RSIとボリンジャーバンドとMACDを組み合わせた戦略をPythonで組んでバックテストをしてみた

stragegy/macd_rsi_bb.py

class MacdBbRsi(StrategyInterface):
    def is_buy(self, df_candle):
        buy = False
        df_candle = bb(df_candle, timeperiod=60*6)
        last_row = df_candle.iloc[-1]
        if last_row.bb2p < last_row.low:
            rsi_value = rsi(df_candle, timeperiod=60*3)
            if rsi_value.values[-1] < 70 and macd_up(df_candle, slow=50, fast=20, smooth=7, limitv=10*0.01):
                buy = True
        return buy


    def is_sell(self, df_candle):
        sell = False
        df_candle = bb(df_candle, timeperiod=60*6)
        last_row = df_candle.iloc[-1]
        if last_row.bb2m > last_row.high:
            rsi_value = rsi(df_candle, timeperiod=60*3)
            if rsi_value.values[-1] > 30 and macd_dw(df_candle, slow=50, fast=20, smooth=7, limitv=10*0.01):
                sell = True
        return sell

import abc

class StrategyInterface(metaclass=abc.ABCMeta):
    @abc.abstractmethod
    def is_buy(self, df_candle):
        raise NotImplementedError()
    
    @abc.abstractmethod
    def is_sell(self, df_candle):
        raise NotImplementedError()

trading_test.py main()

    # stratgy = SimpleMacd()
    stratgy = MacdBbRsi()

trading_test.py main()

            getpips = 30
            losspips = 15

analyzer/indicator.py

def get_macd(df_candle, slow=26, fast=12, smooth=9):
    '''
    macd を得る
    col:macd, signal, hist
        macd:macdライン、slow(長期EMA)とfast(短期EMA)の指数移動平均の差
        signal:macdシグナル、MACDのEMA
        hist:macdラインと、macdシグナルの差
    ゴールデンクロス：買い：signalをmacdがクロスして、macdが下から上に抜ける
    デッドデンクロス：売り：signalをmacdがクロスして、macdが上から下に抜ける
    追従買い：ゴールデンクロスの後、macdがマイナスからプラスに転換
    追従売り：デッドクロスの後、macdがプラスからマイナスに転換
    :param df_candle:
    :param slow:
    :param fast:
    :param smooth:
    :return:
    '''
    price = df_candle.close
    exp1 = price.ewm(span=fast, adjust=False).mean()
    exp2 = price.ewm(span=slow, adjust=False).mean()
    macd = exp1 - exp2
    signal = macd.ewm(span=smooth, adjust=False).mean()
    hist = macd - signal
    df = pd.concat([macd, signal, hist], axis=1)
    df.columns = ['macd', 'signal', 'hist']
    return df

def macd_dw(df_candle, slow=26, fast=12, smooth=9, limitv=None):
    if 'macd' not in df_candle.columns:
        df_macd = get_macd(df_candle, slow=slow, fast=fast, smooth=smooth)
    else:
        df_macd = df_candle
    df_up = df_macd[df_macd.signal < df_macd.macd]
    last_up = df_up.index[-1]
    df_dw = df_macd.loc[last_up:]
    if 4 > len(df_dw) > 1:
        df_dw = df_dw.iloc[1:]
        if (df_dw.signal > df_dw.macd).all():
            if limitv is None or (df_candle.iloc[-1*smooth:].high.max() - df_candle.iloc[-1].close) < limitv:
                print("MACD DW")
                return True
    return False

def macd_up(df_candle, slow=26, fast=12, smooth=9, limitv=None):
    if 'macd' not in df_candle.columns:
        df_macd = get_macd(df_candle, slow=slow, fast=fast, smooth=smooth)
    else:
        df_macd = df_candle
    df_dw = df_macd[df_macd.signal > df_macd.macd]
    last_dw = df_dw.index[-1]
    df_up = df_macd.loc[last_dw:]
    if 4 > len(df_up) > 1:
        df_up = df_up.iloc[1:]
        if (df_up.signal < df_up.macd).all():
            if limitv is None or (df_candle.iloc[-1].close - df_candle.iloc[-1*smooth:].low.min()) < limitv:
                print("MACD UP")
                return True
    return False