# source: https://raw.githubusercontent.com/remiotore/freqtrade/44beaeb6a420cd8e9f2e4ea93e11d6cfa192ee03/strategies/CCIStrategy_2.py

from freqtrade.strategy.interface import IStrategy
from typing import Dict, List
from hyperopt import hp
from functools import reduce
from pandas import DataFrame, Series, DatetimeIndex, merge


import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib


class Github_remiotore_freqtrade__CCIStrategy_2__20260111_210550(IStrategy):


    minimal_roi = {
        "0": 0.1
    }


    stoploss = -0.02

    ticker_interval = '1m'

    def populate_indicators(self, dataframe: DataFrame) -> DataFrame:
        macd = ta.MACD(dataframe)
        dataframe = CCIStrategy.resample(dataframe, self.ticker_interval, 5)

        dataframe['cci_one'] = ta.CCI(dataframe, timeperiod=170)
        dataframe['cci_two'] = ta.CCI(dataframe, timeperiod=34)
        dataframe['rsi'] = ta.RSI(dataframe)
        dataframe['mfi'] = ta.MFI(dataframe)

        dataframe['cmf'] = self.chaikin_mf(dataframe)

        bollinger = qtpylib.bollinger_bands(dataframe['close'], window=20, stds=2)
        dataframe['bb_lowerband'] = bollinger['lower']
        dataframe['bb_upperband'] = bollinger['upper']
        dataframe['bb_middleband'] = bollinger['mid']

        return dataframe

    def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame:
        """
        Based on TA indicators, populates the buy signal for the given dataframe
        :param dataframe: DataFrame
        :return: DataFrame with buy column
        """
        dataframe.loc[
            (
                    (dataframe['cci_one'] < -100)
                    & (dataframe['cci_two'] < -100)
                    & (dataframe['cmf'] < -0.1)
                    & (dataframe['mfi'] < 25)

                    & (dataframe['resample_medium'] > dataframe['resample_short'])
                    & (dataframe['resample_long'] < dataframe['close'])

            ),
            'buy'] = 1

        return dataframe

    def populate_sell_trend(self, dataframe: DataFrame) -> DataFrame:
        """
        Based on TA indicators, populates the sell signal for the given dataframe
        :param dataframe: DataFrame
        :return: DataFrame with buy column
        """
        dataframe.loc[
            (
                    (dataframe['cci_one'] > 100)
                    & (dataframe['cci_two'] > 100)
                    & (dataframe['cmf'] > 0.3)
                    & (dataframe['resample_sma'] < dataframe['resample_medium'])
                    & (dataframe['resample_medium'] < dataframe['resample_short'])

            ),
            'sell'] = 1
        return dataframe

    def chaikin_mf(self, 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)  # float division by zero
        mfv *= volume
        cmf = mfv.rolling(periods).sum() / volume.rolling(periods).sum()

        return Series(cmf, name='cmf')

    @staticmethod
    def resample(dataframe, interval, factor):


        df = dataframe.copy()
        df = df.set_index(DatetimeIndex(df['date']))
        ohlc_dict = {
            'open': 'first',
            'high': 'max',
            'low': 'min',
            'close': 'last'
        }
        df = df.resample(str(int(interval[:-1]) * factor) + 'min', how=ohlc_dict)
        df['resample_sma'] = ta.SMA(df, timeperiod=100, price='close')
        df['resample_medium'] = ta.SMA(df, timeperiod=50, price='close')
        df['resample_short'] = ta.SMA(df, timeperiod=25, price='close')
        df['resample_long'] = ta.SMA(df, timeperiod=200, price='close')
        df = df.drop(columns=['open', 'high', 'low', 'close'])
        df = df.resample(interval[:-1] + 'min')
        df = df.interpolate(method='time')
        df['date'] = df.index
        df.index = range(len(df))
        dataframe = merge(dataframe, df, on='date', how='left')
        return dataframe