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). 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 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). 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 (more information can be found here). 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. 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) 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.
`weight_factor`	Weight training data points according to their recency (see details here). Datatype: Positive float (typically < 1).
`indicator_max_period_candles`	No longer used (#7325). Replaced by `startup_candle_count` which is set in the 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 2^nd data point into a separate dataset to be pulled from during training/testing. See details about how it works here. 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 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. 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. Datatype: Boolean.
`svm_params`	All parameters available in Sklearn's `SGDOneClassSVM()`. See details about some select parameters here. 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. 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. 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 (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 selected model library. For example, if you use `LightGBMRegressor`, this dictionary can contain any parameter available by the `LightGBMRegressor` here (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 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.