# source: https://raw.githubusercontent.com/plcacs/Harmonizing_ml_results/f650bc774e45cb18138fdec18c21d682e8b9562c/ManyTypes4py_benchmarks/o3_mini_2nd_run/15/FreqaiExampleHybridStrategy_04bd2f.py
from typing import Any, Dict
import logging
import numpy as np
import pandas as pd
import talib.abstract as ta
from pandas import DataFrame
from technical import qtpylib
from freqtrade.strategy import IntParameter, IStrategy, merge_informative_pair

logger = logging.getLogger(__name__)


class Github_plcacs_Harmonizing_ml_results__FreqaiExampleHybridStrategy_04bd2f__20250731_222317(IStrategy):
    """
    Example of a hybrid FreqAI strat, designed to illustrate how a user may employ
    FreqAI to bolster a typical Freqtrade strategy.
    """
    minimal_roi = {'60': 0.01, '30': 0.02, '0': 0.04}
    plot_config = {
        'main_plot': {'tema': {}},
        'subplots': {
            'MACD': {'macd': {'color': 'blue'}, 'macdsignal': {'color': 'orange'}},
            'RSI': {'rsi': {'color': 'red'}},
            'Up_or_down': {'&s-up_or_down': {'color': 'green'}}
        }
    }
    process_only_new_candles = True
    stoploss = -0.05
    use_exit_signal = True
    startup_candle_count = 30
    can_short = True

    buy_rsi: IntParameter = IntParameter(low=1, high=50, default=30, space='buy', optimize=True, load=True)
    sell_rsi: IntParameter = IntParameter(low=50, high=100, default=70, space='sell', optimize=True, load=True)
    short_rsi: IntParameter = IntParameter(low=51, high=100, default=70, space='sell', optimize=True, load=True)
    exit_short_rsi: IntParameter = IntParameter(low=1, high=50, default=30, space='buy', optimize=True, load=True)

    def feature_engineering_expand_all(self, dataframe: DataFrame, period: int, metadata: Dict[str, Any], **kwargs: Any) -> DataFrame:
        """
        *Only functional with FreqAI enabled strategies*
        This function will automatically expand the defined features on the config defined
        `indicator_periods_candles`, `include_timeframes`, `include_shifted_candles`, and
        `include_corr_pairs`. In other words, a single feature defined in this function
        will automatically expand to a total of
        `indicator_periods_candles` * `include_timeframes` * `include_shifted_candles` *
        `include_corr_pairs` numbers of features added to the model.
        """
        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: Any) -> DataFrame:
        """
        *Only functional with FreqAI enabled strategies*
        This function will automatically expand the defined features on the config defined
        `include_timeframes`, `include_shifted_candles`, and `include_corr_pairs`.
        """
        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:
        """
        *Only functional with FreqAI enabled strategies*
        This optional function will be called once with the dataframe of the base timeframe.
        """
        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:
        """
        *Only functional with FreqAI enabled strategies*
        Required function to set the targets for the model.
        """
        self.freqai.class_names = ['down', 'up']
        dataframe['&s-up_or_down'] = np.where(dataframe['close'].shift(-50) > dataframe['close'], 'up', 'down')
        return dataframe

    def populate_indicators(self, dataframe: DataFrame, metadata: Dict[str, Any]) -> DataFrame:
        dataframe = self.freqai.start(dataframe, metadata, self)
        dataframe['rsi'] = ta.RSI(dataframe)
        bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
        dataframe['bb_lowerband'] = bollinger['lower']
        dataframe['bb_middleband'] = bollinger['mid']
        dataframe['bb_upperband'] = bollinger['upper']
        dataframe['bb_percent'] = (dataframe['close'] - dataframe['bb_lowerband']) / (dataframe['bb_upperband'] - dataframe['bb_lowerband'])
        dataframe['bb_width'] = (dataframe['bb_upperband'] - dataframe['bb_lowerband']) / dataframe['bb_middleband']
        dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
        return dataframe

    def populate_entry_trend(self, df: DataFrame, metadata: Dict[str, Any]) -> DataFrame:
        df.loc[
            qtpylib.crossed_above(df['rsi'], self.buy_rsi.value) &
            (df['tema'] <= df['bb_middleband']) &
            (df['tema'] > df['tema'].shift(1)) &
            (df['volume'] > 0) &
            (df['do_predict'] == 1) &
            (df['&s-up_or_down'] == 'up'),
            'enter_long'
        ] = 1
        df.loc[
            qtpylib.crossed_above(df['rsi'], self.short_rsi.value) &
            (df['tema'] > df['bb_middleband']) &
            (df['tema'] < df['tema'].shift(1)) &
            (df['volume'] > 0) &
            (df['do_predict'] == 1) &
            (df['&s-up_or_down'] == 'down'),
            'enter_short'
        ] = 1
        return df

    def populate_exit_trend(self, df: DataFrame, metadata: Dict[str, Any]) -> DataFrame:
        df.loc[
            qtpylib.crossed_above(df['rsi'], self.sell_rsi.value) &
            (df['tema'] > df['bb_middleband']) &
            (df['tema'] < df['tema'].shift(1)) &
            (df['volume'] > 0),
            'exit_long'
        ] = 1
        df.loc[
            qtpylib.crossed_above(df['rsi'], self.exit_short_rsi.value) &
            (df['tema'] <= df['bb_middleband']) &
            (df['tema'] > df['tema'].shift(1)) &
            (df['volume'] > 0),
            'exit_short'
        ] = 1
        return df