factor_data = preprocess.QA_fetch_get_factor_start_date(factor)
factor_data = factor_data.reset_index()
factor_data = factor_data.loc[(factor_data.datetime - factor_data.start_date
) > pd.Timedelta("200D")].set_index(
["datetime", "code"])
factor = factor_data['factor']
# 极值处理，默认使用 "MAD"
factor = preprocess.QA_winsorize_factor(factor)
factor_time_range = factor.index.remove_unused_levels().get_level_values(
"datetime").tolist()
# 数据导入
dataapi = data.DataApi(jq_username="******",
jq_password="******",
factor_time_range=factor_time_range,
industry_cls="sw_l1",
weight_cls="mktcap",
detailed=True,
frequence='1q')
analyzer = analyze.FactorAnalyzer(factor=factor,
**dataapi.apis,
periods=(1, 2),
max_loss=0.9)
# 因子数据
factor_data = analyzer.clean_factor_data.head()
# 简略分析
tears.create_summary_tear_sheet(factor_data=analyzer.clean_factor_data)
factor_data = factor_data.loc[(
factor_data.datetime -
pd.to_datetime(factor_data.start_date).dt.tz_localize(None).dt.
tz_localize('Asia/Shanghai')) > pd.Timedelta("200D")].set_index(
["datetime", "code"])
factor = factor_data['factor']
# 极值处理，默认使用 "MAD"
factor = preprocess.QA_winsorize_factor(factor)
factor_time_range = factor.index.remove_unused_levels().get_level_values(
"datetime").tolist()
# 数据导入
dataapi = data.DataApi(
# jq_username=JQ_USERNAME,
# jq_password=JQ_PASSWORD,
factor_time_range=factor_time_range,
industry_cls="sw_l1",
weight_cls="mktcap",
detailed=True,
frequence='1q')
analyzer = analyze.FactorAnalyzer(factor=factor,
**dataapi.apis,
periods=1,
max_loss=0.9)
# 因子数据
factor_data = analyzer.clean_factor_data.head()
# 简略分析
tears.create_summary_tear_sheet(factor_data=analyzer.clean_factor_data)