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

class Github_remiotore_freqtrade__heikin__20260111_210550(IStrategy):

    timeframe = '1h'

    minimal_roi = {
        "0": 10,
    }
    stoploss = -0.99
    
    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:   
        dataframe['hclose']=(dataframe['open'] + dataframe['high'] + dataframe['low'] + dataframe['close']) / 4
        dataframe['hopen']= ((dataframe['open'].shift(2) + dataframe['close'].shift(2))/ 2) #it is not the same as real Github_remiotore_freqtrade__heikin__20260111_210550 ashi since I found that this is better.
        dataframe['hhigh']=dataframe[['open','close','high']].max(axis=1)
        dataframe['hlow']=dataframe[['open','close','low']].min(axis=1)

        dataframe['emac'] = ta.SMA(dataframe['hclose'], timeperiod=6) #to smooth out the data and thus less noise.
        dataframe['emao'] = ta.SMA(dataframe['hopen'], timeperiod=6)
        return dataframe
        

    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (
                (dataframe['emao'] < dataframe['emac'])
            ),
            'buy'] = 1
        return dataframe

    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (
                (dataframe['emao'] > dataframe['emac'])
            ),
            'sell'] = 1
        return dataframe