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heikin

🏆 League #1895 / 1937
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Ysertysert2/freqtrade-strategies/strategies/heikin/heikin.py · ★3 · ⑂2 · first seen 2026-07-24 · repo updated 2025-09-23

Basics mode: spot timeframe: 1h interface version: 3
Settings stoploss: -0.99 has minimal roi
Indicators SMA talib
15 related strategies (⧉ identical code, ≈ similar name)

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# --- Do not remove these libs --- freqtrade backtesting --strategy SmoothScalp --timerange 20210110-20210410
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

#V1
class heikin(IStrategy):
    INTERFACE_VERSION = 3
    #do not use this strategy in live mod. It is not good enough yet and can only be use to find trends.
    timeframe = '1h'
    #I haven't found the best roi and stoplost, so feel free to explore.
    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 heikin 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_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (
                (dataframe['emao'] < dataframe['emac'])
            ),
            'buy'] = 1
        return dataframe

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