freqtrade_origin/freqtrade/freqai/freqai_interface.py

# import contextlib
import gc
import logging
# import sys
import threading
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Any, Dict, Tuple

import numpy.typing as npt
import pandas as pd
from pandas import DataFrame

from freqtrade.configuration import TimeRange
from freqtrade.enums import RunMode
from freqtrade.exceptions import OperationalException
from freqtrade.freqai.data_drawer import FreqaiDataDrawer
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
from freqtrade.strategy.interface import IStrategy


pd.options.mode.chained_assignment = None
logger = logging.getLogger(__name__)


def threaded(fn):
    def wrapper(*args, **kwargs):
        threading.Thread(target=fn, args=args, kwargs=kwargs).start()
    return wrapper


class IFreqaiModel(ABC):
    """
    Class containing all tools for training and prediction in the strategy.
    User models should inherit from this class as shown in
    templates/ExamplePredictionModel.py where the user overrides
    train(), predict(), fit(), and make_labels().
    Author: Robert Caulk, rob.caulk@gmail.com
    """

    def __init__(self, config: Dict[str, Any]) -> None:

        self.config = config
        self.assert_config(self.config)
        self.freqai_info = config["freqai"]
        self.data_split_parameters = config["freqai"]["data_split_parameters"]
        self.model_training_parameters = config["freqai"]["model_training_parameters"]
        self.feature_parameters = config["freqai"]["feature_parameters"]
        self.time_last_trained = None
        self.current_time = None
        self.model = None
        self.predictions = None
        self.training_on_separate_thread = False
        self.retrain = False
        self.first = True
        self.set_full_path()
        self.data_drawer = FreqaiDataDrawer(Path(self.full_path),
                                            self.config['exchange']['pair_whitelist'])
        self.lock = threading.Lock()

    def assert_config(self, config: Dict[str, Any]) -> None:

        if not config.get('freqai', {}):
            raise OperationalException(
                "No freqai parameters found in configuration file."
            )

    def start(self, dataframe: DataFrame, metadata: dict, strategy: IStrategy) -> DataFrame:
        """
        Entry point to the FreqaiModel from a specific pair, it will train a new model if
        necessary before making the prediction.

        :params:
        :dataframe: Full dataframe coming from strategy - it contains entire
        backtesting timerange + additional historical data necessary to train
        the model.
        :metadata: pair metadata coming from strategy.
        """

        self.live = strategy.dp.runmode in (RunMode.DRY_RUN, RunMode.LIVE)
        self.data_drawer.set_pair_dict_info(metadata)

        # For live, we may be training new models on a separate thread while other pairs still need
        # to inference their historical models. Here we use a training queue system to handle this
        # and we keep the flag self.training_on_separate_threaad in the current object to help
        # determine what the current pair will do
        if self.live:
            if (not self.training_on_separate_thread and
                    self.data_drawer.pair_dict[metadata['pair']]['priority'] == 1):

                self.dh = FreqaiDataKitchen(self.config, self.data_drawer,
                                            self.live, metadata["pair"])
                dh = self.start_live(dataframe, metadata, strategy, self.dh)
            else:
                # we will have at max 2 separate instances of the kitchen at once.
                self.dh_fg = FreqaiDataKitchen(self.config, self.data_drawer,
                                               self.live, metadata["pair"])
                dh = self.start_live(dataframe, metadata, strategy, self.dh_fg)

            # return (dh.full_predictions, dh.full_do_predict,
            #         dh.full_target_mean, dh.full_target_std)

        # For backtesting, each pair enters and then gets trained for each window along the
        # sliding window defined by "train_period" (training window) and "backtest_period"
        # (backtest window, i.e. window immediately following the training window).
        # FreqAI slides the window and sequentially builds the backtesting results before returning
        # the concatenated results for the full backtesting period back to the strategy.
        else:
            self.dh = FreqaiDataKitchen(self.config, self.data_drawer, self.live, metadata["pair"])
            logger.info(f'Training {len(self.dh.training_timeranges)} timeranges')
            dh = self.start_backtesting(dataframe, metadata, self.dh)

        return (dh.full_predictions, dh.full_do_predict,
                dh.full_target_mean, dh.full_target_std)

    def start_backtesting(self, dataframe: DataFrame, metadata: dict,
                          dh: FreqaiDataKitchen) -> FreqaiDataKitchen:
        """
        The main broad execution for backtesting. For backtesting, each pair enters and then gets
        trained for each window along the sliding window defined by "train_period" (training window)
        and "backtest_period" (backtest window, i.e. window immediately following the
        training window). FreqAI slides the window and sequentially builds the backtesting results
        before returning the concatenated results for the full backtesting period back to the
        strategy.
        :params:
        dataframe: DataFrame = strategy passed dataframe
        metadata: Dict = pair metadata
        dh: FreqaiDataKitchen = Data management/analysis tool assoicated to present pair only
        :returns:
        dh: FreqaiDataKitchen = Data management/analysis tool assoicated to present pair only
        """

        # Loop enforcing the sliding window training/backtesting paradigm
        # tr_train is the training time range e.g. 1 historical month
        # tr_backtest is the backtesting time range e.g. the week directly
        # following tr_train. Both of these windows slide through the
        # entire backtest
        for tr_train, tr_backtest in zip(
            dh.training_timeranges, dh.backtesting_timeranges
        ):
            (_, _, _) = self.data_drawer.get_pair_dict_info(metadata)
            gc.collect()
            dh.data = {}  # clean the pair specific data between training window sliding
            self.training_timerange = tr_train
            # self.training_timerange_timerange = tr_train
            dataframe_train = dh.slice_dataframe(tr_train, dataframe)
            dataframe_backtest = dh.slice_dataframe(tr_backtest, dataframe)
            logger.info("training %s for %s", metadata["pair"], tr_train)
            trained_timestamp = tr_train  # TimeRange.parse_timerange(tr_train)
            dh.data_path = Path(dh.full_path /
                                str("sub-train" + "-" + metadata['pair'].split("/")[0] +
                                    str(int(trained_timestamp.stopts))))
            if not self.model_exists(metadata["pair"], dh,
                                     trained_timestamp=trained_timestamp.stopts):
                self.model = self.train(dataframe_train, metadata, dh)
                self.data_drawer.pair_dict[metadata['pair']][
                                        'trained_timestamp'] = trained_timestamp.stopts
                dh.set_new_model_names(metadata, trained_timestamp)
                dh.save_data(self.model, metadata['pair'])
            else:
                self.model = dh.load_data(metadata['pair'])

            self.check_if_feature_list_matches_strategy(dataframe_train, dh)

            preds, do_preds = self.predict(dataframe_backtest, dh)

            dh.append_predictions(preds, do_preds, len(dataframe_backtest))
            print('predictions', len(dh.full_predictions),
                  'do_predict', len(dh.full_do_predict))

        dh.fill_predictions(len(dataframe))

        return dh

    def start_live(self, dataframe: DataFrame, metadata: dict,
                   strategy: IStrategy, dh: FreqaiDataKitchen) -> FreqaiDataKitchen:
        """
        The main broad execution for dry/live. This function will check if a retraining should be
        performed, and if so, retrain and reset the model.
        :params:
        dataframe: DataFrame = strategy passed dataframe
        metadata: Dict = pair metadata
        strategy: IStrategy = currently employed strategy
        dh: FreqaiDataKitchen = Data management/analysis tool assoicated to present pair only
        :returns:
        dh: FreqaiDataKitchen = Data management/analysis tool assoicated to present pair only
        """

        (model_filename,
         trained_timestamp,
         coin_first) = self.data_drawer.get_pair_dict_info(metadata)

        if not self.training_on_separate_thread:
            file_exists = False

            if trained_timestamp != 0:  # historical model available
                dh.set_paths(metadata, trained_timestamp)
                file_exists = self.model_exists(metadata['pair'],
                                                dh,
                                                trained_timestamp=trained_timestamp,
                                                model_filename=model_filename)

            (self.retrain,
             new_trained_timerange) = dh.check_if_new_training_required(trained_timestamp)
            dh.set_paths(metadata, new_trained_timerange.stopts)

            if self.retrain or not file_exists:
                if coin_first:
                    self.train_model_in_series(new_trained_timerange, metadata, strategy, dh)
                else:
                    self.training_on_separate_thread = True  # acts like a lock
                    self.retrain_model_on_separate_thread(new_trained_timerange,
                                                          metadata, strategy, dh)

        else:
            logger.info("FreqAI training a new model on background thread.")

        self.model = dh.load_data(coin=metadata['pair'])

        self.check_if_feature_list_matches_strategy(dataframe, dh)

        preds, do_preds = self.predict(dataframe, dh)
        dh.append_predictions(preds, do_preds, len(dataframe))

        return dh

    def check_if_feature_list_matches_strategy(self, dataframe: DataFrame,
                                               dh: FreqaiDataKitchen) -> None:
        strategy_provided_features = dh.find_features(dataframe)
        if 'training_features_list_raw' in dh.data:
            feature_list = dh.data['training_features_list_raw']
        else:
            feature_list = dh.training_features_list
        if strategy_provided_features != feature_list:
            raise OperationalException("Trying to access pretrained model with `identifier` "
                                       "but found different features furnished by current strategy."
                                       "Change `identifer` to train from scratch, or ensure the"
                                       "strategy is furnishing the same features as the pretrained"
                                       "model")

    def data_cleaning_train(self, dh: FreqaiDataKitchen) -> None:
        """
        Base data cleaning method for train
        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.freqai_info.get('feature_parameters', {}).get('principal_component_analysis'):
            dh.principal_component_analysis()

        if self.freqai_info.get('feature_parameters', {}).get('use_SVM_to_remove_outliers'):
            dh.use_SVM_to_remove_outliers(predict=False)

        if self.freqai_info.get('feature_parameters', {}).get('DI_threshold'):
            dh.data["avg_mean_dist"] = dh.compute_distances()

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

    def data_cleaning_predict(self, dh: FreqaiDataKitchen, dataframe: DataFrame) -> None:
        """
        Base data cleaning method for predict.
        These functions each modify 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.freqai_info.get('feature_parameters', {}).get('principal_component_analysis'):
            dh.pca_transform(dataframe)

        if self.freqai_info.get('feature_parameters', {}).get('use_SVM_to_remove_outliers'):
            dh.use_SVM_to_remove_outliers(predict=True)

        if self.freqai_info.get('feature_parameters', {}).get('DI_threshold'):
            dh.check_if_pred_in_training_spaces()

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

    def model_exists(self, pair: str, dh: FreqaiDataKitchen, trained_timestamp: int = None,
                     model_filename: str = '') -> bool:
        """
        Given a pair and path, check if a model already exists
        :param pair: pair e.g. BTC/USD
        :param path: path to model
        """
        coin, _ = pair.split("/")

        if not self.live:
            dh.model_filename = model_filename = "cb_" + coin.lower() + "_" + str(trained_timestamp)

        path_to_modelfile = Path(dh.data_path / str(model_filename + "_model.joblib"))
        file_exists = path_to_modelfile.is_file()
        if file_exists:
            logger.info("Found model at %s", dh.data_path / dh.model_filename)
        else:
            logger.info("Could not find model at %s", dh.data_path / dh.model_filename)
        return file_exists

    def set_full_path(self) -> None:
        self.full_path = Path(self.config['user_data_dir'] /
                              "models" /
                              str(self.freqai_info.get('identifier')))

    @threaded
    def retrain_model_on_separate_thread(self, new_trained_timerange: TimeRange, metadata: dict,
                                         strategy: IStrategy, dh: FreqaiDataKitchen):

        # with nostdout():
        dh.download_new_data_for_retraining(new_trained_timerange, metadata, strategy)
        corr_dataframes, base_dataframes = dh.load_pairs_histories(new_trained_timerange,
                                                                   metadata)

        # protecting from common benign errors associated with grabbing new data from exchange:
        try:
            unfiltered_dataframe = dh.use_strategy_to_populate_indicators(strategy,
                                                                          corr_dataframes,
                                                                          base_dataframes,
                                                                          metadata)
        except Exception:
            logger.warning('Mismatched sizes encountered in strategy')
            # self.data_drawer.pair_to_end_of_training_queue(metadata['pair'])
            self.training_on_separate_thread = False
            self.retrain = False
            return

        try:
            model = self.train(unfiltered_dataframe, metadata, dh)
        except ValueError:
            logger.warning('Value error encountered during training')
            # self.data_drawer.pair_to_end_of_training_queue(metadata['pair'])
            self.training_on_separate_thread = False
            self.retrain = False
            return

        self.data_drawer.pair_dict[metadata['pair']][
                                   'trained_timestamp'] = new_trained_timerange.stopts
        dh.set_new_model_names(metadata, new_trained_timerange)
        # logger.info('Training queue'
        #             f'{sorted(self.data_drawer.pair_dict.items(), key=lambda item: item[1])}')
        dh.save_data(model, coin=metadata['pair'])

        if self.data_drawer.pair_dict[metadata['pair']['priority']] == 1:
            self.data_drawer.pair_to_end_of_training_queue(metadata['pair'])
        self.training_on_separate_thread = False
        self.retrain = False
        return

    def train_model_in_series(self, new_trained_timerange: TimeRange, metadata: dict,
                              strategy: IStrategy, dh: FreqaiDataKitchen):

        dh.download_new_data_for_retraining(new_trained_timerange, metadata, strategy)
        corr_dataframes, base_dataframes = dh.load_pairs_histories(new_trained_timerange,
                                                                   metadata)

        unfiltered_dataframe = dh.use_strategy_to_populate_indicators(strategy,
                                                                      corr_dataframes,
                                                                      base_dataframes,
                                                                      metadata)

        model = self.train(unfiltered_dataframe, metadata, dh)

        self.data_drawer.pair_dict[metadata['pair']][
                                   'trained_timestamp'] = new_trained_timerange.stopts
        dh.set_new_model_names(metadata, new_trained_timerange)
        self.data_drawer.pair_dict[metadata['pair']]['first'] = False
        dh.save_data(model, coin=metadata['pair'])
        self.retrain = False

    # Following methods which are overridden by user made prediction models.
    # See freqai/prediction_models/CatboostPredictionModlel.py for an example.

    @abstractmethod
    def train(self, unfiltered_dataframe: DataFrame, metadata: dict, dh: FreqaiDataKitchen) -> Any:
        """
        Filter the training data and train a model to it. Train makes heavy use of the datahandler
        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)
        """

    @abstractmethod
    def fit(self) -> Any:
        """
        Most regressors use the same function names and arguments e.g. user
        can drop in LGBMRegressor in place of CatBoostRegressor and all data
        management will be properly handled by Freqai.
        :params:
        data_dictionary: Dict = the dictionary constructed by DataHandler to hold
        all the training and test data/labels.
        """

        return

    @abstractmethod
    def predict(self, dataframe: DataFrame,
                dh: FreqaiDataKitchen) -> Tuple[npt.ArrayLike, npt.ArrayLike]:
        """
        Filter the prediction features data and predict with it.
        :param:
        unfiltered_dataframe: Full dataframe for the current backtest period.
        dh: FreqaiDataKitchen = Data management/analysis tool assoicated to present pair only
        :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 (i.e. SVM and/or DI index)
        """

    @abstractmethod
    def make_labels(self, dataframe: DataFrame, dh: FreqaiDataKitchen) -> DataFrame:
        """
        User defines the labels here (target values).
        :params:
        dataframe: DataFrame = the full dataframe for the present training period
        dh: FreqaiDataKitchen = Data management/analysis tool assoicated to present pair only
        """

        return