freqtrade_origin/docs/freqai-parameter-table.md

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 2nd 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.