完全重新编写原始问题
我从csv文件“ CloseWeight4.csv”中读取了原始数据
df=pd.read_csv('CloseWeights4.csv')
Date Symbol ClosingPrice Weight
3/1/2010 OGDC 116.51 0.1820219
3/2/2010 OGDC 117.32 0.1820219
3/3/2010 OGDC 116.4 0.1820219
3/4/2010 OGDC 116.58 0.1820219
3/5/2010 OGDC 117.61 0.1820219
3/1/2010 WTI 78.7 0.5348142
3/2/2010 WTI 79.68 0.5348142
3/3/2010 WTI 80.87 0.5348142
3/4/2010 WTI 80.21 0.5348142
3/5/2010 WTI 81.5 0.5348142
3/1/2010 FX 85.07 0.1312427
3/2/2010 FX 85.1077 0.1312427
3/3/2010 FX 85.049 0.1312427
3/4/2010 FX 84.9339 0.1312427
3/5/2010 FX 84.8 0.1312427
3/1/2010 PIB 98.1596499 0.1519211
3/2/2010 PIB 98.1596499 0.1519211
3/3/2010 PIB 98.1764222 0.1519211
3/4/2010 PIB 98.1770656 0.1519211
3/5/2010 PIB 98.1609364 0.1519211
从中我生成一个数据帧df2
df2=df.iloc[:,0:3].pivot('Date', 'Symbol', 'ClosingPrice')
df2
Out[10]:
Symbol FX OGDC PIB WTI
Date
2010-03-01 85.0700 116.51 98.159650 78.70
2010-03-02 85.1077 117.32 98.159650 79.68
2010-03-03 85.0490 116.40 98.176422 80.87
2010-03-04 84.9339 116.58 98.177066 80.21
2010-03-05 84.8000 117.61 98.160936 81.50
据此，我使用以下方法计算收益：
ret=np.log(df2/df2.shift(1))
In [12] ret
Out[12]:
Symbol FX OGDC PIB WTI
Date
2010-03-01 NaN NaN NaN NaN
2010-03-02 0.000443 0.006928 0.000000 0.012375
2010-03-03 -0.000690 -0.007873 0.000171 0.014824
2010-03-04 -0.001354 0.001545 0.000007 -0.008195
2010-03-05 -0.001578 0.008796 -0.000164 0.015955
我有来自df的每种证券的权重
df3=df.iloc[:,[1,3]].drop_duplicates().reset_index(drop=True)
df3
Out[14]:
Weight
Symbol
OGDC 0.182022
WTI 0.534814
FX 0.131243
PIB 0.151921
我正在尝试每天获得以下加权收益结果，但不知道如何在熊猫中进行数学运算：
Date Portfolio_weighted_returns
2010-03-02 0.008174751
2010-03-03 0.006061657
2010-03-04 -0.005002414
2010-03-05 0.009058151
where the Portfolio_weighted_returns of 2010-03-02 is calculated as follows:
0.006928*0.182022+.012375*0.534814+0.000443*0.131243+0*0.151921 = 0.007937512315
然后，我需要将这些结果乘以一个衰减因子，其中将衰减因子定义为decFac = decay ^（t）。使用衰减= 0.5得出decFac值为：
Date decFac
2010-03-02 0.0625
2010-03-03 0.125
2010-03-04 0.25
2010-03-05 0.5
然后，我需要将每天的Portfolio_weighted_returns平方和的总SQRT乘以相应的decFac，如下所示：
SQRT(Sum(0.008174751^2*.0625+0.006061657^2*.125+(-0.005002414^2)*.25+.009058151^2*.5)) = 0.007487
最佳答案
IIUC您可以通过以下方式进行操作：
In [267]: port_ret = ret.dot(df3)
In [268]: port_ret
Out[268]:
Weight
Date
2010-03-01 NaN
2010-03-02 0.007938
2010-03-03 0.006431
2010-03-04 -0.004278
2010-03-05 0.009902
In [269]: decay = 0.5
In [270]: decay_df = pd.DataFrame({'decFac':decay**np.arange(len(ret), 0, -1)}, index=ret.index)
In [271]: decay_df
Out[271]:
decFac
Date
2010-03-01 0.03125
2010-03-02 0.06250
2010-03-03 0.12500
2010-03-04 0.25000
2010-03-05 0.50000
In [272]: (port_ret.Weight**2 * decay_df.decFac).sum() ** 0.5
Out[272]: 0.007918790111274962
port_ret.Weight**2 * decay_df.decFacIn [277]: port_ret.Weight**2 * decay_df.decFac
Out[277]:
Date
2010-03-01 NaN
2010-03-02 0.000004
2010-03-03 0.000005
2010-03-04 0.000005
2010-03-05 0.000049
dtype: float64