我有一個如下所示的數(shù)據(jù)框： | symbol | date | close ----|--------|------------|---------- 0 | APX | 5/31/2017 | 4.04 1 | APX | 6/30/2017 | 5.4 2 | APX | 7/31/2017 | 4.15 3 | APX | 8/31/2017 | 9.95 4 | APX | 9/30/2017 | 10.3 5 | APX | 10/31/2017 | 5.58 6 | APX | 11/30/2017 | 8.47 7 | APX | 12/31/2017 | 15.66 8 | APX | 1/31/2018 | 10.55 9 | APX | 2/28/2018 | 9.8 10 | APX | 3/31/2018 | 7.43 11 | APX | 4/30/2018 | 8.93 12 | APX | 5/31/2018 | 7.61 13 | APX | 6/30/2018 | 7.79 14 | AURA | 1/31/2018 | 0.221382 15 | AURA | 2/28/2018 | 0.222236 16 | AURA | 3/31/2018 | 0.075488 17 | AURA | 4/30/2018 | 0.180699 18 | AURA | 5/31/2018 | 0.220009 19 | AURA | 6/30/2018 | 0.199029 20 | BASH | 11/30/2016 | 0.000447 21 | BASH | 12/31/2016 | 0.000376 22 | BASH | 1/31/2017 | 0.000452 23 | BASH | 2/28/2017 | 0.000414 24 | BASH | 3/31/2017 | 0.00045 25 | BASH | 4/30/2017 | 0.000754 26 | BASH | 5/31/2017 | 0.009115 27 | BASH | 6/30/2017 | 0.03419 28 | BASH | 7/31/2017 | 0.014037 29 | BASH | 8/31/2017 | 0.009117 30 | BASH | 9/30/2017 | 0.002333 31 | BASH | 10/31/2017 | 0.00258 32 | BASH | 11/30/2017 | 0.003415 33 | BASH | 12/31/2017 | 0.003756 34 | BASH | 1/31/2018 | 0.005454 35 | BASH | 2/28/2018 | 0.006186 36 | BASH | 3/31/2018 | 0.004155 37 | BASH | 4/30/2018 | 0.005078 38 | BASH | 5/31/2018 | 0.003696 39 | BASH | 6/30/2018 | 0.003442 我想計算每個符號的 6 個月尾隨方差，并將其作為新列添加到數(shù)據(jù)框中。應(yīng)根據(jù)close列中的值計算方差。例如，對于 APX，有 14 個觀測值，因此第一個方差應(yīng)根據(jù)值 4.04、5.4、4.15、9.95、10.3 和 5.58 計算。下一個方差應(yīng)根據(jù) 5.4、4.15、9.95、10.3、5.58 和 8.47 等計算。我假設(shè)我需要使用該df.var函數(shù)來計算方差，但是我如何告訴每個交易品種在過去 6 個月的基礎(chǔ)上進行計算？
1 回答
吃雞游戲
TA貢獻1829條經(jīng)驗 獲得超7個贊
您可以使用groupby，并rolling(6)一起var()獲得滾動方差為前6組的意見在各組的數(shù)據(jù)分開。設(shè)置min_periods為 6 將強制函數(shù)使用至少 6 個值進行計算，如果不設(shè)置此值，前 5 個結(jié)果將使用較少的觀察值。
df['trailing_var'] = df.groupby('symbol')['close'].rolling(6, min_periods=6).var().reset_index(drop=True)
結(jié)果：
symbol date close trailing_var
0 APX 5/31/2017 4.040000 NaN
1 APX 6/30/2017 5.400000 NaN
2 APX 7/31/2017 4.150000 NaN
3 APX 8/31/2017 9.950000 NaN
4 APX 9/30/2017 10.30000 NaN
5 APX 10/31/2017 5.580000 7.988720e+00
6 APX 11/30/2017 8.470000 6.776377e+00
7 APX 12/31/2017 15.66000 1.648918e+01
8 APX 1/31/2018 10.55000 1.085291e+01
9 APX 2/28/2018 9.800000 1.086476e+01
10 APX 3/31/2018 7.430000 1.196206e+01
11 APX 4/30/2018 8.930000 8.470240e+00
12 APX 5/31/2018 7.610000 9.167987e+00
13 APX 6/30/2018 7.790000 1.662630e+00
14 AURA 1/31/2018 0.221382 NaN
15 AURA 2/28/2018 0.222236 NaN
16 AURA 3/31/2018 0.075488 NaN
17 AURA 4/30/2018 0.180699 NaN
18 AURA 5/31/2018 0.220009 NaN
19 AURA 6/30/2018 0.199029 3.226191e-03
20 BASH 11/30/2016 0.000447 NaN
21 BASH 12/31/2016 0.000376 NaN
22 BASH 1/31/2017 0.000452 NaN
23 BASH 2/28/2017 0.000414 NaN
24 BASH 3/31/2017 0.000450 NaN
25 BASH 4/30/2017 0.000754 1.859857e-08
26 BASH 5/31/2017 0.009115 1.241904e-05
27 BASH 6/30/2017 0.034190 1.820075e-04
28 BASH 7/31/2017 0.014037 1.741278e-04
29 BASH 8/31/2017 0.009117 1.539841e-04
30 BASH 9/30/2017 0.002333 1.464200e-04
31 BASH 10/31/2017 0.002580 1.390604e-04
32 BASH 11/30/2017 0.003415 1.508145e-04
33 BASH 12/31/2017 0.003756 2.221467e-05
34 BASH 1/31/2018 0.005454 6.464003e-06
35 BASH 2/28/2018 0.006186 2.415413e-06
36 BASH 3/31/2018 0.004155 1.787309e-06
37 BASH 4/30/2018 0.005078 1.150985e-06
38 BASH 5/31/2018 0.003696 1.022634e-06
39 BASH 6/30/2018 0.003442 1.160249e-06
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