Merge branch 'develop' into add-inlier-metric
This commit is contained in:
Robert Caulk
GitHub
commit
d7585161b2
@ -53,7 +53,6 @@
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],
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"freqai": {
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"enabled": true,
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"startup_candles": 10000,
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"purge_old_models": true,
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"train_period_days": 15,
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"backtest_period_days": 7,
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@ -75,8 +74,10 @@
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"weight_factor": 0.9,
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"principal_component_analysis": false,
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"use_SVM_to_remove_outliers": true,
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"indicator_max_period_candles": 20,
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"indicator_periods_candles": [10, 20]
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"indicator_periods_candles": [
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10,
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20
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]
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},
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"data_split_parameters": {
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"test_size": 0.33,
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Before Width: | Height: | Size: 191 KiB After Width: | Height: | Size: 185 KiB |
@ -61,8 +61,8 @@ Binance supports [time_in_force](configuration.md#understand-order_time_in_force
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### Binance Blacklist
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For Binance, please add `"BNB/<STAKE>"` to your blacklist to avoid issues.
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Accounts having BNB accounts use this to pay for fees - if your first trade happens to be on `BNB`, further trades will consume this position and make the initial BNB trade unsellable as the expected amount is not there anymore.
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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.
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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.
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### Binance Futures
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@ -205,8 +205,8 @@ Kucoin supports [time_in_force](configuration.md#understand-order_time_in_force)
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### Kucoin Blacklists
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For Kucoin, please add `"KCS/<STAKE>"` to your blacklist to avoid issues.
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Accounts having KCS accounts use this to pay for fees - if your first trade happens to be on `KCS`, further trades will consume this position and make the initial KCS trade unsellable as the expected amount is not there anymore.
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For Kucoin, it is suggested to add `"KCS/<STAKE>"` to your blacklist to avoid issues, unless you are willing to maintain enough extra `KCS` on the account or unless you're willing to disable using `KCS` for fees.
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Kucoin accounts may use `KCS` for fees, and if a trade happens to be on `KCS`, further trades may consume this position and make the initial `KCS` trade unsellable as the expected amount is not there anymore.
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## Huobi
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@ -38,14 +38,14 @@ The example strategy, example prediction model, and example config can be found
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The user provides FreqAI with a set of custom *base* indicators (the same way as in a typical Freqtrade strategy) as well as target values (*labels*).
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FreqAI trains a model to predict the target values based on the input of custom indicators, for each pair in the whitelist. These models are consistently retrained to adapt to market conditions. FreqAI offers the ability to both backtest strategies (emulating reality with periodic retraining) and deploy dry/live runs. In dry/live conditions, FreqAI can be set to constant retraining in a background thread in an effort to keep models as up to date as possible.
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An overview of the algorithm is shown below, explaining the data processing pipeline and the model usage.
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An overview of the algorithm is shown below, explaining the data processing pipeline and the model usage.
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### Important machine learning vocabulary
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**Features** - the quantities with which a model is trained. All features for a single candle is stored as a vector. In FreqAI, the user
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builds the feature sets from anything they can construct in the strategy.
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builds the feature sets from anything they can construct in the strategy.
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**Labels** - the target values that a model is trained
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toward. Each set of features is associated with a single label that is
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@ -53,12 +53,12 @@ defined by the user within the strategy. These labels intentionally look into th
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future, and are not available to the model during dry/live/backtesting.
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**Training** - the process of feeding individual feature sets, composed of historic data, with associated labels into the
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model with the goal of matching input feature sets to associated labels.
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model with the goal of matching input feature sets to associated labels.
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**Train data** - a subset of the historic data that is fed to the model during
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training. This data directly influences weight connections in the model.
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**Test data** - a subset of the historic data that is used to evaluate the performance of the model after training. This data does not influence nodal weights within the model.
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**Test data** - a subset of the historic data that is used to evaluate the performance of the model after training. This data does not influence nodal weights within the model.
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## Install prerequisites
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@ -89,10 +89,10 @@ Mandatory parameters are marked as **Required**, which means that they are requi
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|------------|-------------|
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| | **General configuration parameters**
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| `freqai` | **Required.** <br> The parent dictionary containing all the parameters for controlling FreqAI. <br> **Datatype:** Dictionary.
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| `startup_candles` | Number of candles needed for *backtesting only* to ensure all indicators are non NaNs at the start of the first train period. <br> **Datatype:** Positive integer.
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| `purge_old_models` | Delete obsolete models (otherwise, all historic models will remain on disk). <br> **Datatype:** Boolean. Default: `False`.
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| `train_period_days` | **Required.** <br> Number of days to use for the training data (width of the sliding window). <br> **Datatype:** Positive integer.
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| `backtest_period_days` | **Required.** <br> Number of days to inference from the trained model before sliding the window defined above, and retraining the model. This can be fractional days, but beware that the user-provided `timerange` will be divided by this number to yield the number of trainings necessary to complete the backtest. <br> **Datatype:** Float.
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| `save_backtest_models` | Backtesting operates most efficiently by saving the prediction data and reusing them directly for subsequent runs (when users wish to tune entry/exit parameters). If a user wishes to save models to disk when running backtesting, they should activate `save_backtest_models`. A user may wish to do this if they plan to use the same model files for starting a dry/live instance with the same `identifier`. <br> **Datatype:** Boolean. Default: `False`.
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| `identifier` | **Required.** <br> A unique name for the current model. This can be reused to reload pre-trained models/data. <br> **Datatype:** String.
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| `live_retrain_hours` | Frequency of retraining during dry/live runs. <br> Default set to 0, which means the model will retrain as often as possible. <br> **Datatype:** Float > 0.
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| `expiration_hours` | Avoid making predictions if a model is more than `expiration_hours` old. <br> Defaults set to 0, which means models never expire. <br> **Datatype:** Positive integer.
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@ -104,25 +104,25 @@ Mandatory parameters are marked as **Required**, which means that they are requi
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| `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](#feature-engineering)) will be created for each coin in this list, and that set of features is added to the base asset feature set. <br> **Datatype:** List of assets (strings).
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| `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). The user can create custom labels, making use of this parameter or not. <br> **Datatype:** Positive integer.
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| `include_shifted_candles` | Add features from previous candles to subsequent candles to add historical information. FreqAI takes all features from the `include_shifted_candles` previous candles, duplicates and shifts them so that the information is available for the subsequent candle. <br> **Datatype:** Positive integer.
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| `weight_factor` | Used to set weights for training data points according to their recency. See details about how it works [here](#controlling-the-model-learning-process). <br> **Datatype:** Positive float (typically < 1).
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| `indicator_max_period_candles` | The maximum period used in `populate_any_indicators()` for indicator creation. FreqAI uses this information in combination with the maximum timeframe to calculate how many data points that should be downloaded so that the first data point does not have a NaN. <br> **Datatype:** Positive integer.
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| `weight_factor` | Used to set weights for training data points according to their recency. See details about how it works [here](#controlling-the-model-learning-process). <br> **Datatype:** Positive float (typically < 1).
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| `indicator_max_period_candles` | **No longer used**. User must use the strategy set `startup_candle_count` which defines the maximum *period* used in `populate_any_indicators()` for indicator creation (timeframe independent). FreqAI uses this information in combination with the maximum timeframe to calculate how many data points it should download so that the first data point does not have a NaN <br> **Datatype:** positive integer.
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| `indicator_periods_candles` | Calculate indicators for `indicator_periods_candles` time periods and add them to the feature set. <br> **Datatype:** List of positive integers.
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| `stratify_training_data` | This value is used to indicate the grouping of the data. For example, 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](#stratifying-the-data-for-training-and-testing-the-model) <br> **Datatype:** Positive integer.
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| `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.
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| `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.
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| `DI_threshold` | Activates the Dissimilarity Index for outlier detection when > 0. See details about how it works [here](#removing-outliers-with-the-dissimilarity-index). <br> **Datatype:** Positive float (typically < 1).
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| `use_SVM_to_remove_outliers` | Train a support vector machine to detect and remove outliers from the training data set, as well as from incoming data points. See details about how it works [here](#removing-outliers-using-a-support-vector-machine-svm). <br> **Datatype:** Boolean.
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| `svm_params` | All parameters available in Sklearn's `SGDOneClassSVM()`. See details about some select parameters [here](#removing-outliers-using-a-support-vector-machine-svm). <br> **Datatype:** Dictionary.
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| `use_DBSCAN_to_remove_outliers` | Cluster data using DBSCAN to identify and remove outliers from training and prediction data. See details about how it works [here](#removing-outliers-with-dbscan). <br> **Datatype:** Boolean.
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| `outlier_protection_percentage` | If more than `outlier_protection_percentage` fraction of points are removed as outliers, FreqAI will log a warning message and ignore outlier detection while keeping the original dataset intact. <br> **Datatype:** float. Default: `30`
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| `reverse_train_test_order` | If true, FreqAI will train on the latest data split 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, users should be careful to understand unorthodox nature of this parameter before employing it. <br> **Datatype:** bool. Default: False
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| `inlier_metric_window` | If set, FreqAI will add the `inlier_metric` to the training feature set and set the lookback to be the `inlier_metric_window`. Details of how the `inlier_metric` is computed can be found [here](#using-the-inliermetric) <br> **Datatype:** int. Default: 0
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| `noise_standard_deviation` | If > 0, FreqAI adds noise to the training features. FreqAI generates random deviates from a gaussian distribution with a standard deviation of `noise_standard_deviation` and adds them to all data points. Value should be kept relative to the normalized space between -1 and 1). In other words, since data is always normalized between -1 and 1 in FreqAI, the user can expect a `noise_standard_deviation: 0.05` to see 32% of data randomly increased/decreased by more than 2.5% (i.e. the percent of data falling within the first standard deviation). Good for preventing overfitting. <br> **Datatype:** int. Default: 0
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| `outlier_protection_percentage` | 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 while keeping the original dataset intact. If the outlier protection is triggered, no predictions will be made based on the training data. <br> **Datatype:** Float. Default: `30`
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| `reverse_train_test_order` | If true, FreqAI will train on the latest data split 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, users should be careful to understand unorthodox nature of this parameter before employing it. <br> **Datatype:** Boolean. Default: False
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| | **Data split parameters**
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| `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.
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| `test_size` | Fraction of data that should be used for testing instead of training. <br> **Datatype:** Positive float < 1.
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| `shuffle` | Shuffle the training data points during training. Typically, for time-series forecasting, this is set to `False`. <br>
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| `shuffle` | Shuffle the training data points during training. Typically, for time-series forecasting, this is set to `False`. <br> **Datatype:** Boolean.
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| | **Model training parameters**
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| `model_training_parameters` | A flexible dictionary that includes all parameters available by the user selected model library. For example, if the user uses `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 the user selects a different model, this dictionary can contain any parameter from that model. <br> **Datatype:** Dictionary.**Datatype:** Boolean.
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| `model_training_parameters` | A flexible dictionary that includes all parameters available by the user selected model library. For example, if the user uses `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 the user selects a different model, this dictionary can contain any parameter from that model. <br> **Datatype:** Dictionary.
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| `n_estimators` | The number of boosted trees to fit in regression. <br> **Datatype:** Integer.
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| `learning_rate` | Boosting learning rate during regression. <br> **Datatype:** Float.
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| `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.
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@ -169,7 +169,6 @@ The user interface is isolated to the typical Freqtrade config file. A FreqAI co
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],
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"label_period_candles": 24,
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"include_shifted_candles": 2,
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"indicator_max_period_candles": 20,
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"indicator_periods_candles": [10, 20]
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},
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"data_split_parameters" : {
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@ -186,6 +185,9 @@ The user interface is isolated to the typical Freqtrade config file. A FreqAI co
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The FreqAI strategy requires the user to include the following lines of code in the standard Freqtrade strategy:
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```python
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# user should define the maximum startup candle count (the largest number of candles
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# passed to any single indicator)
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startup_candle_count: int = 20
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def informative_pairs(self):
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whitelist_pairs = self.dp.current_whitelist()
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@ -202,9 +204,9 @@ The FreqAI strategy requires the user to include the following lines of code in
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def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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# the model will return all labels created by user in `populate_any_indicators`
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# (& appended targets), an indication of whether or not the prediction should be accepted,
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# the target mean/std values for each of the labels created by user in
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# the model will return all labels created by user in `populate_any_indicators`
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# (& appended targets), an indication of whether or not the prediction should be accepted,
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# the target mean/std values for each of the labels created by user in
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# `populate_any_indicators()` for each training period.
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dataframe = self.freqai.start(dataframe, metadata, self)
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@ -279,6 +281,17 @@ The FreqAI strategy requires the user to include the following lines of code in
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Notice how the `populate_any_indicators()` is where the user adds their own features ([more information](#feature-engineering)) and labels ([more information](#setting-classifier-targets)). See a full example at `templates/FreqaiExampleStrategy.py`.
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### Setting the `startup_candle_count`
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Users need to take care to set the `startup_candle_count` in their strategy the same way they would for any normal 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 on the `dataprovider` to avoid any NaNs at the beginning of the first training. Users can easily set this value by identifying the longest period (in candle units) that they pass to their indicator creation functions (e.g. talib functions). In the present example, the user would pass 20 to as this value (since it is the maximum value in their `indicators_periods_candles`).
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!!! Note
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Typically it is best for users to be safe and multiply their expected `startup_candle_count` by 2. There are instances where the talib functions actually require more data than just the passed `period`. Anecdotally, multiplying the `startup_candle_count` by 2 always leads to a fully NaN free training dataset. Look out for this log message to confirm that your data is clean:
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```
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2022-08-31 15:14:04 - freqtrade.freqai.data_kitchen - INFO - dropped 0 training points due to NaNs in populated dataset 4319.
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```
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## Creating a dynamic target
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The `&*_std/mean` return values describe the statistical fit of the user defined label *during the most recent training*. This value allows the user to know the rarity of a given prediction. For example, `templates/FreqaiExampleStrategy.py`, creates a `target_roi` which is based on filtering out predictions that are below a given z-score of 1.25.
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@ -312,7 +325,7 @@ The user is encouraged to inherit `train()` and `predict()` to let them customiz
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## Feature engineering
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Features are added by the user inside the `populate_any_indicators()` method of the strategy
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by prepending indicators with `%`, and labels with `&`.
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by prepending indicators with `%`, and labels with `&`.
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There are some important components/structures that the user *must* include when building their feature set; the use of these is shown below:
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@ -421,13 +434,13 @@ In total, the number of features the user of the presented example strat has cre
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length of `include_timeframes` * no. features in `populate_any_indicators()` * length of `include_corr_pairlist` * no. `include_shifted_candles` * length of `indicator_periods_candles`
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$= 3 * 3 * 3 * 2 * 2 = 108$.
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Another structure to consider is the location of the labels at the bottom of the example function (below `if set_generalized_indicators:`).
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Another structure to consider is the location of the labels at the bottom of the example function (below `if set_generalized_indicators:`).
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This is where the user will add single features and labels to their feature set to avoid duplication of them from
|
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various configuration parameters that multiply the feature set, such as `include_timeframes`.
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!!! Note
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Features **must** be defined in `populate_any_indicators()`. Definining FreqAI features in `populate_indicators()`
|
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will cause the algorithm to fail in live/dry mode. If the user wishes to add generalized features that are not associated with
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will cause the algorithm to fail in live/dry mode. If the user wishes to add generalized features that are not associated with
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a specific pair or timeframe, they should use the following structure inside `populate_any_indicators()`
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(as exemplified in `freqtrade/templates/FreqaiExampleStrategy.py`):
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@ -436,7 +449,7 @@ various configuration parameters that multiply the feature set, such as `include
|
||||
|
||||
...
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||||
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# Add generalized indicators here (because in live, it will call only this function to populate
|
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# Add generalized indicators here (because in live, it will call only this function to populate
|
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# indicators for retraining). Notice how we ensure not to add them multiple times by associating
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# these generalized indicators to the basepair/timeframe
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if set_generalized_indicators:
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@ -504,10 +517,10 @@ and if a full `live_retrain_hours` has elapsed since the end of the loaded model
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The FreqAI backtesting module can be executed with the following command:
|
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```bash
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freqtrade backtesting --strategy FreqaiExampleStrategy --config config_freqai.example.json --freqaimodel LightGBMRegressor --timerange 20210501-20210701
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freqtrade backtesting --strategy FreqaiExampleStrategy --strategy-path freqtrade/templates --config config_examples/config_freqai.example.json --freqaimodel LightGBMRegressor --timerange 20210501-20210701
|
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```
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Backtesting mode requires the user to have the data pre-downloaded (unlike in dry/live mode where FreqAI automatically downloads the necessary data). The user should be careful to consider that the time range of the downloaded data is more than the backtesting time range. This is because FreqAI needs data prior to the desired backtesting time range in order to train a model to be ready to make predictions on the first candle of the user-set backtesting time range. More details on how to calculate the data to download can be found [here](#deciding-the-sliding-training-window-and-backtesting-duration).
|
||||
Backtesting mode requires the user to have the data [pre-downloaded](#downloading-data-for-backtesting) (unlike in dry/live mode where FreqAI automatically downloads the necessary data). The user should be careful to consider that the time range of the downloaded data is more than the backtesting time range. This is because FreqAI needs data prior to the desired backtesting time range in order to train a model to be ready to make predictions on the first candle of the user-set backtesting time range. More details on how to calculate the data to download can be found [here](#deciding-the-sliding-training-window-and-backtesting-duration).
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If this command has never been executed with the existing config file, it will train a new model
|
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for each pair, for each backtesting window within the expanded `--timerange`.
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@ -531,23 +544,17 @@ the user is asking FreqAI to use a training period of 30 days and backtest on th
|
||||
This means that if the user sets `--timerange 20210501-20210701`,
|
||||
FreqAI will train have trained 8 separate models at the end of `--timerange` (because the full range comprises 8 weeks). 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`.
|
||||
|
||||
In live mode, the required training data is automatically computed and downloaded. However, in backtesting mode,
|
||||
the user must manually enter the required number of `startup_candles` in the config. This value
|
||||
is used to increase the data to FreqAI, which should be sufficient to enable all indicators
|
||||
to be NaN free at the beginning of the first training. This is done by identifying the
|
||||
longest timeframe (`4h` in presented example config) and the longest indicator period (`20` days in presented example config)
|
||||
and adding this to the `train_period_days`. The units need to be in the base candle time frame:
|
||||
`startup_candles` = ( 4 hours * 20 max period * 60 minutes/hour + 30 day train_period_days * 1440 minutes per day ) / 5 min (base time frame) = 9360.
|
||||
|
||||
!!! Note
|
||||
In dry/live mode, this is all precomputed and handled automatically. Thus, `startup_candle` has no influence on dry/live mode.
|
||||
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||||
!!! Note
|
||||
Although fractional `backtest_period_days` is allowed, the user should be aware that the `--timerange` is divided by this value to determine the number of models that FreqAI will need to train in order to backtest the full range. For example, if the user wants to set a `--timerange` of 10 days, and asks for a `backtest_period_days` of 0.1, FreqAI will need to train 100 models per pair to complete the full backtest. Because of this, a true backtest of FreqAI adaptive training would take a *very* long time. The best way to fully test a model is to run it dry and let it constantly train. In this case, backtesting would take the exact same amount of time as a dry run.
|
||||
|
||||
### Downloading data for backtesting
|
||||
Live/dry instances will download the data automatically for the user, but users who wish to use backtesting functionality still need to download the necessary data using `download-data` (details [here](data-download.md#data-downloading)). FreqAI users need to pay careful attention to understanding how much *additional* data needs to be downloaded to ensure that they have a sufficient amount of training data *before* the start of their 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` ([details](#setting-the-startupcandlecount)) from the beginning of the desired backtesting timerange.
|
||||
|
||||
As an example, if we wish to backtest the `--timerange` above of `20210501-20210701`, and we use the example config which sets `train_period_days` to 15. The startup candle count is 40 on a maximum `include_timeframes` of 1h. We would need 20210501 - 15 days - 40 * 1h / 24 hours = 20210414 (16.7 days earlier than the start of the desired training timerange).
|
||||
|
||||
### Defining model expirations
|
||||
|
||||
During dry/live mode, FreqAI trains each coin pair sequentially (on separate threads/GPU from the main Freqtrade bot). This means that there is always an age discrepancy between models. If a user is training on 50 pairs, and each pair requires 5 minutes to train, the oldest model will be over 4 hours old. This may be undesirable if the characteristic time scale (the trade duration target) for a strategy is less than 4 hours. The user can decide to only make trade entries if the model is less than
|
||||
During dry/live mode, FreqAI trains each coin pair sequentially (on separate threads/GPU from the main Freqtrade bot). This means that there is always an age discrepancy between models. If a user is training on 50 pairs, and each pair requires 5 minutes to train, the oldest model will be over 4 hours old. This may be undesirable if the characteristic time scale (the trade duration target) for a strategy is less than 4 hours. The user can decide to only make trade entries if the model is less than
|
||||
a certain number of hours old by setting the `expiration_hours` in the config file:
|
||||
|
||||
```json
|
||||
@ -634,9 +641,9 @@ The user can stratify (group) the training/testing data using:
|
||||
|
||||
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.
|
||||
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 either does not capture the complexity of the data, the test data is significantly different from the train data, or a different model should be used.
|
||||
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 either does not capture the complexity of the data, the test data is significantly different from the train data, or a different model should be used.
|
||||
|
||||
### Using the `inlier_metric`
|
||||
|
||||
@ -654,7 +661,7 @@ This function does **not** remove outliers from the data set.
|
||||
|
||||
Model training parameters are unique to the machine learning library selected by the user. FreqAI allows the user to set any parameter for any library using the `model_training_parameters` dictionary in the user configuration file. The example configuration file (found in `config_examples/config_freqai.example.json`) show some of the example parameters associated with `Catboost` and `LightGBM`, but the user can add any parameters available in those libraries.
|
||||
|
||||
Data split parameters are defined in `data_split_parameters` which can be any parameters associated with `Sklearn`'s `train_test_split()` function.
|
||||
Data split parameters are defined in `data_split_parameters` which can be any parameters associated with `Sklearn`'s `train_test_split()` function.
|
||||
|
||||
FreqAI includes some additional parameters such as `weight_factor`, which allows the user to weight more recent data more strongly
|
||||
than past data via an exponential function:
|
||||
@ -684,7 +691,7 @@ The user can tell FreqAI to remove outlier data points from the training/test da
|
||||
}
|
||||
```
|
||||
|
||||
Equity and crypto markets suffer from a high level of non-patterned noise in the form of outlier data points. The Dissimilarity Index (DI) aims to quantify the uncertainty associated with each prediction made by the model. The DI allows predictions which are outliers (not existent in the model feature space) to be thrown out due to low levels of certainty.
|
||||
Equity and crypto markets suffer from a high level of non-patterned noise in the form of outlier data points. The Dissimilarity Index (DI) aims to quantify the uncertainty associated with each prediction made by the model. 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:
|
||||
|
||||
@ -702,7 +709,7 @@ which enables the estimation of the Dissimilarity Index as:
|
||||
|
||||
$$ DI_k = d_k/\overline{d} $$
|
||||
|
||||
The user can tweak the DI through the `DI_threshold` to increase or decrease the extrapolation of the trained model.
|
||||
The user can tweak the DI through the `DI_threshold` to increase or decrease the extrapolation of the trained model.
|
||||
|
||||
Below is a figure that describes the DI for a 3D data set.
|
||||
|
||||
@ -721,11 +728,11 @@ The user can tell FreqAI to remove outlier data points from the training/test da
|
||||
```
|
||||
|
||||
FreqAI will train an SVM on the training data (or components of it if the user activated
|
||||
`principal_component_analysis`) and remove any data point that the SVM deems to be beyond the feature space.
|
||||
`principal_component_analysis`) and remove any data point that the SVM deems to be beyond the feature space.
|
||||
|
||||
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.
|
||||
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.
|
||||
|
||||
The parameter `nu`, *very* broadly, is the amount of data points that should be considered outliers.
|
||||
The parameter `nu`, *very* broadly, is the amount of data points that should be considered outliers.
|
||||
|
||||
#### Removing outliers with DBSCAN
|
||||
|
||||
@ -745,7 +752,7 @@ Given a number of data points $N$, and a distance $\varepsilon$, DBSCAN clusters
|
||||
|
||||
|
||||
|
||||
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)) with `min_samples` ($N$) taken as double the no. of user-defined features, and `eps` ($\varepsilon$) taken as the longest distance 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)) with `min_samples` ($N$) taken as 1/4 of the no. of time points in the feature set, and `eps` ($\varepsilon$) taken 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.
|
||||
|
||||
## Additional information
|
||||
|
||||
@ -770,5 +777,5 @@ Code review, software architecture brainstorming:
|
||||
@xmatthias
|
||||
|
||||
Beta testing and bug reporting:
|
||||
@bloodhunter4rc, Salah Lamkadem @ikonx, @ken11o2, @longyu, @paranoidandy, @smidelis, @smarm
|
||||
@bloodhunter4rc, Salah Lamkadem @ikonx, @ken11o2, @longyu, @paranoidandy, @smidelis, @smarm,
|
||||
Juha Nykänen @suikula, Wagner Costa @wagnercosta
|
||||
|
||||
@ -13,7 +13,7 @@
|
||||
Please only use advanced trading modes when you know how freqtrade (and your strategy) works.
|
||||
Also, never risk more than what you can afford to lose.
|
||||
|
||||
Please read the [strategy migration guide](strategy_migration.md#strategy-migration-between-v2-and-v3) to migrate your strategy from a freqtrade v2 strategy, to v3 strategy that can short and trade futures.
|
||||
If you already have an existing strategy, please read the [strategy migration guide](strategy_migration.md#strategy-migration-between-v2-and-v3) to migrate your strategy from a freqtrade v2 strategy, to strategy of version 3 which can short and trade futures.
|
||||
|
||||
## Shorting
|
||||
|
||||
@ -62,6 +62,13 @@ You will also have to pick a "margin mode" (explanation below) - with freqtrade
|
||||
"margin_mode": "isolated"
|
||||
```
|
||||
|
||||
##### Pair namings
|
||||
|
||||
Freqtrade follows the [ccxt naming conventions for futures](https://docs.ccxt.com/en/latest/manual.html?#perpetual-swap-perpetual-future).
|
||||
A futures pair will therefore have the naming of `base/quote:settle` (e.g. `ETH/USDT:USDT`).
|
||||
|
||||
Binance is currently still an exception to this naming scheme, where pairs are named `ETH/USDT` also for futures markets, but will be aligned as soon as CCXT is ready.
|
||||
|
||||
### Margin mode
|
||||
|
||||
On top of `trading_mode` - you will also have to configure your `margin_mode`.
|
||||
|
||||
@ -166,7 +166,7 @@ Additional technical libraries can be installed as necessary, or custom indicato
|
||||
|
||||
Most indicators have an instable startup period, in which they are either not available (NaN), or the calculation is incorrect. This can lead to inconsistencies, since Freqtrade does not know how long this instable period should be.
|
||||
To account for this, the strategy can be assigned the `startup_candle_count` attribute.
|
||||
This should be set to the maximum number of candles that the strategy requires to calculate stable indicators.
|
||||
This should be set to the maximum number of candles that the strategy requires to calculate stable indicators. In the case where a user includes higher timeframes with informative pairs, the `startup_candle_count` does not necessarily change. The value is the maximum period (in candles) that any of the informatives timeframes need to compute stable indicators.
|
||||
|
||||
In this example strategy, this should be set to 100 (`startup_candle_count = 100`), since the longest needed history is 100 candles.
|
||||
|
||||
@ -824,6 +824,8 @@ Options:
|
||||
- Merge the dataframe without lookahead bias
|
||||
- Forward-fill (optional)
|
||||
|
||||
For a full sample, please refer to the [complete data provider example](#complete-data-provider-sample) below.
|
||||
|
||||
All columns of the informative dataframe will be available on the returning dataframe in a renamed fashion:
|
||||
|
||||
!!! Example "Column renaming"
|
||||
|
||||
@ -91,9 +91,9 @@ class DataProvider:
|
||||
timerange = TimeRange.parse_timerange(None if self._config.get(
|
||||
'timerange') is None else str(self._config.get('timerange')))
|
||||
# Move informative start time respecting startup_candle_count
|
||||
timerange.subtract_start(
|
||||
timeframe_to_seconds(str(timeframe)) * self._config.get('startup_candle_count', 0)
|
||||
)
|
||||
startup_candles = self.get_required_startup(str(timeframe))
|
||||
tf_seconds = timeframe_to_seconds(str(timeframe))
|
||||
timerange.subtract_start(tf_seconds * startup_candles)
|
||||
self.__cached_pairs_backtesting[saved_pair] = load_pair_history(
|
||||
pair=pair,
|
||||
timeframe=timeframe or self._config['timeframe'],
|
||||
@ -105,6 +105,21 @@ class DataProvider:
|
||||
)
|
||||
return self.__cached_pairs_backtesting[saved_pair].copy()
|
||||
|
||||
def get_required_startup(self, timeframe: str) -> int:
|
||||
freqai_config = self._config.get('freqai', {})
|
||||
if not freqai_config.get('enabled', False):
|
||||
return self._config.get('startup_candle_count', 0)
|
||||
else:
|
||||
startup_candles = self._config.get('startup_candle_count', 0)
|
||||
indicator_periods = freqai_config['feature_parameters']['indicator_periods_candles']
|
||||
# make sure the startupcandles is at least the set maximum indicator periods
|
||||
self._config['startup_candle_count'] = max(startup_candles, max(indicator_periods))
|
||||
tf_seconds = timeframe_to_seconds(timeframe)
|
||||
train_candles = freqai_config['train_period_days'] * 86400 / tf_seconds
|
||||
total_candles = int(self._config['startup_candle_count'] + train_candles)
|
||||
logger.info(f'Increasing startup_candle_count for freqai to {total_candles}')
|
||||
return total_candles
|
||||
|
||||
def get_pair_dataframe(
|
||||
self,
|
||||
pair: str,
|
||||
|
||||
@ -2600,7 +2600,7 @@ class Exchange:
|
||||
is_short: bool,
|
||||
amount: float, # Absolute value of position size
|
||||
stake_amount: float,
|
||||
wallet_balance: float = 0.0,
|
||||
wallet_balance: float,
|
||||
mm_ex_1: float = 0.0, # (Binance) Cross only
|
||||
upnl_ex_1: float = 0.0, # (Binance) Cross only
|
||||
) -> Optional[float]:
|
||||
|
||||
@ -76,6 +76,8 @@ class FreqaiDataDrawer:
|
||||
self.full_path / f"follower_dictionary-{self.follower_name}.json"
|
||||
)
|
||||
self.historic_predictions_path = Path(self.full_path / "historic_predictions.pkl")
|
||||
self.historic_predictions_bkp_path = Path(
|
||||
self.full_path / "historic_predictions.backup.pkl")
|
||||
self.pair_dictionary_path = Path(self.full_path / "pair_dictionary.json")
|
||||
self.follow_mode = follow_mode
|
||||
if follow_mode:
|
||||
@ -118,13 +120,21 @@ class FreqaiDataDrawer:
|
||||
"""
|
||||
exists = self.historic_predictions_path.is_file()
|
||||
if exists:
|
||||
with open(self.historic_predictions_path, "rb") as fp:
|
||||
self.historic_predictions = cloudpickle.load(fp)
|
||||
logger.info(
|
||||
f"Found existing historic predictions at {self.full_path}, but beware "
|
||||
"that statistics may be inaccurate if the bot has been offline for "
|
||||
"an extended period of time."
|
||||
)
|
||||
try:
|
||||
with open(self.historic_predictions_path, "rb") as fp:
|
||||
self.historic_predictions = cloudpickle.load(fp)
|
||||
logger.info(
|
||||
f"Found existing historic predictions at {self.full_path}, but beware "
|
||||
"that statistics may be inaccurate if the bot has been offline for "
|
||||
"an extended period of time."
|
||||
)
|
||||
except EOFError:
|
||||
logger.warning(
|
||||
'Historical prediction file was corrupted. Trying to load backup file.')
|
||||
with open(self.historic_predictions_bkp_path, "rb") as fp:
|
||||
self.historic_predictions = cloudpickle.load(fp)
|
||||
logger.warning('FreqAI successfully loaded the backup historical predictions file.')
|
||||
|
||||
elif not self.follow_mode:
|
||||
logger.info("Could not find existing historic_predictions, starting from scratch")
|
||||
else:
|
||||
@ -142,6 +152,9 @@ class FreqaiDataDrawer:
|
||||
with open(self.historic_predictions_path, "wb") as fp:
|
||||
cloudpickle.dump(self.historic_predictions, fp, protocol=cloudpickle.DEFAULT_PROTOCOL)
|
||||
|
||||
# create a backup
|
||||
shutil.copy(self.historic_predictions_path, self.historic_predictions_bkp_path)
|
||||
|
||||
def save_drawer_to_disk(self):
|
||||
"""
|
||||
Save data drawer full of all pair model metadata in present model folder.
|
||||
|
||||
@ -18,8 +18,6 @@ from sklearn.model_selection import train_test_split
|
||||
from sklearn.neighbors import NearestNeighbors
|
||||
|
||||
from freqtrade.configuration import TimeRange
|
||||
from freqtrade.data.dataprovider import DataProvider
|
||||
from freqtrade.data.history.history_utils import refresh_backtest_ohlcv_data
|
||||
from freqtrade.exceptions import OperationalException
|
||||
from freqtrade.exchange import timeframe_to_seconds
|
||||
from freqtrade.strategy.interface import IStrategy
|
||||
@ -73,6 +71,8 @@ class FreqaiDataKitchen:
|
||||
self.label_list: List = []
|
||||
self.training_features_list: List = []
|
||||
self.model_filename: str = ""
|
||||
self.backtesting_results_path = Path()
|
||||
self.backtest_predictions_folder: str = "backtesting_predictions"
|
||||
self.live = live
|
||||
self.pair = pair
|
||||
|
||||
@ -291,6 +291,7 @@ class FreqaiDataKitchen:
|
||||
:returns:
|
||||
:data_dictionary: updated dictionary with standardized values.
|
||||
"""
|
||||
|
||||
# standardize the data by training stats
|
||||
train_max = data_dictionary["train_features"].max()
|
||||
train_min = data_dictionary["train_features"].min()
|
||||
@ -324,10 +325,24 @@ class FreqaiDataKitchen:
|
||||
- 1
|
||||
)
|
||||
|
||||
self.data[f"{item}_max"] = train_labels_max # .to_dict()
|
||||
self.data[f"{item}_min"] = train_labels_min # .to_dict()
|
||||
self.data[f"{item}_max"] = train_labels_max
|
||||
self.data[f"{item}_min"] = train_labels_min
|
||||
return data_dictionary
|
||||
|
||||
def normalize_single_dataframe(self, df: DataFrame) -> DataFrame:
|
||||
|
||||
train_max = df.max()
|
||||
train_min = df.min()
|
||||
df = (
|
||||
2 * (df - train_min) / (train_max - train_min) - 1
|
||||
)
|
||||
|
||||
for item in train_max.keys():
|
||||
self.data[item + "_max"] = train_max[item]
|
||||
self.data[item + "_min"] = train_min[item]
|
||||
|
||||
return df
|
||||
|
||||
def normalize_data_from_metadata(self, df: DataFrame) -> DataFrame:
|
||||
"""
|
||||
Normalize a set of data using the mean and standard deviation from
|
||||
@ -441,7 +456,8 @@ class FreqaiDataKitchen:
|
||||
start = datetime.fromtimestamp(timerange.startts, tz=timezone.utc)
|
||||
stop = datetime.fromtimestamp(timerange.stopts, tz=timezone.utc)
|
||||
df = df.loc[df["date"] >= start, :]
|
||||
df = df.loc[df["date"] <= stop, :]
|
||||
if not self.live:
|
||||
df = df.loc[df["date"] < stop, :]
|
||||
|
||||
return df
|
||||
|
||||
@ -454,22 +470,23 @@ class FreqaiDataKitchen:
|
||||
|
||||
from sklearn.decomposition import PCA # avoid importing if we dont need it
|
||||
|
||||
n_components = self.data_dictionary["train_features"].shape[1]
|
||||
pca = PCA(n_components=n_components)
|
||||
pca = PCA(0.999)
|
||||
pca = pca.fit(self.data_dictionary["train_features"])
|
||||
n_keep_components = np.argmin(pca.explained_variance_ratio_.cumsum() < 0.999)
|
||||
pca2 = PCA(n_components=n_keep_components)
|
||||
n_keep_components = pca.n_components_
|
||||
self.data["n_kept_components"] = n_keep_components
|
||||
pca2 = pca2.fit(self.data_dictionary["train_features"])
|
||||
n_components = self.data_dictionary["train_features"].shape[1]
|
||||
logger.info("reduced feature dimension by %s", n_components - n_keep_components)
|
||||
logger.info("explained variance %f", np.sum(pca2.explained_variance_ratio_))
|
||||
train_components = pca2.transform(self.data_dictionary["train_features"])
|
||||
logger.info("explained variance %f", np.sum(pca.explained_variance_ratio_))
|
||||
|
||||
train_components = pca.transform(self.data_dictionary["train_features"])
|
||||
self.data_dictionary["train_features"] = pd.DataFrame(
|
||||
data=train_components,
|
||||
columns=["PC" + str(i) for i in range(0, n_keep_components)],
|
||||
index=self.data_dictionary["train_features"].index,
|
||||
)
|
||||
# normalsing transformed training features
|
||||
self.data_dictionary["train_features"] = self.normalize_single_dataframe(
|
||||
self.data_dictionary["train_features"])
|
||||
|
||||
# keeping a copy of the non-transformed features so we can check for errors during
|
||||
# model load from disk
|
||||
@ -477,15 +494,18 @@ class FreqaiDataKitchen:
|
||||
self.training_features_list = self.data_dictionary["train_features"].columns
|
||||
|
||||
if self.freqai_config.get('data_split_parameters', {}).get('test_size', 0.1) != 0:
|
||||
test_components = pca2.transform(self.data_dictionary["test_features"])
|
||||
test_components = pca.transform(self.data_dictionary["test_features"])
|
||||
self.data_dictionary["test_features"] = pd.DataFrame(
|
||||
data=test_components,
|
||||
columns=["PC" + str(i) for i in range(0, n_keep_components)],
|
||||
index=self.data_dictionary["test_features"].index,
|
||||
)
|
||||
# normalise transformed test feature to transformed training features
|
||||
self.data_dictionary["test_features"] = self.normalize_data_from_metadata(
|
||||
self.data_dictionary["test_features"])
|
||||
|
||||
self.data["n_kept_components"] = n_keep_components
|
||||
self.pca = pca2
|
||||
self.pca = pca
|
||||
|
||||
logger.info(f"PCA reduced total features from {n_components} to {n_keep_components}")
|
||||
|
||||
@ -506,6 +526,9 @@ class FreqaiDataKitchen:
|
||||
columns=["PC" + str(i) for i in range(0, self.data["n_kept_components"])],
|
||||
index=filtered_dataframe.index,
|
||||
)
|
||||
# normalise transformed predictions to transformed training features
|
||||
self.data_dictionary["prediction_features"] = self.normalize_data_from_metadata(
|
||||
self.data_dictionary["prediction_features"])
|
||||
|
||||
def compute_distances(self) -> float:
|
||||
"""
|
||||
@ -885,9 +908,10 @@ class FreqaiDataKitchen:
|
||||
weights = np.exp(-np.arange(num_weights) / (wfactor * num_weights))[::-1]
|
||||
return weights
|
||||
|
||||
def append_predictions(self, predictions: DataFrame, do_predict: npt.ArrayLike) -> None:
|
||||
def get_predictions_to_append(self, predictions: DataFrame,
|
||||
do_predict: npt.ArrayLike) -> DataFrame:
|
||||
"""
|
||||
Append backtest prediction from current backtest period to all previous periods
|
||||
Get backtest prediction from current backtest period
|
||||
"""
|
||||
|
||||
append_df = DataFrame()
|
||||
@ -902,13 +926,18 @@ class FreqaiDataKitchen:
|
||||
if self.freqai_config["feature_parameters"].get("DI_threshold", 0) > 0:
|
||||
append_df["DI_values"] = self.DI_values
|
||||
|
||||
return append_df
|
||||
|
||||
def append_predictions(self, append_df: DataFrame) -> None:
|
||||
"""
|
||||
Append backtest prediction from current backtest period to all previous periods
|
||||
"""
|
||||
|
||||
if self.full_df.empty:
|
||||
self.full_df = append_df
|
||||
else:
|
||||
self.full_df = pd.concat([self.full_df, append_df], axis=0)
|
||||
|
||||
return
|
||||
|
||||
def fill_predictions(self, dataframe):
|
||||
"""
|
||||
Back fill values to before the backtesting range so that the dataframe matches size
|
||||
@ -1008,9 +1037,7 @@ class FreqaiDataKitchen:
|
||||
# We notice that users like to use exotic indicators where
|
||||
# they do not know the required timeperiod. Here we include a factor
|
||||
# of safety by multiplying the user considered "max" by 2.
|
||||
max_period = self.freqai_config["feature_parameters"].get(
|
||||
"indicator_max_period_candles", 20
|
||||
) * 2
|
||||
max_period = self.config.get('startup_candle_count', 20) * 2
|
||||
additional_seconds = max_period * max_tf_seconds
|
||||
|
||||
if trained_timestamp != 0:
|
||||
@ -1056,31 +1083,6 @@ class FreqaiDataKitchen:
|
||||
|
||||
self.model_filename = f"cb_{coin.lower()}_{int(trained_timerange.stopts)}"
|
||||
|
||||
def download_all_data_for_training(self, timerange: TimeRange, dp: DataProvider) -> None:
|
||||
"""
|
||||
Called only once upon start of bot to download the necessary data for
|
||||
populating indicators and training the model.
|
||||
:param timerange: TimeRange = The full data timerange for populating the indicators
|
||||
and training the model.
|
||||
:param dp: DataProvider instance attached to the strategy
|
||||
"""
|
||||
new_pairs_days = int((timerange.stopts - timerange.startts) / SECONDS_IN_DAY)
|
||||
if not dp._exchange:
|
||||
# Not realistic - this is only called in live mode.
|
||||
raise OperationalException("Dataprovider did not have an exchange attached.")
|
||||
refresh_backtest_ohlcv_data(
|
||||
dp._exchange,
|
||||
pairs=self.all_pairs,
|
||||
timeframes=self.freqai_config["feature_parameters"].get("include_timeframes"),
|
||||
datadir=self.config["datadir"],
|
||||
timerange=timerange,
|
||||
new_pairs_days=new_pairs_days,
|
||||
erase=False,
|
||||
data_format=self.config.get("dataformat_ohlcv", "json"),
|
||||
trading_mode=self.config.get("trading_mode", "spot"),
|
||||
prepend=self.config.get("prepend_data", False),
|
||||
)
|
||||
|
||||
def set_all_pairs(self) -> None:
|
||||
|
||||
self.all_pairs = copy.deepcopy(
|
||||
@ -1194,3 +1196,50 @@ class FreqaiDataKitchen:
|
||||
if self.unique_classes:
|
||||
for label in self.unique_classes:
|
||||
self.unique_class_list += list(self.unique_classes[label])
|
||||
|
||||
def save_backtesting_prediction(
|
||||
self, append_df: DataFrame
|
||||
) -> None:
|
||||
|
||||
"""
|
||||
Save prediction dataframe from backtesting to h5 file format
|
||||
:param append_df: dataframe for backtesting period
|
||||
"""
|
||||
full_predictions_folder = Path(self.full_path / self.backtest_predictions_folder)
|
||||
if not full_predictions_folder.is_dir():
|
||||
full_predictions_folder.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
append_df.to_hdf(self.backtesting_results_path, key='append_df', mode='w')
|
||||
|
||||
def get_backtesting_prediction(
|
||||
self
|
||||
) -> DataFrame:
|
||||
|
||||
"""
|
||||
Get prediction dataframe from h5 file format
|
||||
"""
|
||||
append_df = pd.read_hdf(self.backtesting_results_path)
|
||||
return append_df
|
||||
|
||||
def check_if_backtest_prediction_exists(
|
||||
self
|
||||
) -> bool:
|
||||
"""
|
||||
Check if a backtesting prediction already exists
|
||||
:param dk: FreqaiDataKitchen
|
||||
:return:
|
||||
:boolean: whether the prediction file exists or not.
|
||||
"""
|
||||
path_to_predictionfile = Path(self.full_path /
|
||||
self.backtest_predictions_folder /
|
||||
f"{self.model_filename}_prediction.h5")
|
||||
self.backtesting_results_path = path_to_predictionfile
|
||||
|
||||
file_exists = path_to_predictionfile.is_file()
|
||||
if file_exists:
|
||||
logger.info(f"Found backtesting prediction file at {path_to_predictionfile}")
|
||||
else:
|
||||
logger.info(
|
||||
f"Could not find backtesting prediction file at {path_to_predictionfile}"
|
||||
)
|
||||
return file_exists
|
||||
|
||||
@ -6,7 +6,7 @@ from abc import ABC, abstractmethod
|
||||
from datetime import datetime, timezone
|
||||
from pathlib import Path
|
||||
from threading import Lock
|
||||
from typing import Any, Dict, Tuple
|
||||
from typing import Any, Dict, List, Tuple
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
@ -26,13 +26,6 @@ pd.options.mode.chained_assignment = None
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
def threaded(fn):
|
||||
def wrapper(*args, **kwargs):
|
||||
threading.Thread(target=fn, args=args, kwargs=kwargs).start()
|
||||
|
||||
return wrapper
|
||||
|
||||
|
||||
class IFreqaiModel(ABC):
|
||||
"""
|
||||
Class containing all tools for training and prediction in the strategy.
|
||||
@ -69,6 +62,9 @@ class IFreqaiModel(ABC):
|
||||
self.first = True
|
||||
self.set_full_path()
|
||||
self.follow_mode: bool = self.freqai_info.get("follow_mode", False)
|
||||
self.save_backtest_models: bool = self.freqai_info.get("save_backtest_models", False)
|
||||
if self.save_backtest_models:
|
||||
logger.info('Backtesting module configured to save all models.')
|
||||
self.dd = FreqaiDataDrawer(Path(self.full_path), self.config, self.follow_mode)
|
||||
self.identifier: str = self.freqai_info.get("identifier", "no_id_provided")
|
||||
self.scanning = False
|
||||
@ -92,6 +88,9 @@ class IFreqaiModel(ABC):
|
||||
self.begin_time_train: float = 0
|
||||
self.base_tf_seconds = timeframe_to_seconds(self.config['timeframe'])
|
||||
|
||||
self._threads: List[threading.Thread] = []
|
||||
self._stop_event = threading.Event()
|
||||
|
||||
def assert_config(self, config: Dict[str, Any]) -> None:
|
||||
|
||||
if not config.get("freqai", {}):
|
||||
@ -125,10 +124,9 @@ class IFreqaiModel(ABC):
|
||||
elif not self.follow_mode:
|
||||
self.dk = FreqaiDataKitchen(self.config, self.live, metadata["pair"])
|
||||
logger.info(f"Training {len(self.dk.training_timeranges)} timeranges")
|
||||
with self.analysis_lock:
|
||||
dataframe = self.dk.use_strategy_to_populate_indicators(
|
||||
strategy, prediction_dataframe=dataframe, pair=metadata["pair"]
|
||||
)
|
||||
dataframe = self.dk.use_strategy_to_populate_indicators(
|
||||
strategy, prediction_dataframe=dataframe, pair=metadata["pair"]
|
||||
)
|
||||
dk = self.start_backtesting(dataframe, metadata, self.dk)
|
||||
|
||||
dataframe = dk.remove_features_from_df(dk.return_dataframe)
|
||||
@ -146,15 +144,34 @@ class IFreqaiModel(ABC):
|
||||
self.model = None
|
||||
self.dk = None
|
||||
|
||||
@threaded
|
||||
def start_scanning(self, strategy: IStrategy) -> None:
|
||||
def shutdown(self):
|
||||
"""
|
||||
Cleans up threads on Shutdown, set stop event. Join threads to wait
|
||||
for current training iteration.
|
||||
"""
|
||||
logger.info("Stopping FreqAI")
|
||||
self._stop_event.set()
|
||||
|
||||
logger.info("Waiting on Training iteration")
|
||||
for _thread in self._threads:
|
||||
_thread.join()
|
||||
|
||||
def start_scanning(self, *args, **kwargs) -> None:
|
||||
"""
|
||||
Start `self._start_scanning` in a separate thread
|
||||
"""
|
||||
_thread = threading.Thread(target=self._start_scanning, args=args, kwargs=kwargs)
|
||||
self._threads.append(_thread)
|
||||
_thread.start()
|
||||
|
||||
def _start_scanning(self, strategy: IStrategy) -> None:
|
||||
"""
|
||||
Function designed to constantly scan pairs for retraining on a separate thread (intracandle)
|
||||
to improve model youth. This function is agnostic to data preparation/collection/storage,
|
||||
it simply trains on what ever data is available in the self.dd.
|
||||
:param strategy: IStrategy = The user defined strategy class
|
||||
"""
|
||||
while 1:
|
||||
while not self._stop_event.is_set():
|
||||
time.sleep(1)
|
||||
for pair in self.config.get("exchange", {}).get("pair_whitelist"):
|
||||
|
||||
@ -225,28 +242,39 @@ class IFreqaiModel(ABC):
|
||||
"trains"
|
||||
)
|
||||
|
||||
trained_timestamp_int = int(trained_timestamp.stopts)
|
||||
dk.data_path = Path(
|
||||
dk.full_path
|
||||
/
|
||||
f"sub-train-{metadata['pair'].split('/')[0]}_{int(trained_timestamp.stopts)}"
|
||||
f"sub-train-{metadata['pair'].split('/')[0]}_{trained_timestamp_int}"
|
||||
)
|
||||
if not self.model_exists(
|
||||
metadata["pair"], dk, trained_timestamp=int(trained_timestamp.stopts)
|
||||
):
|
||||
dk.find_features(dataframe_train)
|
||||
self.model = self.train(dataframe_train, metadata["pair"], dk)
|
||||
self.dd.pair_dict[metadata["pair"]]["trained_timestamp"] = int(
|
||||
trained_timestamp.stopts)
|
||||
dk.set_new_model_names(metadata["pair"], trained_timestamp)
|
||||
self.dd.save_data(self.model, metadata["pair"], dk)
|
||||
|
||||
dk.set_new_model_names(metadata["pair"], trained_timestamp)
|
||||
|
||||
if dk.check_if_backtest_prediction_exists():
|
||||
append_df = dk.get_backtesting_prediction()
|
||||
dk.append_predictions(append_df)
|
||||
else:
|
||||
self.model = self.dd.load_data(metadata["pair"], dk)
|
||||
if not self.model_exists(
|
||||
metadata["pair"], dk, trained_timestamp=trained_timestamp_int
|
||||
):
|
||||
dk.find_features(dataframe_train)
|
||||
self.model = self.train(dataframe_train, metadata["pair"], dk)
|
||||
self.dd.pair_dict[metadata["pair"]]["trained_timestamp"] = int(
|
||||
trained_timestamp.stopts)
|
||||
|
||||
self.check_if_feature_list_matches_strategy(dataframe_train, dk)
|
||||
if self.save_backtest_models:
|
||||
logger.info('Saving backtest model to disk.')
|
||||
self.dd.save_data(self.model, metadata["pair"], dk)
|
||||
else:
|
||||
self.model = self.dd.load_data(metadata["pair"], dk)
|
||||
|
||||
pred_df, do_preds = self.predict(dataframe_backtest, dk)
|
||||
self.check_if_feature_list_matches_strategy(dataframe_train, dk)
|
||||
|
||||
dk.append_predictions(pred_df, do_preds)
|
||||
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)
|
||||
dk.save_backtesting_prediction(append_df)
|
||||
|
||||
dk.fill_predictions(dataframe)
|
||||
|
||||
@ -291,14 +319,8 @@ class IFreqaiModel(ABC):
|
||||
)
|
||||
dk.set_paths(metadata["pair"], new_trained_timerange.stopts)
|
||||
|
||||
# download candle history if it is not already in memory
|
||||
# load candle history into memory if it is not yet.
|
||||
if not self.dd.historic_data:
|
||||
logger.info(
|
||||
"Downloading all training data for all pairs in whitelist and "
|
||||
"corr_pairlist, this may take a while if you do not have the "
|
||||
"data saved"
|
||||
)
|
||||
dk.download_all_data_for_training(data_load_timerange, strategy.dp)
|
||||
self.dd.load_all_pair_histories(data_load_timerange, dk)
|
||||
|
||||
if not self.scanning:
|
||||
@ -471,11 +493,6 @@ class IFreqaiModel(ABC):
|
||||
:return:
|
||||
:boolean: whether the model file exists or not.
|
||||
"""
|
||||
coin, _ = pair.split("/")
|
||||
|
||||
if not self.live:
|
||||
dk.model_filename = model_filename = f"cb_{coin.lower()}_{trained_timestamp}"
|
||||
|
||||
path_to_modelfile = Path(dk.data_path / f"{model_filename}_model.joblib")
|
||||
file_exists = path_to_modelfile.is_file()
|
||||
if file_exists and not scanning:
|
||||
@ -628,8 +645,8 @@ class IFreqaiModel(ABC):
|
||||
logger.info(
|
||||
f'Total time spent inferencing pairlist {self.inference_time:.2f} seconds')
|
||||
if self.inference_time > 0.25 * self.base_tf_seconds:
|
||||
logger.warning('Inference took over 25/% of the candle time. Reduce pairlist to'
|
||||
' avoid blinding open trades and degrading performance.')
|
||||
logger.warning("Inference took over 25% of the candle time. Reduce pairlist to"
|
||||
" avoid blinding open trades and degrading performance.")
|
||||
self.pair_it = 0
|
||||
self.inference_time = 0
|
||||
return
|
||||
|
||||
134
freqtrade/freqai/utils.py
Normal file
134
freqtrade/freqai/utils.py
Normal file
@ -0,0 +1,134 @@
|
||||
import logging
|
||||
from datetime import datetime, timezone
|
||||
|
||||
from freqtrade.configuration import TimeRange
|
||||
from freqtrade.data.dataprovider import DataProvider
|
||||
from freqtrade.data.history.history_utils import refresh_backtest_ohlcv_data
|
||||
from freqtrade.exceptions import OperationalException
|
||||
from freqtrade.exchange import timeframe_to_seconds
|
||||
from freqtrade.exchange.exchange import market_is_active
|
||||
from freqtrade.plugins.pairlist.pairlist_helpers import dynamic_expand_pairlist
|
||||
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
def download_all_data_for_training(dp: DataProvider, config: dict) -> None:
|
||||
"""
|
||||
Called only once upon start of bot to download the necessary data for
|
||||
populating indicators and training the model.
|
||||
:param timerange: TimeRange = The full data timerange for populating the indicators
|
||||
and training the model.
|
||||
:param dp: DataProvider instance attached to the strategy
|
||||
"""
|
||||
|
||||
if dp._exchange is None:
|
||||
raise OperationalException('No exchange object found.')
|
||||
markets = [p for p, m in dp._exchange.markets.items() if market_is_active(m)
|
||||
or config.get('include_inactive')]
|
||||
|
||||
all_pairs = dynamic_expand_pairlist(config, markets)
|
||||
|
||||
timerange = get_required_data_timerange(config)
|
||||
|
||||
new_pairs_days = int((timerange.stopts - timerange.startts) / 86400)
|
||||
|
||||
refresh_backtest_ohlcv_data(
|
||||
dp._exchange,
|
||||
pairs=all_pairs,
|
||||
timeframes=config["freqai"]["feature_parameters"].get("include_timeframes"),
|
||||
datadir=config["datadir"],
|
||||
timerange=timerange,
|
||||
new_pairs_days=new_pairs_days,
|
||||
erase=False,
|
||||
data_format=config.get("dataformat_ohlcv", "json"),
|
||||
trading_mode=config.get("trading_mode", "spot"),
|
||||
prepend=config.get("prepend_data", False),
|
||||
)
|
||||
|
||||
|
||||
def get_required_data_timerange(
|
||||
config: dict
|
||||
) -> TimeRange:
|
||||
"""
|
||||
Used to compute the required data download time range
|
||||
for auto data-download in FreqAI
|
||||
"""
|
||||
time = datetime.now(tz=timezone.utc).timestamp()
|
||||
|
||||
timeframes = config["freqai"]["feature_parameters"].get("include_timeframes")
|
||||
|
||||
max_tf_seconds = 0
|
||||
for tf in timeframes:
|
||||
secs = timeframe_to_seconds(tf)
|
||||
if secs > max_tf_seconds:
|
||||
max_tf_seconds = secs
|
||||
|
||||
startup_candles = config.get('startup_candle_count', 0)
|
||||
indicator_periods = config["freqai"]["feature_parameters"]["indicator_periods_candles"]
|
||||
|
||||
# factor the max_period as a factor of safety.
|
||||
max_period = int(max(startup_candles, max(indicator_periods)) * 1.5)
|
||||
config['startup_candle_count'] = max_period
|
||||
logger.info(f'FreqAI auto-downloader using {max_period} startup candles.')
|
||||
|
||||
additional_seconds = max_period * max_tf_seconds
|
||||
|
||||
startts = int(
|
||||
time
|
||||
- config["freqai"].get("train_period_days", 0) * 86400
|
||||
- additional_seconds
|
||||
)
|
||||
stopts = int(time)
|
||||
data_load_timerange = TimeRange('date', 'date', startts, stopts)
|
||||
|
||||
return data_load_timerange
|
||||
|
||||
|
||||
# Keep below for when we wish to download heterogeneously lengthed data for FreqAI.
|
||||
# def download_all_data_for_training(dp: DataProvider, config: dict) -> None:
|
||||
# """
|
||||
# Called only once upon start of bot to download the necessary data for
|
||||
# populating indicators and training a FreqAI model.
|
||||
# :param timerange: TimeRange = The full data timerange for populating the indicators
|
||||
# and training the model.
|
||||
# :param dp: DataProvider instance attached to the strategy
|
||||
# """
|
||||
|
||||
# if dp._exchange is not None:
|
||||
# markets = [p for p, m in dp._exchange.markets.items() if market_is_active(m)
|
||||
# or config.get('include_inactive')]
|
||||
# else:
|
||||
# # This should not occur:
|
||||
# raise OperationalException('No exchange object found.')
|
||||
|
||||
# all_pairs = dynamic_expand_pairlist(config, markets)
|
||||
|
||||
# if not dp._exchange:
|
||||
# # Not realistic - this is only called in live mode.
|
||||
# raise OperationalException("Dataprovider did not have an exchange attached.")
|
||||
|
||||
# time = datetime.now(tz=timezone.utc).timestamp()
|
||||
|
||||
# for tf in config["freqai"]["feature_parameters"].get("include_timeframes"):
|
||||
# timerange = TimeRange()
|
||||
# timerange.startts = int(time)
|
||||
# timerange.stopts = int(time)
|
||||
# startup_candles = dp.get_required_startup(str(tf))
|
||||
# tf_seconds = timeframe_to_seconds(str(tf))
|
||||
# timerange.subtract_start(tf_seconds * startup_candles)
|
||||
# new_pairs_days = int((timerange.stopts - timerange.startts) / 86400)
|
||||
# # FIXME: now that we are looping on `refresh_backtest_ohlcv_data`, the function
|
||||
# # redownloads the funding rate for each pair.
|
||||
# refresh_backtest_ohlcv_data(
|
||||
# dp._exchange,
|
||||
# pairs=all_pairs,
|
||||
# timeframes=[tf],
|
||||
# datadir=config["datadir"],
|
||||
# timerange=timerange,
|
||||
# new_pairs_days=new_pairs_days,
|
||||
# erase=False,
|
||||
# data_format=config.get("dataformat_ohlcv", "json"),
|
||||
# trading_mode=config.get("trading_mode", "spot"),
|
||||
# prepend=config.get("prepend_data", False),
|
||||
# )
|
||||
@ -142,15 +142,20 @@ class FreqtradeBot(LoggingMixin):
|
||||
:return: None
|
||||
"""
|
||||
logger.info('Cleaning up modules ...')
|
||||
try:
|
||||
# Wrap db activities in shutdown to avoid problems if database is gone,
|
||||
# and raises further exceptions.
|
||||
if self.config['cancel_open_orders_on_exit']:
|
||||
self.cancel_all_open_orders()
|
||||
|
||||
if self.config['cancel_open_orders_on_exit']:
|
||||
self.cancel_all_open_orders()
|
||||
self.check_for_open_trades()
|
||||
|
||||
self.check_for_open_trades()
|
||||
finally:
|
||||
self.strategy.ft_bot_cleanup()
|
||||
|
||||
self.rpc.cleanup()
|
||||
Trade.commit()
|
||||
self.exchange.close()
|
||||
self.rpc.cleanup()
|
||||
Trade.commit()
|
||||
self.exchange.close()
|
||||
|
||||
def startup(self) -> None:
|
||||
"""
|
||||
@ -281,7 +286,7 @@ class FreqtradeBot(LoggingMixin):
|
||||
pair=trade.pair,
|
||||
amount=trade.amount,
|
||||
is_short=trade.is_short,
|
||||
open_date=trade.open_date_utc
|
||||
open_date=trade.date_last_filled_utc
|
||||
)
|
||||
trade.funding_fees = funding_fees
|
||||
else:
|
||||
@ -726,10 +731,11 @@ class FreqtradeBot(LoggingMixin):
|
||||
fee = self.exchange.get_fee(symbol=pair, taker_or_maker='maker')
|
||||
base_currency = self.exchange.get_pair_base_currency(pair)
|
||||
open_date = datetime.now(timezone.utc)
|
||||
funding_fees = self.exchange.get_funding_fees(
|
||||
pair=pair, amount=amount, is_short=is_short, open_date=open_date)
|
||||
|
||||
# This is a new trade
|
||||
if trade is None:
|
||||
funding_fees = self.exchange.get_funding_fees(
|
||||
pair=pair, amount=amount, is_short=is_short, open_date=open_date)
|
||||
trade = Trade(
|
||||
pair=pair,
|
||||
base_currency=base_currency,
|
||||
@ -1484,7 +1490,7 @@ class FreqtradeBot(LoggingMixin):
|
||||
pair=trade.pair,
|
||||
amount=trade.amount,
|
||||
is_short=trade.is_short,
|
||||
open_date=trade.open_date_utc,
|
||||
open_date=trade.date_last_filled_utc,
|
||||
)
|
||||
exit_type = 'exit'
|
||||
exit_reason = exit_tag or exit_check.exit_reason
|
||||
@ -1778,7 +1784,7 @@ class FreqtradeBot(LoggingMixin):
|
||||
self.rpc.send_msg(msg)
|
||||
|
||||
def apply_fee_conditional(self, trade: Trade, trade_base_currency: str,
|
||||
amount: float, fee_abs: float) -> float:
|
||||
amount: float, fee_abs: float, order_obj: Order) -> Optional[float]:
|
||||
"""
|
||||
Applies the fee to amount (either from Order or from Trades).
|
||||
Can eat into dust if more than the required asset is available.
|
||||
@ -1786,40 +1792,42 @@ class FreqtradeBot(LoggingMixin):
|
||||
never in base currency.
|
||||
"""
|
||||
self.wallets.update()
|
||||
if fee_abs != 0 and self.wallets.get_free(trade_base_currency) >= amount:
|
||||
amount_ = amount
|
||||
if order_obj.ft_order_side == trade.exit_side or order_obj.ft_order_side == 'stoploss':
|
||||
# check against remaining amount!
|
||||
amount_ = trade.amount - amount
|
||||
|
||||
if fee_abs != 0 and self.wallets.get_free(trade_base_currency) >= amount_:
|
||||
# Eat into dust if we own more than base currency
|
||||
logger.info(f"Fee amount for {trade} was in base currency - "
|
||||
f"Eating Fee {fee_abs} into dust.")
|
||||
elif fee_abs != 0:
|
||||
real_amount = self.exchange.amount_to_precision(trade.pair, amount - fee_abs)
|
||||
logger.info(f"Applying fee on amount for {trade} "
|
||||
f"(from {amount} to {real_amount}).")
|
||||
return real_amount
|
||||
return amount
|
||||
logger.info(f"Applying fee on amount for {trade}, fee={fee_abs}.")
|
||||
return fee_abs
|
||||
return None
|
||||
|
||||
def handle_order_fee(self, trade: Trade, order_obj: Order, order: Dict[str, Any]) -> None:
|
||||
# Try update amount (binance-fix)
|
||||
try:
|
||||
new_amount = self.get_real_amount(trade, order, order_obj)
|
||||
if not isclose(safe_value_fallback(order, 'filled', 'amount'), new_amount,
|
||||
abs_tol=constants.MATH_CLOSE_PREC):
|
||||
order_obj.ft_fee_base = trade.amount - new_amount
|
||||
fee_abs = self.get_real_amount(trade, order, order_obj)
|
||||
if fee_abs is not None:
|
||||
order_obj.ft_fee_base = fee_abs
|
||||
except DependencyException as exception:
|
||||
logger.warning("Could not update trade amount: %s", exception)
|
||||
|
||||
def get_real_amount(self, trade: Trade, order: Dict, order_obj: Order) -> float:
|
||||
def get_real_amount(self, trade: Trade, order: Dict, order_obj: Order) -> Optional[float]:
|
||||
"""
|
||||
Detect and update trade fee.
|
||||
Calls trade.update_fee() upon correct detection.
|
||||
Returns modified amount if the fee was taken from the destination currency.
|
||||
Necessary for exchanges which charge fees in base currency (e.g. binance)
|
||||
:return: identical (or new) amount for the trade
|
||||
:return: Absolute fee to apply for this order or None
|
||||
"""
|
||||
# Init variables
|
||||
order_amount = safe_value_fallback(order, 'filled', 'amount')
|
||||
# Only run for closed orders
|
||||
if trade.fee_updated(order.get('side', '')) or order['status'] == 'open':
|
||||
return order_amount
|
||||
return None
|
||||
|
||||
trade_base_currency = self.exchange.get_pair_base_currency(trade.pair)
|
||||
# use fee from order-dict if possible
|
||||
@ -1836,13 +1844,14 @@ class FreqtradeBot(LoggingMixin):
|
||||
if trade_base_currency == fee_currency:
|
||||
# Apply fee to amount
|
||||
return self.apply_fee_conditional(trade, trade_base_currency,
|
||||
amount=order_amount, fee_abs=fee_cost)
|
||||
return order_amount
|
||||
amount=order_amount, fee_abs=fee_cost,
|
||||
order_obj=order_obj)
|
||||
return None
|
||||
return self.fee_detection_from_trades(
|
||||
trade, order, order_obj, order_amount, order.get('trades', []))
|
||||
|
||||
def fee_detection_from_trades(self, trade: Trade, order: Dict, order_obj: Order,
|
||||
order_amount: float, trades: List) -> float:
|
||||
order_amount: float, trades: List) -> Optional[float]:
|
||||
"""
|
||||
fee-detection fallback to Trades.
|
||||
Either uses provided trades list or the result of fetch_my_trades to get correct fee.
|
||||
@ -1853,7 +1862,7 @@ class FreqtradeBot(LoggingMixin):
|
||||
|
||||
if len(trades) == 0:
|
||||
logger.info("Applying fee on amount for %s failed: myTrade-Dict empty found", trade)
|
||||
return order_amount
|
||||
return None
|
||||
fee_currency = None
|
||||
amount = 0
|
||||
fee_abs = 0.0
|
||||
@ -1895,10 +1904,9 @@ class FreqtradeBot(LoggingMixin):
|
||||
raise DependencyException("Half bought? Amounts don't match")
|
||||
|
||||
if fee_abs != 0:
|
||||
return self.apply_fee_conditional(trade, trade_base_currency,
|
||||
amount=amount, fee_abs=fee_abs)
|
||||
else:
|
||||
return amount
|
||||
return self.apply_fee_conditional(
|
||||
trade, trade_base_currency, amount=amount, fee_abs=fee_abs, order_obj=order_obj)
|
||||
return None
|
||||
|
||||
def get_valid_price(self, custom_price: float, proposed_price: float) -> float:
|
||||
"""
|
||||
|
||||
@ -212,21 +212,12 @@ class Backtesting:
|
||||
"""
|
||||
self.progress.init_step(BacktestState.DATALOAD, 1)
|
||||
|
||||
if self.config.get('freqai', {}).get('enabled', False):
|
||||
startup_candles = int(self.config.get('freqai', {}).get('startup_candles', 0))
|
||||
if not startup_candles:
|
||||
raise OperationalException('FreqAI backtesting module requires user set '
|
||||
'startup_candles in config.')
|
||||
self.required_startup += int(self.config.get('freqai', {}).get('startup_candles', 0))
|
||||
logger.info(f'Increasing startup_candle_count for freqai to {self.required_startup}')
|
||||
self.config['startup_candle_count'] = self.required_startup
|
||||
|
||||
data = history.load_data(
|
||||
datadir=self.config['datadir'],
|
||||
pairs=self.pairlists.whitelist,
|
||||
timeframe=self.timeframe,
|
||||
timerange=self.timerange,
|
||||
startup_candles=self.required_startup,
|
||||
startup_candles=self.dataprovider.get_required_startup(self.timeframe),
|
||||
fail_without_data=True,
|
||||
data_format=self.config.get('dataformat_ohlcv', 'json'),
|
||||
candle_type=self.config.get('candle_type_def', CandleType.SPOT)
|
||||
@ -695,7 +686,7 @@ class Backtesting:
|
||||
self.futures_data[trade.pair],
|
||||
amount=trade.amount,
|
||||
is_short=trade.is_short,
|
||||
open_date=trade.open_date_utc,
|
||||
open_date=trade.date_last_filled_utc,
|
||||
close_date=exit_candle_time,
|
||||
)
|
||||
|
||||
|
||||
@ -421,9 +421,10 @@ class Hyperopt:
|
||||
preprocessed = self.backtesting.strategy.advise_all_indicators(data)
|
||||
|
||||
# Trim startup period from analyzed dataframe to get correct dates for output.
|
||||
processed = trim_dataframes(preprocessed, self.timerange, self.backtesting.required_startup)
|
||||
self.min_date, self.max_date = get_timerange(processed)
|
||||
return processed
|
||||
trimmed = trim_dataframes(preprocessed, self.timerange, self.backtesting.required_startup)
|
||||
self.min_date, self.max_date = get_timerange(trimmed)
|
||||
# Real trimming will happen as part of backtesting.
|
||||
return preprocessed
|
||||
|
||||
def prepare_hyperopt_data(self) -> None:
|
||||
HyperoptStateContainer.set_state(HyperoptState.DATALOAD)
|
||||
|
||||
@ -212,17 +212,18 @@ def migrate_orders_table(engine, table_back_name: str, cols_order: List):
|
||||
ft_fee_base = get_column_def(cols_order, 'ft_fee_base', 'null')
|
||||
average = get_column_def(cols_order, 'average', 'null')
|
||||
stop_price = get_column_def(cols_order, 'stop_price', 'null')
|
||||
funding_fee = get_column_def(cols_order, 'funding_fee', '0.0')
|
||||
|
||||
# sqlite does not support literals for booleans
|
||||
with engine.begin() as connection:
|
||||
connection.execute(text(f"""
|
||||
insert into orders (id, ft_trade_id, ft_order_side, ft_pair, ft_is_open, order_id,
|
||||
status, symbol, order_type, side, price, amount, filled, average, remaining, cost,
|
||||
stop_price, order_date, order_filled_date, order_update_date, ft_fee_base)
|
||||
stop_price, order_date, order_filled_date, order_update_date, ft_fee_base, funding_fee)
|
||||
select id, ft_trade_id, ft_order_side, ft_pair, ft_is_open, order_id,
|
||||
status, symbol, order_type, side, price, amount, filled, {average} average, remaining,
|
||||
cost, {stop_price} stop_price, order_date, order_filled_date,
|
||||
order_update_date, {ft_fee_base} ft_fee_base
|
||||
order_update_date, {ft_fee_base} ft_fee_base, {funding_fee} funding_fee
|
||||
from {table_back_name}
|
||||
"""))
|
||||
|
||||
@ -307,9 +308,10 @@ def check_migrate(engine, decl_base, previous_tables) -> None:
|
||||
# Check if migration necessary
|
||||
# Migrates both trades and orders table!
|
||||
# if ('orders' not in previous_tables
|
||||
# or not has_column(cols_orders, 'stop_price')):
|
||||
# or not has_column(cols_orders, 'funding_fee')):
|
||||
migrating = False
|
||||
if not has_column(cols_trades, 'contract_size'):
|
||||
# if not has_column(cols_trades, 'contract_size'):
|
||||
if not has_column(cols_orders, 'funding_fee'):
|
||||
migrating = True
|
||||
logger.info(f"Running database migration for trades - "
|
||||
f"backup: {table_back_name}, {order_table_bak_name}")
|
||||
|
||||
@ -65,6 +65,8 @@ class Order(_DECL_BASE):
|
||||
order_filled_date = Column(DateTime, nullable=True)
|
||||
order_update_date = Column(DateTime, nullable=True)
|
||||
|
||||
funding_fee = Column(Float, nullable=True)
|
||||
|
||||
ft_fee_base = Column(Float, nullable=True)
|
||||
|
||||
@property
|
||||
@ -72,6 +74,13 @@ class Order(_DECL_BASE):
|
||||
""" Order-date with UTC timezoneinfo"""
|
||||
return self.order_date.replace(tzinfo=timezone.utc)
|
||||
|
||||
@property
|
||||
def order_filled_utc(self) -> Optional[datetime]:
|
||||
""" last order-date with UTC timezoneinfo"""
|
||||
return (
|
||||
self.order_filled_date.replace(tzinfo=timezone.utc) if self.order_filled_date else None
|
||||
)
|
||||
|
||||
@property
|
||||
def safe_price(self) -> float:
|
||||
return self.average or self.price
|
||||
@ -119,6 +128,10 @@ class Order(_DECL_BASE):
|
||||
self.ft_is_open = True
|
||||
if self.status in NON_OPEN_EXCHANGE_STATES:
|
||||
self.ft_is_open = False
|
||||
if self.trade:
|
||||
# Assign funding fee up to this point
|
||||
# (represents the funding fee since the last order)
|
||||
self.funding_fee = self.trade.funding_fees
|
||||
if (order.get('filled', 0.0) or 0.0) > 0:
|
||||
self.order_filled_date = datetime.now(timezone.utc)
|
||||
self.order_update_date = datetime.now(timezone.utc)
|
||||
@ -179,6 +192,10 @@ class Order(_DECL_BASE):
|
||||
self.remaining = 0
|
||||
self.status = 'closed'
|
||||
self.ft_is_open = False
|
||||
# Assign funding fees to Order.
|
||||
# Assumes backtesting will use date_last_filled_utc to calculate future funding fees.
|
||||
self.funding_fee = trade.funding_fees
|
||||
|
||||
if (self.ft_order_side == trade.entry_side):
|
||||
trade.open_rate = self.price
|
||||
trade.recalc_trade_from_orders()
|
||||
@ -346,6 +363,15 @@ class LocalTrade():
|
||||
else:
|
||||
return self.amount
|
||||
|
||||
@property
|
||||
def date_last_filled_utc(self) -> datetime:
|
||||
""" Date of the last filled order"""
|
||||
orders = self.select_filled_orders()
|
||||
if not orders:
|
||||
return self.open_date_utc
|
||||
return max([self.open_date_utc,
|
||||
max(o.order_filled_utc for o in orders if o.order_filled_utc)])
|
||||
|
||||
@property
|
||||
def open_date_utc(self):
|
||||
return self.open_date.replace(tzinfo=timezone.utc)
|
||||
@ -648,7 +674,6 @@ class LocalTrade():
|
||||
"""
|
||||
self.close_rate = rate
|
||||
self.close_date = self.close_date or datetime.utcnow()
|
||||
self.close_profit_abs = self.calc_profit(rate) + self.realized_profit
|
||||
self.is_open = False
|
||||
self.exit_order_status = 'closed'
|
||||
self.open_order_id = None
|
||||
@ -844,10 +869,14 @@ class LocalTrade():
|
||||
close_profit = 0.0
|
||||
close_profit_abs = 0.0
|
||||
profit = None
|
||||
for o in self.orders:
|
||||
# Reset funding fees
|
||||
self.funding_fees = 0.0
|
||||
funding_fees = 0.0
|
||||
ordercount = len(self.orders) - 1
|
||||
for i, o in enumerate(self.orders):
|
||||
if o.ft_is_open or not o.filled:
|
||||
continue
|
||||
|
||||
funding_fees += (o.funding_fee or 0.0)
|
||||
tmp_amount = FtPrecise(o.safe_amount_after_fee)
|
||||
tmp_price = FtPrecise(o.safe_price)
|
||||
|
||||
@ -862,7 +891,11 @@ class LocalTrade():
|
||||
avg_price = current_stake / current_amount
|
||||
|
||||
if is_exit:
|
||||
# Process partial exits
|
||||
# Process exits
|
||||
if i == ordercount and is_closing:
|
||||
# Apply funding fees only to the last closing order
|
||||
self.funding_fees = funding_fees
|
||||
|
||||
exit_rate = o.safe_price
|
||||
exit_amount = o.safe_amount_after_fee
|
||||
profit = self.calc_profit(rate=exit_rate, amount=exit_amount,
|
||||
@ -872,6 +905,7 @@ class LocalTrade():
|
||||
exit_rate, amount=exit_amount, open_rate=avg_price)
|
||||
else:
|
||||
total_stake = total_stake + self._calc_open_trade_value(tmp_amount, price)
|
||||
self.funding_fees = funding_fees
|
||||
|
||||
if close_profit:
|
||||
self.close_profit = close_profit
|
||||
|
||||
@ -52,7 +52,7 @@ class PrecisionFilter(IPairList):
|
||||
:return: True if the pair can stay, false if it should be removed
|
||||
"""
|
||||
if ticker.get('last', None) is None:
|
||||
self.log_once(f"Removed {ticker['symbol']} from whitelist, because "
|
||||
self.log_once(f"Removed {pair} from whitelist, because "
|
||||
"ticker['last'] is empty (Usually no trade in the last 24h).",
|
||||
logger.info)
|
||||
return False
|
||||
@ -62,10 +62,10 @@ class PrecisionFilter(IPairList):
|
||||
sp = self._exchange.price_to_precision(pair, stop_price)
|
||||
|
||||
stop_gap_price = self._exchange.price_to_precision(pair, stop_price * 0.99)
|
||||
logger.debug(f"{ticker['symbol']} - {sp} : {stop_gap_price}")
|
||||
logger.debug(f"{pair} - {sp} : {stop_gap_price}")
|
||||
|
||||
if sp <= stop_gap_price:
|
||||
self.log_once(f"Removed {ticker['symbol']} from whitelist, because "
|
||||
self.log_once(f"Removed {pair} from whitelist, because "
|
||||
f"stop price {sp} would be <= stop limit {stop_gap_price}", logger.info)
|
||||
return False
|
||||
|
||||
|
||||
@ -186,6 +186,7 @@ class VolumePairList(IPairList):
|
||||
needed_pairs, since_ms=since_ms, cache=False
|
||||
)
|
||||
for i, p in enumerate(filtered_tickers):
|
||||
contract_size = self._exchange.markets[p['symbol']].get('contractSize', 1.0) or 1.0
|
||||
pair_candles = candles[
|
||||
(p['symbol'], self._lookback_timeframe, self._def_candletype)
|
||||
] if (
|
||||
@ -199,6 +200,7 @@ class VolumePairList(IPairList):
|
||||
|
||||
pair_candles['quoteVolume'] = (
|
||||
pair_candles['volume'] * pair_candles['typical_price']
|
||||
* contract_size
|
||||
)
|
||||
else:
|
||||
# Exchange ohlcv data is in quote volume already.
|
||||
|
||||
@ -261,11 +261,15 @@ class RPC:
|
||||
profit_str += f" ({fiat_profit:.2f})"
|
||||
fiat_profit_sum = fiat_profit if isnan(fiat_profit_sum) \
|
||||
else fiat_profit_sum + fiat_profit
|
||||
open_order = (trade.select_order_by_order_id(
|
||||
trade.open_order_id) if trade.open_order_id else None)
|
||||
|
||||
detail_trade = [
|
||||
f'{trade.id} {direction_str}',
|
||||
trade.pair + ('*' if (trade.open_order_id is not None
|
||||
and trade.close_rate_requested is None) else '')
|
||||
+ ('**' if (trade.close_rate_requested is not None) else ''),
|
||||
trade.pair + ('*' if (open_order
|
||||
and open_order.ft_order_side == trade.entry_side) else '')
|
||||
+ ('**' if (open_order and
|
||||
open_order.ft_order_side == trade.exit_side is not None) else ''),
|
||||
shorten_date(arrow.get(trade.open_date).humanize(only_distance=True)),
|
||||
profit_str
|
||||
]
|
||||
|
||||
@ -6,6 +6,7 @@ This module manage Telegram communication
|
||||
import json
|
||||
import logging
|
||||
import re
|
||||
from copy import deepcopy
|
||||
from dataclasses import dataclass
|
||||
from datetime import date, datetime, timedelta
|
||||
from functools import partial
|
||||
@ -374,7 +375,7 @@ class Telegram(RPCHandler):
|
||||
message += f"\n*Duration:* `{msg['duration']} ({msg['duration_min']:.1f} min)`"
|
||||
return message
|
||||
|
||||
def compose_message(self, msg: Dict[str, Any], msg_type: RPCMessageType) -> str:
|
||||
def compose_message(self, msg: Dict[str, Any], msg_type: RPCMessageType) -> Optional[str]:
|
||||
if msg_type in [RPCMessageType.ENTRY, RPCMessageType.ENTRY_FILL]:
|
||||
message = self._format_entry_msg(msg)
|
||||
|
||||
@ -411,7 +412,8 @@ class Telegram(RPCHandler):
|
||||
elif msg_type == RPCMessageType.STRATEGY_MSG:
|
||||
message = f"{msg['msg']}"
|
||||
else:
|
||||
raise NotImplementedError(f"Unknown message type: {msg_type}")
|
||||
logger.debug("Unknown message type: %s", msg_type)
|
||||
return None
|
||||
return message
|
||||
|
||||
def send_msg(self, msg: Dict[str, Any]) -> None:
|
||||
@ -438,9 +440,9 @@ class Telegram(RPCHandler):
|
||||
# Notification disabled
|
||||
return
|
||||
|
||||
message = self.compose_message(msg, msg_type)
|
||||
|
||||
self._send_msg(message, disable_notification=(noti == 'silent'))
|
||||
message = self.compose_message(deepcopy(msg), msg_type)
|
||||
if message:
|
||||
self._send_msg(message, disable_notification=(noti == 'silent'))
|
||||
|
||||
def _get_sell_emoji(self, msg):
|
||||
"""
|
||||
|
||||
@ -148,10 +148,19 @@ class IStrategy(ABC, HyperStrategyMixin):
|
||||
def load_freqAI_model(self) -> None:
|
||||
if self.config.get('freqai', {}).get('enabled', False):
|
||||
# Import here to avoid importing this if freqAI is disabled
|
||||
from freqtrade.freqai.utils import download_all_data_for_training
|
||||
from freqtrade.resolvers.freqaimodel_resolver import FreqaiModelResolver
|
||||
|
||||
self.freqai = FreqaiModelResolver.load_freqaimodel(self.config)
|
||||
self.freqai_info = self.config["freqai"]
|
||||
|
||||
# download the desired data in dry/live
|
||||
if self.config.get('runmode') in (RunMode.DRY_RUN, RunMode.LIVE):
|
||||
logger.info(
|
||||
"Downloading all training data for all pairs in whitelist and "
|
||||
"corr_pairlist, this may take a while if the data is not "
|
||||
"already on disk."
|
||||
)
|
||||
download_all_data_for_training(self.dp, self.config)
|
||||
else:
|
||||
# Gracious failures if freqAI is disabled but "start" is called.
|
||||
class DummyClass():
|
||||
@ -159,6 +168,10 @@ class IStrategy(ABC, HyperStrategyMixin):
|
||||
raise OperationalException(
|
||||
'freqAI is not enabled. '
|
||||
'Please enable it in your config to use this strategy.')
|
||||
|
||||
def shutdown(self, *args, **kwargs):
|
||||
pass
|
||||
|
||||
self.freqai = DummyClass() # type: ignore
|
||||
|
||||
def ft_bot_start(self, **kwargs) -> None:
|
||||
@ -172,6 +185,12 @@ class IStrategy(ABC, HyperStrategyMixin):
|
||||
|
||||
self.ft_load_hyper_params(self.config.get('runmode') == RunMode.HYPEROPT)
|
||||
|
||||
def ft_bot_cleanup(self) -> None:
|
||||
"""
|
||||
Clean up FreqAI and child threads
|
||||
"""
|
||||
self.freqai.shutdown()
|
||||
|
||||
@abstractmethod
|
||||
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
"""
|
||||
|
||||
@ -43,7 +43,8 @@ class FreqaiExampleStrategy(IStrategy):
|
||||
process_only_new_candles = True
|
||||
stoploss = -0.05
|
||||
use_exit_signal = True
|
||||
startup_candle_count: int = 300
|
||||
# this is the maximum period fed to talib (timeframe independent)
|
||||
startup_candle_count: int = 40
|
||||
can_short = False
|
||||
|
||||
linear_roi_offset = DecimalParameter(
|
||||
|
||||
@ -45,7 +45,6 @@ class FreqaiExampleHybridStrategy(IStrategy):
|
||||
"weight_factor": 0.9,
|
||||
"principal_component_analysis": false,
|
||||
"use_SVM_to_remove_outliers": true,
|
||||
"indicator_max_period_candles": 20,
|
||||
"indicator_periods_candles": [10, 20]
|
||||
},
|
||||
"data_split_parameters": {
|
||||
|
||||
@ -10,7 +10,7 @@ flake8==5.0.4
|
||||
flake8-tidy-imports==4.8.0
|
||||
mypy==0.971
|
||||
pre-commit==2.20.0
|
||||
pytest==7.1.2
|
||||
pytest==7.1.3
|
||||
pytest-asyncio==0.19.0
|
||||
pytest-cov==3.0.0
|
||||
pytest-mock==3.8.2
|
||||
|
||||
@ -1,22 +1,22 @@
|
||||
numpy==1.23.2
|
||||
pandas==1.4.3
|
||||
pandas==1.4.4
|
||||
pandas-ta==0.3.14b
|
||||
|
||||
ccxt==1.92.84
|
||||
ccxt==1.93.3
|
||||
# Pin cryptography for now due to rust build errors with piwheels
|
||||
cryptography==37.0.4
|
||||
aiohttp==3.8.1
|
||||
SQLAlchemy==1.4.40
|
||||
python-telegram-bot==13.13
|
||||
arrow==1.2.2
|
||||
python-telegram-bot==13.14
|
||||
arrow==1.2.3
|
||||
cachetools==4.2.2
|
||||
requests==2.28.1
|
||||
urllib3==1.26.12
|
||||
jsonschema==4.14.0
|
||||
jsonschema==4.15.0
|
||||
TA-Lib==0.4.24
|
||||
technical==1.3.0
|
||||
tabulate==0.8.10
|
||||
pycoingecko==2.2.0
|
||||
pycoingecko==3.0.0
|
||||
jinja2==3.1.2
|
||||
tables==3.7.0
|
||||
blosc==1.10.6
|
||||
@ -34,17 +34,17 @@ orjson==3.8.0
|
||||
sdnotify==0.3.2
|
||||
|
||||
# API Server
|
||||
fastapi==0.81.0
|
||||
fastapi==0.82.0
|
||||
uvicorn==0.18.3
|
||||
pyjwt==2.4.0
|
||||
aiofiles==0.8.0
|
||||
psutil==5.9.1
|
||||
psutil==5.9.2
|
||||
|
||||
# Support for colorized terminal output
|
||||
colorama==0.4.5
|
||||
# Building config files interactively
|
||||
questionary==1.10.0
|
||||
prompt-toolkit==3.0.30
|
||||
prompt-toolkit==3.0.31
|
||||
# Extensions to datetime library
|
||||
python-dateutil==2.8.2
|
||||
|
||||
|
||||
@ -1,4 +1,3 @@
|
||||
from math import isclose
|
||||
from pathlib import Path
|
||||
from unittest.mock import MagicMock
|
||||
|
||||
@ -269,7 +268,7 @@ def test_create_cum_profit(testdatadir):
|
||||
"cum_profits", timeframe="5m")
|
||||
assert "cum_profits" in cum_profits.columns
|
||||
assert cum_profits.iloc[0]['cum_profits'] == 0
|
||||
assert isclose(cum_profits.iloc[-1]['cum_profits'], 8.723007518796964e-06)
|
||||
assert pytest.approx(cum_profits.iloc[-1]['cum_profits']) == 8.723007518796964e-06
|
||||
|
||||
|
||||
def test_create_cum_profit1(testdatadir):
|
||||
@ -287,7 +286,7 @@ def test_create_cum_profit1(testdatadir):
|
||||
"cum_profits", timeframe="5m")
|
||||
assert "cum_profits" in cum_profits.columns
|
||||
assert cum_profits.iloc[0]['cum_profits'] == 0
|
||||
assert isclose(cum_profits.iloc[-1]['cum_profits'], 8.723007518796964e-06)
|
||||
assert pytest.approx(cum_profits.iloc[-1]['cum_profits']) == 8.723007518796964e-06
|
||||
|
||||
with pytest.raises(ValueError, match='Trade dataframe empty.'):
|
||||
create_cum_profit(df.set_index('date'), bt_data[bt_data["pair"] == 'NOTAPAIR'],
|
||||
|
||||
@ -2,7 +2,6 @@ import copy
|
||||
import logging
|
||||
from copy import deepcopy
|
||||
from datetime import datetime, timedelta, timezone
|
||||
from math import isclose
|
||||
from random import randint
|
||||
from unittest.mock import MagicMock, Mock, PropertyMock, patch
|
||||
|
||||
@ -407,10 +406,10 @@ def test__get_stake_amount_limit(mocker, default_conf) -> None:
|
||||
# min
|
||||
result = exchange.get_min_pair_stake_amount('ETH/BTC', 1, stoploss)
|
||||
expected_result = 2 * (1 + 0.05) / (1 - abs(stoploss))
|
||||
assert isclose(result, expected_result)
|
||||
assert pytest.approx(result) == expected_result
|
||||
# With Leverage
|
||||
result = exchange.get_min_pair_stake_amount('ETH/BTC', 1, stoploss, 3.0)
|
||||
assert isclose(result, expected_result / 3)
|
||||
assert pytest.approx(result) == expected_result / 3
|
||||
# max
|
||||
result = exchange.get_max_pair_stake_amount('ETH/BTC', 2)
|
||||
assert result == 10000
|
||||
@ -426,10 +425,10 @@ def test__get_stake_amount_limit(mocker, default_conf) -> None:
|
||||
)
|
||||
result = exchange.get_min_pair_stake_amount('ETH/BTC', 2, stoploss)
|
||||
expected_result = 2 * 2 * (1 + 0.05) / (1 - abs(stoploss))
|
||||
assert isclose(result, expected_result)
|
||||
assert pytest.approx(result) == expected_result
|
||||
# With Leverage
|
||||
result = exchange.get_min_pair_stake_amount('ETH/BTC', 2, stoploss, 5.0)
|
||||
assert isclose(result, expected_result / 5)
|
||||
assert pytest.approx(result) == expected_result / 5
|
||||
# max
|
||||
result = exchange.get_max_pair_stake_amount('ETH/BTC', 2)
|
||||
assert result == 20000
|
||||
@ -445,10 +444,10 @@ def test__get_stake_amount_limit(mocker, default_conf) -> None:
|
||||
)
|
||||
result = exchange.get_min_pair_stake_amount('ETH/BTC', 2, stoploss)
|
||||
expected_result = max(2, 2 * 2) * (1 + 0.05) / (1 - abs(stoploss))
|
||||
assert isclose(result, expected_result)
|
||||
assert pytest.approx(result) == expected_result
|
||||
# With Leverage
|
||||
result = exchange.get_min_pair_stake_amount('ETH/BTC', 2, stoploss, 10)
|
||||
assert isclose(result, expected_result / 10)
|
||||
assert pytest.approx(result) == expected_result / 10
|
||||
|
||||
# min amount and cost are set (amount is minial)
|
||||
markets["ETH/BTC"]["limits"] = {
|
||||
@ -461,20 +460,20 @@ def test__get_stake_amount_limit(mocker, default_conf) -> None:
|
||||
)
|
||||
result = exchange.get_min_pair_stake_amount('ETH/BTC', 2, stoploss)
|
||||
expected_result = max(8, 2 * 2) * (1 + 0.05) / (1 - abs(stoploss))
|
||||
assert isclose(result, expected_result)
|
||||
assert pytest.approx(result) == expected_result
|
||||
# With Leverage
|
||||
result = exchange.get_min_pair_stake_amount('ETH/BTC', 2, stoploss, 7.0)
|
||||
assert isclose(result, expected_result / 7.0)
|
||||
assert pytest.approx(result) == expected_result / 7.0
|
||||
# Max
|
||||
result = exchange.get_max_pair_stake_amount('ETH/BTC', 2)
|
||||
assert result == 1000
|
||||
|
||||
result = exchange.get_min_pair_stake_amount('ETH/BTC', 2, -0.4)
|
||||
expected_result = max(8, 2 * 2) * 1.5
|
||||
assert isclose(result, expected_result)
|
||||
assert pytest.approx(result) == expected_result
|
||||
# With Leverage
|
||||
result = exchange.get_min_pair_stake_amount('ETH/BTC', 2, -0.4, 8.0)
|
||||
assert isclose(result, expected_result / 8.0)
|
||||
assert pytest.approx(result) == expected_result / 8.0
|
||||
# Max
|
||||
result = exchange.get_max_pair_stake_amount('ETH/BTC', 2)
|
||||
assert result == 1000
|
||||
@ -482,10 +481,10 @@ def test__get_stake_amount_limit(mocker, default_conf) -> None:
|
||||
# Really big stoploss
|
||||
result = exchange.get_min_pair_stake_amount('ETH/BTC', 2, -1)
|
||||
expected_result = max(8, 2 * 2) * 1.5
|
||||
assert isclose(result, expected_result)
|
||||
assert pytest.approx(result) == expected_result
|
||||
# With Leverage
|
||||
result = exchange.get_min_pair_stake_amount('ETH/BTC', 2, -1, 12.0)
|
||||
assert isclose(result, expected_result / 12)
|
||||
assert pytest.approx(result) == expected_result / 12
|
||||
# Max
|
||||
result = exchange.get_max_pair_stake_amount('ETH/BTC', 2)
|
||||
assert result == 1000
|
||||
@ -501,7 +500,7 @@ def test__get_stake_amount_limit(mocker, default_conf) -> None:
|
||||
|
||||
# Contract size 0.01
|
||||
result = exchange.get_min_pair_stake_amount('ETH/BTC', 2, -1)
|
||||
assert isclose(result, expected_result * 0.01)
|
||||
assert pytest.approx(result) == expected_result * 0.01
|
||||
# Max
|
||||
result = exchange.get_max_pair_stake_amount('ETH/BTC', 2)
|
||||
assert result == 10
|
||||
@ -513,7 +512,7 @@ def test__get_stake_amount_limit(mocker, default_conf) -> None:
|
||||
)
|
||||
# With Leverage, Contract size 10
|
||||
result = exchange.get_min_pair_stake_amount('ETH/BTC', 2, -1, 12.0)
|
||||
assert isclose(result, (expected_result / 12) * 10.0)
|
||||
assert pytest.approx(result) == (expected_result / 12) * 10.0
|
||||
# Max
|
||||
result = exchange.get_max_pair_stake_amount('ETH/BTC', 2)
|
||||
assert result == 10000
|
||||
@ -3239,7 +3238,7 @@ def test_get_trades_for_order(default_conf, mocker, exchange_name, trading_mode,
|
||||
orders = exchange.get_trades_for_order(order_id, 'ETH/USDT:USDT', since)
|
||||
assert len(orders) == 1
|
||||
assert orders[0]['price'] == 165
|
||||
assert isclose(orders[0]['amount'], amount)
|
||||
assert pytest.approx(orders[0]['amount']) == amount
|
||||
assert api_mock.fetch_my_trades.call_count == 1
|
||||
# since argument should be
|
||||
assert isinstance(api_mock.fetch_my_trades.call_args[0][1], int)
|
||||
@ -3776,8 +3775,8 @@ def test__get_funding_fees_from_exchange(default_conf, mocker, exchange_name):
|
||||
since=unix_time
|
||||
)
|
||||
|
||||
assert (isclose(expected_fees, fees_from_datetime))
|
||||
assert (isclose(expected_fees, fees_from_unix_time))
|
||||
assert pytest.approx(expected_fees) == fees_from_datetime
|
||||
assert pytest.approx(expected_fees) == fees_from_unix_time
|
||||
|
||||
ccxt_exceptionhandlers(
|
||||
mocker,
|
||||
@ -4514,7 +4513,7 @@ def test_liquidation_price(
|
||||
default_conf['liquidation_buffer'] = 0.0
|
||||
exchange = get_patched_exchange(mocker, default_conf, id=exchange_name)
|
||||
exchange.get_maintenance_ratio_and_amt = MagicMock(return_value=(mm_ratio, maintenance_amt))
|
||||
assert isclose(round(exchange.get_liquidation_price(
|
||||
assert pytest.approx(round(exchange.get_liquidation_price(
|
||||
pair='DOGE/USDT',
|
||||
open_rate=open_rate,
|
||||
is_short=is_short,
|
||||
@ -4523,7 +4522,7 @@ def test_liquidation_price(
|
||||
upnl_ex_1=upnl_ex_1,
|
||||
amount=amount,
|
||||
stake_amount=open_rate * amount,
|
||||
), 2), expected)
|
||||
), 2)) == expected
|
||||
|
||||
|
||||
def test_get_max_pair_stake_amount(
|
||||
@ -4868,8 +4867,8 @@ def test_get_max_leverage_futures(default_conf, mocker, leverage_tiers):
|
||||
assert exchange.get_max_leverage("BNB/BUSD", 1.0) == 20.0
|
||||
assert exchange.get_max_leverage("BNB/USDT", 100.0) == 75.0
|
||||
assert exchange.get_max_leverage("BTC/USDT", 170.30) == 125.0
|
||||
assert isclose(exchange.get_max_leverage("BNB/BUSD", 99999.9), 5.000005)
|
||||
assert isclose(exchange.get_max_leverage("BNB/USDT", 1500), 33.333333333333333)
|
||||
assert pytest.approx(exchange.get_max_leverage("BNB/BUSD", 99999.9)) == 5.000005
|
||||
assert pytest.approx(exchange.get_max_leverage("BNB/USDT", 1500)) == 33.333333333333333
|
||||
assert exchange.get_max_leverage("BTC/USDT", 300000000) == 2.0
|
||||
assert exchange.get_max_leverage("BTC/USDT", 600000000) == 1.0 # Last tier
|
||||
|
||||
@ -4986,6 +4985,7 @@ def test_get_liquidation_price1(mocker, default_conf):
|
||||
is_short=False,
|
||||
amount=0.8,
|
||||
stake_amount=18.884 * 0.8,
|
||||
wallet_balance=18.884 * 0.8,
|
||||
)
|
||||
assert liq_price == 17.47
|
||||
|
||||
@ -4997,6 +4997,7 @@ def test_get_liquidation_price1(mocker, default_conf):
|
||||
is_short=False,
|
||||
amount=0.8,
|
||||
stake_amount=18.884 * 0.8,
|
||||
wallet_balance=18.884 * 0.8,
|
||||
)
|
||||
assert liq_price == 17.540699999999998
|
||||
|
||||
@ -5008,6 +5009,7 @@ def test_get_liquidation_price1(mocker, default_conf):
|
||||
is_short=False,
|
||||
amount=0.8,
|
||||
stake_amount=18.884 * 0.8,
|
||||
wallet_balance=18.884 * 0.8,
|
||||
)
|
||||
assert liq_price is None
|
||||
default_conf['trading_mode'] = 'margin'
|
||||
@ -5020,6 +5022,7 @@ def test_get_liquidation_price1(mocker, default_conf):
|
||||
is_short=False,
|
||||
amount=0.8,
|
||||
stake_amount=18.884 * 0.8,
|
||||
wallet_balance=18.884 * 0.8,
|
||||
)
|
||||
|
||||
|
||||
@ -5145,7 +5148,7 @@ def test_get_liquidation_price(
|
||||
else:
|
||||
buffer_amount = liquidation_buffer * abs(open_rate - expected_liq)
|
||||
expected_liq = expected_liq - buffer_amount if is_short else expected_liq + buffer_amount
|
||||
assert isclose(expected_liq, liq)
|
||||
assert pytest.approx(expected_liq) == liq
|
||||
|
||||
|
||||
@pytest.mark.parametrize('contract_size,order_amount', [
|
||||
|
||||
@ -45,7 +45,6 @@ def freqai_conf(default_conf, tmpdir):
|
||||
"principal_component_analysis": False,
|
||||
"use_SVM_to_remove_outliers": True,
|
||||
"stratify_training_data": 0,
|
||||
"indicator_max_period_candles": 10,
|
||||
"indicator_periods_candles": [10],
|
||||
},
|
||||
"data_split_parameters": {"test_size": 0.33, "random_state": 1},
|
||||
|
||||
@ -48,10 +48,4 @@ def test_freqai_backtest_load_data(freqai_conf, mocker, caplog):
|
||||
|
||||
assert log_has_re('Increasing startup_candle_count for freqai to.*', caplog)
|
||||
|
||||
del freqai_conf['freqai']['startup_candles']
|
||||
backtesting = Backtesting(freqai_conf)
|
||||
with pytest.raises(OperationalException,
|
||||
match=r'FreqAI backtesting module.*startup_candles in config.'):
|
||||
backtesting.load_bt_data()
|
||||
|
||||
Backtesting.cleanup()
|
||||
|
||||
@ -1,5 +1,5 @@
|
||||
import datetime
|
||||
import shutil
|
||||
from datetime import datetime, timedelta, timezone
|
||||
from pathlib import Path
|
||||
|
||||
import pytest
|
||||
@ -57,16 +57,13 @@ def test_split_timerange(
|
||||
shutil.rmtree(Path(dk.full_path))
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"timestamp, expected",
|
||||
[
|
||||
(datetime.datetime.now(tz=datetime.timezone.utc).timestamp() - 7200, True),
|
||||
(datetime.datetime.now(tz=datetime.timezone.utc).timestamp(), False),
|
||||
],
|
||||
)
|
||||
def test_check_if_model_expired(mocker, freqai_conf, timestamp, expected):
|
||||
def test_check_if_model_expired(mocker, freqai_conf):
|
||||
|
||||
dk = get_patched_data_kitchen(mocker, freqai_conf)
|
||||
assert dk.check_if_model_expired(timestamp) == expected
|
||||
now = datetime.now(tz=timezone.utc).timestamp()
|
||||
assert dk.check_if_model_expired(now) is False
|
||||
now = (datetime.now(tz=timezone.utc) - timedelta(hours=2)).timestamp()
|
||||
assert dk.check_if_model_expired(now) is True
|
||||
shutil.rmtree(Path(dk.full_path))
|
||||
|
||||
|
||||
@ -75,7 +72,7 @@ def test_use_DBSCAN_to_remove_outliers(mocker, freqai_conf, caplog):
|
||||
# freqai_conf['freqai']['feature_parameters'].update({"outlier_protection_percentage": 1})
|
||||
freqai.dk.use_DBSCAN_to_remove_outliers(predict=False)
|
||||
assert log_has_re(
|
||||
"DBSCAN found eps of 2.42.",
|
||||
"DBSCAN found eps of 2.36.",
|
||||
caplog,
|
||||
)
|
||||
|
||||
@ -84,7 +81,7 @@ 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) == 2.56
|
||||
assert round(avg_mean_dist, 2) == 2.54
|
||||
|
||||
|
||||
def test_use_SVM_to_remove_outliers_and_outlier_protection(mocker, freqai_conf, caplog):
|
||||
@ -92,7 +89,7 @@ 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 8.46%",
|
||||
"SVM detected 8.09%",
|
||||
caplog,
|
||||
)
|
||||
|
||||
|
||||
@ -176,6 +176,7 @@ def test_extract_data_and_train_model_LightGBMClassifier(mocker, freqai_conf):
|
||||
|
||||
def test_start_backtesting(mocker, freqai_conf):
|
||||
freqai_conf.update({"timerange": "20180120-20180130"})
|
||||
freqai_conf.get("freqai", {}).update({"save_backtest_models": True})
|
||||
strategy = get_patched_freqai_strategy(mocker, freqai_conf)
|
||||
exchange = get_patched_exchange(mocker, freqai_conf)
|
||||
strategy.dp = DataProvider(freqai_conf, exchange)
|
||||
@ -194,7 +195,7 @@ 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) == 5
|
||||
assert len(model_folders) == 6
|
||||
|
||||
shutil.rmtree(Path(freqai.dk.full_path))
|
||||
|
||||
@ -202,6 +203,7 @@ def test_start_backtesting(mocker, freqai_conf):
|
||||
def test_start_backtesting_subdaily_backtest_period(mocker, freqai_conf):
|
||||
freqai_conf.update({"timerange": "20180120-20180124"})
|
||||
freqai_conf.get("freqai", {}).update({"backtest_period_days": 0.5})
|
||||
freqai_conf.get("freqai", {}).update({"save_backtest_models": True})
|
||||
strategy = get_patched_freqai_strategy(mocker, freqai_conf)
|
||||
exchange = get_patched_exchange(mocker, freqai_conf)
|
||||
strategy.dp = DataProvider(freqai_conf, exchange)
|
||||
@ -219,13 +221,14 @@ def test_start_backtesting_subdaily_backtest_period(mocker, freqai_conf):
|
||||
metadata = {"pair": "LTC/BTC"}
|
||||
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) == 8
|
||||
assert len(model_folders) == 9
|
||||
|
||||
shutil.rmtree(Path(freqai.dk.full_path))
|
||||
|
||||
|
||||
def test_start_backtesting_from_existing_folder(mocker, freqai_conf, caplog):
|
||||
freqai_conf.update({"timerange": "20180120-20180130"})
|
||||
freqai_conf.get("freqai", {}).update({"save_backtest_models": True})
|
||||
strategy = get_patched_freqai_strategy(mocker, freqai_conf)
|
||||
exchange = get_patched_exchange(mocker, freqai_conf)
|
||||
strategy.dp = DataProvider(freqai_conf, exchange)
|
||||
@ -244,7 +247,7 @@ def test_start_backtesting_from_existing_folder(mocker, freqai_conf, caplog):
|
||||
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) == 5
|
||||
assert len(model_folders) == 6
|
||||
|
||||
# without deleting the exiting folder structure, re-run
|
||||
|
||||
@ -265,10 +268,14 @@ def test_start_backtesting_from_existing_folder(mocker, freqai_conf, caplog):
|
||||
freqai.start_backtesting(df, metadata, freqai.dk)
|
||||
|
||||
assert log_has_re(
|
||||
"Found model at ",
|
||||
"Found backtesting prediction file ",
|
||||
caplog,
|
||||
)
|
||||
|
||||
path = (freqai.dd.full_path / freqai.dk.backtest_predictions_folder)
|
||||
prediction_files = [x for x in path.iterdir() if x.is_file()]
|
||||
assert len(prediction_files) == 5
|
||||
|
||||
shutil.rmtree(Path(freqai.dk.full_path))
|
||||
|
||||
|
||||
|
||||
@ -1,5 +1,3 @@
|
||||
from math import isclose
|
||||
|
||||
import pytest
|
||||
|
||||
from freqtrade.leverage import interest
|
||||
@ -30,9 +28,9 @@ twentyfive_hours = FtPrecise(25.0)
|
||||
def test_interest(exchange, interest_rate, hours, expected):
|
||||
borrowed = FtPrecise(60.0)
|
||||
|
||||
assert isclose(interest(
|
||||
assert pytest.approx(float(interest(
|
||||
exchange_name=exchange,
|
||||
borrowed=borrowed,
|
||||
rate=FtPrecise(interest_rate),
|
||||
hours=hours
|
||||
), expected)
|
||||
))) == expected
|
||||
|
||||
@ -37,6 +37,7 @@ def generate_mock_trade(pair: str, fee: float, is_open: bool,
|
||||
trade.orders.append(Order(
|
||||
ft_order_side=trade.entry_side,
|
||||
order_id=f'{pair}-{trade.entry_side}-{trade.open_date}',
|
||||
ft_is_open=False,
|
||||
ft_pair=pair,
|
||||
amount=trade.amount,
|
||||
filled=trade.amount,
|
||||
@ -51,6 +52,7 @@ def generate_mock_trade(pair: str, fee: float, is_open: bool,
|
||||
trade.orders.append(Order(
|
||||
ft_order_side=trade.exit_side,
|
||||
order_id=f'{pair}-{trade.exit_side}-{trade.close_date}',
|
||||
ft_is_open=False,
|
||||
ft_pair=pair,
|
||||
amount=trade.amount,
|
||||
filled=trade.amount,
|
||||
|
||||
@ -2138,11 +2138,11 @@ def test_send_msg_strategy_msg_notification(default_conf, mocker) -> None:
|
||||
|
||||
|
||||
def test_send_msg_unknown_type(default_conf, mocker) -> None:
|
||||
telegram, _, _ = get_telegram_testobject(mocker, default_conf)
|
||||
with pytest.raises(NotImplementedError, match=r'Unknown message type: None'):
|
||||
telegram.send_msg({
|
||||
'type': None,
|
||||
})
|
||||
telegram, _, msg_mock = get_telegram_testobject(mocker, default_conf)
|
||||
telegram.send_msg({
|
||||
'type': None,
|
||||
})
|
||||
msg_mock.call_count == 0
|
||||
|
||||
|
||||
@pytest.mark.parametrize('message_type,enter,enter_signal,leverage', [
|
||||
|
||||
@ -1,5 +1,3 @@
|
||||
from math import isclose
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import pytest
|
||||
@ -165,7 +163,7 @@ def test_stoploss_from_open():
|
||||
or (side == 'short' and expected_stop_price < current_price)):
|
||||
assert stoploss == 0
|
||||
else:
|
||||
assert isclose(stop_price, expected_stop_price, rel_tol=0.00001)
|
||||
assert pytest.approx(stop_price) == expected_stop_price
|
||||
|
||||
|
||||
def test_stoploss_from_absolute():
|
||||
|
||||
@ -1051,8 +1051,6 @@ def test_add_stoploss_on_exchange(mocker, default_conf_usdt, limit_order, is_sho
|
||||
mocker.patch('freqtrade.freqtradebot.FreqtradeBot.handle_trade', MagicMock(return_value=True))
|
||||
mocker.patch('freqtrade.exchange.Exchange.fetch_order', return_value=order)
|
||||
mocker.patch('freqtrade.exchange.Exchange.get_trades_for_order', return_value=[])
|
||||
mocker.patch('freqtrade.freqtradebot.FreqtradeBot.get_real_amount',
|
||||
return_value=order['amount'])
|
||||
|
||||
stoploss = MagicMock(return_value={'id': 13434334})
|
||||
mocker.patch('freqtrade.exchange.Binance.stoploss', stoploss)
|
||||
@ -1875,8 +1873,6 @@ def test_exit_positions(mocker, default_conf_usdt, limit_order, is_short, caplog
|
||||
mocker.patch('freqtrade.exchange.Exchange.fetch_order',
|
||||
return_value=limit_order[entry_side(is_short)])
|
||||
mocker.patch('freqtrade.exchange.Exchange.get_trades_for_order', return_value=[])
|
||||
mocker.patch('freqtrade.freqtradebot.FreqtradeBot.get_real_amount',
|
||||
return_value=limit_order[entry_side(is_short)]['amount'])
|
||||
|
||||
trade = MagicMock()
|
||||
trade.is_short = is_short
|
||||
@ -1886,14 +1882,13 @@ def test_exit_positions(mocker, default_conf_usdt, limit_order, is_short, caplog
|
||||
n = freqtrade.exit_positions(trades)
|
||||
assert n == 0
|
||||
# Test amount not modified by fee-logic
|
||||
assert not log_has(
|
||||
'Applying fee to amount for Trade {} from 30.0 to 90.81'.format(trade), caplog
|
||||
)
|
||||
assert not log_has_re(r'Applying fee to amount for Trade .*', caplog)
|
||||
|
||||
mocker.patch('freqtrade.freqtradebot.FreqtradeBot.get_real_amount', return_value=90.81)
|
||||
gra = mocker.patch('freqtrade.freqtradebot.FreqtradeBot.get_real_amount', return_value=0.0)
|
||||
# test amount modified by fee-logic
|
||||
n = freqtrade.exit_positions(trades)
|
||||
assert n == 0
|
||||
assert gra.call_count == 0
|
||||
|
||||
|
||||
@pytest.mark.parametrize("is_short", [False, True])
|
||||
@ -1927,8 +1922,7 @@ def test_update_trade_state(mocker, default_conf_usdt, limit_order, is_short, ca
|
||||
mocker.patch('freqtrade.freqtradebot.FreqtradeBot.handle_trade', MagicMock(return_value=True))
|
||||
mocker.patch('freqtrade.exchange.Exchange.fetch_order', return_value=order)
|
||||
mocker.patch('freqtrade.exchange.Exchange.get_trades_for_order', return_value=[])
|
||||
mocker.patch('freqtrade.freqtradebot.FreqtradeBot.get_real_amount',
|
||||
return_value=order['amount'])
|
||||
mocker.patch('freqtrade.freqtradebot.FreqtradeBot.get_real_amount', return_value=0.0)
|
||||
order_id = order['id']
|
||||
|
||||
trade = Trade(
|
||||
@ -1960,11 +1954,11 @@ def test_update_trade_state(mocker, default_conf_usdt, limit_order, is_short, ca
|
||||
assert trade.amount == order['amount']
|
||||
|
||||
trade.open_order_id = order_id
|
||||
mocker.patch('freqtrade.freqtradebot.FreqtradeBot.get_real_amount', return_value=90.81)
|
||||
assert trade.amount != 90.81
|
||||
mocker.patch('freqtrade.freqtradebot.FreqtradeBot.get_real_amount', return_value=0.01)
|
||||
assert trade.amount == 30.0
|
||||
# test amount modified by fee-logic
|
||||
freqtrade.update_trade_state(trade, order_id)
|
||||
assert trade.amount == 90.81
|
||||
assert trade.amount == 29.99
|
||||
assert trade.open_order_id is None
|
||||
|
||||
trade.is_open = True
|
||||
@ -4268,10 +4262,10 @@ def test_get_real_amount_quote(default_conf_usdt, trades_for_order, buy_order_fe
|
||||
caplog.clear()
|
||||
order_obj = Order.parse_from_ccxt_object(buy_order_fee, 'LTC/ETH', 'buy')
|
||||
# Amount is reduced by "fee"
|
||||
assert freqtrade.get_real_amount(trade, buy_order_fee, order_obj) == amount - (amount * 0.001)
|
||||
assert freqtrade.get_real_amount(trade, buy_order_fee, order_obj) == (amount * 0.001)
|
||||
assert log_has(
|
||||
'Applying fee on amount for Trade(id=None, pair=LTC/ETH, amount=8.00000000, is_short=False,'
|
||||
' leverage=1.0, open_rate=0.24544100, open_since=closed) (from 8.0 to 7.992).',
|
||||
' leverage=1.0, open_rate=0.24544100, open_since=closed), fee=0.008.',
|
||||
caplog
|
||||
)
|
||||
|
||||
@ -4296,7 +4290,7 @@ def test_get_real_amount_quote_dust(default_conf_usdt, trades_for_order, buy_ord
|
||||
walletmock.reset_mock()
|
||||
order_obj = Order.parse_from_ccxt_object(buy_order_fee, 'LTC/ETH', 'buy')
|
||||
# Amount is kept as is
|
||||
assert freqtrade.get_real_amount(trade, buy_order_fee, order_obj) == amount
|
||||
assert freqtrade.get_real_amount(trade, buy_order_fee, order_obj) is None
|
||||
assert walletmock.call_count == 1
|
||||
assert log_has_re(r'Fee amount for Trade.* was in base currency '
|
||||
'- Eating Fee 0.008 into dust', caplog)
|
||||
@ -4319,7 +4313,7 @@ def test_get_real_amount_no_trade(default_conf_usdt, buy_order_fee, caplog, mock
|
||||
|
||||
order_obj = Order.parse_from_ccxt_object(buy_order_fee, 'LTC/ETH', 'buy')
|
||||
# Amount is reduced by "fee"
|
||||
assert freqtrade.get_real_amount(trade, buy_order_fee, order_obj) == amount
|
||||
assert freqtrade.get_real_amount(trade, buy_order_fee, order_obj) is None
|
||||
assert log_has(
|
||||
'Applying fee on amount for Trade(id=None, pair=LTC/ETH, amount=8.00000000, '
|
||||
'is_short=False, leverage=1.0, open_rate=0.24544100, open_since=closed) failed: '
|
||||
@ -4343,8 +4337,7 @@ def test_get_real_amount_no_trade(default_conf_usdt, buy_order_fee, caplog, mock
|
||||
# from order
|
||||
({'cost': 0.004, 'currency': 'LTC'}, 0.004, False, (
|
||||
'Applying fee on amount for Trade(id=None, pair=LTC/ETH, amount=8.00000000, '
|
||||
'is_short=False, leverage=1.0, open_rate=0.24544100, open_since=closed) (from'
|
||||
' 8.0 to 7.996).'
|
||||
'is_short=False, leverage=1.0, open_rate=0.24544100, open_since=closed), fee=0.004.'
|
||||
)),
|
||||
# invalid, no currency in from fee dict
|
||||
({'cost': 0.008, 'currency': None}, 0, True, None),
|
||||
@ -4376,7 +4369,11 @@ def test_get_real_amount(
|
||||
|
||||
caplog.clear()
|
||||
order_obj = Order.parse_from_ccxt_object(buy_order_fee, 'LTC/ETH', 'buy')
|
||||
assert freqtrade.get_real_amount(trade, buy_order, order_obj) == amount - fee_reduction_amount
|
||||
res = freqtrade.get_real_amount(trade, buy_order, order_obj)
|
||||
if fee_reduction_amount == 0:
|
||||
assert res is None
|
||||
else:
|
||||
assert res == fee_reduction_amount
|
||||
|
||||
if expected_log:
|
||||
assert log_has(expected_log, caplog)
|
||||
@ -4422,14 +4419,14 @@ def test_get_real_amount_multi(
|
||||
return_value={'ask': 0.19, 'last': 0.2})
|
||||
|
||||
# Amount is reduced by "fee"
|
||||
expected_amount = amount - (amount * fee_reduction_amount)
|
||||
expected_amount = amount * fee_reduction_amount
|
||||
order_obj = Order.parse_from_ccxt_object(buy_order_fee, 'LTC/ETH', 'buy')
|
||||
assert freqtrade.get_real_amount(trade, buy_order_fee, order_obj) == expected_amount
|
||||
assert log_has(
|
||||
(
|
||||
'Applying fee on amount for Trade(id=None, pair=LTC/ETH, amount=8.00000000, '
|
||||
'is_short=False, leverage=1.0, open_rate=0.24544100, open_since=closed) '
|
||||
f'(from 8.0 to {expected_log_amount}).'
|
||||
'is_short=False, leverage=1.0, open_rate=0.24544100, open_since=closed), '
|
||||
f'fee={expected_amount}.'
|
||||
),
|
||||
caplog
|
||||
)
|
||||
@ -4462,7 +4459,7 @@ def test_get_real_amount_invalid_order(default_conf_usdt, trades_for_order, buy_
|
||||
|
||||
order_obj = Order.parse_from_ccxt_object(buy_order_fee, 'LTC/ETH', 'buy')
|
||||
# Amount does not change
|
||||
assert freqtrade.get_real_amount(trade, limit_buy_order_usdt, order_obj) == amount
|
||||
assert freqtrade.get_real_amount(trade, limit_buy_order_usdt, order_obj) is None
|
||||
|
||||
|
||||
def test_get_real_amount_fees_order(default_conf_usdt, market_buy_order_usdt_doublefee,
|
||||
@ -4485,7 +4482,7 @@ def test_get_real_amount_fees_order(default_conf_usdt, market_buy_order_usdt_dou
|
||||
# Amount does not change
|
||||
assert trade.fee_open == 0.0025
|
||||
order_obj = Order.parse_from_ccxt_object(market_buy_order_usdt_doublefee, 'LTC/ETH', 'buy')
|
||||
assert freqtrade.get_real_amount(trade, market_buy_order_usdt_doublefee, order_obj) == 30.0
|
||||
assert freqtrade.get_real_amount(trade, market_buy_order_usdt_doublefee, order_obj) is None
|
||||
assert tfo_mock.call_count == 0
|
||||
# Fetch fees from trades dict if available to get "proper" values
|
||||
assert round(trade.fee_open, 4) == 0.001
|
||||
@ -4537,7 +4534,7 @@ def test_get_real_amount_wrong_amount_rounding(default_conf_usdt, trades_for_ord
|
||||
order_obj = Order.parse_from_ccxt_object(buy_order_fee, 'LTC/ETH', 'buy')
|
||||
# Amount changes by fee amount.
|
||||
assert pytest.approx(freqtrade.get_real_amount(
|
||||
trade, limit_buy_order_usdt, order_obj)) == amount - (amount * 0.001)
|
||||
trade, limit_buy_order_usdt, order_obj)) == (amount * 0.001)
|
||||
|
||||
|
||||
def test_get_real_amount_open_trade_usdt(default_conf_usdt, fee, mocker):
|
||||
@ -4559,7 +4556,7 @@ def test_get_real_amount_open_trade_usdt(default_conf_usdt, fee, mocker):
|
||||
}
|
||||
freqtrade = get_patched_freqtradebot(mocker, default_conf_usdt)
|
||||
order_obj = Order.parse_from_ccxt_object(order, 'LTC/ETH', 'buy')
|
||||
assert freqtrade.get_real_amount(trade, order, order_obj) == amount
|
||||
assert freqtrade.get_real_amount(trade, order, order_obj) is None
|
||||
|
||||
|
||||
def test_get_real_amount_in_point(default_conf_usdt, buy_order_fee, fee, mocker, caplog):
|
||||
@ -4616,7 +4613,7 @@ def test_get_real_amount_in_point(default_conf_usdt, buy_order_fee, fee, mocker,
|
||||
|
||||
order_obj = Order.parse_from_ccxt_object(buy_order_fee, 'LTC/ETH', 'buy')
|
||||
res = freqtrade.get_real_amount(trade, limit_buy_order_usdt, order_obj)
|
||||
assert res == amount
|
||||
assert res is None
|
||||
assert trade.fee_open_currency is None
|
||||
assert trade.fee_open_cost is None
|
||||
message = "Not updating buy-fee - rate: None, POINT."
|
||||
@ -4624,7 +4621,7 @@ def test_get_real_amount_in_point(default_conf_usdt, buy_order_fee, fee, mocker,
|
||||
caplog.clear()
|
||||
freqtrade.config['exchange']['unknown_fee_rate'] = 1
|
||||
res = freqtrade.get_real_amount(trade, limit_buy_order_usdt, order_obj)
|
||||
assert res == amount
|
||||
assert res is None
|
||||
assert trade.fee_open_currency == 'POINT'
|
||||
assert pytest.approx(trade.fee_open_cost) == 0.3046651026
|
||||
assert trade.fee_open == 0.002
|
||||
@ -4633,12 +4630,12 @@ def test_get_real_amount_in_point(default_conf_usdt, buy_order_fee, fee, mocker,
|
||||
|
||||
|
||||
@pytest.mark.parametrize('amount,fee_abs,wallet,amount_exp', [
|
||||
(8.0, 0.0, 10, 8),
|
||||
(8.0, 0.0, 0, 8),
|
||||
(8.0, 0.1, 0, 7.9),
|
||||
(8.0, 0.1, 10, 8),
|
||||
(8.0, 0.1, 8.0, 8.0),
|
||||
(8.0, 0.1, 7.9, 7.9),
|
||||
(8.0, 0.0, 10, None),
|
||||
(8.0, 0.0, 0, None),
|
||||
(8.0, 0.1, 0, 0.1),
|
||||
(8.0, 0.1, 10, None),
|
||||
(8.0, 0.1, 8.0, None),
|
||||
(8.0, 0.1, 7.9, 0.1),
|
||||
])
|
||||
def test_apply_fee_conditional(default_conf_usdt, fee, mocker,
|
||||
amount, fee_abs, wallet, amount_exp):
|
||||
@ -4653,11 +4650,17 @@ def test_apply_fee_conditional(default_conf_usdt, fee, mocker,
|
||||
fee_close=fee.return_value,
|
||||
open_order_id="123456"
|
||||
)
|
||||
order = Order(
|
||||
ft_order_side='buy',
|
||||
order_id='100',
|
||||
ft_pair=trade.pair,
|
||||
ft_is_open=True,
|
||||
)
|
||||
freqtrade = get_patched_freqtradebot(mocker, default_conf_usdt)
|
||||
|
||||
walletmock.reset_mock()
|
||||
# Amount is kept as is
|
||||
assert freqtrade.apply_fee_conditional(trade, 'LTC', amount, fee_abs) == amount_exp
|
||||
assert freqtrade.apply_fee_conditional(trade, 'LTC', amount, fee_abs, order) == amount_exp
|
||||
assert walletmock.call_count == 1
|
||||
|
||||
|
||||
|
||||
@ -1,7 +1,6 @@
|
||||
# pragma pylint: disable=missing-docstring, C0103
|
||||
import logging
|
||||
from datetime import datetime, timedelta, timezone
|
||||
from math import isclose
|
||||
from pathlib import Path
|
||||
from types import FunctionType
|
||||
from unittest.mock import MagicMock
|
||||
@ -582,25 +581,25 @@ def test_update_market_order(market_buy_order_usdt, market_sell_order_usdt, fee,
|
||||
@pytest.mark.parametrize(
|
||||
'exchange,is_short,lev,open_value,close_value,profit,profit_ratio,trading_mode,funding_fees', [
|
||||
("binance", False, 1, 60.15, 65.835, 5.685, 0.09451371, spot, 0.0),
|
||||
("binance", True, 1, 59.850, 66.1663784375, -6.3163784375, -0.1055368, margin, 0.0),
|
||||
("binance", True, 1, 65.835, 60.151253125, 5.68374687, 0.08633321, margin, 0.0),
|
||||
("binance", False, 3, 60.15, 65.83416667, 5.68416667, 0.28349958, margin, 0.0),
|
||||
("binance", True, 3, 59.85, 66.1663784375, -6.3163784375, -0.31661044, margin, 0.0),
|
||||
("binance", True, 3, 65.835, 60.151253125, 5.68374687, 0.25899963, margin, 0.0),
|
||||
|
||||
("kraken", False, 1, 60.15, 65.835, 5.685, 0.09451371, spot, 0.0),
|
||||
("kraken", True, 1, 59.850, 66.231165, -6.381165, -0.1066192, margin, 0.0),
|
||||
("kraken", True, 1, 65.835, 60.21015, 5.62485, 0.0854386, margin, 0.0),
|
||||
("kraken", False, 3, 60.15, 65.795, 5.645, 0.28154613, margin, 0.0),
|
||||
("kraken", True, 3, 59.850, 66.231165, -6.381165, -0.3198578, margin, 0.0),
|
||||
("kraken", True, 3, 65.835, 60.21015, 5.62485, 0.25631579, margin, 0.0),
|
||||
|
||||
("binance", False, 1, 60.15, 65.835, 5.685, 0.09451371, futures, 0.0),
|
||||
("binance", False, 1, 60.15, 66.835, 6.685, 0.11113881, futures, 1.0),
|
||||
("binance", True, 1, 59.85, 66.165, -6.315, -0.10551378, futures, 0.0),
|
||||
("binance", True, 1, 59.85, 67.165, -7.315, -0.12222222, futures, -1.0),
|
||||
("binance", True, 1, 65.835, 60.15, 5.685, 0.08635224, futures, 0.0),
|
||||
("binance", True, 1, 65.835, 61.15, 4.685, 0.07116276, futures, -1.0),
|
||||
("binance", True, 3, 65.835, 59.15, 6.685, 0.3046252, futures, 1.0),
|
||||
("binance", False, 3, 60.15, 64.835, 4.685, 0.23366583, futures, -1.0),
|
||||
("binance", True, 3, 59.85, 65.165, -5.315, -0.26641604, futures, 1.0),
|
||||
])
|
||||
@pytest.mark.usefixtures("init_persistence")
|
||||
def test_calc_open_close_trade_price(
|
||||
limit_buy_order_usdt, limit_sell_order_usdt, fee, exchange, is_short, lev,
|
||||
limit_order, fee, exchange, is_short, lev,
|
||||
open_value, close_value, profit, profit_ratio, trading_mode, funding_fees
|
||||
):
|
||||
trade: Trade = Trade(
|
||||
@ -616,24 +615,30 @@ def test_calc_open_close_trade_price(
|
||||
is_short=is_short,
|
||||
leverage=lev,
|
||||
trading_mode=trading_mode,
|
||||
funding_fees=funding_fees
|
||||
)
|
||||
|
||||
entry_order = limit_order[trade.entry_side]
|
||||
exit_order = limit_order[trade.exit_side]
|
||||
trade.open_order_id = f'something-{is_short}-{lev}-{exchange}'
|
||||
|
||||
oobj = Order.parse_from_ccxt_object(limit_buy_order_usdt, 'ADA/USDT', 'buy')
|
||||
oobj = Order.parse_from_ccxt_object(entry_order, 'ADA/USDT', trade.entry_side)
|
||||
oobj.trade = trade
|
||||
oobj.update_from_ccxt_object(entry_order)
|
||||
trade.update_trade(oobj)
|
||||
|
||||
oobj = Order.parse_from_ccxt_object(limit_sell_order_usdt, 'ADA/USDT', 'sell')
|
||||
trade.funding_fees = funding_fees
|
||||
|
||||
oobj = Order.parse_from_ccxt_object(exit_order, 'ADA/USDT', trade.exit_side)
|
||||
oobj.trade = trade
|
||||
oobj.update_from_ccxt_object(exit_order)
|
||||
trade.update_trade(oobj)
|
||||
|
||||
trade.open_rate = 2.0
|
||||
trade.close_rate = 2.2
|
||||
trade.recalc_open_trade_value()
|
||||
assert isclose(trade._calc_open_trade_value(trade.amount, trade.open_rate), open_value)
|
||||
assert isclose(trade.calc_close_trade_value(trade.close_rate), close_value)
|
||||
assert isclose(trade.calc_profit(trade.close_rate), round(profit, 8))
|
||||
assert pytest.approx(trade.calc_profit_ratio(trade.close_rate)) == profit_ratio
|
||||
assert trade.is_open is False
|
||||
assert trade.funding_fees == funding_fees
|
||||
|
||||
assert pytest.approx(trade._calc_open_trade_value(trade.amount, trade.open_rate)) == open_value
|
||||
assert pytest.approx(trade.calc_close_trade_value(trade.close_rate)) == close_value
|
||||
assert pytest.approx(trade.close_profit_abs) == profit
|
||||
assert pytest.approx(trade.close_profit) == profit_ratio
|
||||
|
||||
|
||||
@pytest.mark.usefixtures("init_persistence")
|
||||
@ -655,6 +660,7 @@ def test_trade_close(fee):
|
||||
trade.orders.append(Order(
|
||||
ft_order_side=trade.entry_side,
|
||||
order_id=f'{trade.pair}-{trade.entry_side}-{trade.open_date}',
|
||||
ft_is_open=False,
|
||||
ft_pair=trade.pair,
|
||||
amount=trade.amount,
|
||||
filled=trade.amount,
|
||||
@ -668,6 +674,7 @@ def test_trade_close(fee):
|
||||
trade.orders.append(Order(
|
||||
ft_order_side=trade.exit_side,
|
||||
order_id=f'{trade.pair}-{trade.exit_side}-{trade.open_date}',
|
||||
ft_is_open=False,
|
||||
ft_pair=trade.pair,
|
||||
amount=trade.amount,
|
||||
filled=trade.amount,
|
||||
|
||||
Loading…
Reference in New Issue
Block a user