股票相关性分析 

 1 import numpy as np
 2 from matplotlib.pyplot import plot
 3 from matplotlib.pyplot import show
 4 
 5 bhp = np.loadtxt('BHP.csv', delimiter=',', usecols=(6,), unpack=True)
 6 
 7 bhp_returns = np.diff(bhp) / bhp[ : -1]
 8 
 9 vale = np.loadtxt('VALE.csv', delimiter=',', usecols=(6,), unpack=True)
10 
11 vale_returns = np.diff(vale) / vale[ : -1]
12 
13 covariance = np.cov(bhp_returns, vale_returns) 
14 print("Covariance", covariance)
15 
16 print ("Covariance diagonal", covariance.diagonal()_
17 print( "Covariance trace", covariance.trace())
18 
19 print (covariance/ (bhp_returns.std() * vale_returns.std()))
20 
21 print ("Correlation coefficient", np.corrcoef(bhp_returns, vale_returns))
22 
23 difference = bhp - vale
24 avg = np.mean(difference)
25 dev = np.std(difference)
26 
27 print ("Out of sync", np.abs(difference[-1] - avg) > 2 * dev)
28 
29 t = np.arange(len(bhp_returns))
30 plot(t, bhp_returns, lw=1)
31 plot(t, vale_returns, lw=2)
32 show()

参考资料：《Numpy学习指南》

posted @ 2017-06-15 10:52  Jason&Hymer  阅读(876)  评论(0)    收藏  举报