函數coinT()使用ADF檢驗和Hurst指數檢驗兩個時間序列是否平穩。時間序列存儲在1511x6 CSV檔案中，但是對于測試，函數stock()隻傳回第5列的向量。總共有50個檔案。程式似乎占用了太多記憶體，因為它會使電腦在運作約30秒後崩潰。它可以很好地處理15個檔案，但在較大的檔案集(大于50個)時崩潰。在
有人能幫我找出是什麼東西占用了這麼多記憶體嗎？我嘗試過将計算拆分為多個函數并删除對象，但沒有太大幫助。在import numpy as np
import pandas as pd
import statsmodels.tsa.stattools as ts
import csv
import timeit
from numpy import log, polyfit, sqrt, std, subtract
from pandas.stats.api import ols
import os
src = 'C:/Users/PC/Desktop/Magistr/Ibpython/testing/'
filenames = next(os.walk(src))[2] #load all stock file names into array
cointegratedPairs = []
def hurst(ts):
"""Returns the Hurst Exponent of the time series vector ts
H<0.5 - The time series is mean reverting
H=0.5 - The time series is a Geometric Brownian Motion
H>0.5 - The time series is trending"""
# Create the range of lag values
lags = range(2, 100)
# Calculate the array of the variances of the lagged differences
tau = [sqrt(std(subtract(ts[lag:], ts[:-lag]))) for lag in lags]
# Use a linear fit to estimate the Hurst Exponent
poly = polyfit(log(lags), log(tau), 1)
del lags
del tau
# Return the Hurst exponent from the polyfit output
return poly[0]*2.0
#Convert file into an array
def stock(filename):
#read file into array and get it's length
delimiter = ","
with open(src + filename,'r') as dest_f:
data_iter = csv.reader(dest_f,
delimiter = delimiter,
quotechar = '"')
data = [data for data in data_iter]
data_array = np.asarray(data)[:,5]
return data_array
del data
del data_array
#Check if two time series are cointegrated
def coinTest(itemX, itemY):
indVar = map(float, stock(itemX)[0:1000]) #2009.05.22 - 2013.05.14
depVar = map(float, stock(itemY)[0:1000]) #2009.05.22 - 2013.05.14
#Calculate optimal hedge ratio "beta"
df = pd.DataFrame()
df[itemX] = indVar
df[itemY] = depVar
res = ols(y=df[itemY], x=df[itemX])
beta_hr = res.beta.x
alpha = res.beta.intercept
df["res"] = df[itemY] - beta_hr*df[itemX] - alpha
#Calculate the CADF test on the residuals
cadf = ts.adfuller(df["res"])
#Reject the null hypothesis at 1% confidence level
if cadf[4]['1%'] > cadf[0]:
#Hurst exponent test if residuals are mean reverting
if hurst(df["res"]) < 0.4:
cointegratedPairs.append((itemY,itemX))
del indVar
del depVar
del df[itemX]
del df[itemY]
del df["res"]
del cadf
#Main function
def coinT():
limit = 0
TotalPairs = 0
for itemX in filenames:
for itemY in filenames[limit:]:
TotalPairs +=1
if itemX == itemY:
next
else:
coinTest(itemX, itemY)
limit +=1