keep_value_list].copy(
)
for factor in change_factor_list:
this_factor_df = this_date_components_df.loc[:, [factor, 'Size']]
#去空
this_factor_df = xyk_common_data_processing.delete_none(this_factor_df)
#做回归
size_sr = this_factor_df.loc[:, 'Size']
this_factor_sr = this_factor_df.loc[:, factor]
X_np = sm.add_constant(size_sr)
ols_model = sm.OLS(this_factor_sr, X_np)
results = ols_model.fit()
this_factor_np = results.resid
this_factor_df.loc[:, factor] = this_factor_np
#储存
output_data_dict[date] = pd.concat(
[output_data_dict[date], this_factor_df.loc[:, factor]],
axis=1,
join='outer')
for i, date in enumerate(daily_date_list):
print date, 'inserting...'
temp_insert_data = output_data_dict[date]
temp_insert_data['curr_date'] = date
temp_insert_data.index.name = 'stock_id'
temp_insert_data = temp_insert_data.set_index(
[temp_insert_data['curr_date'],
temp_insert_data.index]).drop(['curr_date'], axis=1)
db_interaction.insert_df_append(Output_Table_Name,
temp_insert_data,
index_name_list=["curr_date", "stock_id"])
X = sm.add_constant(this_index_ROR_list)
Y = this_minus_list
wls_model = sm.WLS(Y, X, weights=half_life_list)
results = wls_model.fit()
this_beta = float(results.params[1])
resid_list = results.resid
this_resid_mean = sum(resid_list) / float(len(resid_list))
this_treated_list = []
for resid_data in resid_list:
this_treated_list.append((resid_data - this_resid_mean) *
(resid_data - this_resid_mean))
this_HSIGMA = math.sqrt(
xyk_common_data_processing.weighted_mean(this_treated_list,
half_life_list,
use_df=1))
result_list.append([stock, data[0], this_beta, this_HSIGMA])
'''
***输出至DB***
'''
result_pd = xyk_common_data_processing.change_data_format_with_df(
"list of lists",
"DataFrame",
result_list,
columns_name_list=["stock_id", "curr_date", "Beta", "HSIGMA"])
result_pd.set_index(["stock_id", "curr_date"], inplace=True)
print "Begin inserting..."
table_name = "daily_stock_descriptors_" + Now_Index + "_unique"
db_interaction.insert_df_append(table_name,
result_pd,
index_name_list=["stock_id", "curr_date"])