# source: https://raw.githubusercontent.com/DerSalvador/freqtrade-helm-chart/a669dc11b640b0eb63aa8f8b51e9f181fd7ee43c/chart/deployed_strategies/ClucMay72018.py
# --- Do not remove these libs ---
from freqtrade.strategy.interface import IStrategy
from typing import Dict, List
from functools import reduce
from pandas import DataFrame
# --------------------------------
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
from typing import Dict, List
from functools import reduce
from pandas import DataFrame, DatetimeIndex, merge
# --------------------------------
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
import numpy  # noqa

class Github_DerSalvador_freqtrade_helm_chart__ClucMay72018__20260115_122204(IStrategy):
    INTERFACE_VERSION = 3
    '\n\n    author@: Gert Wohlgemuth\n\n    works on new objectify branch!\n\n    '
    # Minimal ROI designed for the strategy.
    # This attribute will be overridden if the config file contains "minimal_roi"
    minimal_roi = {'0': 0.01}
    # Optimal stoploss designed for the strategy
    # This attribute will be overridden if the config file contains "stoploss"
    stoploss = -0.05
    # Optimal timeframe for the strategy
    timeframe = '5m'

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=5)
        rsiframe = DataFrame(dataframe['rsi']).rename(columns={'rsi': 'close'})
        dataframe['emarsi'] = ta.EMA(rsiframe, timeperiod=5)
        macd = ta.MACD(dataframe)
        dataframe['macd'] = macd['macd']
        dataframe['adx'] = ta.ADX(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['ema100'] = ta.EMA(dataframe, timeperiod=50)
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Based on TA indicators, populates the entry signal for the given dataframe
        :param dataframe: DataFrame
        :return: DataFrame with entry column
        """
        dataframe.loc[(dataframe['close'] < dataframe['ema100']) & (dataframe['close'] < 0.985 * dataframe['bb_lowerband']) & (dataframe['volume'] < dataframe['volume'].rolling(window=30).mean().shift(1) * 20), 'enter_long'] = 1
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Based on TA indicators, populates the exit signal for the given dataframe
        :param dataframe: DataFrame
        :return: DataFrame with entry column
        """
        dataframe.loc[dataframe['close'] > dataframe['bb_middleband'], 'exit_long'] = 1
        return dataframe