def cal_factor_liquidity_stom(beg_date, end_date):
"""
LIQUIDITY_STOM 最近21个交易日的换手率总和的对数值
"""
# params
##################################################################################
raw_factor_name = "RAW_CNE5_LIQUIDITY_STOM"
factor_name = "NORMAL_CNE5_LIQUIDITY_STOM"
M = 21
# read data
##################################################################################
turnover_daily = Stock().get_factor_h5("TurnOver_Daily", None, 'primary_mfc').T
data_beg_date = Date().get_trade_date_offset(beg_date, -M)
end_date = Date().change_to_str(end_date)
turnover_daily = turnover_daily.ix[data_beg_date:end_date, :]
turnover_month = turnover_daily.rolling(window=M).sum().applymap(np.log)
turnover_month = turnover_month.ix[beg_date:end_date, :]
turnover_month = turnover_month.replace(-np.inf, np.nan)
turnover_month = turnover_month.replace(np.inf, np.nan)
turnover_month = turnover_month.dropna(how='all').T
# save data
##################################################################################
Stock().write_factor_h5(turnover_month, raw_factor_name, 'barra_risk_dfc')
turnover_month = FactorPreProcess().remove_extreme_value_mad(turnover_month)
turnover_month = FactorPreProcess().standardization_free_mv(turnover_month)
Stock().write_factor_h5(turnover_month, factor_name, 'barra_risk_dfc')
##################################################################################
return turnover_month
def cal_factor_barra_cumulative_range(self, beg_date, end_date):
""" 过去1-12月最大累计收益 和最小累计收益的差 """
# param
t = 12
month_days = 21
pct_chg = Stock().read_factor_h5("Pct_chg").applymap(
lambda x: np.log(x / 100 + 1)).T
date_series = Date().get_trade_date_series(beg_date, end_date)
date_series = list(set(date_series) & set(pct_chg.columns))
date_series.sort()
pct_chg_panel = pd.Panel()
for i in range(t):
length = month_days * (i + 1)
pct_chg_sum = pct_chg.rolling(length).sum()
pct_chg_sum = pct_chg_sum.dropna(how='all')
pct_chg_panel = pd.concat([pct_chg_panel, pct_chg_sum], axis=0)
pct_max = pct_chg_panel.max(axis=0)
pct_max = pct_max.applymap(lambda x: np.log(x + 1)).T
pct_min = pct_chg_panel.min(axis=0)
pct_min = pct_min.applymap(lambda x: np.log(x + 1)).T
res = pct_max.sub(pct_min)
self.save_risk_factor_exposure(res, self.raw_factor_name_range)
res = Stock().remove_extreme_value_mad(res)
res = Stock().standardization(res)
self.save_risk_factor_exposure(res, self.factor_name_range)
def cal_factor_liquidity_yearly(self):
""" LIQUIDITY_STOM 最近252个交易日的换手率总和的对数值 """
P = 252
turnover_daily = Stock().read_factor_h5("TurnOver_Daily").T
turnover_period = turnover_daily.rolling(window=P).sum().applymap(np.log)
turnover_period = turnover_period.T.dropna(how='all')
self.save_risk_factor_exposure(turnover_period, self.raw_factor_name_yearly)
turnover_period = FactorPreProcess().remove_extreme_value_mad(turnover_period)
turnover_period = FactorPreProcess().standardization(turnover_period)
self.save_risk_factor_exposure(turnover_period, self.factor_name_yearly)
def cal_factor_barra_growth_5years_profit_growth(self, beg_date, end_date):
""" 过去5年每股盈利 回归 等差数列 的系数 除以 每股盈利的平均值 """
report_data = Stock().read_factor_h5("ReportDateDaily")
eps = Stock().read_factor_h5("EPS_basic").T
eps_ttm = eps.rolling(4).sum()
month = eps_ttm.index[-1][4:6]
eps_ttm_quarter = eps_ttm.index
eps_ttm_year = list(filter(lambda x: x[4:6] == month, list(eps_ttm.index)))
eps_ttm = eps_ttm.loc[eps_ttm_year, :]
eps_ttm_growth = eps_ttm.rolling(5).apply(self.slope)
eps_ttm_growth = eps_ttm_growth.loc[eps_ttm_quarter, :]
eps_ttm_growth = eps_ttm_growth.fillna(method='pad', limit=3).T
eps_ttm_growth = StockFactor().change_quarter_to_daily_with_disclosure_date(eps_ttm_growth, report_data,
beg_date, end_date)
self.save_risk_factor_exposure(eps_ttm_growth, self.raw_factor_name_5y_profit)
eps_ttm_growth = FactorPreProcess().remove_extreme_value_mad(eps_ttm_growth)
eps_ttm_growth = FactorPreProcess().standardization(eps_ttm_growth)
self.save_risk_factor_exposure(eps_ttm_growth, self.factor_name_5y_profit)
