# source: https://raw.githubusercontent.com/plcacs/Harmonizing_ml_results/7c866e63ef59a5355e43f9333c4b0811a37bb9dc/ManyTypes4py_benchmarks/HiTyper_1st_run/freqai_test_classifier_bd29bc.py
import logging
from functools import reduce
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 Github_plcacs_Harmonizing_ml_results__freqai_test_classifier_bd29bc__20250811_003639(IStrategy):
    """
    Test strategy - used for testing freqAI 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 = 300
    can_short = False
    linear_roi_offset = DecimalParameter(0.0, 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 informative_pairs(self) -> list[tuple[typing.Union[str,tuple[typing.Union[str,int]]]]]:
        whitelist_pairs = self.dp.current_whitelist()
        corr_pairs = self.config['freqai']['feature_parameters']['include_corr_pairlist']
        informative_pairs = []
        for tf in self.config['freqai']['feature_parameters']['include_timeframes']:
            for pair in whitelist_pairs:
                informative_pairs.append((pair, tf))
            for pair in corr_pairs:
                if pair in whitelist_pairs:
                    continue
                informative_pairs.append((pair, tf))
        return informative_pairs

    def feature_engineering_expand_all(self, dataframe: Union[pandas.DataFrame, pandas.Series], period: Union[pandas.DataFrame, pandas.Series], metadata: Union[pandas.DataFrame, list[tuple[str]], numpy.array], **kwargs) -> Union[pandas.DataFrame, pandas.Series]:
        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: Union[pandas.DataFrame, dict], metadata: Union[dict, pandas.DataFrame], **kwargs) -> pandas.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: Union[pandas.DataFrame, dict], metadata: Union[dict, pandas.DataFrame], **kwargs) -> pandas.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: pandas.DataFrame, metadata: Union[dict, pandas.DataFrame], **kwargs) -> pandas.DataFrame:
        self.freqai.class_names = ['down', 'up']
        dataframe['&s-up_or_down'] = np.where(dataframe['close'].shift(-100) > dataframe['close'], 'up', 'down')
        return dataframe

    def populate_indicators(self, dataframe: Union[pandas.DataFrame, dict], metadata: Union[pandas.DataFrame, dict]) -> Union[list, list[list], dict[str, float]]:
        self.freqai_info = self.config['freqai']
        dataframe = self.freqai.start(dataframe, metadata, self)
        return dataframe

    def populate_entry_trend(self, df: Union[pandas.DataFrame, pandas.Series], metadata: Union[dict, pandas.DataFrame]) -> Union[pandas.DataFrame, pandas.Series]:
        enter_long_conditions = [df['&s-up_or_down'] == 'up']
        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['&s-up_or_down'] == 'down']
        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: Union[dict, pandas.DataFrame], metadata: Union[dict, pandas.DataFrame]) -> Union[dict, pandas.DataFrame]:
        return df