# source: https://raw.githubusercontent.com/plcacs/Harmonizing_ml_results/9b70afe47f502f4432b81918922b4d6e41310981/ManyTypes4py_benchmarks/claude3_sonnet_1st_run/3/freqai_test_strat_0dc220.py
import logging
from functools import reduce
from typing import Dict, Any, List, Optional

import talib.abstract as ta
from pandas import DataFrame
from freqtrade.strategy import DecimalParameter, IntParameter, IStrategy

logger = logging.getLogger(__name__)

class Github_plcacs_Harmonizing_ml_results__freqai_test_strat_0dc220__20250723_231527(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, Dict] = {'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)
    freqai_info: Dict[str, Any]

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

    def populate_indicators(self, dataframe: DataFrame, metadata: Dict[str, Any]) -> 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[str, Any]) -> DataFrame:
        enter_long_conditions: List[Any] = [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: List[Any] = [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[str, Any]) -> DataFrame:
        exit_long_conditions: List[Any] = [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: List[Any] = [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