finta.py 文件源码

def KAMA(cls, ohlc, er=10, ema_fast=2, ema_slow=30, period=20):
        """Developed by Perry Kaufman, Kaufman's Adaptive Moving Average (KAMA) is a moving average designed to account for market noise or volatility.
        Its main advantage is that it takes into consideration not just the direction, but the market volatility as well."""

        er = cls.ER(ohlc, er)
        fast_alpha = 2 / (ema_fast + 1)
        slow_alpha = 2 / (ema_slow + 1)
        sc = pd.Series((er * (fast_alpha - slow_alpha) + slow_alpha)**2, name="smoothing_constant") ## smoothing constant

        sma = pd.Series(ohlc["close"].rolling(period).mean(), name="SMA") ## first KAMA is SMA
        kama = []
        # Current KAMA = Prior KAMA + smoothing_constant * (Price - Prior KAMA)
        for s, ma, price in zip(sc.iteritems(), sma.shift().iteritems(), ohlc["close"].iteritems()):
            try:
                kama.append(kama[-1] + s[1] * (price[1] - kama[-1]))
            except:
                if pd.notnull(ma[1]):
                    kama.append(ma[1] + s[1] * (price[1] - ma[1]))
                else:
                    kama.append(None)

        sma["KAMA"] = pd.Series(kama, index=sma.index, name="{0} period KAMA.".format(period)) ## apply the kama list to existing index
        return sma["KAMA"]