# source: https://raw.githubusercontent.com/DerSalvador/freqtrade-helm-chart/a669dc11b640b0eb63aa8f8b51e9f181fd7ee43c/chart/deployed_strategies/binance-michael-k8s-namespace/adxbbrsi2.py
# --- Do not remove these libs ---
from freqtrade.strategy.interface import IStrategy
from pandas import DataFrame
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
# --------------------------------

class Github_DerSalvador_freqtrade_helm_chart__adxbbrsi2__20260115_122204(IStrategy):
    INTERFACE_VERSION = 3
    '\n\n    author@: Gert Wohlgemuth\n\n    converted from:\n\n        https://github.com/sthewissen/Mynt/blob/master/src/Mynt.Core/Strategies/AdxMomentum.cs\n\n    '
    # Minimal ROI designed for the strategy.
    # adjust based on market conditions. We would recommend to keep it low for quick turn arounds
    # This attribute will be overridden if the config file contains "minimal_roi"
    minimal_roi = {'0': 0.16083, '33': 0.04139, '85': 0.01225, '197': 0}
    # Optimal stoploss designed for the strategy
    stoploss = -0.32237
    # Optimal timeframe for the strategy
    timeframe = '1h'
    # Number of candles the strategy requires before producing valid signals
    startup_candle_count: int = 20
    ##Trailing stop:
    trailing_stop = True
    trailing_stop_positive = 0.1195
    trailing_stop_positive_offset = 0.1568
    trailing_only_offset_is_reached = True

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe['adx'] = ta.ADX(dataframe, timeperiod=14)
        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
        # Bollinger bands
        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['plus_di'] = ta.PLUS_DI(dataframe, timeperiod=25)
        # dataframe['minus_di'] = ta.MINUS_DI(dataframe, timeperiod=25)
        # dataframe['mom'] = ta.MOM(dataframe, timeperiod=14)
        ## Stochastic
        stoch_fast = ta.STOCHF(dataframe)
        dataframe['fastd'] = stoch_fast['fastd']
        dataframe['fastk'] = stoch_fast['fastk']
        # MFI
        dataframe['mfi'] = ta.MFI(dataframe)
        #SAR
        dataframe['sar'] = ta.SAR(dataframe)
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # (dataframe['mom'] > 0) &
        # (dataframe['minus_di'] > 25) &
        # (dataframe['plus_di'] > dataframe['minus_di'])
        dataframe.loc[(dataframe['adx'] > 47) & (dataframe['fastd'] < 41) & (dataframe['close'] < dataframe['bb_lowerband']), 'entry'] = 1
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # (dataframe['rsi'] > 97) &
        # (dataframe['sar'] < 97)
        #fastd 74
        #mfi 97
        # (dataframe['mom'] < 0) &
        # (dataframe['minus_di'] > 25) &
        # (dataframe["close"] > dataframe['bb_upperband'])
        # (dataframe['plus_di'] < dataframe['minus_di'])
        dataframe.loc[(dataframe['adx'] > 67) & (dataframe['mfi'] > 97) & (dataframe['fastd'] < 74), 'exit'] = 1
        return dataframe