# source: https://raw.githubusercontent.com/plcacs/Harmonizing_ml_results/c70345b09693dfd05f7b9798406e20f822d554a8/ManyTypes4py_benchmarks/deep_seek/15/FreqaiExampleHybridStrategy_04bd2f.py
import logging
from typing import Dict, Any, List
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__20250802_160724(IStrategy):
    """
    Example of a hybrid FreqAI strat, designed to illustrate how a user may employ
    FreqAI to bolster a typical Freqtrade strategy.
    """
    minimal_roi: Dict[str, float] = {'60': 0.01, '30': 0.02, '0': 0.04}
    plot_config: Dict[str, Any] = {
        '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: bool = True
    stoploss: float = -0.05
    use_exit_signal: bool = True
    startup_candle_count: int = 30
    can_short: bool = 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, **kwargs) -> DataFrame:
        """
        *Only functional with FreqAI enabled strategies*
        """
        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, **kwargs) -> DataFrame:
        """
        *Only functional with FreqAI enabled strategies*
        """
        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, **kwargs) -> DataFrame:
        """
        *Only functional with FreqAI enabled strategies*
        """
        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, **kwargs) -> DataFrame:
        """
        *Only functional with FreqAI enabled strategies*
        """
        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) -> 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) -> 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) -> 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