def cal_factor_exposure(self, beg_date, end_date):
""" 计算因子暴露 """
# read data
cfo = Stock().read_factor_h5("NetOperateCashFlow")
cfo_ttm = Stock().change_single_quarter_to_ttm_quarter(cfo)
total_share = Stock().read_factor_h5("TotalShare")
price_unadjust = Stock().read_factor_h5("Price_Unadjust")
report_data = Stock().read_factor_h5("ReportDateDaily")
# data precessing
cfo_ttm = Stock().change_quarter_to_daily_with_disclosure_date(
cfo_ttm, report_data, beg_date, end_date)
[total_share,
price_unadjust] = FactorPreProcess().make_same_index_columns(
[total_share, price_unadjust])
total_mv = total_share.mul(price_unadjust) / 100000000
[cfo_ttm,
total_mv] = Stock().make_same_index_columns([cfo_ttm, total_mv])
cfno2p = cfo_ttm.div(total_mv)
# save data
cfno2p = cfno2p.T.dropna(how='all').T
self.save_alpha_factor_exposure(cfno2p, self.raw_factor_name)
def TotalAssetTurnBias(beg_date, end_date):
# 总资产周转率环减
# 因子说明：当季度（营业收入TTM / 总资产加权）- 本季度（营业收入TTM / 总资产加权）
# TTM 为统一财报期
# param
#################################################################################
factor_name = 'TotalAssetTurnBias'
ipo_num = 90
# read data
#################################################################################
income_q = Stock().get_factor_h5("OperatingIncome", None, 'primary_mfc')
total_asset = Stock().get_factor_h5("TotalAsset", None, 'primary_mfc')
total_asset = StockFactorOperate().change_single_quarter_to_ttm_quarter(total_asset)
total_asset /= 4.0
turnover = income_q.div(total_asset)
turnover_qoq = turnover.T.diff().T
turnover_qoq = StockFactorOperate().change_quarter_to_daily_with_report_date(turnover_qoq, beg_date, end_date)
# code set & date set
#################################################################################
pass
res = turnover_qoq.T.dropna(how='all').T
# save data
#############################################################################
Stock().write_factor_h5(res, factor_name, "alpha_dfc")
return res
def cal_factor_barra_growth_5years_sales_growth(self, beg_date, end_date):
""" 过去5年每股营收 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")
total_income = Stock().read_factor_h5("OperatingIncomeTotal")
total_share = Stock().read_factor_h5("TotalShare") / 100000000
normal_date_series = Date().get_normal_date_series(total_share.columns[0], total_share.columns[-1])
total_share = total_share.loc[:, normal_date_series]
total_share = total_share.T.fillna(method='pad', limit=10).T
total_share, total_income = StockFactor().make_same_index_columns([total_share, total_income])
income_pre_share = total_income.div(total_share).T
ips_ttm_growth = income_pre_share.rolling(4).sum()
month = ips_ttm_growth.index[-1][4:6]
ips_ttm_quarter = ips_ttm_growth.index
ips_ttm_year = list(filter(lambda x: x[4:6] == month, list(ips_ttm_growth.index)))
ips_ttm_growth = ips_ttm_growth.loc[ips_ttm_year, :]
ips_ttm_growth = ips_ttm_growth.rolling(5).apply(self.slope)
ips_ttm_growth = ips_ttm_growth.loc[ips_ttm_quarter, :]
ips_ttm_growth = ips_ttm_growth.fillna(method='pad', limit=3).T
ips_ttm_growth = StockFactor().change_quarter_to_daily_with_disclosure_date(ips_ttm_growth, report_data,
beg_date, end_date)
self.save_risk_factor_exposure(ips_ttm_growth, self.raw_factor_name_5y_sale)
ips_ttm_growth = FactorPreProcess().remove_extreme_value_mad(ips_ttm_growth)
ips_ttm_growth = FactorPreProcess().standardization(ips_ttm_growth)
self.save_risk_factor_exposure(ips_ttm_growth, self.factor_name_5y_sale)
def EBITIncomeBias(beg_date, end_date):
"""
因子说明: 息税前利润/营业总收入 环比增长率
披露日期 为 最近财报
"""
# param
#################################################################################
factor_name = 'EBITIncomeBias'
ipo_num = 90
# read data
#################################################################################
ebit = Stock().get_factor_h5("EBIT", None, "primary_mfc")
income = Stock().get_factor_h5("OperatingIncome", None, "primary_mfc")
[income, ebit] = Stock().make_same_index_columns([income, ebit])
margin = ebit.div(income).T
margin = margin.diff().T
margin = StockFactorOperate().change_quarter_to_daily_with_report_date(
margin, beg_date, end_date)
# data precessing
#################################################################################
pass
# calculate data daily
#################################################################################
res = margin.T.dropna(how='all').T
# save data
#############################################################################
Stock().write_factor_h5(res, factor_name, "alpha_dfc")
return res
def RetainMarketValue(beg_date, end_date):
"""
因子说明: 留存收益 / 总市值
披露日期 为 最近财报
"""
# param
#################################################################################
factor_name = 'RetainMarketValue'
ipo_num = 90
# read data
#################################################################################
retain = Stock().get_factor_h5("RetainedEarnings", None, "primary_mfc")
mv = Stock().get_factor_h5("TotalMarketValue", None, "alpha_dfc")
retain = StockFactorOperate().change_quarter_to_daily_with_report_date(
retain, beg_date, end_date)
[retain, mv] = Stock().make_same_index_columns([retain, mv])
retain_ratio = retain.div(mv)
# data precessing
#################################################################################
pass
# calculate data daily
#################################################################################
res = retain_ratio.T.dropna(how='all').T
# save data
#############################################################################
Stock().write_factor_h5(res, factor_name, "alpha_dfc")
return res
def CashMarketValue(beg_date, end_date):
"""
因子说明: 货币资金/总市值, 根据最新财报更新数据
披露日期 为 最近财报
"""
# param
#################################################################################
factor_name = 'CashMarketValue'
ipo_num = 90
# read data
#################################################################################
cash = Stock().get_factor_h5("CashEquivalents", None, "primary_mfc")
total_mv = Stock().get_factor_h5("TotalMarketValue", None, "alpha_dfc")
cash = StockFactorOperate().change_quarter_to_daily_with_report_date(
cash, beg_date, end_date)
[cash, total_mv] = Stock().make_same_index_columns([cash, total_mv])
cash_price = cash.div(total_mv)
# data precessing
#################################################################################
pass
# calculate data daily
#################################################################################
res = cash_price.T.dropna(how='all').T
# save data
#############################################################################
Stock().write_factor_h5(res, factor_name, "alpha_dfc")
return res
def NetProfitQOQ(beg_date, end_date):
"""
因子说明: 净利润环比增长率
披露日期 为 最近财报
"""
# param
#################################################################################
factor_name = 'NetProfitQOQ'
ipo_num = 90
# read data
#################################################################################
netprofit = Stock().get_factor_h5("NetProfit", None, "primary_mfc").T
netprofit_1 = netprofit.shift(1)
netprofit_qoq = netprofit.div(netprofit_1) - 1.0
netprofit_qoq = netprofit_qoq.T
netprofit_qoq = StockFactorOperate(
).change_quarter_to_daily_with_report_date(netprofit_qoq, beg_date,
end_date)
# data precessing
#################################################################################
pass
# calculate data daily
#################################################################################
res = netprofit_qoq.T.dropna(how='all').T
# save data
#############################################################################
Stock().write_factor_h5(res, factor_name, "alpha_dfc")
return res
def PEG(beg_date, end_date):
"""
因子说明：PE_TTM / 净利润增长速度
当两者都是负数的时候 结果也为负数
披露日期 为 最近财报
"""
# param
#################################################################################
factor_name = 'PEG'
ipo_num = 90
# read data
#################################################################################
net_profit = Stock().get_factor_h5("NetProfit", None, "primary_mfc")
net_profit = StockFactorOperate().change_single_quarter_to_ttm_quarter(
net_profit).T
net_profit_4 = net_profit.shift(4)
net_profit_g = net_profit / net_profit_4 - 1.0
net_profit_g_mean = net_profit_g.rolling(window=4).median().T
report_data = Stock().get_factor_h5("NetProfit" + "Daily", "ReportDate",
'primary_mfc')
net_profit_g_mean = StockFactorOperate().\
change_quarter_to_daily_with_disclosure_date(net_profit_g_mean, report_data, beg_date, end_date)
pe_ttm = Stock().get_factor_h5("PE_ttm", None, "primary_mfc")
[pe_ttm, net_profit_g_mean
] = Stock().make_same_index_columns([pe_ttm, net_profit_g_mean])
pe_ttm_negative = pe_ttm < 0.0
net_profit_g_mean_negative = net_profit_g_mean < 0.0
double_negative = pe_ttm_negative & net_profit_g_mean_negative
peg = pe_ttm.div(net_profit_g_mean)
peg[double_negative] = -peg[double_negative]
# data precessing
#################################################################################
pass
# calculate data daily
#################################################################################
res = peg.T.dropna(how='all').T
# save data
#############################################################################
Stock().write_factor_h5(res, factor_name, "alpha_dfc")
return res
def cal_factor_exposure(self, beg_date, end_date):
""" 计算因子暴露 """
income = Stock().read_factor_h5("OperatingIncome")
income_ttm = Stock().change_single_quarter_to_ttm_quarter(income).T
income_ttm_1 = income_ttm.shift(1)
income_qoq = income_ttm.div(income_ttm_1) - 1.0
income_qoq = income_qoq.T
report_data = Stock().read_factor_h5("ReportDateDaily")
income_qoq = Stock().change_quarter_to_daily_with_disclosure_date(
income_qoq, report_data, beg_date, end_date)
res = income_qoq.T.dropna(how='all').T
self.save_alpha_factor_exposure(res, self.raw_factor_name)
def cal_factor_exposure(self, beg_date, end_date):
""" 计算因子暴露 """
profit = Stock().read_factor_h5("NetProfitDeducted")
profit_ttm = Stock().change_single_quarter_to_ttm_quarter(profit).T
profit_ttm_1 = profit_ttm.shift(1)
profit_qoq = profit_ttm.div(profit_ttm_1) - 1.0
profit_qoq = profit_qoq.T
report_data = Stock().read_factor_h5("ReportDateDaily")
profit_qoq = Stock().change_quarter_to_daily_with_disclosure_date(
profit_qoq, report_data, beg_date, end_date)
res = profit_qoq.T.dropna(how='all').T
self.save_alpha_factor_exposure(res, self.raw_factor_name)
def cal_factor_barra_leverage_debt_to_asset(self):
""" 资产负债比 = 总负债/总资产 """
total_debt = Stock().read_factor_h5("TotalLiabilityDaily")
total_asset = Stock().read_factor_h5('TotalAssetDaily')
debt_to_asset = total_debt.div(total_asset)
debt_to_asset = debt_to_asset.T.dropna(how='all').T
self.save_risk_factor_exposure(debt_to_asset,
self.raw_factor_name_debt_to_asset)
debt_to_asset = FactorPreProcess().remove_extreme_value_mad(
debt_to_asset)
debt_to_asset = FactorPreProcess().standardization(debt_to_asset)
self.save_risk_factor_exposure(debt_to_asset,
self.factor_name_debt_to_asset)
def cal_factor_exposure(self, beg_date, end_date):
""" 计算因子暴露 """
# read data
net_profit = Stock().read_factor_h5("NetProfitDeducted")
asset = Stock().read_factor_h5("TotalAsset") / 100000000.0
[net_profit,
asset] = Stock().make_same_index_columns([net_profit, asset])
roe = 4 * net_profit.div(asset)
report_data = Stock().read_factor_h5("ReportDateDaily")
roe = Stock().change_quarter_to_daily_with_disclosure_date(
roe, report_data, beg_date, end_date)
res = roe.T.dropna(how='all').T
self.save_alpha_factor_exposure(res, self.raw_factor_name)
def cal_factor_exposure(self, beg_date, end_date):
""" 计算因子暴露 """
income = Stock().read_factor_h5("OperatingIncome")
total_asset = Stock().read_factor_h5("TotalAsset") / 100000000
report_data = Stock().read_factor_h5("ReportDateDaily")
income_ttm = Stock().change_single_quarter_to_ttm_quarter(income)
total_asset = Stock().change_single_quarter_to_ttm_quarter(
total_asset) / 4.0
turnover = income_ttm.div(total_asset)
at_bias = turnover.T.diff().T
at_bias = Stock().change_quarter_to_daily_with_disclosure_date(
at_bias, report_data, beg_date, end_date)
at_bias = at_bias.T.dropna(how='all').T
self.save_alpha_factor_exposure(at_bias, self.raw_factor_name)
def cal_factor_exposure(self, beg_date, end_date):
""" 计算因子暴露 """
# param
cfo = Stock().read_factor_h5("NetOperateCashFlow")
cfo_ttm = Stock().change_single_quarter_to_ttm_quarter(cfo)
debt = Stock().read_factor_h5("TotalLiability") / 100000000
debt_ttm = Stock().change_single_quarter_to_ttm_quarter(debt) / 4.0
cfo2d = cfo_ttm.div(debt_ttm)
report_data = Stock().read_factor_h5("ReportDateDaily")
cfo2d = Stock().change_quarter_to_daily_with_disclosure_date(
cfo2d, report_data, beg_date, end_date)
res = cfo2d.T.dropna(how='all').T
self.save_alpha_factor_exposure(res, self.raw_factor_name)
def cal_factor_exposure(self, beg_date, end_date):
""" 计算因子暴露 """
# param
cfo = Stock().read_factor_h5("NetOperateCashFlow")
cfo_ttm = Stock().change_single_quarter_to_ttm_quarter(cfo)
report_data = Stock().read_factor_h5("ReportDateDaily")
cfo_ttm = Stock().change_quarter_to_daily_with_disclosure_date(
cfo_ttm, report_data, beg_date, end_date)
ev = Stock().read_factor_h5("Ev2") / 100000000
# data precessing
[cfo_ttm, ev] = Stock().make_same_index_columns([cfo_ttm, ev])
res = cfo_ttm.div(ev)
res = res.T.dropna(how='all').T
self.save_alpha_factor_exposure(res, self.raw_factor_name)
def cal_factor_exposure(self, beg_date, end_date):
""" 计算因子暴露 """
# param
cfo = Stock().read_factor_h5("NetOperateCashFlow")
cfo_ttm = Stock().change_single_quarter_to_ttm_quarter(cfo)
income = Stock().read_factor_h5("OperatingIncome")
income_ttm = Stock().change_single_quarter_to_ttm_quarter(income) / 4.0
cfo2income = cfo_ttm.div(income_ttm)
report_data = Stock().read_factor_h5("ReportDateDaily")
cfo2income = Stock().change_quarter_to_daily_with_disclosure_date(
cfo2income, report_data, beg_date, end_date)
res = cfo2income.T.dropna(how='all').T
self.save_alpha_factor_exposure(res, self.raw_factor_name)
def cal_predicted_earnings_to_price_ratio(self):
""" 预期盈利 / 总市值 """
e1_predicted = Stock().read_factor_h5("FE_1")
price_unadjust = Stock().read_factor_h5("Price_Unadjust")
e1_predicted, price_unadjust = FactorPreProcess(
).make_same_index_columns([e1_predicted, price_unadjust])
ep1_predicted = e1_predicted.div(price_unadjust)
ep1_predicted = ep1_predicted.T.dropna(how='all').T
self.save_risk_factor_exposure(ep1_predicted,
self.raw_factor_name_predicted)
ep1_predicted = FactorPreProcess().remove_extreme_value_mad(
ep1_predicted)
ep1_predicted = FactorPreProcess().standardization(ep1_predicted)
self.save_risk_factor_exposure(ep1_predicted,
self.factor_name_predicted)
def cal_factor_exposure(self, beg_date, end_date):
""" 计算因子暴露 """
# read data
advance = Stock().read_factor_h5("AdvanceReceipts")
total_share = Stock().read_factor_h5("TotalShare")
price_unadjust = Stock().read_factor_h5("Price_Unadjust")
report_data = Stock().read_factor_h5("ReportDateDaily")
# data precessing
advance = Stock().change_quarter_to_daily_with_disclosure_date(advance, report_data, beg_date, end_date)
[total_share, price_unadjust] = Stock().make_same_index_columns([total_share, price_unadjust])
total_mv = total_share.mul(price_unadjust)
[advance, total_mv] = Stock().make_same_index_columns([advance, total_mv])
ar2p = advance.div(total_mv)
res = ar2p.T.dropna(how='all').T
self.save_alpha_factor_exposure(res, self.raw_factor_name)
def cal_factor_exposure(self, beg_date, end_date):
""" 计算因子暴露 """
# read data
cfo = Stock().read_factor_h5("NetOperateCashFlow")
cfo_ttm = Stock().change_single_quarter_to_ttm_quarter(cfo)
asset = Stock().read_factor_h5("TotalAsset") / 100000000.0
asset_ttm = Stock().change_single_quarter_to_ttm_quarter(asset) / 4.0
[cfo_ttm, asset_ttm] = Stock().make_same_index_columns([cfo_ttm, asset_ttm])
roa = cfo_ttm.div(asset_ttm)
report_data = Stock().read_factor_h5("ReportDateDaily")
roa = Stock().change_quarter_to_daily_with_disclosure_date(roa, report_data, beg_date, end_date)
res = roa.T.dropna(how='all').T
self.save_alpha_factor_exposure(res, self.raw_factor_name)
def cal_factor_exposure(self, beg_date, end_date):
""" 计算因子暴露 """
# read data
net_profit = Stock().read_factor_h5("NetProfitDeducted")
holder = Stock().read_factor_h5("TotalShareHoldeRequity") / 100000000.0
holder_ttm = Stock().change_single_quarter_to_ttm_quarter(holder) / 4.0
# holder_ttm[holder_ttm < 1.0] = np.nan
[net_profit, holder_ttm
] = Stock().make_same_index_columns([net_profit, holder_ttm])
roe = 4 * net_profit.div(holder_ttm)
report_data = Stock().read_factor_h5("ReportDateDaily")
roe = Stock().change_quarter_to_daily_with_disclosure_date(
roe, report_data, beg_date, end_date)
res = roe.T.dropna(how='all').T
self.save_alpha_factor_exposure(res, self.raw_factor_name)
def CFNOTTMTotalDebt(beg_date, end_date):
"""
因子说明: 经营活动现金净流量(TTM)/负债合计, 根据最新财报更新数据
披露日期 为 最近财报
总负债可能是负值 000637.SZ 20120331 应交税费为负值 导致整体负债为负值
"""
# param
#################################################################################
factor_name = 'CFNOTTMTotalDebt'
ipo_num = 90
# read data
#################################################################################
operate_cash = Stock().get_factor_h5("NetOperateCashFlow", None,
"primary_mfc")
total_liability = Stock().get_factor_h5("TotalLiability", None,
"primary_mfc")
operate_cash = StockFactorOperate().change_single_quarter_to_ttm_quarter(
operate_cash) / 4
[operate_cash, total_liability
] = Stock().make_same_index_columns([operate_cash, total_liability])
total_liability[total_liability < 0.0] = np.nan
cfo_td = operate_cash.div(total_liability)
cfo_td = StockFactorOperate().change_quarter_to_daily_with_report_date(
cfo_td, beg_date, end_date)
# data precessing
#################################################################################
pass
# calculate data daily
#################################################################################
res = cfo_td.T.dropna(how='all').T
# save data
#############################################################################
Stock().write_factor_h5(res, factor_name, "alpha_dfc")
return res
def cal_factor_exposure(self, beg_date, end_date):
""" 计算因子暴露 """
# read data
holder = Stock().read_factor_h5("TotalShareHoldeRequity")
total_share = Stock().read_factor_h5("TotalShare")
price_unadjust = Stock().read_factor_h5("Price_Unadjust")
report_data = Stock().read_factor_h5("ReportDateDaily")
# data precessing
holder = Stock().change_quarter_to_daily_with_disclosure_date(holder, report_data, beg_date, end_date)
[total_share, price_unadjust] = FactorPreProcess().make_same_index_columns([total_share, price_unadjust])
total_mv = total_share.mul(price_unadjust)
[holder, total_mv] = Stock().make_same_index_columns([holder, total_mv])
bp = holder.div(total_mv)
# save data
bp = bp.T.dropna(how='all').T
self.save_alpha_factor_exposure(bp, self.raw_factor_name)
def cal_factor_exposure(self, beg_date, end_date):
""" 计算因子暴露 """
# read data
cash = Stock().read_factor_h5("CashEquivalents")
total_share = Stock().read_factor_h5("TotalShare")
price_unadjust = Stock().read_factor_h5("Price_Unadjust")
report_data = Stock().read_factor_h5("ReportDateDaily")
# data precessing
cash = Stock().change_quarter_to_daily_with_disclosure_date(cash, report_data, beg_date, end_date)
[total_share, price_unadjust] = FactorPreProcess().make_same_index_columns([total_share, price_unadjust])
total_mv = total_share.mul(price_unadjust)
[cash, total_mv] = Stock().make_same_index_columns([cash, total_mv])
cp = 4 * cash.div(total_mv)
# save data
cp = cp.T.dropna(how='all').T
self.save_alpha_factor_exposure(cp, self.raw_factor_name)
def cal_factor_exposure(self, beg_date, end_date):
""" 计算因子暴露 """
# read data
total_share = Stock().read_factor_h5("TotalShare")
price_unadjust = Stock().read_factor_h5("Price_Unadjust")
income = Stock().read_factor_h5("OperatingIncome")
income = Stock().change_single_quarter_to_ttm_quarter(income)
report_data = Stock().read_factor_h5("ReportDateDaily")
income = Stock().change_quarter_to_daily_with_disclosure_date(income, report_data, beg_date, end_date)
# data precessing
[total_share, price_unadjust] = FactorPreProcess().make_same_index_columns([total_share, price_unadjust])
total_mv = total_share.mul(price_unadjust) / 100000000
[income, total_mv] = Stock().make_same_index_columns([income, total_mv])
sp = income.div(total_mv)
# save data
sp = sp.T.dropna(how='all').T
self.save_alpha_factor_exposure(sp, self.raw_factor_name)
def cal_factor_exposure(self, beg_date, end_date):
""" 计算因子暴露 """
# read data
research = Stock().read_factor_h5("ResearchDevelopmentExpense")
research = research.fillna(0.0)
income = Stock().read_factor_h5("OperatingIncome")
research_ttm = Stock().change_single_quarter_to_ttm_quarter(research)
income_ttm = Stock().change_single_quarter_to_ttm_quarter(income) / 4.0
[research_ttm, income_ttm
] = Stock().make_same_index_columns([research_ttm, income_ttm])
roa = research_ttm.div(income_ttm)
report_data = Stock().read_factor_h5("ReportDateDaily")
roa = Stock().change_quarter_to_daily_with_disclosure_date(
roa, report_data, beg_date, end_date)
res = roa.T.dropna(how='all').T
self.save_alpha_factor_exposure(res, self.raw_factor_name)
def ROEQuarterDaily(beg_date, end_date):
"""
因子说明: ROE 单季
披露日期 为 最近财报
"""
# param
#################################################################################
factor_name = 'ROEQuarterDaily'
ipo_num = 90
beg_date = Date().get_trade_date_offset(beg_date, -250)
# read data
#################################################################################
net_profit = Stock().get_factor_h5("NetProfit", None, "primary_mfc")
holder = Stock().get_factor_h5("TotalShareHoldeRequity", None,
"primary_mfc")
[net_profit,
holder] = Stock().make_same_index_columns([net_profit, holder])
roe = net_profit.div(holder)
report_data = Stock().get_factor_h5("NetProfit" + "Daily", "ReportDate",
'primary_mfc')
roe = StockFactorOperate().change_quarter_to_daily_with_disclosure_date(
roe, report_data, beg_date, end_date)
# data precessing
#################################################################################
pass
# calculate data daily
#################################################################################
res = roe.T.dropna(how='all').T
# save data
#############################################################################
Stock().write_factor_h5(res, factor_name, "alpha_dfc")
return res
def EPQuarterDaily(beg_date, end_date):
"""
因子说明: 当季净利润EP
披露日期 为 最近财报
"""
# param
#################################################################################
factor_name = 'EPQuarterDaily'
ipo_num = 90
# read data
#################################################################################
netprofit = Stock().get_factor_h5("NetProfit", None, "primary_mfc")
total_mv = Stock().get_factor_h5("TotalMarketValue", None, "alpha_dfc")
report_data = Stock().get_factor_h5("OperatingIncome" + "Daily",
"ReportDate", 'primary_mfc')
netprofit = StockFactorOperate(
).change_quarter_to_daily_with_disclosure_date(netprofit, report_data,
beg_date, end_date)
[total_mv,
netprofit] = Stock().make_same_index_columns([total_mv, netprofit])
total_mv /= 100000000
ratio = netprofit.div(total_mv)
# data precessing
#################################################################################
pass
# calculate data daily
#################################################################################
res = ratio.T.dropna(how='all').T
# save data
#############################################################################
Stock().write_factor_h5(res, factor_name, "alpha_dfc")
return res
def cal_cash_earnings_to_price_ratio(self, beg_date, end_date):
""" 经营性现金流净额 / 总市值 """
nocf = Stock().read_factor_h5("NetOperateCashFlow")
report_data = Stock().read_factor_h5("ReportDateDaily")
nocf = Stock().change_single_quarter_to_ttm_quarter(nocf)
nocf = Stock().change_quarter_to_daily_with_disclosure_date(
nocf, report_data, beg_date, end_date)
total_share = Stock().read_factor_h5("TotalShare")
price_unadjust = Stock().read_factor_h5("Price_Unadjust")
result = FactorPreProcess().make_same_index_columns(
[nocf, total_share, price_unadjust])
nocf, total_share, price_unadjust = result
total_mv = total_share.mul(price_unadjust) / 100000000
nocf_mv = nocf.div(total_mv)
nocf_mv = nocf_mv.T.dropna(how='all').T
self.save_risk_factor_exposure(nocf_mv, self.raw_factor_name_cash)
nocf_mv = FactorPreProcess().remove_extreme_value_mad(nocf_mv)
nocf_mv = FactorPreProcess().standardization(nocf_mv)
self.save_risk_factor_exposure(nocf_mv, self.factor_name_cash)
def cal_factor_exposure(self, beg_date, end_date):
""" 计算因子暴露 """
# read data
income = Stock().read_factor_h5("OperatingIncome")
cost = Stock().read_factor_h5("OperatingCost")
total_share = Stock().read_factor_h5("TotalShare")
price_unadjust = Stock().read_factor_h5("Price_Unadjust")
report_data = Stock().read_factor_h5("ReportDateDaily")
profit = income.sub(cost)
# data precessing
profit = Stock().change_quarter_to_daily_with_disclosure_date(profit, report_data, beg_date, end_date)
[total_share, price_unadjust] = FactorPreProcess().make_same_index_columns([total_share, price_unadjust])
total_mv = total_share.mul(price_unadjust) / 100000000
[profit, total_mv] = Stock().make_same_index_columns([profit, total_mv])
gross_ep = 4 * profit.div(total_mv)
# save data
gross_ep = gross_ep.T.dropna(how='all').T
self.save_alpha_factor_exposure(gross_ep, self.raw_factor_name)
def cal_factor_exposure(self, beg_date, end_date):
""" 计算因子暴露 """
# read data
retain = Stock().read_factor_h5("RetainedEarnings")
total_share = Stock().read_factor_h5("TotalShare")
price_unadjust = Stock().read_factor_h5("Price_Unadjust")
report_data = Stock().read_factor_h5("ReportDateDaily")
# data precessing
retain = Stock().change_quarter_to_daily_with_disclosure_date(
retain, report_data, beg_date, end_date)
[total_share,
price_unadjust] = FactorPreProcess().make_same_index_columns(
[total_share, price_unadjust])
total_mv = total_share.mul(price_unadjust) / 100000000
[retain,
total_mv] = Stock().make_same_index_columns([retain, total_mv])
retain2p = 4 * retain.div(total_mv)
# save data
retain2p = retain2p.T.dropna(how='all').T
self.save_alpha_factor_exposure(retain2p, self.raw_factor_name)
