoid "unsellable" buys of very
low value pairs.
:param pair: Pair that's currently validated
:param ticker: ticker dict as returned from ccxt.fetch_tickers()
:param ticker: ticker dict as returned from ccxt.fetch_ticker
:return: True if the pair can stay, false if it should be removed
"""
if ticker.get('last', None) is None:

2
freqtrade/plugins/pairlist/PriceFilter.py
View File

@ -68,7 +68,7 @@ class PriceFilter(IPairList):
"""
Check if if one price-step (pip) is > than a certain barrier.
:param pair: Pair that's currently validated
:param ticker: ticker dict as returned from ccxt.fetch_tickers()
:param ticker: ticker dict as returned from ccxt.fetch_ticker
:return: True if the pair can stay, false if it should be removed
"""
if ticker.get('last', None) is None or ticker.get('last') == 0:

90
freqtrade/plugins/pairlist/ProducerPairList.py Normal file
View File

@ -0,0 +1,90 @@
"""
External Pair List provider
Provides pair list from Leader data
"""
import logging
from typing import Any, Dict, List, Optional
from freqtrade.exceptions import OperationalException
from freqtrade.plugins.pairlist.IPairList import IPairList
logger = logging.getLogger(__name__)
class ProducerPairList(IPairList):
"""
PairList plugin for use with external_message_consumer.
Will use pairs given from leader data.
Usage:
"pairlists": [
{
"method": "ProducerPairList",
"number_assets": 5,
"producer_name": "default",
}
],
"""
def __init__(self, exchange, pairlistmanager,
config: Dict[str, Any], pairlistconfig: Dict[str, Any],
pairlist_pos: int) -> None:
super().__init__(exchange, pairlistmanager, config, pairlistconfig, pairlist_pos)
self._num_assets: int = self._pairlistconfig.get('number_assets', 0)
self._producer_name = self._pairlistconfig.get('producer_name', 'default')
if not config.get('external_message_consumer', {}).get('enabled'):
raise OperationalException(
"ProducerPairList requires external_message_consumer to be enabled.")
@property
def needstickers(self) -> bool:
"""
Boolean property defining if tickers are necessary.
If no Pairlist requires tickers, an empty Dict is passed
as tickers argument to filter_pairlist
"""
return False
def short_desc(self) -> str:
"""
Short whitelist method description - used for startup-messages
-> Please overwrite in subclasses
"""
return f"{self.name} - {self._producer_name}"
def _filter_pairlist(self, pairlist: Optional[List[str]]):
upstream_pairlist = self._pairlistmanager._dataprovider.get_producer_pairs(
self._producer_name)
if pairlist is None:
pairlist = self._pairlistmanager._dataprovider.get_producer_pairs(self._producer_name)
pairs = list(dict.fromkeys(pairlist + upstream_pairlist))
if self._num_assets:
pairs = pairs[:self._num_assets]
return pairs
def gen_pairlist(self, tickers: Dict) -> List[str]:
"""
Generate the pairlist
:param tickers: Tickers (from exchange.get_tickers). May be cached.
:return: List of pairs
"""
pairs = self._filter_pairlist(None)
self.log_once(f"Received pairs: {pairs}", logger.debug)
pairs = self._whitelist_for_active_markets(self.verify_whitelist(pairs, logger.info))
return pairs
def filter_pairlist(self, pairlist: List[str], tickers: Dict) -> List[str]:
"""
Filters and sorts pairlist and returns the whitelist again.
Called on each bot iteration - please use internal caching if necessary
:param pairlist: pairlist to filter or sort
:param tickers: Tickers (from exchange.get_tickers). May be cached.
:return: new whitelist
"""
return self._filter_pairlist(pairlist)

2
freqtrade/plugins/pairlist/ShuffleFilter.py
View File

@ -52,7 +52,7 @@ class ShuffleFilter(IPairList):
Filters and sorts pairlist and returns the whitelist again.
Called on each bot iteration - please use internal caching if necessary
:param pairlist: pairlist to filter or sort
:param tickers: Tickers (from exchange.get_tickers()). May be cached.
:param tickers: Tickers (from exchange.get_tickers). May be cached.
:return: new whitelist
"""
# Shuffle is done inplace

2
freqtrade/plugins/pairlist/SpreadFilter.py
View File

@ -48,7 +48,7 @@ class SpreadFilter(IPairList):
"""
Validate spread for the ticker
:param pair: Pair that's currently validated
:param ticker: ticker dict as returned from ccxt.fetch_tickers()
:param ticker: ticker dict as returned from ccxt.fetch_ticker
:return: True if the pair can stay, false if it should be removed
"""
if 'bid' in ticker and 'ask' in ticker and ticker['ask'] and ticker['bid']:

4
freqtrade/plugins/pairlist/StaticPairList.py
View File

@ -42,7 +42,7 @@ class StaticPairList(IPairList):
def gen_pairlist(self, tickers: Dict) -> List[str]:
"""
Generate the pairlist
:param tickers: Tickers (from exchange.get_tickers()). May be cached.
:param tickers: Tickers (from exchange.get_tickers). May be cached.
:return: List of pairs
"""
if self._allow_inactive:
@ -58,7 +58,7 @@ class StaticPairList(IPairList):
Filters and sorts pairlist and returns the whitelist again.
Called on each bot iteration - please use internal caching if necessary
:param pairlist: pairlist to filter or sort
:param tickers: Tickers (from exchange.get_tickers()). May be cached.
:param tickers: Tickers (from exchange.get_tickers). May be cached.
:return: new whitelist
"""
pairlist_ = deepcopy(pairlist)

2
freqtrade/plugins/pairlist/VolatilityFilter.py
View File

@ -66,7 +66,7 @@ class VolatilityFilter(IPairList):
"""
Validate trading range
:param pairlist: pairlist to filter or sort
:param tickers: Tickers (from exchange.get_tickers()). May be cached.
:param tickers: Tickers (from exchange.get_tickers). May be cached.
:return: new allowlist
"""
needed_pairs: ListPairsWithTimeframes = [

6
freqtrade/plugins/pairlist/VolumePairList.py
View File

@ -113,7 +113,7 @@ class VolumePairList(IPairList):
def gen_pairlist(self, tickers: Dict) -> List[str]:
"""
Generate the pairlist
:param tickers: Tickers (from exchange.get_tickers()). May be cached.
:param tickers: Tickers (from exchange.get_tickers). May be cached.
:return: List of pairs
"""
# Generate dynamic whitelist
@ -150,7 +150,7 @@ class VolumePairList(IPairList):
Filters and sorts pairlist and returns the whitelist again.
Called on each bot iteration - please use internal caching if necessary
:param pairlist: pairlist to filter or sort
:param tickers: Tickers (from exchange.get_tickers()). May be cached.
:param tickers: Tickers (from exchange.get_tickers). May be cached.
:return: new whitelist
"""
if self._use_range:
@ -232,6 +232,4 @@ class VolumePairList(IPairList):
# Limit pairlist to the requested number of pairs
pairs = pairs[:self._number_pairs]
self.log_once(f"Searching {self._number_pairs} pairs: {pairs}", logger.info)
return pairs

2
freqtrade/plugins/pairlist/pairlist_helpers.py
View File

@ -12,7 +12,7 @@ def expand_pairlist(wildcardpl: List[str], available_pairs: List[str],
:param wildcardpl: List of Pairlists, which may contain regex
:param available_pairs: List of all available pairs (`exchange.get_markets().keys()`)
:param keep_invalid: If sets to True, drops invalid pairs silently while expanding regexes
:return expanded pairlist, with Regexes from wildcardpl applied to match all available pairs.
:return: expanded pairlist, with Regexes from wildcardpl applied to match all available pairs.
:raises: ValueError if a wildcard is invalid (like '*/BTC' - which should be `.*/BTC`)
"""
result = []

2
freqtrade/plugins/pairlist/rangestabilityfilter.py
View File

@ -64,7 +64,7 @@ class RangeStabilityFilter(IPairList):
"""
Validate trading range
:param pairlist: pairlist to filter or sort
:param tickers: Tickers (from exchange.get_tickers()). May be cached.
:param tickers: Tickers (from exchange.get_tickers). May be cached.
:return: new allowlist
"""
needed_pairs: ListPairsWithTimeframes = [

8
freqtrade/plugins/pairlistmanager.py
View File

@ -3,11 +3,12 @@ PairList manager class
"""
import logging
from functools import partial
from typing import Dict, List
from typing import Dict, List, Optional
from cachetools import TTLCache, cached
from freqtrade.constants import Config, ListPairsWithTimeframes
from freqtrade.data.dataprovider import DataProvider
from freqtrade.enums import CandleType
from freqtrade.exceptions import OperationalException
from freqtrade.mixins import LoggingMixin
@ -21,13 +22,14 @@ logger = logging.getLogger(__name__)
class PairListManager(LoggingMixin):
def __init__(self, exchange, config: Config) -> None:
def __init__(self, exchange, config: Config, dataprovider: DataProvider = None) -> None:
self._exchange = exchange
self._config = config
self._whitelist = self._config['exchange'].get('pair_whitelist')
self._blacklist = self._config['exchange'].get('pair_blacklist', [])
self._pairlist_handlers: List[IPairList] = []
self._tickers_needed = False
self._dataprovider: Optional[DataProvider] = dataprovider
for pairlist_handler_config in self._config.get('pairlists', []):
pairlist_handler = PairListResolver.load_pairlist(
pairlist_handler_config['method'],
@ -96,6 +98,8 @@ class PairListManager(LoggingMixin):
# to ensure blacklist is respected.
pairlist = self.verify_blacklist(pairlist, logger.warning)
self.log_once(f"Whitelist with {len(pairlist)} pairs: {pairlist}", logger.info)
self._whitelist = pairlist
def verify_blacklist(self, pairlist: List[str], logmethod) -> List[str]:

6
freqtrade/rpc/api_server/webserver.py
View File

@ -198,8 +198,10 @@ class ApiServer(RPCHandler):
logger.debug(f"Found message of type: {message.get('type')}")
# Broadcast it
await self._ws_channel_manager.broadcast(message)
# Sleep, make this configurable?
await asyncio.sleep(0.1)
# Limit messages per sec.
# Could cause problems with queue size if too low, and
# problems with network traffik if too high.
await asyncio.sleep(0.001)
except asyncio.CancelledError:
pass

2
freqtrade/rpc/discord.py
View File

@ -30,9 +30,9 @@ class Discord(Webhook):
pass
def send_msg(self, msg) -> None:
logger.info(f"Sending discord message: {msg}")
if msg['type'].value in self.config['discord']:
logger.info(f"Sending discord message: {msg}")
msg['strategy'] = self.strategy
msg['timeframe'] = self.timeframe

2
freqtrade/rpc/external_message_consumer.py
View File

@ -284,7 +284,7 @@ class ExternalMessageConsumer:
logger.error(f"Empty message received from `{producer_name}`")
return
logger.info(f"Received message of type `{producer_message.type}` from `{producer_name}`")
logger.debug(f"Received message of type `{producer_message.type}` from `{producer_name}`")
message_handler = self._message_handlers.get(producer_message.type)

10
freqtrade/rpc/fiat_convert.py
View File

@ -3,8 +3,8 @@ Module that define classes to convert Crypto-currency to FIAT
e.g BTC to USD
"""
import datetime
import logging
from datetime import datetime
from typing import Dict, List
from cachetools import TTLCache
@ -46,7 +46,9 @@ class CryptoToFiatConverter(LoggingMixin):
if CryptoToFiatConverter.__instance is None:
CryptoToFiatConverter.__instance = object.__new__(cls)
try:
CryptoToFiatConverter._coingekko = CoinGeckoAPI()
# Limit retires to 1 (0 and 1)
# otherwise we risk bot impact if coingecko is down.
CryptoToFiatConverter._coingekko = CoinGeckoAPI(retries=1)
except BaseException:
CryptoToFiatConverter._coingekko = None
return CryptoToFiatConverter.__instance
@ -67,7 +69,7 @@ class CryptoToFiatConverter(LoggingMixin):
logger.warning(
"Too many requests for CoinGecko API, backing off and trying again later.")
# Set backoff timestamp to 60 seconds in the future
self._backoff = datetime.datetime.now().timestamp() + 60
self._backoff = datetime.now().timestamp() + 60
return
# If the request is not a 429 error we want to raise the normal error
logger.error(
@ -81,7 +83,7 @@ class CryptoToFiatConverter(LoggingMixin):
def _get_gekko_id(self, crypto_symbol):
if not self._coinlistings:
if self._backoff <= datetime.datetime.now().timestamp():
if self._backoff <= datetime.now().timestamp():
self._load_cryptomap()
# Still not loaded.
if not self._coinlistings:

2
freqtrade/rpc/rpc_manager.py
View File

@ -67,7 +67,7 @@ class RPCManager:
'status': 'stopping bot'
}
"""
if msg.get('type') is not RPCMessageType.ANALYZED_DF:
if msg.get('type') not in (RPCMessageType.ANALYZED_DF, RPCMessageType.WHITELIST):
logger.info('Sending rpc message: %s', msg)
if 'pair' in msg:
msg.update({

8
freqtrade/rpc/webhook.py
View File

@ -61,6 +61,14 @@ class Webhook(RPCHandler):
RPCMessageType.STARTUP,
RPCMessageType.WARNING):
valuedict = whconfig.get('webhookstatus')
elif msg['type'] in (
RPCMessageType.PROTECTION_TRIGGER,
RPCMessageType.PROTECTION_TRIGGER_GLOBAL,
RPCMessageType.WHITELIST,
RPCMessageType.ANALYZED_DF,
RPCMessageType.STRATEGY_MSG):
# Don't fail for non-implemented types
return
else:
raise NotImplementedError('Unknown message type: {}'.format(msg['type']))
if not valuedict:

1
freqtrade/templates/sample_strategy.py
View File

@ -5,6 +5,7 @@
import numpy as np # noqa
import pandas as pd # noqa
from pandas import DataFrame
from typing import Optional, Union
from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter,
IStrategy, IntParameter)

14
requirements-dev.txt
View File

@ -8,23 +8,23 @@
coveralls==3.3.1
flake8==5.0.4
flake8-tidy-imports==4.8.0
mypy==0.971
mypy==0.982
pre-commit==2.20.0
pytest==7.1.3
pytest-asyncio==0.19.0
pytest-cov==3.0.0
pytest-mock==3.8.2
pytest-cov==4.0.0
pytest-mock==3.10.0
pytest-random-order==1.0.4
isort==5.10.1
# For datetime mocking
time-machine==2.8.1
time-machine==2.8.2
# Convert jupyter notebooks to markdown documents
nbconvert==7.0.0
nbconvert==7.2.1
# mypy types
types-cachetools==5.2.1
types-filelock==3.2.7
types-requests==2.28.11
types-tabulate==0.8.11
types-requests==2.28.11.2
types-tabulate==0.9.0.0
types-python-dateutil==2.8.19

2
requirements-freqai.txt
View File

@ -4,6 +4,6 @@
# Required for freqai
scikit-learn==1.1.2
joblib==1.2.0
catboost==1.0.6; platform_machine != 'aarch64'
catboost==1.1; platform_machine != 'aarch64'
lightgbm==3.3.2
xgboost==1.6.2

2
requirements-hyperopt.txt
View File

@ -2,7 +2,7 @@
-r requirements.txt
# Required for hyperopt
scipy==1.9.1
scipy==1.9.2
scikit-learn==1.1.2
scikit-optimize==0.9.0
filelock==3.8.0

5
requirements.txt
View File

@ -4,7 +4,7 @@ pandas==1.5.0; platform_machine != 'armv7l'
pandas==1.4.3; platform_machine == 'armv7l'
pandas-ta==0.3.14b
ccxt==1.93.98
ccxt==1.95.30
# Pin cryptography for now due to rust build errors with piwheels
cryptography==38.0.1
aiohttp==3.8.3
@ -17,7 +17,7 @@ urllib3==1.26.12
jsonschema==4.16.0
TA-Lib==0.4.25
technical==1.3.0
tabulate==0.8.10
tabulate==0.9.0
pycoingecko==3.0.0
jinja2==3.1.2
tables==3.7.0
@ -38,6 +38,7 @@ sdnotify==0.3.2
# API Server
fastapi==0.85.0
pydantic>=1.8.0
uvicorn==0.18.3
pyjwt==2.5.0
aiofiles==22.1.0

327
scripts/ws_client.py Normal file
View File

@ -0,0 +1,327 @@
#!/usr/bin/env python3
"""
Simple command line client for Testing/debugging
a Freqtrade bot's message websocket
Should not import anything from freqtrade,
so it can be used as a standalone script.
"""
import argparse
import asyncio
import logging
import socket
import sys
import time
from pathlib import Path
import orjson
import pandas
import rapidjson
import websockets
from dateutil.relativedelta import relativedelta
logger = logging.getLogger("WebSocketClient")
# ---------------------------------------------------------------------------
def setup_logging(filename: str):
logging.basicConfig(
level=logging.INFO,
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
handlers=[
logging.FileHandler(filename),
logging.StreamHandler()
]
)
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument(
'-c',
'--config',
help='Specify configuration file (default: %(default)s). ',
dest='config',
type=str,
metavar='PATH',
default='config.json'
)
parser.add_argument(
'-l',
'--logfile',
help='The filename to log to.',
dest='logfile',
type=str,
default='ws_client.log'
)
args = parser.parse_args()
return vars(args)
def load_config(configfile):
file = Path(configfile)
if file.is_file():
with file.open("r") as f:
config = rapidjson.load(f, parse_mode=rapidjson.PM_COMMENTS |
rapidjson.PM_TRAILING_COMMAS)
return config
else:
logger.warning(f"Could not load config file {file}.")
sys.exit(1)
def readable_timedelta(delta):
"""
Convert a dateutil.relativedelta to a readable format
:param delta: A dateutil.relativedelta
:returns: The readable time difference string
"""
attrs = ['years', 'months', 'days', 'hours', 'minutes', 'seconds', 'microseconds']
return ", ".join([
'%d %s' % (getattr(delta, attr), attr if getattr(delta, attr) > 1 else attr[:-1])
for attr in attrs if getattr(delta, attr)
])
# ----------------------------------------------------------------------------
def json_serialize(message):
"""
Serialize a message to JSON using orjson
:param message: The message to serialize
"""
return str(orjson.dumps(message), "utf-8")
def json_deserialize(message):
"""
Deserialize JSON to a dict
:param message: The message to deserialize
"""
def json_to_dataframe(data: str) -> pandas.DataFrame:
dataframe = pandas.read_json(data, orient='split')
if 'date' in dataframe.columns:
dataframe['date'] = pandas.to_datetime(dataframe['date'], unit='ms', utc=True)
return dataframe
def _json_object_hook(z):
if z.get('__type__') == 'dataframe':
return json_to_dataframe(z.get('__value__'))
return z
return rapidjson.loads(message, object_hook=_json_object_hook)
# ---------------------------------------------------------------------------
class ClientProtocol:
logger = logging.getLogger("WebSocketClient.Protocol")
_MESSAGE_COUNT = 0
_LAST_RECEIVED_AT = 0 # The epoch we received a message most recently
async def on_connect(self, websocket):
# On connection we have to send our initial requests
initial_requests = [
{
"type": "subscribe", # The subscribe request should always be first
"data": ["analyzed_df", "whitelist"] # The message types we want
},
{
"type": "whitelist",
"data": None,
},
{
"type": "analyzed_df",
"data": {"limit": 1500}
}
]
for request in initial_requests:
await websocket.send(json_serialize(request))
async def on_message(self, websocket, name, message):
deserialized = json_deserialize(message)
message_size = sys.getsizeof(message)
message_type = deserialized.get('type')
message_data = deserialized.get('data')
self.logger.info(
f"Received message of type {message_type} [{message_size} bytes] @ [{name}]"
)
time_difference = self._calculate_time_difference()
if self._MESSAGE_COUNT > 0:
self.logger.info(f"Time since last message: {time_difference}")
message_handler = getattr(self, f"_handle_{message_type}", None) or self._handle_default
await message_handler(name, message_type, message_data)
self._MESSAGE_COUNT += 1
self.logger.info(f"[{self._MESSAGE_COUNT}] total messages..")
self.logger.info("-" * 80)
def _calculate_time_difference(self):
old_last_received_at = self._LAST_RECEIVED_AT
self._LAST_RECEIVED_AT = time.time() * 1000
time_delta = relativedelta(microseconds=(self._LAST_RECEIVED_AT - old_last_received_at))
return readable_timedelta(time_delta)
async def _handle_whitelist(self, name, type, data):
self.logger.info(data)
async def _handle_analyzed_df(self, name, type, data):
key, la, df = data['key'], data['la'], data['df']
if not df.empty:
columns = ", ".join([str(column) for column in df.columns])
self.logger.info(key)
self.logger.info(f"Last analyzed datetime: {la}")
self.logger.info(f"Latest candle datetime: {df.iloc[-1]['date']}")
self.logger.info(f"DataFrame length: {len(df)}")
self.logger.info(f"DataFrame columns: {columns}")
else:
self.logger.info("Empty DataFrame")
async def _handle_default(self, name, type, data):
self.logger.info("Unkown message of type {type} received...")
self.logger.info(data)
async def create_client(
host,
port,
token,
name='default',
protocol=ClientProtocol(),
sleep_time=10,
ping_timeout=10,
wait_timeout=30,
**kwargs
):
"""
Create a websocket client and listen for messages
:param host: The host
:param port: The port
:param token: The websocket auth token
:param name: The name of the producer
:param **kwargs: Any extra kwargs passed to websockets.connect
"""
while 1:
try:
websocket_url = f"ws://{host}:{port}/api/v1/message/ws?token={token}"
logger.info(f"Attempting to connect to {name} @ {host}:{port}")
async with websockets.connect(websocket_url, **kwargs) as ws:
logger.info("Connection successful...")
await protocol.on_connect(ws)
# Now listen for messages
while 1:
try:
message = await asyncio.wait_for(
ws.recv(),
timeout=wait_timeout
)
await protocol.on_message(ws, name, message)
except (
asyncio.TimeoutError,
websockets.exceptions.ConnectionClosed
):
# Try pinging
try:
pong = ws.ping()
await asyncio.wait_for(
pong,
timeout=ping_timeout
)
logger.info("Connection still alive...")
continue
except asyncio.TimeoutError:
logger.error(f"Ping timed out, retrying in {sleep_time}s")
await asyncio.sleep(sleep_time)
break
except (
socket.gaierror,
ConnectionRefusedError,
websockets.exceptions.InvalidStatusCode,
websockets.exceptions.InvalidMessage
) as e:
logger.error(f"Connection Refused - {e} retrying in {sleep_time}s")
await asyncio.sleep(sleep_time)
continue
except (
websockets.exceptions.ConnectionClosedError,
websockets.exceptions.ConnectionClosedOK
):
# Just keep trying to connect again indefinitely
await asyncio.sleep(sleep_time)
continue
except Exception as e:
# An unforseen error has occurred, log and try reconnecting again
logger.error("Unexpected error has occurred:")
logger.exception(e)
await asyncio.sleep(sleep_time)
continue
# ---------------------------------------------------------------------------
async def _main(args):
setup_logging(args['logfile'])
config = load_config(args['config'])
emc_config = config.get('external_message_consumer', {})
producers = emc_config.get('producers', [])
producer = producers[0]
wait_timeout = emc_config.get('wait_timeout', 300)
ping_timeout = emc_config.get('ping_timeout', 10)
sleep_time = emc_config.get('sleep_time', 10)
message_size_limit = (emc_config.get('message_size_limit', 8) << 20)
await create_client(
producer['host'],
producer['port'],
producer['ws_token'],
producer['name'],
sleep_time=sleep_time,
ping_timeout=ping_timeout,
wait_timeout=wait_timeout,
max_size=message_size_limit
)
def main():
args = parse_args()
try:
asyncio.run(_main(args))
except KeyboardInterrupt:
logger.info("Exiting...")
if __name__ == "__main__":
main()

3
setup.py
View File

@ -72,9 +72,10 @@ setup(
'pandas',
'tables',
'blosc',
'joblib',
'joblib>=1.2.0',
'pyarrow; platform_machine != "armv7l"',
'fastapi',
'pydantic>=1.8.0',
'uvicorn',
'psutil',
'pyjwt',

31
tests/conftest.py
View File

@ -10,6 +10,7 @@ from unittest.mock import MagicMock, Mock, PropertyMock
import arrow
import numpy as np
import pandas as pd
import pytest
from telegram import Chat, Message, Update
@ -19,6 +20,7 @@ from freqtrade.data.converter import ohlcv_to_dataframe
from freqtrade.edge import PairInfo
from freqtrade.enums import CandleType, MarginMode, RunMode, SignalDirection, TradingMode
from freqtrade.exchange import Exchange
from freqtrade.exchange.exchange import timeframe_to_minutes
from freqtrade.freqtradebot import FreqtradeBot
from freqtrade.persistence import LocalTrade, Order, Trade, init_db
from freqtrade.resolvers import ExchangeResolver
@ -82,6 +84,33 @@ def get_args(args):
return Arguments(args).get_parsed_arg()
def generate_test_data(timeframe: str, size: int, start: str = '2020-07-05'):
np.random.seed(42)
tf_mins = timeframe_to_minutes(timeframe)
base = np.random.normal(20, 2, size=size)
date = pd.date_range(start, periods=size, freq=f'{tf_mins}min', tz='UTC')
df = pd.DataFrame({
'date': date,
'open': base,
'high': base + np.random.normal(2, 1, size=size),
'low': base - np.random.normal(2, 1, size=size),
'close': base + np.random.normal(0, 1, size=size),
'volume': np.random.normal(200, size=size)
}
)
df = df.dropna()
return df
def generate_test_data_raw(timeframe: str, size: int, start: str = '2020-07-05'):
""" Generates data in the ohlcv format used by ccxt """
df = generate_test_data(timeframe, size, start)
df['date'] = df.loc[:, 'date'].view(np.int64) // 1000 // 1000
return list(list(x) for x in zip(*(df[x].values.tolist() for x in df.columns)))
# Source: https://stackoverflow.com/questions/29881236/how-to-mock-asyncio-coroutines
# TODO: This should be replaced with AsyncMock once support for python 3.7 is dropped.
def get_mock_coro(return_value=None, side_effect=None):
@ -200,6 +229,8 @@ def patch_freqtradebot(mocker, config) -> None:
mocker.patch('freqtrade.freqtradebot.RPCManager._init', MagicMock())
mocker.patch('freqtrade.freqtradebot.RPCManager.send_msg', MagicMock())
patch_whitelist(mocker, config)
mocker.patch('freqtrade.freqtradebot.ExternalMessageConsumer')
mocker.patch('freqtrade.configuration.config_validation._validate_consumers')
def get_patched_freqtradebot(mocker, config) -> FreqtradeBot:

2
tests/data/test_btanalysis.py
View File

@ -235,7 +235,7 @@ def test_calculate_market_change(testdatadir):
data = load_data(datadir=testdatadir, pairs=pairs, timeframe='5m')
result = calculate_market_change(data)
assert isinstance(result, float)
assert pytest.approx(result) == 0.00955514
assert pytest.approx(result) == 0.01100002
def test_combine_dataframes_with_mean(testdatadir):

4
tests/data/test_datahandler.py
View File

@ -139,10 +139,10 @@ def test_jsondatahandler_ohlcv_purge(mocker, testdatadir):
def test_jsondatahandler_ohlcv_load(testdatadir, caplog):
dh = JsonDataHandler(testdatadir)
df = dh.ohlcv_load('XRP/ETH', '5m', 'spot')
assert len(df) == 711
assert len(df) == 712
df_mark = dh.ohlcv_load('UNITTEST/USDT', '1h', candle_type="mark")
assert len(df_mark) == 99
assert len(df_mark) == 100
df_no_mark = dh.ohlcv_load('UNITTEST/USDT', '1h', 'spot')
assert len(df_no_mark) == 0

4
tests/data/test_entryexitanalysis.py
View File

@ -124,8 +124,8 @@ def test_backtest_analysis_nomock(default_conf, mocker, caplog, testdatadir, tmp
assert '0' in captured.out
assert '0.01616' in captured.out
assert '34.049' in captured.out
assert '0.104104' in captured.out
assert '47.0996' in captured.out
assert '0.104411' in captured.out
assert '52.8292' in captured.out
# test group 1
args = get_args(base_args + ['--analysis-groups', "1"])

10
tests/data/test_history.py
View File

@ -377,8 +377,8 @@ def test_load_partial_missing(testdatadir, caplog) -> None:
td = ((end - start).total_seconds() // 60 // 5) + 1
assert td != len(data['UNITTEST/BTC'])
# Shift endtime with +5 - as last candle is dropped (partial candle)
end_real = arrow.get(data['UNITTEST/BTC'].iloc[-1, 0]).shift(minutes=5)
# Shift endtime with +5
end_real = arrow.get(data['UNITTEST/BTC'].iloc[-1, 0])
assert log_has(f'UNITTEST/BTC, spot, 5m, '
f'data ends at {end_real.strftime(DATETIME_PRINT_FORMAT)}',
caplog)
@ -447,7 +447,7 @@ def test_get_timerange(default_conf, mocker, testdatadir) -> None:
)
min_date, max_date = get_timerange(data)
assert min_date.isoformat() == '2017-11-04T23:02:00+00:00'
assert max_date.isoformat() == '2017-11-14T22:58:00+00:00'
assert max_date.isoformat() == '2017-11-14T22:59:00+00:00'
def test_validate_backtest_data_warn(default_conf, mocker, caplog, testdatadir) -> None:
@ -470,7 +470,7 @@ def test_validate_backtest_data_warn(default_conf, mocker, caplog, testdatadir)
min_date, max_date, timeframe_to_minutes('1m'))
assert len(caplog.record_tuples) == 1
assert log_has(
"UNITTEST/BTC has missing frames: expected 14396, got 13680, that's 716 missing values",
"UNITTEST/BTC has missing frames: expected 14397, got 13681, that's 716 missing values",
caplog)
@ -480,7 +480,7 @@ def test_validate_backtest_data(default_conf, mocker, caplog, testdatadir) -> No
default_conf.update({'strategy': CURRENT_TEST_STRATEGY})
strategy = StrategyResolver.load_strategy(default_conf)
timerange = TimeRange('index', 'index', 200, 250)
timerange = TimeRange()
data = strategy.advise_all_indicators(
load_data(
datadir=testdatadir,

22
tests/exchange/test_binance.py
View File

@ -5,7 +5,7 @@ from unittest.mock import MagicMock, PropertyMock
import ccxt
import pytest
from freqtrade.enums import MarginMode, TradingMode
from freqtrade.enums import CandleType, MarginMode, TradingMode
from freqtrade.exceptions import DependencyException, InvalidOrderException, OperationalException
from tests.conftest import get_mock_coro, get_patched_exchange, log_has_re
from tests.exchange.test_exchange import ccxt_exceptionhandlers
@ -501,6 +501,24 @@ def test_fill_leverage_tiers_binance_dryrun(default_conf, mocker, leverage_tiers
assert len(v) == len(value)
def test_additional_exchange_init_binance(default_conf, mocker):
api_mock = MagicMock()
api_mock.fapiPrivateGetPositionsideDual = MagicMock(return_value={"dualSidePosition": True})
api_mock.fapiPrivateGetMultiAssetsMargin = MagicMock(return_value={"multiAssetsMargin": True})
default_conf['dry_run'] = False
default_conf['trading_mode'] = TradingMode.FUTURES
default_conf['margin_mode'] = MarginMode.ISOLATED
with pytest.raises(OperationalException,
match=r"Hedge Mode is not supported.*\nMulti-Asset Mode is not supported.*"):
get_patched_exchange(mocker, default_conf, id="binance", api_mock=api_mock)
api_mock.fapiPrivateGetPositionsideDual = MagicMock(return_value={"dualSidePosition": False})
api_mock.fapiPrivateGetMultiAssetsMargin = MagicMock(return_value={"multiAssetsMargin": False})
exchange = get_patched_exchange(mocker, default_conf, id="binance", api_mock=api_mock)
assert exchange
ccxt_exceptionhandlers(mocker, default_conf, api_mock, 'binance',
"additional_exchange_init", "fapiPrivateGetPositionsideDual")
def test__set_leverage_binance(mocker, default_conf):
api_mock = MagicMock()
@ -524,7 +542,7 @@ def test__set_leverage_binance(mocker, default_conf):
@pytest.mark.asyncio
@pytest.mark.parametrize('candle_type', ['mark', ''])
@pytest.mark.parametrize('candle_type', [CandleType.MARK, ''])
async def test__async_get_historic_ohlcv_binance(default_conf, mocker, caplog, candle_type):
ohlcv = [
[

10
tests/exchange/test_ccxt_compat.py
View File

@ -137,6 +137,7 @@ def exchange_futures(request, exchange_conf, class_mocker):
'freqtrade.exchange.binance.Binance.fill_leverage_tiers')
class_mocker.patch('freqtrade.exchange.exchange.Exchange.fetch_trading_fees')
class_mocker.patch('freqtrade.exchange.okx.Okx.additional_exchange_init')
class_mocker.patch('freqtrade.exchange.binance.Binance.additional_exchange_init')
class_mocker.patch('freqtrade.exchange.exchange.Exchange.load_cached_leverage_tiers',
return_value=None)
class_mocker.patch('freqtrade.exchange.exchange.Exchange.cache_leverage_tiers')
@ -267,9 +268,8 @@ class TestCCXTExchange():
now = datetime.now(timezone.utc) - timedelta(minutes=(timeframe_to_minutes(timeframe) * 2))
assert exchange.klines(pair_tf).iloc[-1]['date'] >= timeframe_to_prev_date(timeframe, now)
def ccxt__async_get_candle_history(self, exchange, exchangename, pair, timeframe):
def ccxt__async_get_candle_history(self, exchange, exchangename, pair, timeframe, candle_type):
candle_type = CandleType.SPOT
timeframe_ms = timeframe_to_msecs(timeframe)
now = timeframe_to_prev_date(
timeframe, datetime.now(timezone.utc))
@ -301,7 +301,8 @@ class TestCCXTExchange():
return
pair = EXCHANGES[exchangename]['pair']
timeframe = EXCHANGES[exchangename]['timeframe']
self.ccxt__async_get_candle_history(exchange, exchangename, pair, timeframe)
self.ccxt__async_get_candle_history(
exchange, exchangename, pair, timeframe, CandleType.SPOT)
def test_ccxt__async_get_candle_history_futures(self, exchange_futures):
exchange, exchangename = exchange_futures
@ -310,7 +311,8 @@ class TestCCXTExchange():
return
pair = EXCHANGES[exchangename].get('futures_pair', EXCHANGES[exchangename]['pair'])
timeframe = EXCHANGES[exchangename]['timeframe']
self.ccxt__async_get_candle_history(exchange, exchangename, pair, timeframe)
self.ccxt__async_get_candle_history(
exchange, exchangename, pair, timeframe, CandleType.FUTURES)
def test_ccxt_fetch_funding_rate_history(self, exchange_futures):
exchange, exchangename = exchange_futures

111
tests/exchange/test_exchange.py
View File

@ -22,7 +22,8 @@ from freqtrade.exchange.common import (API_FETCH_ORDER_RETRY_COUNT, API_RETRY_CO
calculate_backoff, remove_credentials)
from freqtrade.exchange.exchange import amount_to_contract_precision
from freqtrade.resolvers.exchange_resolver import ExchangeResolver
from tests.conftest import get_mock_coro, get_patched_exchange, log_has, log_has_re, num_log_has_re
from tests.conftest import (generate_test_data_raw, get_mock_coro, get_patched_exchange, log_has,
log_has_re, num_log_has_re)
# Make sure to always keep one exchange here which is NOT subclassed!!
@ -2083,7 +2084,7 @@ async def test__async_get_historic_ohlcv(default_conf, mocker, caplog, exchange_
def test_refresh_latest_ohlcv(mocker, default_conf, caplog, candle_type) -> None:
ohlcv = [
[
(arrow.utcnow().int_timestamp - 1) * 1000, # unix timestamp ms
(arrow.utcnow().shift(minutes=-5).int_timestamp) * 1000, # unix timestamp ms
1, # open
2, # high
3, # low
@ -2140,10 +2141,22 @@ def test_refresh_latest_ohlcv(mocker, default_conf, caplog, candle_type) -> None
assert len(res) == len(pairs)
assert exchange._api_async.fetch_ohlcv.call_count == 0
exchange.required_candle_call_count = 1
assert log_has(f"Using cached candle (OHLCV) data for {pairs[0][0]}, "
f"{pairs[0][1]}, {candle_type} ...",
caplog)
caplog.clear()
# Reset refresh times - must do 2 call per pair as cache is expired
exchange._pairs_last_refresh_time = {}
res = exchange.refresh_latest_ohlcv(
[('IOTA/ETH', '5m', candle_type), ('XRP/ETH', '5m', candle_type)])
assert len(res) == len(pairs)
assert exchange._api_async.fetch_ohlcv.call_count == 4
# cache - but disabled caching
exchange._api_async.fetch_ohlcv.reset_mock()
exchange.required_candle_call_count = 1
pairlist = [
('IOTA/ETH', '5m', candle_type),
('XRP/ETH', '5m', candle_type),
@ -2159,6 +2172,7 @@ def test_refresh_latest_ohlcv(mocker, default_conf, caplog, candle_type) -> None
assert exchange._api_async.fetch_ohlcv.call_count == 3
exchange._api_async.fetch_ohlcv.reset_mock()
caplog.clear()
# Call with invalid timeframe
res = exchange.refresh_latest_ohlcv([('IOTA/ETH', '3m', candle_type)], cache=False)
if candle_type != CandleType.MARK:
@ -2169,6 +2183,91 @@ def test_refresh_latest_ohlcv(mocker, default_conf, caplog, candle_type) -> None
assert len(res) == 1
@pytest.mark.parametrize('candle_type', [CandleType.FUTURES, CandleType.MARK, CandleType.SPOT])
def test_refresh_latest_ohlcv_cache(mocker, default_conf, candle_type, time_machine) -> None:
start = datetime(2021, 8, 1, 0, 0, 0, 0, tzinfo=timezone.utc)
ohlcv = generate_test_data_raw('1h', 100, start.strftime('%Y-%m-%d'))
time_machine.move_to(start + timedelta(hours=99, minutes=30))
exchange = get_patched_exchange(mocker, default_conf)
exchange._api_async.fetch_ohlcv = get_mock_coro(ohlcv)
pair1 = ('IOTA/ETH', '1h', candle_type)
pair2 = ('XRP/ETH', '1h', candle_type)
pairs = [pair1, pair2]
# No caching
assert not exchange._klines
res = exchange.refresh_latest_ohlcv(pairs, cache=False)
assert exchange._api_async.fetch_ohlcv.call_count == 2
assert len(res) == 2
assert len(res[pair1]) == 99
assert len(res[pair2]) == 99
assert not exchange._klines
exchange._api_async.fetch_ohlcv.reset_mock()
# With caching
res = exchange.refresh_latest_ohlcv(pairs)
assert exchange._api_async.fetch_ohlcv.call_count == 2
assert len(res) == 2
assert len(res[pair1]) == 99
assert len(res[pair2]) == 99
assert exchange._klines
assert exchange._pairs_last_refresh_time[pair1] == ohlcv[-1][0] // 1000
exchange._api_async.fetch_ohlcv.reset_mock()
# Returned from cache
res = exchange.refresh_latest_ohlcv(pairs)
assert exchange._api_async.fetch_ohlcv.call_count == 0
assert len(res) == 2
assert len(res[pair1]) == 99
assert len(res[pair2]) == 99
assert exchange._pairs_last_refresh_time[pair1] == ohlcv[-1][0] // 1000
# Move time 1 candle further but result didn't change yet
time_machine.move_to(start + timedelta(hours=101))
res = exchange.refresh_latest_ohlcv(pairs)
assert exchange._api_async.fetch_ohlcv.call_count == 2
assert len(res) == 2
assert len(res[pair1]) == 99
assert len(res[pair2]) == 99
assert exchange._pairs_last_refresh_time[pair1] == ohlcv[-1][0] // 1000
refresh_pior = exchange._pairs_last_refresh_time[pair1]
# New candle on exchange - only return 50 candles (but one candle further)
new_startdate = (start + timedelta(hours=51)).strftime('%Y-%m-%d %H:%M')
ohlcv = generate_test_data_raw('1h', 50, new_startdate)
exchange._api_async.fetch_ohlcv = get_mock_coro(ohlcv)
res = exchange.refresh_latest_ohlcv(pairs)
assert exchange._api_async.fetch_ohlcv.call_count == 2
assert len(res) == 2
assert len(res[pair1]) == 100
assert len(res[pair2]) == 100
assert refresh_pior != exchange._pairs_last_refresh_time[pair1]
assert exchange._pairs_last_refresh_time[pair1] == ohlcv[-1][0] // 1000
assert exchange._pairs_last_refresh_time[pair2] == ohlcv[-1][0] // 1000
exchange._api_async.fetch_ohlcv.reset_mock()
# Retry same call - no action.
res = exchange.refresh_latest_ohlcv(pairs)
assert exchange._api_async.fetch_ohlcv.call_count == 0
assert len(res) == 2
assert len(res[pair1]) == 100
assert len(res[pair2]) == 100
# Move to distant future (so a 1 call would cause a hole in the data)
time_machine.move_to(start + timedelta(hours=2000))
ohlcv = generate_test_data_raw('1h', 100, start + timedelta(hours=1900))
exchange._api_async.fetch_ohlcv = get_mock_coro(ohlcv)
res = exchange.refresh_latest_ohlcv(pairs)
assert exchange._api_async.fetch_ohlcv.call_count == 2
assert len(res) == 2
# Cache eviction - new data.
assert len(res[pair1]) == 99
assert len(res[pair2]) == 99
@pytest.mark.asyncio
@pytest.mark.parametrize("exchange_name", EXCHANGES)
async def test__async_get_candle_history(default_conf, mocker, caplog, exchange_name):
@ -2240,7 +2339,8 @@ async def test__async_kucoin_get_candle_history(default_conf, mocker, caplog):
for _ in range(3):
with pytest.raises(DDosProtection, match=r'429 Too Many Requests'):
await exchange._async_get_candle_history(
"ETH/BTC", "5m", (arrow.utcnow().int_timestamp - 2000) * 1000, count=3)
"ETH/BTC", "5m", CandleType.SPOT,
since_ms=(arrow.utcnow().int_timestamp - 2000) * 1000, count=3)
assert num_log_has_re(msg, caplog) == 3
caplog.clear()
@ -2256,7 +2356,8 @@ async def test__async_kucoin_get_candle_history(default_conf, mocker, caplog):
for _ in range(3):
with pytest.raises(DDosProtection, match=r'429 Too Many Requests'):
await exchange._async_get_candle_history(
"ETH/BTC", "5m", (arrow.utcnow().int_timestamp - 2000) * 1000, count=3)
"ETH/BTC", "5m", CandleType.SPOT,
(arrow.utcnow().int_timestamp - 2000) * 1000, count=3)
# Expect the "returned exception" message 12 times (4 retries * 3 (loop))
assert num_log_has_re(msg, caplog) == 12
assert num_log_has_re(msg2, caplog) == 9

85
tests/exchange/test_exchange_utils.py Normal file
View File

@ -0,0 +1,85 @@
# pragma pylint: disable=missing-docstring, protected-access, invalid-name
import pytest
from freqtrade.enums import RunMode
from freqtrade.exceptions import OperationalException
from freqtrade.exchange.check_exchange import check_exchange
from tests.conftest import log_has_re
def test_check_exchange(default_conf, caplog) -> None:
# Test an officially supported by Freqtrade team exchange
default_conf['runmode'] = RunMode.DRY_RUN
default_conf.get('exchange').update({'name': 'BITTREX'})
assert check_exchange(default_conf)
assert log_has_re(r"Exchange .* is officially supported by the Freqtrade development team\.",
caplog)
caplog.clear()
# Test an officially supported by Freqtrade team exchange
default_conf.get('exchange').update({'name': 'binance'})
assert check_exchange(default_conf)
assert log_has_re(
r"Exchange \"binance\" is officially supported by the Freqtrade development team\.",
caplog)
caplog.clear()
# Test an officially supported by Freqtrade team exchange
default_conf.get('exchange').update({'name': 'binanceus'})
assert check_exchange(default_conf)
assert log_has_re(
r"Exchange \"binanceus\" is officially supported by the Freqtrade development team\.",
caplog)
caplog.clear()
# Test an officially supported by Freqtrade team exchange - with remapping
default_conf.get('exchange').update({'name': 'okex'})
assert check_exchange(default_conf)
assert log_has_re(
r"Exchange \"okex\" is officially supported by the Freqtrade development team\.",
caplog)
caplog.clear()
# Test an available exchange, supported by ccxt
default_conf.get('exchange').update({'name': 'huobipro'})
assert check_exchange(default_conf)
assert log_has_re(r"Exchange .* is known to the the ccxt library, available for the bot, "
r"but not officially supported "
r"by the Freqtrade development team\. .*", caplog)
caplog.clear()
# Test a 'bad' exchange, which known to have serious problems
default_conf.get('exchange').update({'name': 'bitmex'})
with pytest.raises(OperationalException,
match=r"Exchange .* will not work with Freqtrade\..*"):
check_exchange(default_conf)
caplog.clear()
# Test a 'bad' exchange with check_for_bad=False
default_conf.get('exchange').update({'name': 'bitmex'})
assert check_exchange(default_conf, False)
assert log_has_re(r"Exchange .* is known to the the ccxt library, available for the bot, "
r"but not officially supported "
r"by the Freqtrade development team\. .*", caplog)
caplog.clear()
# Test an invalid exchange
default_conf.get('exchange').update({'name': 'unknown_exchange'})
with pytest.raises(
OperationalException,
match=r'Exchange "unknown_exchange" is not known to the ccxt library '
r'and therefore not available for the bot.*'
):
check_exchange(default_conf)
# Test no exchange...
default_conf.get('exchange').update({'name': ''})
default_conf['runmode'] = RunMode.PLOT
assert check_exchange(default_conf)
# Test no exchange...
default_conf.get('exchange').update({'name': ''})
default_conf['runmode'] = RunMode.UTIL_EXCHANGE
with pytest.raises(OperationalException,
match=r'This command requires a configured exchange.*'):
check_exchange(default_conf)

7
tests/freqai/conftest.py
View File

@ -29,15 +29,16 @@ def freqai_conf(default_conf, tmpdir):
"enabled": True,
"startup_candles": 10000,
"purge_old_models": True,
"train_period_days": 5,
"train_period_days": 2,
"backtest_period_days": 2,
"live_retrain_hours": 0,
"expiration_hours": 1,
"identifier": "uniqe-id100",
"live_trained_timestamp": 0,
"data_kitchen_thread_count": 2,
"feature_parameters": {
"include_timeframes": ["5m"],
"include_corr_pairlist": ["ADA/BTC", "DASH/BTC"],
"include_corr_pairlist": ["ADA/BTC"],
"label_period_candles": 20,
"include_shifted_candles": 1,
"DI_threshold": 0.9,
@ -47,7 +48,7 @@ def freqai_conf(default_conf, tmpdir):
"stratify_training_data": 0,
"indicator_periods_candles": [10],
},
"data_split_parameters": {"test_size": 0.33, "random_state": 1},
"data_split_parameters": {"test_size": 0.33, "shuffle": False},
"model_training_parameters": {"n_estimators": 100},
},
"config_files": [Path('config_examples', 'config_freqai.example.json')]

2
tests/freqai/test_freqai_backtesting.py
View File

@ -29,7 +29,7 @@ def test_freqai_backtest_start_backtest_list(freqai_conf, mocker, testdatadir, c
'--config', 'config.json',
'--datadir', str(testdatadir),
'--strategy-path', str(Path(__file__).parents[1] / 'strategy/strats'),
'--timeframe', '1h',
'--timeframe', '1m',
'--strategy-list', CURRENT_TEST_STRATEGY
]
args = get_args(args)

2
tests/freqai/test_freqai_datadrawer.py
View File

@ -90,5 +90,5 @@ def test_use_strategy_to_populate_indicators(mocker, freqai_conf):
df = freqai.dk.use_strategy_to_populate_indicators(strategy, corr_df, base_df, 'LTC/BTC')
assert len(df.columns) == 45
assert len(df.columns) == 33
shutil.rmtree(Path(freqai.dk.full_path))

10
tests/freqai/test_freqai_datakitchen.py
View File

@ -71,14 +71,14 @@ def test_use_DBSCAN_to_remove_outliers(mocker, freqai_conf, caplog):
freqai = make_data_dictionary(mocker, freqai_conf)
# freqai_conf['freqai']['feature_parameters'].update({"outlier_protection_percentage": 1})
freqai.dk.use_DBSCAN_to_remove_outliers(predict=False)
assert log_has_re(r"DBSCAN found eps of 2\.3\d\.", caplog)
assert log_has_re(r"DBSCAN found eps of 1\.7\d\.", caplog)
def test_compute_distances(mocker, freqai_conf):
freqai = make_data_dictionary(mocker, freqai_conf)
freqai_conf['freqai']['feature_parameters'].update({"DI_threshold": 1})
avg_mean_dist = freqai.dk.compute_distances()
assert round(avg_mean_dist, 2) == 2.54
assert round(avg_mean_dist, 2) == 1.99
def test_use_SVM_to_remove_outliers_and_outlier_protection(mocker, freqai_conf, caplog):
@ -86,7 +86,7 @@ def test_use_SVM_to_remove_outliers_and_outlier_protection(mocker, freqai_conf,
freqai_conf['freqai']['feature_parameters'].update({"outlier_protection_percentage": 0.1})
freqai.dk.use_SVM_to_remove_outliers(predict=False)
assert log_has_re(
"SVM detected 8.09%",
"SVM detected 7.36%",
caplog,
)
@ -125,7 +125,7 @@ def test_normalize_data(mocker, freqai_conf):
freqai = make_data_dictionary(mocker, freqai_conf)
data_dict = freqai.dk.data_dictionary
freqai.dk.normalize_data(data_dict)
assert len(freqai.dk.data) == 56
assert len(freqai.dk.data) == 32
def test_filter_features(mocker, freqai_conf):
@ -139,7 +139,7 @@ def test_filter_features(mocker, freqai_conf):
training_filter=True,
)
assert len(filtered_df.columns) == 26
assert len(filtered_df.columns) == 14
def test_make_train_test_datasets(mocker, freqai_conf):

88
tests/freqai/test_freqai_interface.py
View File

@ -7,7 +7,11 @@ import pytest
from freqtrade.configuration import TimeRange
from freqtrade.data.dataprovider import DataProvider
from freqtrade.enums import RunMode
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
from freqtrade.freqai.utils import download_all_data_for_training, get_required_data_timerange
from freqtrade.optimize.backtesting import Backtesting
from freqtrade.persistence import Trade
from freqtrade.plugins.pairlistmanager import PairListManager
from tests.conftest import get_patched_exchange, log_has_re
from tests.freqai.conftest import get_patched_freqai_strategy
@ -18,15 +22,21 @@ def is_arm() -> bool:
return "arm" in machine or "aarch64" in machine
def is_mac() -> bool:
machine = platform.system()
return "Darwin" in machine
@pytest.mark.parametrize('model', [
'LightGBMRegressor',
'XGBoostRegressor',
'CatboostRegressor',
])
def test_extract_data_and_train_model_Regressors(mocker, freqai_conf, model):
def test_extract_data_and_train_model_Standard(mocker, freqai_conf, model):
if is_arm() and model == 'CatboostRegressor':
pytest.skip("CatBoost is not supported on ARM")
model_save_ext = 'joblib'
freqai_conf.update({"freqaimodel": model})
freqai_conf.update({"timerange": "20180110-20180130"})
freqai_conf.update({"strategy": "freqai_test_strat"})
@ -43,16 +53,16 @@ def test_extract_data_and_train_model_Regressors(mocker, freqai_conf, model):
freqai.dd.pair_dict = MagicMock()
data_load_timerange = TimeRange.parse_timerange("20180110-20180130")
new_timerange = TimeRange.parse_timerange("20180120-20180130")
data_load_timerange = TimeRange.parse_timerange("20180125-20180130")
new_timerange = TimeRange.parse_timerange("20180127-20180130")
freqai.extract_data_and_train_model(
new_timerange, "ADA/BTC", strategy, freqai.dk, data_load_timerange)
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_model.joblib").is_file()
assert Path(freqai.dk.data_path /
f"{freqai.dk.model_filename}_model.{model_save_ext}").is_file()
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_metadata.json").is_file()
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_trained_df.pkl").is_file()
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_svm_model.joblib").is_file()
shutil.rmtree(Path(freqai.dk.full_path))
@ -92,7 +102,7 @@ def test_extract_data_and_train_model_MultiTargets(mocker, freqai_conf, model):
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_metadata.json").is_file()
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_trained_df.pkl").is_file()
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_svm_model.joblib").is_file()
assert len(freqai.dk.data['training_features_list']) == 26
assert len(freqai.dk.data['training_features_list']) == 14
shutil.rmtree(Path(freqai.dk.full_path))
@ -136,9 +146,28 @@ def test_extract_data_and_train_model_Classifiers(mocker, freqai_conf, model):
shutil.rmtree(Path(freqai.dk.full_path))
def test_start_backtesting(mocker, freqai_conf):
freqai_conf.update({"timerange": "20180120-20180130"})
@pytest.mark.parametrize(
"model, num_files, strat",
[
("LightGBMRegressor", 6, "freqai_test_strat"),
("XGBoostRegressor", 6, "freqai_test_strat"),
("CatboostRegressor", 6, "freqai_test_strat"),
("XGBoostClassifier", 6, "freqai_test_classifier"),
("LightGBMClassifier", 6, "freqai_test_classifier"),
("CatboostClassifier", 6, "freqai_test_classifier")
],
)
def test_start_backtesting(mocker, freqai_conf, model, num_files, strat):
freqai_conf.get("freqai", {}).update({"save_backtest_models": True})
freqai_conf['runmode'] = RunMode.BACKTEST
Trade.use_db = False
if is_arm() and "Catboost" in model:
pytest.skip("CatBoost is not supported on ARM")
freqai_conf.update({"freqaimodel": model})
freqai_conf.update({"timerange": "20180120-20180130"})
freqai_conf.update({"strategy": strat})
strategy = get_patched_freqai_strategy(mocker, freqai_conf)
exchange = get_patched_exchange(mocker, freqai_conf)
strategy.dp = DataProvider(freqai_conf, exchange)
@ -157,8 +186,8 @@ def test_start_backtesting(mocker, freqai_conf):
freqai.start_backtesting(df, metadata, freqai.dk)
model_folders = [x for x in freqai.dd.full_path.iterdir() if x.is_dir()]
assert len(model_folders) == 6
assert len(model_folders) == num_files
Backtesting.cleanup()
shutil.rmtree(Path(freqai.dk.full_path))
@ -211,7 +240,7 @@ def test_start_backtesting_from_existing_folder(mocker, freqai_conf, caplog):
assert len(model_folders) == 6
# without deleting the exiting folder structure, re-run
# without deleting the existing folder structure, re-run
freqai_conf.update({"timerange": "20180120-20180130"})
strategy = get_patched_freqai_strategy(mocker, freqai_conf)
@ -375,3 +404,40 @@ def test_freqai_informative_pairs(mocker, freqai_conf, timeframes, corr_pairs):
pairs_b = strategy.gather_informative_pairs()
# we expect unique pairs * timeframes
assert len(pairs_b) == len(set(pairlist + corr_pairs)) * len(timeframes)
def test_start_set_train_queue(mocker, freqai_conf, caplog):
strategy = get_patched_freqai_strategy(mocker, freqai_conf)
exchange = get_patched_exchange(mocker, freqai_conf)
pairlist = PairListManager(exchange, freqai_conf)
strategy.dp = DataProvider(freqai_conf, exchange, pairlist)
strategy.freqai_info = freqai_conf.get("freqai", {})
freqai = strategy.freqai
freqai.live = False
freqai.train_queue = freqai._set_train_queue()
assert log_has_re(
"Set fresh train queue from whitelist.",
caplog,
)
def test_get_required_data_timerange(mocker, freqai_conf):
time_range = get_required_data_timerange(freqai_conf)
assert (time_range.stopts - time_range.startts) == 177300
def test_download_all_data_for_training(mocker, freqai_conf, caplog, tmpdir):
strategy = get_patched_freqai_strategy(mocker, freqai_conf)
exchange = get_patched_exchange(mocker, freqai_conf)
pairlist = PairListManager(exchange, freqai_conf)
strategy.dp = DataProvider(freqai_conf, exchange, pairlist)
freqai_conf['pairs'] = freqai_conf['exchange']['pair_whitelist']
freqai_conf['datadir'] = Path(tmpdir)
download_all_data_for_training(strategy.dp, freqai_conf)
assert log_has_re(
"Downloading",
caplog,
)

32
tests/optimize/test_backtesting.py
View File

@ -80,7 +80,7 @@ def load_data_test(what, testdatadir):
data.loc[:, 'close'] = np.sin(data.index * hz) / 1000 + base
return {'UNITTEST/BTC': clean_ohlcv_dataframe(data, timeframe='1m', pair='UNITTEST/BTC',
fill_missing=True)}
fill_missing=True, drop_incomplete=True)}
# FIX: fixturize this?
@ -323,7 +323,7 @@ def test_data_to_dataframe_bt(default_conf, mocker, testdatadir) -> None:
backtesting = Backtesting(default_conf)
backtesting._set_strategy(backtesting.strategylist[0])
processed = backtesting.strategy.advise_all_indicators(data)
assert len(processed['UNITTEST/BTC']) == 102
assert len(processed['UNITTEST/BTC']) == 103
# Load strategy to compare the result between Backtesting function and strategy are the same
strategy = StrategyResolver.load_strategy(default_conf)
@ -1165,9 +1165,9 @@ def test_backtest_start_timerange(default_conf, mocker, caplog, testdatadir):
'Parameter --timerange detected: 1510694220-1510700340 ...',
f'Using data directory: {testdatadir} ...',
'Loading data from 2017-11-14 20:57:00 '
'up to 2017-11-14 22:58:00 (0 days).',
'up to 2017-11-14 22:59:00 (0 days).',
'Backtesting with data from 2017-11-14 21:17:00 '
'up to 2017-11-14 22:58:00 (0 days).',
'up to 2017-11-14 22:59:00 (0 days).',
'Parameter --enable-position-stacking detected ...'
]
@ -1244,9 +1244,9 @@ def test_backtest_start_multi_strat(default_conf, mocker, caplog, testdatadir):
'Parameter --timerange detected: 1510694220-1510700340 ...',
f'Using data directory: {testdatadir} ...',
'Loading data from 2017-11-14 20:57:00 '
'up to 2017-11-14 22:58:00 (0 days).',
'up to 2017-11-14 22:59:00 (0 days).',
'Backtesting with data from 2017-11-14 21:17:00 '
'up to 2017-11-14 22:58:00 (0 days).',
'up to 2017-11-14 22:59:00 (0 days).',
'Parameter --enable-position-stacking detected ...',
f'Running backtesting for Strategy {CURRENT_TEST_STRATEGY}',
'Running backtesting for Strategy StrategyTestV2',
@ -1355,9 +1355,9 @@ def test_backtest_start_multi_strat_nomock(default_conf, mocker, caplog, testdat
'Parameter --timerange detected: 1510694220-1510700340 ...',
f'Using data directory: {testdatadir} ...',
'Loading data from 2017-11-14 20:57:00 '
'up to 2017-11-14 22:58:00 (0 days).',
'up to 2017-11-14 22:59:00 (0 days).',
'Backtesting with data from 2017-11-14 21:17:00 '
'up to 2017-11-14 22:58:00 (0 days).',
'up to 2017-11-14 22:59:00 (0 days).',
'Parameter --enable-position-stacking detected ...',
f'Running backtesting for Strategy {CURRENT_TEST_STRATEGY}',
'Running backtesting for Strategy StrategyTestV2',
@ -1371,7 +1371,7 @@ def test_backtest_start_multi_strat_nomock(default_conf, mocker, caplog, testdat
assert 'EXIT REASON STATS' in captured.out
assert 'DAY BREAKDOWN' in captured.out
assert 'LEFT OPEN TRADES REPORT' in captured.out
assert '2017-11-14 21:17:00 -> 2017-11-14 22:58:00 | Max open trades : 1' in captured.out
assert '2017-11-14 21:17:00 -> 2017-11-14 22:59:00 | Max open trades : 1' in captured.out
assert 'STRATEGY SUMMARY' in captured.out
@ -1503,9 +1503,9 @@ def test_backtest_start_nomock_futures(default_conf_usdt, mocker,
'Parameter -i/--timeframe detected ... Using timeframe: 1h ...',
f'Using data directory: {testdatadir} ...',
'Loading data from 2021-11-17 01:00:00 '
'up to 2021-11-21 03:00:00 (4 days).',
'up to 2021-11-21 04:00:00 (4 days).',
'Backtesting with data from 2021-11-17 21:00:00 '
'up to 2021-11-21 03:00:00 (3 days).',
'up to 2021-11-21 04:00:00 (3 days).',
'XRP/USDT, funding_rate, 8h, data starts at 2021-11-18 00:00:00',
'XRP/USDT, mark, 8h, data starts at 2021-11-18 00:00:00',
f'Running backtesting for Strategy {CURRENT_TEST_STRATEGY}',
@ -1616,9 +1616,9 @@ def test_backtest_start_multi_strat_nomock_detail(default_conf, mocker,
'Parameter --timeframe-detail detected, using 1m for intra-candle backtesting ...',
f'Using data directory: {testdatadir} ...',
'Loading data from 2019-10-11 00:00:00 '
'up to 2019-10-13 11:10:00 (2 days).',
'up to 2019-10-13 11:15:00 (2 days).',
'Backtesting with data from 2019-10-11 01:40:00 '
'up to 2019-10-13 11:10:00 (2 days).',
'up to 2019-10-13 11:15:00 (2 days).',
f'Running backtesting for Strategy {CURRENT_TEST_STRATEGY}',
]
@ -1719,7 +1719,7 @@ def test_backtest_start_multi_strat_caching(default_conf, mocker, caplog, testda
'Parameter --timerange detected: 1510694220-1510700340 ...',
f'Using data directory: {testdatadir} ...',
'Loading data from 2017-11-14 20:57:00 '
'up to 2017-11-14 22:58:00 (0 days).',
'up to 2017-11-14 22:59:00 (0 days).',
'Parameter --enable-position-stacking detected ...',
]
@ -1732,7 +1732,7 @@ def test_backtest_start_multi_strat_caching(default_conf, mocker, caplog, testda
'Running backtesting for Strategy StrategyTestV2',
'Running backtesting for Strategy StrategyTestV3',
'Ignoring max_open_trades (--disable-max-market-positions was used) ...',
'Backtesting with data from 2017-11-14 21:17:00 up to 2017-11-14 22:58:00 (0 days).',
'Backtesting with data from 2017-11-14 21:17:00 up to 2017-11-14 22:59:00 (0 days).',
]
elif run_id == '2' and min_backtest_date < start_time:
assert backtestmock.call_count == 0
@ -1745,7 +1745,7 @@ def test_backtest_start_multi_strat_caching(default_conf, mocker, caplog, testda
'Reusing result of previous backtest for StrategyTestV2',
'Running backtesting for Strategy StrategyTestV3',
'Ignoring max_open_trades (--disable-max-market-positions was used) ...',
'Backtesting with data from 2017-11-14 21:17:00 up to 2017-11-14 22:58:00 (0 days).',
'Backtesting with data from 2017-11-14 21:17:00 up to 2017-11-14 22:59:00 (0 days).',
]
assert backtestmock.call_count == 1

21
tests/optimize/test_backtesting_adjust_position.py
View File

@ -93,11 +93,16 @@ def test_backtest_position_adjustment(default_conf, fee, mocker, testdatadir) ->
t["close_rate"], 6) < round(ln.iloc[0]["high"], 6))
def test_backtest_position_adjustment_detailed(default_conf, fee, mocker) -> None:
@pytest.mark.parametrize('leverage', [
1, 2
])
def test_backtest_position_adjustment_detailed(default_conf, fee, mocker, leverage) -> None:
default_conf['use_exit_signal'] = False
mocker.patch('freqtrade.exchange.Exchange.get_fee', fee)
mocker.patch("freqtrade.exchange.Exchange.get_min_pair_stake_amount", return_value=10)
mocker.patch("freqtrade.exchange.Exchange.get_max_pair_stake_amount", return_value=float('inf'))
mocker.patch("freqtrade.exchange.Exchange.get_max_leverage", return_value=10)
patch_exchange(mocker)
default_conf.update({
"stake_amount": 100.0,
@ -105,6 +110,7 @@ def test_backtest_position_adjustment_detailed(default_conf, fee, mocker) -> Non
"strategy": "StrategyTestV3"
})
backtesting = Backtesting(default_conf)
backtesting._can_short = True
backtesting._set_strategy(backtesting.strategylist[0])
pair = 'XRP/USDT'
row = [
@ -120,18 +126,19 @@ def test_backtest_position_adjustment_detailed(default_conf, fee, mocker) -> Non
'', # enter_tag
'', # exit_tag
]
backtesting.strategy.leverage = MagicMock(return_value=leverage)
trade = backtesting._enter_trade(pair, row=row, direction='long')
trade.orders[0].close_bt_order(row[0], trade)
assert trade
assert pytest.approx(trade.stake_amount) == 100.0
assert pytest.approx(trade.amount) == 47.61904762
assert pytest.approx(trade.amount) == 47.61904762 * leverage
assert len(trade.orders) == 1
backtesting.strategy.adjust_trade_position = MagicMock(return_value=None)
trade = backtesting._get_adjust_trade_entry_for_candle(trade, row)
assert trade
assert pytest.approx(trade.stake_amount) == 100.0
assert pytest.approx(trade.amount) == 47.61904762
assert pytest.approx(trade.amount) == 47.61904762 * leverage
assert len(trade.orders) == 1
# Increase position by 100
backtesting.strategy.adjust_trade_position = MagicMock(return_value=100)
@ -140,7 +147,7 @@ def test_backtest_position_adjustment_detailed(default_conf, fee, mocker) -> Non
assert trade
assert pytest.approx(trade.stake_amount) == 200.0
assert pytest.approx(trade.amount) == 95.23809524
assert pytest.approx(trade.amount) == 95.23809524 * leverage
assert len(trade.orders) == 2
# Reduce by more than amount - no change to trade.
@ -150,7 +157,7 @@ def test_backtest_position_adjustment_detailed(default_conf, fee, mocker) -> Non
assert trade
assert pytest.approx(trade.stake_amount) == 200.0
assert pytest.approx(trade.amount) == 95.23809524
assert pytest.approx(trade.amount) == 95.23809524 * leverage
assert len(trade.orders) == 2
assert trade.nr_of_successful_entries == 2
@ -160,7 +167,7 @@ def test_backtest_position_adjustment_detailed(default_conf, fee, mocker) -> Non
assert trade
assert pytest.approx(trade.stake_amount) == 100.0
assert pytest.approx(trade.amount) == 47.61904762
assert pytest.approx(trade.amount) == 47.61904762 * leverage
assert len(trade.orders) == 3
assert trade.nr_of_successful_entries == 2
assert trade.nr_of_successful_exits == 1
@ -171,7 +178,7 @@ def test_backtest_position_adjustment_detailed(default_conf, fee, mocker) -> Non
assert trade
assert pytest.approx(trade.stake_amount) == 100.0
assert pytest.approx(trade.amount) == 47.61904762
assert pytest.approx(trade.amount) == 47.61904762 * leverage
assert len(trade.orders) == 3
assert trade.nr_of_successful_entries == 2
assert trade.nr_of_successful_exits == 1

7
tests/optimize/test_hyperopt.py
View File

@ -297,6 +297,7 @@ def test_params_no_optimize_details(hyperopt) -> None:
def test_start_calls_optimizer(mocker, hyperopt_conf, capsys) -> None:
dumper = mocker.patch('freqtrade.optimize.hyperopt.dump')
dumper2 = mocker.patch('freqtrade.optimize.hyperopt.Hyperopt._save_result')
mocker.patch('freqtrade.optimize.hyperopt.calculate_market_change', return_value=1.5)
mocker.patch('freqtrade.optimize.hyperopt.file_dump_json')
mocker.patch('freqtrade.optimize.backtesting.Backtesting.load_bt_data',
@ -530,6 +531,7 @@ def test_print_json_spaces_all(mocker, hyperopt_conf, capsys) -> None:
dumper = mocker.patch('freqtrade.optimize.hyperopt.dump')
dumper2 = mocker.patch('freqtrade.optimize.hyperopt.Hyperopt._save_result')
mocker.patch('freqtrade.optimize.hyperopt.file_dump_json')
mocker.patch('freqtrade.optimize.hyperopt.calculate_market_change', return_value=1.5)
mocker.patch('freqtrade.optimize.backtesting.Backtesting.load_bt_data',
MagicMock(return_value=(MagicMock(), None)))
@ -581,6 +583,7 @@ def test_print_json_spaces_default(mocker, hyperopt_conf, capsys) -> None:
dumper = mocker.patch('freqtrade.optimize.hyperopt.dump')
dumper2 = mocker.patch('freqtrade.optimize.hyperopt.Hyperopt._save_result')
mocker.patch('freqtrade.optimize.hyperopt.file_dump_json')
mocker.patch('freqtrade.optimize.hyperopt.calculate_market_change', return_value=1.5)
mocker.patch('freqtrade.optimize.backtesting.Backtesting.load_bt_data',
MagicMock(return_value=(MagicMock(), None)))
mocker.patch(
@ -622,6 +625,7 @@ def test_print_json_spaces_default(mocker, hyperopt_conf, capsys) -> None:
def test_print_json_spaces_roi_stoploss(mocker, hyperopt_conf, capsys) -> None:
dumper = mocker.patch('freqtrade.optimize.hyperopt.dump')
dumper2 = mocker.patch('freqtrade.optimize.hyperopt.Hyperopt._save_result')
mocker.patch('freqtrade.optimize.hyperopt.calculate_market_change', return_value=1.5)
mocker.patch('freqtrade.optimize.hyperopt.file_dump_json')
mocker.patch('freqtrade.optimize.backtesting.Backtesting.load_bt_data',
MagicMock(return_value=(MagicMock(), None)))
@ -663,6 +667,7 @@ def test_print_json_spaces_roi_stoploss(mocker, hyperopt_conf, capsys) -> None:
def test_simplified_interface_roi_stoploss(mocker, hyperopt_conf, capsys) -> None:
dumper = mocker.patch('freqtrade.optimize.hyperopt.dump')
dumper2 = mocker.patch('freqtrade.optimize.hyperopt.Hyperopt._save_result')
mocker.patch('freqtrade.optimize.hyperopt.calculate_market_change', return_value=1.5)
mocker.patch('freqtrade.optimize.hyperopt.file_dump_json')
mocker.patch('freqtrade.optimize.backtesting.Backtesting.load_bt_data',
MagicMock(return_value=(MagicMock(), None)))
@ -736,6 +741,7 @@ def test_simplified_interface_all_failed(mocker, hyperopt_conf, caplog) -> None:
def test_simplified_interface_buy(mocker, hyperopt_conf, capsys) -> None:
dumper = mocker.patch('freqtrade.optimize.hyperopt.dump')
dumper2 = mocker.patch('freqtrade.optimize.hyperopt.Hyperopt._save_result')
mocker.patch('freqtrade.optimize.hyperopt.calculate_market_change', return_value=1.5)
mocker.patch('freqtrade.optimize.hyperopt.file_dump_json')
mocker.patch('freqtrade.optimize.backtesting.Backtesting.load_bt_data',
MagicMock(return_value=(MagicMock(), None)))
@ -778,6 +784,7 @@ def test_simplified_interface_buy(mocker, hyperopt_conf, capsys) -> None:
def test_simplified_interface_sell(mocker, hyperopt_conf, capsys) -> None:
dumper = mocker.patch('freqtrade.optimize.hyperopt.dump')
dumper2 = mocker.patch('freqtrade.optimize.hyperopt.Hyperopt._save_result')
mocker.patch('freqtrade.optimize.hyperopt.calculate_market_change', return_value=1.5)
mocker.patch('freqtrade.optimize.hyperopt.file_dump_json')
mocker.patch('freqtrade.optimize.backtesting.Backtesting.load_bt_data',
MagicMock(return_value=(MagicMock(), None)))

95
tests/plugins/test_pairlist.py
View File

@ -9,6 +9,7 @@ import pytest
import time_machine
from freqtrade.constants import AVAILABLE_PAIRLISTS
from freqtrade.data.dataprovider import DataProvider
from freqtrade.enums import CandleType, RunMode
from freqtrade.exceptions import OperationalException
from freqtrade.persistence import Trade
@ -40,6 +41,12 @@ def whitelist_conf(default_conf):
"sort_key": "quoteVolume",
},
]
default_conf.update({
"external_message_consumer": {
"enabled": True,
"producers": [],
}
})
return default_conf
@ -126,7 +133,7 @@ def test_log_cached(mocker, static_pl_conf, markets, tickers):
def test_load_pairlist_noexist(mocker, markets, default_conf):
freqtrade = get_patched_freqtradebot(mocker, default_conf)
mocker.patch('freqtrade.exchange.Exchange.markets', PropertyMock(return_value=markets))
plm = PairListManager(freqtrade.exchange, default_conf)
plm = PairListManager(freqtrade.exchange, default_conf, MagicMock())
with pytest.raises(OperationalException,
match=r"Impossible to load Pairlist 'NonexistingPairList'. "
r"This class does not exist or contains Python code errors."):
@ -137,7 +144,7 @@ def test_load_pairlist_noexist(mocker, markets, default_conf):
def test_load_pairlist_verify_multi(mocker, markets_static, default_conf):
freqtrade = get_patched_freqtradebot(mocker, default_conf)
mocker.patch('freqtrade.exchange.Exchange.markets', PropertyMock(return_value=markets_static))
plm = PairListManager(freqtrade.exchange, default_conf)
plm = PairListManager(freqtrade.exchange, default_conf, MagicMock())
# Call different versions one after the other, should always consider what was passed in
# and have no side-effects (therefore the same check multiple times)
assert plm.verify_whitelist(['ETH/BTC', 'XRP/BTC', ], print) == ['ETH/BTC', 'XRP/BTC']
@ -269,7 +276,7 @@ def test_refresh_pairlist_dynamic(mocker, shitcoinmarkets, tickers, whitelist_co
with pytest.raises(OperationalException,
match=r'`number_assets` not specified. Please check your configuration '
r'for "pairlist.config.number_assets"'):
PairListManager(freqtrade.exchange, whitelist_conf)
PairListManager(freqtrade.exchange, whitelist_conf, MagicMock())
def test_refresh_pairlist_dynamic_2(mocker, shitcoinmarkets, tickers, whitelist_conf_2):
@ -694,7 +701,7 @@ def test_PrecisionFilter_error(mocker, whitelist_conf) -> None:
with pytest.raises(OperationalException,
match=r"PrecisionFilter can only work with stoploss defined\..*"):
PairListManager(MagicMock, whitelist_conf)
PairListManager(MagicMock, whitelist_conf, MagicMock())
def test_PerformanceFilter_error(mocker, whitelist_conf, caplog) -> None:
@ -703,7 +710,7 @@ def test_PerformanceFilter_error(mocker, whitelist_conf, caplog) -> None:
del Trade.query
mocker.patch('freqtrade.exchange.Exchange.exchange_has', MagicMock(return_value=True))
exchange = get_patched_exchange(mocker, whitelist_conf)
pm = PairListManager(exchange, whitelist_conf)
pm = PairListManager(exchange, whitelist_conf, MagicMock())
pm.refresh_pairlist()
assert log_has("PerformanceFilter is not available in this mode.", caplog)
@ -1167,6 +1174,10 @@ def test_spreadfilter_invalid_data(mocker, default_conf, markets, tickers, caplo
"[{'OffsetFilter': 'OffsetFilter - Taking 10 Pairs, starting from 5.'}]",
None
),
({"method": "ProducerPairList"},
"[{'ProducerPairList': 'ProducerPairList - default'}]",
None
),
])
def test_pricefilter_desc(mocker, whitelist_conf, markets, pairlistconfig,
desc_expected, exception_expected):
@ -1341,3 +1352,77 @@ def test_expand_pairlist_keep_invalid(wildcardlist, pairs, expected):
expand_pairlist(wildcardlist, pairs, keep_invalid=True)
else:
assert sorted(expand_pairlist(wildcardlist, pairs, keep_invalid=True)) == sorted(expected)
def test_ProducerPairlist_no_emc(mocker, whitelist_conf):
mocker.patch('freqtrade.exchange.Exchange.exchange_has', MagicMock(return_value=True))
whitelist_conf['pairlists'] = [
{
"method": "ProducerPairList",
"number_assets": 10,
"producer_name": "hello_world",
}
]
del whitelist_conf['external_message_consumer']
with pytest.raises(OperationalException,
match=r"ProducerPairList requires external_message_consumer to be enabled."):
get_patched_freqtradebot(mocker, whitelist_conf)
def test_ProducerPairlist(mocker, whitelist_conf, markets):
mocker.patch('freqtrade.exchange.Exchange.exchange_has', MagicMock(return_value=True))
mocker.patch.multiple('freqtrade.exchange.Exchange',
markets=PropertyMock(return_value=markets),
exchange_has=MagicMock(return_value=True),
)
whitelist_conf['pairlists'] = [
{
"method": "ProducerPairList",
"number_assets": 2,
"producer_name": "hello_world",
}
]
whitelist_conf.update({
"external_message_consumer": {
"enabled": True,
"producers": [
{
"name": "hello_world",
"host": "null",
"port": 9891,
"ws_token": "dummy",
}
]
}
})
exchange = get_patched_exchange(mocker, whitelist_conf)
dp = DataProvider(whitelist_conf, exchange, None)
pairs = ['ETH/BTC', 'LTC/BTC', 'XRP/BTC']
# different producer
dp._set_producer_pairs(pairs + ['MEEP/USDT'], 'default')
pm = PairListManager(exchange, whitelist_conf, dp)
pm.refresh_pairlist()
assert pm.whitelist == []
# proper producer
dp._set_producer_pairs(pairs, 'hello_world')
pm.refresh_pairlist()
# Pairlist reduced to 2
assert pm.whitelist == pairs[:2]
assert len(pm.whitelist) == 2
whitelist_conf['exchange']['pair_whitelist'] = ['TKN/BTC']
whitelist_conf['pairlists'] = [
{"method": "StaticPairList"},
{
"method": "ProducerPairList",
"producer_name": "hello_world",
}
]
pm = PairListManager(exchange, whitelist_conf, dp)
pm.refresh_pairlist()
assert len(pm.whitelist) == 4
assert pm.whitelist == ['TKN/BTC'] + pairs

45
tests/rpc/test_rpc_emc.py
View File

@ -188,15 +188,19 @@ async def test_emc_create_connection_success(default_conf, caplog, mocker):
emc.shutdown()
async def test_emc_create_connection_invalid_port(default_conf, caplog, mocker):
@pytest.mark.parametrize('host,port', [
(_TEST_WS_HOST, -1),
("10000.1241..2121/", _TEST_WS_PORT),
])
async def test_emc_create_connection_invalid_url(default_conf, caplog, mocker, host, port):
default_conf.update({
"external_message_consumer": {
"enabled": True,
"producers": [
{
"name": "default",
"host": _TEST_WS_HOST,
"port": -1,
"host": host,
"port": port,
"ws_token": _TEST_WS_TOKEN
}
],
@ -207,38 +211,13 @@ async def test_emc_create_connection_invalid_port(default_conf, caplog, mocker):
})
dp = DataProvider(default_conf, None, None, None)
# Handle start explicitly to avoid messing with threading in tests
mocker.patch("freqtrade.rpc.external_message_consumer.ExternalMessageConsumer.start",)
emc = ExternalMessageConsumer(default_conf, dp)
try:
await asyncio.sleep(0.01)
assert log_has_re(r".+ is an invalid WebSocket URL .+", caplog)
finally:
emc.shutdown()
async def test_emc_create_connection_invalid_host(default_conf, caplog, mocker):
default_conf.update({
"external_message_consumer": {
"enabled": True,
"producers": [
{
"name": "default",
"host": "10000.1241..2121/",
"port": _TEST_WS_PORT,
"ws_token": _TEST_WS_TOKEN
}
],
"wait_timeout": 60,
"ping_timeout": 60,
"sleep_timeout": 60
}
})
dp = DataProvider(default_conf, None, None, None)
emc = ExternalMessageConsumer(default_conf, dp)
try:
await asyncio.sleep(0.01)
emc._running = True
await emc._create_connection(emc.producers[0], asyncio.Lock())
assert log_has_re(r".+ is an invalid WebSocket URL .+", caplog)
finally:
emc.shutdown()
@ -276,6 +255,8 @@ async def test_emc_create_connection_error(default_conf, caplog, mocker):
async def test_emc_receive_messages_valid(default_conf, caplog, mocker):
caplog.set_level(logging.DEBUG)
default_conf.update({
"external_message_consumer": {
"enabled": True,

8
tests/rpc/test_rpc_webhook.py
View File

@ -365,6 +365,14 @@ def test_exception_send_msg(default_conf, mocker, caplog):
with pytest.raises(NotImplementedError):
webhook.send_msg(msg)
# Test no failure for not implemented but known messagetypes
for e in RPCMessageType:
msg = {
'type': e,
'status': 'whatever'
}
webhook.send_msg(msg)
def test__send_msg(default_conf, mocker, caplog):
default_conf["webhook"] = get_webhook_dict()

4
tests/strategy/test_interface.py
View File

@ -288,7 +288,7 @@ def test_advise_all_indicators(default_conf, testdatadir) -> None:
data = load_data(testdatadir, '1m', ['UNITTEST/BTC'], timerange=timerange,
fill_up_missing=True)
processed = strategy.advise_all_indicators(data)
assert len(processed['UNITTEST/BTC']) == 102 # partial candle was removed
assert len(processed['UNITTEST/BTC']) == 103
def test_populate_any_indicators(default_conf, testdatadir) -> None:
@ -300,7 +300,7 @@ def test_populate_any_indicators(default_conf, testdatadir) -> None:
processed = strategy.populate_any_indicators('UNITTEST/BTC', data, '5m')
assert processed == data
assert id(processed) == id(data)
assert len(processed['UNITTEST/BTC']) == 102 # partial candle was removed
assert len(processed['UNITTEST/BTC']) == 103
def test_freqai_not_initialized(default_conf) -> None:

25
tests/strategy/test_strategy_helpers.py
View File

@ -5,29 +5,8 @@ import pytest
from freqtrade.data.dataprovider import DataProvider
from freqtrade.enums import CandleType
from freqtrade.resolvers.strategy_resolver import StrategyResolver
from freqtrade.strategy import (merge_informative_pair, stoploss_from_absolute, stoploss_from_open,
timeframe_to_minutes)
from tests.conftest import get_patched_exchange
def generate_test_data(timeframe: str, size: int, start: str = '2020-07-05'):
np.random.seed(42)
tf_mins = timeframe_to_minutes(timeframe)
base = np.random.normal(20, 2, size=size)
date = pd.date_range(start, periods=size, freq=f'{tf_mins}min', tz='UTC')
df = pd.DataFrame({
'date': date,
'open': base,
'high': base + np.random.normal(2, 1, size=size),
'low': base - np.random.normal(2, 1, size=size),
'close': base + np.random.normal(0, 1, size=size),
'volume': np.random.normal(200, size=size)
}
)
df = df.dropna()
return df
from freqtrade.strategy import merge_informative_pair, stoploss_from_absolute, stoploss_from_open
from tests.conftest import generate_test_data, get_patched_exchange
def test_merge_informative_pair():

88
tests/test_configuration.py
View File

@ -11,7 +11,7 @@ import pytest
from jsonschema import ValidationError
from freqtrade.commands import Arguments
from freqtrade.configuration import Configuration, check_exchange, validate_config_consistency
from freqtrade.configuration import Configuration, validate_config_consistency
from freqtrade.configuration.config_validation import validate_config_schema
from freqtrade.configuration.deprecated_settings import (check_conflicting_settings,
process_deprecated_setting,
@ -584,67 +584,6 @@ def test_hyperopt_with_arguments(mocker, default_conf, caplog) -> None:
assert config['runmode'] == RunMode.HYPEROPT
def test_check_exchange(default_conf, caplog) -> None:
# Test an officially supported by Freqtrade team exchange
default_conf['runmode'] = RunMode.DRY_RUN
default_conf.get('exchange').update({'name': 'BITTREX'})
assert check_exchange(default_conf)
assert log_has_re(r"Exchange .* is officially supported by the Freqtrade development team\.",
caplog)
caplog.clear()
# Test an officially supported by Freqtrade team exchange
default_conf.get('exchange').update({'name': 'binance'})
assert check_exchange(default_conf)
assert log_has_re(r"Exchange .* is officially supported by the Freqtrade development team\.",
caplog)
caplog.clear()
# Test an available exchange, supported by ccxt
default_conf.get('exchange').update({'name': 'huobipro'})
assert check_exchange(default_conf)
assert log_has_re(r"Exchange .* is known to the the ccxt library, available for the bot, "
r"but not officially supported "
r"by the Freqtrade development team\. .*", caplog)
caplog.clear()
# Test a 'bad' exchange, which known to have serious problems
default_conf.get('exchange').update({'name': 'bitmex'})
with pytest.raises(OperationalException,
match=r"Exchange .* will not work with Freqtrade\..*"):
check_exchange(default_conf)
caplog.clear()
# Test a 'bad' exchange with check_for_bad=False
default_conf.get('exchange').update({'name': 'bitmex'})
assert check_exchange(default_conf, False)
assert log_has_re(r"Exchange .* is known to the the ccxt library, available for the bot, "
r"but not officially supported "
r"by the Freqtrade development team\. .*", caplog)
caplog.clear()
# Test an invalid exchange
default_conf.get('exchange').update({'name': 'unknown_exchange'})
with pytest.raises(
OperationalException,
match=r'Exchange "unknown_exchange" is not known to the ccxt library '
r'and therefore not available for the bot.*'
):
check_exchange(default_conf)
# Test no exchange...
default_conf.get('exchange').update({'name': ''})
default_conf['runmode'] = RunMode.PLOT
assert check_exchange(default_conf)
# Test no exchange...
default_conf.get('exchange').update({'name': ''})
default_conf['runmode'] = RunMode.UTIL_EXCHANGE
with pytest.raises(OperationalException,
match=r'This command requires a configured exchange.*'):
check_exchange(default_conf)
def test_cli_verbose_with_params(default_conf, mocker, caplog) -> None:
patched_configuration_load_config_file(mocker, default_conf)
@ -1089,6 +1028,31 @@ def test__validate_pricing_rules(default_conf, caplog) -> None:
validate_config_consistency(conf)
def test__validate_freqai_include_timeframes(default_conf, caplog) -> None:
conf = deepcopy(default_conf)
conf.update({
"freqai": {
"enabled": True,
"feature_parameters": {
"include_timeframes": ["1m", "5m"],
"include_corr_pairlist": [],
},
"data_split_parameters": {},
"model_training_parameters": {}
}
})
with pytest.raises(OperationalException, match=r"Main timeframe of .*"):
validate_config_consistency(conf)
# Validation pass
conf.update({'timeframe': '1m'})
validate_config_consistency(conf)
conf.update({'analyze_per_epoch': True})
with pytest.raises(OperationalException,
match=r"Using analyze-per-epoch .* not supported with a FreqAI strategy."):
validate_config_consistency(conf)
def test__validate_consumers(default_conf, caplog) -> None:
conf = deepcopy(default_conf)
conf.update({

114
tests/test_freqtradebot.py
View File

@ -28,6 +28,7 @@ from tests.conftest import (create_mock_trades, create_mock_trades_usdt, get_pat
from tests.conftest_trades import (MOCK_TRADE_COUNT, entry_side, exit_side, mock_order_1,
mock_order_2, mock_order_2_sell, mock_order_3, mock_order_3_sell,
mock_order_4, mock_order_5_stoploss, mock_order_6_sell)
from tests.conftest_trades_usdt import mock_trade_usdt_4
def patch_RPCManager(mocker) -> MagicMock:
@ -1060,6 +1061,7 @@ def test_add_stoploss_on_exchange(mocker, default_conf_usdt, limit_order, is_sho
freqtrade = FreqtradeBot(default_conf_usdt)
freqtrade.strategy.order_types['stoploss_on_exchange'] = True
# TODO: should not be magicmock
trade = MagicMock()
trade.is_short = is_short
trade.open_order_id = None
@ -1101,6 +1103,7 @@ def test_handle_stoploss_on_exchange(mocker, default_conf_usdt, fee, caplog, is_
# First case: when stoploss is not yet set but the order is open
# should get the stoploss order id immediately
# and should return false as no trade actually happened
# TODO: should not be magicmock
trade = MagicMock()
trade.is_short = is_short
trade.is_open = True
@ -1879,6 +1882,7 @@ def test_exit_positions(mocker, default_conf_usdt, limit_order, is_short, caplog
return_value=limit_order[entry_side(is_short)])
mocker.patch('freqtrade.exchange.Exchange.get_trades_for_order', return_value=[])
# TODO: should not be magicmock
trade = MagicMock()
trade.is_short = is_short
trade.open_order_id = '123'
@ -1902,6 +1906,7 @@ def test_exit_positions_exception(mocker, default_conf_usdt, limit_order, caplog
order = limit_order[entry_side(is_short)]
mocker.patch('freqtrade.exchange.Exchange.fetch_order', return_value=order)
# TODO: should not be magicmock
trade = MagicMock()
trade.is_short = is_short
trade.open_order_id = None
@ -2042,6 +2047,7 @@ def test_update_trade_state_exception(mocker, default_conf_usdt, is_short, limit
freqtrade = get_patched_freqtradebot(mocker, default_conf_usdt)
mocker.patch('freqtrade.exchange.Exchange.fetch_order', return_value=order)
# TODO: should not be magicmock
trade = MagicMock()
trade.open_order_id = '123'
trade.amount = 123
@ -2060,6 +2066,7 @@ def test_update_trade_state_orderexception(mocker, default_conf_usdt, caplog) ->
mocker.patch('freqtrade.exchange.Exchange.fetch_order',
MagicMock(side_effect=InvalidOrderException))
# TODO: should not be magicmock
trade = MagicMock()
trade.open_order_id = '123'
@ -2661,6 +2668,7 @@ def test_manage_open_orders_exit_usercustom(
rpc_mock = patch_RPCManager(mocker)
cancel_order_mock = MagicMock()
patch_exchange(mocker)
mocker.patch('freqtrade.exchange.Exchange.get_min_pair_stake_amount', return_value=0.0)
et_mock = mocker.patch('freqtrade.freqtradebot.FreqtradeBot.execute_trade_exit')
mocker.patch.multiple(
'freqtrade.exchange.Exchange',
@ -2673,7 +2681,6 @@ def test_manage_open_orders_exit_usercustom(
open_trade_usdt.open_date = arrow.utcnow().shift(hours=-5).datetime
open_trade_usdt.close_date = arrow.utcnow().shift(minutes=-601).datetime
open_trade_usdt.close_profit_abs = 0.001
open_trade_usdt.is_open = False
Trade.query.session.add(open_trade_usdt)
Trade.commit()
@ -2687,7 +2694,6 @@ def test_manage_open_orders_exit_usercustom(
freqtrade.manage_open_orders()
assert cancel_order_mock.call_count == 0
assert rpc_mock.call_count == 1
assert open_trade_usdt.is_open is False
assert freqtrade.strategy.check_exit_timeout.call_count == 1
assert freqtrade.strategy.check_entry_timeout.call_count == 0
@ -2697,7 +2703,6 @@ def test_manage_open_orders_exit_usercustom(
freqtrade.manage_open_orders()
assert cancel_order_mock.call_count == 0
assert rpc_mock.call_count == 1
assert open_trade_usdt.is_open is False
assert freqtrade.strategy.check_exit_timeout.call_count == 1
assert freqtrade.strategy.check_entry_timeout.call_count == 0
@ -2707,7 +2712,6 @@ def test_manage_open_orders_exit_usercustom(
freqtrade.manage_open_orders()
assert cancel_order_mock.call_count == 1
assert rpc_mock.call_count == 2
assert open_trade_usdt.is_open is True
assert freqtrade.strategy.check_exit_timeout.call_count == 1
assert freqtrade.strategy.check_entry_timeout.call_count == 0
@ -2748,14 +2752,14 @@ def test_manage_open_orders_exit(
'freqtrade.exchange.Exchange',
fetch_ticker=ticker_usdt,
fetch_order=MagicMock(return_value=limit_sell_order_old),
cancel_order=cancel_order_mock
cancel_order=cancel_order_mock,
get_min_pair_stake_amount=MagicMock(return_value=0),
)
freqtrade = FreqtradeBot(default_conf_usdt)
open_trade_usdt.open_date = arrow.utcnow().shift(hours=-5).datetime
open_trade_usdt.close_date = arrow.utcnow().shift(minutes=-601).datetime
open_trade_usdt.close_profit_abs = 0.001
open_trade_usdt.is_open = False
open_trade_usdt.is_short = is_short
Trade.query.session.add(open_trade_usdt)
@ -2796,7 +2800,6 @@ def test_check_handle_cancelled_exit(
open_trade_usdt.open_date = arrow.utcnow().shift(hours=-5).datetime
open_trade_usdt.close_date = arrow.utcnow().shift(minutes=-601).datetime
open_trade_usdt.is_open = False
open_trade_usdt.is_short = is_short
Trade.query.session.add(open_trade_usdt)
@ -2984,7 +2987,7 @@ def test_manage_open_orders_exception(default_conf_usdt, ticker_usdt, open_trade
@pytest.mark.parametrize("is_short", [False, True])
def test_handle_cancel_enter(mocker, caplog, default_conf_usdt, limit_order, is_short) -> None:
def test_handle_cancel_enter(mocker, caplog, default_conf_usdt, limit_order, is_short, fee) -> None:
patch_RPCManager(mocker)
patch_exchange(mocker)
l_order = limit_order[entry_side(is_short)]
@ -2998,15 +3001,12 @@ def test_handle_cancel_enter(mocker, caplog, default_conf_usdt, limit_order, is_
freqtrade = FreqtradeBot(default_conf_usdt)
freqtrade._notify_enter_cancel = MagicMock()
# TODO: Convert to real trade
trade = MagicMock()
trade.pair = 'LTC/USDT'
trade.open_rate = 200
trade.is_short = False
trade.entry_side = "buy"
trade = mock_trade_usdt_4(fee, is_short)
Trade.query.session.add(trade)
Trade.commit()
l_order['filled'] = 0.0
l_order['status'] = 'open'
trade.nr_of_successful_entries = 0
reason = CANCEL_REASON['TIMEOUT']
assert freqtrade.handle_cancel_enter(trade, l_order, reason)
assert cancel_order_mock.call_count == 1
@ -3038,7 +3038,7 @@ def test_handle_cancel_enter(mocker, caplog, default_conf_usdt, limit_order, is_
@pytest.mark.parametrize("is_short", [False, True])
@pytest.mark.parametrize("limit_buy_order_canceled_empty", ['binance', 'ftx', 'kraken', 'bittrex'],
indirect=['limit_buy_order_canceled_empty'])
def test_handle_cancel_enter_exchanges(mocker, caplog, default_conf_usdt, is_short,
def test_handle_cancel_enter_exchanges(mocker, caplog, default_conf_usdt, is_short, fee,
limit_buy_order_canceled_empty) -> None:
patch_RPCManager(mocker)
patch_exchange(mocker)
@ -3049,11 +3049,10 @@ def test_handle_cancel_enter_exchanges(mocker, caplog, default_conf_usdt, is_sho
freqtrade = FreqtradeBot(default_conf_usdt)
reason = CANCEL_REASON['TIMEOUT']
# TODO: Convert to real trade
trade = MagicMock()
trade.nr_of_successful_entries = 0
trade.pair = 'LTC/ETH'
trade.entry_side = "sell" if is_short else "buy"
trade = mock_trade_usdt_4(fee, is_short)
Trade.query.session.add(trade)
Trade.commit()
assert freqtrade.handle_cancel_enter(trade, limit_buy_order_canceled_empty, reason)
assert cancel_order_mock.call_count == 0
assert log_has_re(
@ -3071,7 +3070,7 @@ def test_handle_cancel_enter_exchanges(mocker, caplog, default_conf_usdt, is_sho
'String Return value',
123
])
def test_handle_cancel_enter_corder_empty(mocker, default_conf_usdt, limit_order, is_short,
def test_handle_cancel_enter_corder_empty(mocker, default_conf_usdt, limit_order, is_short, fee,
cancelorder) -> None:
patch_RPCManager(mocker)
patch_exchange(mocker)
@ -3079,19 +3078,15 @@ def test_handle_cancel_enter_corder_empty(mocker, default_conf_usdt, limit_order
cancel_order_mock = MagicMock(return_value=cancelorder)
mocker.patch.multiple(
'freqtrade.exchange.Exchange',
cancel_order=cancel_order_mock
cancel_order=cancel_order_mock,
fetch_order=MagicMock(side_effect=InvalidOrderException)
)
freqtrade = FreqtradeBot(default_conf_usdt)
freqtrade._notify_enter_cancel = MagicMock()
# TODO: Convert to real trade
trade = MagicMock()
trade.pair = 'LTC/USDT'
trade.entry_side = "buy"
trade.open_rate = 200
trade.entry_side = "buy"
trade.open_order_id = "open_order_noop"
trade.nr_of_successful_entries = 0
trade = mock_trade_usdt_4(fee, is_short)
Trade.query.session.add(trade)
Trade.commit()
l_order['filled'] = 0.0
l_order['status'] = 'open'
reason = CANCEL_REASON['TIMEOUT']
@ -3100,6 +3095,9 @@ def test_handle_cancel_enter_corder_empty(mocker, default_conf_usdt, limit_order
cancel_order_mock.reset_mock()
l_order['filled'] = 1.0
order = deepcopy(l_order)
order['status'] = 'canceled'
mocker.patch('freqtrade.exchange.Exchange.fetch_order', return_value=order)
assert not freqtrade.handle_cancel_enter(trade, l_order, reason)
assert cancel_order_mock.call_count == 1
@ -3113,6 +3111,9 @@ def test_handle_cancel_exit_limit(mocker, default_conf_usdt, fee) -> None:
cancel_order=cancel_order_mock,
)
mocker.patch('freqtrade.exchange.Exchange.get_rate', return_value=0.245441)
mocker.patch('freqtrade.exchange.Exchange.get_min_pair_stake_amount', return_value=0.2)
mocker.patch('freqtrade.freqtradebot.FreqtradeBot.handle_order_fee')
freqtrade = FreqtradeBot(default_conf_usdt)
@ -3121,20 +3122,21 @@ def test_handle_cancel_exit_limit(mocker, default_conf_usdt, fee) -> None:
amount=2,
exchange='binance',
open_rate=0.245441,
open_order_id="123456",
open_order_id="sell_123456",
open_date=arrow.utcnow().shift(days=-2).datetime,
fee_open=fee.return_value,
fee_close=fee.return_value,
close_rate=0.555,
close_date=arrow.utcnow().datetime,
exit_reason="sell_reason_whatever",
stake_amount=0.245441 * 2,
)
trade.orders = [
Order(
Order(
ft_order_side='buy',
ft_pair=trade.pair,
ft_is_open=True,
order_id='123456',
ft_is_open=False,
order_id='buy_123456',
status="closed",
symbol=trade.pair,
order_type="market",
@ -3147,21 +3149,42 @@ def test_handle_cancel_exit_limit(mocker, default_conf_usdt, fee) -> None:
order_date=trade.open_date,
order_filled_date=trade.open_date,
),
Order(
ft_order_side='sell',
ft_pair=trade.pair,
ft_is_open=True,
order_id='sell_123456',
status="open",
symbol=trade.pair,
order_type="limit",
side="sell",
price=trade.open_rate,
average=trade.open_rate,
filled=0.0,
remaining=trade.amount,
cost=trade.open_rate * trade.amount,
order_date=trade.open_date,
order_filled_date=trade.open_date,
),
]
order = {'id': "123456",
order = {'id': "sell_123456",
'remaining': 1,
'amount': 1,
'status': "open"}
reason = CANCEL_REASON['TIMEOUT']
send_msg_mock.reset_mock()
assert freqtrade.handle_cancel_exit(trade, order, reason)
assert cancel_order_mock.call_count == 1
assert send_msg_mock.call_count == 2
assert send_msg_mock.call_count == 1
assert trade.close_rate is None
assert trade.exit_reason is None
assert trade.open_order_id is None
send_msg_mock.reset_mock()
# Partial exit - below exit threshold
order['amount'] = 2
order['filled'] = 1.9
assert not freqtrade.handle_cancel_exit(trade, order, reason)
# Assert cancel_order was not called (callcount remains unchanged)
assert cancel_order_mock.call_count == 1
@ -3171,21 +3194,32 @@ def test_handle_cancel_exit_limit(mocker, default_conf_usdt, fee) -> None:
assert not freqtrade.handle_cancel_exit(trade, order, reason)
send_msg_mock.call_args_list[0][0][0]['reason'] = CANCEL_REASON['PARTIALLY_FILLED_KEEP_OPEN']
assert (send_msg_mock.call_args_list[0][0][0]['reason']
== CANCEL_REASON['PARTIALLY_FILLED_KEEP_OPEN'])
# Message should not be iterated again
assert trade.exit_order_status == CANCEL_REASON['PARTIALLY_FILLED_KEEP_OPEN']
assert send_msg_mock.call_count == 1
send_msg_mock.reset_mock()
order['filled'] = 1
assert freqtrade.handle_cancel_exit(trade, order, reason)
assert send_msg_mock.call_count == 1
assert (send_msg_mock.call_args_list[0][0][0]['reason']
== CANCEL_REASON['PARTIALLY_FILLED'])
def test_handle_cancel_exit_cancel_exception(mocker, default_conf_usdt) -> None:
patch_RPCManager(mocker)
patch_exchange(mocker)
mocker.patch(
'freqtrade.exchange.Exchange.cancel_order_with_result', side_effect=InvalidOrderException())
mocker.patch('freqtrade.exchange.Exchange.get_min_pair_stake_amount', return_value=0.0)
mocker.patch('freqtrade.exchange.Exchange.cancel_order_with_result',
side_effect=InvalidOrderException())
freqtrade = FreqtradeBot(default_conf_usdt)
# TODO: should not be magicmock
trade = MagicMock()
reason = CANCEL_REASON['TIMEOUT']
order = {'remaining': 1,

50
tests/test_integration.py
View File

@ -2,7 +2,7 @@ from unittest.mock import MagicMock
import pytest
from freqtrade.enums import ExitCheckTuple, ExitType
from freqtrade.enums import ExitCheckTuple, ExitType, TradingMode
from freqtrade.persistence import Trade
from freqtrade.persistence.models import Order
from freqtrade.rpc.rpc import RPC
@ -351,8 +351,13 @@ def test_dca_short(default_conf_usdt, ticker_usdt, fee, mocker) -> None:
assert trade.nr_of_successful_exits == 1
def test_dca_order_adjust(default_conf_usdt, ticker_usdt, fee, mocker) -> None:
@pytest.mark.parametrize('leverage', [
1, 2
])
def test_dca_order_adjust(default_conf_usdt, ticker_usdt, leverage, fee, mocker) -> None:
default_conf_usdt['position_adjustment_enable'] = True
default_conf_usdt['trading_mode'] = 'futures'
default_conf_usdt['margin_mode'] = 'isolated'
freqtrade = get_patched_freqtradebot(mocker, default_conf_usdt)
mocker.patch.multiple(
@ -363,9 +368,14 @@ def test_dca_order_adjust(default_conf_usdt, ticker_usdt, fee, mocker) -> None:
price_to_precision=lambda s, x, y: y,
)
mocker.patch('freqtrade.exchange.Exchange._is_dry_limit_order_filled', return_value=False)
mocker.patch("freqtrade.exchange.Exchange.get_max_leverage", return_value=10)
mocker.patch("freqtrade.exchange.Exchange.get_funding_fees", return_value=0)
mocker.patch("freqtrade.exchange.Exchange.get_maintenance_ratio_and_amt", return_value=(0, 0))
patch_get_signal(freqtrade)
freqtrade.strategy.custom_entry_price = lambda **kwargs: ticker_usdt['ask'] * 0.96
freqtrade.strategy.leverage = MagicMock(return_value=leverage)
freqtrade.strategy.minimal_roi = {0: 0.2}
freqtrade.enter_positions()
@ -377,6 +387,8 @@ def test_dca_order_adjust(default_conf_usdt, ticker_usdt, fee, mocker) -> None:
assert trade.open_rate == 1.96
assert trade.stop_loss_pct is None
assert trade.stop_loss == 0.0
assert trade.leverage == leverage
assert trade.stake_amount == 60
assert trade.initial_stop_loss == 0.0
assert trade.initial_stop_loss_pct is None
# No adjustment
@ -396,6 +408,7 @@ def test_dca_order_adjust(default_conf_usdt, ticker_usdt, fee, mocker) -> None:
assert trade.open_rate == 1.96
assert trade.stop_loss_pct is None
assert trade.stop_loss == 0.0
assert trade.stake_amount == 60
assert trade.initial_stop_loss == 0.0
assert trade.initial_stop_loss_pct is None
@ -407,9 +420,10 @@ def test_dca_order_adjust(default_conf_usdt, ticker_usdt, fee, mocker) -> None:
assert trade.open_order_id is None
# Open rate is not adjusted yet
assert trade.open_rate == 1.99
assert trade.stake_amount == 60
assert trade.stop_loss_pct == -0.1
assert trade.stop_loss == 1.99 * 0.9
assert trade.initial_stop_loss == 1.99 * 0.9
assert pytest.approx(trade.stop_loss) == 1.99 * (1 - 0.1 / leverage)
assert pytest.approx(trade.initial_stop_loss) == 1.99 * (1 - 0.1 / leverage)
assert trade.initial_stop_loss_pct == -0.1
# 2nd order - not filling
@ -422,7 +436,7 @@ def test_dca_order_adjust(default_conf_usdt, ticker_usdt, fee, mocker) -> None:
assert trade.open_order_id is not None
assert trade.open_rate == 1.99
assert trade.orders[-1].price == 1.96
assert trade.orders[-1].cost == 120
assert trade.orders[-1].cost == 120 * leverage
# Replace new order with diff. order at a lower price
freqtrade.strategy.adjust_entry_price = MagicMock(return_value=1.95)
@ -432,8 +446,9 @@ def test_dca_order_adjust(default_conf_usdt, ticker_usdt, fee, mocker) -> None:
assert len(trade.orders) == 4
assert trade.open_order_id is not None
assert trade.open_rate == 1.99
assert trade.stake_amount == 60
assert trade.orders[-1].price == 1.95
assert pytest.approx(trade.orders[-1].cost) == 120
assert pytest.approx(trade.orders[-1].cost) == 120 * leverage
# Fill DCA order
freqtrade.strategy.adjust_trade_position = MagicMock(return_value=None)
@ -446,19 +461,21 @@ def test_dca_order_adjust(default_conf_usdt, ticker_usdt, fee, mocker) -> None:
assert trade.open_order_id is None
assert pytest.approx(trade.open_rate) == 1.963153456
assert trade.orders[-1].price == 1.95
assert pytest.approx(trade.orders[-1].cost) == 120
assert pytest.approx(trade.orders[-1].cost) == 120 * leverage
assert trade.orders[-1].status == 'closed'
assert pytest.approx(trade.amount) == 91.689215
assert pytest.approx(trade.amount) == 91.689215 * leverage
# Check the 2 filled orders equal the above amount
assert pytest.approx(trade.orders[1].amount) == 30.150753768
assert pytest.approx(trade.orders[-1].amount) == 61.538461232
assert pytest.approx(trade.orders[1].amount) == 30.150753768 * leverage
assert pytest.approx(trade.orders[-1].amount) == 61.538461232 * leverage
def test_dca_exiting(default_conf_usdt, ticker_usdt, fee, mocker, caplog) -> None:
@pytest.mark.parametrize('leverage', [1, 2])
def test_dca_exiting(default_conf_usdt, ticker_usdt, fee, mocker, caplog, leverage) -> None:
default_conf_usdt['position_adjustment_enable'] = True
freqtrade = get_patched_freqtradebot(mocker, default_conf_usdt)
freqtrade.trading_mode = TradingMode.FUTURES
mocker.patch.multiple(
'freqtrade.exchange.Exchange',
fetch_ticker=ticker_usdt,
@ -467,15 +484,17 @@ def test_dca_exiting(default_conf_usdt, ticker_usdt, fee, mocker, caplog) -> Non
price_to_precision=lambda s, x, y: y,
get_min_pair_stake_amount=MagicMock(return_value=10),
)
mocker.patch("freqtrade.exchange.Exchange.get_max_leverage", return_value=10)
patch_get_signal(freqtrade)
freqtrade.strategy.leverage = MagicMock(return_value=leverage)
freqtrade.enter_positions()
assert len(Trade.get_trades().all()) == 1
trade = Trade.get_trades().first()
assert len(trade.orders) == 1
assert pytest.approx(trade.stake_amount) == 60
assert pytest.approx(trade.amount) == 30.0
assert pytest.approx(trade.amount) == 30.0 * leverage
assert trade.open_rate == 2.0
# Too small size
@ -484,8 +503,9 @@ def test_dca_exiting(default_conf_usdt, ticker_usdt, fee, mocker, caplog) -> Non
trade = Trade.get_trades().first()
assert len(trade.orders) == 1
assert pytest.approx(trade.stake_amount) == 60
assert pytest.approx(trade.amount) == 30.0
assert log_has_re("Remaining amount of 1.6.* would be smaller than the minimum of 10.", caplog)
assert pytest.approx(trade.amount) == 30.0 * leverage
assert log_has_re(
r"Remaining amount of \d\.\d+.* would be smaller than the minimum of 10.", caplog)
freqtrade.strategy.adjust_trade_position = MagicMock(return_value=-20)
@ -494,7 +514,7 @@ def test_dca_exiting(default_conf_usdt, ticker_usdt, fee, mocker, caplog) -> Non
assert len(trade.orders) == 2
assert trade.orders[-1].ft_order_side == 'sell'
assert pytest.approx(trade.stake_amount) == 40.198
assert pytest.approx(trade.amount) == 20.099
assert pytest.approx(trade.amount) == 20.099 * leverage
assert trade.open_rate == 2.0
assert trade.is_open
caplog.clear()

2
tests/test_plotting.py
View File

@ -63,7 +63,7 @@ def test_init_plotscript(default_conf, mocker, testdatadir):
def test_add_indicators(default_conf, testdatadir, caplog):
pair = "UNITTEST/BTC"
timerange = TimeRange(None, 'line', 0, -1000)
timerange = TimeRange()
data = history.load_pair_history(pair=pair, timeframe='1m',
datadir=testdatadir, timerange=timerange)