量化交易----常见指标的python实现01

quant001

顺势指标CCICCI中文译名为:随顺市势指标。它属于超买超卖指标中较特殊的一种。波动于广向正值无限大和微向负值无限小之间。本指标专门用以测量股价是否已超出常态分布范围

计算公式

CCI（N日）=（TP－MA）÷Std÷0.015

其中，TP=（最高价+最低价+收盘价）÷3
MA=近N日收盘价的累计之和÷N
Std=近N日数据的标准差

1. # Load the necessary packages and modules
   
2. import pandas as pd
   
3. import pandas.io.data as web
   
4. import matplotlib.pyplot as plt
   
5. # Commodity Channel Index
   
6. def CCI(data, ndays):
   
7. TP = (data['High'] + data['Low'] + data['Close']) / 3
   
8. CCI = pd.Series((TP - pd.rolling_mean(TP, ndays)) / (0.015 * pd.rolling_std(TP, ndays)),
   
9. name = 'CCI')
   
10. data = data.join(CCI)
    
11. return data
    
12. 
    
13. # Retrieve the Nifty data from Yahoo finance:
    
14. data = web.DataReader('^NSEI',data_source='yahoo',start='1/1/2014', end='1/1/2016')
    
15. data = pd.DataFrame(data)
    
16. 
    
17. # Compute the Commodity Channel Index(CCI) for NIFTY based on the 20-day Moving average
    
18. n = 20
    
19. NIFTY_CCI = CCI(data, n)
    
20. CCI = NIFTY_CCI['CCI']
    
21. 
    
22. # Plotting the Price Series chart and the Commodity Channel index below
    
23. fig = plt.figure(figsize=(7,5))
    
24. ax = fig.add_subplot(2, 1, 1)
    
25. ax.set_xticklabels([])
    
26. plt.plot(data['Close'],lw=1)
    
27. plt.title('NSE Price Chart')
    
28. plt.ylabel('Close Price')
    
29. plt.grid(True)
    
30. bx = fig.add_subplot(2, 1, 2)
    
31. plt.plot(CCI,'k',lw=0.75,linestyle='-',label='CCI')
    
32. plt.legend(loc=2,prop={'size':9.5})
    
33. plt.ylabel('CCI values')
    
34. plt.grid(True)
    
35. plt.setp(plt.gca().get_xticklabels(), rotation=30)
    
36. 
    

复制代码