freqai_test_multimodel_classifier_strat

import logging
from functools import reduce
from typing import Dict

import numpy as np
import talib.abstract as ta
from pandas import DataFrame

from freqtrade.strategy import DecimalParameter, IntParameter, IStrategy


logger = logging.getLogger(__name__)


class freqai_test_multimodel_classifier_strat(IStrategy):
    """
    Test strategy - used for testing freqAI multimodel functionalities.
    DO not use in production.
    """

    minimal_roi = {"0": 0.1, "240": -1}

    plot_config = {
        "main_plot": {},
        "subplots": {
            "prediction": {"prediction": {"color": "blue"}},
            "target_roi": {
                "target_roi": {"color": "brown"},
            },
            "do_predict": {
                "do_predict": {"color": "brown"},
            },
        },
    }

    process_only_new_candles = True
    stoploss = -0.05
    use_exit_signal = True
    startup_candle_count: int = 300
    can_short = False

    linear_roi_offset = DecimalParameter(
        0.00, 0.02, default=0.005, space="sell", optimize=False, load=True
    )
    max_roi_time_long = IntParameter(0, 800, default=400, space="sell", optimize=False, load=True)

    def feature_engineering_expand_all(self, dataframe: DataFrame, period: int,
                                       metadata: Dict, **kwargs):

        dataframe["%-rsi-period"] = ta.RSI(dataframe, timeperiod=period)
        dataframe["%-mfi-period"] = ta.MFI(dataframe, timeperiod=period)
        dataframe["%-adx-period"] = ta.ADX(dataframe, timeperiod=period)

        return dataframe

    def feature_engineering_expand_basic(self, dataframe: DataFrame, metadata: Dict, **kwargs):

        dataframe["%-pct-change"] = dataframe["close"].pct_change()
        dataframe["%-raw_volume"] = dataframe["volume"]
        dataframe["%-raw_price"] = dataframe["close"]

        return dataframe

    def feature_engineering_standard(self, dataframe: DataFrame, metadata: Dict, **kwargs):

        dataframe["%-day_of_week"] = dataframe["date"].dt.dayofweek
        dataframe["%-hour_of_day"] = dataframe["date"].dt.hour

        return dataframe

    def set_freqai_targets(self, dataframe: DataFrame, metadata: Dict, **kwargs):

        dataframe['&s-up_or_down'] = np.where(dataframe["close"].shift(-50) >
                                              dataframe["close"], 'up', 'down')

        dataframe['&s-up_or_down2'] = np.where(dataframe["close"].shift(-50) >
                                               dataframe["close"], 'up2', 'down2')

        return dataframe

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:

        self.freqai_info = self.config["freqai"]

        dataframe = self.freqai.start(dataframe, metadata, self)

        dataframe["target_roi"] = dataframe["&-s_close_mean"] + dataframe["&-s_close_std"] * 1.25
        dataframe["sell_roi"] = dataframe["&-s_close_mean"] - dataframe["&-s_close_std"] * 1.25
        return dataframe

    def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:

        enter_long_conditions = [df["do_predict"] == 1, df["&-s_close"] > df["target_roi"]]

        if enter_long_conditions:
            df.loc[
                reduce(lambda x, y: x & y, enter_long_conditions), ["enter_long", "enter_tag"]
            ] = (1, "long")

        enter_short_conditions = [df["do_predict"] == 1, df["&-s_close"] < df["sell_roi"]]

        if enter_short_conditions:
            df.loc[
                reduce(lambda x, y: x & y, enter_short_conditions), ["enter_short", "enter_tag"]
            ] = (1, "short")

        return df

    def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
        exit_long_conditions = [df["do_predict"] == 1, df["&-s_close"] < df["sell_roi"] * 0.25]
        if exit_long_conditions:
            df.loc[reduce(lambda x, y: x & y, exit_long_conditions), "exit_long"] = 1

        exit_short_conditions = [df["do_predict"] == 1, df["&-s_close"] > df["target_roi"] * 0.25]
        if exit_short_conditions:
            df.loc[reduce(lambda x, y: x & y, exit_short_conditions), "exit_short"] = 1

        return df