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feat: add some freqAI parameter descriptions

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Matthias 2024-07-26 20:23:21 +02:00
parent edf66deb96
commit 06bbcf4c9f
1 changed files with 97 additions and 14 deletions
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111
freqtrade/configuration/config_schema.py
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@ -986,24 +986,98 @@ CONF_SCHEMA = {
"feature_parameters": {
"type": "object",
"properties": {
"include_corr_pairlist": {"type": "array"},
"include_timeframes": {"type": "array"},
"label_period_candles": {"type": "integer"},
"include_shifted_candles": {"type": "integer", "default": 0},
"DI_threshold": {"type": "number", "default": 0},
"weight_factor": {"type": "number", "default": 0},
"principal_component_analysis": {"type": "boolean", "default": False},
"use_SVM_to_remove_outliers": {"type": "boolean", "default": False},
"plot_feature_importances": {"type": "integer", "default": 0},
"include_corr_pairlist": {
"description": "List of correlated pairs to include in the features.",
"type": "array",
},
"include_timeframes": {
"description": (
"A list of timeframes that all indicators in "
"`feature_engineering_expand_*()` will be created for."
),
"type": "array",
},
"label_period_candles": {
"description": (
"Number of candles into the future to use for labeling the period."
"This can be used in `set_freqai_targets()`."
),
"type": "integer",
},
"include_shifted_candles": {
"description": (
"Add features from previous candles to subsequent candles with "
"the intent of adding historical information."
),
"type": "integer",
"default": 0,
},
"DI_threshold": {
"description": (
"Activates the use of the Dissimilarity Index for "
"outlier detection when set to > 0."
),
"type": "number",
"default": 0,
},
"weight_factor": {
"description": (
"Weight training data points according to their recency."
),
"type": "number",
"default": 0,
},
"principal_component_analysis": {
"description": (
"Automatically reduce the dimensionality of the data set using "
"Principal Component Analysis"
),
"type": "boolean",
"default": False,
},
"use_SVM_to_remove_outliers": {
"description": "Use SVM to remove outliers from the features.",
"type": "boolean",
"default": False,
},
"plot_feature_importances": {
"description": "Create feature importance plots for each model.",
"type": "integer",
"default": 0,
},
"svm_params": {
"description": (
"All parameters available in Sklearn's `SGDOneClassSVM()`."
),
"type": "object",
"properties": {
"shuffle": {"type": "boolean", "default": False},
"nu": {"type": "number", "default": 0.1},
"shuffle": {
"description": "Whether to shuffle data before applying SVM.",
"type": "boolean",
"default": False,
},
"nu": {
"type": "number",
"default": 0.1,
},
},
"shuffle_after_split": {"type": "boolean", "default": False},
"buffer_train_data_candles": {"type": "integer", "default": 0},
},
"shuffle_after_split": {
"description": (
"Split the data into train and test sets, and then shuffle "
"both sets individually."
),
"type": "boolean",
"default": False,
},
"buffer_train_data_candles": {
"description": (
"Cut `buffer_train_data_candles` off the beginning and end of the "
"training data *after* the indicators were populated."
),
"type": "integer",
"default": 0,
},
},
"required": [
"include_timeframes",
@ -1011,6 +1085,9 @@ CONF_SCHEMA = {
],
},
"data_split_parameters": {
"descriptions": (
"Additional parameters for scikit-learn's test_train_split() function."
),
"type": "object",
"properties": {
"test_size": {"type": "number"},
@ -1018,7 +1095,13 @@ CONF_SCHEMA = {
"shuffle": {"type": "boolean", "default": False},
},
},
"model_training_parameters": {"type": "object"},
"model_training_parameters": {
"description": (
"Flexible dictionary that includes all parameters available by "
"the selected model library. "
),
"type": "object",
},
"rl_config": {
"type": "object",
"properties": {