python类ewma()的实例源码

def draw_trend(timeSeries, size):
    rol_mean = pd.rolling_mean(timeSeries,size)
    rol_weighted_mean = pd.ewma(timeSeries, span=size)
    # f = plt.figure(facecolor='white')
    # plt.plot(timeSeries,'b',rol_mean,'g',rol_weighted_mean,'r')
    # plt.legend(loc='best')
    # plt.title('Rolling Mean')
    # plt.show()
    return  rol_weighted_mean

def EMA(df, n):  
    EMA = pd.Series(pd.ewma(df['Close'], span = n, min_periods = n - 1), name = 'EMA_' + str(n))  
    df = df.join(EMA)  
    return df

#Momentum

def ATR(df, n):  
    i = 0  
    TR_l = [0]  
    while i < df.index[-1]:  
        TR = max(df.get_value(i + 1, 'High'), df.get_value(i, 'Close')) - min(df.get_value(i + 1, 'Low'), df.get_value(i, 'Close'))  
        TR_l.append(TR)  
        i = i + 1  
    TR_s = pd.Series(TR_l)  
    ATR = pd.Series(pd.ewma(TR_s, span = n, min_periods = n), name = 'ATR_' + str(n))  
    df = df.join(ATR)  
    return df

#Bollinger Bands

def STO(df, n):  
    SOk = pd.Series((df['Close'] - df['Low']) / (df['High'] - df['Low']), name = 'SO%k')  
    SOd = pd.Series(pd.ewma(SOk, span = n, min_periods = n - 1), name = 'SO%d_' + str(n))  
    df = df.join(SOd)  
    return df

#Trix

def TRIX(df, n):  
    EX1 = pd.ewma(df['Close'], span = n, min_periods = n - 1)  
    EX2 = pd.ewma(EX1, span = n, min_periods = n - 1)  
    EX3 = pd.ewma(EX2, span = n, min_periods = n - 1)  
    i = 0  
    ROC_l = [0]  
    while i + 1 <= df.index[-1]:  
        ROC = (EX3[i + 1] - EX3[i]) / EX3[i]  
        ROC_l.append(ROC)  
        i = i + 1  
    Trix = pd.Series(ROC_l, name = 'Trix_' + str(n))  
    df = df.join(Trix)  
    return df

#Average Directional Movement Index

def ADX(df, n, n_ADX):  
    i = 0  
    UpI = []  
    DoI = []  
    while i + 1 <= df.index[-1]:  
        UpMove = df.get_value(i + 1, 'High') - df.get_value(i, 'High')  
        DoMove = df.get_value(i, 'Low') - df.get_value(i + 1, 'Low')  
        if UpMove > DoMove and UpMove > 0:  
            UpD = UpMove  
        else: UpD = 0  
        UpI.append(UpD)  
        if DoMove > UpMove and DoMove > 0:  
            DoD = DoMove  
        else: DoD = 0  
        DoI.append(DoD)  
        i = i + 1  
    i = 0  
    TR_l = [0]  
    while i < df.index[-1]:  
        TR = max(df.get_value(i + 1, 'High'), df.get_value(i, 'Close')) - min(df.get_value(i + 1, 'Low'), df.get_value(i, 'Close'))  
        TR_l.append(TR)  
        i = i + 1  
    TR_s = pd.Series(TR_l)  
    ATR = pd.Series(pd.ewma(TR_s, span = n, min_periods = n))  
    UpI = pd.Series(UpI)  
    DoI = pd.Series(DoI)  
    PosDI = pd.Series(pd.ewma(UpI, span = n, min_periods = n - 1) / ATR)  
    NegDI = pd.Series(pd.ewma(DoI, span = n, min_periods = n - 1) / ATR)  
    ADX = pd.Series(pd.ewma(abs(PosDI - NegDI) / (PosDI + NegDI), span = n_ADX, min_periods = n_ADX - 1), name = 'ADX_' + str(n) + '_' + str(n_ADX))  
    df = df.join(ADX)  
    return df

#MACD, MACD Signal and MACD difference

def MassI(df):  
    Range = df['High'] - df['Low']  
    EX1 = pd.ewma(Range, span = 9, min_periods = 8)  
    EX2 = pd.ewma(EX1, span = 9, min_periods = 8)  
    Mass = EX1 / EX2  
    MassI = pd.Series(pd.rolling_sum(Mass, 25), name = 'Mass Index')  
    df = df.join(MassI)  
    return df

#Vortex Indicator: http://www.vortexindicator.com/VFX_VORTEX.PDF

def TSI(df, r, s):  
    M = pd.Series(df['Close'].diff(1))  
    aM = abs(M)  
    EMA1 = pd.Series(pd.ewma(M, span = r, min_periods = r - 1))  
    aEMA1 = pd.Series(pd.ewma(aM, span = r, min_periods = r - 1))  
    EMA2 = pd.Series(pd.ewma(EMA1, span = s, min_periods = s - 1))  
    aEMA2 = pd.Series(pd.ewma(aEMA1, span = s, min_periods = s - 1))  
    TSI = pd.Series(EMA2 / aEMA2, name = 'TSI_' + str(r) + '_' + str(s))  
    df = df.join(TSI)  
    return df

#Accumulation/Distribution

def Chaikin(df):  
    ad = (2 * df['Close'] - df['High'] - df['Low']) / (df['High'] - df['Low']) * df['Volume']  
    Chaikin = pd.Series(pd.ewma(ad, span = 3, min_periods = 2) - pd.ewma(ad, span = 10, min_periods = 9), name = 'Chaikin')  
    df = df.join(Chaikin)  
    return df

#Money Flow Index and Ratio

def COPP(df, n):  
    M = df['Close'].diff(int(n * 11 / 10) - 1)  
    N = df['Close'].shift(int(n * 11 / 10) - 1)  
    ROC1 = M / N  
    M = df['Close'].diff(int(n * 14 / 10) - 1)  
    N = df['Close'].shift(int(n * 14 / 10) - 1)  
    ROC2 = M / N  
    Copp = pd.Series(pd.ewma(ROC1 + ROC2, span = n, min_periods = n), name = 'Copp_' + str(n))  
    df = df.join(Copp)  
    return df

#Keltner Channel

def calculate_ema(self, window, name=None, col='Adj_Close'):
        self.set_max_win(window)
        if name is None:
            name = "ema_%s" % (window)
        self.df[name] = pd.ewma(self.df[col], span=window, min_periods=window)
        return name

def calculate_macd(self, signal=9, fast=12, slow=26, col='Adj_Close'):
        """ MACD """
        self.set_max_win(slow)
        signal_name = "signal_%s" % (signal)
        # ignore warnings for now # TODO
        fast_ema = pd.ewma(self.df[col], span=fast, min_periods=fast)
        slow_ema = pd.ewma(self.df[col], span=slow, min_periods=slow)
        name = "macd_%s_%s" % (fast, slow)
        self.df[name] = fast_ema - slow_ema
        self.calculate_ema(signal, col=name, name=signal_name)
        return name

def calculate_adx(self, window=14, window_adx=14):
        """ Average Directional Index Discover if trend is developing
                Sum((+DI-(-DI))/(+DI+(-DI))/n
        """
        self.set_max_win(window)
        i = 0  
        UpI = []  
        DoI = []  
        while i + 1 <= self.df.index[-1]:  
            UpMove = self.df.get_value(i + 1, 'High') - self.df.get_value(i, 'High')  
            DoMove = self.df.get_value(i, 'Low') - self.df.get_value(i + 1, 'Low')  
            if UpMove > DoMove and UpMove > 0:  
                UpD = UpMove  
            else: UpD = 0  
            UpI.append(UpD)  
            if DoMove > UpMove and DoMove > 0:  
                DoD = DoMove  
            else: DoD = 0  
            DoI.append(DoD)  
            i = i + 1  
        atr_name = "atr_%s" % (window)
        self.calculate_atr(window)
        PosDI = pd.ewma(pd.Series(UpI), span=window, min_periods=window-1) / self.df[atr_name]
        NegDI = pd.ewma(pd.Series(DoI), span=window, min_periods=window-1) / self.df[atr_name]
        name = "adx_%s_%s" % (window, window_adx)
        self.df[name] = pd.Series(pd.ewma(abs(PosDI - NegDI) / (PosDI + NegDI), span=window_adx, min_periods=window_adx-1))
        return name

def ema(df, periods=12):
    # compute exponential moving average
    #return pd.ewma(df, span = periods)
      return df.ewm(span=periods, adjust=True, min_periods=0, ignore_na=False).mean()

def ema(df, periods=12):
    # compute exponential moving average
    #return pd.ewma(df, span = periods)
    return df.ewm(span=periods, adjust=True, min_periods=0, ignore_na=False).mean()

def EMA(df, n):  
    EMA = pd.Series(pd.ewma(df['Close'], span = n, min_periods = n - 1), name = 'EMA_' + str(n))  
    df = df.join(EMA)  
    return df

#Momentum

def ATR(df, n):  
    i = 0  
    TR_l = [0]  
    while i < df.index[-1]:  
        TR = max(df.get_value(i + 1, 'High'), df.get_value(i, 'Close')) - min(df.get_value(i + 1, 'Low'), df.get_value(i, 'Close'))  
        TR_l.append(TR)  
        i = i + 1  
    TR_s = pd.Series(TR_l)  
    ATR = pd.Series(pd.ewma(TR_s, span = n, min_periods = n), name = 'ATR_' + str(n))  
    df = df.join(ATR)  
    return df

#Bollinger Bands

def STO(df, n):  
    SOk = pd.Series((df['Close'] - df['Low']) / (df['High'] - df['Low']), name = 'SO%k')  
    SOd = pd.Series(pd.ewma(SOk, span = n, min_periods = n - 1), name = 'SO%d_' + str(n))  
    df = df.join(SOd)  
    return df

#Trix

def TRIX(df, n):  
    EX1 = pd.ewma(df['Close'], span = n, min_periods = n - 1)  
    EX2 = pd.ewma(EX1, span = n, min_periods = n - 1)  
    EX3 = pd.ewma(EX2, span = n, min_periods = n - 1)  
    i = 0  
    ROC_l = [0]  
    while i + 1 <= df.index[-1]:  
        ROC = (EX3[i + 1] - EX3[i]) / EX3[i]  
        ROC_l.append(ROC)  
        i = i + 1  
    Trix = pd.Series(ROC_l, name = 'Trix_' + str(n))  
    df = df.join(Trix)  
    return df

#Average Directional Movement Index

def ADX(df, n, n_ADX):  
    i = 0  
    UpI = []  
    DoI = []  
    while i + 1 <= df.index[-1]:  
        UpMove = df.get_value(i + 1, 'High') - df.get_value(i, 'High')  
        DoMove = df.get_value(i, 'Low') - df.get_value(i + 1, 'Low')  
        if UpMove > DoMove and UpMove > 0:  
            UpD = UpMove  
        else: UpD = 0  
        UpI.append(UpD)  
        if DoMove > UpMove and DoMove > 0:  
            DoD = DoMove  
        else: DoD = 0  
        DoI.append(DoD)  
        i = i + 1  
    i = 0  
    TR_l = [0]  
    while i < df.index[-1]:  
        TR = max(df.get_value(i + 1, 'High'), df.get_value(i, 'Close')) - min(df.get_value(i + 1, 'Low'), df.get_value(i, 'Close'))  
        TR_l.append(TR)  
        i = i + 1  
    TR_s = pd.Series(TR_l)  
    ATR = pd.Series(pd.ewma(TR_s, span = n, min_periods = n))  
    UpI = pd.Series(UpI)  
    DoI = pd.Series(DoI)  
    PosDI = pd.Series(pd.ewma(UpI, span = n, min_periods = n - 1) / ATR)  
    NegDI = pd.Series(pd.ewma(DoI, span = n, min_periods = n - 1) / ATR)  
    ADX = pd.Series(pd.ewma(abs(PosDI - NegDI) / (PosDI + NegDI), span = n_ADX, min_periods = n_ADX - 1), name = 'ADX_' + str(n) + '_' + str(n_ADX))  
    df = df.join(ADX)  
    return df

#MACD, MACD Signal and MACD difference