def __init__(self):
AlphaFactor.__init__(self)
self.exposure_path = self.data_path
self.raw_factor_name = 'alpha_raw_profit_yoy'
def __init__(self):
AlphaFactor.__init__(self)
self.exposure_path = self.data_path
self.raw_factor_name = 'alpha_raw_illiquidity'
def __init__(self):
AlphaFactor.__init__(self)
self.exposure_path = self.data_path
self.raw_factor_name = 'alpha_raw_amount_ln_20d'
def split_alpha(self,
beg_date,
end_date,
factor_name,
period="W",
stock_pool_name="AllChinaStockFilter"):
""" 计算残差Alpha 回归风格因子和行业 计算在风格和行业上的暴露 """
alpha = AlphaFactor().get_standard_alpha_factor(factor_name)
date_series = Date().get_trade_date_series(beg_date,
end_date,
period=period)
barra_date_series = Barra().get_exposure_date_series()
date_series = list(
set(date_series) & set(alpha.columns) & set(barra_date_series))
date_series.sort()
res_alpha = pd.DataFrame()
exposure_risk = pd.DataFrame()
for i_date in range(len(date_series)):
date = date_series[i_date]
alpha_date = pd.DataFrame(alpha[date])
alpha_date.columns = ['Alpha']
alpha_date = alpha_date.dropna()
risk_exposure = Barra().get_factor_exposure_date(
date, type_list=['STYLE', 'INDUSTRY', "COUNTRY"])
stock_pool = Stock().get_invest_stock_pool(
date=date, stock_pool_name=stock_pool_name)
stock_pool = list(
set(stock_pool) & set(risk_exposure.index)
& set(alpha_date.index))
stock_pool.sort()
alpha_date = alpha_date.loc[stock_pool, "Alpha"]
risk_exposure = risk_exposure.loc[stock_pool, :]
concat_data = pd.concat([alpha_date, risk_exposure], axis=1)
concat_data = concat_data.dropna()
if len(concat_data) > self.min_stock_number:
factor_val = concat_data.iloc[:, 0]
neutral_val = concat_data.iloc[:, 1:]
print("Split %s Alpha Exposure At %s %s" %
(factor_name, date, stock_pool_name))
model = sm.OLS(factor_val.values, neutral_val.values)
regress = model.fit()
params = pd.DataFrame(regress.params,
index=neutral_val.columns,
columns=['param'])
factor_res = factor_val - regress.predict(neutral_val)
params = pd.DataFrame(params)
params.columns = [date]
res_alpha_date = pd.DataFrame(factor_res)
res_alpha_date.columns = [date]
exposure_risk = pd.concat([exposure_risk, params], axis=1)
res_alpha = pd.concat([res_alpha, res_alpha_date], axis=1)
else:
print("Split %s Alpha Exposure At %s %s is Null" %
(factor_name, date, stock_pool_name))
res_alpha = FactorPreProcess().remove_extreme_value_mad(res_alpha)
res_alpha = FactorPreProcess().standardization(res_alpha)
self.save_alpha_risk_exposure(exposure_risk, factor_name,
stock_pool_name)
self.save_alpha_res_exposure(res_alpha, factor_name, stock_pool_name)
def __init__(self):
AlphaFactor.__init__(self)
self.exposure_path = self.data_path
self.raw_factor_name = 'alpha_raw_average_holder'
def __init__(self):
AlphaFactor.__init__(self)
self.exposure_path = self.data_path
self.raw_factor_name = 'alpha_raw_to_bias_6m'
