# source: https://raw.githubusercontent.com/plcacs/Harmonizing_ml_results/a66800d87059b5b854166f27c66f5104ecc830b8/cloned_repos/o1_mini/3/freqai_test_strat_0dc220.py
import logging
from functools import reduce
from typing import Any, Dict
import talib.abstract as ta
from pandas import DataFrame
from freqtrade.strategy import DecimalParameter, IntParameter, IStrategy

logger: logging.Logger = logging.getLogger(__name__)

class Github_plcacs_Harmonizing_ml_results__freqai_test_strat_0dc220__20250519_020121(IStrategy):
    """
    Test strategy - used for testing freqAI functionalities.
    DO not use in production.
    """
    minimal_roi: Dict[str, float] = {'0': 0.1, '240': -1}
    plot_config: Dict[str, Any] = {
        'main_plot': {},
        'subplots': {
            'prediction': {'prediction': {'color': 'blue'}},
            'target_roi': {'target_roi': {'color': 'brown'}},
            'do_predict': {'do_predict': {'color': 'brown'}}
        }
    }
    process_only_new_candles: bool = True
    stoploss: float = -0.05
    use_exit_signal: bool = True
    startup_candle_count: int = 300
    can_short: bool = False
    linear_roi_offset: DecimalParameter = DecimalParameter(
        0.0, 0.02, default=0.005, space='sell', optimize=False, load=True
    )
    max_roi_time_long: IntParameter = IntParameter(
        0, 800, default=400, space='sell', optimize=False, load=True
    )

    def feature_engineering_expand_all(
        self,
        dataframe: DataFrame,
        period: int,
        metadata: Dict[str, Any],
        **kwargs: Any
    ) -> DataFrame:
        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[str, Any],
        **kwargs: Any
    ) -> DataFrame:
        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[str, Any],
        **kwargs: Any
    ) -> DataFrame:
        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[str, Any],
        **kwargs: Any
    ) -> DataFrame:
        label_period_candles: int = self.freqai_info['feature_parameters']['label_period_candles']
        dataframe['&-s_close'] = (
            dataframe['close']
            .shift(-label_period_candles)
            .rolling(label_period_candles)
            .mean()
            / dataframe['close'] - 1
        )
        return dataframe

    def populate_indicators(
        self,
        dataframe: DataFrame,
        metadata: Dict[str, Any]
    ) -> DataFrame:
        self.freqai_info: Dict[str, Any] = 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[str, Any]
    ) -> DataFrame:
        enter_long_conditions: list = [
            df['do_predict'] == 1,
            df['&-s_close'] > df['target_roi']
        ]
        if all(enter_long_conditions):
            condition = reduce(lambda x, y: x & y, enter_long_conditions)
            df.loc[condition, ['enter_long', 'enter_tag']] = (1, 'long')

        enter_short_conditions: list = [
            df['do_predict'] == 1,
            df['&-s_close'] < df['sell_roi']
        ]
        if all(enter_short_conditions):
            condition = reduce(lambda x, y: x & y, enter_short_conditions)
            df.loc[condition, ['enter_short', 'enter_tag']] = (1, 'short')

        return df

    def populate_exit_trend(
        self,
        df: DataFrame,
        metadata: Dict[str, Any]
    ) -> DataFrame:
        exit_long_conditions: list = [
            df['do_predict'] == 1,
            df['&-s_close'] < df['sell_roi'] * 0.25
        ]
        if all(exit_long_conditions):
            condition = reduce(lambda x, y: x & y, exit_long_conditions)
            df.loc[condition, 'exit_long'] = 1

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

        return df