def cal_factor_return(self, sf_ids):
sfs = []
for sf_id in sf_ids:
sfs.append(
StockFactor.subclass(sf_id, StockFactor.stock_factors[sf_id]))
close = StockAsset.all_stock_nav()
ret = close.pct_change()
ret = ret[StockAsset.all_stock_info().index]
dates = ret.index
dates = dates[dates > '2000-01-01']
df_ret = pd.DataFrame(columns=sf_ids)
df_sret = pd.DataFrame(columns=StockAsset.all_stock_info().index)
for date, next_date in zip(dates[:-1], dates[1:]):
print 'cal_factor_return:', date
tmp_exposure = {}
tmp_ret = ret.loc[next_date].values
for sf in sfs:
tmp_exposure[sf.factor_id] = sf.exposure.loc[date]
tmp_exposure_df = pd.DataFrame(tmp_exposure)
tmp_exposure_df = tmp_exposure_df[sf_ids].fillna(0.0)
tmp_exposure_df = tmp_exposure_df.loc[
StockAsset.all_stock_info().index]
mod = sm.OLS(tmp_ret, tmp_exposure_df.values, missing='drop').fit()
df_ret.loc[next_date] = mod.params
df_sret.loc[next_date] = tmp_ret - np.dot(tmp_exposure_df.values,
mod.params)
return df_ret, df_sret
def cal_factor_return(self, sf_ids):
period = 21
sfs = []
for sf_id in sf_ids:
sfs.append(
StockFactor.subclass(sf_id, StockFactor.stock_factors[sf_id]))
close = StockAsset.all_stock_nav()
ret = close.pct_change(period).iloc[period:]
ret = ret[StockAsset.all_stock_info().index]
dates = ret.index
dates = dates[dates >= '2005-01-01']
df_ret = pd.DataFrame(columns=sf_ids)
df_sret = pd.DataFrame(columns=StockAsset.all_stock_info().index)
pool = Pool(len(sfs))
sfs = pool.map(multiprocess_load_factor_exposure, sfs)
pool.close()
pool.join()
for date, next_date in zip(dates[:-period], dates[period:]):
tmp_exposure = {}
tmp_ret = ret.loc[next_date].values
for sf in sfs:
tmp_exposure[sf.factor_id] = sf.exposure.loc[date]
#tmp_exposure[sf.factor_id] = fed[sf.factor_id].loc[date]
tmp_exposure_df = pd.DataFrame(tmp_exposure)
tmp_exposure_df = tmp_exposure_df[sf_ids].fillna(0.0)
tmp_exposure_df = tmp_exposure_df.loc[
StockAsset.all_stock_info().index]
mod = sm.OLS(tmp_ret, tmp_exposure_df.values, missing='drop').fit()
# mod = sm.WLS(tmp_ret, tmp_exposure_df.values, weights = tmp_amount, missing = 'drop').fit()
# print(mod.summary())
df_ret.loc[next_date] = mod.params
df_sret.loc[next_date] = tmp_ret - np.dot(tmp_exposure_df.values,
mod.params)
return df_ret, df_sret