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

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_remiotore_freqtrade__DuperFivish__20260111_210550(IStrategy):


    minimal_roi = {
        "0": 0.15
    }


    stoploss = -0.05
    trailing_stop = True
    trailing_stop_positive = 0.0025
    trailing_stop_positive_offset = 0.006
    trailing_only_offset_is_reached = True

    ticker_interval = '5m'

    order_types = {
    'buy': 'limit',
    'sell': 'limit',
    'emergencysell': 'market',
    'stoploss': 'limit',
    'stoploss_on_exchange': True,
    'stoploss_on_exchange_interval': 60,
    'stoploss_on_exchange_limit_ratio': 0.99
}

    resample_factor = 12
    resample_factor2 = 3

    EMA_SHORT_TERM = 5
    EMA_MEDIUM_TERM = 10
    EMA_LONG_TERM = 20


    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe = self.resample(dataframe, self.ticker_interval, self.resample_factor)



        dataframe['ema_{}'.format(self.EMA_SHORT_TERM)] = ta.EMA(
            dataframe, timeperiod=self.EMA_SHORT_TERM
        )
        dataframe['ema_{}'.format(self.EMA_MEDIUM_TERM)] = ta.EMA(
            dataframe, timeperiod=self.EMA_MEDIUM_TERM
        )
        dataframe['ema_{}'.format(self.EMA_LONG_TERM)] = ta.EMA(
            dataframe, timeperiod=self.EMA_LONG_TERM
        )


        

        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['min'] = ta.MIN(dataframe, timeperiod=self.EMA_MEDIUM_TERM)
        dataframe['max'] = ta.MAX(dataframe, timeperiod=self.EMA_MEDIUM_TERM)

        dataframe['cci'] = ta.CCI(dataframe)
        dataframe['mfi'] = ta.MFI(dataframe)
        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=7)

        dataframe['average'] = (dataframe['close'] + dataframe['open'] + dataframe['high'] + dataframe['low']) / 4

        dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5)
        dataframe['ema12'] = ta.EMA(dataframe, timeperiod=12)
        dataframe['ema20'] = ta.EMA(dataframe, timeperiod=20)


        dataframe['adx'] = ta.ADX(dataframe, timeperiod=14)


        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']

        macd = ta.MACD(dataframe)
        dataframe['macd'] = macd['macd']
        dataframe['macdsignal'] = macd['macdsignal']
        dataframe['macdhist'] = macd['macdhist']

        return dataframe

    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Based on TA indicators, populates the buy signal for the given dataframe
        :param dataframe: DataFrame
        :return: DataFrame with buy column
        """
        dataframe.loc[
            (
                    (
                            (

                                    (dataframe['close'] <= dataframe['bb_lowerband'])&
                                    (dataframe['close'].shift(1) < dataframe['ema_{}'.format(self.EMA_SHORT_TERM)]) &
                                    (dataframe['close'] < dataframe['ema_{}'.format(self.EMA_SHORT_TERM)]) &
                                    (dataframe['ema_{}'.format(self.EMA_SHORT_TERM)] < dataframe['ema_{}'.format(self.EMA_LONG_TERM)]) &
                                    (dataframe['ema_{}'.format(self.EMA_LONG_TERM)] > dataframe['ema_{}'.format(self.EMA_MEDIUM_TERM)]) 




                                    
                                    
                            )
                            |



                            (
                                    (dataframe['average'].shift(5) > dataframe['average'].shift(4))
                                    & (dataframe['average'].shift(4) > dataframe['average'].shift(3))
                                    & (dataframe['average'].shift(3) > dataframe['average'].shift(2))
                                    & (dataframe['average'].shift(2) > dataframe['average'].shift(1))
                                    & (dataframe['average'].shift(1) < dataframe['average'].shift(0))
                                    & (dataframe['low'].shift(1) < dataframe['bb_middleband'])
                                    & (dataframe['cci'].shift(1) < -100)
                                    & (dataframe['rsi'].shift(1) < 30)
                                    & (dataframe['mfi'].shift(1) > 20)

                            )
                    )

                    &
                    (
                            (dataframe['volume'] < (dataframe['volume'].rolling(window=30).mean().shift(1) * 20)) &
                            (dataframe['resample_sma'] < dataframe['close']) &
                            (dataframe['resample_sma'].shift(1) < dataframe['resample_sma'])
                    )
            )
            ,
            'buy'] = 1

        return dataframe

    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Based on TA indicators, populates the sell signal for the given dataframe
        :param dataframe: DataFrame
        :return: DataFrame with buy column
        """
        dataframe.loc[
            (
                    (dataframe['close'] > dataframe['ema_{}'.format(self.EMA_SHORT_TERM)]) &
                    (dataframe['close'] > dataframe['ema_{}'.format(self.EMA_MEDIUM_TERM)]) &
                    (dataframe['close'] >= dataframe['max']) &
                    (dataframe['close'] >= dataframe['bb_upperband']) &
                    (dataframe['mfi'] > 80)
            ) |


            (
                    (dataframe['open'] < dataframe['close']) &
                    (dataframe['open'].shift(1) < dataframe['close'].shift(1)) &
                    (dataframe['open'].shift(2) < dataframe['close'].shift(2)) &
                    (dataframe['open'].shift(3) < dataframe['close'].shift(3)) &
                    (dataframe['open'].shift(4) < dataframe['close'].shift(4)) &
                    (dataframe['open'].shift(5) < dataframe['close'].shift(5)) &
                    (dataframe['open'].shift(6) < dataframe['close'].shift(6)) &
                    (dataframe['open'].shift(7) < dataframe['close'].shift(7)) &
                    (dataframe['rsi'] > 70)
            )
            ,
            'sell'
        ] = 1
        return dataframe

    def resample(self, 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',
                         label="right").agg(ohlc_dict).dropna(how='any')
        df['resample_sma'] = ta.SMA(df, timeperiod=25, 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