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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print('hello')
hello
