def save_ic_analysis_result(self, factor_name):
with MongoConnect(DatabaseName.MULTI_FACTOR_DATA.value):
ic_df = self.ic_df.copy()
p_value_df = self.p_value_df.copy()
ic_df.index = ic_df.index.format()
p_value_df.index = p_value_df.index.format()
doc = FactorIcAnalysisResult(
factor_name=factor_name,
# 因子数据开始时间
begin_date=self.factor.index[0],
# 因子数据结束时间
end_date=self.factor.index[-1],
# IC信号衰减计算，index 是时间序列， columns是decay周期，[1, self.ic_decay], 闭区间
ic=ic_df,
# p值信号衰减计算，index 是时间序列， columns是decay周期，[1, self.ic_decay], 闭区间
p_value=p_value_df,
ic_result=self.ic_result)
doc.save()
def save_ic_analysis_result(self, factor_name):
with MongoConnect(DatabaseName.MULTI_FACTOR_DATA.value):
ic_df = self.ic_df.copy()
p_value_df = self.p_value_df.copy()
ic_df.index = ic_df.index.format()
p_value_df.index = p_value_df.index.format()
doc = FactorIcAnalysisResult(
factor_name=factor_name,
# 因子数据开始时间
begin_date=self.factor.index[0],
# 因子数据结束时间
end_date=self.factor.index[-1],
# IC信号衰减计算，index 是时间序列， columns是decay周期，[1, self.ic_decay], 闭区间
ic=ic_df,
# p值信号衰减计算，index 是时间序列， columns是decay周期，[1, self.ic_decay], 闭区间
p_value=p_value_df,
# IC均值、 IC标准差、 IC_IR比率、 IC > 0 占比、 | IC | > 0.02 占比(绝对值)、 偏度、 峰度、
# 正相关显著比例、负相关显著比例、状态切换比例、同向比例
# index_list=['ic_mean', 'ic_std', 'ic_ir', 'ic_ratio', 'ic_abs_ratio', 'ic_skewness', 'ic_kurtosis',
# 'ic_positive_ratio', 'ic_negative_ratio', 'ic_change_ratio', 'ic_unchange_ratio', ]
ic_result=self.ic_result)
doc.save()
factor_ic = {}
factor_return = {}
for factor_name in factor_list:
factor_regression_analysis_result = FactorRegressionAnalysisResult.objects(factor_name=factor_name) \
.only('factor_name') \
.only('begin_date') \
.only('end_date') \
.only('factor_return') \
.as_pymongo()
factor_return[factor_name] = pd.DataFrame(
factor_regression_analysis_result[0]['factor_return'])
factor_return[factor_name].index = pd.DatetimeIndex(
factor_return[factor_name].index)
factor_ic_result = FactorIcAnalysisResult.objects(factor_name=factor_name) \
.only('factor_name') \
.only('begin_date') \
.only('end_date') \
.only('ic') \
.as_pymongo()
# print(factor_ic_result)
factor_ic[factor_name] = pd.DataFrame(factor_ic_result[0]['ic'])
factor_ic[factor_name].index = pd.DatetimeIndex(
factor_ic[factor_name].index)
factor_weighting_obj = FactorWeighting(factor_data)
# factor_weighted = factor_weighting_obj.weighting(weight_method='return_half_life', data=factor_return, half_life=5)
factor_weighted = factor_weighting_obj.weighting(weight_method='max_ic_ir',
data=factor_ic,
window=20)