# source: https://raw.githubusercontent.com/DerSalvador/freqtrade-helm-chart/a669dc11b640b0eb63aa8f8b51e9f181fd7ee43c/chart/deployed_strategies/TheRealPullbackV2.py
from freqtrade.strategy import IStrategy, merge_informative_pair
from pandas import DataFrame, Series
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
from technical.indicators import RMI
# The main idea of this strategy is to entry in dips and exit after recovery.

def chaikin_mf(df, periods=20):
    close = df['close']
    low = df['low']
    high = df['high']
    volume = df['volume']
    mfv = (close - low - (high - close)) / (high - low)
    mfv = mfv.fillna(0.0)
    mfv *= volume
    cmf = mfv.rolling(periods).sum() / volume.rolling(periods).sum()
    return Series(cmf, name='cmf')

class Github_DerSalvador_freqtrade_helm_chart__TheRealPullbackV2__20260115_122204(IStrategy):
    INTERFACE_VERSION = 3
    minimal_roi = {'0': 100}
    stoploss = -0.035
    timeframe = '5m'
    process_only_new_candles = True
    ignore_roi_if_entry_signal = True
    startup_candle_count = 200

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        bollinger = qtpylib.bollinger_bands(dataframe['close'], window=20, stds=2)
        dataframe['bb_lowerband'] = bollinger['lower']
        dataframe['bb_middleband'] = bollinger['mid']
        dataframe['bb_upperband'] = bollinger['upper']
        dataframe['bb_width'] = (dataframe['bb_upperband'] - dataframe['bb_lowerband']) / dataframe['bb_middleband']
        dataframe['bb_bottom_cross'] = qtpylib.crossed_below(dataframe['close'], dataframe['bb_lowerband']).astype('int')
        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=10)
        dataframe['plus_di'] = ta.PLUS_DI(dataframe)
        dataframe['minus_di'] = ta.MINUS_DI(dataframe)
        dataframe['cci'] = ta.CCI(dataframe, 30)
        dataframe['mfi'] = ta.MFI(dataframe, timeperiod=14)
        dataframe['cmf'] = chaikin_mf(dataframe)
        dataframe['rmi'] = RMI(dataframe, length=8, mom=4)
        stoch = ta.STOCHRSI(dataframe, 15, 20, 2, 2)
        dataframe['srsi_fk'] = stoch['fastk']
        dataframe['srsi_fd'] = stoch['fastd']
        dataframe['fastEMA'] = ta.EMA(dataframe['volume'], timeperiod=12)
        dataframe['slowEMA'] = ta.EMA(dataframe['volume'], timeperiod=26)
        dataframe['pvo'] = (dataframe['fastEMA'] - dataframe['slowEMA']) / dataframe['slowEMA'] * 100
        # Maybe comment mfi and cmf to make more trades
        dataframe['is_dip'] = ((dataframe['rmi'] < 20) & (dataframe['cci'] <= -150) & (dataframe['srsi_fk'] < 20) & (dataframe['mfi'] < 25) & (dataframe['cmf'] <= -0.1)).astype('int')
        dataframe['is_break'] = ((dataframe['bb_width'] > 0.025) & (dataframe['bb_bottom_cross'].rolling(10).sum() > 1) & (dataframe['close'] < 0.99 * dataframe['bb_lowerband'])).astype('int')
        dataframe['entry_signal'] = ((dataframe['is_dip'] > 0) & (dataframe['is_break'] > 0)).astype('int')
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[dataframe['entry_signal'] > 0, 'enter_long'] = 1
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[qtpylib.crossed_below(dataframe['close'], dataframe['bb_middleband']) | qtpylib.crossed_below(dataframe['close'], dataframe['bb_upperband']), 'exit_long'] = 1
        return dataframe