大 pandas 数据框的对数返回 [英] Logarithmic returns in pandas dataframe

问题描述

Python熊猫具有pct_change函数，可用于计算数据框中股票价格的回报:

Python pandas has a pct_change function which I use to calculate the returns for stock prices in a dataframe:

ndf['Return']= ndf['TypicalPrice'].pct_change()

我正在使用以下代码获取对数返回值，但它给出的值与pct.change()函数完全相同:

I am using the following code to get logarithmic returns, but it gives the exact same values as the pct.change() function:

ndf['retlog']=np.log(ndf['TypicalPrice'].astype('float64')/ndf['TypicalPrice'].astype('float64').shift(1))
#np is for numpy

推荐答案

这里是使用.shift()计算日志返回的一种方法.结果与pct_change()计算的总收益相似但不相同.您可以上传示例数据的副本(Dropbox共享链接)来重现您看到的不一致之处吗?

Here is one way to calculate log return using .shift(). And the result is similar to but not the same as the gross return calculated by pct_change(). Can you upload a copy of your sample data (dropbox share link) to reproduce the inconsistency you saw?

import pandas as pd
import numpy as np

np.random.seed(0)
df = pd.DataFrame(100 + np.random.randn(100).cumsum(), columns=['price'])
df['pct_change'] = df.price.pct_change()
df['log_ret'] = np.log(df.price) - np.log(df.price.shift(1))

Out[56]: 
       price  pct_change  log_ret
0   101.7641         NaN      NaN
1   102.1642      0.0039   0.0039
2   103.1429      0.0096   0.0095
3   105.3838      0.0217   0.0215
4   107.2514      0.0177   0.0176
5   106.2741     -0.0091  -0.0092
6   107.2242      0.0089   0.0089
7   107.0729     -0.0014  -0.0014
..       ...         ...      ...
92  101.6160      0.0021   0.0021
93  102.5926      0.0096   0.0096
94  102.9490      0.0035   0.0035
95  103.6555      0.0069   0.0068
96  103.6660      0.0001   0.0001
97  105.4519      0.0172   0.0171
98  105.5788      0.0012   0.0012
99  105.9808      0.0038   0.0038

[100 rows x 3 columns]

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