def every_stock_pool_cal(stock):
print stock
if len(hq_dict_no_suspension[stock]) > 372:
for i, data in enumerate(hq_dict_no_suspension[stock]):
if data[0] >= cal_daily_date_list[0] and i >= 372:
temp_ROR_list = []
this_shibor_list = []
this_index_ROR_list = []
j = 0
while j < 252:
temp_ROR_list.append(hq_dict_no_suspension[stock][i - j][2] / hq_dict_no_suspension[stock][i - j - 1][2] - 1.0)
if SHIBOR_dict.has_key(hq_dict_no_suspension[stock][i - j][0]) == True:
this_shibor_list.append(SHIBOR_dict[hq_dict_no_suspension[stock][i - j][0]])
else:
this_shibor_list.append(SHIBOR_dict['20061008'])
this_index_ROR_list.append(index_return_dict[(Now_Index, hq_dict_no_suspension[stock][i - j][0])])
j += 1
this_minus_list = xyk_common_data_processing.element_cal_between_list(temp_ROR_list, this_shibor_list, "-")
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))
result_list.append([stock, data[0], this_beta, this_HSIGMA])
pass
X = sm.add_constant(this_whole_stock_factors_list)
wls_model = sm.WLS(ROR_list, X, weights=sqrt_liquid_list)
results = wls_model.fit()
U_list_temp = results.resid #残差项，也就是特质因子序列
T_value_list_temp = results.tvalues #模型对每个因子的t值
R_squared_temp = results.rsquared #模型R2值
R_squared_adj_temp = results.rsquared_adj #模型调整后的R2值
f_list_temp = results.params #模型参数，也就是因子收益率序列
U_list.append(U_list_temp)
T_value_list.append(T_value_list_temp)
R_squared.append(R_squared_temp)
R_squared_adj.append(R_squared_adj_temp)
f_list.append(f_list_temp)
U2_list_temp = xyk_common_data_processing.element_cal_between_list(
U_list_temp, U_list_temp, "*")
WU2_list_temp = xyk_common_data_processing.element_cal_between_list(
U2_list_temp, sqrt_liquid_list, "*")
Y2_list_temp = xyk_common_data_processing.element_cal_between_list(
ROR_list, ROR_list, "*")
WY2_list_temp = xyk_common_data_processing.element_cal_between_list(
Y2_list_temp, sqrt_liquid_list, "*")
R_squared_barra = 1 - sum(WU2_list_temp) / sum(WY2_list_temp)
R_squared_barra_list.append(R_squared_barra)
output_list.append(
[Now_Index, Now_Range, "r_square", "1", date, R_squared_temp])
output_list.append(
[Now_Index, Now_Range, "r_square_adj", "1", date, R_squared_adj_temp])
output_list.append(
[Now_Index, Now_Range, "R_squared_barra", "1", date, R_squared_barra])
this_date_volatility_list = []
for stock in this_date_list:
if factor_dict.has_key((stock, date)) == True:
this_stock_daily_factor_list = factor_dict[(stock, date)]
null_count = 0
for value in this_stock_daily_factor_list:
if value == "" or value == None:
null_count += 1
if null_count == 0:
this_date_id_list.append(stock)
this_date_size_list.append(factor_dict[(stock, date)][0])
this_date_beta_list.append(factor_dict[(stock, date)][1])
this_date_volatility_list.append(factor_dict[(stock,
date)][2])
size_temp_list = xyk_common_data_processing.element_cal_between_list(
this_date_size_list, para_dict[date][1], "*")
beta_temp_list = xyk_common_data_processing.element_cal_between_list(
this_date_beta_list, para_dict[date][2], "*")
minus_1_list = xyk_common_data_processing.element_cal_between_list(
this_date_volatility_list, para_dict[date][0], "-")
minus_size_list = xyk_common_data_processing.element_cal_between_list(
minus_1_list, size_temp_list, "-")
residual_list = xyk_common_data_processing.element_cal_between_list(
minus_size_list, beta_temp_list, "-")
resi_temp_list = xyk_common_data_processing.element_cal_between_list(
residual_list, para_dict2[date][0], "-")
resi_standard_list = xyk_common_data_processing.element_cal_between_list(
resi_temp_list, para_dict2[date][1], "/")
i = 0
while i < len(resi_standard_list):
j = 0
while j < 252:
temp_ROR_list.append(
hq_dict_no_suspension[stock][i - j][2] /
hq_dict_no_suspension[stock][i - j - 1][2] - 1.0)
if SHIBOR_dict.has_key(
hq_dict_no_suspension[stock][i - j][0]) == True:
this_shibor_list.append(
SHIBOR_dict[hq_dict_no_suspension[stock][i -
j][0]])
else:
this_shibor_list.append(SHIBOR_dict['20061008'])
this_index_ROR_list.append(index_return_dict[(
Now_Index, hq_dict_no_suspension[stock][i - j][0])])
j += 1
this_minus_list = xyk_common_data_processing.element_cal_between_list(
temp_ROR_list, this_shibor_list, "-")
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))
temp_ln_ROR_list = []
this_ln_shibor_list = []
j = 0
while j < 20:
temp_ln_ROR_list.append(
math.log(hq_dict_no_suspension[stock][i - j][2] /
hq_dict_no_suspension[stock][i - j - 1][2]))
if LN_SHIBOR_dict.has_key(
hq_dict_no_suspension[stock][i - j][0]) == True:
this_ln_shibor_list.append(
LN_SHIBOR_dict[hq_dict_no_suspension[stock][i -
j][0]])
else:
this_ln_shibor_list.append(LN_SHIBOR_dict['20061008'])
j += 1
this_minus_list = xyk_common_data_processing.element_cal_between_list(
temp_ln_ROR_list, this_ln_shibor_list, "-")
this_short_momentum_value = xyk_common_data_processing.weighted_mean(
this_minus_list, half_life_list, use_df=1)
result_list2.append(
[stock, data[0], this_short_momentum_value])
half_life_list = xyk_common_data_processing.get_half_life_list(120, 126)
result_list3 = []
count = 0
for stock in hq_dict_no_suspension.keys():
print count, 'medium'
count += 1
if len(hq_dict_no_suspension[stock]) > After_date + 120:
for i, data in enumerate(hq_dict_no_suspension[stock]):
if data[0] >= cal_daily_date_list[0] and i >= After_date + 120:
temp_ln_ROR_list = []
if null_count < 4:
if hq_dict.has_key((stock, date)) == True:
this_stock_liquid_list.append(hq_dict[(stock, date)][0])
daily_des_value_list.append(
copy.deepcopy(this_stock_daily_des_value_list))
daily_des_value_list_T = xyk_common_data_processing.exchange_sequence(
daily_des_value_list)
daily_des_value_list_T_standard = []
i = 0
while i < len(daily_des_value_list_T):
para_dict[(date, i)] = [
xyk_common_data_processing.weighted_mean(
daily_des_value_list_T[i], this_stock_liquid_list, 2),
xyk_common_data_processing.dev_n(daily_des_value_list_T[i])
]
temp_list = xyk_common_data_processing.element_cal_between_list(
daily_des_value_list_T[i], para_dict[(date, i)][0], "-")
temp_list2 = xyk_common_data_processing.element_cal_between_list(
temp_list, para_dict[(date, i)][1], "/")
daily_des_value_list_T_standard.append(temp_list2)
i += 1
daily_des_value_list_standard = xyk_common_data_processing.exchange_sequence(
daily_des_value_list_T_standard)
factor_list = factor_aggregation(daily_des_value_list_standard, 1)
daily_factor_list_T = xyk_common_data_processing.exchange_sequence(
factor_list)
i = 0
while i < len(daily_factor_list_T):
para_dict2[(date, i)] = [
xyk_common_data_processing.weighted_mean(
daily_factor_list_T[i], this_stock_liquid_list, 2),
xyk_common_data_processing.dev_n(daily_factor_list_T[i])