More docstring changes
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@ -92,11 +92,11 @@ class IFreqaiModel(ABC):
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Entry point to the FreqaiModel from a specific pair, it will train a new model if
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Entry point to the FreqaiModel from a specific pair, it will train a new model if
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necessary before making the prediction.
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necessary before making the prediction.
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:params:
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:param dataframe: Full dataframe coming from strategy - it contains entire
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:dataframe: Full dataframe coming from strategy - it contains entire
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backtesting timerange + additional historical data necessary to train
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backtesting timerange + additional historical data necessary to train
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the model.
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the model.
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:metadata: pair metadata coming from strategy.
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:param metadata: pair metadata coming from strategy.
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:param strategy: Strategy to train on
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"""
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"""
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self.live = strategy.dp.runmode in (RunMode.DRY_RUN, RunMode.LIVE)
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self.live = strategy.dp.runmode in (RunMode.DRY_RUN, RunMode.LIVE)
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@ -32,10 +32,9 @@ class BaseRegressionModel(IFreqaiModel):
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"""
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"""
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Filter the training data and train a model to it. Train makes heavy use of the datakitchen
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Filter the training data and train a model to it. Train makes heavy use of the datakitchen
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for storing, saving, loading, and analyzing the data.
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for storing, saving, loading, and analyzing the data.
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:params:
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:param unfiltered_dataframe: Full dataframe for the current training period
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:unfiltered_dataframe: Full dataframe for the current training period
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:param metadata: pair metadata from strategy.
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:metadata: pair metadata from strategy.
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:return:
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:returns:
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:model: Trained model which can be used to inference (self.predict)
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:model: Trained model which can be used to inference (self.predict)
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"""
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"""
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@ -31,9 +31,8 @@ class BaseTensorFlowModel(IFreqaiModel):
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"""
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"""
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Filter the training data and train a model to it. Train makes heavy use of the datakitchen
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Filter the training data and train a model to it. Train makes heavy use of the datakitchen
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for storing, saving, loading, and analyzing the data.
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for storing, saving, loading, and analyzing the data.
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:params:
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:param unfiltered_dataframe: Full dataframe for the current training period
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:unfiltered_dataframe: Full dataframe for the current training period
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:param metadata: pair metadata from strategy.
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:metadata: pair metadata from strategy.
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:returns:
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:returns:
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:model: Trained model which can be used to inference (self.predict)
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:model: Trained model which can be used to inference (self.predict)
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"""
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"""
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@ -19,8 +19,7 @@ class CatboostPredictionModel(BaseRegressionModel):
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def fit(self, data_dictionary: Dict) -> Any:
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def fit(self, data_dictionary: Dict) -> Any:
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"""
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"""
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User sets up the training and test data to fit their desired model here
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User sets up the training and test data to fit their desired model here
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:params:
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:param data_dictionary: the dictionary constructed by DataHandler to hold
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:data_dictionary: the dictionary constructed by DataHandler to hold
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all the training and test data/labels.
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all the training and test data/labels.
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"""
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"""
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@ -20,8 +20,7 @@ class CatboostPredictionMultiModel(BaseRegressionModel):
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def fit(self, data_dictionary: Dict) -> Any:
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def fit(self, data_dictionary: Dict) -> Any:
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"""
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"""
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User sets up the training and test data to fit their desired model here
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User sets up the training and test data to fit their desired model here
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:params:
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:param data_dictionary: the dictionary constructed by DataHandler to hold
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:data_dictionary: the dictionary constructed by DataHandler to hold
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all the training and test data/labels.
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all the training and test data/labels.
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"""
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"""
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@ -21,8 +21,7 @@ class LightGBMPredictionModel(BaseRegressionModel):
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Most regressors use the same function names and arguments e.g. user
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Most regressors use the same function names and arguments e.g. user
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can drop in LGBMRegressor in place of CatBoostRegressor and all data
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can drop in LGBMRegressor in place of CatBoostRegressor and all data
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management will be properly handled by Freqai.
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management will be properly handled by Freqai.
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:params:
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:param data_dictionary: the dictionary constructed by DataHandler to hold
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:data_dictionary: the dictionary constructed by DataHandler to hold
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all the training and test data/labels.
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all the training and test data/labels.
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"""
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"""
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@ -20,8 +20,7 @@ class LightGBMPredictionMultiModel(BaseRegressionModel):
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def fit(self, data_dictionary: Dict) -> Any:
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def fit(self, data_dictionary: Dict) -> Any:
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"""
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"""
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User sets up the training and test data to fit their desired model here
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User sets up the training and test data to fit their desired model here
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:params:
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:param data_dictionary: the dictionary constructed by DataHandler to hold
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:data_dictionary: the dictionary constructed by DataHandler to hold
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all the training and test data/labels.
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all the training and test data/labels.
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"""
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"""
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