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量化交易吧 /  数理科学 帖子：3364677 新帖：0

时间序列

Peace发表于：8 月 22 日 16：52回复(1)

时间序列

import pandas as pd
hz= attribute_history('601088.XSHG',count=2*6*60+60, unit='1d',fields='close')['close']
fin=[]
h1=[]
h=[]
beta=[]
for i in range(12*60+60):
    h1.append(hz[i])  #得到list 全局变量 不改
h=h1 #得到list 全局变量 每次循环会减少一个

for j in range(12*60):
    x=[]
    h=h1[len(h1)-60:len(h1)]
    for i in range(60-1):
        x.append(h[i+1]/h[i]-1)
    X = np.array(x)  
    #N代表最大的片段值（即不对X分割时）
    N = X.size
    T = np.arange(1, N + 1)

    Y = np.cumsum(X)
    
#分别计算不同长度的片段的均值
 
    Ave_T = Y / T

#每个片段的最大差距R_T

 
    R_T = np.zeros(N)
#对应的每段的标准差S_T
 
    S_T = np.zeros(N)
    
#分别对不同的大小的切片计算R_T和S_T
 
    for i in range(N):
        S_T[i] = np.std(X[:i + 1])
        Z_T = Y - T * Ave_T[i]
        R_T[i] = np.ptp(Z_T[:i + 1])
        
#计算R/S
 
    
    R_S = R_T / S_T 
    R_S = np.log(R_S)[1:] 
    n = np.log(T)[1:]
    A = np.column_stack((n, np.ones(n.size)))

#将会得到一个斜率，也就是hurst指数
 
    [m, c] = np.linalg.lstsq(np.nan_to_num(A), np.nan_to_num(R_S))[0]
    H = m
    fin.append(H)
    regindex = np.polyfit(x,np.arange(len(x)),1)
    beta.append(regindex[0])  #得到beta
    h1.pop()  #每次循环去掉最后一个
    x.clear()  #每次循环后清空x 不清空不知道为什么会报错


f1=list(reversed(fin))
print(f1)
b1=list(reversed(beta))
print(b1)
print(len(f1))

   

/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:45: RuntimeWarning: invalid value encountered in true_divide
/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:59: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.
To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.

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720

print('hello')

hello

       

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