【发布时间】:2016-04-23 17:50:39
【问题描述】:
我正在尝试估计股票收益的每日赫斯特指数值(例如,每天也有赫斯特指数 - 类似的东西:https://www.quandl.com/data/PE/CKEC_HURST-Hurst-Exponent-of-Carmike-Cinemas-Inc-Common-Stock-CKEC-NASDAQ)。
我正在使用这个 Python 代码(取自 https://www.quantstart.com/articles/Basics-of-Statistical-Mean-Reversion-Testing),但我不知道如何将它用于每日 Hurst 值,而不仅仅是一个值:
from datetime import datetime
from pandas.io.data import DataReader
from numpy import cumsum, log, polyfit, sqrt, std, subtract
from numpy.random import randn
def hurst(ts):
"""Returns the Hurst Exponent of the time series vector ts"""
# 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)
# Return the Hurst exponent from the polyfit output
return poly[0]*2.0
# Download the stock prices series from Yahoo
aapl = DataReader("AAPL", "yahoo", datetime(2012,1,1), datetime(2015,9,18))
# Call the function
hurst(aapl['Adj Close'])
【问题讨论】:
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您的问题是什么?代码应该做什么?
标签: python