pandas MultiIndex 滚动平均值

Question

前言：我是新手，但在这里和 pandas documentation without success. I've also read Wes's book.

中搜索了几个小时

我正在为一家对冲基金建模股票市场数据，并且有一个简单的 MultiIndexed-DataFrame，其中包含代码、日期（每日）和字段。这里的样本来自彭博社。 3 个月 - 2016 年 12 月至 2017 年 2 月，3 个代码（AAPL、IBM、MSFT）。

import numpy as np
import pandas as pd
import os

# get data from Excel
curr_directory = os.getcwd()
filename = 'Sample Data File.xlsx'
filepath = os.path.join(curr_directory, filename)
df = pd.read_excel(filepath, sheetname = 'Sheet1', index_col = [0,1], parse_cols = 'A:D')

# sort
df.sort_index(inplace=True)

# sample of the data
df.head(15)
Out[4]: 
                           PX_LAST  PX_VOLUME
Security Name  date                          
AAPL US Equity 2016-12-01   109.49   37086862
               2016-12-02   109.90   26527997
               2016-12-05   109.11   34324540
               2016-12-06   109.95   26195462
               2016-12-07   111.03   29998719
               2016-12-08   112.12   27068316
               2016-12-09   113.95   34402627
               2016-12-12   113.30   26374377
               2016-12-13   115.19   43733811
               2016-12-14   115.19   34031834
               2016-12-15   115.82   46524544
               2016-12-16   115.97   44351134
               2016-12-19   116.64   27779423
               2016-12-20   116.95   21424965
               2016-12-21   117.06   23783165

df.tail(15)
Out[5]: 
                           PX_LAST  PX_VOLUME
Security Name  date                          
MSFT US Equity 2017-02-07    63.43   20277226
               2017-02-08    63.34   18096358
               2017-02-09    64.06   22644443
               2017-02-10    64.00   18170729
               2017-02-13    64.72   22920101
               2017-02-14    64.57   23108426
               2017-02-15    64.53   17005157
               2017-02-16    64.52   20546345
               2017-02-17    64.62   21248818
               2017-02-21    64.49   20655869
               2017-02-22    64.36   19292651
               2017-02-23    64.62   20273128
               2017-02-24    64.62   21796800
               2017-02-27    64.23   15871507
               2017-02-28    63.98   23239825

当我像这样计算每日价格变化时，它似乎有效，只有第一天是 NaN，它应该是：

df.head(5)
Out[7]: 
                           PX_LAST  PX_VOLUME  px_change_%
Security Name  date                                       
AAPL US Equity 2016-12-01   109.49   37086862          NaN
               2016-12-02   109.90   26527997     0.003745
               2016-12-05   109.11   34324540    -0.007188
               2016-12-06   109.95   26195462     0.007699
               2016-12-07   111.03   29998719     0.009823

但每日 30 天交易量却没有。它应该只在前 29 天为 NaN，但所有的天数都是 NaN：

# daily change from 30 day volume - doesn't work
df['30_day_volume'] = df.groupby(level=0,group_keys=True)['PX_VOLUME'].rolling(window=30).mean()
df['volume_change_%'] = (df['PX_VOLUME'] - df['30_day_volume']) / df['30_day_volume']

df.iloc[:,3:].tail(40)
Out[12]: 
                           30_day_volume  volume_change_%
Security Name  date                                      
MSFT US Equity 2016-12-30            NaN              NaN
               2017-01-03            NaN              NaN
               2017-01-04            NaN              NaN
               2017-01-05            NaN              NaN
               2017-01-06            NaN              NaN
               2017-01-09            NaN              NaN
               2017-01-10            NaN              NaN
               2017-01-11            NaN              NaN
               2017-01-12            NaN              NaN
               2017-01-13            NaN              NaN
               2017-01-17            NaN              NaN
               2017-01-18            NaN              NaN
               2017-01-19            NaN              NaN
               2017-01-20            NaN              NaN
               2017-01-23            NaN              NaN
               2017-01-24            NaN              NaN
               2017-01-25            NaN              NaN
               2017-01-26            NaN              NaN
               2017-01-27            NaN              NaN
               2017-01-30            NaN              NaN
               2017-01-31            NaN              NaN
               2017-02-01            NaN              NaN
               2017-02-02            NaN              NaN
               2017-02-03            NaN              NaN
               2017-02-06            NaN              NaN
               2017-02-07            NaN              NaN
               2017-02-08            NaN              NaN
               2017-02-09            NaN              NaN
               2017-02-10            NaN              NaN
               2017-02-13            NaN              NaN
               2017-02-14            NaN              NaN
               2017-02-15            NaN              NaN
               2017-02-16            NaN              NaN
               2017-02-17            NaN              NaN
               2017-02-21            NaN              NaN
               2017-02-22            NaN              NaN
               2017-02-23            NaN              NaN
               2017-02-24            NaN              NaN
               2017-02-27            NaN              NaN
               2017-02-28            NaN              NaN

由于 pandas 似乎是专门为金融设计的，我很惊讶这并不简单。

编辑： 我也尝试过一些其他方法。

尝试将其转换为 Panel (3D)，但没有找到 Windows 的任何内置函数，除了转换为 DataFrame 并返回，因此没有优势。
试图创建一个主元 table，但无法找到仅引用 MultiIndex 第一层的方法。 df.index.levels[0] 或 ...levels[1] 不工作。

谢谢！

Answer 1

你可以尝试以下方法看看是否有效吗？

df['30_day_volume'] = df.groupby(level=0)['PX_VOLUME'].rolling(window=30).mean().values

df['volume_change_%'] = (df['PX_VOLUME'] - df['30_day_volume']) / df['30_day_volume']

Answer 2

我可以验证 Allen 的答案在使用 pandas_datareader 时有效，修改 datareader 多索引的 groupby 操作的索引级别。

import pandas_datareader.data as web
import datetime

start = datetime.datetime(2016, 12, 1)
end = datetime.datetime(2017, 2, 28)
data = web.DataReader(['AAPL', 'IBM', 'MSFT'], 'yahoo', start, end).to_frame()

data['30_day_volume'] = data.groupby(level=1).rolling(window=30)['Volume'].mean().values

data['volume_change_%'] = (data['Volume'] - data['30_day_volume']) / data['30_day_volume']

# double-check that it computed starting at 30 trading days. 
data.loc['2017-1-17':'2017-1-30']

发帖者可能会尝试编辑此行：

df['30_day_volume'] = df.groupby(level=0,group_keys=True)['PX_VOLUME'].rolling(window=30).mean()

以下，使用 mean().values:

df['30_day_volume'] = df.groupby(level=0,group_keys=True)['PX_VOLUME'].rolling(window=30).mean().values

如果不这样做，数据将无法正确对齐，从而导致 NaN。

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