最后一个文件是perd_attrib.py，这个文件主要是做绩效归因的，把股票的收益率归因为某些factor能解释的部分，就是common_returns,这些因素不能解释的部分，就是特别收益
import pandas
as pd
def
perf_attrib(
returns,
positions,
factor_returns,
factor_loadings):
"""
Attributes the performance of a returns stream to a set of risk factors.
Performance attribution determines how much each risk factor, e.g.,
momentum, the technology sector, etc., contributed to total returns, as
well as the daily exposure to each of the risk factors. The returns that
can be attributed to one of the given risk factors are the
`common_returns`, and the returns that _cannot_ be attributed to a risk
factor are the `specific_returns`. The `common_returns` and
`specific_returns` summed together will always equal the total returns.
Parameters
----------
returns : pd.Series
Returns for each day in the date range.
- Example:
2017-01-01 -0.017098
2017-01-02 0.002683
2017-01-03 -0.008669
positions: pd.Series
Daily holdings in percentages, indexed by date.
- Examples:
dt ticker
2017-01-01 AAPL 0.417582
TLT 0.010989
XOM 0.571429
2017-01-02 AAPL 0.202381
TLT 0.535714
XOM 0.261905
factor_returns : pd.DataFrame
Returns by factor, with date as index and factors as columns
- Example:
momentum reversal
2017-01-01 0.002779 -0.005453
2017-01-02 0.001096 0.010290
factor_loadings : pd.DataFrame
Factor loadings for all days in the date range, with date and ticker as
index, and factors as columns.
- Example:
momentum reversal
dt ticker
2017-01-01 AAPL -1.592914 0.852830
TLT 0.184864 0.895534
XOM 0.993160 1.149353
2017-01-02 AAPL -0.140009 -0.524952
TLT -1.066978 0.185435
XOM -1.798401 0.761549
Returns
-------
tuple of (risk_exposures_portfolio, perf_attribution)
risk_exposures_portfolio : pd.DataFrame
df indexed by datetime, with factors as columns
- Example:
momentum reversal
dt
2017-01-01 -0.238655 0.077123
2017-01-02 0.821872 1.520515
perf_attribution : pd.DataFrame
df with factors, common returns, and specific returns as columns,
and datetimes as index
- Example:
momentum reversal common_returns specific_returns
dt
2017-01-01 0.249087 0.935925 1.185012 1.185012
2017-01-02 -0.003194 -0.400786 -0.403980 -0.403980
Note
----
See https://en.wikipedia.org/wiki/Performance_attribution for more details.
"""
risk_exposures_portfolio = compute_exposures(positions,
factor_loadings)
perf_attrib_by_factor = risk_exposures_portfolio.multiply(factor_returns)
common_returns = perf_attrib_by_factor.sum(
axis=
'columns')
specific_returns = returns - common_returns
returns_df = pd.DataFrame({
'total_returns': returns,
'common_returns': common_returns,
'specific_returns': specific_returns})
return (risk_exposures_portfolio,
pd.concat([perf_attrib_by_factor, returns_df],
axis=
'columns'))
def
compute_exposures(
positions,
factor_loadings):
"""
Compute daily risk factor exposures.
Parameters
----------
positions: pd.Series
A series of holdings as percentages indexed by date and ticker.
- Examples:
dt ticker
2017-01-01 AAPL 0.417582
TLT 0.010989
XOM 0.571429
2017-01-02 AAPL 0.202381
TLT 0.535714
XOM 0.261905
factor_loadings : pd.DataFrame
Factor loadings for all days in the date range, with date and ticker as
index, and factors as columns.
- Example:
momentum reversal
dt ticker
2017-01-01 AAPL -1.592914 0.852830
TLT 0.184864 0.895534
XOM 0.993160 1.149353
2017-01-02 AAPL -0.140009 -0.524952
TLT -1.066978 0.185435
XOM -1.798401 0.761549
Returns
-------
risk_exposures_portfolio : pd.DataFrame
df indexed by datetime, with factors as columns
- Example:
momentum reversal
dt
2017-01-01 -0.238655 0.077123
2017-01-02 0.821872 1.520515
"""
risk_exposures = factor_loadings.multiply(positions,
axis=
'rows')
return risk_exposures.groupby(
level=
'dt').sum()