卡夫曼自适应移动平均也是一种趋势模型，其很好得解决了固定均线模型无法适应不同市场状况的问题。
KAMA根据市场趋势变化速度自主调节，既能够避免震荡行情中的虚假信号，也能消除长期趋势中的滞后性。
在牛市或者熊市中自适应均线紧随指数向上或者向下变化，而市场震荡时期，其变化减弱。
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import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
import tushare as ts
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szzz = ts.get_hist_data(code = 'sh000001', start='2000-01-01', ktype='D')['close']
szzz = szzz.sort_index()
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# KAMA函数
def KAMA(data, er, ma1, ma2):
data = pd.DataFrame(data)
# efficiencu ratio
data['cha'] = np.abs(data.diff(periods=er))
data['vol'] = np.abs(data['close'].diff(periods=1)).rolling(window=er).sum()
data['ER'] = data['cha'] / data['vol']
# smoothing constant
data['SC'] = (data['ER'] * (2/(ma1 + 1) - 2/(ma2 + 1)) + 2/(ma2 + 1)) ** 2
# current kama = prior kama + sc * (price - prior kama)
KAMA = list(np.repeat(np.nan, er-1))
KAMA.append(data['close'].iloc[0:er].mean())
for i in data['SC'].dropna().index:
KAMA.append(KAMA[-1] + data['SC'][i] * (data['close'][i] - KAMA[-1]))
data['KAMA'] = pd.Series(KAMA, index=data.index)
return data
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er = 10
ma1 = 2
ma2 = 30
data = szzz
result = KAMA(data = szzz, er=10, ma1=2, ma2=30)
#result['sign'] = []
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result[['close', 'KAMA']].plot()
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sns.timeseries(result[['close','KAMA']])
#sns.lineplot(data = result['close'])
#sns.lineplot(data = result['KAMA'])