From 68a900a9b7325b0a2b0e1c84f0c694fa862104bc Mon Sep 17 00:00:00 2001 From: robcaulk Date: Sun, 11 Sep 2022 17:29:14 +0200 Subject: [PATCH 1/9] reorganize freqai docs for easier reading, add detailed file structure description --- docs/freqai-configuration.md | 283 +++++++++++ docs/freqai-data-handling.md | 129 +++++ docs/freqai-feature-engineering.md | 274 +++++++++++ docs/freqai-running.md | 104 ++++ docs/freqai.md | 760 +---------------------------- mkdocs.yml | 7 +- 6 files changed, 816 insertions(+), 741 deletions(-) create mode 100644 docs/freqai-configuration.md create mode 100644 docs/freqai-data-handling.md create mode 100644 docs/freqai-feature-engineering.md create mode 100644 docs/freqai-running.md diff --git a/docs/freqai-configuration.md b/docs/freqai-configuration.md new file mode 100644 index 000000000..5190c59c9 --- /dev/null +++ b/docs/freqai-configuration.md @@ -0,0 +1,283 @@ +# Configuration + +## Setting up the configuration file + +The user interface is isolated to the typical Freqtrade config file. Although there are plenty of additional parameters that a user can choose from, as highlighted in the [parameter table](#parameter-table), a FreqAI config must at minimum include the following parameters (the example inputs are not requires): + +```json + "freqai": { + "enabled": true, + "purge_old_models": true, + "train_period_days": 30, + "backtest_period_days": 7, + "identifier" : "unique-id", + "feature_parameters" : { + "include_timeframes": ["5m","15m","4h"], + "include_corr_pairlist": [ + "ETH/USD", + "LINK/USD", + "BNB/USD" + ], + "label_period_candles": 24, + "include_shifted_candles": 2, + "indicator_periods_candles": [10, 20] + }, + "data_split_parameters" : { + "test_size": 0.25 + }, + "model_training_parameters" : { + "n_estimators": 100 + }, + } +``` + +## Building a FreqAI strategy + +The FreqAI strategy requires the user to include the following lines of code in the standard Freqtrade strategy: + +```python + # user should define the maximum startup candle count (the largest number of candles + # passed to any single indicator) + startup_candle_count: int = 20 + + def informative_pairs(self): + whitelist_pairs = self.dp.current_whitelist() + corr_pairs = self.config["freqai"]["feature_parameters"]["include_corr_pairlist"] + informative_pairs = [] + for tf in self.config["freqai"]["feature_parameters"]["include_timeframes"]: + for pair in whitelist_pairs: + informative_pairs.append((pair, tf)) + for pair in corr_pairs: + if pair in whitelist_pairs: + continue # avoid duplication + informative_pairs.append((pair, tf)) + return informative_pairs + + def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: + + # the model will return all labels created by user in `populate_any_indicators` + # (& appended targets), an indication of whether or not the prediction should be accepted, + # the target mean/std values for each of the labels created by user in + # `populate_any_indicators()` for each training period. + + dataframe = self.freqai.start(dataframe, metadata, self) + + return dataframe + + def populate_any_indicators( + self, pair, df, tf, informative=None, set_generalized_indicators=False + ): + """ + Function designed to automatically generate, name and merge features + from user indicated timeframes in the configuration file. User controls the indicators + passed to the training/prediction by prepending indicators with `'%-' + coin ` + (see convention below). I.e. user should not prepend any supporting metrics + (e.g. bb_lowerband below) with % unless they explicitly want to pass that metric to the + model. + :param pair: pair to be used as informative + :param df: strategy dataframe which will receive merges from informatives + :param tf: timeframe of the dataframe which will modify the feature names + :param informative: the dataframe associated with the informative pair + :param coin: the name of the coin which will modify the feature names. + """ + + coin = pair.split('/')[0] + + if informative is None: + informative = self.dp.get_pair_dataframe(pair, tf) + + # first loop is automatically duplicating indicators for time periods + for t in self.freqai_info["feature_parameters"]["indicator_periods_candles"]: + t = int(t) + informative[f"%-{coin}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t) + informative[f"%-{coin}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t) + informative[f"%-{coin}adx-period_{t}"] = ta.ADX(informative, window=t) + + indicators = [col for col in informative if col.startswith("%")] + # This loop duplicates and shifts all indicators to add a sense of recency to data + for n in range(self.freqai_info["feature_parameters"]["include_shifted_candles"] + 1): + if n == 0: + continue + informative_shift = informative[indicators].shift(n) + informative_shift = informative_shift.add_suffix("_shift-" + str(n)) + informative = pd.concat((informative, informative_shift), axis=1) + + df = merge_informative_pair(df, informative, self.config["timeframe"], tf, ffill=True) + skip_columns = [ + (s + "_" + tf) for s in ["date", "open", "high", "low", "close", "volume"] + ] + df = df.drop(columns=skip_columns) + + # Add generalized indicators here (because in live, it will call this + # function to populate indicators during training). Notice how we ensure not to + # add them multiple times + if set_generalized_indicators: + + # user adds targets here by prepending them with &- (see convention below) + # If user wishes to use multiple targets, a multioutput prediction model + # needs to be used such as templates/CatboostPredictionMultiModel.py + df["&-s_close"] = ( + df["close"] + .shift(-self.freqai_info["feature_parameters"]["label_period_candles"]) + .rolling(self.freqai_info["feature_parameters"]["label_period_candles"]) + .mean() + / df["close"] + - 1 + ) + + return df + + +``` + +Notice how the `populate_any_indicators()` is where the user adds their own features ([more information](freqai-feature_engineering.md#feature-engineering)) and labels ([more information](freqai-running.md#setting-classifier-targets)). See a full example at `templates/FreqaiExampleStrategy.py`. + +Another structure to consider is the location of the labels at the bottom of the example function (below `if set_generalized_indicators:`). This is where the user will add single features and labels to their feature set to avoid duplication of them from various configuration parameters that multiply the feature set, such as `include_timeframes`. + +!!! Note + Features **must** be defined in `populate_any_indicators()`. Defining FreqAI features in `populate_indicators()` + will cause the algorithm to fail in live/dry mode. If the user wishes to add generalized features that are not associated with + a specific pair or timeframe, they should use the following structure inside `populate_any_indicators()` + (as exemplified in `freqtrade/templates/FreqaiExampleStrategy.py`): + + ```python + def populate_any_indicators(self, metadata, pair, df, tf, informative=None, coin="", set_generalized_indicators=False): + + ... + + # Add generalized indicators here (because in live, it will call only this function to populate + # indicators for retraining). Notice how we ensure not to add them multiple times by associating + # these generalized indicators to the basepair/timeframe + if set_generalized_indicators: + df['%-day_of_week'] = (df["date"].dt.dayofweek + 1) / 7 + df['%-hour_of_day'] = (df['date'].dt.hour + 1) / 25 + + # user adds targets here by prepending them with &- (see convention below) + # If user wishes to use multiple targets, a multioutput prediction model + # needs to be used such as templates/CatboostPredictionMultiModel.py + df["&-s_close"] = ( + df["close"] + .shift(-self.freqai_info["feature_parameters"]["label_period_candles"]) + .rolling(self.freqai_info["feature_parameters"]["label_period_candles"]) + .mean() + / df["close"] + - 1 + ) + ``` + + (Please see the example script located in `freqtrade/templates/FreqaiExampleStrategy.py` for a full example of `populate_any_indicators()`.) + +*Important*: The `self.freqai.start()` function cannot be called outside the `populate_indicators()`. + +## Setting the `startup_candle_count` +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`). + +!!! Note + 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: + + ``` + 2022-08-31 15:14:04 - freqtrade.freqai.data_kitchen - INFO - dropped 0 training points due to NaNs in populated dataset 4319. + ``` + +## Creating a dynamic target threshold + +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. + +```python +dataframe["target_roi"] = dataframe["&-s_close_mean"] + dataframe["&-s_close_std"] * 1.25 +dataframe["sell_roi"] = dataframe["&-s_close_mean"] - dataframe["&-s_close_std"] * 1.25 +``` + +If the user wishes to consider the population +of *historical predictions* for creating the dynamic target instead of the trained labels, (as discussed above) the user +can do so by setting `fit_live_prediction_candles` in the config to the number of historical prediction candles +the user wishes to use to generate target statistics. + +```json + "freqai": { + "fit_live_prediction_candles": 300, + } +``` + +If the user sets this value, FreqAI will initially use the predictions from the training data +and subsequently begin introducing real prediction data as it is generated. FreqAI will save +this historical data to be reloaded if the user stops and restarts a model with the same `identifier`. + + +## Parameter table + +The table below will list all configuration parameters available for FreqAI, presented in the same order as `config_examples/config_freqai.example.json`. + +Mandatory parameters are marked as **Required**, which means that they are required to be set in one of the possible ways. + +| Parameter | Description | +|------------|-------------| +| | **General configuration parameters** +| `freqai` | **Required.**
The parent dictionary containing all the parameters for controlling FreqAI.
**Datatype:** Dictionary. +| `purge_old_models` | Delete obsolete models (otherwise, all historic models will remain on disk).
**Datatype:** Boolean. Default: `False`. +| `train_period_days` | **Required.**
Number of days to use for the training data (width of the sliding window).
**Datatype:** Positive integer. +| `backtest_period_days` | **Required.**
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.
**Datatype:** Float. +| `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`.
**Datatype:** Boolean. Default: `False`. +| `identifier` | **Required.**
A unique name for the current model. This can be reused to reload pre-trained models/data.
**Datatype:** String. +| `live_retrain_hours` | Frequency of retraining during dry/live runs.
Default set to 0, which means the model will retrain as often as possible.
**Datatype:** Float > 0. +| `expiration_hours` | Avoid making predictions if a model is more than `expiration_hours` old.
Defaults set to 0, which means models never expire.
**Datatype:** Positive integer. +| `fit_live_predictions_candles` | Number of historical candles to use for computing target (label) statistics from prediction data, instead of from the training data set.
**Datatype:** Positive integer. +| `follow_mode` | If true, this instance of FreqAI will look for models associated with `identifier` and load those for inferencing. A `follower` will **not** train new models.
**Datatype:** Boolean. Default: `False`. +| `continual_learning` | If true, FreqAI will start training new models from the final state of the most recently trained model.
**Datatype:** Boolean. Default: `False`. +| | **Feature parameters** +| `feature_parameters` | A dictionary containing the parameters used to engineer the feature set. Details and examples are shown [here](#feature-engineering).
**Datatype:** Dictionary. +| `include_timeframes` | A list of timeframes that all indicators in `populate_any_indicators` will be created for. The list is added as features to the base asset feature set.
**Datatype:** List of timeframes (strings). +| `include_corr_pairlist` | A list of correlated coins that FreqAI will add as additional features to all `pair_whitelist` coins. All indicators set in `populate_any_indicators` during feature engineering (see details [here](#feature-engineering)) will be created for each coin in this list, and that set of features is added to the base asset feature set.
**Datatype:** List of assets (strings). +| `label_period_candles` | Number of candles into the future that the labels are created for. This is used in `populate_any_indicators` (see `templates/FreqaiExampleStrategy.py` for detailed usage). The user can create custom labels, making use of this parameter or not.
**Datatype:** Positive integer. +| `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.
**Datatype:** Positive integer. +| `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).
**Datatype:** Positive float (typically < 1). +| `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
**Datatype:** positive integer. +| `indicator_periods_candles` | Calculate indicators for `indicator_periods_candles` time periods and add them to the feature set.
**Datatype:** List of positive integers. +| `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)
**Datatype:** Positive integer. +| `principal_component_analysis` | Automatically reduce the dimensionality of the data set using Principal Component Analysis. See details about how it works [here](#reducing-data-dimensionality-with-principal-component-analysis)
**Datatype:** Boolean. +| `DI_threshold` | Activates the Dissimilarity Index for outlier detection when > 0. See details about how it works [here](#removing-outliers-with-the-dissimilarity-index).
**Datatype:** Positive float (typically < 1). +| `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).
**Datatype:** Boolean. +| `svm_params` | All parameters available in Sklearn's `SGDOneClassSVM()`. See details about some select parameters [here](#removing-outliers-using-a-support-vector-machine-svm).
**Datatype:** Dictionary. +| `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).
**Datatype:** Boolean. +| `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)
**Datatype:** int. Default: 0 +| `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.
**Datatype:** int. Default: 0 +| `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.
**Datatype:** Float. Default: `30` +| `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.
**Datatype:** Boolean. Default: False +| | **Data split parameters** +| `data_split_parameters` | Include any additional parameters available from Scikit-learn `test_train_split()`, which are shown [here](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html) (external website).
**Datatype:** Dictionary. +| `test_size` | Fraction of data that should be used for testing instead of training.
**Datatype:** Positive float < 1. +| `shuffle` | Shuffle the training data points during training. Typically, for time-series forecasting, this is set to `False`.
**Datatype:** Boolean. +| | **Model training parameters** +| `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.
**Datatype:** Dictionary. +| `n_estimators` | The number of boosted trees to fit in regression.
**Datatype:** Integer. +| `learning_rate` | Boosting learning rate during regression.
**Datatype:** Float. +| `n_jobs`, `thread_count`, `task_type` | Set the number of threads for parallel processing and the `task_type` (`gpu` or `cpu`). Different model libraries use different parameter names.
**Datatype:** Float. +| | **Extraneous parameters** +| `keras` | If your model makes use of Keras (typical for Tensorflow-based prediction models), activate this flag so that the model save/loading follows Keras standards.
**Datatype:** Boolean. Default: `False`. +| `conv_width` | The width of a convolutional neural network input tensor. This replaces the need for shifting candles (`include_shifted_candles`) by feeding in historical data points as the second dimension of the tensor. Technically, this parameter can also be used for regressors, but it only adds computational overhead and does not change the model training/prediction.
**Datatype:** Integer. Default: 2. + +## Important dataframe key patterns + +Below are the values the user can expect to include/use inside a typical strategy dataframe (`df[]`): + +| DataFrame Key | Description | +|------------|-------------| +| `df['&*']` | Any dataframe column prepended with `&` in `populate_any_indicators()` is treated as a training target (label) inside FreqAI (typically following the naming convention `&-s*`). The names of these dataframe columns are fed back to the user as the predictions. For example, if the user wishes to predict the price change in the next 40 candles (similar to `templates/FreqaiExampleStrategy.py`), they set `df['&-s_close']`. FreqAI makes the predictions and gives them back under the same key (`df['&-s_close']`) to be used in `populate_entry/exit_trend()`.
**Datatype:** Depends on the output of the model. +| `df['&*_std/mean']` | Standard deviation and mean values of the user-defined labels during training (or live tracking with `fit_live_predictions_candles`). Commonly used to understand the rarity of a prediction (use the z-score as shown in `templates/FreqaiExampleStrategy.py` to evaluate how often a particular prediction was observed during training or historically with `fit_live_predictions_candles`).
**Datatype:** Float. +| `df['do_predict']` | Indication of an outlier data point. The return value is integer between -1 and 2, which lets the user know if the prediction is trustworthy or not. `do_predict==1` means the prediction is trustworthy. If the Dissimilarity Index (DI, see details [here](#removing-outliers-with-the-dissimilarity-index)) of the input data point is above the user-defined threshold, FreqAI will subtract 1 from `do_predict`, resulting in `do_predict==0`. If `use_SVM_to_remove_outliers()` is active, the Support Vector Machine (SVM) may also detect outliers in training and prediction data. In this case, the SVM will also subtract 1 from `do_predict`. If the input data point was considered an outlier by the SVM but not by the DI, the result will be `do_predict==0`. If both the DI and the SVM considers the input data point to be an outlier, the result will be `do_predict==-1`. A particular case is when `do_predict == 2`, which means that the model has expired due to exceeding `expired_hours`.
**Datatype:** Integer between -1 and 2. +| `df['DI_values']` | Dissimilarity Index values are proxies to the level of confidence FreqAI has in the prediction. A lower DI means the prediction is close to the training data, i.e., higher prediction confidence.
**Datatype:** Float. +| `df['%*']` | Any dataframe column prepended with `%` in `populate_any_indicators()` is treated as a training feature. For example, the user can include the RSI in the training feature set (similar to in `templates/FreqaiExampleStrategy.py`) by setting `df['%-rsi']`. See more details on how this is done [here](#feature-engineering).
**Note**: Since the number of features prepended with `%` can multiply very quickly (10s of thousands of features is easily engineered using the multiplictative functionality described in the `feature_parameters` table shown above), these features are removed from the dataframe upon return from FreqAI. If the user wishes to keep a particular type of feature for plotting purposes, they can prepend it with `%%`.
**Datatype:** Depends on the output of the model. + +## Building a custom prediction model + +FreqAI has multiple example prediction model libraries, such as `Catboost` regression (`freqai/prediction_models/CatboostRegressor.py`), `LightGBM`, `XGBoost` regression. However, the user can customize and create their own prediction models using the `IFreqaiModel` class. The user is encouraged to inherit `fit()`, `train()` and `predict()` to let them customize various aspects of their training procedures. + +## Setting classifier targets + +`FreqAI` includes a variety of classifiers, such as the `CatboostClassifier` via the flag `--freqaimodel CatboostClassifier`. If the user elects to use a classifier, they must ensure the classes are set using strings. For example: + +```python +df['&s-up_or_down'] = np.where( df["close"].shift(-100) > df["close"], 'up', 'down') +``` + +Additionally, the example classifier models do not accommodate multiple labels, but they do allow multi-class classification within a single label column. diff --git a/docs/freqai-data-handling.md b/docs/freqai-data-handling.md new file mode 100644 index 000000000..7ed51599d --- /dev/null +++ b/docs/freqai-data-handling.md @@ -0,0 +1,129 @@ +# Data handling + +`FreqAI` aims to organize prediction data, model files, and meta data in a way that automates crash resilient reloading and simplifies post-processing. Data is organized into `user_data_dir/models/` and contains all the data associated with the trainings and backtests. This file structure is heavily controlled and inferenced by the `FreqaiDataKitchen()` and should therefore not be manually modified. + +## File structure + +The file structure is automatically generated based on the user set `identifier` in the configuration file. The following structure shows where the data is stored for post processing: + +* `config_*.json` + * a copy of the user submitted configuration file +* `historic_predictions.pkl` + * all historic predictions generated during the lifetime of the `identifier` live deployment. These are also used to reload the model after a crash or a config change. A backup file is always held incase of corruption on the main file - FreqAI automatically detects corruption and replaces the corrupted file with the backup. +* `pair_dictionary.json` + * contains the training queue as well as the location of the most recently trained model on disk. +* `sub-train-*_TIMESTAMP` + * a folder containing all the files associated with a single model, such as: + * `*_metadata.json` + * metadata for the model, such as normalization max/mins, expected training feature list, etc. + * `*_model.*` + * the model file saved to disk for reloading from crash. Can be `joblib` (typical boosting libs), `zip` (stable_baselines), `hd5` (keras type), etc. + * `*_pca_object.pkl` + * the PCA transform (if the user set `principal_component_analysis: true` in their config) which will be used to transform unseen prediction features. + * `*_svm_model.pkl` + * the Support Vector Machine model which is used to detect outliers in unseen prediction features. + * `*_trained_df.pkl` + * the dataframe containing all the training features used to train the particular model. This is used for computing the Dissimilarity Index and can be used for post-processing. + * `*_trained_dates.df.pkl` + * dates associated with the `trained_df.pkl`, useful for post-processing. + +The example file structure would look like this: + +``` +├── models +│   └── unique-id +│   ├── config_freqai.example.json +│   ├── historic_predictions.backup.pkl +│   ├── historic_predictions.pkl +│   ├── pair_dictionary.json +│   ├── sub-train-1INCH_1662821319 +│   │   ├── cb_1inch_1662821319_metadata.json +│   │   ├── cb_1inch_1662821319_model.joblib +│   │   ├── cb_1inch_1662821319_pca_object.pkl +│   │   ├── cb_1inch_1662821319_svm_model.joblib +│   │   ├── cb_1inch_1662821319_trained_dates_df.pkl +│   │   └── cb_1inch_1662821319_trained_df.pkl +│   ├── sub-train-1INCH_1662821371 +│   │   ├── cb_1inch_1662821371_metadata.json +│   │   ├── cb_1inch_1662821371_model.joblib +│   │   ├── cb_1inch_1662821371_pca_object.pkl +│   │   ├── cb_1inch_1662821371_svm_model.joblib +│   │   ├── cb_1inch_1662821371_trained_dates_df.pkl +│   │   └── cb_1inch_1662821371_trained_df.pkl +│   ├── sub-train-ADA_1662821344 +│   │   ├── cb_ada_1662821344_metadata.json +│   │   ├── cb_ada_1662821344_model.joblib +│   │   ├── cb_ada_1662821344_pca_object.pkl +│   │   ├── cb_ada_1662821344_svm_model.joblib +│   │   ├── cb_ada_1662821344_trained_dates_df.pkl +│   │   └── cb_ada_1662821344_trained_df.pkl +│   └── sub-train-ADA_1662821399 +│   ├── cb_ada_1662821399_metadata.json +│   ├── cb_ada_1662821399_model.joblib +│   ├── cb_ada_1662821399_pca_object.pkl +│   ├── cb_ada_1662821399_svm_model.joblib +│   ├── cb_ada_1662821399_trained_dates_df.pkl +│   └── cb_ada_1662821399_trained_df.pkl +``` + +## Backtesting + +When users run a backtest, `FreqAI` will automatically save the predictions to be reused for future runs under the same `identifier`. This is a performance enhancement geared towards enabling high-level hyperopting of entry/exit criteria. That means the user will see an additional directory created in their `unique-id` folder called `predictions` which contains all the predictions stored in `hdf` format. + +If users wish to change their features, they **must** use a new identifier which will signal to `FreqAI` to train new models. If users wish to save the models generated during a particular backtest so they can start a live deployment without an initial training, they must set `save_backtest_models` to `True` in their configuration file. + +### 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](freqai-configuration.md#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). + +## Live deployments + +### Auto data download + +`FreqAI` automatically downloads and proper amount of data to ensure it can train a model using the user defined `train_period_days` and the strategy defined `startup_candle_count`. + +### Historical predictions + +The historical predictions are collected for the life-time of a single `identifier` and stored in `historical_predictions.pkl` + +### 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 +a certain number of hours old by setting the `expiration_hours` in the config file: + +```json + "freqai": { + "expiration_hours": 0.5, + } +``` + +In the presented example config, the user will only allow predictions on models that are less than 1/2 hours old. + +### Purging old model data + +FreqAI stores new model files each time it retrains. These files become obsolete as new models are trained and FreqAI adapts to new market conditions. Users planning to leave FreqAI running for extended periods of time with high frequency retraining should enable `purge_old_models` in their config: + +```json + "freqai": { + "purge_old_models": true, + } +``` + +This will automatically purge all models older than the two most recently trained ones. + +### Returning additional info from training + +The user may find that there are some important metrics that they'd like to return to the strategy at the end of each model training. +The user can include these metrics by assigning them to `dk.data['extra_returns_per_train']['my_new_value'] = XYZ` inside their custom prediction model class. FreqAI takes the `my_new_value` assigned in this dictionary and expands it to fit the return dataframe to the strategy. +The user can then use the value in the strategy with `dataframe['my_new_value']`. An example of how this is already used in FreqAI is +the `&*_mean` and `&*_std` values, which indicate the mean and standard deviation of the particular target (label) during the most recent training. +An example, where the user wants to use live metrics from the trade database, is shown below: + +```json + "freqai": { + "extra_returns_per_train": {"total_profit": 4} + } +``` + +The user needs to set the standard dictionary in the config so that FreqAI can return proper dataframe shapes. These values will likely be overridden by the prediction model, but in the case where the model has yet to set them, or needs a default initial value, this is the value that will be returned. diff --git a/docs/freqai-feature-engineering.md b/docs/freqai-feature-engineering.md new file mode 100644 index 000000000..ebde5aa81 --- /dev/null +++ b/docs/freqai-feature-engineering.md @@ -0,0 +1,274 @@ +# Feature engineering + +Feature engineering is handled within the `FreqAI` config file and the user strategy. The user adds all their `base features` to their strategy, such as `RSI`, `MFI`, `EMA`, `SMA` etc. These can be custom indicators or they can be imported from any technical-analysis library that the user can find. These features are added by the user inside the `populate_any_indicators()` method of the strategy by prepending indicators with `%`, and labels with `&`. + +Users should start from an existing `populate_any_indicators()` to ensure they are following some of the conventions that help with feature engineering. Here is an example: + +```python + def populate_any_indicators( + self, pair, df, tf, informative=None, set_generalized_indicators=False + ): + """ + Function designed to automatically generate, name, and merge features + from user-indicated timeframes in the configuration file. The user controls the indicators + passed to the training/prediction by prepending indicators with `'%-' + coin ` + (see convention below). I.e., the user should not prepend any supporting metrics + (e.g., bb_lowerband below) with % unless they explicitly want to pass that metric to the + model. + :param pair: pair to be used as informative + :param df: strategy dataframe which will receive merges from informatives + :param tf: timeframe of the dataframe which will modify the feature names + :param informative: the dataframe associated with the informative pair + :param coin: the name of the coin which will modify the feature names. + """ + + coin = pair.split('/')[0] + + if informative is None: + informative = self.dp.get_pair_dataframe(pair, tf) + + # first loop is automatically duplicating indicators for time periods + for t in self.freqai_info["feature_parameters"]["indicator_periods_candles"]: + t = int(t) + informative[f"%-{coin}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t) + informative[f"%-{coin}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t) + informative[f"%-{coin}adx-period_{t}"] = ta.ADX(informative, window=t) + + bollinger = qtpylib.bollinger_bands( + qtpylib.typical_price(informative), window=t, stds=2.2 + ) + informative[f"{coin}bb_lowerband-period_{t}"] = bollinger["lower"] + informative[f"{coin}bb_middleband-period_{t}"] = bollinger["mid"] + informative[f"{coin}bb_upperband-period_{t}"] = bollinger["upper"] + + informative[f"%-{coin}bb_width-period_{t}"] = ( + informative[f"{coin}bb_upperband-period_{t}"] + - informative[f"{coin}bb_lowerband-period_{t}"] + ) / informative[f"{coin}bb_middleband-period_{t}"] + informative[f"%-{coin}close-bb_lower-period_{t}"] = ( + informative["close"] / informative[f"{coin}bb_lowerband-period_{t}"] + ) + + informative[f"%-{coin}relative_volume-period_{t}"] = ( + informative["volume"] / informative["volume"].rolling(t).mean() + ) + + indicators = [col for col in informative if col.startswith("%")] + # This loop duplicates and shifts all indicators to add a sense of recency to data + for n in range(self.freqai_info["feature_parameters"]["include_shifted_candles"] + 1): + if n == 0: + continue + informative_shift = informative[indicators].shift(n) + informative_shift = informative_shift.add_suffix("_shift-" + str(n)) + informative = pd.concat((informative, informative_shift), axis=1) + + df = merge_informative_pair(df, informative, self.config["timeframe"], tf, ffill=True) + skip_columns = [ + (s + "_" + tf) for s in ["date", "open", "high", "low", "close", "volume"] + ] + df = df.drop(columns=skip_columns) + + # Add generalized indicators here (because in live, it will call this + # function to populate indicators during training). Notice how we ensure not to + # add them multiple times + if set_generalized_indicators: + df["%-day_of_week"] = (df["date"].dt.dayofweek + 1) / 7 + df["%-hour_of_day"] = (df["date"].dt.hour + 1) / 25 + + # user adds targets here by prepending them with &- (see convention below) + # If user wishes to use multiple targets, a multioutput prediction model + # needs to be used such as templates/CatboostPredictionMultiModel.py + df["&-s_close"] = ( + df["close"] + .shift(-self.freqai_info["feature_parameters"]["label_period_candles"]) + .rolling(self.freqai_info["feature_parameters"]["label_period_candles"]) + .mean() + / df["close"] + - 1 + ) + + return df +``` + +In the presented example strategy, the user does not wish to pass the `bb_lowerband` as a feature to the model, +and has therefore not prepended it with `%`. The user does, however, wish to pass `bb_width` to the +model for training/prediction and has therefore prepended it with `%`. + +Now that the user has set their `base features`, they will next expand upon their base features using the powerful `feature_parameters` in their configuration file: + +```json + "freqai": { + ... + "feature_parameters" : { + "include_timeframes": ["5m","15m","4h"], + "include_corr_pairlist": [ + "ETH/USD", + "LINK/USD", + "BNB/USD" + ], + "label_period_candles": 24, + "include_shifted_candles": 2, + "indicator_periods_candles": [10, 20] + }, + ... + } +``` + +The `include_timeframes` in the config above are the timeframes (`tf`) of each call to `populate_any_indicators()` in the strategy. In the present case, the user is asking for the `5m`, `15m`, and `4h` timeframes of the `rsi`, `mfi`, `roc`, and `bb_width` to be included in the feature set. + +The user can ask for each of the defined features to be included also from informative pairs using the `include_corr_pairlist`. This means that the feature set will include all the features from `populate_any_indicators` on all the `include_timeframes` for each of the correlated pairs defined in the config (`ETH/USD`, `LINK/USD`, and `BNB/USD`). + +`include_shifted_candles` indicates the number of previous candles to include in the feature set. For example, `include_shifted_candles: 2` tells `FreqAI` to include the past 2 candles for each of the features in the feature set. + +In total, the number of features the user of the presented example strat has created is: length of `include_timeframes` * no. features in `populate_any_indicators()` * length of `include_corr_pairlist` * no. `include_shifted_candles` * length of `indicator_periods_candles` + $= 3 * 3 * 3 * 2 * 2 = 108$. + + +### Feature normalization + +`FreqAI` is strict when it comes to data normalization - all data is always automatically normalized to the training feature space according to industry standards. This includes all test data and unseen prediction data (dry/live/backtest). `FreqAI` stores all the metadata required to ensure that prediction features will be properly normalized and that predictions are properly denormalized. For this reason, it is not recommended to eschew industry standards and modify `FreqAI` internals - however - advanced users can do so by inheriting `train()` in their custom `IFreqaiModel` and using their own normalization functions. + +### Reducing data dimensionality with Principal Component Analysis + +Users can reduce the dimensionality of their features by activating the `principal_component_analysis` in the config: + +```json + "freqai": { + "feature_parameters" : { + "principal_component_analysis": true + } + } +``` + +This will perform PCA on the features and reduce the dimensionality of the data so that the explained variance of the data set is >= 0.999. + +### Stratifying the data for training and testing the model + +The user can stratify (group) the training/testing data using: + +```json + "freqai": { + "feature_parameters" : { + "stratify_training_data": 3 + } + } +``` + +This will split the data chronologically so that every Xth data point is used to test the model after training. In the +example above, the user is asking for every third data point in the dataframe to be used for +testing; the other points are used for training. + +The test data is used to evaluate the performance of the model after training. If the test score is high, the model is able to capture the behavior of the data well. If the test score is low, either the model 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` + +The `inlier_metric` is a metric aimed at quantifying how different a prediction data point is from the most recent historic data points. + +User can set `inlier_metric_window` to set the look back window. FreqAI will compute the distance between the present prediction point and each of the previous data points (total of `inlier_metric_window` points). + +This function goes one step further - during training, it computes the `inlier_metric` for all training data points and builds weibull distributions for each each lookback point. The cumulative distribution function for the weibull distribution is used to produce a quantile for each of the data points. The quantiles for each lookback point are averaged to create the `inlier_metric`. + +FreqAI adds this `inlier_metric` score to the training features! In other words, your model is trained to recognize how this temporal inlier metric is related to the user set labels. + +This function does **not** remove outliers from the data set. + +### Controlling the model learning process + +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. + +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: + +$$ W_i = \exp(\frac{-i}{\alpha*n}) $$ + +where $W_i$ is the weight of data point $i$ in a total set of $n$ data points. Below is a figure showing the effect of different weight factors on the data points (candles) in a feature set. + +![weight-factor](assets/freqai_weight-factor.jpg) + +`train_test_split()` has a parameters called `shuffle` that allows the user to keep the data unshuffled. This is particularly useful to avoid biasing training with temporally auto-correlated data. + +Finally, `label_period_candles` defines the offset (number of candles into the future) used for the `labels`. In the presented example config, +the user is asking for `labels` that are 24 candles in the future. + +#### Continual learning + +Users can choose to adopt a "continual learning" strategy by setting `"continual_learning": true` in their configuration file. This setting will train an initial model from scratch, and subsequent trainings will start from the final model state of the preceding training. By default, this is set to `false` which trains a new model from scratch upon each subsequent training. + +### Outlier removal + +#### Removing outliers with the Dissimilarity Index + +The user can tell FreqAI to remove outlier data points from the training/test data sets using a Dissimilarity Index by including the following statement in the config: + +```json + "freqai": { + "feature_parameters" : { + "DI_threshold": 1 + } + } +``` + +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: + +$$ d_{ab} = \sqrt{\sum_{j=1}^p(X_{a,j}-X_{b,j})^2} $$ + +where $d_{ab}$ is the distance between the normalized points $a$ and $b$. $p$ is the number of features, i.e., the length of the vector $X$. The characteristic distance, $\overline{d}$ for a set of training data points is simply the mean of the average distances: + +$$ \overline{d} = \sum_{a=1}^n(\sum_{b=1}^n(d_{ab}/n)/n) $$ + +$\overline{d}$ quantifies the spread of the training data, which is compared to the distance between a new prediction feature vectors, $X_k$ and all the training data: + +$$ d_k = \arg \min d_{k,i} $$ + +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. + +Below is a figure that describes the DI for a 3D data set. + +![DI](assets/freqai_DI.jpg) + +#### Removing outliers using a Support Vector Machine (SVM) + +The user can tell FreqAI to remove outlier data points from the training/test data sets using a SVM by setting: + +```json + "freqai": { + "feature_parameters" : { + "use_SVM_to_remove_outliers": true + } + } +``` + +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. + +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. + +#### Removing outliers with DBSCAN + +The user can configure FreqAI to use DBSCAN to cluster and remove outliers from the training/test data set or incoming outliers from predictions, by activating `use_DBSCAN_to_remove_outliers` in the config: + +```json + "freqai": { + "feature_parameters" : { + "use_DBSCAN_to_remove_outliers": true + } + } +``` + +DBSCAN is an unsupervised machine learning algorithm that clusters data without needing to know how many clusters there should be. + +Given a number of data points $N$, and a distance $\varepsilon$, DBSCAN clusters the data set by setting all data points that have $N-1$ other data points within a distance of $\varepsilon$ as *core points*. A data point that is within a distance of $\varepsilon$ from a *core point* but that does not have $N-1$ other data points within a distance of $\varepsilon$ from itself is considered an *edge point*. A cluster is then the collection of *core points* and *edge points*. Data points that have no other data points at a distance $<\varepsilon$ are considered outliers. The figure below shows a cluster with $N = 3$. + +![dbscan](assets/freqai_dbscan.jpg) + +FreqAI uses `sklearn.cluster.DBSCAN` (details are available on scikit-learn's webpage [here](#https://scikit-learn.org/stable/modules/generated/sklearn.cluster.DBSCAN.html)) 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. diff --git a/docs/freqai-running.md b/docs/freqai-running.md new file mode 100644 index 000000000..90856c841 --- /dev/null +++ b/docs/freqai-running.md @@ -0,0 +1,104 @@ +# Running FreqAI + +There are two ways to train and deploy an adaptive machine learning model. FreqAI enables live deployment as well as backtesting analyses. In both cases, a model is trained periodically, as shown in the following figure. + +![freqai-window](assets/freqai_moving-window.jpg) + +### Running the model live + +FreqAI can be run dry/live using the following command: + +```bash +freqtrade trade --strategy FreqaiExampleStrategy --config config_freqai.example.json --freqaimodel LightGBMRegressor +``` + +By default, FreqAI will not find any existing models and will start by training a new one +based on the user's configuration settings. Following training, the model will be used to make predictions on incoming candles until a new model is available. New models are typically generated as often as possible, with FreqAI managing an internal queue of the coin pairs to try to keep all models equally up to date. FreqAI will always use the most recently trained model to make predictions on incoming live data. If the user does not want FreqAI to retrain new models as often as possible, they can set `live_retrain_hours` to tell FreqAI to wait at least that number of hours before training a new model. Additionally, the user can set `expired_hours` to tell FreqAI to avoid making predictions on models that are older than that number of hours. + +If the user wishes to start a dry/live run from a saved backtest model (or from a previously crashed dry/live session), the user only needs to reuse +the same `identifier` parameter: + +```json + "freqai": { + "identifier": "example", + "live_retrain_hours": 0.5 + } +``` + +In this case, although FreqAI will initiate with a +pre-trained model, it will still check to see how much time has elapsed since the model was trained, +and if a full `live_retrain_hours` has elapsed since the end of the loaded model, FreqAI will retrain. + +### Backtesting + +The FreqAI backtesting module can be executed with the following command: + +```bash +freqtrade backtesting --strategy FreqaiExampleStrategy --strategy-path freqtrade/templates --config config_examples/config_freqai.example.json --freqaimodel LightGBMRegressor --timerange 20210501-20210701 +``` + +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). + +If this command has never been executed with the existing config file, it will train a new model +for each pair, for each backtesting window within the expanded `--timerange`. + +!!! Note "Model reuse" + Once the training is completed, the user can execute the backtesting again with the same config file and + FreqAI will find the trained models and load them instead of spending time training. This is useful + if the user wants to tweak (or even hyperopt) buy and sell criteria inside the strategy. If the user + *wants* to retrain a new model with the same config file, then they should simply change the `identifier`. + This way, the user can return to using any model they wish by simply specifying the `identifier`. + +--- + +### Hyperopt + +Users can hyperopt using the same command as typical [hyperopt](hyperopt.md): + +```bash +freqtrade hyperopt --hyperopt-loss SharpeHyperOptLoss --strategy FreqaiExampleStrategy --freqaimodel LightGBMRegressor --strategy-path freqtrade/templates --config config_examples/config_freqai.example.json --timerange 20220428-20220507 +``` + +Users need to have the data pre-downloaded in the same fashion as if they were doing a FreqAI [backtest](#backtesting). In addition, users must consider some restrictions when trying to [Hyperopt](hyperopt.md) FreqAI strategies: + +- The `--analyze-per-epoch` hyperopt parameter is not compatible with FreqAI. +- It's not possible to hyperopt indicators in `populate_any_indicators()` function. This means that the user cannot optimize model parameters using hyperopt. Apart from this exception, it is possible to optimize all other [spaces](hyperopt.md#running-hyperopt-with-smaller-search-space). +- The [Backtesting](#backtesting) instructions also apply to Hyperopt. + +The best method for combining hyperopt and FreqAI is to focus on hyperopting entry/exit thresholds/criteria. Users need to focus on hyperopting parameters that are not used in their FreqAI features. For example, users should not try to hyperopt rolling window lengths in their feature creation, or any of their FreqAI config which changes predictions. In order to efficiently hyperopt the FreqAI strategy, FreqAI stores predictions as dataframes and reuses them. Hence the requirement to hyperopt entry/exit thresholds/criteria only. + +A good example of a hyperoptable parameter in FreqAI is a value for `DI_values` beyond which we consider outliers and below which we consider inliers: + +```python +di_max = IntParameter(low=1, high=20, default=10, space='buy', optimize=True, load=True) +dataframe['outlier'] = np.where(dataframe['DI_values'] > self.di_max.value/10, 1, 0) +``` + +Which would help the user understand the appropriate Dissimilarity Index values for their particular parameter space. + +### Deciding the size of the sliding training window and backtesting duration + +The user defines the backtesting timerange with the typical `--timerange` parameter in the configuration file. The duration of the sliding training window is set by `train_period_days`, whilst `backtest_period_days` is the sliding backtesting window, both in number of days (`backtest_period_days` can be +a float to indicate sub-daily retraining in live/dry mode). In the presented example config, the user is asking FreqAI to use a training period of 30 days and backtest on the subsequent 7 days. 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`. + +!!! 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). + +### Setting up a follower + +The user can define: + +```json + "freqai": { + "follow_mode": true, + "identifier": "example" + } +``` + +to indicate to the bot that it should not train models, but instead should look for models trained by a leader with the same `identifier`. In this example, the user has a leader bot with the `identifier: "example"`. The leader bot is already running or launching simultaneously as the follower. +The follower will load models created by the leader and inference them to obtain predictions. diff --git a/docs/freqai.md b/docs/freqai.md index 5f523f58a..955e49250 100644 --- a/docs/freqai.md +++ b/docs/freqai.md @@ -6,18 +6,18 @@ FreqAI is a module designed to automate a variety of tasks associated with train Features include: -* **Self-adaptive retraining**: retrain models during [live deployments](#running-the-model-live) to self-adapt to the market in an unsupervised manner. -* **Rapid feature engineering**: create large rich [feature sets](#feature-engineering) (10k+ features) based on simple user-created strategies. +* **Self-adaptive retraining**: retrain models during [live deployments](freqai-running.md#running-the-model-live) to self-adapt to the market in an unsupervised manner. +* **Rapid feature engineering**: create large rich [feature sets](freqai-feature-engineering.md#feature-engineering) (10k+ features) based on simple user-created strategies. * **High performance**: adaptive retraining occurs on a separate thread (or on GPU if available) from inferencing and bot trade operations. Newest models and data are kept in memory for rapid inferencing. -* **Realistic backtesting**: emulate self-adaptive retraining with a [backtesting module](#backtesting) that automates past retraining. -* **Modifiability**: use the generalized and robust architecture for incorporating any [machine learning library/method](#building-a-custom-prediction-model) available in Python. Eight examples are currently available, including classifiers, regressors, and a convolutional neural network. -* **Smart outlier removal**: remove outliers from training and prediction data sets using a variety of [outlier detection techniques](#outlier-removal). -* **Crash resilience**: store model to disk to make reloading from a crash fast and easy, and [purge obsolete files](#purging-old-model-data) for sustained dry/live runs. -* **Automatic data normalization**: [normalize the data](#feature-normalization) in a smart and statistically safe way. +* **Realistic backtesting**: emulate self-adaptive retraining with a [backtesting module](freqai-running.md#backtesting) that automates past retraining. +* **Extensibility**: use the generalized and robust architecture for incorporating any [machine learning library/method](freqai-configuration.md#building-a-custom-prediction-model) available in Python. Eight examples are currently available, including classifiers, regressors, and a convolutional neural network. +* **Smart outlier removal**: remove outliers from training and prediction data sets using a variety of [outlier detection techniques](freqai-feature-engineering.md#outlier-removal). +* **Crash resilience**: store model to disk to make reloading from a crash fast and easy, and [purge obsolete files](freqai-data-handling.md#purging-old-model-data) for sustained dry/live runs. +* **Automatic data normalization**: [normalize the data](freqai-feature-engineering.md#feature-normalization) in a smart and statistically safe way. * **Automatic data download**: compute the data download timerange and update historic data (in live deployments). * **Cleaning of incoming data**: handle NaNs safely before training and prediction. -* **Dimensionality reduction**: reduce the size of the training data via [Principal Component Analysis](#reducing-data-dimensionality-with-principal-component-analysis). -* **Deploying bot fleets**: set one bot to train models while a fleet of [follower bots](#setting-up-a-follower) inference the models and handle trades. +* **Dimensionality reduction**: reduce the size of the training data via [Principal Component Analysis](freqai-feature-engineering.md#reducing-data-dimensionality-with-principal-component-analysis). +* **Deploying bot fleets**: set one bot to train models while a fleet of [follower bots](freqai-running.md#setting-up-a-follower) inference the models and handle trades. ## Quick start @@ -35,8 +35,7 @@ The example strategy, example prediction model, and example config can be found ## General approach -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*). -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. +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*). 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. An overview of the algorithm is shown below, explaining the data processing pipeline and the model usage. @@ -44,19 +43,13 @@ An overview of the algorithm is shown below, explaining the data processing pipe ### Important machine learning vocabulary -**Features** - the quantities with which a model is trained. All features for a single candle is stored as a vector. In FreqAI, the user -builds the feature sets from anything they can construct in the strategy. +**Features** - the quantities with which a model is trained. All features for a single candle is stored as a vector. In FreqAI, the user builds the feature sets from anything they can construct in the strategy. -**Labels** - the target values that a model is trained -toward. Each set of features is associated with a single label that is -defined by the user within the strategy. These labels intentionally look into the -future, and are not available to the model during dry/live/backtesting. +**Labels** - the target values that a model is trained toward. Each set of features is associated with a single label that is defined by the user within the strategy. These labels intentionally look into the future, and are not available to the model during dry/live/backtesting. -**Training** - the process of feeding individual feature sets, composed of historic data, with associated labels into the -model with the goal of matching input feature sets to associated labels. +**Training** - the process of feeding individual feature sets, composed of historic data, with associated labels into the model with the goal of matching input feature sets to associated labels. -**Train data** - a subset of the historic data that is fed to the model during -training. This data directly influences weight connections in the model. +**Train data** - a subset of the historic data that is fed to the model during training. This data directly influences weight connections in the model. **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. @@ -73,723 +66,7 @@ pip install -r requirements-freqai.txt ### Usage with docker -For docker users, a dedicated tag with freqAI dependencies is available as `:freqai`. -As such - you can replace the image line in your docker-compose file with `image: freqtradeorg/freqtrade:develop_freqai`. -This image contains the regular freqAI dependencies. Similar to native installs, Catboost will not be available on ARM based devices. - -## Setting up FreqAI - -### Parameter table - -The table below will list all configuration parameters available for FreqAI, presented in the same order as `config_examples/config_freqai.example.json`. - -Mandatory parameters are marked as **Required**, which means that they are required to be set in one of the possible ways. - -| Parameter | Description | -|------------|-------------| -| | **General configuration parameters** -| `freqai` | **Required.**
The parent dictionary containing all the parameters for controlling FreqAI.
**Datatype:** Dictionary. -| `purge_old_models` | Delete obsolete models (otherwise, all historic models will remain on disk).
**Datatype:** Boolean. Default: `False`. -| `train_period_days` | **Required.**
Number of days to use for the training data (width of the sliding window).
**Datatype:** Positive integer. -| `backtest_period_days` | **Required.**
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.
**Datatype:** Float. -| `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`.
**Datatype:** Boolean. Default: `False`. -| `identifier` | **Required.**
A unique name for the current model. This can be reused to reload pre-trained models/data.
**Datatype:** String. -| `live_retrain_hours` | Frequency of retraining during dry/live runs.
Default set to 0, which means the model will retrain as often as possible.
**Datatype:** Float > 0. -| `expiration_hours` | Avoid making predictions if a model is more than `expiration_hours` old.
Defaults set to 0, which means models never expire.
**Datatype:** Positive integer. -| `fit_live_predictions_candles` | Number of historical candles to use for computing target (label) statistics from prediction data, instead of from the training data set.
**Datatype:** Positive integer. -| `follow_mode` | If true, this instance of FreqAI will look for models associated with `identifier` and load those for inferencing. A `follower` will **not** train new models.
**Datatype:** Boolean. Default: `False`. -| `continual_learning` | If true, FreqAI will start training new models from the final state of the most recently trained model.
**Datatype:** Boolean. Default: `False`. -| | **Feature parameters** -| `feature_parameters` | A dictionary containing the parameters used to engineer the feature set. Details and examples are shown [here](#feature-engineering).
**Datatype:** Dictionary. -| `include_timeframes` | A list of timeframes that all indicators in `populate_any_indicators` will be created for. The list is added as features to the base asset feature set.
**Datatype:** List of timeframes (strings). -| `include_corr_pairlist` | A list of correlated coins that FreqAI will add as additional features to all `pair_whitelist` coins. All indicators set in `populate_any_indicators` during feature engineering (see details [here](#feature-engineering)) will be created for each coin in this list, and that set of features is added to the base asset feature set.
**Datatype:** List of assets (strings). -| `label_period_candles` | Number of candles into the future that the labels are created for. This is used in `populate_any_indicators` (see `templates/FreqaiExampleStrategy.py` for detailed usage). The user can create custom labels, making use of this parameter or not.
**Datatype:** Positive integer. -| `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.
**Datatype:** Positive integer. -| `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).
**Datatype:** Positive float (typically < 1). -| `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
**Datatype:** positive integer. -| `indicator_periods_candles` | Calculate indicators for `indicator_periods_candles` time periods and add them to the feature set.
**Datatype:** List of positive integers. -| `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)
**Datatype:** Positive integer. -| `principal_component_analysis` | Automatically reduce the dimensionality of the data set using Principal Component Analysis. See details about how it works [here](#reducing-data-dimensionality-with-principal-component-analysis)
**Datatype:** Boolean. -| `DI_threshold` | Activates the Dissimilarity Index for outlier detection when > 0. See details about how it works [here](#removing-outliers-with-the-dissimilarity-index).
**Datatype:** Positive float (typically < 1). -| `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).
**Datatype:** Boolean. -| `svm_params` | All parameters available in Sklearn's `SGDOneClassSVM()`. See details about some select parameters [here](#removing-outliers-using-a-support-vector-machine-svm).
**Datatype:** Dictionary. -| `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).
**Datatype:** Boolean. -| `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)
**Datatype:** int. Default: 0 -| `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.
**Datatype:** int. Default: 0 -| `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.
**Datatype:** Float. Default: `30` -| `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.
**Datatype:** Boolean. Default: False -| | **Data split parameters** -| `data_split_parameters` | Include any additional parameters available from Scikit-learn `test_train_split()`, which are shown [here](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html) (external website).
**Datatype:** Dictionary. -| `test_size` | Fraction of data that should be used for testing instead of training.
**Datatype:** Positive float < 1. -| `shuffle` | Shuffle the training data points during training. Typically, for time-series forecasting, this is set to `False`.
**Datatype:** Boolean. -| | **Model training parameters** -| `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.
**Datatype:** Dictionary. -| `n_estimators` | The number of boosted trees to fit in regression.
**Datatype:** Integer. -| `learning_rate` | Boosting learning rate during regression.
**Datatype:** Float. -| `n_jobs`, `thread_count`, `task_type` | Set the number of threads for parallel processing and the `task_type` (`gpu` or `cpu`). Different model libraries use different parameter names.
**Datatype:** Float. -| | **Extraneous parameters** -| `keras` | If your model makes use of Keras (typical for Tensorflow-based prediction models), activate this flag so that the model save/loading follows Keras standards.
**Datatype:** Boolean. Default: `False`. -| `conv_width` | The width of a convolutional neural network input tensor. This replaces the need for shifting candles (`include_shifted_candles`) by feeding in historical data points as the second dimension of the tensor. Technically, this parameter can also be used for regressors, but it only adds computational overhead and does not change the model training/prediction.
**Datatype:** Integer. Default: 2. - -### Important dataframe key patterns - -Below are the values the user can expect to include/use inside a typical strategy dataframe (`df[]`): - -| DataFrame Key | Description | -|------------|-------------| -| `df['&*']` | Any dataframe column prepended with `&` in `populate_any_indicators()` is treated as a training target (label) inside FreqAI (typically following the naming convention `&-s*`). The names of these dataframe columns are fed back to the user as the predictions. For example, if the user wishes to predict the price change in the next 40 candles (similar to `templates/FreqaiExampleStrategy.py`), they set `df['&-s_close']`. FreqAI makes the predictions and gives them back under the same key (`df['&-s_close']`) to be used in `populate_entry/exit_trend()`.
**Datatype:** Depends on the output of the model. -| `df['&*_std/mean']` | Standard deviation and mean values of the user-defined labels during training (or live tracking with `fit_live_predictions_candles`). Commonly used to understand the rarity of a prediction (use the z-score as shown in `templates/FreqaiExampleStrategy.py` to evaluate how often a particular prediction was observed during training or historically with `fit_live_predictions_candles`).
**Datatype:** Float. -| `df['do_predict']` | Indication of an outlier data point. The return value is integer between -1 and 2, which lets the user know if the prediction is trustworthy or not. `do_predict==1` means the prediction is trustworthy. If the Dissimilarity Index (DI, see details [here](#removing-outliers-with-the-dissimilarity-index)) of the input data point is above the user-defined threshold, FreqAI will subtract 1 from `do_predict`, resulting in `do_predict==0`. If `use_SVM_to_remove_outliers()` is active, the Support Vector Machine (SVM) may also detect outliers in training and prediction data. In this case, the SVM will also subtract 1 from `do_predict`. If the input data point was considered an outlier by the SVM but not by the DI, the result will be `do_predict==0`. If both the DI and the SVM considers the input data point to be an outlier, the result will be `do_predict==-1`. A particular case is when `do_predict == 2`, which means that the model has expired due to exceeding `expired_hours`.
**Datatype:** Integer between -1 and 2. -| `df['DI_values']` | Dissimilarity Index values are proxies to the level of confidence FreqAI has in the prediction. A lower DI means the prediction is close to the training data, i.e., higher prediction confidence.
**Datatype:** Float. -| `df['%*']` | Any dataframe column prepended with `%` in `populate_any_indicators()` is treated as a training feature. For example, the user can include the RSI in the training feature set (similar to in `templates/FreqaiExampleStrategy.py`) by setting `df['%-rsi']`. See more details on how this is done [here](#feature-engineering).
**Note**: Since the number of features prepended with `%` can multiply very quickly (10s of thousands of features is easily engineered using the multiplictative functionality described in the `feature_parameters` table shown above), these features are removed from the dataframe upon return from FreqAI. If the user wishes to keep a particular type of feature for plotting purposes, they can prepend it with `%%`.
**Datatype:** Depends on the output of the model. - -### File structure - -`user_data_dir/models/` contains all the data associated with the trainings and backtests. -This file structure is heavily controlled and inferenced by the `FreqaiDataKitchen()` -and should therefore not be modified. - -### Example config file - -The user interface is isolated to the typical Freqtrade config file. A FreqAI config should include: - -```json - "freqai": { - "enabled": true, - "startup_candles": 10000, - "purge_old_models": true, - "train_period_days": 30, - "backtest_period_days": 7, - "identifier" : "unique-id", - "feature_parameters" : { - "include_timeframes": ["5m","15m","4h"], - "include_corr_pairlist": [ - "ETH/USD", - "LINK/USD", - "BNB/USD" - ], - "label_period_candles": 24, - "include_shifted_candles": 2, - "indicator_periods_candles": [10, 20] - }, - "data_split_parameters" : { - "test_size": 0.25 - }, - "model_training_parameters" : { - "n_estimators": 100 - }, - } -``` - -## Building a FreqAI strategy - -The FreqAI strategy requires the user to include the following lines of code in the standard Freqtrade strategy: - -```python - # user should define the maximum startup candle count (the largest number of candles - # passed to any single indicator) - startup_candle_count: int = 20 - - def informative_pairs(self): - whitelist_pairs = self.dp.current_whitelist() - corr_pairs = self.config["freqai"]["feature_parameters"]["include_corr_pairlist"] - informative_pairs = [] - for tf in self.config["freqai"]["feature_parameters"]["include_timeframes"]: - for pair in whitelist_pairs: - informative_pairs.append((pair, tf)) - for pair in corr_pairs: - if pair in whitelist_pairs: - continue # avoid duplication - informative_pairs.append((pair, tf)) - return informative_pairs - - def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: - - # the model will return all labels created by user in `populate_any_indicators` - # (& appended targets), an indication of whether or not the prediction should be accepted, - # the target mean/std values for each of the labels created by user in - # `populate_any_indicators()` for each training period. - - dataframe = self.freqai.start(dataframe, metadata, self) - - return dataframe - - def populate_any_indicators( - self, pair, df, tf, informative=None, set_generalized_indicators=False - ): - """ - Function designed to automatically generate, name and merge features - from user indicated timeframes in the configuration file. User controls the indicators - passed to the training/prediction by prepending indicators with `'%-' + coin ` - (see convention below). I.e. user should not prepend any supporting metrics - (e.g. bb_lowerband below) with % unless they explicitly want to pass that metric to the - model. - :param pair: pair to be used as informative - :param df: strategy dataframe which will receive merges from informatives - :param tf: timeframe of the dataframe which will modify the feature names - :param informative: the dataframe associated with the informative pair - :param coin: the name of the coin which will modify the feature names. - """ - - coin = pair.split('/')[0] - - if informative is None: - informative = self.dp.get_pair_dataframe(pair, tf) - - # first loop is automatically duplicating indicators for time periods - for t in self.freqai_info["feature_parameters"]["indicator_periods_candles"]: - t = int(t) - informative[f"%-{coin}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t) - informative[f"%-{coin}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t) - informative[f"%-{coin}adx-period_{t}"] = ta.ADX(informative, window=t) - - indicators = [col for col in informative if col.startswith("%")] - # This loop duplicates and shifts all indicators to add a sense of recency to data - for n in range(self.freqai_info["feature_parameters"]["include_shifted_candles"] + 1): - if n == 0: - continue - informative_shift = informative[indicators].shift(n) - informative_shift = informative_shift.add_suffix("_shift-" + str(n)) - informative = pd.concat((informative, informative_shift), axis=1) - - df = merge_informative_pair(df, informative, self.config["timeframe"], tf, ffill=True) - skip_columns = [ - (s + "_" + tf) for s in ["date", "open", "high", "low", "close", "volume"] - ] - df = df.drop(columns=skip_columns) - - # Add generalized indicators here (because in live, it will call this - # function to populate indicators during training). Notice how we ensure not to - # add them multiple times - if set_generalized_indicators: - - # user adds targets here by prepending them with &- (see convention below) - # If user wishes to use multiple targets, a multioutput prediction model - # needs to be used such as templates/CatboostPredictionMultiModel.py - df["&-s_close"] = ( - df["close"] - .shift(-self.freqai_info["feature_parameters"]["label_period_candles"]) - .rolling(self.freqai_info["feature_parameters"]["label_period_candles"]) - .mean() - / df["close"] - - 1 - ) - - return df - - -``` - -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`. - -*Important*: The `self.freqai.start()` function cannot be called outside the `populate_indicators()`. - -### Setting the `startup_candle_count` -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`). - -!!! Note - 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: - - ``` - 2022-08-31 15:14:04 - freqtrade.freqai.data_kitchen - INFO - dropped 0 training points due to NaNs in populated dataset 4319. - ``` - - -## Creating a dynamic target - -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. - -```python -dataframe["target_roi"] = dataframe["&-s_close_mean"] + dataframe["&-s_close_std"] * 1.25 -dataframe["sell_roi"] = dataframe["&-s_close_mean"] - dataframe["&-s_close_std"] * 1.25 -``` - -If the user wishes to consider the population -of *historical predictions* for creating the dynamic target instead of the trained labels, (as discussed above) the user -can do so by setting `fit_live_prediction_candles` in the config to the number of historical prediction candles -the user wishes to use to generate target statistics. - -```json - "freqai": { - "fit_live_prediction_candles": 300, - } -``` - -If the user sets this value, FreqAI will initially use the predictions from the training data -and subsequently begin introducing real prediction data as it is generated. FreqAI will save -this historical data to be reloaded if the user stops and restarts a model with the same `identifier`. - -## Building a custom prediction model - -FreqAI has multiple example prediction model libraries, such as `Catboost` regression (`freqai/prediction_models/CatboostRegressor.py`) and `LightGBM` regression. -However, the user can customize and create their own prediction models using the `IFreqaiModel` class. -The user is encouraged to inherit `train()` and `predict()` to let them customize various aspects of their training procedures. - -## Feature engineering - -Features are added by the user inside the `populate_any_indicators()` method of the strategy -by prepending indicators with `%`, and labels with `&`. - -There are some important components/structures that the user *must* include when building their feature set; the use of these is shown below: - -```python - def populate_any_indicators( - self, pair, df, tf, informative=None, set_generalized_indicators=False - ): - """ - Function designed to automatically generate, name, and merge features - from user-indicated timeframes in the configuration file. The user controls the indicators - passed to the training/prediction by prepending indicators with `'%-' + coin ` - (see convention below). I.e., the user should not prepend any supporting metrics - (e.g., bb_lowerband below) with % unless they explicitly want to pass that metric to the - model. - :param pair: pair to be used as informative - :param df: strategy dataframe which will receive merges from informatives - :param tf: timeframe of the dataframe which will modify the feature names - :param informative: the dataframe associated with the informative pair - :param coin: the name of the coin which will modify the feature names. - """ - - coin = pair.split('/')[0] - - if informative is None: - informative = self.dp.get_pair_dataframe(pair, tf) - - # first loop is automatically duplicating indicators for time periods - for t in self.freqai_info["feature_parameters"]["indicator_periods_candles"]: - t = int(t) - informative[f"%-{coin}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t) - informative[f"%-{coin}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t) - informative[f"%-{coin}adx-period_{t}"] = ta.ADX(informative, window=t) - - bollinger = qtpylib.bollinger_bands( - qtpylib.typical_price(informative), window=t, stds=2.2 - ) - informative[f"{coin}bb_lowerband-period_{t}"] = bollinger["lower"] - informative[f"{coin}bb_middleband-period_{t}"] = bollinger["mid"] - informative[f"{coin}bb_upperband-period_{t}"] = bollinger["upper"] - - informative[f"%-{coin}bb_width-period_{t}"] = ( - informative[f"{coin}bb_upperband-period_{t}"] - - informative[f"{coin}bb_lowerband-period_{t}"] - ) / informative[f"{coin}bb_middleband-period_{t}"] - informative[f"%-{coin}close-bb_lower-period_{t}"] = ( - informative["close"] / informative[f"{coin}bb_lowerband-period_{t}"] - ) - - informative[f"%-{coin}relative_volume-period_{t}"] = ( - informative["volume"] / informative["volume"].rolling(t).mean() - ) - - indicators = [col for col in informative if col.startswith("%")] - # This loop duplicates and shifts all indicators to add a sense of recency to data - for n in range(self.freqai_info["feature_parameters"]["include_shifted_candles"] + 1): - if n == 0: - continue - informative_shift = informative[indicators].shift(n) - informative_shift = informative_shift.add_suffix("_shift-" + str(n)) - informative = pd.concat((informative, informative_shift), axis=1) - - df = merge_informative_pair(df, informative, self.config["timeframe"], tf, ffill=True) - skip_columns = [ - (s + "_" + tf) for s in ["date", "open", "high", "low", "close", "volume"] - ] - df = df.drop(columns=skip_columns) - - # Add generalized indicators here (because in live, it will call this - # function to populate indicators during training). Notice how we ensure not to - # add them multiple times - if set_generalized_indicators: - df["%-day_of_week"] = (df["date"].dt.dayofweek + 1) / 7 - df["%-hour_of_day"] = (df["date"].dt.hour + 1) / 25 - - # user adds targets here by prepending them with &- (see convention below) - # If user wishes to use multiple targets, a multioutput prediction model - # needs to be used such as templates/CatboostPredictionMultiModel.py - df["&-s_close"] = ( - df["close"] - .shift(-self.freqai_info["feature_parameters"]["label_period_candles"]) - .rolling(self.freqai_info["feature_parameters"]["label_period_candles"]) - .mean() - / df["close"] - - 1 - ) - - return df -``` - -In the presented example strategy, the user does not wish to pass the `bb_lowerband` as a feature to the model, -and has therefore not prepended it with `%`. The user does, however, wish to pass `bb_width` to the -model for training/prediction and has therefore prepended it with `%`. - -The `include_timeframes` in the example config above are the timeframes (`tf`) of each call to `populate_any_indicators()` in the strategy. In the present case, the user is asking for the -`5m`, `15m`, and `4h` timeframes of the `rsi`, `mfi`, `roc`, and `bb_width` to be included in the feature set. - -The user can ask for each of the defined features to be included also from -informative pairs using the `include_corr_pairlist`. This means that the feature -set will include all the features from `populate_any_indicators` on all the `include_timeframes` for each of the correlated pairs defined in the config (`ETH/USD`, `LINK/USD`, and `BNB/USD`). - -`include_shifted_candles` indicates the number of previous -candles to include in the feature set. For example, `include_shifted_candles: 2` tells -FreqAI to include the past 2 candles for each of the features in the feature set. - -In total, the number of features the user of the presented example strat has created is: -length of `include_timeframes` * no. features in `populate_any_indicators()` * length of `include_corr_pairlist` * no. `include_shifted_candles` * length of `indicator_periods_candles` - $= 3 * 3 * 3 * 2 * 2 = 108$. - -Another structure to consider is the location of the labels at the bottom of the example function (below `if set_generalized_indicators:`). -This is where the user will add single features and labels to their feature set to avoid duplication of them from -various configuration parameters that multiply the feature set, such as `include_timeframes`. - -!!! Note - Features **must** be defined in `populate_any_indicators()`. Definining FreqAI features in `populate_indicators()` - will cause the algorithm to fail in live/dry mode. If the user wishes to add generalized features that are not associated with - a specific pair or timeframe, they should use the following structure inside `populate_any_indicators()` - (as exemplified in `freqtrade/templates/FreqaiExampleStrategy.py`): - - ```python - def populate_any_indicators(self, metadata, pair, df, tf, informative=None, coin="", set_generalized_indicators=False): - - ... - - # Add generalized indicators here (because in live, it will call only this function to populate - # indicators for retraining). Notice how we ensure not to add them multiple times by associating - # these generalized indicators to the basepair/timeframe - if set_generalized_indicators: - df['%-day_of_week'] = (df["date"].dt.dayofweek + 1) / 7 - df['%-hour_of_day'] = (df['date'].dt.hour + 1) / 25 - - # user adds targets here by prepending them with &- (see convention below) - # If user wishes to use multiple targets, a multioutput prediction model - # needs to be used such as templates/CatboostPredictionMultiModel.py - df["&-s_close"] = ( - df["close"] - .shift(-self.freqai_info["feature_parameters"]["label_period_candles"]) - .rolling(self.freqai_info["feature_parameters"]["label_period_candles"]) - .mean() - / df["close"] - - 1 - ) - ``` - - (Please see the example script located in `freqtrade/templates/FreqaiExampleStrategy.py` for a full example of `populate_any_indicators()`.) - -## Setting classifier targets - -FreqAI includes the `CatboostClassifier` via the flag `--freqaimodel CatboostClassifier`. The user should take care to set the classes using strings: - -```python -df['&s-up_or_down'] = np.where( df["close"].shift(-100) > df["close"], 'up', 'down') -``` - -Additionally, the example classifier models do not accommodate multiple labels, but they do allow multi-class classification within a single label column. - -## Running FreqAI - -There are two ways to train and deploy an adaptive machine learning model. FreqAI enables live deployment as well as backtesting analyses. In both cases, a model is trained periodically, as shown in the following figure. - -![freqai-window](assets/freqai_moving-window.jpg) - -### Running the model live - -FreqAI can be run dry/live using the following command: - -```bash -freqtrade trade --strategy FreqaiExampleStrategy --config config_freqai.example.json --freqaimodel LightGBMRegressor -``` - -By default, FreqAI will not find any existing models and will start by training a new one -based on the user's configuration settings. Following training, the model will be used to make predictions on incoming candles until a new model is available. New models are typically generated as often as possible, with FreqAI managing an internal queue of the coin pairs to try to keep all models equally up to date. FreqAI will always use the most recently trained model to make predictions on incoming live data. If the user does not want FreqAI to retrain new models as often as possible, they can set `live_retrain_hours` to tell FreqAI to wait at least that number of hours before training a new model. Additionally, the user can set `expired_hours` to tell FreqAI to avoid making predictions on models that are older than that number of hours. - -If the user wishes to start a dry/live run from a saved backtest model (or from a previously crashed dry/live session), the user only needs to reuse -the same `identifier` parameter: - -```json - "freqai": { - "identifier": "example", - "live_retrain_hours": 0.5 - } -``` - -In this case, although FreqAI will initiate with a -pre-trained model, it will still check to see how much time has elapsed since the model was trained, -and if a full `live_retrain_hours` has elapsed since the end of the loaded model, FreqAI will retrain. - -### Backtesting - -The FreqAI backtesting module can be executed with the following command: - -```bash -freqtrade backtesting --strategy FreqaiExampleStrategy --strategy-path freqtrade/templates --config config_examples/config_freqai.example.json --freqaimodel LightGBMRegressor --timerange 20210501-20210701 -``` - -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). - -If this command has never been executed with the existing config file, it will train a new model -for each pair, for each backtesting window within the expanded `--timerange`. - -!!! Note "Model reuse" - Once the training is completed, the user can execute the backtesting again with the same config file and - FreqAI will find the trained models and load them instead of spending time training. This is useful - if the user wants to tweak (or even hyperopt) buy and sell criteria inside the strategy. If the user - *wants* to retrain a new model with the same config file, then they should simply change the `identifier`. - This way, the user can return to using any model they wish by simply specifying the `identifier`. - ---- - -### Hyperopt - -Users can hyperopt using the same command as typical [hyperopt](hyperopt.md): - -```bash -freqtrade hyperopt --hyperopt-loss SharpeHyperOptLoss --strategy FreqaiExampleStrategy --freqaimodel LightGBMRegressor --strategy-path freqtrade/templates --config config_examples/config_freqai.example.json --timerange 20220428-20220507 -``` - -Users need to have the data pre-downloaded in the same fashion as if they were doing a FreqAI [backtest](#backtesting). In addition, users must consider some restrictions when trying to [Hyperopt](hyperopt.md) FreqAI strategies: - -- The `--analyze-per-epoch` hyperopt parameter is not compatible with FreqAI. -- It's not possible to hyperopt indicators in `populate_any_indicators()` function. This means that the user cannot optimize model parameters using hyperopt. Apart from this exception, it is possible to optimize all other [spaces](hyperopt.md#running-hyperopt-with-smaller-search-space). -- The [Backtesting](#backtesting) instructions also apply to Hyperopt. - -The best method for combining hyperopt and FreqAI is to focus on hyperopting entry/exit thresholds/criteria. Users need to focus on hyperopting parameters that are not used in their FreqAI features. For example, users should not try to hyperopt rolling window lengths in their feature creation, or any of their FreqAI config which changes predictions. In order to efficiently hyperopt the FreqAI strategy, FreqAI stores predictions as dataframes and reuses them. Hence the requirement to hyperopt entry/exit thresholds/criteria only. - -A good example of a hyperoptable parameter in FreqAI is a value for `DI_values` beyond which we consider outliers and below which we consider inliers: - -```python -di_max = IntParameter(low=1, high=20, default=10, space='buy', optimize=True, load=True) -dataframe['outlier'] = np.where(dataframe['DI_values'] > self.di_max.value/10, 1, 0) -``` - -Which would help the user understand the appropriate Dissimilarity Index values for their particular parameter space. - -### Deciding the size of the sliding training window and backtesting duration - -The user defines the backtesting timerange with the typical `--timerange` parameter in the -configuration file. The duration of the sliding training window is set by `train_period_days`, whilst -`backtest_period_days` is the sliding backtesting window, both in number of days (`backtest_period_days` can be -a float to indicate sub-daily retraining in live/dry mode). In the presented example config, -the user is asking FreqAI to use a training period of 30 days and backtest on the subsequent 7 days. -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`. - -!!! 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 -a certain number of hours old by setting the `expiration_hours` in the config file: - -```json - "freqai": { - "expiration_hours": 0.5, - } -``` - -In the presented example config, the user will only allow predictions on models that are less than 1/2 hours old. - -### Purging old model data - -FreqAI stores new model files each time it retrains. These files become obsolete as new models are trained and FreqAI adapts to new market conditions. Users planning to leave FreqAI running for extended periods of time with high frequency retraining should enable `purge_old_models` in their config: - -```json - "freqai": { - "purge_old_models": true, - } -``` - -This will automatically purge all models older than the two most recently trained ones. - -### Returning additional info from training - -The user may find that there are some important metrics that they'd like to return to the strategy at the end of each model training. -The user can include these metrics by assigning them to `dk.data['extra_returns_per_train']['my_new_value'] = XYZ` inside their custom prediction model class. FreqAI takes the `my_new_value` assigned in this dictionary and expands it to fit the return dataframe to the strategy. -The user can then use the value in the strategy with `dataframe['my_new_value']`. An example of how this is already used in FreqAI is -the `&*_mean` and `&*_std` values, which indicate the mean and standard deviation of the particular target (label) during the most recent training. -An example, where the user wants to use live metrics from the trade database, is shown below: - -```json - "freqai": { - "extra_returns_per_train": {"total_profit": 4} - } -``` - -The user needs to set the standard dictionary in the config so that FreqAI can return proper dataframe shapes. These values will likely be overridden by the prediction model, but in the case where the model has yet to set them, or needs a default initial value, this is the value that will be returned. - -### Setting up a follower - -The user can define: - -```json - "freqai": { - "follow_mode": true, - "identifier": "example" - } -``` - -to indicate to the bot that it should not train models, but instead should look for models trained by a leader with the same `identifier`. In this example, the user has a leader bot with the `identifier: "example"`. The leader bot is already running or launching simultaneously as the follower. -The follower will load models created by the leader and inference them to obtain predictions. - -## Data manipulation techniques - -### Feature normalization - -The feature set created by the user is automatically normalized to the training data. This includes all test data and unseen prediction data (dry/live/backtest). - -### Reducing data dimensionality with Principal Component Analysis - -Users can reduce the dimensionality of their features by activating the `principal_component_analysis` in the config: - -```json - "freqai": { - "feature_parameters" : { - "principal_component_analysis": true - } - } -``` - -This will perform PCA on the features and reduce the dimensionality of the data so that the explained variance of the data set is >= 0.999. - -### Stratifying the data for training and testing the model - -The user can stratify (group) the training/testing data using: - -```json - "freqai": { - "feature_parameters" : { - "stratify_training_data": 3 - } - } -``` - -This will split the data chronologically so that every Xth data point is used to test the model after training. In the -example above, the user is asking for every third data point in the dataframe to be used for -testing; the other points are used for training. - -The test data is used to evaluate the performance of the model after training. If the test score is high, the model is able to capture the behavior of the data well. If the test score is low, either the model 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` - -The `inlier_metric` is a metric aimed at quantifying how different a prediction data point is from the most recent historic data points. - -User can set `inlier_metric_window` to set the look back window. FreqAI will compute the distance between the present prediction point and each of the previous data points (total of `inlier_metric_window` points). - -This function goes one step further - during training, it computes the `inlier_metric` for all training data points and builds weibull distributions for each each lookback point. The cumulative distribution function for the weibull distribution is used to produce a quantile for each of the data points. The quantiles for each lookback point are averaged to create the `inlier_metric`. - -FreqAI adds this `inlier_metric` score to the training features! In other words, your model is trained to recognize how this temporal inlier metric is related to the user set labels. - -This function does **not** remove outliers from the data set. - -### Controlling the model learning process - -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. - -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: - -$$ W_i = \exp(\frac{-i}{\alpha*n}) $$ - -where $W_i$ is the weight of data point $i$ in a total set of $n$ data points. Below is a figure showing the effect of different weight factors on the data points (candles) in a feature set. - -![weight-factor](assets/freqai_weight-factor.jpg) - -`train_test_split()` has a parameters called `shuffle` that allows the user to keep the data unshuffled. This is particularly useful to avoid biasing training with temporally auto-correlated data. - -Finally, `label_period_candles` defines the offset (number of candles into the future) used for the `labels`. In the presented example config, -the user is asking for `labels` that are 24 candles in the future. - -### Outlier removal - -#### Removing outliers with the Dissimilarity Index - -The user can tell FreqAI to remove outlier data points from the training/test data sets using a Dissimilarity Index by including the following statement in the config: - -```json - "freqai": { - "feature_parameters" : { - "DI_threshold": 1 - } - } -``` - -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: - -$$ d_{ab} = \sqrt{\sum_{j=1}^p(X_{a,j}-X_{b,j})^2} $$ - -where $d_{ab}$ is the distance between the normalized points $a$ and $b$. $p$ is the number of features, i.e., the length of the vector $X$. The characteristic distance, $\overline{d}$ for a set of training data points is simply the mean of the average distances: - -$$ \overline{d} = \sum_{a=1}^n(\sum_{b=1}^n(d_{ab}/n)/n) $$ - -$\overline{d}$ quantifies the spread of the training data, which is compared to the distance between a new prediction feature vectors, $X_k$ and all the training data: - -$$ d_k = \arg \min d_{k,i} $$ - -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. - -Below is a figure that describes the DI for a 3D data set. - -![DI](assets/freqai_DI.jpg) - -#### Removing outliers using a Support Vector Machine (SVM) - -The user can tell FreqAI to remove outlier data points from the training/test data sets using a SVM by setting: - -```json - "freqai": { - "feature_parameters" : { - "use_SVM_to_remove_outliers": true - } - } -``` - -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. - -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. - -#### Removing outliers with DBSCAN - -The user can configure FreqAI to use DBSCAN to cluster and remove outliers from the training/test data set or incoming outliers from predictions, by activating `use_DBSCAN_to_remove_outliers` in the config: - -```json - "freqai": { - "feature_parameters" : { - "use_DBSCAN_to_remove_outliers": true - } - } -``` - -DBSCAN is an unsupervised machine learning algorithm that clusters data without needing to know how many clusters there should be. - -Given a number of data points $N$, and a distance $\varepsilon$, DBSCAN clusters the data set by setting all data points that have $N-1$ other data points within a distance of $\varepsilon$ as *core points*. A data point that is within a distance of $\varepsilon$ from a *core point* but that does not have $N-1$ other data points within a distance of $\varepsilon$ from itself is considered an *edge point*. A cluster is then the collection of *core points* and *edge points*. Data points that have no other data points at a distance $<\varepsilon$ are considered outliers. The figure below shows a cluster with $N = 3$. - -![dbscan](assets/freqai_dbscan.jpg) - -FreqAI uses `sklearn.cluster.DBSCAN` (details are available on scikit-learn's webpage [here](#https://scikit-learn.org/stable/modules/generated/sklearn.cluster.DBSCAN.html)) 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 - -### Common pitfalls - -FreqAI cannot be combined with dynamic `VolumePairlists` (or any pairlist filter that adds and removes pairs dynamically). -This is for performance reasons - FreqAI relies on making quick predictions/retrains. To do this effectively, -it needs to download all the training data at the beginning of a dry/live instance. FreqAI stores and appends -new candles automatically for future retrains. This means that if new pairs arrive later in the dry run due to a volume pairlist, it will not have the data ready. However, FreqAI does work with the `ShufflePairlist` or a `VolumePairlist` which keeps the total pairlist constant (but reorders the pairs according to volume). +For docker users, a dedicated tag with freqAI dependencies is available as `:freqai`. As such - you can replace the image line in your docker-compose file with `image: freqtradeorg/freqtrade:develop_freqai`. This image contains the regular freqAI dependencies. Similar to native installs, Catboost will not be available on ARM based devices. ## Credits @@ -804,6 +81,9 @@ Elin Törnquist @th0rntwig Code review, software architecture brainstorming: @xmatthias +Development: +Wagner Costa @wagnercosta + Beta testing and bug reporting: -@bloodhunter4rc, Salah Lamkadem @ikonx, @ken11o2, @longyu, @paranoidandy, @smidelis, @smarm, -Juha Nykänen @suikula, Wagner Costa @wagnercosta +Stefan Gehring @bloodhunter4rc, @longyu, @paranoidandy, @smidelis, Ryan McMullan @smarmau, +Juha Nykänen @suikula, Johan van der Vlugt @jooopiert, Richárd Józsa @richardjosza diff --git a/mkdocs.yml b/mkdocs.yml index 2e5e0c8c9..95106ae1e 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -23,6 +23,12 @@ nav: - Data Downloading: data-download.md - Backtesting: backtesting.md - Hyperopt: hyperopt.md + - FreqAI: + - Introduction: freqai.md + - Setting-up: freqai-configuration.md + - Feature engineering: freqai-feature-engineering.md + - Running: freqai-running.md + - Data handling: freqai-data-handling.md - Short / Leverage: leverage.md - Utility Sub-commands: utils.md - Plotting: plotting.md @@ -35,7 +41,6 @@ nav: - Advanced Post-installation Tasks: advanced-setup.md - Advanced Strategy: strategy-advanced.md - Advanced Hyperopt: advanced-hyperopt.md - - FreqAI: freqai.md - Edge Positioning: edge.md - Sandbox Testing: sandbox-testing.md - FAQ: faq.md From ede282392f1d9b497da91825017439555646119e Mon Sep 17 00:00:00 2001 From: robcaulk Date: Sun, 11 Sep 2022 17:50:50 +0200 Subject: [PATCH 2/9] add a developers guide --- docs/assets/freqai_algorithm-diagram.jpg | Bin 0 -> 491920 bytes docs/freqai-configuration.md | 2 +- docs/freqai-developers.md | 28 +++++++++++++++++++++++ mkdocs.yml | 1 + 4 files changed, 30 insertions(+), 1 deletion(-) create mode 100644 docs/assets/freqai_algorithm-diagram.jpg create mode 100644 docs/freqai-developers.md diff --git a/docs/assets/freqai_algorithm-diagram.jpg b/docs/assets/freqai_algorithm-diagram.jpg new file mode 100644 index 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ze;j(f?!|%~yC2%|_pt?)u=KyTojmX4`IP6Iif>xJne)xBT7BdR9QZ4EwW5Lf$Klsy z`@J3iB+S*dp9tK3Gqv~p!9OpA_C%2!ILCR}XX?LawAg#6{%ezrMa7SQ?G{U8-_7}U zuR7m$`@GxdbDwYfe7o}P9@+||J}j7bsQ#f0r%6+viAVL}Uos!#`bY6-7*I70EaCbX QKgTZk0C0lpNA>@k0D^-PmjD0& literal 0 HcmV?d00001 diff --git a/docs/freqai-configuration.md b/docs/freqai-configuration.md index 5190c59c9..706524744 100644 --- a/docs/freqai-configuration.md +++ b/docs/freqai-configuration.md @@ -130,7 +130,7 @@ The FreqAI strategy requires the user to include the following lines of code in ``` -Notice how the `populate_any_indicators()` is where the user adds their own features ([more information](freqai-feature_engineering.md#feature-engineering)) and labels ([more information](freqai-running.md#setting-classifier-targets)). See a full example at `templates/FreqaiExampleStrategy.py`. +Notice how the `populate_any_indicators()` is where the user adds their own features ([more information](freqai-feature-engineering.md#feature-engineering)) and labels ([more information](freqai-running.md#setting-classifier-targets)). See a full example at `templates/FreqaiExampleStrategy.py`. Another structure to consider is the location of the labels at the bottom of the example function (below `if set_generalized_indicators:`). This is where the user will add single features and labels to their feature set to avoid duplication of them from various configuration parameters that multiply the feature set, such as `include_timeframes`. diff --git a/docs/freqai-developers.md b/docs/freqai-developers.md new file mode 100644 index 000000000..01cdf1e71 --- /dev/null +++ b/docs/freqai-developers.md @@ -0,0 +1,28 @@ +# Development + +The class structure and details algorithmic overview is depicted in the following diagram: + +![image](assets/freqai_algorithm-diagram.jpg) + +As shown, there are three distinct objects comprising `FreqAI`: + +* IFreqaiModel + * Singular persistent object containing all the necessary logic to collect data, store data, process data, engineer features, run training, and inference models. +* FreqaiDataKitchen + * A non-persistent object which is created uniquely for each unique asset/model. Beyond metadata, it also contains a variety of data processing tools. +* FreqaiDataDrawer + * Singular persistent object containing all the historical predictions, models, and save/load methods. + +There are a variety of built-in prediction models which inherit directly from `IFreqaiModel` including: + +* CatboostRegressor +* CatboostRegressorMultiTarget +* CatboostClassifier +* LightGBMRegressor +* LightGBMRegressorMultiTarget +* LightGBMClassifier +* XGBoostRegressor +* XGBoostRegressorMultiTarget +* XGBoostClassifier + +Each of these have full access to all methods in `IFreqaiModel`. And can therefore, override any of those functions at will. However, advanced users will likely stick to overriding `fit()`, `train()`, `predict()`, and `data_cleaning_train/predict()`. \ No newline at end of file diff --git a/mkdocs.yml b/mkdocs.yml index 95106ae1e..726924b78 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -29,6 +29,7 @@ nav: - Feature engineering: freqai-feature-engineering.md - Running: freqai-running.md - Data handling: freqai-data-handling.md + - Developer guide: freqai-developers.md - Short / Leverage: leverage.md - Utility Sub-commands: utils.md - Plotting: plotting.md From c210d6614cb9582348287fb5de2586f83b6e7e65 Mon Sep 17 00:00:00 2001 From: th0rntwig Date: Sat, 17 Sep 2022 17:43:39 +0200 Subject: [PATCH 3/9] Reorganise multipage doc --- docs/assets/freqai_algorithm-diagram.jpg | Bin 491920 -> 517742 bytes docs/freqai-configuration.md | 164 +++++++---------------- docs/freqai-data-handling.md | 102 ++++++-------- docs/freqai-developers.md | 29 ++-- docs/freqai-feature-engineering.md | 149 +++----------------- 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ze;j(f?!|%~yC2%|_pt?)u=KyTojmX4`IP6Iif>xJne)xBT7BdR9QZ4EwW5Lf$Klsy z`@J3iB+S*dp9tK3Gqv~p!9OpA_C%2!ILCR}XX?LawAg#6{%ezrMa7SQ?G{U8-_7}U zuR7m$`@GxdbDwYfe7o}P9@+||J}j7bsQ#f0r%6+viAVL}Uos!#`bY6-7*I70EaCbX QKgTZk0C0lpNA>@k0D^-PmjD0& diff --git a/docs/freqai-configuration.md b/docs/freqai-configuration.md index 706524744..4e41ff8b3 100644 --- a/docs/freqai-configuration.md +++ b/docs/freqai-configuration.md @@ -1,8 +1,10 @@ # Configuration +FreqAI is configured through the typical [Freqtrade config file](configuration.md) and the standard [Freqtrade strategy](strategy-customization.md). Examples of a FreqAI config and strategy file can be found in `config_examples/config_freqai.example.json` and `freqtrade/templates/FreqaiExampleStrategy.py`, respectively. + ## Setting up the configuration file -The user interface is isolated to the typical Freqtrade config file. Although there are plenty of additional parameters that a user can choose from, as highlighted in the [parameter table](#parameter-table), a FreqAI config must at minimum include the following parameters (the example inputs are not requires): + Although there are plenty of additional parameters that a user can choose from, as highlighted in the [parameter table](freqai-parameter-table.md#parameter-table), a FreqAI config must at minimum include the following parameters (the parameter values are only examples): ```json "freqai": { @@ -30,29 +32,17 @@ The user interface is isolated to the typical Freqtrade config file. Although th }, } ``` +A full example config is available in `config_examples/config_freqai.example.json`. ## Building a FreqAI strategy -The FreqAI strategy requires the user to include the following lines of code in the standard Freqtrade strategy: +The FreqAI strategy requires the user to include the following lines of code in the standard [Freqtrade strategy](strategy-customization.md): ```python # user should define the maximum startup candle count (the largest number of candles # passed to any single indicator) startup_candle_count: int = 20 - def informative_pairs(self): - whitelist_pairs = self.dp.current_whitelist() - corr_pairs = self.config["freqai"]["feature_parameters"]["include_corr_pairlist"] - informative_pairs = [] - for tf in self.config["freqai"]["feature_parameters"]["include_timeframes"]: - for pair in whitelist_pairs: - informative_pairs.append((pair, tf)) - for pair in corr_pairs: - if pair in whitelist_pairs: - continue # avoid duplication - informative_pairs.append((pair, tf)) - return informative_pairs - def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # the model will return all labels created by user in `populate_any_indicators` @@ -130,14 +120,16 @@ The FreqAI strategy requires the user to include the following lines of code in ``` -Notice how the `populate_any_indicators()` is where the user adds their own features ([more information](freqai-feature-engineering.md#feature-engineering)) and labels ([more information](freqai-running.md#setting-classifier-targets)). See a full example at `templates/FreqaiExampleStrategy.py`. +Notice how the `populate_any_indicators()` is where the user adds their own [features](freqai-feature-engineering.md#feature-engineering) and labels/tragets. A full example strategy is available in `templates/FreqaiExampleStrategy.py`. -Another structure to consider is the location of the labels at the bottom of the example function (below `if set_generalized_indicators:`). This is where the user will add single features and labels to their feature set to avoid duplication of them from various configuration parameters that multiply the feature set, such as `include_timeframes`. +Notice also the location of the labels under `if set_generalized_indicators:` at the bottom of the example. This is where the user will add single features and labels/targets to their feature set to avoid duplication of them from various configuration parameters that multiply the feature set, such as `include_timeframes`. + +!!! Note + The `self.freqai.start()` function cannot be called outside the `populate_indicators()`. !!! Note Features **must** be defined in `populate_any_indicators()`. Defining FreqAI features in `populate_indicators()` - will cause the algorithm to fail in live/dry mode. If the user wishes to add generalized features that are not associated with - a specific pair or timeframe, they should use the following structure inside `populate_any_indicators()` + will cause the algorithm to fail in live/dry mode. If the user wishes to add generalized features that are not associated with a specific pair or timeframe, they should use the following structure inside `populate_any_indicators()` (as exemplified in `freqtrade/templates/FreqaiExampleStrategy.py`): ```python @@ -165,96 +157,7 @@ Another structure to consider is the location of the labels at the bottom of the ) ``` - (Please see the example script located in `freqtrade/templates/FreqaiExampleStrategy.py` for a full example of `populate_any_indicators()`.) - -*Important*: The `self.freqai.start()` function cannot be called outside the `populate_indicators()`. - -## Setting the `startup_candle_count` -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`). - -!!! Note - 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: - - ``` - 2022-08-31 15:14:04 - freqtrade.freqai.data_kitchen - INFO - dropped 0 training points due to NaNs in populated dataset 4319. - ``` - -## Creating a dynamic target threshold - -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. - -```python -dataframe["target_roi"] = dataframe["&-s_close_mean"] + dataframe["&-s_close_std"] * 1.25 -dataframe["sell_roi"] = dataframe["&-s_close_mean"] - dataframe["&-s_close_std"] * 1.25 -``` - -If the user wishes to consider the population -of *historical predictions* for creating the dynamic target instead of the trained labels, (as discussed above) the user -can do so by setting `fit_live_prediction_candles` in the config to the number of historical prediction candles -the user wishes to use to generate target statistics. - -```json - "freqai": { - "fit_live_prediction_candles": 300, - } -``` - -If the user sets this value, FreqAI will initially use the predictions from the training data -and subsequently begin introducing real prediction data as it is generated. FreqAI will save -this historical data to be reloaded if the user stops and restarts a model with the same `identifier`. - - -## Parameter table - -The table below will list all configuration parameters available for FreqAI, presented in the same order as `config_examples/config_freqai.example.json`. - -Mandatory parameters are marked as **Required**, which means that they are required to be set in one of the possible ways. - -| Parameter | Description | -|------------|-------------| -| | **General configuration parameters** -| `freqai` | **Required.**
The parent dictionary containing all the parameters for controlling FreqAI.
**Datatype:** Dictionary. -| `purge_old_models` | Delete obsolete models (otherwise, all historic models will remain on disk).
**Datatype:** Boolean. Default: `False`. -| `train_period_days` | **Required.**
Number of days to use for the training data (width of the sliding window).
**Datatype:** Positive integer. -| `backtest_period_days` | **Required.**
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.
**Datatype:** Float. -| `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`.
**Datatype:** Boolean. Default: `False`. -| `identifier` | **Required.**
A unique name for the current model. This can be reused to reload pre-trained models/data.
**Datatype:** String. -| `live_retrain_hours` | Frequency of retraining during dry/live runs.
Default set to 0, which means the model will retrain as often as possible.
**Datatype:** Float > 0. -| `expiration_hours` | Avoid making predictions if a model is more than `expiration_hours` old.
Defaults set to 0, which means models never expire.
**Datatype:** Positive integer. -| `fit_live_predictions_candles` | Number of historical candles to use for computing target (label) statistics from prediction data, instead of from the training data set.
**Datatype:** Positive integer. -| `follow_mode` | If true, this instance of FreqAI will look for models associated with `identifier` and load those for inferencing. A `follower` will **not** train new models.
**Datatype:** Boolean. Default: `False`. -| `continual_learning` | If true, FreqAI will start training new models from the final state of the most recently trained model.
**Datatype:** Boolean. Default: `False`. -| | **Feature parameters** -| `feature_parameters` | A dictionary containing the parameters used to engineer the feature set. Details and examples are shown [here](#feature-engineering).
**Datatype:** Dictionary. -| `include_timeframes` | A list of timeframes that all indicators in `populate_any_indicators` will be created for. The list is added as features to the base asset feature set.
**Datatype:** List of timeframes (strings). -| `include_corr_pairlist` | A list of correlated coins that FreqAI will add as additional features to all `pair_whitelist` coins. All indicators set in `populate_any_indicators` during feature engineering (see details [here](#feature-engineering)) will be created for each coin in this list, and that set of features is added to the base asset feature set.
**Datatype:** List of assets (strings). -| `label_period_candles` | Number of candles into the future that the labels are created for. This is used in `populate_any_indicators` (see `templates/FreqaiExampleStrategy.py` for detailed usage). The user can create custom labels, making use of this parameter or not.
**Datatype:** Positive integer. -| `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.
**Datatype:** Positive integer. -| `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).
**Datatype:** Positive float (typically < 1). -| `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
**Datatype:** positive integer. -| `indicator_periods_candles` | Calculate indicators for `indicator_periods_candles` time periods and add them to the feature set.
**Datatype:** List of positive integers. -| `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)
**Datatype:** Positive integer. -| `principal_component_analysis` | Automatically reduce the dimensionality of the data set using Principal Component Analysis. See details about how it works [here](#reducing-data-dimensionality-with-principal-component-analysis)
**Datatype:** Boolean. -| `DI_threshold` | Activates the Dissimilarity Index for outlier detection when > 0. See details about how it works [here](#removing-outliers-with-the-dissimilarity-index).
**Datatype:** Positive float (typically < 1). -| `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).
**Datatype:** Boolean. -| `svm_params` | All parameters available in Sklearn's `SGDOneClassSVM()`. See details about some select parameters [here](#removing-outliers-using-a-support-vector-machine-svm).
**Datatype:** Dictionary. -| `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).
**Datatype:** Boolean. -| `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)
**Datatype:** int. Default: 0 -| `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.
**Datatype:** int. Default: 0 -| `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.
**Datatype:** Float. Default: `30` -| `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.
**Datatype:** Boolean. Default: False -| | **Data split parameters** -| `data_split_parameters` | Include any additional parameters available from Scikit-learn `test_train_split()`, which are shown [here](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html) (external website).
**Datatype:** Dictionary. -| `test_size` | Fraction of data that should be used for testing instead of training.
**Datatype:** Positive float < 1. -| `shuffle` | Shuffle the training data points during training. Typically, for time-series forecasting, this is set to `False`.
**Datatype:** Boolean. -| | **Model training parameters** -| `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.
**Datatype:** Dictionary. -| `n_estimators` | The number of boosted trees to fit in regression.
**Datatype:** Integer. -| `learning_rate` | Boosting learning rate during regression.
**Datatype:** Float. -| `n_jobs`, `thread_count`, `task_type` | Set the number of threads for parallel processing and the `task_type` (`gpu` or `cpu`). Different model libraries use different parameter names.
**Datatype:** Float. -| | **Extraneous parameters** -| `keras` | If your model makes use of Keras (typical for Tensorflow-based prediction models), activate this flag so that the model save/loading follows Keras standards.
**Datatype:** Boolean. Default: `False`. -| `conv_width` | The width of a convolutional neural network input tensor. This replaces the need for shifting candles (`include_shifted_candles`) by feeding in historical data points as the second dimension of the tensor. Technically, this parameter can also be used for regressors, but it only adds computational overhead and does not change the model training/prediction.
**Datatype:** Integer. Default: 2. + Please see the example script located in `freqtrade/templates/FreqaiExampleStrategy.py` for a full example of `populate_any_indicators()`. ## Important dataframe key patterns @@ -263,16 +166,45 @@ Below are the values the user can expect to include/use inside a typical strateg | DataFrame Key | Description | |------------|-------------| | `df['&*']` | Any dataframe column prepended with `&` in `populate_any_indicators()` is treated as a training target (label) inside FreqAI (typically following the naming convention `&-s*`). The names of these dataframe columns are fed back to the user as the predictions. For example, if the user wishes to predict the price change in the next 40 candles (similar to `templates/FreqaiExampleStrategy.py`), they set `df['&-s_close']`. FreqAI makes the predictions and gives them back under the same key (`df['&-s_close']`) to be used in `populate_entry/exit_trend()`.
**Datatype:** Depends on the output of the model. -| `df['&*_std/mean']` | Standard deviation and mean values of the user-defined labels during training (or live tracking with `fit_live_predictions_candles`). Commonly used to understand the rarity of a prediction (use the z-score as shown in `templates/FreqaiExampleStrategy.py` to evaluate how often a particular prediction was observed during training or historically with `fit_live_predictions_candles`).
**Datatype:** Float. -| `df['do_predict']` | Indication of an outlier data point. The return value is integer between -1 and 2, which lets the user know if the prediction is trustworthy or not. `do_predict==1` means the prediction is trustworthy. If the Dissimilarity Index (DI, see details [here](#removing-outliers-with-the-dissimilarity-index)) of the input data point is above the user-defined threshold, FreqAI will subtract 1 from `do_predict`, resulting in `do_predict==0`. If `use_SVM_to_remove_outliers()` is active, the Support Vector Machine (SVM) may also detect outliers in training and prediction data. In this case, the SVM will also subtract 1 from `do_predict`. If the input data point was considered an outlier by the SVM but not by the DI, the result will be `do_predict==0`. If both the DI and the SVM considers the input data point to be an outlier, the result will be `do_predict==-1`. A particular case is when `do_predict == 2`, which means that the model has expired due to exceeding `expired_hours`.
**Datatype:** Integer between -1 and 2. -| `df['DI_values']` | Dissimilarity Index values are proxies to the level of confidence FreqAI has in the prediction. A lower DI means the prediction is close to the training data, i.e., higher prediction confidence.
**Datatype:** Float. -| `df['%*']` | Any dataframe column prepended with `%` in `populate_any_indicators()` is treated as a training feature. For example, the user can include the RSI in the training feature set (similar to in `templates/FreqaiExampleStrategy.py`) by setting `df['%-rsi']`. See more details on how this is done [here](#feature-engineering).
**Note**: Since the number of features prepended with `%` can multiply very quickly (10s of thousands of features is easily engineered using the multiplictative functionality described in the `feature_parameters` table shown above), these features are removed from the dataframe upon return from FreqAI. If the user wishes to keep a particular type of feature for plotting purposes, they can prepend it with `%%`.
**Datatype:** Depends on the output of the model. +| `df['&*_std/mean']` | Standard deviation and mean values of the user defined labels during training (or live tracking with `fit_live_predictions_candles`). Commonly used to understand the rarity of a prediction (use the z-score as shown in `templates/FreqaiExampleStrategy.py` and explained [here](#creating-a-dynamic-target-threshold) to evaluate how often a particular prediction was observed during training or historically with `fit_live_predictions_candles`).
**Datatype:** Float. +| `df['do_predict']` | Indication of an outlier data point. The return value is integer between -1 and 2, which lets the user know if the prediction is trustworthy or not. `do_predict==1` means that the prediction is trustworthy. If the Dissimilarity Index (DI, see details [here](freqai-outlier-detection.md#identifying-outliers-with-the-dissimilarity-index)) of the input data point is above the user-defined threshold, FreqAI will subtract 1 from `do_predict`, resulting in `do_predict==0`. If `use_SVM_to_remove_outliers()` is active, the Support Vector Machine (SVM, see details [here](freqai-outlier-detection.md#identifying-outliers-using-a-support-vector-machine-svm)) may also detect outliers in training and prediction data. In this case, the SVM will also subtract 1 from `do_predict`. If the input data point was considered an outlier by the SVM but not by the DI, or vice versa, the result will be `do_predict==0`. If both the DI and the SVM considers the input data point to be an outlier, the result will be `do_predict==-1`. A particular case is when `do_predict == 2`, which means that the model has expired due to exceeding `expired_hours`.
**Datatype:** Integer between -1 and 2. +| `df['DI_values']` | Dissimilarity Index (DI) values are proxies for the level of confidence FreqAI has in the prediction. A lower DI means the prediction is close to the training data, i.e., higher prediction confidence. See details about the DI [here](freqai-outlier-detection.md#identifying-outliers-with-the-dissimilarity-index).
**Datatype:** Float. +| `df['%*']` | Any dataframe column prepended with `%` in `populate_any_indicators()` is treated as a training feature. For example, the user can include the RSI in the training feature set (similar to in `templates/FreqaiExampleStrategy.py`) by setting `df['%-rsi']`. See more details on how this is done [here](freqai-feature-engineering.md).
**Note:** Since the number of features prepended with `%` can multiply very quickly (10s of thousands of features is easily engineered using the multiplictative functionality described in the `feature_parameters` table shown above), these features are removed from the dataframe upon return from FreqAI. If the user wishes to keep a particular type of feature for plotting purposes, they can prepend it with `%%`.
**Datatype:** Depends on the output of the model. -## Building a custom prediction model +## Setting the `startup_candle_count` +The user needs to take care to set the `startup_candle_count` in their strategy the same way as in the standard Freqtrade strategy (see details [here](strategy-customization.md#strategy-startup-period)). This value is used by Freqtrade to ensure that a sufficient amount of data is provided when calling the `dataprovider`, to avoid any NaNs at the beginning of the first training. The user 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 presented example, `startup_candle_count` is 20 since this is the maximum value in `indicators_periods_candles`. -FreqAI has multiple example prediction model libraries, such as `Catboost` regression (`freqai/prediction_models/CatboostRegressor.py`), `LightGBM`, `XGBoost` regression. However, the user can customize and create their own prediction models using the `IFreqaiModel` class. The user is encouraged to inherit `fit()`, `train()` and `predict()` to let them customize various aspects of their training procedures. +!!! Note + There are instances where the talib functions actually require more data than just the passed `period` or else the feature dataset gets populated with NaNs. Anecdotally, multiplying the `startup_candle_count` by 2 always leads to a fully NaN free training dataset. Hence, it is typically safest to multiply the expected `startup_candle_count` by 2. Look out for this log message to confirm that the data is clean: -## Setting classifier targets + ``` + 2022-08-31 15:14:04 - freqtrade.freqai.data_kitchen - INFO - dropped 0 training points due to NaNs in populated dataset 4319. + ``` + +## Creating a dynamic target threshold + +Deciding when to enter or exit a trade can be done in a dynamic way to reflect current market conditions. FreqAI allows the user to return additional information from the training of a model (more info [here](freqai-data-handling.md#returning-additional-info-from-training)). For example, the `&*_std/mean` return values describe the statistical distribution of the user defined target/label *during the most recent training*. Comparing a given prediction to these values allows the user to know the rarity of the prediction. In `templates/FreqaiExampleStrategy.py`, the `target_roi` and `sell_roi` are defined to be 1.25 z-scores away from the mean which causes predictions that are closer to the mean to be filtered out. + +```python +dataframe["target_roi"] = dataframe["&-s_close_mean"] + dataframe["&-s_close_std"] * 1.25 +dataframe["sell_roi"] = dataframe["&-s_close_mean"] - dataframe["&-s_close_std"] * 1.25 +``` + +If the user wishes to consider the population of *historical predictions* for creating the dynamic target instead of information from the training as discussed above, the user can do so by setting `fit_live_prediction_candles` in the config to the number of historical prediction candles the user wishes to use to generate target statistics. + +```json + "freqai": { + "fit_live_prediction_candles": 300, + } +``` + +If the user sets this value, FreqAI will initially use the predictions from the training data and subsequently begin introducing real prediction data as it is generated. FreqAI will save this historical data to be reloaded if the user stops and restarts a model with the same `identifier`. + +## Using different prediction models + +FreqAI has multiple example prediction model libraries that are ready to be used as is via the flag `--freqaimodel`. These libraries include `Catboost`, `LightGBM`, and `XGBoost` regression, classification, and multi-target models, and can be found in `freqai/prediction_models/`. For the more interested user, it is possible to customize and create their own prediction models using the `IFreqaiModel` class. The user is encouraged to inherit `fit()`, `train()`, and `predict()` to let these customize various aspects of their training procedures. + +### Setting classifier targets `FreqAI` includes a variety of classifiers, such as the `CatboostClassifier` via the flag `--freqaimodel CatboostClassifier`. If the user elects to use a classifier, they must ensure the classes are set using strings. For example: diff --git a/docs/freqai-data-handling.md b/docs/freqai-data-handling.md index 7ed51599d..64983744e 100644 --- a/docs/freqai-data-handling.md +++ b/docs/freqai-data-handling.md @@ -1,31 +1,23 @@ # Data handling -`FreqAI` aims to organize prediction data, model files, and meta data in a way that automates crash resilient reloading and simplifies post-processing. Data is organized into `user_data_dir/models/` and contains all the data associated with the trainings and backtests. This file structure is heavily controlled and inferenced by the `FreqaiDataKitchen()` and should therefore not be manually modified. +`FreqAI` aims to organize model files, prediction data, and meta data in a way that simplifies post-processing and enhances crash recililence by automatic data reloading. The data is saved in a file structure,`user_data_dir/models/`, which contains all the data associated with the trainings and backtests. The `FreqaiDataKitchen()` relies heavily on the file structure for proper training and inferencing and should therefore not be manually modified. ## File structure -The file structure is automatically generated based on the user set `identifier` in the configuration file. The following structure shows where the data is stored for post processing: +The file structure is automatically generated based on the model `identifier` set by the user in the [config](freqai-configuration.md#setting-up-the-configuration-file). The following structure shows where the data is stored for post processing: -* `config_*.json` - * a copy of the user submitted configuration file -* `historic_predictions.pkl` - * all historic predictions generated during the lifetime of the `identifier` live deployment. These are also used to reload the model after a crash or a config change. A backup file is always held incase of corruption on the main file - FreqAI automatically detects corruption and replaces the corrupted file with the backup. -* `pair_dictionary.json` - * contains the training queue as well as the location of the most recently trained model on disk. -* `sub-train-*_TIMESTAMP` - * a folder containing all the files associated with a single model, such as: - * `*_metadata.json` - * metadata for the model, such as normalization max/mins, expected training feature list, etc. - * `*_model.*` - * the model file saved to disk for reloading from crash. Can be `joblib` (typical boosting libs), `zip` (stable_baselines), `hd5` (keras type), etc. - * `*_pca_object.pkl` - * the PCA transform (if the user set `principal_component_analysis: true` in their config) which will be used to transform unseen prediction features. - * `*_svm_model.pkl` - * the Support Vector Machine model which is used to detect outliers in unseen prediction features. - * `*_trained_df.pkl` - * the dataframe containing all the training features used to train the particular model. This is used for computing the Dissimilarity Index and can be used for post-processing. - * `*_trained_dates.df.pkl` - * dates associated with the `trained_df.pkl`, useful for post-processing. +| Structure | Description | +|-----------|-------------| +| `config_*.json` | A copy of the model specific configuration file. | +| `historic_predictions.pkl` | A file containing all historic predictions generated during the lifetime of the `identifier` model during live deployment. `historic_predictions.pkl` is used to reload the model after a crash or a config change. A backup file is always held incase of corruption on the main file. **FreqAI automatically detects corruption and replaces the corrupted file with the backup**. | +| `pair_dictionary.json` | A file containing the training queue as well as the on disk location of the most recently trained model. | +| `sub-train-*_TIMESTAMP` | A folder containing all the files associated with a single model, such as:
+|| `*_metadata.json` - Metadata for the model, such as normalization max/mins, expected training feature list, etc.
+|| `*_model.*` - The model file saved to disk for reloading from a crash. Can be `joblib` (typical boosting libs), `zip` (stable_baselines), `hd5` (keras type), etc.
+|| `*_pca_object.pkl` - The [Principal component analysis (PCA)](freqai-feature-engineering.md#data-dimensionality-reduction-with-principal-component-analysis) transform (if the user set `principal_component_analysis: true` in their config) which will be used to transform unseen prediction features.
+|| `*_svm_model.pkl` - The [Support Vector Machine (SVM)](freqai-outlier-detection.md#identifying-outliers-using-a-support-vector-machine-svm) model which is used to detect outliers in unseen prediction features.
+|| `*_trained_df.pkl` - The dataframe containing all the training features used to train the `identifier` model. This is used for computing the [Dissimilarity Index (DI)](freqai-outlier-detection.md#identifying-outliers-with-the-dissimilarity-index-di) and can also be used for post-processing.
+|| `*_trained_dates.df.pkl` - The dates associated with the `trained_df.pkl`, which is useful for post-processing. | The example file structure would look like this: @@ -66,43 +58,19 @@ The example file structure would look like this: │   └── cb_ada_1662821399_trained_df.pkl ``` -## Backtesting - -When users run a backtest, `FreqAI` will automatically save the predictions to be reused for future runs under the same `identifier`. This is a performance enhancement geared towards enabling high-level hyperopting of entry/exit criteria. That means the user will see an additional directory created in their `unique-id` folder called `predictions` which contains all the predictions stored in `hdf` format. - -If users wish to change their features, they **must** use a new identifier which will signal to `FreqAI` to train new models. If users wish to save the models generated during a particular backtest so they can start a live deployment without an initial training, they must set `save_backtest_models` to `True` in their configuration file. - -### 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](freqai-configuration.md#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). - ## Live deployments -### Auto data download +### Automatic data download -`FreqAI` automatically downloads and proper amount of data to ensure it can train a model using the user defined `train_period_days` and the strategy defined `startup_candle_count`. +FreqAI automatically downloads the proper amount of data needed to ensure training of a model through the user defined `train_period_days` and `startup_candle_count` (see the [parameter table](freqai-parameter-table.md) for detailed descriptions of these parameters). -### Historical predictions +### Saving prediction data -The historical predictions are collected for the life-time of a single `identifier` and stored in `historical_predictions.pkl` - -### 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 -a certain number of hours old by setting the `expiration_hours` in the config file: - -```json - "freqai": { - "expiration_hours": 0.5, - } -``` - -In the presented example config, the user will only allow predictions on models that are less than 1/2 hours old. +All predictions made during the lifetime of a specific `identifier` model are stored in `historical_predictions.pkl` to allow for reloading after a crash or changes made to the config. ### Purging old model data -FreqAI stores new model files each time it retrains. These files become obsolete as new models are trained and FreqAI adapts to new market conditions. Users planning to leave FreqAI running for extended periods of time with high frequency retraining should enable `purge_old_models` in their config: +FreqAI stores new model files after each successful training. These files become obsolete as new models are generated to adapt to new market conditions. The user who is planning to leave FreqAI running for extended periods of time with high frequency retraining should enable `purge_old_models` in their config: ```json "freqai": { @@ -110,15 +78,15 @@ FreqAI stores new model files each time it retrains. These files become obsolete } ``` -This will automatically purge all models older than the two most recently trained ones. +This will automatically purge all models older than the two most recently trained ones to save disk space. ### Returning additional info from training -The user may find that there are some important metrics that they'd like to return to the strategy at the end of each model training. -The user can include these metrics by assigning them to `dk.data['extra_returns_per_train']['my_new_value'] = XYZ` inside their custom prediction model class. FreqAI takes the `my_new_value` assigned in this dictionary and expands it to fit the return dataframe to the strategy. -The user can then use the value in the strategy with `dataframe['my_new_value']`. An example of how this is already used in FreqAI is -the `&*_mean` and `&*_std` values, which indicate the mean and standard deviation of the particular target (label) during the most recent training. -An example, where the user wants to use live metrics from the trade database, is shown below: +The user may find that there are important metrics that they would like to return to their strategy at the end of each model training. Such metrics are returned by assigning them to `dk.data['extra_returns_per_train']['my_new_value'] = XYZ` inside the user's custom prediction model class. + +FreqAI takes the `my_new_value` assigned in this dictionary and expands it to fit the dataframe that is returned to the strategy. The user can then use the returned metrics in their strategy through `dataframe['my_new_value']`. An example of how return values can be used in FreqAI are the `&*_mean` and `&*_std` values that are used to [created a dynamic target threshold](freqai-configuration.md#creating-a-dynamic-target-threshold). + +Another example, where the user wants to use live metrics from the trade database, is shown below: ```json "freqai": { @@ -126,4 +94,22 @@ An example, where the user wants to use live metrics from the trade database, is } ``` -The user needs to set the standard dictionary in the config so that FreqAI can return proper dataframe shapes. These values will likely be overridden by the prediction model, but in the case where the model has yet to set them, or needs a default initial value, this is the value that will be returned. +The user needs to set the standard dictionary in the config so that FreqAI can return proper dataframe shapes. These values will likely be overridden by the prediction model, but in the case where the model has yet to set them, or needs a default initial value, the preset values are what will be returned. + +## Backtesting + +### Saving prediction data + +To allow the user to tweak their strategy (**not** the features!), FreqAI will automatically save the predictions during backtesting so that they can be reused for future backtests and live runs using the same `identifier` model. This provides a performance enhancement geared towards enabling **high-level hyperopting** of entry/exit criteria. + +An additional directory called `predictions`, which contains all the predictions stored in `hdf` format, will be created in the `unique-id` folder. + +If the user wishes to change their **features**, they **must** set a new `identifier` in the config to signal to `FreqAI` to train new models. + +If the user wishes to save the models generated during a particular backtest so that they can start a live deployment from one of them instead of training a new model, they must set `save_backtest_models` to `True` in their configuration file. + +### Downloading data to cover the full backtest period + +For live/dry deployments, FreqAI will download the necessary data automatically. However, the user who wishes to use backtesting functionality needs 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` (see the [parameter table](freqai-parameter-table.md) for detailed descriptions of these parameters) from the beginning of the desired backtesting timerange. + +As an example, to backtest the `--timerange 20210501-20210701` using the [example config](freqai-configuration.md#setting-up-the-configuration-file) which sets `train_period_days` to 30, together with `startup_candle_count: 40` on a maximum `include_timeframes` of 1h, the start date for the downloaded data needs to be `20210501` - 30 days - 40 * 1h / 24 hours = 20210330 (31.7 days earlier than the start of the desired training timerange). diff --git a/docs/freqai-developers.md b/docs/freqai-developers.md index 01cdf1e71..96a020783 100644 --- a/docs/freqai-developers.md +++ b/docs/freqai-developers.md @@ -1,28 +1,15 @@ # Development -The class structure and details algorithmic overview is depicted in the following diagram: +The architechture and functions of FreqAI are generalized to encourage users to develop their own features, functions, models, etc. + +The class structure and a detailed algorithmic overview is depicted in the following diagram: ![image](assets/freqai_algorithm-diagram.jpg) -As shown, there are three distinct objects comprising `FreqAI`: +As shown, there are three distinct objects comprising FreqAI: -* IFreqaiModel - * Singular persistent object containing all the necessary logic to collect data, store data, process data, engineer features, run training, and inference models. -* FreqaiDataKitchen - * A non-persistent object which is created uniquely for each unique asset/model. Beyond metadata, it also contains a variety of data processing tools. -* FreqaiDataDrawer - * Singular persistent object containing all the historical predictions, models, and save/load methods. +* **IFreqaiModel** - A singular persistent object containing all the necessary logic to collect, store, and process data, engineer features, run training, and inference models. +* **FreqaiDataKitchen** - A non-persistent object which is created uniquely for each unique asset/model. Beyond metadata, it also contains a variety of data processing tools. +* **FreqaiDataDrawer** - A singular persistent object containing all the historical predictions, models, and save/load methods. -There are a variety of built-in prediction models which inherit directly from `IFreqaiModel` including: - -* CatboostRegressor -* CatboostRegressorMultiTarget -* CatboostClassifier -* LightGBMRegressor -* LightGBMRegressorMultiTarget -* LightGBMClassifier -* XGBoostRegressor -* XGBoostRegressorMultiTarget -* XGBoostClassifier - -Each of these have full access to all methods in `IFreqaiModel`. And can therefore, override any of those functions at will. However, advanced users will likely stick to overriding `fit()`, `train()`, `predict()`, and `data_cleaning_train/predict()`. \ No newline at end of file +There are a variety of built-in [prediction models](freqai-configuration.md#using-different-prediction-models) which inherit directly from `IFreqaiModel`. Each of these models have full access to all methods in `IFreqaiModel` and can therefore override any of those functions at will. However, advanced users will likely stick to overriding `fit()`, `train()`, `predict()`, and `data_cleaning_train/predict()`. diff --git a/docs/freqai-feature-engineering.md b/docs/freqai-feature-engineering.md index ebde5aa81..753c289f7 100644 --- a/docs/freqai-feature-engineering.md +++ b/docs/freqai-feature-engineering.md @@ -1,8 +1,10 @@ # Feature engineering -Feature engineering is handled within the `FreqAI` config file and the user strategy. The user adds all their `base features` to their strategy, such as `RSI`, `MFI`, `EMA`, `SMA` etc. These can be custom indicators or they can be imported from any technical-analysis library that the user can find. These features are added by the user inside the `populate_any_indicators()` method of the strategy by prepending indicators with `%`, and labels with `&`. +## Defining the features -Users should start from an existing `populate_any_indicators()` to ensure they are following some of the conventions that help with feature engineering. Here is an example: +Feature engineering is handled within `"feature_parameters":{}` in the `FreqAI` config file and in the user strategy. The user adds all their `base features`, such as, e.g., `RSI`, `MFI`, `EMA`, `SMA`, etc., to their strategy. The `base features` can be custom indicators or they can be imported from any technical-analysis library that the user can find. The `base features` are added by the user inside the `populate_any_indicators()` method of the strategy by prepending indicators with `%`, and labels with `&`. + +It is advisable for the user to start from the `populate_any_indicators()` in the example strategy (found in `templates/FreqaiExampleStrategy.py`) to ensure that they are following the correct conventions. Here is an example of how to set the indicators and labels in the strategy: ```python def populate_any_indicators( @@ -90,11 +92,11 @@ Users should start from an existing `populate_any_indicators()` to ensure they a return df ``` -In the presented example strategy, the user does not wish to pass the `bb_lowerband` as a feature to the model, +In the presented example, the user does not wish to pass the `bb_lowerband` as a feature to the model, and has therefore not prepended it with `%`. The user does, however, wish to pass `bb_width` to the model for training/prediction and has therefore prepended it with `%`. -Now that the user has set their `base features`, they will next expand upon their base features using the powerful `feature_parameters` in their configuration file: +After having defined the `base features`, the next step is to expand upon them using the powerful `feature_parameters` in the configuration file: ```json "freqai": { @@ -114,9 +116,9 @@ Now that the user has set their `base features`, they will next expand upon thei } ``` -The `include_timeframes` in the config above are the timeframes (`tf`) of each call to `populate_any_indicators()` in the strategy. In the present case, the user is asking for the `5m`, `15m`, and `4h` timeframes of the `rsi`, `mfi`, `roc`, and `bb_width` to be included in the feature set. +The `include_timeframes` in the config above are the timeframes (`tf`) of each call to `populate_any_indicators()` in the strategy. In the presented case, the user is asking for the `5m`, `15m`, and `4h` timeframes of the `rsi`, `mfi`, `roc`, and `bb_width` to be included in the feature set. -The user can ask for each of the defined features to be included also from informative pairs using the `include_corr_pairlist`. This means that the feature set will include all the features from `populate_any_indicators` on all the `include_timeframes` for each of the correlated pairs defined in the config (`ETH/USD`, `LINK/USD`, and `BNB/USD`). +The user can ask for each of the defined features to be included also for informative pairs using the `include_corr_pairlist`. This means that the feature set will include all the features from `populate_any_indicators` on all the `include_timeframes` for each of the correlated pairs defined in the config (`ETH/USD`, `LINK/USD`, and `BNB/USD` in the presented example). `include_shifted_candles` indicates the number of previous candles to include in the feature set. For example, `include_shifted_candles: 2` tells `FreqAI` to include the past 2 candles for each of the features in the feature set. @@ -124,13 +126,13 @@ In total, the number of features the user of the presented example strat has cre $= 3 * 3 * 3 * 2 * 2 = 108$. -### Feature normalization +## Feature normalization -`FreqAI` is strict when it comes to data normalization - all data is always automatically normalized to the training feature space according to industry standards. This includes all test data and unseen prediction data (dry/live/backtest). `FreqAI` stores all the metadata required to ensure that prediction features will be properly normalized and that predictions are properly denormalized. For this reason, it is not recommended to eschew industry standards and modify `FreqAI` internals - however - advanced users can do so by inheriting `train()` in their custom `IFreqaiModel` and using their own normalization functions. +FreqAI is strict when it comes to data normalization. The train features are always normalized to [-1, 1] and all other data (test data and unseen prediction data in dry/live/backtest) is always automatically normalized to the training feature space according to industry standards. FreqAI stores all the metadata required to ensure that test and prediction features will be properly normalized and that predictions are properly denormalized. For this reason, it is not recommended to eschew industry standards and modify FreqAI internals - however - advanced users can do so by inheriting `train()` in their custom `IFreqaiModel` and using their own normalization functions. -### Reducing data dimensionality with Principal Component Analysis +## Data dimensionality reduction with Principal Component Analysis -Users can reduce the dimensionality of their features by activating the `principal_component_analysis` in the config: +The user can reduce the dimensionality of their features by activating the `principal_component_analysis` in the config: ```json "freqai": { @@ -140,135 +142,24 @@ Users can reduce the dimensionality of their features by activating the `princip } ``` -This will perform PCA on the features and reduce the dimensionality of the data so that the explained variance of the data set is >= 0.999. +This will perform PCA on the features and reduce their dimensionality so that the explained variance of the data set is >= 0.999. Reducing data dimensionality makes training the model faster and hence allows for more up-to-date models. -### Stratifying the data for training and testing the model +## Inlier metric -The user can stratify (group) the training/testing data using: +The `inlier_metric` is a metric aimed at quantifying how similar a the features of a data point are to the most recent historic data points. -```json - "freqai": { - "feature_parameters" : { - "stratify_training_data": 3 - } - } -``` +The user defines the lookback window by setting `inlier_metric_window` and FreqAI computes the distance between the present time point and each of the previous `inlier_metric_window` lookback points. A Weibull function is fit to each of the lookback distributions and its cumulative distribution function is used to produce a quantile for each lookback point. The `inlier_metric` is then computed for each time point as the average of the corresponding lookback quantiles. -This will split the data chronologically so that every Xth data point is used to test the model after training. In the -example above, the user is asking for every third data point in the dataframe to be used for -testing; the other points are used for training. - -The test data is used to evaluate the performance of the model after training. If the test score is high, the model is able to capture the behavior of the data well. If the test score is low, either the model 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` - -The `inlier_metric` is a metric aimed at quantifying how different a prediction data point is from the most recent historic data points. - -User can set `inlier_metric_window` to set the look back window. FreqAI will compute the distance between the present prediction point and each of the previous data points (total of `inlier_metric_window` points). - -This function goes one step further - during training, it computes the `inlier_metric` for all training data points and builds weibull distributions for each each lookback point. The cumulative distribution function for the weibull distribution is used to produce a quantile for each of the data points. The quantiles for each lookback point are averaged to create the `inlier_metric`. - -FreqAI adds this `inlier_metric` score to the training features! In other words, your model is trained to recognize how this temporal inlier metric is related to the user set labels. +FreqAI adds the `inlier_metric` to the training features and hence gives the model access to a novel type of temporal information. This function does **not** remove outliers from the data set. -### Controlling the model learning process +## Weighting features for temporal importance -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. - -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: +FreqAI allows the user to set a `weight_factor` to weight recent data more strongly than past data via an exponential function: $$ W_i = \exp(\frac{-i}{\alpha*n}) $$ -where $W_i$ is the weight of data point $i$ in a total set of $n$ data points. Below is a figure showing the effect of different weight factors on the data points (candles) in a feature set. +where $W_i$ is the weight of data point $i$ in a total set of $n$ data points. Below is a figure showing the effect of different weight factors on the data points in a feature set. ![weight-factor](assets/freqai_weight-factor.jpg) - -`train_test_split()` has a parameters called `shuffle` that allows the user to keep the data unshuffled. This is particularly useful to avoid biasing training with temporally auto-correlated data. - -Finally, `label_period_candles` defines the offset (number of candles into the future) used for the `labels`. In the presented example config, -the user is asking for `labels` that are 24 candles in the future. - -#### Continual learning - -Users can choose to adopt a "continual learning" strategy by setting `"continual_learning": true` in their configuration file. This setting will train an initial model from scratch, and subsequent trainings will start from the final model state of the preceding training. By default, this is set to `false` which trains a new model from scratch upon each subsequent training. - -### Outlier removal - -#### Removing outliers with the Dissimilarity Index - -The user can tell FreqAI to remove outlier data points from the training/test data sets using a Dissimilarity Index by including the following statement in the config: - -```json - "freqai": { - "feature_parameters" : { - "DI_threshold": 1 - } - } -``` - -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: - -$$ d_{ab} = \sqrt{\sum_{j=1}^p(X_{a,j}-X_{b,j})^2} $$ - -where $d_{ab}$ is the distance between the normalized points $a$ and $b$. $p$ is the number of features, i.e., the length of the vector $X$. The characteristic distance, $\overline{d}$ for a set of training data points is simply the mean of the average distances: - -$$ \overline{d} = \sum_{a=1}^n(\sum_{b=1}^n(d_{ab}/n)/n) $$ - -$\overline{d}$ quantifies the spread of the training data, which is compared to the distance between a new prediction feature vectors, $X_k$ and all the training data: - -$$ d_k = \arg \min d_{k,i} $$ - -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. - -Below is a figure that describes the DI for a 3D data set. - -![DI](assets/freqai_DI.jpg) - -#### Removing outliers using a Support Vector Machine (SVM) - -The user can tell FreqAI to remove outlier data points from the training/test data sets using a SVM by setting: - -```json - "freqai": { - "feature_parameters" : { - "use_SVM_to_remove_outliers": true - } - } -``` - -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. - -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. - -#### Removing outliers with DBSCAN - -The user can configure FreqAI to use DBSCAN to cluster and remove outliers from the training/test data set or incoming outliers from predictions, by activating `use_DBSCAN_to_remove_outliers` in the config: - -```json - "freqai": { - "feature_parameters" : { - "use_DBSCAN_to_remove_outliers": true - } - } -``` - -DBSCAN is an unsupervised machine learning algorithm that clusters data without needing to know how many clusters there should be. - -Given a number of data points $N$, and a distance $\varepsilon$, DBSCAN clusters the data set by setting all data points that have $N-1$ other data points within a distance of $\varepsilon$ as *core points*. A data point that is within a distance of $\varepsilon$ from a *core point* but that does not have $N-1$ other data points within a distance of $\varepsilon$ from itself is considered an *edge point*. A cluster is then the collection of *core points* and *edge points*. Data points that have no other data points at a distance $<\varepsilon$ are considered outliers. The figure below shows a cluster with $N = 3$. - -![dbscan](assets/freqai_dbscan.jpg) - -FreqAI uses `sklearn.cluster.DBSCAN` (details are available on scikit-learn's webpage [here](#https://scikit-learn.org/stable/modules/generated/sklearn.cluster.DBSCAN.html)) 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. diff --git a/docs/freqai-outlier-detection.md b/docs/freqai-outlier-detection.md new file mode 100644 index 000000000..1f41e91cd --- /dev/null +++ b/docs/freqai-outlier-detection.md @@ -0,0 +1,79 @@ +# Outlier detection + +Equity and crypto markets suffer from a high level of non-patterned noise in the form of outlier data points. FreqAI implements a variety of methods to identify such outliers and hence mitigate risk. + +## Identifying outliers with the Dissimilarity Index (DI) + + The Dissimilarity Index (DI) aims to quantify the uncertainty associated with each prediction made by the model. + +The user can tell FreqAI to remove outlier data points from the training/test data sets using the DI by including the following statement in the config: + +```json + "freqai": { + "feature_parameters" : { + "DI_threshold": 1 + } + } +``` + + The DI allows predictions which are outliers (not existent in the model feature space) to be thrown out due to low levels of certainty. To do so, FreqAI measures the distance between each training data point (feature vector), $X_{a}$, and all other training data points: + +$$ d_{ab} = \sqrt{\sum_{j=1}^p(X_{a,j}-X_{b,j})^2} $$ + +where $d_{ab}$ is the distance between the normalized points $a$ and $b$, and $p$ is the number of features, i.e., the length of the vector $X$. The characteristic distance, $\overline{d}$, for a set of training data points is simply the mean of the average distances: + +$$ \overline{d} = \sum_{a=1}^n(\sum_{b=1}^n(d_{ab}/n)/n) $$ + +$\overline{d}$ quantifies the spread of the training data, which is compared to the distance between a new prediction feature vectors, $X_k$ and all the training data: + +$$ d_k = \arg \min d_{k,i} $$ + +This 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. A higher `DI_threshold` means that the DI is more lenient and allows predictions further away from the training data to be used whilst a lower `DI_threshold` has the opposite effect and hence discards more predictions. + +Below is a figure that describes the DI for a 3D data set. + +![DI](assets/freqai_DI.jpg) + +## Identifying outliers using a Support Vector Machine (SVM) + +The user can tell FreqAI to remove outlier data points from the training/test data sets using a Support Vector Machine (SVM) by including the following statement in the config: + +```json + "freqai": { + "feature_parameters" : { + "use_SVM_to_remove_outliers": true + } + } +``` + +The SVM will be trained on the training data and any data point that the SVM deems to be beyond the feature space will be removed. + +FreqAI uses `sklearn.linear_model.SGDOneClassSVM` (details are available on scikit-learn's webpage [here](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDOneClassSVM.html) (external website)) and the user can elect to provide additional parameters for the SVM, such as `shuffle`, and `nu`. + +The parameter `shuffle` is by default set to `False` to ensure consistent results. If it is set to `True`, running the SVM multiple times on the same data set might result in different outcomes due to `max_iter` being to low for the algorithm to reach the demanded `tol`. Increasing `max_iter` solves this issue but causes the procedure to take longer time. + +The parameter `nu`, *very* broadly, is the amount of data points that should be considered outliers and should be between 0 and 1. + +## Identifying outliers with DBSCAN + +The user can configure FreqAI to use DBSCAN to cluster and remove outliers from the training/test data set or incoming outliers from predictions, by activating `use_DBSCAN_to_remove_outliers` in the config: + +```json + "freqai": { + "feature_parameters" : { + "use_DBSCAN_to_remove_outliers": true + } + } +``` + +DBSCAN is an unsupervised machine learning algorithm that clusters data without needing to know how many clusters there should be. + +Given a number of data points $N$, and a distance $\varepsilon$, DBSCAN clusters the data set by setting all data points that have $N-1$ other data points within a distance of $\varepsilon$ as *core points*. A data point that is within a distance of $\varepsilon$ from a *core point* but that does not have $N-1$ other data points within a distance of $\varepsilon$ from itself is considered an *edge point*. A cluster is then the collection of *core points* and *edge points*. Data points that have no other data points at a distance $<\varepsilon$ are considered outliers. The figure below shows a cluster with $N = 3$. + +![dbscan](assets/freqai_dbscan.jpg) + +FreqAI uses `sklearn.cluster.DBSCAN` (details are available on scikit-learn's webpage [here](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.DBSCAN.html) (external website)) with `min_samples` ($N$) taken as 1/4 of the no. of time points in the feature set. `eps` ($\varepsilon$) is computed automatically as the elbow point in the *k-distance graph* computed from the nearest neighbors in the pairwise distances of all data points in the feature set. diff --git a/docs/freqai-parameter-table.md b/docs/freqai-parameter-table.md new file mode 100644 index 000000000..f26762809 --- /dev/null +++ b/docs/freqai-parameter-table.md @@ -0,0 +1,51 @@ +# Parameter table + +The table below will list all configuration parameters available for FreqAI. Some of the parameters are exemplified in `config_examples/config_freqai.example.json`. + +Mandatory parameters are marked as **Required** and have to be set in one of the suggested ways. + +| Parameter | Description | +|------------|-------------| +| | **General configuration parameters** +| `freqai` | **Required.**
The parent dictionary containing all the parameters for controlling FreqAI.
**Datatype:** Dictionary. +| `train_period_days` | **Required.**
Number of days to use for the training data (width of the sliding window).
**Datatype:** Positive integer. +| `backtest_period_days` | **Required.**
Number of days to inference from the trained model before sliding the `train_period_days` window defined above, and retraining the model during backtesting (more info [here](freqai-running.md#backtesting)). This can be fractional days, but beware that the user provided `timerange` will be divided by this number to yield the number of trainings necessary to complete the backtest.
**Datatype:** Float. +| `identifier` | **Required.**
A unique ID for the current model. If models are saved to disk, the `identifier` allows the user to reload specific pre-trained models/data.
**Datatype:** String. +| `live_retrain_hours` | Frequency of retraining during dry/live runs.
**Datatype:** Float > 0.
Default: 0 (models retrain as often as possible). +| `expiration_hours` | Avoid making predictions if a model is more than `expiration_hours` old.
**Datatype:** Positive integer.
Default: 0 (models never expire). +| `purge_old_models` | Delete obsolete models.
**Datatype:** Boolean.
Default: `False` (all historic models remain on disk). +| `save_backtest_models` | Save models to disk when running backtesting. Backtesting operates most efficiently by saving the prediction data and reusing them directly for subsequent runs (when the user wishes to tune entry/exit parameters). Saving backtesting models to disk also allows to use the same model files for starting a dry/live instance with the same model `identifier`.
**Datatype:** Boolean.
Default: `False` (no models are saved). +| `fit_live_predictions_candles` | Number of historical candles to use for computing target (label) statistics from prediction data, instead of from the training dataset (more information can be found [here](freqai-configuration.md#creating-a-dynamic-target-threshold)).
**Datatype:** Positive integer. +| `follow_mode` | Use a `follower` that will look for models associated with a specific `identifier` and load those for inferencing. A `follower` will **not** train new models.
**Datatype:** Boolean.
Default: `False`. +| `continual_learning` | Use the final state of the most recently trained model as starting point for the new model, allowing for incremental learning.
**Datatype:** Boolean.
Default: `False`. +| | **Feature parameters** +| `feature_parameters` | A dictionary containing the parameters used to engineer the feature set. Details and examples are shown [here](freqai-feature-engineering.md).
**Datatype:** Dictionary. +| `include_timeframes` | A list of timeframes that all indicators in `populate_any_indicators` will be created for. The list is added as features to the base indicators dataset.
**Datatype:** List of timeframes (strings). +| `include_corr_pairlist` | A list of correlated coins that FreqAI will add as additional features to all `pair_whitelist` coins. All indicators set in `populate_any_indicators` during feature engineering (see details [here](freqai-feature-engineering.md)) will be created for each correlated coin. The correlated coins features are added to the base indicators dataset.
**Datatype:** List of assets (strings). +| `label_period_candles` | Number of candles into the future that the labels are created for. This is used in `populate_any_indicators` (see `templates/FreqaiExampleStrategy.py` for detailed usage). The user can create custom labels and choose whether to make use of this parameter or not.
**Datatype:** Positive integer. +| `include_shifted_candles` | Add features from previous candles to subsequent candles with the intent of adding historical information. If used, FreqAI will duplicate and shift all features from the `include_shifted_candles` previous candles so that the information is available for the subsequent candle.
**Datatype:** Positive integer. +| `weight_factor` | Weight training data points according to their recency (see details [here](freqai-feature-engineering.md#weighting-features-for-temporal-importance)).
**Datatype:** Positive float (typically < 1). +| `indicator_max_period_candles` | **No longer used (#7325)**. Replaced by `startup_candle_count` which is set in the [strategy](freqai-configuration.md#building-a-freqai-strategy). `startup_candle_count` is timeframe independent and defines the maximum *period* used in `populate_any_indicators()` for indicator creation. FreqAI uses this parameter together with the maximum timeframe in `include_time_frames` to calculate how many data points to download such that the first data point does not include a NaN
**Datatype:** Positive integer. +| `indicator_periods_candles` | Time periods to calculate indicators for. The indicators are added to the base indicator dataset.
**Datatype:** List of positive integers. +| `stratify_training_data` | Split the feature set into training and testing datasets. For example, `stratify_training_data: 2` would set every 2nd data point into a separate dataset to be pulled from during training/testing. See details about how it works [here](freqai-running.md#data-stratification-for-training-and-testing-the-model).
**Datatype:** Positive integer. +| `principal_component_analysis` | Reduce the dimensionality of the dataset using Principal Component Analysis. See details about how it works [here](freqai-feature-engineering.md#data-dimensionality-reduction-with-principal-component-analysis).
**Datatype:** Boolean. +| `DI_threshold` | Activates the use of the Dissimilarity Index for outlier detection when set to > 0. See details about how it works [here](freqai-outlier-detection.md#identifying-outliers-with-the-dissimilarity-index).
**Datatype:** Positive float (typically < 1). +| `use_SVM_to_remove_outliers` | Train a support vector machine to detect and remove outliers from the training dataset, as well as from incoming data points. See details about how it works [here](freqai-outlier-detection.md#identifying-outliers-using-a-support-vector-machine-svm).
**Datatype:** Boolean. +| `svm_params` | All parameters available in Sklearn's `SGDOneClassSVM()`. See details about some select parameters [here](freqai-outlier-detection.md#identifying-outliers-using-a-support-vector-machine-svm).
**Datatype:** Dictionary. +| `use_DBSCAN_to_remove_outliers` | Cluster data using the DBSCAN algorithm to identify and remove outliers from training and prediction data. See details about how it works [here](freqai-outlier-detection.md#identifying-outliers-with-dbscan).
**Datatype:** Boolean. +| `inlier_metric_window` | If set, FreqAI adds an `inlier_metric` to the training feature set and set the lookback to be the `inlier_metric_window`, i.e., the number of previous time points to compare the current candle to. Details of how the `inlier_metric` is computed can be found [here](freqai-feature-engineering.md#inlier-metric).
**Datatype:** Integer.
Default: 0. +| `noise_standard_deviation` | If set, FreqAI adds noise to the training features with the aim of preventing overfitting. FreqAI generates random deviates from a gaussian distribution with a standard deviation of `noise_standard_deviation` and adds them to all data points. `noise_standard_deviation` should be kept relative to the normalized space, i.e., between -1 and 1. In other words, since data in FreqAI is always normalized to be between -1 and 1, `noise_standard_deviation: 0.05` would result in 32% of the data being randomly increased/decreased by more than 2.5% (i.e., the percent of data falling within the first standard deviation).
**Datatype:** Integer.
Default: 0. +| `outlier_protection_percentage` | Enable to prevent outlier detection methods from discarding too much data. If more than `outlier_protection_percentage` % of points are detected as outliers by the SVM or DBSCAN, FreqAI will log a warning message and ignore outlier detection, i.e., the original dataset will be kept intact. If the outlier protection is triggered, no predictions will be made based on the training dataset.
**Datatype:** Float.
Default: `30`. +| `reverse_train_test_order` | Split the feature dataset (see below) and use the latest data split for training and test on historical split of the data. This allows the model to be trained up to the most recent data point, while avoiding overfitting. However, users should be careful to understand the unorthodox nature of this parameter before employing it.
**Datatype:** Boolean.
Default: `False` (no reversal). +| | **Data split parameters** +| `data_split_parameters` | Include any additional parameters available from Scikit-learn `test_train_split()`, which are shown [here](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html) (external website).
**Datatype:** Dictionary. +| `test_size` | The fraction of data that should be used for testing instead of training.
**Datatype:** Positive float < 1. +| `shuffle` | Shuffle the training data points during training. Typically, for time-series forecasting, this is set to `False`.
**Datatype:** Boolean. +| | **Model training parameters** +| `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.
**Datatype:** Dictionary. +| `n_estimators` | The number of boosted trees to fit in regression.
**Datatype:** Integer. +| `learning_rate` | Boosting learning rate during regression.
**Datatype:** Float. +| `n_jobs`, `thread_count`, `task_type` | Set the number of threads for parallel processing and the `task_type` (`gpu` or `cpu`). Different model libraries use different parameter names.
**Datatype:** Float. +| | **Extraneous parameters** +| `keras` | If the user selected model makes use of Keras (typical for Tensorflow-based prediction models), this flag needs to be activated so that the model save/loading follows Keras standards.
**Datatype:** Boolean.
Default: `False`. +| `conv_width` | The width of a convolutional neural network input tensor. This replaces the need for shifting candles (`include_shifted_candles`) by feeding in historical data points as the second dimension of the tensor. Technically, this parameter can also be used for regressors, but it only adds computational overhead and does not change the model training/prediction.
**Datatype:** Integer.
Default: 2. diff --git a/docs/freqai-running.md b/docs/freqai-running.md index 90856c841..875b0c149 100644 --- a/docs/freqai-running.md +++ b/docs/freqai-running.md @@ -1,10 +1,10 @@ # Running FreqAI -There are two ways to train and deploy an adaptive machine learning model. FreqAI enables live deployment as well as backtesting analyses. In both cases, a model is trained periodically, as shown in the following figure. +There are two ways to train and deploy an adaptive machine learning model - live deployment and deployment for backtesting analysis. FreqAI can be used for both and allows for periodic training of models in live and backtesting, as shown in the following figure. ![freqai-window](assets/freqai_moving-window.jpg) -### Running the model live +## Running the model live FreqAI can be run dry/live using the following command: @@ -12,11 +12,11 @@ FreqAI can be run dry/live using the following command: freqtrade trade --strategy FreqaiExampleStrategy --config config_freqai.example.json --freqaimodel LightGBMRegressor ``` -By default, FreqAI will not find any existing models and will start by training a new one -based on the user's configuration settings. Following training, the model will be used to make predictions on incoming candles until a new model is available. New models are typically generated as often as possible, with FreqAI managing an internal queue of the coin pairs to try to keep all models equally up to date. FreqAI will always use the most recently trained model to make predictions on incoming live data. If the user does not want FreqAI to retrain new models as often as possible, they can set `live_retrain_hours` to tell FreqAI to wait at least that number of hours before training a new model. Additionally, the user can set `expired_hours` to tell FreqAI to avoid making predictions on models that are older than that number of hours. +When launched, FreqAI will start training a new model based on the user's config settings. Following training, the model will be used to make predictions on incoming candles until a new model is available. New models are typically generated as often as possible, with FreqAI managing an internal queue of the coin pairs to try to keep all models equally up to date. FreqAI will always use the most recently trained model to make predictions on incoming live data. If the user does not want FreqAI to retrain new models as often as possible, they can set `live_retrain_hours` to tell FreqAI to wait at least that number of hours before training a new model. Additionally, the user can set `expired_hours` to tell FreqAI to avoid making predictions on models that are older than that number of hours. -If the user wishes to start a dry/live run from a saved backtest model (or from a previously crashed dry/live session), the user only needs to reuse -the same `identifier` parameter: +Trained models are by default saved to disk to allow for reuse during backtesting or after a crash. The user can opt to purge old models to save disk space by setting `"purge_old_models": true` in the config. + +If the user wishes to start a dry/live run from a saved backtest model (or from a previously crashed dry/live session), the user only needs to specify the `identifier` of the specific model: ```json "freqai": { @@ -25,11 +25,9 @@ the same `identifier` parameter: } ``` -In this case, although FreqAI will initiate with a -pre-trained model, it will still check to see how much time has elapsed since the model was trained, -and if a full `live_retrain_hours` has elapsed since the end of the loaded model, FreqAI will retrain. +In this case, although FreqAI will initiate with a pre-trained model, it will still check to see how much time has elapsed since the model was trained. If a full `live_retrain_hours` has elapsed since the end of the loaded model, FreqAI will start training a new model. -### Backtesting +## Backtesting The FreqAI backtesting module can be executed with the following command: @@ -37,11 +35,11 @@ The FreqAI backtesting module can be executed with the following command: freqtrade backtesting --strategy FreqaiExampleStrategy --strategy-path freqtrade/templates --config config_examples/config_freqai.example.json --freqaimodel LightGBMRegressor --timerange 20210501-20210701 ``` -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). - -If this command has never been executed with the existing config file, it will train a new model +If this command has never been executed with the existing config file, FreqAI will train a new model for each pair, for each backtesting window within the expanded `--timerange`. +Backtesting mode requires the user [download the necessary data](freqai-data-handling.md#downloading-data-to-cover-the-full-backtest-period) before deployment (unlike in dry/live mode where FreqAI handles the data downloading automatically). 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-size-of-the-sliding-training-window-and-backtesting-duration). + !!! Note "Model reuse" Once the training is completed, the user can execute the backtesting again with the same config file and FreqAI will find the trained models and load them instead of spending time training. This is useful @@ -51,47 +49,85 @@ for each pair, for each backtesting window within the expanded `--timerange`. --- -### Hyperopt +## Deciding the size of the sliding training window and backtesting duration -Users can hyperopt using the same command as typical [hyperopt](hyperopt.md): +The user defines the backtesting timerange with the typical `--timerange` parameter in the configuration file. The duration of the sliding training window is set by `train_period_days`, whilst `backtest_period_days` is the sliding backtesting window, both in number of days (`backtest_period_days` can be +a float to indicate sub-daily retraining in live/dry mode). In the presented [example config](freqai-configuration.md#setting-up-the-configuration-file) (found in `config_examples/config_freqai.example.json`), the user is asking FreqAI to use a training period of 30 days and backtest on the subsequent 7 days. After the training of the model, FreqAI will backtest the subsequent 7 days. The "sliding window" then moves one week forward (emulating FreqAI retraining once per week in live mode) and the new model uses the previous 30 days (including the 7 days used for backtesting by the previous model) to train. This is repeated until the end of `--timerange`. This means that if the user sets `--timerange 20210501-20210701`, FreqAI will have trained 8 separate models at the end of `--timerange` (because the full range comprises 8 weeks). + +!!! 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 train constantly. In this case, backtesting would take the exact same amount of time as a dry run. + +## 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 +a certain number of hours old by setting the `expiration_hours` in the config file: + +```json + "freqai": { + "expiration_hours": 0.5, + } +``` + +In the presented example config, the user will only allow predictions on models that are less than 1/2 hours old. + +## Data stratification for training and testing the model + +The user can stratify (group) the training/testing data using: + +```json + "freqai": { + "feature_parameters" : { + "stratify_training_data": 3 + } + } +``` + +This will split the data chronologically so that every Xth data point is used to test the model after training. In the +example above, the user is asking for every third data point in the dataframe to be used for +testing; the other points are used for training. + +The test data is used to evaluate the performance of the model after training. If the test score is high, the model is able to capture the behavior of the data well. If the test score is low, either the model does not capture the complexity of the data, the test data is significantly different from the train data, or a different type of model should be used. + +## Controlling the model learning process + +Model training parameters are unique to the 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 config (found in `config_examples/config_freqai.example.json`) shows some of the example parameters associated with `Catboost` and `LightGBM`, but the user can add any parameters available in those libraries or any other machine learning library they choose to implement. + +Data split parameters are defined in `data_split_parameters` which can be any parameters associated with Scikit-learn's `train_test_split()` function. `train_test_split()` has a parameters called `shuffle` which allows the user to shuffle the data or keep it unshuffled. This is particularly useful to avoid biasing training with temporally auto-correlated data. More details about these parameters can be found the [Scikit-learn website](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html) (external website). + +The FreqAI specific parameter `label_period_candles` defines the offset (number of candles into the future) used for the `labels`. In the presented [example config](freqai-configuration.md#setting-up-the-configuration-file), the user is asking for `labels` that are 24 candles in the future. + +## Continual learning + +The user can choose to adopt a continual learning scheme by setting `"continual_learning": true` in their configuration file. By enabling `continual_learning`, after training an initial model from scratch, subsequent trainings will start from the final model state of the preceding training. This gives the new model a "memory" of the previous state. By default, this is set to `false` which means that all new models are trained from scratch, without input from previous models. + +## Hyperopt + +The user can hyperopt using the same command as for [typical Freqtrade hyperopt](hyperopt.md): ```bash freqtrade hyperopt --hyperopt-loss SharpeHyperOptLoss --strategy FreqaiExampleStrategy --freqaimodel LightGBMRegressor --strategy-path freqtrade/templates --config config_examples/config_freqai.example.json --timerange 20220428-20220507 ``` -Users need to have the data pre-downloaded in the same fashion as if they were doing a FreqAI [backtest](#backtesting). In addition, users must consider some restrictions when trying to [Hyperopt](hyperopt.md) FreqAI strategies: +`hyperopt` requires the user to have the data pre-downloaded in the same fashion as if they were doing [backtesting](#backtesting). In addition, the user must consider some restrictions when trying to hyperopt FreqAI strategies: - The `--analyze-per-epoch` hyperopt parameter is not compatible with FreqAI. -- It's not possible to hyperopt indicators in `populate_any_indicators()` function. This means that the user cannot optimize model parameters using hyperopt. Apart from this exception, it is possible to optimize all other [spaces](hyperopt.md#running-hyperopt-with-smaller-search-space). -- The [Backtesting](#backtesting) instructions also apply to Hyperopt. +- It's not possible to hyperopt indicators in the `populate_any_indicators()` function. This means that the user cannot optimize model parameters using hyperopt. Apart from this exception, it is possible to optimize all other [spaces](hyperopt.md#running-hyperopt-with-smaller-search-space). +- The backtesting instructions also apply to hyperopt. -The best method for combining hyperopt and FreqAI is to focus on hyperopting entry/exit thresholds/criteria. Users need to focus on hyperopting parameters that are not used in their FreqAI features. For example, users should not try to hyperopt rolling window lengths in their feature creation, or any of their FreqAI config which changes predictions. In order to efficiently hyperopt the FreqAI strategy, FreqAI stores predictions as dataframes and reuses them. Hence the requirement to hyperopt entry/exit thresholds/criteria only. +The best method for combining hyperopt and FreqAI is to focus on hyperopting entry/exit thresholds/criteria. The user needs to focus on hyperopting parameters that are not used in their FreqAI features. For example, the user should not try to hyperopt rolling window lengths in their feature creation, or any part of their FreqAI config which changes predictions. In order to efficiently hyperopt the FreqAI strategy, FreqAI stores predictions as dataframes and reuses them. Hence the requirement to hyperopt entry/exit thresholds/criteria only. -A good example of a hyperoptable parameter in FreqAI is a value for `DI_values` beyond which we consider outliers and below which we consider inliers: +A good example of a hyperoptable parameter in FreqAI is a threshold for the [Dissimilarity Index (DI)](freqai-outlier-detection.md#identifying-outliers-with-the-dissimilarity-index-di) `DI_values` beyond which we consider data points as outliers: ```python di_max = IntParameter(low=1, high=20, default=10, space='buy', optimize=True, load=True) dataframe['outlier'] = np.where(dataframe['DI_values'] > self.di_max.value/10, 1, 0) ``` -Which would help the user understand the appropriate Dissimilarity Index values for their particular parameter space. +This specific hyperopt would help the user understand the appropriate `DI_values` for their particular parameter space. -### Deciding the size of the sliding training window and backtesting duration +## Setting up a follower -The user defines the backtesting timerange with the typical `--timerange` parameter in the configuration file. The duration of the sliding training window is set by `train_period_days`, whilst `backtest_period_days` is the sliding backtesting window, both in number of days (`backtest_period_days` can be -a float to indicate sub-daily retraining in live/dry mode). In the presented example config, the user is asking FreqAI to use a training period of 30 days and backtest on the subsequent 7 days. 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`. - -!!! 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). - -### Setting up a follower - -The user can define: +The user can indicate to the bot that it should not train models, but instead should look for models trained by a leader with a specific `identifier` by defining: ```json "freqai": { @@ -100,5 +136,4 @@ The user can define: } ``` -to indicate to the bot that it should not train models, but instead should look for models trained by a leader with the same `identifier`. In this example, the user has a leader bot with the `identifier: "example"`. The leader bot is already running or launching simultaneously as the follower. -The follower will load models created by the leader and inference them to obtain predictions. +In this example, the user has a leader bot with the `"identifier": "example"`. The leader bot is already running or is launched simultaneously with the follower. The follower will load models created by the leader and inference them to obtain predictions instead of training its own models. diff --git a/docs/freqai.md b/docs/freqai.md index 955e49250..a025c13b9 100644 --- a/docs/freqai.md +++ b/docs/freqai.md @@ -1,27 +1,29 @@ ![freqai-logo](assets/freqai_doc_logo.svg) -# FreqAI +# FreqAI + +##Introduction FreqAI is a module designed to automate a variety of tasks associated with training a predictive machine learning model to generate market forecasts given a set of input features. Features include: -* **Self-adaptive retraining**: retrain models during [live deployments](freqai-running.md#running-the-model-live) to self-adapt to the market in an unsupervised manner. -* **Rapid feature engineering**: create large rich [feature sets](freqai-feature-engineering.md#feature-engineering) (10k+ features) based on simple user-created strategies. -* **High performance**: adaptive retraining occurs on a separate thread (or on GPU if available) from inferencing and bot trade operations. Newest models and data are kept in memory for rapid inferencing. -* **Realistic backtesting**: emulate self-adaptive retraining with a [backtesting module](freqai-running.md#backtesting) that automates past retraining. -* **Extensibility**: use the generalized and robust architecture for incorporating any [machine learning library/method](freqai-configuration.md#building-a-custom-prediction-model) available in Python. Eight examples are currently available, including classifiers, regressors, and a convolutional neural network. -* **Smart outlier removal**: remove outliers from training and prediction data sets using a variety of [outlier detection techniques](freqai-feature-engineering.md#outlier-removal). -* **Crash resilience**: store model to disk to make reloading from a crash fast and easy, and [purge obsolete files](freqai-data-handling.md#purging-old-model-data) for sustained dry/live runs. -* **Automatic data normalization**: [normalize the data](freqai-feature-engineering.md#feature-normalization) in a smart and statistically safe way. -* **Automatic data download**: compute the data download timerange and update historic data (in live deployments). -* **Cleaning of incoming data**: handle NaNs safely before training and prediction. -* **Dimensionality reduction**: reduce the size of the training data via [Principal Component Analysis](freqai-feature-engineering.md#reducing-data-dimensionality-with-principal-component-analysis). -* **Deploying bot fleets**: set one bot to train models while a fleet of [follower bots](freqai-running.md#setting-up-a-follower) inference the models and handle trades. +* **Self-adaptive retraining** - Retrain models during [live deployments](freqai-running.md#running-the-model-live) to self-adapt to the market in a supervised manner +* **Rapid feature engineering** - Create large rich [feature sets](freqai-feature-engineering.md#feature-engineering) (10k+ features) based on simple user-created strategies +* **High performance** - Threading allows for adaptive model retraining on a separate thread (or on GPU if available) from model inferencing (prediction) and bot trade operations. Newest models and data are kept in RAM for rapid inferencing +* **Realistic backtesting** - Emulate self-adaptive training on historic data with a [backtesting module](freqai-running.md#backtesting) that automates retraining +* **Extensibility** - The generalized and robust architecture allows for incorporating any [machine learning library/method](freqai-configuration.md#using-different-prediction-models) available in Python. Eight examples are currently available, including classifiers, regressors, and a convolutional neural network +* **Smart outlier removal** - Remove outliers from training and prediction data sets using a variety of [outlier detection techniques](freqai-outlier-detection.md) +* **Crash resilience** - Store trained models to disk to make reloading from a crash fast and easy, and [purge obsolete files](freqai-data-handling.md#purging-old-model-data) for sustained dry/live runs +* **Automatic data normalization** - [Normalize the data](freqai-feature-engineering.md#feature-normalization) in a smart and statistically safe way +* **Automatic data download** - Compute timeranges for data downloads and update historic data (in live deployments) +* **Cleaning of incoming data** - Handle NaNs safely before training and model inferencing +* **Dimensionality reduction** - Reduce the size of the training data via [Principal Component Analysis](freqai-feature-engineering.md#data-dimensionality-reduction-with-principal-component-analysis) +* **Deploying bot fleets** - Set one bot to train models while a fleet of [follower bots](freqai-running.md#setting-up-a-follower) inference the models and handle trades ## Quick start -The easiest way to quickly test FreqAI is to run it in dry mode with the following command +The easiest way to quickly test FreqAI is to run it in dry mode with the following command: ```bash freqtrade trade --config config_examples/config_freqai.example.json --strategy FreqaiExampleStrategy --freqaimodel LightGBMRegressor --strategy-path freqtrade/templates @@ -29,29 +31,31 @@ freqtrade trade --config config_examples/config_freqai.example.json --strategy F The user will see the boot-up process of automatic data downloading, followed by simultaneous training and trading. -The example strategy, example prediction model, and example config can be found in +An example strategy, prediction model, and config to use as a starting points can be found in `freqtrade/templates/FreqaiExampleStrategy.py`, `freqtrade/freqai/prediction_models/LightGBMRegressor.py`, and `config_examples/config_freqai.example.json`, respectively. ## General approach -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*). 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. +The user provides FreqAI with a set of custom *base indicators* (the same way as in a [typical Freqtrade strategy](strategy-customization.md)) as well as target values (*labels*). For each pair in the whitelist, FreqAI trains a model to predict the target values based on the input of custom indicators. The models are then consistently retrained, with a frequency set by the user, to adapt to market conditions. FreqAI offers the ability to both backtest strategies (emulating reality with periodic retraining on historic data) and deploy dry/live runs. In dry/live conditions, FreqAI can be set to constant retraining in a background thread to keep models as up to date as possible. -An overview of the algorithm is shown below, explaining the data processing pipeline and the model usage. +An overview of the algorithm, explaining the data processing pipeline and model usage, is shown below. ![freqai-algo](assets/freqai_algo.jpg) ### Important machine learning vocabulary -**Features** - the quantities with which a model is trained. All features for a single candle is stored as a vector. In FreqAI, the user builds the feature sets from anything they can construct in the strategy. +**Features** - the parameters, based on historic data, on which a model is trained. All features for a single candle is stored as a vector. In FreqAI, the user builds a feature data sets from anything they can construct in the strategy. -**Labels** - the target values that a model is trained toward. Each set of features is associated with a single label that is defined by the user within the strategy. These labels intentionally look into the future, and are not available to the model during dry/live/backtesting. +**Labels** - the target values that a model is trained toward. Each feature vector is associated with a single label that is defined by the user within the strategy. These labels intentionally look into the future, and are not available to the model during dry/live/backtesting. -**Training** - the process of feeding individual feature sets, composed of historic data, with associated labels into the model with the goal of matching input feature sets to associated labels. +**Training** - the process of "teaching" the model to match the feature sets to the associated labels. Different types of models "learn" in different ways. More information about the different models can be found [here](freqai-configuration.md#using-different-prediction-models). -**Train data** - a subset of the historic data that is fed to the model during training. This data directly influences weight connections in the model. +**Train data** - a subset of the feature data set that is fed to the model during training. This data directly influences weight connections in the model. -**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. +**Test data** - a subset of the feature data set that is used to evaluate the performance of the model after training. This data does not influence nodal weights within the model. + +**Inferencing** - the process of feeding a trained model new data on which it will make a prediction. ## Install prerequisites @@ -62,26 +66,26 @@ pip install -r requirements-freqai.txt ``` !!! Note - Catboost will not be installed on arm devices (raspberry, Mac M1, ARM based VPS, ...), since Catboost does not provide wheels for this platform. + Catboost will not be installed on arm devices (raspberry, Mac M1, ARM based VPS, ...), since it does not provide wheels for this platform. ### Usage with docker -For docker users, a dedicated tag with freqAI dependencies is available as `:freqai`. As such - you can replace the image line in your docker-compose file with `image: freqtradeorg/freqtrade:develop_freqai`. This image contains the regular freqAI dependencies. Similar to native installs, Catboost will not be available on ARM based devices. +For docker users, a dedicated tag with FreqAI dependencies is available as `:freqai`. As such - you can replace the image line in your docker-compose file with `image: freqtradeorg/freqtrade:develop_freqai`. This image contains the regular FreqAI dependencies. Similar to native installs, Catboost will not be available on ARM based devices. ## Credits -FreqAI was developed by a group of individuals who all contributed specific skillsets to the project. +FreqAI is developed by a group of individuals who all contribute specific skillsets to the project. Conception and software development: Robert Caulk @robcaulk -Theoretical brainstorming, data analysis: +Theoretical brainstorming and data analysis: Elin Törnquist @th0rntwig -Code review, software architecture brainstorming: +Code review and software architecture brainstorming: @xmatthias -Development: +Software development: Wagner Costa @wagnercosta Beta testing and bug reporting: diff --git a/mkdocs.yml b/mkdocs.yml index 726924b78..d8c23f174 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -25,9 +25,11 @@ nav: - Hyperopt: hyperopt.md - FreqAI: - Introduction: freqai.md - - Setting-up: freqai-configuration.md + - Configuration: freqai-configuration.md + - Parameter table: freqai-parameter-table.md - Feature engineering: freqai-feature-engineering.md - - Running: freqai-running.md + - Outlier detection: freqai-outlier-detection.md + - Running FreqAI: freqai-running.md - Data handling: freqai-data-handling.md - Developer guide: freqai-developers.md - Short / Leverage: leverage.md From edbe9137dab0f884bab083036f7a2b07455589e8 Mon Sep 17 00:00:00 2001 From: th0rntwig Date: Sun, 18 Sep 2022 14:51:11 +0200 Subject: [PATCH 4/9] Add Common pitfalls --- docs/freqai.md | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/docs/freqai.md b/docs/freqai.md index a025c13b9..d110c3e7a 100644 --- a/docs/freqai.md +++ b/docs/freqai.md @@ -72,6 +72,10 @@ pip install -r requirements-freqai.txt For docker users, a dedicated tag with FreqAI dependencies is available as `:freqai`. As such - you can replace the image line in your docker-compose file with `image: freqtradeorg/freqtrade:develop_freqai`. This image contains the regular FreqAI dependencies. Similar to native installs, Catboost will not be available on ARM based devices. +## Common pitfalls + +FreqAI cannot be combined with dynamic `VolumePairlists` (or any pairlist filter that adds and removes pairs dynamically). This is for performance reasons - FreqAI relies on making quick predictions/retrains. To do this effectively, it needs to download all the training data at the beginning of a dry/live instance. FreqAI stores and appends new candles automatically for future retrains. This means that if new pairs arrive later in the dry run due to a volume pairlist, it will not have the data ready. However, FreqAI does work with the `ShufflePairlist` or a `VolumePairlist` which keeps the total pairlist constant (but reorders the pairs according to volume). + ## Credits FreqAI is developed by a group of individuals who all contribute specific skillsets to the project. 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additional parameters that a user can choose from, as highlighted in the [parameter table](freqai-parameter-table.md#parameter-table), a FreqAI config must at minimum include the following parameters (the parameter values are only examples): + Although there are plenty of additional parameters to choose from, as highlighted in the [parameter table](freqai-parameter-table.md#parameter-table), a FreqAI config must at minimum include the following parameters (the parameter values are only examples): ```json "freqai": { @@ -36,7 +36,7 @@ A full example config is available in `config_examples/config_freqai.example.jso ## Building a FreqAI strategy -The FreqAI strategy requires the user to include the following lines of code in the standard [Freqtrade strategy](strategy-customization.md): +The FreqAI strategy requires including the following lines of code in the standard [Freqtrade strategy](strategy-customization.md): ```python # user should define the maximum startup candle count (the largest number of candles @@ -120,16 +120,16 @@ The FreqAI strategy requires the user to include the following lines of code in ``` -Notice how the `populate_any_indicators()` is where the user adds their own [features](freqai-feature-engineering.md#feature-engineering) and labels/tragets. A full example strategy is available in `templates/FreqaiExampleStrategy.py`. +Notice how the `populate_any_indicators()` is where [features](freqai-feature-engineering.md#feature-engineering) and labels/tragets are added. A full example strategy is available in `templates/FreqaiExampleStrategy.py`. -Notice also the location of the labels under `if set_generalized_indicators:` at the bottom of the example. This is where the user will add single features and labels/targets to their feature set to avoid duplication of them from various configuration parameters that multiply the feature set, such as `include_timeframes`. +Notice also the location of the labels under `if set_generalized_indicators:` at the bottom of the example. This is where single features and labels/targets should be added to the feature set to avoid duplication of them from various configuration parameters that multiply the feature set, such as `include_timeframes`. !!! Note The `self.freqai.start()` function cannot be called outside the `populate_indicators()`. !!! Note Features **must** be defined in `populate_any_indicators()`. Defining FreqAI features in `populate_indicators()` - will cause the algorithm to fail in live/dry mode. If the user wishes to add generalized features that are not associated with a specific pair or timeframe, they should use the following structure inside `populate_any_indicators()` + will cause the algorithm to fail in live/dry mode. In order to add generalized features that are not associated with a specific pair or timeframe, the following structure inside `populate_any_indicators()` should be used (as exemplified in `freqtrade/templates/FreqaiExampleStrategy.py`): ```python @@ -161,18 +161,18 @@ Notice also the location of the labels under `if set_generalized_indicators:` at ## Important dataframe key patterns -Below are the values the user can expect to include/use inside a typical strategy dataframe (`df[]`): +Below are the values you can expect to include/use inside a typical strategy dataframe (`df[]`): | DataFrame Key | Description | |------------|-------------| -| `df['&*']` | Any dataframe column prepended with `&` in `populate_any_indicators()` is treated as a training target (label) inside FreqAI (typically following the naming convention `&-s*`). The names of these dataframe columns are fed back to the user as the predictions. For example, if the user wishes to predict the price change in the next 40 candles (similar to `templates/FreqaiExampleStrategy.py`), they set `df['&-s_close']`. FreqAI makes the predictions and gives them back under the same key (`df['&-s_close']`) to be used in `populate_entry/exit_trend()`.
**Datatype:** Depends on the output of the model. -| `df['&*_std/mean']` | Standard deviation and mean values of the user defined labels during training (or live tracking with `fit_live_predictions_candles`). Commonly used to understand the rarity of a prediction (use the z-score as shown in `templates/FreqaiExampleStrategy.py` and explained [here](#creating-a-dynamic-target-threshold) to evaluate how often a particular prediction was observed during training or historically with `fit_live_predictions_candles`).
**Datatype:** Float. -| `df['do_predict']` | Indication of an outlier data point. The return value is integer between -1 and 2, which lets the user know if the prediction is trustworthy or not. `do_predict==1` means that the prediction is trustworthy. If the Dissimilarity Index (DI, see details [here](freqai-outlier-detection.md#identifying-outliers-with-the-dissimilarity-index)) of the input data point is above the user-defined threshold, FreqAI will subtract 1 from `do_predict`, resulting in `do_predict==0`. If `use_SVM_to_remove_outliers()` is active, the Support Vector Machine (SVM, see details [here](freqai-outlier-detection.md#identifying-outliers-using-a-support-vector-machine-svm)) may also detect outliers in training and prediction data. In this case, the SVM will also subtract 1 from `do_predict`. If the input data point was considered an outlier by the SVM but not by the DI, or vice versa, the result will be `do_predict==0`. If both the DI and the SVM considers the input data point to be an outlier, the result will be `do_predict==-1`. A particular case is when `do_predict == 2`, which means that the model has expired due to exceeding `expired_hours`.
**Datatype:** Integer between -1 and 2. -| `df['DI_values']` | Dissimilarity Index (DI) values are proxies for the level of confidence FreqAI has in the prediction. A lower DI means the prediction is close to the training data, i.e., higher prediction confidence. See details about the DI [here](freqai-outlier-detection.md#identifying-outliers-with-the-dissimilarity-index).
**Datatype:** Float. -| `df['%*']` | Any dataframe column prepended with `%` in `populate_any_indicators()` is treated as a training feature. For example, the user can include the RSI in the training feature set (similar to in `templates/FreqaiExampleStrategy.py`) by setting `df['%-rsi']`. See more details on how this is done [here](freqai-feature-engineering.md).
**Note:** Since the number of features prepended with `%` can multiply very quickly (10s of thousands of features is easily engineered using the multiplictative functionality described in the `feature_parameters` table shown above), these features are removed from the dataframe upon return from FreqAI. If the user wishes to keep a particular type of feature for plotting purposes, they can prepend it with `%%`.
**Datatype:** Depends on the output of the model. +| `df['&*']` | Any dataframe column prepended with `&` in `populate_any_indicators()` is treated as a training target (label) inside FreqAI (typically following the naming convention `&-s*`). The names of these dataframe columns are fed back as the predictions. For example, to predict the price change in the next 40 candles (similar to `templates/FreqaiExampleStrategy.py`), you would set `df['&-s_close']`. FreqAI makes the predictions and gives them back under the same key (`df['&-s_close']`) to be used in `populate_entry/exit_trend()`.
**Datatype:** Depends on the output of the model. +| `df['&*_std/mean']` | Standard deviation and mean values of the defined labels during training (or live tracking with `fit_live_predictions_candles`). Commonly used to understand the rarity of a prediction (use the z-score as shown in `templates/FreqaiExampleStrategy.py` and explained [here](#creating-a-dynamic-target-threshold) to evaluate how often a particular prediction was observed during training or historically with `fit_live_predictions_candles`).
**Datatype:** Float. +| `df['do_predict']` | Indication of an outlier data point. The return value is integer between -1 and 2, which lets you know if the prediction is trustworthy or not. `do_predict==1` means that the prediction is trustworthy. If the Dissimilarity Index (DI, see details [here](freqai-feature-engineering.md#identifying-outliers-with-the-dissimilarity-index-di)) of the input data point is above the threshold defined in the config, FreqAI will subtract 1 from `do_predict`, resulting in `do_predict==0`. If `use_SVM_to_remove_outliers()` is active, the Support Vector Machine (SVM, see details [here](freqai-feature-engineering.md#identifying-outliers-using-a-support-vector-machine-svm)) may also detect outliers in training and prediction data. In this case, the SVM will also subtract 1 from `do_predict`. If the input data point was considered an outlier by the SVM but not by the DI, or vice versa, the result will be `do_predict==0`. If both the DI and the SVM considers the input data point to be an outlier, the result will be `do_predict==-1`. A particular case is when `do_predict == 2`, which means that the model has expired due to exceeding `expired_hours`.
**Datatype:** Integer between -1 and 2. +| `df['DI_values']` | Dissimilarity Index (DI) values are proxies for the level of confidence FreqAI has in the prediction. A lower DI means the prediction is close to the training data, i.e., higher prediction confidence. See details about the DI [here](freqai-feature-engineering.md#identifying-outliers-with-the-dissimilarity-index-di).
**Datatype:** Float. +| `df['%*']` | Any dataframe column prepended with `%` in `populate_any_indicators()` is treated as a training feature. For example, you can include the RSI in the training feature set (similar to in `templates/FreqaiExampleStrategy.py`) by setting `df['%-rsi']`. See more details on how this is done [here](freqai-feature-engineering.md).
**Note:** Since the number of features prepended with `%` can multiply very quickly (10s of thousands of features is easily engineered using the multiplictative functionality described in the `feature_parameters` table shown above), these features are removed from the dataframe upon return from FreqAI. To keep a particular type of feature for plotting purposes, you would prepend it with `%%`.
**Datatype:** Depends on the output of the model. ## Setting the `startup_candle_count` -The user needs to take care to set the `startup_candle_count` in their strategy the same way as in the standard Freqtrade strategy (see details [here](strategy-customization.md#strategy-startup-period)). This value is used by Freqtrade to ensure that a sufficient amount of data is provided when calling the `dataprovider`, to avoid any NaNs at the beginning of the first training. The user 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 presented example, `startup_candle_count` is 20 since this is the maximum value in `indicators_periods_candles`. +The `startup_candle_count` in the FreqAI strategy needs to be set up in the same way as in the standard Freqtrade strategy (see details [here](strategy-customization.md#strategy-startup-period)). This value is used by Freqtrade to ensure that a sufficient amount of data is provided when calling the `dataprovider`, to avoid any NaNs at the beginning of the first training. You can easily set this value by identifying the longest period (in candle units) which is passed to the indicator creation functions (e.g., talib functions). In the presented example, `startup_candle_count` is 20 since this is the maximum value in `indicators_periods_candles`. !!! Note There are instances where the talib functions actually require more data than just the passed `period` or else the feature dataset gets populated with NaNs. Anecdotally, multiplying the `startup_candle_count` by 2 always leads to a fully NaN free training dataset. Hence, it is typically safest to multiply the expected `startup_candle_count` by 2. Look out for this log message to confirm that the data is clean: @@ -183,14 +183,14 @@ The user needs to take care to set the `startup_candle_count` in their strategy ## Creating a dynamic target threshold -Deciding when to enter or exit a trade can be done in a dynamic way to reflect current market conditions. FreqAI allows the user to return additional information from the training of a model (more info [here](freqai-data-handling.md#returning-additional-info-from-training)). For example, the `&*_std/mean` return values describe the statistical distribution of the user defined target/label *during the most recent training*. Comparing a given prediction to these values allows the user to know the rarity of the prediction. In `templates/FreqaiExampleStrategy.py`, the `target_roi` and `sell_roi` are defined to be 1.25 z-scores away from the mean which causes predictions that are closer to the mean to be filtered out. +Deciding when to enter or exit a trade can be done in a dynamic way to reflect current market conditions. FreqAI allows you to return additional information from the training of a model (more info [here](freqai-feature-engineering.md#returning-additional-info-from-training)). For example, the `&*_std/mean` return values describe the statistical distribution of the target/label *during the most recent training*. Comparing a given prediction to these values allows you to know the rarity of the prediction. In `templates/FreqaiExampleStrategy.py`, the `target_roi` and `sell_roi` are defined to be 1.25 z-scores away from the mean which causes predictions that are closer to the mean to be filtered out. ```python dataframe["target_roi"] = dataframe["&-s_close_mean"] + dataframe["&-s_close_std"] * 1.25 dataframe["sell_roi"] = dataframe["&-s_close_mean"] - dataframe["&-s_close_std"] * 1.25 ``` -If the user wishes to consider the population of *historical predictions* for creating the dynamic target instead of information from the training as discussed above, the user can do so by setting `fit_live_prediction_candles` in the config to the number of historical prediction candles the user wishes to use to generate target statistics. +To consider the population of *historical predictions* for creating the dynamic target instead of information from the training as discussed above, you would set `fit_live_prediction_candles` in the config to the number of historical prediction candles you wish to use to generate target statistics. ```json "freqai": { @@ -198,15 +198,15 @@ If the user wishes to consider the population of *historical predictions* for cr } ``` -If the user sets this value, FreqAI will initially use the predictions from the training data and subsequently begin introducing real prediction data as it is generated. FreqAI will save this historical data to be reloaded if the user stops and restarts a model with the same `identifier`. +If this value is set, FreqAI will initially use the predictions from the training data and subsequently begin introducing real prediction data as it is generated. FreqAI will save this historical data to be reloaded if you stop and restart a model with the same `identifier`. ## Using different prediction models -FreqAI has multiple example prediction model libraries that are ready to be used as is via the flag `--freqaimodel`. These libraries include `Catboost`, `LightGBM`, and `XGBoost` regression, classification, and multi-target models, and can be found in `freqai/prediction_models/`. For the more interested user, it is possible to customize and create their own prediction models using the `IFreqaiModel` class. The user is encouraged to inherit `fit()`, `train()`, and `predict()` to let these customize various aspects of their training procedures. +FreqAI has multiple example prediction model libraries that are ready to be used as is via the flag `--freqaimodel`. These libraries include `Catboost`, `LightGBM`, and `XGBoost` regression, classification, and multi-target models, and can be found in `freqai/prediction_models/`. However, it is possible to customize and create your own prediction models using the `IFreqaiModel` class. You are encouraged to inherit `fit()`, `train()`, and `predict()` to let these customize various aspects of the training procedures. ### Setting classifier targets -`FreqAI` includes a variety of classifiers, such as the `CatboostClassifier` via the flag `--freqaimodel CatboostClassifier`. If the user elects to use a classifier, they must ensure the classes are set using strings. For example: +`FreqAI` includes a variety of classifiers, such as the `CatboostClassifier` via the flag `--freqaimodel CatboostClassifier`. If you elects to use a classifier, the classes need to be set using strings. For example: ```python df['&s-up_or_down'] = np.where( df["close"].shift(-100) > df["close"], 'up', 'down') diff --git a/docs/freqai-data-handling.md b/docs/freqai-data-handling.md deleted file mode 100644 index 64983744e..000000000 --- a/docs/freqai-data-handling.md +++ /dev/null @@ -1,115 +0,0 @@ -# Data handling - -`FreqAI` aims to organize model files, prediction data, and meta data in a way that simplifies post-processing and enhances crash recililence by automatic data reloading. The data is saved in a file structure,`user_data_dir/models/`, which contains all the data associated with the trainings and backtests. The `FreqaiDataKitchen()` relies heavily on the file structure for proper training and inferencing and should therefore not be manually modified. - -## File structure - -The file structure is automatically generated based on the model `identifier` set by the user in the [config](freqai-configuration.md#setting-up-the-configuration-file). The following structure shows where the data is stored for post processing: - -| Structure | Description | -|-----------|-------------| -| `config_*.json` | A copy of the model specific configuration file. | -| `historic_predictions.pkl` | A file containing all historic predictions generated during the lifetime of the `identifier` model during live deployment. `historic_predictions.pkl` is used to reload the model after a crash or a config change. A backup file is always held incase of corruption on the main file. **FreqAI automatically detects corruption and replaces the corrupted file with the backup**. | -| `pair_dictionary.json` | A file containing the training queue as well as the on disk location of the most recently trained model. | -| `sub-train-*_TIMESTAMP` | A folder containing all the files associated with a single model, such as:
-|| `*_metadata.json` - Metadata for the model, such as normalization max/mins, expected training feature list, etc.
-|| `*_model.*` - The model file saved to disk for reloading from a crash. Can be `joblib` (typical boosting libs), `zip` (stable_baselines), `hd5` (keras type), etc.
-|| `*_pca_object.pkl` - The [Principal component analysis (PCA)](freqai-feature-engineering.md#data-dimensionality-reduction-with-principal-component-analysis) transform (if the user set `principal_component_analysis: true` in their config) which will be used to transform unseen prediction features.
-|| `*_svm_model.pkl` - The [Support Vector Machine (SVM)](freqai-outlier-detection.md#identifying-outliers-using-a-support-vector-machine-svm) model which is used to detect outliers in unseen prediction features.
-|| `*_trained_df.pkl` - The dataframe containing all the training features used to train the `identifier` model. This is used for computing the [Dissimilarity Index (DI)](freqai-outlier-detection.md#identifying-outliers-with-the-dissimilarity-index-di) and can also be used for post-processing.
-|| `*_trained_dates.df.pkl` - The dates associated with the `trained_df.pkl`, which is useful for post-processing. | - -The example file structure would look like this: - -``` -├── models -│   └── unique-id -│   ├── config_freqai.example.json -│   ├── historic_predictions.backup.pkl -│   ├── historic_predictions.pkl -│   ├── pair_dictionary.json -│   ├── sub-train-1INCH_1662821319 -│   │   ├── cb_1inch_1662821319_metadata.json -│   │   ├── cb_1inch_1662821319_model.joblib -│   │   ├── cb_1inch_1662821319_pca_object.pkl -│   │   ├── cb_1inch_1662821319_svm_model.joblib -│   │   ├── cb_1inch_1662821319_trained_dates_df.pkl -│   │   └── cb_1inch_1662821319_trained_df.pkl -│   ├── sub-train-1INCH_1662821371 -│   │   ├── cb_1inch_1662821371_metadata.json -│   │   ├── cb_1inch_1662821371_model.joblib -│   │   ├── cb_1inch_1662821371_pca_object.pkl -│   │   ├── cb_1inch_1662821371_svm_model.joblib -│   │   ├── cb_1inch_1662821371_trained_dates_df.pkl -│   │   └── cb_1inch_1662821371_trained_df.pkl -│   ├── sub-train-ADA_1662821344 -│   │   ├── cb_ada_1662821344_metadata.json -│   │   ├── cb_ada_1662821344_model.joblib -│   │   ├── cb_ada_1662821344_pca_object.pkl -│   │   ├── cb_ada_1662821344_svm_model.joblib -│   │   ├── cb_ada_1662821344_trained_dates_df.pkl -│   │   └── cb_ada_1662821344_trained_df.pkl -│   └── sub-train-ADA_1662821399 -│   ├── cb_ada_1662821399_metadata.json -│   ├── cb_ada_1662821399_model.joblib -│   ├── cb_ada_1662821399_pca_object.pkl -│   ├── cb_ada_1662821399_svm_model.joblib -│   ├── cb_ada_1662821399_trained_dates_df.pkl -│   └── cb_ada_1662821399_trained_df.pkl -``` - -## Live deployments - -### Automatic data download - -FreqAI automatically downloads the proper amount of data needed to ensure training of a model through the user defined `train_period_days` and `startup_candle_count` (see the [parameter table](freqai-parameter-table.md) for detailed descriptions of these parameters). - -### Saving prediction data - -All predictions made during the lifetime of a specific `identifier` model are stored in `historical_predictions.pkl` to allow for reloading after a crash or changes made to the config. - -### Purging old model data - -FreqAI stores new model files after each successful training. These files become obsolete as new models are generated to adapt to new market conditions. The user who is planning to leave FreqAI running for extended periods of time with high frequency retraining should enable `purge_old_models` in their config: - -```json - "freqai": { - "purge_old_models": true, - } -``` - -This will automatically purge all models older than the two most recently trained ones to save disk space. - -### Returning additional info from training - -The user may find that there are important metrics that they would like to return to their strategy at the end of each model training. Such metrics are returned by assigning them to `dk.data['extra_returns_per_train']['my_new_value'] = XYZ` inside the user's custom prediction model class. - -FreqAI takes the `my_new_value` assigned in this dictionary and expands it to fit the dataframe that is returned to the strategy. The user can then use the returned metrics in their strategy through `dataframe['my_new_value']`. An example of how return values can be used in FreqAI are the `&*_mean` and `&*_std` values that are used to [created a dynamic target threshold](freqai-configuration.md#creating-a-dynamic-target-threshold). - -Another example, where the user wants to use live metrics from the trade database, is shown below: - -```json - "freqai": { - "extra_returns_per_train": {"total_profit": 4} - } -``` - -The user needs to set the standard dictionary in the config so that FreqAI can return proper dataframe shapes. These values will likely be overridden by the prediction model, but in the case where the model has yet to set them, or needs a default initial value, the preset values are what will be returned. - -## Backtesting - -### Saving prediction data - -To allow the user to tweak their strategy (**not** the features!), FreqAI will automatically save the predictions during backtesting so that they can be reused for future backtests and live runs using the same `identifier` model. This provides a performance enhancement geared towards enabling **high-level hyperopting** of entry/exit criteria. - -An additional directory called `predictions`, which contains all the predictions stored in `hdf` format, will be created in the `unique-id` folder. - -If the user wishes to change their **features**, they **must** set a new `identifier` in the config to signal to `FreqAI` to train new models. - -If the user wishes to save the models generated during a particular backtest so that they can start a live deployment from one of them instead of training a new model, they must set `save_backtest_models` to `True` in their configuration file. - -### Downloading data to cover the full backtest period - -For live/dry deployments, FreqAI will download the necessary data automatically. However, the user who wishes to use backtesting functionality needs 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` (see the [parameter table](freqai-parameter-table.md) for detailed descriptions of these parameters) from the beginning of the desired backtesting timerange. - -As an example, to backtest the `--timerange 20210501-20210701` using the [example config](freqai-configuration.md#setting-up-the-configuration-file) which sets `train_period_days` to 30, together with `startup_candle_count: 40` on a maximum `include_timeframes` of 1h, the start date for the downloaded data needs to be `20210501` - 30 days - 40 * 1h / 24 hours = 20210330 (31.7 days earlier than the start of the desired training timerange). diff --git a/docs/freqai-developers.md b/docs/freqai-developers.md index 96a020783..97c0938b4 100644 --- a/docs/freqai-developers.md +++ b/docs/freqai-developers.md @@ -1,6 +1,8 @@ # Development -The architechture and functions of FreqAI are generalized to encourage users to develop their own features, functions, models, etc. +## Project architecture + +The architechture and functions of FreqAI are generalized to encourages development of unique features, functions, models, etc. The class structure and a detailed algorithmic overview is depicted in the following diagram: @@ -13,3 +15,64 @@ As shown, there are three distinct objects comprising FreqAI: * **FreqaiDataDrawer** - A singular persistent object containing all the historical predictions, models, and save/load methods. There are a variety of built-in [prediction models](freqai-configuration.md#using-different-prediction-models) which inherit directly from `IFreqaiModel`. Each of these models have full access to all methods in `IFreqaiModel` and can therefore override any of those functions at will. However, advanced users will likely stick to overriding `fit()`, `train()`, `predict()`, and `data_cleaning_train/predict()`. + +## Data handling + +`FreqAI` aims to organize model files, prediction data, and meta data in a way that simplifies post-processing and enhances crash recililence by automatic data reloading. The data is saved in a file structure,`user_data_dir/models/`, which contains all the data associated with the trainings and backtests. The `FreqaiDataKitchen()` relies heavily on the file structure for proper training and inferencing and should therefore not be manually modified. + +### File structure + +The file structure is automatically generated based on the model `identifier` set in the [config](freqai-configuration.md#setting-up-the-configuration-file). The following structure shows where the data is stored for post processing: + +| Structure | Description | +|-----------|-------------| +| `config_*.json` | A copy of the model specific configuration file. | +| `historic_predictions.pkl` | A file containing all historic predictions generated during the lifetime of the `identifier` model during live deployment. `historic_predictions.pkl` is used to reload the model after a crash or a config change. A backup file is always held incase of corruption on the main file. **FreqAI automatically detects corruption and replaces the corrupted file with the backup**. | +| `pair_dictionary.json` | A file containing the training queue as well as the on disk location of the most recently trained model. | +| `sub-train-*_TIMESTAMP` | A folder containing all the files associated with a single model, such as:
+|| `*_metadata.json` - Metadata for the model, such as normalization max/mins, expected training feature list, etc.
+|| `*_model.*` - The model file saved to disk for reloading from a crash. Can be `joblib` (typical boosting libs), `zip` (stable_baselines), `hd5` (keras type), etc.
+|| `*_pca_object.pkl` - The [Principal component analysis (PCA)](freqai-feature-engineering.md#data-dimensionality-reduction-with-principal-component-analysis) transform (if `principal_component_analysis: true` is set in the config) which will be used to transform unseen prediction features.
+|| `*_svm_model.pkl` - The [Support Vector Machine (SVM)](freqai-feature-engineering.md#identifying-outliers-using-a-support-vector-machine-svm) model which is used to detect outliers in unseen prediction features.
+|| `*_trained_df.pkl` - The dataframe containing all the training features used to train the `identifier` model. This is used for computing the [Dissimilarity Index (DI)](freqai-feature-engineering.md#identifying-outliers-with-the-dissimilarity-index-di) and can also be used for post-processing.
+|| `*_trained_dates.df.pkl` - The dates associated with the `trained_df.pkl`, which is useful for post-processing. | + +The example file structure would look like this: + +``` +├── models +│   └── unique-id +│   ├── config_freqai.example.json +│   ├── historic_predictions.backup.pkl +│   ├── historic_predictions.pkl +│   ├── pair_dictionary.json +│   ├── sub-train-1INCH_1662821319 +│   │   ├── cb_1inch_1662821319_metadata.json +│   │   ├── cb_1inch_1662821319_model.joblib +│   │   ├── cb_1inch_1662821319_pca_object.pkl +│   │   ├── cb_1inch_1662821319_svm_model.joblib +│   │   ├── cb_1inch_1662821319_trained_dates_df.pkl +│   │   └── cb_1inch_1662821319_trained_df.pkl +│   ├── sub-train-1INCH_1662821371 +│   │   ├── cb_1inch_1662821371_metadata.json +│   │   ├── cb_1inch_1662821371_model.joblib +│   │   ├── cb_1inch_1662821371_pca_object.pkl +│   │   ├── cb_1inch_1662821371_svm_model.joblib +│   │   ├── cb_1inch_1662821371_trained_dates_df.pkl +│   │   └── cb_1inch_1662821371_trained_df.pkl +│   ├── sub-train-ADA_1662821344 +│   │   ├── cb_ada_1662821344_metadata.json +│   │   ├── cb_ada_1662821344_model.joblib +│   │   ├── cb_ada_1662821344_pca_object.pkl +│   │   ├── cb_ada_1662821344_svm_model.joblib +│   │   ├── cb_ada_1662821344_trained_dates_df.pkl +│   │   └── cb_ada_1662821344_trained_df.pkl +│   └── sub-train-ADA_1662821399 +│   ├── cb_ada_1662821399_metadata.json +│   ├── cb_ada_1662821399_model.joblib +│   ├── cb_ada_1662821399_pca_object.pkl +│   ├── cb_ada_1662821399_svm_model.joblib +│   ├── cb_ada_1662821399_trained_dates_df.pkl +│   └── cb_ada_1662821399_trained_df.pkl + +``` diff --git a/docs/freqai-feature-engineering.md b/docs/freqai-feature-engineering.md index 753c289f7..7dd41fd88 100644 --- a/docs/freqai-feature-engineering.md +++ b/docs/freqai-feature-engineering.md @@ -2,9 +2,9 @@ ## Defining the features -Feature engineering is handled within `"feature_parameters":{}` in the `FreqAI` config file and in the user strategy. The user adds all their `base features`, such as, e.g., `RSI`, `MFI`, `EMA`, `SMA`, etc., to their strategy. The `base features` can be custom indicators or they can be imported from any technical-analysis library that the user can find. The `base features` are added by the user inside the `populate_any_indicators()` method of the strategy by prepending indicators with `%`, and labels with `&`. +Feature engineering is handled within `"feature_parameters":{}` in the `FreqAI` config and strategy. All `base features` you wish to use, such as, e.g., `RSI`, `MFI`, `EMA`, `SMA`, etc., should be added to the strategy. The `base features` can be custom indicators or they can be imported from any technical-analysis library that you can find. The `base features` are added inside the `populate_any_indicators()` method of the strategy by prepending indicators with `%`, and labels with `&`. -It is advisable for the user to start from the `populate_any_indicators()` in the example strategy (found in `templates/FreqaiExampleStrategy.py`) to ensure that they are following the correct conventions. Here is an example of how to set the indicators and labels in the strategy: +It is advisable to start from the `populate_any_indicators()` in the example strategy (found in `templates/FreqaiExampleStrategy.py`) to ensure that the feature definitions are following the correct conventions. Here is an example of how to set the indicators and labels in the strategy: ```python def populate_any_indicators( @@ -118,21 +118,40 @@ After having defined the `base features`, the next step is to expand upon them u The `include_timeframes` in the config above are the timeframes (`tf`) of each call to `populate_any_indicators()` in the strategy. In the presented case, the user is asking for the `5m`, `15m`, and `4h` timeframes of the `rsi`, `mfi`, `roc`, and `bb_width` to be included in the feature set. -The user can ask for each of the defined features to be included also for informative pairs using the `include_corr_pairlist`. This means that the feature set will include all the features from `populate_any_indicators` on all the `include_timeframes` for each of the correlated pairs defined in the config (`ETH/USD`, `LINK/USD`, and `BNB/USD` in the presented example). +You can ask for each of the defined features to be included also for informative pairs using the `include_corr_pairlist`. This means that the feature set will include all the features from `populate_any_indicators` on all the `include_timeframes` for each of the correlated pairs defined in the config (`ETH/USD`, `LINK/USD`, and `BNB/USD` in the presented example). `include_shifted_candles` indicates the number of previous candles to include in the feature set. For example, `include_shifted_candles: 2` tells `FreqAI` to include the past 2 candles for each of the features in the feature set. In total, the number of features the user of the presented example strat has created is: length of `include_timeframes` * no. features in `populate_any_indicators()` * length of `include_corr_pairlist` * no. `include_shifted_candles` * length of `indicator_periods_candles` $= 3 * 3 * 3 * 2 * 2 = 108$. +### Returning additional info from training + +Important metrics can be returned to the strategy at the end of each model training by assigning them to `dk.data['extra_returns_per_train']['my_new_value'] = XYZ` inside the custom prediction model class. + +FreqAI takes the `my_new_value` assigned in this dictionary and expands it to fit the dataframe that is returned to the strategy. You can then use the returned metrics in your strategy through `dataframe['my_new_value']`. An example of how return values can be used in FreqAI are the `&*_mean` and `&*_std` values that are used to [created a dynamic target threshold](freqai-configuration.md#creating-a-dynamic-target-threshold). + +Another example, where the user wants to use live metrics from the trade database, is shown below: + +```json + "freqai": { + "extra_returns_per_train": {"total_profit": 4} + } +``` + +You need to set the standard dictionary in the config so that FreqAI can return proper dataframe shapes. These values will likely be overridden by the prediction model, but in the case where the model has yet to set them, or needs a default initial value, the preset values are what will be returned. ## Feature normalization -FreqAI is strict when it comes to data normalization. The train features are always normalized to [-1, 1] and all other data (test data and unseen prediction data in dry/live/backtest) is always automatically normalized to the training feature space according to industry standards. FreqAI stores all the metadata required to ensure that test and prediction features will be properly normalized and that predictions are properly denormalized. For this reason, it is not recommended to eschew industry standards and modify FreqAI internals - however - advanced users can do so by inheriting `train()` in their custom `IFreqaiModel` and using their own normalization functions. +FreqAI is strict when it comes to data normalization. The train features, $X^{train}$, are always normalized to [-1, 1] using a shifted min-max normalization: + +$$X^{train}_{norm} = 2 * \frac{X^{train} - X^{train}.min()}{X^{train}.max() - X^{train}.min()} - 1$$ + +All other data (test data and unseen prediction data in dry/live/backtest) is always automatically normalized to the training feature space according to industry standards. FreqAI stores all the metadata required to ensure that test and prediction features will be properly normalized and that predictions are properly denormalized. For this reason, it is not recommended to eschew industry standards and modify FreqAI internals - however - advanced users can do so by inheriting `train()` in their custom `IFreqaiModel` and using their own normalization functions. ## Data dimensionality reduction with Principal Component Analysis -The user can reduce the dimensionality of their features by activating the `principal_component_analysis` in the config: +You can reduce the dimensionality of your features by activating the `principal_component_analysis` in the config: ```json "freqai": { @@ -142,13 +161,15 @@ The user can reduce the dimensionality of their features by activating the `prin } ``` -This will perform PCA on the features and reduce their dimensionality so that the explained variance of the data set is >= 0.999. Reducing data dimensionality makes training the model faster and hence allows for more up-to-date models. +This will perform PCA on the features and reduce their dimensionality so that the explained variance of the data set is >= 0.999. Reducing data dimensionality makes training the model faster and hence allows for more up-to-date models. ## Inlier metric The `inlier_metric` is a metric aimed at quantifying how similar a the features of a data point are to the most recent historic data points. -The user defines the lookback window by setting `inlier_metric_window` and FreqAI computes the distance between the present time point and each of the previous `inlier_metric_window` lookback points. A Weibull function is fit to each of the lookback distributions and its cumulative distribution function is used to produce a quantile for each lookback point. The `inlier_metric` is then computed for each time point as the average of the corresponding lookback quantiles. +You define the lookback window by setting `inlier_metric_window` and FreqAI computes the distance between the present time point and each of the previous `inlier_metric_window` lookback points. A Weibull function is fit to each of the lookback distributions and its cumulative distribution function (CDF) is used to produce a quantile for each lookback point. The `inlier_metric` is then computed for each time point as the average of the corresponding lookback quantiles. The figure below explains the concept for an `inlier_metric_window` of 5. + +![inlier-metric](assets/freqai_inlier-metric.jpg) FreqAI adds the `inlier_metric` to the training features and hence gives the model access to a novel type of temporal information. @@ -156,10 +177,90 @@ This function does **not** remove outliers from the data set. ## Weighting features for temporal importance -FreqAI allows the user to set a `weight_factor` to weight recent data more strongly than past data via an exponential function: +FreqAI allows you to set a `weight_factor` to weight recent data more strongly than past data via an exponential function: $$ W_i = \exp(\frac{-i}{\alpha*n}) $$ where $W_i$ is the weight of data point $i$ in a total set of $n$ data points. Below is a figure showing the effect of different weight factors on the data points in a feature set. ![weight-factor](assets/freqai_weight-factor.jpg) + +## Outlier detection + +Equity and crypto markets suffer from a high level of non-patterned noise in the form of outlier data points. FreqAI implements a variety of methods to identify such outliers and hence mitigate risk. + +### Identifying outliers with the Dissimilarity Index (DI) + + The Dissimilarity Index (DI) aims to quantify the uncertainty associated with each prediction made by the model. + +You can tell FreqAI to remove outlier data points from the training/test data sets using the DI by including the following statement in the config: + +```json + "freqai": { + "feature_parameters" : { + "DI_threshold": 1 + } + } +``` + + The DI allows predictions which are outliers (not existent in the model feature space) to be thrown out due to low levels of certainty. To do so, FreqAI measures the distance between each training data point (feature vector), $X_{a}$, and all other training data points: + +$$ d_{ab} = \sqrt{\sum_{j=1}^p(X_{a,j}-X_{b,j})^2} $$ + +where $d_{ab}$ is the distance between the normalized points $a$ and $b$, and $p$ is the number of features, i.e., the length of the vector $X$. The characteristic distance, $\overline{d}$, for a set of training data points is simply the mean of the average distances: + +$$ \overline{d} = \sum_{a=1}^n(\sum_{b=1}^n(d_{ab}/n)/n) $$ + +$\overline{d}$ quantifies the spread of the training data, which is compared to the distance between a new prediction feature vectors, $X_k$ and all the training data: + +$$ d_k = \arg \min d_{k,i} $$ + +This enables the estimation of the Dissimilarity Index as: + +$$ DI_k = d_k/\overline{d} $$ + +You can tweak the DI through the `DI_threshold` to increase or decrease the extrapolation of the trained model. A higher `DI_threshold` means that the DI is more lenient and allows predictions further away from the training data to be used whilst a lower `DI_threshold` has the opposite effect and hence discards more predictions. + +Below is a figure that describes the DI for a 3D data set. + +![DI](assets/freqai_DI.jpg) + +### Identifying outliers using a Support Vector Machine (SVM) + +You can tell FreqAI to remove outlier data points from the training/test data sets using a Support Vector Machine (SVM) by including the following statement in the config: + +```json + "freqai": { + "feature_parameters" : { + "use_SVM_to_remove_outliers": true + } + } +``` + +The SVM will be trained on the training data and any data point that the SVM deems to be beyond the feature space will be removed. + +FreqAI uses `sklearn.linear_model.SGDOneClassSVM` (details are available on scikit-learn's webpage [here](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDOneClassSVM.html) (external website)) and you can elect to provide additional parameters for the SVM, such as `shuffle`, and `nu`. + +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 and should be between 0 and 1. + +### Identifying outliers with DBSCAN + +You can configure FreqAI to use DBSCAN to cluster and remove outliers from the training/test data set or incoming outliers from predictions, by activating `use_DBSCAN_to_remove_outliers` in the config: + +```json + "freqai": { + "feature_parameters" : { + "use_DBSCAN_to_remove_outliers": true + } + } +``` + +DBSCAN is an unsupervised machine learning algorithm that clusters data without needing to know how many clusters there should be. + +Given a number of data points $N$, and a distance $\varepsilon$, DBSCAN clusters the data set by setting all data points that have $N-1$ other data points within a distance of $\varepsilon$ as *core points*. A data point that is within a distance of $\varepsilon$ from a *core point* but that does not have $N-1$ other data points within a distance of $\varepsilon$ from itself is considered an *edge point*. A cluster is then the collection of *core points* and *edge points*. Data points that have no other data points at a distance $<\varepsilon$ are considered outliers. The figure below shows a cluster with $N = 3$. + +![dbscan](assets/freqai_dbscan.jpg) + +FreqAI uses `sklearn.cluster.DBSCAN` (details are available on scikit-learn's webpage [here](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.DBSCAN.html) (external website)) with `min_samples` ($N$) taken as 1/4 of the no. of time points in the feature set. `eps` ($\varepsilon$) is computed automatically as the elbow point in the *k-distance graph* computed from the nearest neighbors in the pairwise distances of all data points in the feature set. diff --git a/docs/freqai-outlier-detection.md b/docs/freqai-outlier-detection.md deleted file mode 100644 index 1f41e91cd..000000000 --- a/docs/freqai-outlier-detection.md +++ /dev/null @@ -1,79 +0,0 @@ -# Outlier detection - -Equity and crypto markets suffer from a high level of non-patterned noise in the form of outlier data points. FreqAI implements a variety of methods to identify such outliers and hence mitigate risk. - -## Identifying outliers with the Dissimilarity Index (DI) - - The Dissimilarity Index (DI) aims to quantify the uncertainty associated with each prediction made by the model. - -The user can tell FreqAI to remove outlier data points from the training/test data sets using the DI by including the following statement in the config: - -```json - "freqai": { - "feature_parameters" : { - "DI_threshold": 1 - } - } -``` - - The DI allows predictions which are outliers (not existent in the model feature space) to be thrown out due to low levels of certainty. To do so, FreqAI measures the distance between each training data point (feature vector), $X_{a}$, and all other training data points: - -$$ d_{ab} = \sqrt{\sum_{j=1}^p(X_{a,j}-X_{b,j})^2} $$ - -where $d_{ab}$ is the distance between the normalized points $a$ and $b$, and $p$ is the number of features, i.e., the length of the vector $X$. The characteristic distance, $\overline{d}$, for a set of training data points is simply the mean of the average distances: - -$$ \overline{d} = \sum_{a=1}^n(\sum_{b=1}^n(d_{ab}/n)/n) $$ - -$\overline{d}$ quantifies the spread of the training data, which is compared to the distance between a new prediction feature vectors, $X_k$ and all the training data: - -$$ d_k = \arg \min d_{k,i} $$ - -This 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. A higher `DI_threshold` means that the DI is more lenient and allows predictions further away from the training data to be used whilst a lower `DI_threshold` has the opposite effect and hence discards more predictions. - -Below is a figure that describes the DI for a 3D data set. - -![DI](assets/freqai_DI.jpg) - -## Identifying outliers using a Support Vector Machine (SVM) - -The user can tell FreqAI to remove outlier data points from the training/test data sets using a Support Vector Machine (SVM) by including the following statement in the config: - -```json - "freqai": { - "feature_parameters" : { - "use_SVM_to_remove_outliers": true - } - } -``` - -The SVM will be trained on the training data and any data point that the SVM deems to be beyond the feature space will be removed. - -FreqAI uses `sklearn.linear_model.SGDOneClassSVM` (details are available on scikit-learn's webpage [here](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDOneClassSVM.html) (external website)) and the user can elect to provide additional parameters for the SVM, such as `shuffle`, and `nu`. - -The parameter `shuffle` is by default set to `False` to ensure consistent results. If it is set to `True`, running the SVM multiple times on the same data set might result in different outcomes due to `max_iter` being to low for the algorithm to reach the demanded `tol`. Increasing `max_iter` solves this issue but causes the procedure to take longer time. - -The parameter `nu`, *very* broadly, is the amount of data points that should be considered outliers and should be between 0 and 1. - -## Identifying outliers with DBSCAN - -The user can configure FreqAI to use DBSCAN to cluster and remove outliers from the training/test data set or incoming outliers from predictions, by activating `use_DBSCAN_to_remove_outliers` in the config: - -```json - "freqai": { - "feature_parameters" : { - "use_DBSCAN_to_remove_outliers": true - } - } -``` - -DBSCAN is an unsupervised machine learning algorithm that clusters data without needing to know how many clusters there should be. - -Given a number of data points $N$, and a distance $\varepsilon$, DBSCAN clusters the data set by setting all data points that have $N-1$ other data points within a distance of $\varepsilon$ as *core points*. A data point that is within a distance of $\varepsilon$ from a *core point* but that does not have $N-1$ other data points within a distance of $\varepsilon$ from itself is considered an *edge point*. A cluster is then the collection of *core points* and *edge points*. Data points that have no other data points at a distance $<\varepsilon$ are considered outliers. The figure below shows a cluster with $N = 3$. - -![dbscan](assets/freqai_dbscan.jpg) - -FreqAI uses `sklearn.cluster.DBSCAN` (details are available on scikit-learn's webpage [here](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.DBSCAN.html) (external website)) with `min_samples` ($N$) taken as 1/4 of the no. of time points in the feature set. `eps` ($\varepsilon$) is computed automatically as the elbow point in the *k-distance graph* computed from the nearest neighbors in the pairwise distances of all data points in the feature set. diff --git a/docs/freqai-parameter-table.md b/docs/freqai-parameter-table.md index f26762809..605892134 100644 --- a/docs/freqai-parameter-table.md +++ b/docs/freqai-parameter-table.md @@ -9,43 +9,43 @@ Mandatory parameters are marked as **Required** and have to be set in one of the | | **General configuration parameters** | `freqai` | **Required.**
The parent dictionary containing all the parameters for controlling FreqAI.
**Datatype:** Dictionary. | `train_period_days` | **Required.**
Number of days to use for the training data (width of the sliding window).
**Datatype:** Positive integer. -| `backtest_period_days` | **Required.**
Number of days to inference from the trained model before sliding the `train_period_days` window defined above, and retraining the model during backtesting (more info [here](freqai-running.md#backtesting)). This can be fractional days, but beware that the user provided `timerange` will be divided by this number to yield the number of trainings necessary to complete the backtest.
**Datatype:** Float. -| `identifier` | **Required.**
A unique ID for the current model. If models are saved to disk, the `identifier` allows the user to reload specific pre-trained models/data.
**Datatype:** String. +| `backtest_period_days` | **Required.**
Number of days to inference from the trained model before sliding the `train_period_days` window defined above, and retraining the model during backtesting (more info [here](freqai-running.md#backtesting)). This can be fractional days, but beware that the provided `timerange` will be divided by this number to yield the number of trainings necessary to complete the backtest.
**Datatype:** Float. +| `identifier` | **Required.**
A unique ID for the current model. If models are saved to disk, the `identifier` allows for reloading specific pre-trained models/data.
**Datatype:** String. | `live_retrain_hours` | Frequency of retraining during dry/live runs.
**Datatype:** Float > 0.
Default: 0 (models retrain as often as possible). | `expiration_hours` | Avoid making predictions if a model is more than `expiration_hours` old.
**Datatype:** Positive integer.
Default: 0 (models never expire). | `purge_old_models` | Delete obsolete models.
**Datatype:** Boolean.
Default: `False` (all historic models remain on disk). -| `save_backtest_models` | Save models to disk when running backtesting. Backtesting operates most efficiently by saving the prediction data and reusing them directly for subsequent runs (when the user wishes to tune entry/exit parameters). Saving backtesting models to disk also allows to use the same model files for starting a dry/live instance with the same model `identifier`.
**Datatype:** Boolean.
Default: `False` (no models are saved). +| `save_backtest_models` | Save models to disk when running backtesting. Backtesting operates most efficiently by saving the prediction data and reusing them directly for subsequent runs (when you wish to tune entry/exit parameters). Saving backtesting models to disk also allows to use the same model files for starting a dry/live instance with the same model `identifier`.
**Datatype:** Boolean.
Default: `False` (no models are saved). | `fit_live_predictions_candles` | Number of historical candles to use for computing target (label) statistics from prediction data, instead of from the training dataset (more information can be found [here](freqai-configuration.md#creating-a-dynamic-target-threshold)).
**Datatype:** Positive integer. | `follow_mode` | Use a `follower` that will look for models associated with a specific `identifier` and load those for inferencing. A `follower` will **not** train new models.
**Datatype:** Boolean.
Default: `False`. -| `continual_learning` | Use the final state of the most recently trained model as starting point for the new model, allowing for incremental learning.
**Datatype:** Boolean.
Default: `False`. +| `continual_learning` | Use the final state of the most recently trained model as starting point for the new model, allowing for incremental learning (more information can be found [here](freqai-running.md#continual-learning)).
**Datatype:** Boolean.
Default: `False`. | | **Feature parameters** | `feature_parameters` | A dictionary containing the parameters used to engineer the feature set. Details and examples are shown [here](freqai-feature-engineering.md).
**Datatype:** Dictionary. | `include_timeframes` | A list of timeframes that all indicators in `populate_any_indicators` will be created for. The list is added as features to the base indicators dataset.
**Datatype:** List of timeframes (strings). | `include_corr_pairlist` | A list of correlated coins that FreqAI will add as additional features to all `pair_whitelist` coins. All indicators set in `populate_any_indicators` during feature engineering (see details [here](freqai-feature-engineering.md)) will be created for each correlated coin. The correlated coins features are added to the base indicators dataset.
**Datatype:** List of assets (strings). -| `label_period_candles` | Number of candles into the future that the labels are created for. This is used in `populate_any_indicators` (see `templates/FreqaiExampleStrategy.py` for detailed usage). The user can create custom labels and choose whether to make use of this parameter or not.
**Datatype:** Positive integer. +| `label_period_candles` | Number of candles into the future that the labels are created for. This is used in `populate_any_indicators` (see `templates/FreqaiExampleStrategy.py` for detailed usage). You can create custom labels and choose whether to make use of this parameter or not.
**Datatype:** Positive integer. | `include_shifted_candles` | Add features from previous candles to subsequent candles with the intent of adding historical information. If used, FreqAI will duplicate and shift all features from the `include_shifted_candles` previous candles so that the information is available for the subsequent candle.
**Datatype:** Positive integer. | `weight_factor` | Weight training data points according to their recency (see details [here](freqai-feature-engineering.md#weighting-features-for-temporal-importance)).
**Datatype:** Positive float (typically < 1). | `indicator_max_period_candles` | **No longer used (#7325)**. Replaced by `startup_candle_count` which is set in the [strategy](freqai-configuration.md#building-a-freqai-strategy). `startup_candle_count` is timeframe independent and defines the maximum *period* used in `populate_any_indicators()` for indicator creation. FreqAI uses this parameter together with the maximum timeframe in `include_time_frames` to calculate how many data points to download such that the first data point does not include a NaN
**Datatype:** Positive integer. | `indicator_periods_candles` | Time periods to calculate indicators for. The indicators are added to the base indicator dataset.
**Datatype:** List of positive integers. | `stratify_training_data` | Split the feature set into training and testing datasets. For example, `stratify_training_data: 2` would set every 2nd data point into a separate dataset to be pulled from during training/testing. See details about how it works [here](freqai-running.md#data-stratification-for-training-and-testing-the-model).
**Datatype:** Positive integer. | `principal_component_analysis` | Reduce the dimensionality of the dataset using Principal Component Analysis. See details about how it works [here](freqai-feature-engineering.md#data-dimensionality-reduction-with-principal-component-analysis).
**Datatype:** Boolean. -| `DI_threshold` | Activates the use of the Dissimilarity Index for outlier detection when set to > 0. See details about how it works [here](freqai-outlier-detection.md#identifying-outliers-with-the-dissimilarity-index).
**Datatype:** Positive float (typically < 1). -| `use_SVM_to_remove_outliers` | Train a support vector machine to detect and remove outliers from the training dataset, as well as from incoming data points. See details about how it works [here](freqai-outlier-detection.md#identifying-outliers-using-a-support-vector-machine-svm).
**Datatype:** Boolean. -| `svm_params` | All parameters available in Sklearn's `SGDOneClassSVM()`. See details about some select parameters [here](freqai-outlier-detection.md#identifying-outliers-using-a-support-vector-machine-svm).
**Datatype:** Dictionary. -| `use_DBSCAN_to_remove_outliers` | Cluster data using the DBSCAN algorithm to identify and remove outliers from training and prediction data. See details about how it works [here](freqai-outlier-detection.md#identifying-outliers-with-dbscan).
**Datatype:** Boolean. +| `DI_threshold` | Activates the use of the Dissimilarity Index for outlier detection when set to > 0. See details about how it works [here](freqai-feature-engineering.md#identifying-outliers-with-the-dissimilarity-index-di).
**Datatype:** Positive float (typically < 1). +| `use_SVM_to_remove_outliers` | Train a support vector machine to detect and remove outliers from the training dataset, as well as from incoming data points. See details about how it works [here](freqai-feature-engineering.md#identifying-outliers-using-a-support-vector-machine-svm).
**Datatype:** Boolean. +| `svm_params` | All parameters available in Sklearn's `SGDOneClassSVM()`. See details about some select parameters [here](freqai-feature-engineering.md#identifying-outliers-using-a-support-vector-machine-svm).
**Datatype:** Dictionary. +| `use_DBSCAN_to_remove_outliers` | Cluster data using the DBSCAN algorithm to identify and remove outliers from training and prediction data. See details about how it works [here](freqai-feature-engineering.md#identifying-outliers-with-dbscan).
**Datatype:** Boolean. | `inlier_metric_window` | If set, FreqAI adds an `inlier_metric` to the training feature set and set the lookback to be the `inlier_metric_window`, i.e., the number of previous time points to compare the current candle to. Details of how the `inlier_metric` is computed can be found [here](freqai-feature-engineering.md#inlier-metric).
**Datatype:** Integer.
Default: 0. | `noise_standard_deviation` | If set, FreqAI adds noise to the training features with the aim of preventing overfitting. FreqAI generates random deviates from a gaussian distribution with a standard deviation of `noise_standard_deviation` and adds them to all data points. `noise_standard_deviation` should be kept relative to the normalized space, i.e., between -1 and 1. In other words, since data in FreqAI is always normalized to be between -1 and 1, `noise_standard_deviation: 0.05` would result in 32% of the data being randomly increased/decreased by more than 2.5% (i.e., the percent of data falling within the first standard deviation).
**Datatype:** Integer.
Default: 0. | `outlier_protection_percentage` | Enable to prevent outlier detection methods from discarding too much data. If more than `outlier_protection_percentage` % of points are detected as outliers by the SVM or DBSCAN, FreqAI will log a warning message and ignore outlier detection, i.e., the original dataset will be kept intact. If the outlier protection is triggered, no predictions will be made based on the training dataset.
**Datatype:** Float.
Default: `30`. -| `reverse_train_test_order` | Split the feature dataset (see below) and use the latest data split for training and test on historical split of the data. This allows the model to be trained up to the most recent data point, while avoiding overfitting. However, users should be careful to understand the unorthodox nature of this parameter before employing it.
**Datatype:** Boolean.
Default: `False` (no reversal). +| `reverse_train_test_order` | Split the feature dataset (see below) and use the latest data split for training and test on historical split of the data. This allows the model to be trained up to the most recent data point, while avoiding overfitting. However, you should be careful to understand the unorthodox nature of this parameter before employing it.
**Datatype:** Boolean.
Default: `False` (no reversal). | | **Data split parameters** | `data_split_parameters` | Include any additional parameters available from Scikit-learn `test_train_split()`, which are shown [here](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html) (external website).
**Datatype:** Dictionary. | `test_size` | The fraction of data that should be used for testing instead of training.
**Datatype:** Positive float < 1. | `shuffle` | Shuffle the training data points during training. Typically, for time-series forecasting, this is set to `False`.
**Datatype:** Boolean. | | **Model training parameters** -| `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.
**Datatype:** Dictionary. +| `model_training_parameters` | A flexible dictionary that includes all parameters available by the selected model library. For example, if you use `LightGBMRegressor`, this dictionary can contain any parameter available by the `LightGBMRegressor` [here](https://lightgbm.readthedocs.io/en/latest/pythonapi/lightgbm.LGBMRegressor.html) (external website). If you select a different model, this dictionary can contain any parameter from that model.
**Datatype:** Dictionary. | `n_estimators` | The number of boosted trees to fit in regression.
**Datatype:** Integer. | `learning_rate` | Boosting learning rate during regression.
**Datatype:** Float. | `n_jobs`, `thread_count`, `task_type` | Set the number of threads for parallel processing and the `task_type` (`gpu` or `cpu`). Different model libraries use different parameter names.
**Datatype:** Float. | | **Extraneous parameters** -| `keras` | If the user selected model makes use of Keras (typical for Tensorflow-based prediction models), this flag needs to be activated so that the model save/loading follows Keras standards.
**Datatype:** Boolean.
Default: `False`. +| `keras` | If the selected model makes use of Keras (typical for Tensorflow-based prediction models), this flag needs to be activated so that the model save/loading follows Keras standards.
**Datatype:** Boolean.
Default: `False`. | `conv_width` | The width of a convolutional neural network input tensor. This replaces the need for shifting candles (`include_shifted_candles`) by feeding in historical data points as the second dimension of the tensor. Technically, this parameter can also be used for regressors, but it only adds computational overhead and does not change the model training/prediction.
**Datatype:** Integer.
Default: 2. diff --git a/docs/freqai-running.md b/docs/freqai-running.md index 875b0c149..ff9079397 100644 --- a/docs/freqai-running.md +++ b/docs/freqai-running.md @@ -4,7 +4,7 @@ There are two ways to train and deploy an adaptive machine learning model - live ![freqai-window](assets/freqai_moving-window.jpg) -## Running the model live +## Live deployments FreqAI can be run dry/live using the following command: @@ -12,11 +12,11 @@ FreqAI can be run dry/live using the following command: freqtrade trade --strategy FreqaiExampleStrategy --config config_freqai.example.json --freqaimodel LightGBMRegressor ``` -When launched, FreqAI will start training a new model based on the user's config settings. Following training, the model will be used to make predictions on incoming candles until a new model is available. New models are typically generated as often as possible, with FreqAI managing an internal queue of the coin pairs to try to keep all models equally up to date. FreqAI will always use the most recently trained model to make predictions on incoming live data. If the user does not want FreqAI to retrain new models as often as possible, they can set `live_retrain_hours` to tell FreqAI to wait at least that number of hours before training a new model. Additionally, the user can set `expired_hours` to tell FreqAI to avoid making predictions on models that are older than that number of hours. +When launched, FreqAI will start training a new model, with a new `identifier`, based on the config settings. Following training, the model will be used to make predictions on incoming candles until a new model is available. New models are typically generated as often as possible, with FreqAI managing an internal queue of the coin pairs to try to keep all models equally up to date. FreqAI will always use the most recently trained model to make predictions on incoming live data. If you do not want FreqAI to retrain new models as often as possible, you can set `live_retrain_hours` to tell FreqAI to wait at least that number of hours before training a new model. Additionally, you can set `expired_hours` to tell FreqAI to avoid making predictions on models that are older than that number of hours. -Trained models are by default saved to disk to allow for reuse during backtesting or after a crash. The user can opt to purge old models to save disk space by setting `"purge_old_models": true` in the config. +Trained models are by default saved to disk to allow for reuse during backtesting or after a crash. You can opt to [purge old models](#purging-old-model-data) to save disk space by setting `"purge_old_models": true` in the config. -If the user wishes to start a dry/live run from a saved backtest model (or from a previously crashed dry/live session), the user only needs to specify the `identifier` of the specific model: +To start a dry/live run from a saved backtest model (or from a previously crashed dry/live session), you only need to specify the `identifier` of the specific model: ```json "freqai": { @@ -27,6 +27,26 @@ If the user wishes to start a dry/live run from a saved backtest model (or from In this case, although FreqAI will initiate with a pre-trained model, it will still check to see how much time has elapsed since the model was trained. If a full `live_retrain_hours` has elapsed since the end of the loaded model, FreqAI will start training a new model. +### Automatic data download + +FreqAI automatically downloads the proper amount of data needed to ensure training of a model through the defined `train_period_days` and `startup_candle_count` (see the [parameter table](freqai-parameter-table.md) for detailed descriptions of these parameters). + +### Saving prediction data + +All predictions made during the lifetime of a specific `identifier` model are stored in `historical_predictions.pkl` to allow for reloading after a crash or changes made to the config. + +### Purging old model data + +FreqAI stores new model files after each successful training. These files become obsolete as new models are generated to adapt to new market conditions. If you are planning to leave FreqAI running for extended periods of time with high frequency retraining, you should enable `purge_old_models` in the config: + +```json + "freqai": { + "purge_old_models": true, + } +``` + +This will automatically purge all models older than the two most recently trained ones to save disk space. + ## Backtesting The FreqAI backtesting module can be executed with the following command: @@ -38,29 +58,44 @@ freqtrade backtesting --strategy FreqaiExampleStrategy --strategy-path freqtrade If this command has never been executed with the existing config file, FreqAI will train a new model for each pair, for each backtesting window within the expanded `--timerange`. -Backtesting mode requires the user [download the necessary data](freqai-data-handling.md#downloading-data-to-cover-the-full-backtest-period) before deployment (unlike in dry/live mode where FreqAI handles the data downloading automatically). 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-size-of-the-sliding-training-window-and-backtesting-duration). +Backtesting mode requires [downloading the necessary data](#downloading-data-to-cover-the-full-backtest-period) before deployment (unlike in dry/live mode where FreqAI handles the data downloading automatically). You 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 set backtesting time range. More details on how to calculate the data to download can be found [here](#deciding-the-size-of-the-sliding-training-window-and-backtesting-duration). !!! Note "Model reuse" - Once the training is completed, the user can execute the backtesting again with the same config file and + Once the training is completed, you can execute the backtesting again with the same config file and FreqAI will find the trained models and load them instead of spending time training. This is useful - if the user wants to tweak (or even hyperopt) buy and sell criteria inside the strategy. If the user - *wants* to retrain a new model with the same config file, then they should simply change the `identifier`. - This way, the user can return to using any model they wish by simply specifying the `identifier`. + if you want to tweak (or even hyperopt) buy and sell criteria inside the strategy. If you + *want* to retrain a new model with the same config file, you should simply change the `identifier`. + This way, you can return to using any model you wish by simply specifying the `identifier`. --- -## Deciding the size of the sliding training window and backtesting duration +### Saving prediction data -The user defines the backtesting timerange with the typical `--timerange` parameter in the configuration file. The duration of the sliding training window is set by `train_period_days`, whilst `backtest_period_days` is the sliding backtesting window, both in number of days (`backtest_period_days` can be -a float to indicate sub-daily retraining in live/dry mode). In the presented [example config](freqai-configuration.md#setting-up-the-configuration-file) (found in `config_examples/config_freqai.example.json`), the user is asking FreqAI to use a training period of 30 days and backtest on the subsequent 7 days. After the training of the model, FreqAI will backtest the subsequent 7 days. The "sliding window" then moves one week forward (emulating FreqAI retraining once per week in live mode) and the new model uses the previous 30 days (including the 7 days used for backtesting by the previous model) to train. This is repeated until the end of `--timerange`. This means that if the user sets `--timerange 20210501-20210701`, FreqAI will have trained 8 separate models at the end of `--timerange` (because the full range comprises 8 weeks). +To allow for tweaking your strategy (**not** the features!), FreqAI will automatically save the predictions during backtesting so that they can be reused for future backtests and live runs using the same `identifier` model. This provides a performance enhancement geared towards enabling **high-level hyperopting** of entry/exit criteria. + +An additional directory called `predictions`, which contains all the predictions stored in `hdf` format, will be created in the `unique-id` folder. + +To change your **features**, you **must** set a new `identifier` in the config to signal to `FreqAI` to train new models. + +To save the models generated during a particular backtest so that you can start a live deployment from one of them instead of training a new model, you must set `save_backtest_models` to `True` in the config. + +### Downloading data to cover the full backtest period + +For live/dry deployments, FreqAI will download the necessary data automatically. However, to use backtesting functionality, you need to download the necessary data using `download-data` (details [here](data-download.md#data-downloading)). You need to pay careful attention to understanding how much *additional* data needs to be downloaded to ensure that there is a sufficient amount of training data *before* the start of the backtesting timerange. The amount of additional data can be roughly estimated by moving the start date of the timerange backwards by `train_period_days` and the `startup_candle_count` (see the [parameter table](freqai-parameter-table.md) for detailed descriptions of these parameters) from the beginning of the desired backtesting timerange. + +As an example, to backtest the `--timerange 20210501-20210701` using the [example config](freqai-configuration.md#setting-up-the-configuration-file) which sets `train_period_days` to 30, together with `startup_candle_count: 40` on a maximum `include_timeframes` of 1h, the start date for the downloaded data needs to be `20210501` - 30 days - 40 * 1h / 24 hours = 20210330 (31.7 days earlier than the start of the desired training timerange). + +### Deciding the size of the sliding training window and backtesting duration + +The backtesting timerange is defined with the typical `--timerange` parameter in the configuration file. The duration of the sliding training window is set by `train_period_days`, whilst `backtest_period_days` is the sliding backtesting window, both in number of days (`backtest_period_days` can be +a float to indicate sub-daily retraining in live/dry mode). In the presented [example config](freqai-configuration.md#setting-up-the-configuration-file) (found in `config_examples/config_freqai.example.json`), the user is asking FreqAI to use a training period of 30 days and backtest on the subsequent 7 days. After the training of the model, FreqAI will backtest the subsequent 7 days. The "sliding window" then moves one week forward (emulating FreqAI retraining once per week in live mode) and the new model uses the previous 30 days (including the 7 days used for backtesting by the previous model) to train. This is repeated until the end of `--timerange`. This means that if you set `--timerange 20210501-20210701`, FreqAI will have trained 8 separate models at the end of `--timerange` (because the full range comprises 8 weeks). !!! Note - 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 train constantly. In this case, backtesting would take the exact same amount of time as a dry run. + Although fractional `backtest_period_days` is allowed, you 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, by setting a `--timerange` of 10 days, and 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 train constantly. In this case, backtesting would take the exact same amount of time as a dry run. ## 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 -a certain number of hours old by setting the `expiration_hours` in the config file: +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 you are 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. You 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 "freqai": { @@ -72,7 +107,7 @@ In the presented example config, the user will only allow predictions on models ## Data stratification for training and testing the model -The user can stratify (group) the training/testing data using: +You can stratify (group) the training/testing data using: ```json "freqai": { @@ -82,52 +117,51 @@ 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 +This will split the data chronologically so that every Xth data point is used to test the model after training. In the example above, the user is asking for every third data point in the dataframe to be used for testing; the other points are used for training. The test data is used to evaluate the performance of the model after training. If the test score is high, the model is able to capture the behavior of the data well. If the test score is low, either the model does not capture the complexity of the data, the test data is significantly different from the train data, or a different type of model should be used. ## Controlling the model learning process -Model training parameters are unique to the 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 config (found in `config_examples/config_freqai.example.json`) shows some of the example parameters associated with `Catboost` and `LightGBM`, but the user can add any parameters available in those libraries or any other machine learning library they choose to implement. +Model training parameters are unique to the selected machine learning library. FreqAI allows you to set any parameter for any library using the `model_training_parameters` dictionary in the config. The example config (found in `config_examples/config_freqai.example.json`) shows some of the example parameters associated with `Catboost` and `LightGBM`, but you can add any parameters available in those libraries or any other machine learning library you choose to implement. -Data split parameters are defined in `data_split_parameters` which can be any parameters associated with Scikit-learn's `train_test_split()` function. `train_test_split()` has a parameters called `shuffle` which allows the user to shuffle the data or keep it unshuffled. This is particularly useful to avoid biasing training with temporally auto-correlated data. More details about these parameters can be found the [Scikit-learn website](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html) (external website). +Data split parameters are defined in `data_split_parameters` which can be any parameters associated with Scikit-learn's `train_test_split()` function. `train_test_split()` has a parameters called `shuffle` which allows to shuffle the data or keep it unshuffled. This is particularly useful to avoid biasing training with temporally auto-correlated data. More details about these parameters can be found the [Scikit-learn website](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html) (external website). The FreqAI specific parameter `label_period_candles` defines the offset (number of candles into the future) used for the `labels`. In the presented [example config](freqai-configuration.md#setting-up-the-configuration-file), the user is asking for `labels` that are 24 candles in the future. ## Continual learning -The user can choose to adopt a continual learning scheme by setting `"continual_learning": true` in their configuration file. By enabling `continual_learning`, after training an initial model from scratch, subsequent trainings will start from the final model state of the preceding training. This gives the new model a "memory" of the previous state. By default, this is set to `false` which means that all new models are trained from scratch, without input from previous models. +You can choose to adopt a continual learning scheme by setting `"continual_learning": true` in the config. By enabling `continual_learning`, after training an initial model from scratch, subsequent trainings will start from the final model state of the preceding training. This gives the new model a "memory" of the previous state. By default, this is set to `false` which means that all new models are trained from scratch, without input from previous models. ## Hyperopt -The user can hyperopt using the same command as for [typical Freqtrade hyperopt](hyperopt.md): +You can hyperopt using the same command as for [typical Freqtrade hyperopt](hyperopt.md): ```bash freqtrade hyperopt --hyperopt-loss SharpeHyperOptLoss --strategy FreqaiExampleStrategy --freqaimodel LightGBMRegressor --strategy-path freqtrade/templates --config config_examples/config_freqai.example.json --timerange 20220428-20220507 ``` -`hyperopt` requires the user to have the data pre-downloaded in the same fashion as if they were doing [backtesting](#backtesting). In addition, the user must consider some restrictions when trying to hyperopt FreqAI strategies: +`hyperopt` requires you to have the data pre-downloaded in the same fashion as if you were doing [backtesting](#backtesting). In addition, you must consider some restrictions when trying to hyperopt FreqAI strategies: - The `--analyze-per-epoch` hyperopt parameter is not compatible with FreqAI. -- It's not possible to hyperopt indicators in the `populate_any_indicators()` function. This means that the user cannot optimize model parameters using hyperopt. Apart from this exception, it is possible to optimize all other [spaces](hyperopt.md#running-hyperopt-with-smaller-search-space). +- It's not possible to hyperopt indicators in the `populate_any_indicators()` function. This means that you cannot optimize model parameters using hyperopt. Apart from this exception, it is possible to optimize all other [spaces](hyperopt.md#running-hyperopt-with-smaller-search-space). - The backtesting instructions also apply to hyperopt. -The best method for combining hyperopt and FreqAI is to focus on hyperopting entry/exit thresholds/criteria. The user needs to focus on hyperopting parameters that are not used in their FreqAI features. For example, the user should not try to hyperopt rolling window lengths in their feature creation, or any part of their FreqAI config which changes predictions. In order to efficiently hyperopt the FreqAI strategy, FreqAI stores predictions as dataframes and reuses them. Hence the requirement to hyperopt entry/exit thresholds/criteria only. +The best method for combining hyperopt and FreqAI is to focus on hyperopting entry/exit thresholds/criteria. You need to focus on hyperopting parameters that are not used in your features. For example, you should not try to hyperopt rolling window lengths in the feature creation, or any part of the FreqAI config which changes predictions. In order to efficiently hyperopt the FreqAI strategy, FreqAI stores predictions as dataframes and reuses them. Hence the requirement to hyperopt entry/exit thresholds/criteria only. -A good example of a hyperoptable parameter in FreqAI is a threshold for the [Dissimilarity Index (DI)](freqai-outlier-detection.md#identifying-outliers-with-the-dissimilarity-index-di) `DI_values` beyond which we consider data points as outliers: +A good example of a hyperoptable parameter in FreqAI is a threshold for the [Dissimilarity Index (DI)](freqai-feature-engineering.md#identifying-outliers-with-the-dissimilarity-index-di) `DI_values` beyond which we consider data points as outliers: ```python di_max = IntParameter(low=1, high=20, default=10, space='buy', optimize=True, load=True) dataframe['outlier'] = np.where(dataframe['DI_values'] > self.di_max.value/10, 1, 0) ``` -This specific hyperopt would help the user understand the appropriate `DI_values` for their particular parameter space. +This specific hyperopt would help you understand the appropriate `DI_values` for your particular parameter space. ## Setting up a follower -The user can indicate to the bot that it should not train models, but instead should look for models trained by a leader with a specific `identifier` by defining: +You can indicate to the bot that it should not train models, but instead should look for models trained by a leader with a specific `identifier` by defining: ```json "freqai": { diff --git a/docs/freqai.md b/docs/freqai.md index bdfa8b491..4c95000d1 100644 --- a/docs/freqai.md +++ b/docs/freqai.md @@ -8,13 +8,13 @@ FreqAI is a software designed to automate a variety of tasks associated with tra Features include: -* **Self-adaptive retraining** - Retrain models during [live deployments](freqai-running.md#running-the-model-live) to self-adapt to the market in a supervised manner +* **Self-adaptive retraining** - Retrain models during [live deployments](freqai-running.md#live-deployments) to self-adapt to the market in a supervised manner * **Rapid feature engineering** - Create large rich [feature sets](freqai-feature-engineering.md#feature-engineering) (10k+ features) based on simple user-created strategies * **High performance** - Threading allows for adaptive model retraining on a separate thread (or on GPU if available) from model inferencing (prediction) and bot trade operations. Newest models and data are kept in RAM for rapid inferencing * **Realistic backtesting** - Emulate self-adaptive training on historic data with a [backtesting module](freqai-running.md#backtesting) that automates retraining * **Extensibility** - The generalized and robust architecture allows for incorporating any [machine learning library/method](freqai-configuration.md#using-different-prediction-models) available in Python. Eight examples are currently available, including classifiers, regressors, and a convolutional neural network -* **Smart outlier removal** - Remove outliers from training and prediction data sets using a variety of [outlier detection techniques](freqai-outlier-detection.md) -* **Crash resilience** - Store trained models to disk to make reloading from a crash fast and easy, and [purge obsolete files](freqai-data-handling.md#purging-old-model-data) for sustained dry/live runs +* **Smart outlier removal** - Remove outliers from training and prediction data sets using a variety of [outlier detection techniques](freqai-feature-engineering.md#outlier-detection) +* **Crash resilience** - Store trained models to disk to make reloading from a crash fast and easy, and [purge obsolete files](freqai-running.md#purging-old-model-data) for sustained dry/live runs * **Automatic data normalization** - [Normalize the data](freqai-feature-engineering.md#feature-normalization) in a smart and statistically safe way * **Automatic data download** - Compute timeranges for data downloads and update historic data (in live deployments) * **Cleaning of incoming data** - Handle NaNs safely before training and model inferencing @@ -29,7 +29,7 @@ The easiest way to quickly test FreqAI is to run it in dry mode with the followi freqtrade trade --config config_examples/config_freqai.example.json --strategy FreqaiExampleStrategy --freqaimodel LightGBMRegressor --strategy-path freqtrade/templates ``` -The user will see the boot-up process of automatic data downloading, followed by simultaneous training and trading. +You will see the boot-up process of automatic data downloading, followed by simultaneous training and trading. An example strategy, prediction model, and config to use as a starting points can be found in `freqtrade/templates/FreqaiExampleStrategy.py`, `freqtrade/freqai/prediction_models/LightGBMRegressor.py`, and @@ -37,7 +37,7 @@ An example strategy, prediction model, and config to use as a starting points ca ## General approach -The user provides FreqAI with a set of custom *base indicators* (the same way as in a [typical Freqtrade strategy](strategy-customization.md)) as well as target values (*labels*). For each pair in the whitelist, FreqAI trains a model to predict the target values based on the input of custom indicators. The models are then consistently retrained, with a frequency set by the user, to adapt to market conditions. FreqAI offers the ability to both backtest strategies (emulating reality with periodic retraining on historic data) and deploy dry/live runs. In dry/live conditions, FreqAI can be set to constant retraining in a background thread to keep models as up to date as possible. +You provide FreqAI with a set of custom *base indicators* (the same way as in a [typical Freqtrade strategy](strategy-customization.md)) as well as target values (*labels*). For each pair in the whitelist, FreqAI trains a model to predict the target values based on the input of custom indicators. The models are then consistently retrained, with a predetermined frequency, to adapt to market conditions. FreqAI offers the ability to both backtest strategies (emulating reality with periodic retraining on historic data) and deploy dry/live runs. In dry/live conditions, FreqAI can be set to constant retraining in a background thread to keep models as up to date as possible. An overview of the algorithm, explaining the data processing pipeline and model usage, is shown below. @@ -45,9 +45,9 @@ An overview of the algorithm, explaining the data processing pipeline and model ### Important machine learning vocabulary -**Features** - the parameters, based on historic data, on which a model is trained. All features for a single candle is stored as a vector. In FreqAI, the user builds a feature data sets from anything they can construct in the strategy. +**Features** - the parameters, based on historic data, on which a model is trained. All features for a single candle is stored as a vector. In FreqAI, you build a feature data sets from anything you can construct in the strategy. -**Labels** - the target values that a model is trained toward. Each feature vector is associated with a single label that is defined by the user within the strategy. These labels intentionally look into the future, and are not available to the model during dry/live/backtesting. +**Labels** - the target values that a model is trained toward. Each feature vector is associated with a single label that is defined by you within your strategy. These labels intentionally look into the future, and are not available to the model during dry/live/backtesting. **Training** - the process of "teaching" the model to match the feature sets to the associated labels. Different types of models "learn" in different ways. More information about the different models can be found [here](freqai-configuration.md#using-different-prediction-models). @@ -59,7 +59,7 @@ An overview of the algorithm, explaining the data processing pipeline and model ## Install prerequisites -The normal Freqtrade install process will ask the user if they wish to install FreqAI dependencies. The user should reply "yes" to this question if they wish to use FreqAI. If the user did not reply yes, they can manually install these dependencies after the install with: +The normal Freqtrade install process will ask if you wish to install FreqAI dependencies. You should reply "yes" to this question if you wish to use FreqAI. If you did not reply yes, you can manually install these dependencies after the install with: ``` bash pip install -r requirements-freqai.txt @@ -70,7 +70,7 @@ pip install -r requirements-freqai.txt ### Usage with docker -For docker users, a dedicated tag with FreqAI dependencies is available as `:freqai`. As such - you can replace the image line in your docker-compose file with `image: freqtradeorg/freqtrade:develop_freqai`. This image contains the regular FreqAI dependencies. Similar to native installs, Catboost will not be available on ARM based devices. +If you are using docker, a dedicated tag with FreqAI dependencies is available as `:freqai`. As such - you can replace the image line in your docker-compose file with `image: freqtradeorg/freqtrade:develop_freqai`. This image contains the regular FreqAI dependencies. Similar to native installs, Catboost will not be available on ARM based devices. ## Common pitfalls @@ -93,5 +93,5 @@ Software development: Wagner Costa @wagnercosta Beta testing and bug reporting: -Stefan Gehring @bloodhunter4rc, @longyu, @paranoidandy, @smidelis, Ryan McMullan @smarmau, +Stefan Gehring @bloodhunter4rc, @longyu, Andrew Robert Lawless @paranoidandy, Pascal Schmidt @smidelis, Ryan McMullan @smarmau, Juha Nykänen @suikula, Johan van der Vlugt @jooopiert, Richárd Józsa @richardjosza diff --git a/mkdocs.yml b/mkdocs.yml index 80c3e4ecc..31ad758d0 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -28,9 +28,9 @@ nav: - Configuration: freqai-configuration.md - Parameter table: freqai-parameter-table.md - Feature engineering: freqai-feature-engineering.md - - Outlier detection: freqai-outlier-detection.md + # - Outlier detection: freqai-outlier-detection.md - Running FreqAI: freqai-running.md - - Data handling: freqai-data-handling.md + # - Data handling: freqai-data-handling.md - Developer guide: freqai-developers.md - Short / Leverage: leverage.md - Utility Sub-commands: utils.md From 677c5719bfff782c7f3409803fb629b87b4622fb Mon Sep 17 00:00:00 2001 From: robcaulk Date: Sun, 25 Sep 2022 15:22:08 +0200 Subject: [PATCH 7/9] improve inlier-metric figure --- docs/assets/freqai_inlier-metric.jpg | Bin 594092 -> 468744 bytes 1 file changed, 0 insertions(+), 0 deletions(-) diff --git a/docs/assets/freqai_inlier-metric.jpg b/docs/assets/freqai_inlier-metric.jpg index 43029e99757ebcbf3acf23662e81aa1be91e33cf..d9cbbe8a1569ab56130a7e610dd49058d0613dcc 100644 GIT binary patch literal 468744 zcmdqJcUV(R*EbqODN0dM=^!E?(mM%NP>`;o^di02(7Oc?=|y^zj`S8Fp(r4|haN&y zAR$!gC4gVBJ@@mrbA9i1&Oc}Tv1iT9n!VRrvomGQY=2Ds_yV}Bte~U-ARqt$2=M;^ zKM(+U0O7fFXTXQ^cw8X4aN+#<3m1uriAcyUl964ycc;ZbCqauXFwa>4giV*T^rCU;DofKiU9SNQkmYLkS6P0nS|^ zAiP5GqXWQrCguf#GkO06V!Vh%BzqQ1m4ricH_dtCOvTN^fpe?Ad$3Pq!<$k*}g ze+WaNvLgGU@E^@Vp|SFh;-4g%_pRXbpA;q2#olL+uHjoSr2qB3BH4xc(T4QOvm*Gw zr|zAoW;yUL#-H5y%Q-H#2k(~tr10sxY|oNFjQsge@p)Hhd4XctY90S7{zcu}`7p}y z2mrYF%oLpBtCDe)tvg3>iAp6d|D455S1)h@*|MABl?>m>eM|!1AxIrPhLGn1Nc6Gf@@8kCV?UeOs&qWGiL=V z?$?~o?KZgAoZ(wWEopx_W)eh1p#>j z#k86S3!gPw6<*5!{hV{VV%!^6_kdi5svq$tRW05M4KC9dLKv$QgB|pw!jkdk7*m6? 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Features include: From 02dba5304ba675e14c1dd35d9e5249a56994dea4 Mon Sep 17 00:00:00 2001 From: th0rntwig Date: Sun, 25 Sep 2022 12:12:35 +0200 Subject: [PATCH 6/9] Revise FreqAI multipage docs structure --- docs/assets/freqai_inlier-metric.jpg | Bin 0 -> 594092 bytes docs/freqai-configuration.md | 34 ++++---- docs/freqai-data-handling.md | 115 -------------------------- docs/freqai-developers.md | 65 ++++++++++++++- docs/freqai-feature-engineering.md | 117 +++++++++++++++++++++++++-- docs/freqai-outlier-detection.md | 79 ------------------ docs/freqai-parameter-table.md | 24 +++--- docs/freqai-running.md | 90 ++++++++++++++------- docs/freqai.md | 20 ++--- mkdocs.yml | 4 +- 10 files changed, 276 insertions(+), 272 deletions(-) create mode 100644 docs/assets/freqai_inlier-metric.jpg delete mode 100644 docs/freqai-data-handling.md delete mode 100644 docs/freqai-outlier-detection.md diff --git a/docs/assets/freqai_inlier-metric.jpg b/docs/assets/freqai_inlier-metric.jpg new file mode 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zi)yh{_=_uArJsL-M7aZ846RxT20mI=q;Bu#EK|kgoG)5aDlf-gGA9srB*W7XT5O)PLhu!gXeS?^TS)*GyLxzclM%{juIZ(|#kCLm6<+@sN)>+j_UaUZNJ8Fer1YDmt1?zEt$4tn?1$=0m141PuWZ10Yhdro`GoM$HWJIm+HeLb zBNT2r{z)L}MGb`8C$et%m?~a(;+e7Oa7NQ1q7-yD5wXAbDX)nVDMDtc+=AmR46TQ+ zip2mQ+nFkA>tV0L3nCDxH!XUAkX3!V_~Grp`t{Dt3)Lh+Zc#K467j=4t0Rs0Ki_iu zZuFV#_j%r;+JzY`5D+B92<7Y_9|t4>pf5FP&3tU$4qIZPWgEpM0roL}e;r^y=cD{8 z6yz}^??1d=WEJ_~5dOZKy8bRaL{>otAk6bD(-{i~Sb9%n-n8=eW#exRP$S=f+du9- zb$&s|z=-qfYs;_Xtm(t02;=JNl?o@+&4~p5NGIP7CskTO!y)DLsH)t6h-dPC2bo55 zqlFWkYj8+x9jhUzRq|_9mQJQnFSpW4^cggOME0?+QfS|xG^X4^)rOq?Bd6R`>gAMA uDdK?zBiF4E2?={%@wRU+7Ay9wwJO3P6VnH>ZDDx9*vck9LCdh8pZ^El^TAsH From f5535e780cb04247b28c43449a0fac01d37d4782 Mon Sep 17 00:00:00 2001 From: robcaulk Date: Sun, 25 Sep 2022 21:18:51 +0200 Subject: [PATCH 8/9] change wording, switch FreqAI word format --- docs/freqai-configuration.md | 26 +++++++++++------------ docs/freqai-developers.md | 6 +++--- docs/freqai-feature-engineering.md | 34 ++++++++++++++++-------------- docs/freqai-parameter-table.md | 19 +++++++++-------- docs/freqai-running.md | 2 +- docs/freqai.md | 23 +++++++++++--------- 6 files changed, 58 insertions(+), 52 deletions(-) diff --git a/docs/freqai-configuration.md b/docs/freqai-configuration.md index de77ce92b..68d34eddf 100644 --- a/docs/freqai-configuration.md +++ b/docs/freqai-configuration.md @@ -1,10 +1,10 @@ # Configuration -FreqAI is configured through the typical [Freqtrade config file](configuration.md) and the standard [Freqtrade strategy](strategy-customization.md). Examples of a FreqAI config and strategy file can be found in `config_examples/config_freqai.example.json` and `freqtrade/templates/FreqaiExampleStrategy.py`, respectively. +`FreqAI` is configured through the typical [Freqtrade config file](configuration.md) and the standard [Freqtrade strategy](strategy-customization.md). Examples of `FreqAI` config and strategy files can be found in `config_examples/config_freqai.example.json` and `freqtrade/templates/FreqaiExampleStrategy.py`, respectively. ## Setting up the configuration file - Although there are plenty of additional parameters to choose from, as highlighted in the [parameter table](freqai-parameter-table.md#parameter-table), a FreqAI config must at minimum include the following parameters (the parameter values are only examples): + Although there are plenty of additional parameters to choose from, as highlighted in the [parameter table](freqai-parameter-table.md#parameter-table), a `FreqAI` config must at minimum include the following parameters (the parameter values are only examples): ```json "freqai": { @@ -34,9 +34,9 @@ FreqAI is configured through the typical [Freqtrade config file](configuration.m ``` A full example config is available in `config_examples/config_freqai.example.json`. -## Building a FreqAI strategy +## Building a `FreqAI` strategy -The FreqAI strategy requires including the following lines of code in the standard [Freqtrade strategy](strategy-customization.md): +The `FreqAI` strategy requires including the following lines of code in the standard [Freqtrade strategy](strategy-customization.md): ```python # user should define the maximum startup candle count (the largest number of candles @@ -128,7 +128,7 @@ Notice also the location of the labels under `if set_generalized_indicators:` at The `self.freqai.start()` function cannot be called outside the `populate_indicators()`. !!! Note - Features **must** be defined in `populate_any_indicators()`. Defining FreqAI features in `populate_indicators()` + Features **must** be defined in `populate_any_indicators()`. Defining `FreqAI` features in `populate_indicators()` will cause the algorithm to fail in live/dry mode. In order to add generalized features that are not associated with a specific pair or timeframe, the following structure inside `populate_any_indicators()` should be used (as exemplified in `freqtrade/templates/FreqaiExampleStrategy.py`): @@ -165,14 +165,14 @@ Below are the values you can expect to include/use inside a typical strategy dat | DataFrame Key | Description | |------------|-------------| -| `df['&*']` | Any dataframe column prepended with `&` in `populate_any_indicators()` is treated as a training target (label) inside FreqAI (typically following the naming convention `&-s*`). The names of these dataframe columns are fed back as the predictions. For example, to predict the price change in the next 40 candles (similar to `templates/FreqaiExampleStrategy.py`), you would set `df['&-s_close']`. FreqAI makes the predictions and gives them back under the same key (`df['&-s_close']`) to be used in `populate_entry/exit_trend()`.
**Datatype:** Depends on the output of the model. +| `df['&*']` | Any dataframe column prepended with `&` in `populate_any_indicators()` is treated as a training target (label) inside `FreqAI` (typically following the naming convention `&-s*`). The names of these dataframe columns are fed back as the predictions. For example, to predict the price change in the next 40 candles (similar to `templates/FreqaiExampleStrategy.py`), you would set `df['&-s_close']`. `FreqAI` makes the predictions and gives them back under the same key (`df['&-s_close']`) to be used in `populate_entry/exit_trend()`.
**Datatype:** Depends on the output of the model. | `df['&*_std/mean']` | Standard deviation and mean values of the defined labels during training (or live tracking with `fit_live_predictions_candles`). Commonly used to understand the rarity of a prediction (use the z-score as shown in `templates/FreqaiExampleStrategy.py` and explained [here](#creating-a-dynamic-target-threshold) to evaluate how often a particular prediction was observed during training or historically with `fit_live_predictions_candles`).
**Datatype:** Float. -| `df['do_predict']` | Indication of an outlier data point. The return value is integer between -1 and 2, which lets you know if the prediction is trustworthy or not. `do_predict==1` means that the prediction is trustworthy. If the Dissimilarity Index (DI, see details [here](freqai-feature-engineering.md#identifying-outliers-with-the-dissimilarity-index-di)) of the input data point is above the threshold defined in the config, FreqAI will subtract 1 from `do_predict`, resulting in `do_predict==0`. If `use_SVM_to_remove_outliers()` is active, the Support Vector Machine (SVM, see details [here](freqai-feature-engineering.md#identifying-outliers-using-a-support-vector-machine-svm)) may also detect outliers in training and prediction data. In this case, the SVM will also subtract 1 from `do_predict`. If the input data point was considered an outlier by the SVM but not by the DI, or vice versa, the result will be `do_predict==0`. If both the DI and the SVM considers the input data point to be an outlier, the result will be `do_predict==-1`. A particular case is when `do_predict == 2`, which means that the model has expired due to exceeding `expired_hours`.
**Datatype:** Integer between -1 and 2. -| `df['DI_values']` | Dissimilarity Index (DI) values are proxies for the level of confidence FreqAI has in the prediction. A lower DI means the prediction is close to the training data, i.e., higher prediction confidence. See details about the DI [here](freqai-feature-engineering.md#identifying-outliers-with-the-dissimilarity-index-di).
**Datatype:** Float. -| `df['%*']` | Any dataframe column prepended with `%` in `populate_any_indicators()` is treated as a training feature. For example, you can include the RSI in the training feature set (similar to in `templates/FreqaiExampleStrategy.py`) by setting `df['%-rsi']`. See more details on how this is done [here](freqai-feature-engineering.md).
**Note:** Since the number of features prepended with `%` can multiply very quickly (10s of thousands of features is easily engineered using the multiplictative functionality described in the `feature_parameters` table shown above), these features are removed from the dataframe upon return from FreqAI. To keep a particular type of feature for plotting purposes, you would prepend it with `%%`.
**Datatype:** Depends on the output of the model. +| `df['do_predict']` | Indication of an outlier data point. The return value is integer between -1 and 2, which lets you know if the prediction is trustworthy or not. `do_predict==1` means that the prediction is trustworthy. If the Dissimilarity Index (DI, see details [here](freqai-feature-engineering.md#identifying-outliers-with-the-dissimilarity-index-di)) of the input data point is above the threshold defined in the config, `FreqAI` will subtract 1 from `do_predict`, resulting in `do_predict==0`. If `use_SVM_to_remove_outliers()` is active, the Support Vector Machine (SVM, see details [here](freqai-feature-engineering.md#identifying-outliers-using-a-support-vector-machine-svm)) may also detect outliers in training and prediction data. In this case, the SVM will also subtract 1 from `do_predict`. If the input data point was considered an outlier by the SVM but not by the DI, or vice versa, the result will be `do_predict==0`. If both the DI and the SVM considers the input data point to be an outlier, the result will be `do_predict==-1`. A particular case is when `do_predict == 2`, which means that the model has expired due to exceeding `expired_hours`.
**Datatype:** Integer between -1 and 2. +| `df['DI_values']` | Dissimilarity Index (DI) values are proxies for the level of confidence `FreqAI` has in the prediction. A lower DI means the prediction is close to the training data, i.e., higher prediction confidence. See details about the DI [here](freqai-feature-engineering.md#identifying-outliers-with-the-dissimilarity-index-di).
**Datatype:** Float. +| `df['%*']` | Any dataframe column prepended with `%` in `populate_any_indicators()` is treated as a training feature. For example, you can include the RSI in the training feature set (similar to in `templates/FreqaiExampleStrategy.py`) by setting `df['%-rsi']`. See more details on how this is done [here](freqai-feature-engineering.md).
**Note:** Since the number of features prepended with `%` can multiply very quickly (10s of thousands of features is easily engineered using the multiplictative functionality described in the `feature_parameters` table shown above), these features are removed from the dataframe upon return from `FreqAI`. To keep a particular type of feature for plotting purposes, you would prepend it with `%%`.
**Datatype:** Depends on the output of the model. ## Setting the `startup_candle_count` -The `startup_candle_count` in the FreqAI strategy needs to be set up in the same way as in the standard Freqtrade strategy (see details [here](strategy-customization.md#strategy-startup-period)). This value is used by Freqtrade to ensure that a sufficient amount of data is provided when calling the `dataprovider`, to avoid any NaNs at the beginning of the first training. You can easily set this value by identifying the longest period (in candle units) which is passed to the indicator creation functions (e.g., talib functions). In the presented example, `startup_candle_count` is 20 since this is the maximum value in `indicators_periods_candles`. +The `startup_candle_count` in the `FreqAI` strategy needs to be set up in the same way as in the standard Freqtrade strategy (see details [here](strategy-customization.md#strategy-startup-period)). This value is used by Freqtrade to ensure that a sufficient amount of data is provided when calling the `dataprovider`, to avoid any NaNs at the beginning of the first training. You can easily set this value by identifying the longest period (in candle units) which is passed to the indicator creation functions (e.g., talib functions). In the presented example, `startup_candle_count` is 20 since this is the maximum value in `indicators_periods_candles`. !!! Note There are instances where the talib functions actually require more data than just the passed `period` or else the feature dataset gets populated with NaNs. Anecdotally, multiplying the `startup_candle_count` by 2 always leads to a fully NaN free training dataset. Hence, it is typically safest to multiply the expected `startup_candle_count` by 2. Look out for this log message to confirm that the data is clean: @@ -183,7 +183,7 @@ The `startup_candle_count` in the FreqAI strategy needs to be set up in the same ## Creating a dynamic target threshold -Deciding when to enter or exit a trade can be done in a dynamic way to reflect current market conditions. FreqAI allows you to return additional information from the training of a model (more info [here](freqai-feature-engineering.md#returning-additional-info-from-training)). For example, the `&*_std/mean` return values describe the statistical distribution of the target/label *during the most recent training*. Comparing a given prediction to these values allows you to know the rarity of the prediction. In `templates/FreqaiExampleStrategy.py`, the `target_roi` and `sell_roi` are defined to be 1.25 z-scores away from the mean which causes predictions that are closer to the mean to be filtered out. +Deciding when to enter or exit a trade can be done in a dynamic way to reflect current market conditions. `FreqAI` allows you to return additional information from the training of a model (more info [here](freqai-feature-engineering.md#returning-additional-info-from-training)). For example, the `&*_std/mean` return values describe the statistical distribution of the target/label *during the most recent training*. Comparing a given prediction to these values allows you to know the rarity of the prediction. In `templates/FreqaiExampleStrategy.py`, the `target_roi` and `sell_roi` are defined to be 1.25 z-scores away from the mean which causes predictions that are closer to the mean to be filtered out. ```python dataframe["target_roi"] = dataframe["&-s_close_mean"] + dataframe["&-s_close_std"] * 1.25 @@ -198,11 +198,11 @@ To consider the population of *historical predictions* for creating the dynamic } ``` -If this value is set, FreqAI will initially use the predictions from the training data and subsequently begin introducing real prediction data as it is generated. FreqAI will save this historical data to be reloaded if you stop and restart a model with the same `identifier`. +If this value is set, `FreqAI` will initially use the predictions from the training data and subsequently begin introducing real prediction data as it is generated. `FreqAI` will save this historical data to be reloaded if you stop and restart a model with the same `identifier`. ## Using different prediction models -FreqAI has multiple example prediction model libraries that are ready to be used as is via the flag `--freqaimodel`. These libraries include `Catboost`, `LightGBM`, and `XGBoost` regression, classification, and multi-target models, and can be found in `freqai/prediction_models/`. However, it is possible to customize and create your own prediction models using the `IFreqaiModel` class. You are encouraged to inherit `fit()`, `train()`, and `predict()` to let these customize various aspects of the training procedures. +`FreqAI` has multiple example prediction model libraries that are ready to be used as is via the flag `--freqaimodel`. These libraries include `Catboost`, `LightGBM`, and `XGBoost` regression, classification, and multi-target models, and can be found in `freqai/prediction_models/`. However, it is possible to customize and create your own prediction models using the `IFreqaiModel` class. You are encouraged to inherit `fit()`, `train()`, and `predict()` to let these customize various aspects of the training procedures. ### Setting classifier targets diff --git a/docs/freqai-developers.md b/docs/freqai-developers.md index 97c0938b4..1635e785d 100644 --- a/docs/freqai-developers.md +++ b/docs/freqai-developers.md @@ -2,13 +2,13 @@ ## Project architecture -The architechture and functions of FreqAI are generalized to encourages development of unique features, functions, models, etc. +The architechture and functions of `FreqAI` are generalized to encourages development of unique features, functions, models, etc. The class structure and a detailed algorithmic overview is depicted in the following diagram: ![image](assets/freqai_algorithm-diagram.jpg) -As shown, there are three distinct objects comprising FreqAI: +As shown, there are three distinct objects comprising `FreqAI`: * **IFreqaiModel** - A singular persistent object containing all the necessary logic to collect, store, and process data, engineer features, run training, and inference models. * **FreqaiDataKitchen** - A non-persistent object which is created uniquely for each unique asset/model. Beyond metadata, it also contains a variety of data processing tools. @@ -27,7 +27,7 @@ The file structure is automatically generated based on the model `identifier` se | Structure | Description | |-----------|-------------| | `config_*.json` | A copy of the model specific configuration file. | -| `historic_predictions.pkl` | A file containing all historic predictions generated during the lifetime of the `identifier` model during live deployment. `historic_predictions.pkl` is used to reload the model after a crash or a config change. A backup file is always held incase of corruption on the main file. **FreqAI automatically detects corruption and replaces the corrupted file with the backup**. | +| `historic_predictions.pkl` | A file containing all historic predictions generated during the lifetime of the `identifier` model during live deployment. `historic_predictions.pkl` is used to reload the model after a crash or a config change. A backup file is always held incase of corruption on the main file. **`FreqAI` automatically detects corruption and replaces the corrupted file with the backup**. | | `pair_dictionary.json` | A file containing the training queue as well as the on disk location of the most recently trained model. | | `sub-train-*_TIMESTAMP` | A folder containing all the files associated with a single model, such as:
|| `*_metadata.json` - Metadata for the model, such as normalization max/mins, expected training feature list, etc.
diff --git a/docs/freqai-feature-engineering.md b/docs/freqai-feature-engineering.md index 7dd41fd88..34c9c8443 100644 --- a/docs/freqai-feature-engineering.md +++ b/docs/freqai-feature-engineering.md @@ -2,9 +2,11 @@ ## Defining the features -Feature engineering is handled within `"feature_parameters":{}` in the `FreqAI` config and strategy. All `base features` you wish to use, such as, e.g., `RSI`, `MFI`, `EMA`, `SMA`, etc., should be added to the strategy. The `base features` can be custom indicators or they can be imported from any technical-analysis library that you can find. The `base features` are added inside the `populate_any_indicators()` method of the strategy by prepending indicators with `%`, and labels with `&`. +Low level feature engineering is performed in the user strategy within a function called `populate_any_indicators()`. That function sets the `base features` such as, `RSI`, `MFI`, `EMA`, `SMA`, time of day, volume, etc. The `base features` can be custom indicators or they can be imported from any technical-analysis library that you can find. One important syntax rule is that all `base features` string names are prepended with `%`, while labels/targets are prepended with `&`. -It is advisable to start from the `populate_any_indicators()` in the example strategy (found in `templates/FreqaiExampleStrategy.py`) to ensure that the feature definitions are following the correct conventions. Here is an example of how to set the indicators and labels in the strategy: +Meanwhile, high level feature engineering is handled within `"feature_parameters":{}` in the `FreqAI` config. Within this file, it is possible to decide large scale feature expansions on top of the `base_features` such as "including correlated pairs" or "including informative timeframes" or even "including recent candles." + +It is advisable to start from the template `populate_any_indicators()` in the source provided example strategy (found in `templates/FreqaiExampleStrategy.py`) to ensure that the feature definitions are following the correct conventions. Here is an example of how to set the indicators and labels in the strategy: ```python def populate_any_indicators( @@ -129,7 +131,7 @@ In total, the number of features the user of the presented example strat has cre Important metrics can be returned to the strategy at the end of each model training by assigning them to `dk.data['extra_returns_per_train']['my_new_value'] = XYZ` inside the custom prediction model class. -FreqAI takes the `my_new_value` assigned in this dictionary and expands it to fit the dataframe that is returned to the strategy. You can then use the returned metrics in your strategy through `dataframe['my_new_value']`. An example of how return values can be used in FreqAI are the `&*_mean` and `&*_std` values that are used to [created a dynamic target threshold](freqai-configuration.md#creating-a-dynamic-target-threshold). +`FreqAI` takes the `my_new_value` assigned in this dictionary and expands it to fit the dataframe that is returned to the strategy. You can then use the returned metrics in your strategy through `dataframe['my_new_value']`. An example of how return values can be used in `FreqAI` are the `&*_mean` and `&*_std` values that are used to [created a dynamic target threshold](freqai-configuration.md#creating-a-dynamic-target-threshold). Another example, where the user wants to use live metrics from the trade database, is shown below: @@ -139,15 +141,15 @@ Another example, where the user wants to use live metrics from the trade databas } ``` -You need to set the standard dictionary in the config so that FreqAI can return proper dataframe shapes. These values will likely be overridden by the prediction model, but in the case where the model has yet to set them, or needs a default initial value, the preset values are what will be returned. +You need to set the standard dictionary in the config so that `FreqAI` can return proper dataframe shapes. These values will likely be overridden by the prediction model, but in the case where the model has yet to set them, or needs a default initial value, the preset values are what will be returned. ## Feature normalization -FreqAI is strict when it comes to data normalization. The train features, $X^{train}$, are always normalized to [-1, 1] using a shifted min-max normalization: +`FreqAI` is strict when it comes to data normalization. The train features, $X^{train}$, are always normalized to [-1, 1] using a shifted min-max normalization: $$X^{train}_{norm} = 2 * \frac{X^{train} - X^{train}.min()}{X^{train}.max() - X^{train}.min()} - 1$$ -All other data (test data and unseen prediction data in dry/live/backtest) is always automatically normalized to the training feature space according to industry standards. FreqAI stores all the metadata required to ensure that test and prediction features will be properly normalized and that predictions are properly denormalized. For this reason, it is not recommended to eschew industry standards and modify FreqAI internals - however - advanced users can do so by inheriting `train()` in their custom `IFreqaiModel` and using their own normalization functions. +All other data (test data and unseen prediction data in dry/live/backtest) is always automatically normalized to the training feature space according to industry standards. `FreqAI` stores all the metadata required to ensure that test and prediction features will be properly normalized and that predictions are properly denormalized. For this reason, it is not recommended to eschew industry standards and modify `FreqAI` internals - however - advanced users can do so by inheriting `train()` in their custom `IFreqaiModel` and using their own normalization functions. ## Data dimensionality reduction with Principal Component Analysis @@ -167,17 +169,17 @@ This will perform PCA on the features and reduce their dimensionality so that th The `inlier_metric` is a metric aimed at quantifying how similar a the features of a data point are to the most recent historic data points. -You define the lookback window by setting `inlier_metric_window` and FreqAI computes the distance between the present time point and each of the previous `inlier_metric_window` lookback points. A Weibull function is fit to each of the lookback distributions and its cumulative distribution function (CDF) is used to produce a quantile for each lookback point. The `inlier_metric` is then computed for each time point as the average of the corresponding lookback quantiles. The figure below explains the concept for an `inlier_metric_window` of 5. +You define the lookback window by setting `inlier_metric_window` and `FreqAI` computes the distance between the present time point and each of the previous `inlier_metric_window` lookback points. A Weibull function is fit to each of the lookback distributions and its cumulative distribution function (CDF) is used to produce a quantile for each lookback point. The `inlier_metric` is then computed for each time point as the average of the corresponding lookback quantiles. The figure below explains the concept for an `inlier_metric_window` of 5. ![inlier-metric](assets/freqai_inlier-metric.jpg) -FreqAI adds the `inlier_metric` to the training features and hence gives the model access to a novel type of temporal information. +`FreqAI` adds the `inlier_metric` to the training features and hence gives the model access to a novel type of temporal information. This function does **not** remove outliers from the data set. ## Weighting features for temporal importance -FreqAI allows you to set a `weight_factor` to weight recent data more strongly than past data via an exponential function: +`FreqAI` allows you to set a `weight_factor` to weight recent data more strongly than past data via an exponential function: $$ W_i = \exp(\frac{-i}{\alpha*n}) $$ @@ -187,13 +189,13 @@ where $W_i$ is the weight of data point $i$ in a total set of $n$ data points. B ## Outlier detection -Equity and crypto markets suffer from a high level of non-patterned noise in the form of outlier data points. FreqAI implements a variety of methods to identify such outliers and hence mitigate risk. +Equity and crypto markets suffer from a high level of non-patterned noise in the form of outlier data points. `FreqAI` implements a variety of methods to identify such outliers and hence mitigate risk. ### Identifying outliers with the Dissimilarity Index (DI) The Dissimilarity Index (DI) aims to quantify the uncertainty associated with each prediction made by the model. -You can tell FreqAI to remove outlier data points from the training/test data sets using the DI by including the following statement in the config: +You can tell `FreqAI` to remove outlier data points from the training/test data sets using the DI by including the following statement in the config: ```json "freqai": { @@ -203,7 +205,7 @@ You can tell FreqAI to remove outlier data points from the training/test data se } ``` - The DI allows predictions which are outliers (not existent in the model feature space) to be thrown out due to low levels of certainty. To do so, FreqAI measures the distance between each training data point (feature vector), $X_{a}$, and all other training data points: + The DI allows predictions which are outliers (not existent in the model feature space) to be thrown out due to low levels of certainty. To do so, `FreqAI` measures the distance between each training data point (feature vector), $X_{a}$, and all other training data points: $$ d_{ab} = \sqrt{\sum_{j=1}^p(X_{a,j}-X_{b,j})^2} $$ @@ -227,7 +229,7 @@ Below is a figure that describes the DI for a 3D data set. ### Identifying outliers using a Support Vector Machine (SVM) -You can tell FreqAI to remove outlier data points from the training/test data sets using a Support Vector Machine (SVM) by including the following statement in the config: +You can tell `FreqAI` to remove outlier data points from the training/test data sets using a Support Vector Machine (SVM) by including the following statement in the config: ```json "freqai": { @@ -239,7 +241,7 @@ You can tell FreqAI to remove outlier data points from the training/test data se The SVM will be trained on the training data and any data point that the SVM deems to be beyond the feature space will be removed. -FreqAI uses `sklearn.linear_model.SGDOneClassSVM` (details are available on scikit-learn's webpage [here](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDOneClassSVM.html) (external website)) and you can elect to provide additional parameters for the SVM, such as `shuffle`, and `nu`. +`FreqAI` uses `sklearn.linear_model.SGDOneClassSVM` (details are available on scikit-learn's webpage [here](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDOneClassSVM.html) (external website)) and you can elect to provide additional parameters for the SVM, such as `shuffle`, and `nu`. The parameter `shuffle` is by default set to `False` to ensure consistent results. If it is set to `True`, running the SVM multiple times on the same data set might result in different outcomes due to `max_iter` being to low for the algorithm to reach the demanded `tol`. Increasing `max_iter` solves this issue but causes the procedure to take longer time. @@ -247,7 +249,7 @@ The parameter `nu`, *very* broadly, is the amount of data points that should be ### Identifying outliers with DBSCAN -You can configure FreqAI to use DBSCAN to cluster and remove outliers from the training/test data set or incoming outliers from predictions, by activating `use_DBSCAN_to_remove_outliers` in the config: +You can configure `FreqAI` to use DBSCAN to cluster and remove outliers from the training/test data set or incoming outliers from predictions, by activating `use_DBSCAN_to_remove_outliers` in the config: ```json "freqai": { @@ -263,4 +265,4 @@ Given a number of data points $N$, and a distance $\varepsilon$, DBSCAN clusters ![dbscan](assets/freqai_dbscan.jpg) -FreqAI uses `sklearn.cluster.DBSCAN` (details are available on scikit-learn's webpage [here](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.DBSCAN.html) (external website)) with `min_samples` ($N$) taken as 1/4 of the no. of time points in the feature set. `eps` ($\varepsilon$) is computed automatically as the elbow point in the *k-distance graph* computed from the nearest neighbors in the pairwise distances of all data points in the feature set. +`FreqAI` uses `sklearn.cluster.DBSCAN` (details are available on scikit-learn's webpage [here](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.DBSCAN.html) (external website)) with `min_samples` ($N$) taken as 1/4 of the no. of time points in the feature set. `eps` ($\varepsilon$) is computed automatically as the elbow point in the *k-distance graph* computed from the nearest neighbors in the pairwise distances of all data points in the feature set. diff --git a/docs/freqai-parameter-table.md b/docs/freqai-parameter-table.md index 605892134..5969f43c6 100644 --- a/docs/freqai-parameter-table.md +++ b/docs/freqai-parameter-table.md @@ -1,13 +1,13 @@ # Parameter table -The table below will list all configuration parameters available for FreqAI. Some of the parameters are exemplified in `config_examples/config_freqai.example.json`. +The table below will list all configuration parameters available for `FreqAI`. Some of the parameters are exemplified in `config_examples/config_freqai.example.json`. Mandatory parameters are marked as **Required** and have to be set in one of the suggested ways. | Parameter | Description | |------------|-------------| | | **General configuration parameters** -| `freqai` | **Required.**
The parent dictionary containing all the parameters for controlling FreqAI.
**Datatype:** Dictionary. +| `freqai` | **Required.**
The parent dictionary containing all the parameters for controlling `FreqAI`.
**Datatype:** Dictionary. | `train_period_days` | **Required.**
Number of days to use for the training data (width of the sliding window).
**Datatype:** Positive integer. | `backtest_period_days` | **Required.**
Number of days to inference from the trained model before sliding the `train_period_days` window defined above, and retraining the model during backtesting (more info [here](freqai-running.md#backtesting)). This can be fractional days, but beware that the provided `timerange` will be divided by this number to yield the number of trainings necessary to complete the backtest.
**Datatype:** Float. | `identifier` | **Required.**
A unique ID for the current model. If models are saved to disk, the `identifier` allows for reloading specific pre-trained models/data.
**Datatype:** String. @@ -21,21 +21,22 @@ Mandatory parameters are marked as **Required** and have to be set in one of the | | **Feature parameters** | `feature_parameters` | A dictionary containing the parameters used to engineer the feature set. Details and examples are shown [here](freqai-feature-engineering.md).
**Datatype:** Dictionary. | `include_timeframes` | A list of timeframes that all indicators in `populate_any_indicators` will be created for. The list is added as features to the base indicators dataset.
**Datatype:** List of timeframes (strings). -| `include_corr_pairlist` | A list of correlated coins that FreqAI will add as additional features to all `pair_whitelist` coins. All indicators set in `populate_any_indicators` during feature engineering (see details [here](freqai-feature-engineering.md)) will be created for each correlated coin. The correlated coins features are added to the base indicators dataset.
**Datatype:** List of assets (strings). +| `include_corr_pairlist` | A list of correlated coins that `FreqAI` will add as additional features to all `pair_whitelist` coins. All indicators set in `populate_any_indicators` during feature engineering (see details [here](freqai-feature-engineering.md)) will be created for each correlated coin. The correlated coins features are added to the base indicators dataset.
**Datatype:** List of assets (strings). | `label_period_candles` | Number of candles into the future that the labels are created for. This is used in `populate_any_indicators` (see `templates/FreqaiExampleStrategy.py` for detailed usage). You can create custom labels and choose whether to make use of this parameter or not.
**Datatype:** Positive integer. -| `include_shifted_candles` | Add features from previous candles to subsequent candles with the intent of adding historical information. If used, FreqAI will duplicate and shift all features from the `include_shifted_candles` previous candles so that the information is available for the subsequent candle.
**Datatype:** Positive integer. +| `include_shifted_candles` | Add features from previous candles to subsequent candles with the intent of adding historical information. If used, `FreqAI` will duplicate and shift all features from the `include_shifted_candles` previous candles so that the information is available for the subsequent candle.
**Datatype:** Positive integer. | `weight_factor` | Weight training data points according to their recency (see details [here](freqai-feature-engineering.md#weighting-features-for-temporal-importance)).
**Datatype:** Positive float (typically < 1). -| `indicator_max_period_candles` | **No longer used (#7325)**. Replaced by `startup_candle_count` which is set in the [strategy](freqai-configuration.md#building-a-freqai-strategy). `startup_candle_count` is timeframe independent and defines the maximum *period* used in `populate_any_indicators()` for indicator creation. FreqAI uses this parameter together with the maximum timeframe in `include_time_frames` to calculate how many data points to download such that the first data point does not include a NaN
**Datatype:** Positive integer. +| `indicator_max_period_candles` | **No longer used (#7325)**. Replaced by `startup_candle_count` which is set in the [strategy](freqai-configuration.md#building-a-freqai-strategy). `startup_candle_count` is timeframe independent and defines the maximum *period* used in `populate_any_indicators()` for indicator creation. `FreqAI` uses this parameter together with the maximum timeframe in `include_time_frames` to calculate how many data points to download such that the first data point does not include a NaN
**Datatype:** Positive integer. | `indicator_periods_candles` | Time periods to calculate indicators for. The indicators are added to the base indicator dataset.
**Datatype:** List of positive integers. | `stratify_training_data` | Split the feature set into training and testing datasets. For example, `stratify_training_data: 2` would set every 2nd data point into a separate dataset to be pulled from during training/testing. See details about how it works [here](freqai-running.md#data-stratification-for-training-and-testing-the-model).
**Datatype:** Positive integer. -| `principal_component_analysis` | Reduce the dimensionality of the dataset using Principal Component Analysis. See details about how it works [here](freqai-feature-engineering.md#data-dimensionality-reduction-with-principal-component-analysis).
**Datatype:** Boolean. +| `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)
**Datatype:** Boolean. defaults to `false`. +| `plot_feature_importances` | Create a feature importance plot for each model for the top/bottom `plot_feature_importances` number of features.
**Datatype:** Integer, defaults to `0`. | `DI_threshold` | Activates the use of the Dissimilarity Index for outlier detection when set to > 0. See details about how it works [here](freqai-feature-engineering.md#identifying-outliers-with-the-dissimilarity-index-di).
**Datatype:** Positive float (typically < 1). | `use_SVM_to_remove_outliers` | Train a support vector machine to detect and remove outliers from the training dataset, as well as from incoming data points. See details about how it works [here](freqai-feature-engineering.md#identifying-outliers-using-a-support-vector-machine-svm).
**Datatype:** Boolean. | `svm_params` | All parameters available in Sklearn's `SGDOneClassSVM()`. See details about some select parameters [here](freqai-feature-engineering.md#identifying-outliers-using-a-support-vector-machine-svm).
**Datatype:** Dictionary. | `use_DBSCAN_to_remove_outliers` | Cluster data using the DBSCAN algorithm to identify and remove outliers from training and prediction data. See details about how it works [here](freqai-feature-engineering.md#identifying-outliers-with-dbscan).
**Datatype:** Boolean. -| `inlier_metric_window` | If set, FreqAI adds an `inlier_metric` to the training feature set and set the lookback to be the `inlier_metric_window`, i.e., the number of previous time points to compare the current candle to. Details of how the `inlier_metric` is computed can be found [here](freqai-feature-engineering.md#inlier-metric).
**Datatype:** Integer.
Default: 0. -| `noise_standard_deviation` | If set, FreqAI adds noise to the training features with the aim of preventing overfitting. FreqAI generates random deviates from a gaussian distribution with a standard deviation of `noise_standard_deviation` and adds them to all data points. `noise_standard_deviation` should be kept relative to the normalized space, i.e., between -1 and 1. In other words, since data in FreqAI is always normalized to be between -1 and 1, `noise_standard_deviation: 0.05` would result in 32% of the data being randomly increased/decreased by more than 2.5% (i.e., the percent of data falling within the first standard deviation).
**Datatype:** Integer.
Default: 0. -| `outlier_protection_percentage` | Enable to prevent outlier detection methods from discarding too much data. If more than `outlier_protection_percentage` % of points are detected as outliers by the SVM or DBSCAN, FreqAI will log a warning message and ignore outlier detection, i.e., the original dataset will be kept intact. If the outlier protection is triggered, no predictions will be made based on the training dataset.
**Datatype:** Float.
Default: `30`. +| `inlier_metric_window` | If set, `FreqAI` adds an `inlier_metric` to the training feature set and set the lookback to be the `inlier_metric_window`, i.e., the number of previous time points to compare the current candle to. Details of how the `inlier_metric` is computed can be found [here](freqai-feature-engineering.md#inlier-metric).
**Datatype:** Integer.
Default: 0. +| `noise_standard_deviation` | If set, `FreqAI` adds noise to the training features with the aim of preventing overfitting. `FreqAI` generates random deviates from a gaussian distribution with a standard deviation of `noise_standard_deviation` and adds them to all data points. `noise_standard_deviation` should be kept relative to the normalized space, i.e., between -1 and 1. In other words, since data in `FreqAI` is always normalized to be between -1 and 1, `noise_standard_deviation: 0.05` would result in 32% of the data being randomly increased/decreased by more than 2.5% (i.e., the percent of data falling within the first standard deviation).
**Datatype:** Integer.
Default: 0. +| `outlier_protection_percentage` | Enable to prevent outlier detection methods from discarding too much data. If more than `outlier_protection_percentage` % of points are detected as outliers by the SVM or DBSCAN, `FreqAI` will log a warning message and ignore outlier detection, i.e., the original dataset will be kept intact. If the outlier protection is triggered, no predictions will be made based on the training dataset.
**Datatype:** Float.
Default: `30`. | `reverse_train_test_order` | Split the feature dataset (see below) and use the latest data split for training and test on historical split of the data. This allows the model to be trained up to the most recent data point, while avoiding overfitting. However, you should be careful to understand the unorthodox nature of this parameter before employing it.
**Datatype:** Boolean.
Default: `False` (no reversal). | | **Data split parameters** | `data_split_parameters` | Include any additional parameters available from Scikit-learn `test_train_split()`, which are shown [here](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html) (external website).
**Datatype:** Dictionary. diff --git a/docs/freqai-running.md b/docs/freqai-running.md index ff9079397..4d95f33e1 100644 --- a/docs/freqai-running.md +++ b/docs/freqai-running.md @@ -1,6 +1,6 @@ # Running FreqAI -There are two ways to train and deploy an adaptive machine learning model - live deployment and deployment for backtesting analysis. FreqAI can be used for both and allows for periodic training of models in live and backtesting, as shown in the following figure. +There are two ways to train and deploy an adaptive machine learning model - live deployment and historical backtesting. In both cases, `FreqAI` runs/simulates periodic retraining of models as shown in the following figure: ![freqai-window](assets/freqai_moving-window.jpg) diff --git a/docs/freqai.md b/docs/freqai.md index 4c95000d1..748820914 100644 --- a/docs/freqai.md +++ b/docs/freqai.md @@ -1,10 +1,10 @@ ![freqai-logo](assets/freqai_doc_logo.svg) -# FreqAI +# `FreqAI` ##Introduction -FreqAI is a software designed to automate a variety of tasks associated with training a predictive machine learning model to generate market forecasts given a set of input features. +`FreqAI` is a software designed to automate a variety of tasks associated with training a predictive machine learning model to generate market forecasts given a set of input features. Features include: @@ -23,7 +23,7 @@ Features include: ## Quick start -The easiest way to quickly test FreqAI is to run it in dry mode with the following command: +The easiest way to quickly test `FreqAI` is to run it in dry mode with the following command: ```bash freqtrade trade --config config_examples/config_freqai.example.json --strategy FreqaiExampleStrategy --freqaimodel LightGBMRegressor --strategy-path freqtrade/templates @@ -37,7 +37,7 @@ An example strategy, prediction model, and config to use as a starting points ca ## General approach -You provide FreqAI with a set of custom *base indicators* (the same way as in a [typical Freqtrade strategy](strategy-customization.md)) as well as target values (*labels*). For each pair in the whitelist, FreqAI trains a model to predict the target values based on the input of custom indicators. The models are then consistently retrained, with a predetermined frequency, to adapt to market conditions. FreqAI offers the ability to both backtest strategies (emulating reality with periodic retraining on historic data) and deploy dry/live runs. In dry/live conditions, FreqAI can be set to constant retraining in a background thread to keep models as up to date as possible. +You provide `FreqAI` with a set of custom *base indicators* (the same way as in a [typical Freqtrade strategy](strategy-customization.md)) as well as target values (*labels*). For each pair in the whitelist, `FreqAI` trains a model to predict the target values based on the input of custom indicators. The models are then consistently retrained, with a predetermined frequency, to adapt to market conditions. `FreqAI` offers the ability to both backtest strategies (emulating reality with periodic retraining on historic data) and deploy dry/live runs. In dry/live conditions, `FreqAI` can be set to constant retraining in a background thread to keep models as up to date as possible. An overview of the algorithm, explaining the data processing pipeline and model usage, is shown below. @@ -45,7 +45,7 @@ An overview of the algorithm, explaining the data processing pipeline and model ### Important machine learning vocabulary -**Features** - the parameters, based on historic data, on which a model is trained. All features for a single candle is stored as a vector. In FreqAI, you build a feature data sets from anything you can construct in the strategy. +**Features** - the parameters, based on historic data, on which a model is trained. All features for a single candle is stored as a vector. In `FreqAI`, you build a feature data sets from anything you can construct in the strategy. **Labels** - the target values that a model is trained toward. Each feature vector is associated with a single label that is defined by you within your strategy. These labels intentionally look into the future, and are not available to the model during dry/live/backtesting. @@ -59,7 +59,7 @@ An overview of the algorithm, explaining the data processing pipeline and model ## Install prerequisites -The normal Freqtrade install process will ask if you wish to install FreqAI dependencies. You should reply "yes" to this question if you wish to use FreqAI. If you did not reply yes, you can manually install these dependencies after the install with: +The normal Freqtrade install process will ask if you wish to install `FreqAI` dependencies. You should reply "yes" to this question if you wish to use `FreqAI`. If you did not reply yes, you can manually install these dependencies after the install with: ``` bash pip install -r requirements-freqai.txt @@ -70,15 +70,18 @@ pip install -r requirements-freqai.txt ### Usage with docker -If you are using docker, a dedicated tag with FreqAI dependencies is available as `:freqai`. As such - you can replace the image line in your docker-compose file with `image: freqtradeorg/freqtrade:develop_freqai`. This image contains the regular FreqAI dependencies. Similar to native installs, Catboost will not be available on ARM based devices. +If you are using docker, a dedicated tag with `FreqAI` dependencies is available as `:freqai`. As such - you can replace the image line in your docker-compose file with `image: freqtradeorg/freqtrade:develop_freqai`. This image contains the regular `FreqAI` dependencies. Similar to native installs, Catboost will not be available on ARM based devices. -## Common pitfalls +### Common pitfalls -FreqAI cannot be combined with dynamic `VolumePairlists` (or any pairlist filter that adds and removes pairs dynamically). This is for performance reasons - FreqAI relies on making quick predictions/retrains. To do this effectively, it needs to download all the training data at the beginning of a dry/live instance. FreqAI stores and appends new candles automatically for future retrains. This means that if new pairs arrive later in the dry run due to a volume pairlist, it will not have the data ready. However, FreqAI does work with the `ShufflePairlist` or a `VolumePairlist` which keeps the total pairlist constant (but reorders the pairs according to volume). +`FreqAI` cannot be combined with dynamic `VolumePairlists` (or any pairlist filter that adds and removes pairs dynamically). +This is for performance reasons - `FreqAI` relies on making quick predictions/retrains. To do this effectively, +it needs to download all the training data at the beginning of a dry/live instance. `FreqAI` stores and appends +new candles automatically for future retrains. This means that if new pairs arrive later in the dry run due to a volume pairlist, it will not have the data ready. However, `FreqAI` does work with the `ShufflePairlist` or a `VolumePairlist` which keeps the total pairlist constant (but reorders the pairs according to volume). ## Credits -FreqAI is developed by a group of individuals who all contribute specific skillsets to the project. +`FreqAI` is developed by a group of individuals who all contribute specific skillsets to the project. Conception and software development: Robert Caulk @robcaulk From eb36105de4fc0c07658698f3ac067e60ee4d1605 Mon Sep 17 00:00:00 2001 From: Matthias Date: Mon, 26 Sep 2022 19:36:21 +0200 Subject: [PATCH 9/9] Fix some random typos --- docs/freqai-configuration.md | 10 ++++++---- docs/freqai-developers.md | 16 ++++++++-------- docs/freqai-feature-engineering.md | 6 +++--- docs/freqai-running.md | 12 ++++++------ docs/freqai.md | 8 ++++---- 5 files changed, 27 insertions(+), 25 deletions(-) diff --git a/docs/freqai-configuration.md b/docs/freqai-configuration.md index 68d34eddf..50e75b658 100644 --- a/docs/freqai-configuration.md +++ b/docs/freqai-configuration.md @@ -32,7 +32,8 @@ }, } ``` -A full example config is available in `config_examples/config_freqai.example.json`. + +A full example config is available in `config_examples/config_freqai.example.json`. ## Building a `FreqAI` strategy @@ -120,7 +121,7 @@ The `FreqAI` strategy requires including the following lines of code in the stan ``` -Notice how the `populate_any_indicators()` is where [features](freqai-feature-engineering.md#feature-engineering) and labels/tragets are added. A full example strategy is available in `templates/FreqaiExampleStrategy.py`. +Notice how the `populate_any_indicators()` is where [features](freqai-feature-engineering.md#feature-engineering) and labels/targets are added. A full example strategy is available in `templates/FreqaiExampleStrategy.py`. Notice also the location of the labels under `if set_generalized_indicators:` at the bottom of the example. This is where single features and labels/targets should be added to the feature set to avoid duplication of them from various configuration parameters that multiply the feature set, such as `include_timeframes`. @@ -172,10 +173,11 @@ Below are the values you can expect to include/use inside a typical strategy dat | `df['%*']` | Any dataframe column prepended with `%` in `populate_any_indicators()` is treated as a training feature. For example, you can include the RSI in the training feature set (similar to in `templates/FreqaiExampleStrategy.py`) by setting `df['%-rsi']`. See more details on how this is done [here](freqai-feature-engineering.md).
**Note:** Since the number of features prepended with `%` can multiply very quickly (10s of thousands of features is easily engineered using the multiplictative functionality described in the `feature_parameters` table shown above), these features are removed from the dataframe upon return from `FreqAI`. To keep a particular type of feature for plotting purposes, you would prepend it with `%%`.
**Datatype:** Depends on the output of the model. ## Setting the `startup_candle_count` -The `startup_candle_count` in the `FreqAI` strategy needs to be set up in the same way as in the standard Freqtrade strategy (see details [here](strategy-customization.md#strategy-startup-period)). This value is used by Freqtrade to ensure that a sufficient amount of data is provided when calling the `dataprovider`, to avoid any NaNs at the beginning of the first training. You can easily set this value by identifying the longest period (in candle units) which is passed to the indicator creation functions (e.g., talib functions). In the presented example, `startup_candle_count` is 20 since this is the maximum value in `indicators_periods_candles`. + +The `startup_candle_count` in the `FreqAI` strategy needs to be set up in the same way as in the standard Freqtrade strategy (see details [here](strategy-customization.md#strategy-startup-period)). This value is used by Freqtrade to ensure that a sufficient amount of data is provided when calling the `dataprovider`, to avoid any NaNs at the beginning of the first training. You can easily set this value by identifying the longest period (in candle units) which is passed to the indicator creation functions (e.g., Ta-Lib functions). In the presented example, `startup_candle_count` is 20 since this is the maximum value in `indicators_periods_candles`. !!! Note - There are instances where the talib functions actually require more data than just the passed `period` or else the feature dataset gets populated with NaNs. Anecdotally, multiplying the `startup_candle_count` by 2 always leads to a fully NaN free training dataset. Hence, it is typically safest to multiply the expected `startup_candle_count` by 2. Look out for this log message to confirm that the data is clean: + There are instances where the Ta-Lib functions actually require more data than just the passed `period` or else the feature dataset gets populated with NaNs. Anecdotally, multiplying the `startup_candle_count` by 2 always leads to a fully NaN free training dataset. Hence, it is typically safest to multiply the expected `startup_candle_count` by 2. Look out for this log message to confirm that the data is clean: ``` 2022-08-31 15:14:04 - freqtrade.freqai.data_kitchen - INFO - dropped 0 training points due to NaNs in populated dataset 4319. diff --git a/docs/freqai-developers.md b/docs/freqai-developers.md index 1635e785d..4bff46f2f 100644 --- a/docs/freqai-developers.md +++ b/docs/freqai-developers.md @@ -1,24 +1,24 @@ -# Development +# Development ## Project architecture -The architechture and functions of `FreqAI` are generalized to encourages development of unique features, functions, models, etc. +The architecture and functions of `FreqAI` are generalized to encourages development of unique features, functions, models, etc. -The class structure and a detailed algorithmic overview is depicted in the following diagram: +The class structure and a detailed algorithmic overview is depicted in the following diagram: ![image](assets/freqai_algorithm-diagram.jpg) As shown, there are three distinct objects comprising `FreqAI`: -* **IFreqaiModel** - A singular persistent object containing all the necessary logic to collect, store, and process data, engineer features, run training, and inference models. -* **FreqaiDataKitchen** - A non-persistent object which is created uniquely for each unique asset/model. Beyond metadata, it also contains a variety of data processing tools. -* **FreqaiDataDrawer** - A singular persistent object containing all the historical predictions, models, and save/load methods. +* **IFreqaiModel** - A singular persistent object containing all the necessary logic to collect, store, and process data, engineer features, run training, and inference models. +* **FreqaiDataKitchen** - A non-persistent object which is created uniquely for each unique asset/model. Beyond metadata, it also contains a variety of data processing tools. +* **FreqaiDataDrawer** - A singular persistent object containing all the historical predictions, models, and save/load methods. -There are a variety of built-in [prediction models](freqai-configuration.md#using-different-prediction-models) which inherit directly from `IFreqaiModel`. Each of these models have full access to all methods in `IFreqaiModel` and can therefore override any of those functions at will. However, advanced users will likely stick to overriding `fit()`, `train()`, `predict()`, and `data_cleaning_train/predict()`. +There are a variety of built-in [prediction models](freqai-configuration.md#using-different-prediction-models) which inherit directly from `IFreqaiModel`. Each of these models have full access to all methods in `IFreqaiModel` and can therefore override any of those functions at will. However, advanced users will likely stick to overriding `fit()`, `train()`, `predict()`, and `data_cleaning_train/predict()`. ## Data handling -`FreqAI` aims to organize model files, prediction data, and meta data in a way that simplifies post-processing and enhances crash recililence by automatic data reloading. The data is saved in a file structure,`user_data_dir/models/`, which contains all the data associated with the trainings and backtests. The `FreqaiDataKitchen()` relies heavily on the file structure for proper training and inferencing and should therefore not be manually modified. +`FreqAI` aims to organize model files, prediction data, and meta data in a way that simplifies post-processing and enhances crash resilience by automatic data reloading. The data is saved in a file structure,`user_data_dir/models/`, which contains all the data associated with the trainings and backtests. The `FreqaiDataKitchen()` relies heavily on the file structure for proper training and inferencing and should therefore not be manually modified. ### File structure diff --git a/docs/freqai-feature-engineering.md b/docs/freqai-feature-engineering.md index 34c9c8443..8f061b9fd 100644 --- a/docs/freqai-feature-engineering.md +++ b/docs/freqai-feature-engineering.md @@ -1,6 +1,6 @@ # Feature engineering -## Defining the features +## Defining the features Low level feature engineering is performed in the user strategy within a function called `populate_any_indicators()`. That function sets the `base features` such as, `RSI`, `MFI`, `EMA`, `SMA`, time of day, volume, etc. The `base features` can be custom indicators or they can be imported from any technical-analysis library that you can find. One important syntax rule is that all `base features` string names are prepended with `%`, while labels/targets are prepended with `&`. @@ -102,7 +102,7 @@ After having defined the `base features`, the next step is to expand upon them u ```json "freqai": { - ... + //... "feature_parameters" : { "include_timeframes": ["5m","15m","4h"], "include_corr_pairlist": [ @@ -114,7 +114,7 @@ After having defined the `base features`, the next step is to expand upon them u "include_shifted_candles": 2, "indicator_periods_candles": [10, 20] }, - ... + //... } ``` diff --git a/docs/freqai-running.md b/docs/freqai-running.md index 4d95f33e1..6c7b56da1 100644 --- a/docs/freqai-running.md +++ b/docs/freqai-running.md @@ -58,7 +58,7 @@ freqtrade backtesting --strategy FreqaiExampleStrategy --strategy-path freqtrade If this command has never been executed with the existing config file, FreqAI will train a new model for each pair, for each backtesting window within the expanded `--timerange`. -Backtesting mode requires [downloading the necessary data](#downloading-data-to-cover-the-full-backtest-period) before deployment (unlike in dry/live mode where FreqAI handles the data downloading automatically). You 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 set backtesting time range. More details on how to calculate the data to download can be found [here](#deciding-the-size-of-the-sliding-training-window-and-backtesting-duration). +Backtesting mode requires [downloading the necessary data](#downloading-data-to-cover-the-full-backtest-period) before deployment (unlike in dry/live mode where FreqAI handles the data downloading automatically). You 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 set backtesting time range. More details on how to calculate the data to download can be found [here](#deciding-the-size-of-the-sliding-training-window-and-backtesting-duration). !!! Note "Model reuse" Once the training is completed, you can execute the backtesting again with the same config file and @@ -71,11 +71,11 @@ Backtesting mode requires [downloading the necessary data](#downloading-data-to- ### Saving prediction data -To allow for tweaking your strategy (**not** the features!), FreqAI will automatically save the predictions during backtesting so that they can be reused for future backtests and live runs using the same `identifier` model. This provides a performance enhancement geared towards enabling **high-level hyperopting** of entry/exit criteria. +To allow for tweaking your strategy (**not** the features!), FreqAI will automatically save the predictions during backtesting so that they can be reused for future backtests and live runs using the same `identifier` model. This provides a performance enhancement geared towards enabling **high-level hyperopting** of entry/exit criteria. -An additional directory called `predictions`, which contains all the predictions stored in `hdf` format, will be created in the `unique-id` folder. +An additional directory called `predictions`, which contains all the predictions stored in `hdf` format, will be created in the `unique-id` folder. -To change your **features**, you **must** set a new `identifier` in the config to signal to `FreqAI` to train new models. +To change your **features**, you **must** set a new `identifier` in the config to signal to `FreqAI` to train new models. To save the models generated during a particular backtest so that you can start a live deployment from one of them instead of training a new model, you must set `save_backtest_models` to `True` in the config. @@ -132,7 +132,7 @@ The FreqAI specific parameter `label_period_candles` defines the offset (number ## Continual learning -You can choose to adopt a continual learning scheme by setting `"continual_learning": true` in the config. By enabling `continual_learning`, after training an initial model from scratch, subsequent trainings will start from the final model state of the preceding training. This gives the new model a "memory" of the previous state. By default, this is set to `false` which means that all new models are trained from scratch, without input from previous models. +You can choose to adopt a continual learning scheme by setting `"continual_learning": true` in the config. By enabling `continual_learning`, after training an initial model from scratch, subsequent trainings will start from the final model state of the preceding training. This gives the new model a "memory" of the previous state. By default, this is set to `false` which means that all new models are trained from scratch, without input from previous models. ## Hyperopt @@ -148,7 +148,7 @@ freqtrade hyperopt --hyperopt-loss SharpeHyperOptLoss --strategy FreqaiExampleSt - It's not possible to hyperopt indicators in the `populate_any_indicators()` function. This means that you cannot optimize model parameters using hyperopt. Apart from this exception, it is possible to optimize all other [spaces](hyperopt.md#running-hyperopt-with-smaller-search-space). - The backtesting instructions also apply to hyperopt. -The best method for combining hyperopt and FreqAI is to focus on hyperopting entry/exit thresholds/criteria. You need to focus on hyperopting parameters that are not used in your features. For example, you should not try to hyperopt rolling window lengths in the feature creation, or any part of the FreqAI config which changes predictions. In order to efficiently hyperopt the FreqAI strategy, FreqAI stores predictions as dataframes and reuses them. Hence the requirement to hyperopt entry/exit thresholds/criteria only. +The best method for combining hyperopt and FreqAI is to focus on hyperopting entry/exit thresholds/criteria. You need to focus on hyperopting parameters that are not used in your features. For example, you should not try to hyperopt rolling window lengths in the feature creation, or any part of the FreqAI config which changes predictions. In order to efficiently hyperopt the FreqAI strategy, FreqAI stores predictions as dataframes and reuses them. Hence the requirement to hyperopt entry/exit thresholds/criteria only. A good example of a hyperoptable parameter in FreqAI is a threshold for the [Dissimilarity Index (DI)](freqai-feature-engineering.md#identifying-outliers-with-the-dissimilarity-index-di) `DI_values` beyond which we consider data points as outliers: diff --git a/docs/freqai.md b/docs/freqai.md index 748820914..91adbf7ef 100644 --- a/docs/freqai.md +++ b/docs/freqai.md @@ -1,8 +1,8 @@ ![freqai-logo](assets/freqai_doc_logo.svg) -# `FreqAI` +# `FreqAI` -##Introduction +## Introduction `FreqAI` is a software designed to automate a variety of tasks associated with training a predictive machine learning model to generate market forecasts given a set of input features. @@ -47,7 +47,7 @@ An overview of the algorithm, explaining the data processing pipeline and model **Features** - the parameters, based on historic data, on which a model is trained. All features for a single candle is stored as a vector. In `FreqAI`, you build a feature data sets from anything you can construct in the strategy. -**Labels** - the target values that a model is trained toward. Each feature vector is associated with a single label that is defined by you within your strategy. These labels intentionally look into the future, and are not available to the model during dry/live/backtesting. +**Labels** - the target values that a model is trained toward. Each feature vector is associated with a single label that is defined by you within the strategy. These labels intentionally look into the future, and are not available to the model during dry/live/backtesting. **Training** - the process of "teaching" the model to match the feature sets to the associated labels. Different types of models "learn" in different ways. More information about the different models can be found [here](freqai-configuration.md#using-different-prediction-models). @@ -72,7 +72,7 @@ pip install -r requirements-freqai.txt If you are using docker, a dedicated tag with `FreqAI` dependencies is available as `:freqai`. As such - you can replace the image line in your docker-compose file with `image: freqtradeorg/freqtrade:develop_freqai`. This image contains the regular `FreqAI` dependencies. Similar to native installs, Catboost will not be available on ARM based devices. -### Common pitfalls +## Common pitfalls `FreqAI` cannot be combined with dynamic `VolumePairlists` (or any pairlist filter that adds and removes pairs dynamically). This is for performance reasons - `FreqAI` relies on making quick predictions/retrains. To do this effectively,

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