Beta 是衡量投資組合系統(tǒng)性風(fēng)險的指標(biāo)。它的計算方法是將投資組合回報率與基準(zhǔn)/市場的協(xié)方差除以市場方差。我想根據(jù)許多投資組合滾動計算。我有一個 df 如下PERIOD,PORT1,PORT2,BM201504,-0.004,-0.001,-0.013201505,0.017,0.019,0.022201506,-0.027,-0.037,-0.039201507,0.026,0.033,0.017201508,-0.045,-0.054,-0.081201509,-0.033,-0.026,-0.032201510,0.053,0.07,0.09201511,0.03,0.032,0.038201512,-0.05,-0.034,-0.044201601,-0.016,-0.043,-0.057201602,-0.007,-0.007,-0.011201603,0.014,0.014,0.026201604,0.003,0.001,0.01201605,0.046,0.038,0.031除了更多列，如 port1 和 port2。我想創(chuàng)建一個與 BM 列相比具有滾動 beta 的數(shù)據(jù)集。我創(chuàng)建了一個類似的滾動相關(guān)數(shù)據(jù)集df.rolling(3).corr(df['BM'])...它獲取了我的大集合中的每一列，并計算了與我的 BM 列的相關(guān)性。我試圖為 Beta 制作一個自定義函數(shù)，但因為它需要兩個參數(shù)，所以我很掙扎。下面是我的自定義函數(shù)，以及我是如何通過向它提供兩列返回值來讓它工作的。 def beta(arr1,arr2): #ddof = 0 gives population covar. the 0 and 1 coordinates take the arr1 vs arr2 covar from the matrix return (np.cov(arr1,arr2,ddof=0)[0][1])/np.var(arr2) beta_test = beta(df['PORT1'],df['BM'])所以這有助于我找到我輸入的兩列之間的 beta...問題是如何對我上面的數(shù)據(jù)和包含許多列/投資組合的數(shù)據(jù)執(zhí)行此操作？然后如何在滾動的基礎(chǔ)上做到這一點？從我上面看到的相關(guān)性來看，下面應(yīng)該是可能的，在每一列與一個指定列中運行每個滾動的 3 個月數(shù)據(jù)集。beta_data = df.rolling(3).agg(beta(df['BM']))任何指向正確方向的指針將不勝感激
2 回答
拉莫斯之舞
TA貢獻1820條經(jīng)驗 獲得超10個贊
IIUC，你可以set_index使用 PERIOD 和 BMfilter列，其中包含 PORT 的列（如果你有其他列你不想應(yīng)用該beta功能），然后rolling.apply像這樣使用：
print (df.set_index(['PERIOD','BM']).filter(like='PORT')
.rolling(3).apply(lambda x: beta(x, x.index.get_level_values(1)))
.reset_index())
PERIOD BM PORT1 PORT2
0 201504 -0.013 NaN NaN
1 201505 0.022 NaN NaN
2 201506 -0.039 0.714514 0.898613
3 201507 0.017 0.814734 1.055798
4 201508 -0.081 0.736486 0.907336
5 201509 -0.032 0.724490 0.887755
6 201510 0.090 0.598332 0.736964
7 201511 0.038 0.715848 0.789221
8 201512 -0.044 0.787248 0.778703
9 201601 -0.057 0.658877 0.794949
10 201602 -0.011 0.412270 0.789567
11 201603 0.026 0.354829 0.690573
12 201604 0.010 0.562924 0.558083
13 201605 0.031 1.716066 1.530471
胡子哥哥
TA貢獻1825條經(jīng)驗 獲得超6個贊
def getbetas(df, market, window = 45):
""" given an unstacked pandas dataframe (columns instruments, rows
dates), compute the rolling betas vs the market.
"""
nmarket = market/market.rolling(window).var()
thebetas = df.rolling(window).cov(other=nmarket)
return thebetas
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