# source: https://raw.githubusercontent.com/plcacs/Harmonizing_ml_results/9b70afe47f502f4432b81918922b4d6e41310981/ManyTypes4py_benchmarks/gpt4o/13/FreqaiExampleStrategy_4d11fd.py
import logging
from functools import reduce
import talib.abstract as ta
from pandas import DataFrame
from technical import qtpylib
from freqtrade.strategy import IStrategy
from typing import Dict, Any

logger = logging.getLogger(__name__)

class Github_plcacs_Harmonizing_ml_results__FreqaiExampleStrategy_4d11fd__20250723_231527(IStrategy):
    minimal_roi: Dict[str, float] = {'0': 0.1, '240': -1}
    plot_config: Dict[str, Any] = {'main_plot': {}, 'subplots': {'&-s_close': {'&-s_close': {'color': 'blue'}}, '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 = 40
    can_short: bool = True

    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)
        dataframe['%-sma-period'] = ta.SMA(dataframe, timeperiod=period)
        dataframe['%-ema-period'] = ta.EMA(dataframe, timeperiod=period)
        bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=period, stds=2.2)
        dataframe['bb_lowerband-period'] = bollinger['lower']
        dataframe['bb_middleband-period'] = bollinger['mid']
        dataframe['bb_upperband-period'] = bollinger['upper']
        dataframe['%-bb_width-period'] = (dataframe['bb_upperband-period'] - dataframe['bb_lowerband-period']) / dataframe['bb_middleband-period']
        dataframe['%-close-bb_lower-period'] = dataframe['close'] / dataframe['bb_lowerband-period']
        dataframe['%-roc-period'] = ta.ROC(dataframe, timeperiod=period)
        dataframe['%-relative_volume-period'] = dataframe['volume'] / dataframe['volume'].rolling(period).mean()
        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:
        dataframe = self.freqai.start(dataframe, metadata, self)
        return dataframe

    def populate_entry_trend(self, df: DataFrame, metadata: Dict[str, Any]) -> DataFrame:
        enter_long_conditions = [df['do_predict'] == 1, df['&-s_close'] > 0.01]
        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'] < -0.01]
        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 = [df['do_predict'] == 1, df['&-s_close'] < 0]
        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'] > 0]
        if exit_short_conditions:
            df.loc[reduce(lambda x, y: x & y, exit_short_conditions), 'exit_short'] = 1
        return df

    def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float, time_in_force: str, current_time: Any, entry_tag: str, side: str, **kwargs) -> bool:
        df, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
        last_candle = df.iloc[-1].squeeze()
        if side == 'long':
            if rate > last_candle['close'] * (1 + 0.0025):
                return False
        elif rate < last_candle['close'] * (1 - 0.0025):
            return False
        return True