stable/freqtrade/freqai/prediction_models/CatboostPredictionModel.py

import logging
from typing import Any, Dict, Tuple

import pandas as pd
from catboost import CatBoostRegressor, Pool
from pandas import DataFrame

from freqtrade.freqai.freqai_interface import IFreqaiModel


logger = logging.getLogger(__name__)


class CatboostPredictionModel(IFreqaiModel):
    """
    User created prediction model. The class needs to override three necessary
    functions, predict(), train(), fit(). The class inherits ModelHandler which
    has its own DataHandler where data is held, saved, loaded, and managed.
    """

    def make_labels(self, dataframe: DataFrame) -> DataFrame:
        """
        User defines the labels here (target values).
        :params:
        :dataframe: the full dataframe for the present training period
        """

        dataframe["s"] = (
            dataframe["close"]
            .shift(-self.feature_parameters["period"])
            .rolling(self.feature_parameters["period"])
            .mean()
            / dataframe["close"]
            - 1
        )
        self.dh.data["s_mean"] = dataframe["s"].mean()
        self.dh.data["s_std"] = dataframe["s"].std()

        # logger.info("label mean", self.dh.data["s_mean"], "label std", self.dh.data["s_std"])

        return dataframe["s"]

    def train(self, unfiltered_dataframe: DataFrame, metadata: dict) -> Tuple[DataFrame, DataFrame]:
        """
        Filter the training data and train a model to it. Train makes heavy use of the datahkitchen
        for storing, saving, loading, and analyzing the data.
        :params:
        :unfiltered_dataframe: Full dataframe for the current training period
        :metadata: pair metadata from strategy.
        :returns:
        :model: Trained model which can be used to inference (self.predict)
        """
        logger.info("--------------------Starting training--------------------")

        # create the full feature list based on user config info
        self.dh.training_features_list = self.dh.find_features(unfiltered_dataframe)
        unfiltered_labels = self.make_labels(unfiltered_dataframe)
        # filter the features requested by user in the configuration file and elegantly handle NaNs
        features_filtered, labels_filtered = self.dh.filter_features(
            unfiltered_dataframe,
            self.dh.training_features_list,
            unfiltered_labels,
            training_filter=True,
        )

        # split data into train/test data.
        data_dictionary = self.dh.make_train_test_datasets(features_filtered, labels_filtered)
        # standardize all data based on train_dataset only
        data_dictionary = self.dh.standardize_data(data_dictionary)

        # optional additional data cleaning/analysis
        self.data_cleaning_train()

        logger.info(f'Training model on {len(self.dh.training_features_list)} features')
        logger.info(f'Training model on {len(data_dictionary["train_features"])} data points')

        model = self.fit(data_dictionary)

        logger.info(f'--------------------done training {metadata["pair"]}--------------------')

        return model

    def fit(self, data_dictionary: Dict) -> Any:
        """
        User sets up the training and test data to fit their desired model here
        :params:
        :data_dictionary: the dictionary constructed by DataHandler to hold
        all the training and test data/labels.
        """

        train_data = Pool(
            data=data_dictionary["train_features"],
            label=data_dictionary["train_labels"],
            weight=data_dictionary["train_weights"],
        )

        test_data = Pool(
            data=data_dictionary["test_features"],
            label=data_dictionary["test_labels"],
            weight=data_dictionary["test_weights"],
        )

        model = CatBoostRegressor(
            verbose=100, early_stopping_rounds=400, **self.model_training_parameters
        )
        model.fit(X=train_data, eval_set=test_data)

        return model

    def predict(self, unfiltered_dataframe: DataFrame, metadata: dict) -> Tuple[DataFrame,
                                                                                DataFrame]:
        """
        Filter the prediction features data and predict with it.
        :param: unfiltered_dataframe: Full dataframe for the current backtest period.
        :return:
        :predictions: np.array of predictions
        :do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove
        data (NaNs) or felt uncertain about data (PCA and DI index)
        """

        # logger.info("--------------------Starting prediction--------------------")

        original_feature_list = self.dh.find_features(unfiltered_dataframe)
        filtered_dataframe, _ = self.dh.filter_features(
            unfiltered_dataframe, original_feature_list, training_filter=False
        )
        filtered_dataframe = self.dh.standardize_data_from_metadata(filtered_dataframe)
        self.dh.data_dictionary["prediction_features"] = filtered_dataframe

        # optional additional data cleaning/analysis
        self.data_cleaning_predict(filtered_dataframe)

        predictions = self.model.predict(self.dh.data_dictionary["prediction_features"])

        # compute the non-standardized predictions
        self.dh.predictions = (predictions + 1) * (self.dh.data["labels_max"] -
                                                   self.dh.data["labels_min"]) / 2 + self.dh.data[
                                                                                     "labels_min"]

        # logger.info("--------------------Finished prediction--------------------")

        return (self.dh.predictions, self.dh.do_predict)

    def data_cleaning_train(self) -> None:
        """
        User can add data analysis and cleaning here.
        Any function inside this method should drop training data points from the filtered_dataframe
        based on user decided logic. See FreqaiDataKitchen::remove_outliers() for an example
        of how outlier data points are dropped from the dataframe used for training.
        """
        if self.feature_parameters["principal_component_analysis"]:
            self.dh.principal_component_analysis()

        # if self.feature_parameters["determine_statistical_distributions"]:
        #     self.dh.determine_statistical_distributions()
        # if self.feature_parameters["remove_outliers"]:
        #     self.dh.remove_outliers(predict=False)

        if self.feature_parameters["use_SVM_to_remove_outliers"]:
            self.dh.use_SVM_to_remove_outliers(predict=False)
        if self.feature_parameters["DI_threshold"]:
            self.dh.data["avg_mean_dist"] = self.dh.compute_distances()

    def data_cleaning_predict(self, filtered_dataframe: DataFrame) -> None:
        """
        User can add data analysis and cleaning here.
        These functions each modify self.dh.do_predict, which is a dataframe with equal length
        to the number of candles coming from and returning to the strategy. Inside do_predict,
         1 allows prediction and < 0 signals to the strategy that the model is not confident in
         the prediction.
         See FreqaiDataKitchen::remove_outliers() for an example
        of how the do_predict vector is modified. do_predict is ultimately passed back to strategy
        for buy signals.
        """
        if self.feature_parameters["principal_component_analysis"]:
            pca_components = self.dh.pca.transform(filtered_dataframe)
            self.dh.data_dictionary["prediction_features"] = pd.DataFrame(
                data=pca_components,
                columns=["PC" + str(i) for i in range(0, self.dh.data["n_kept_components"])],
                index=filtered_dataframe.index,
            )

        # if self.feature_parameters["determine_statistical_distributions"]:
        #     self.dh.determine_statistical_distributions()
        # if self.feature_parameters["remove_outliers"]:
        #     self.dh.remove_outliers(predict=True)  # creates dropped index

        if self.feature_parameters["use_SVM_to_remove_outliers"]:
            self.dh.use_SVM_to_remove_outliers(predict=True)

        if self.feature_parameters["DI_threshold"]:
            self.dh.check_if_pred_in_training_spaces()  # sets do_predict