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factor_calculate.py
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factor_calculate.py
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# -*- coding: utf-8 -*-
"""
Created on Wed Jul 4 16:56:01 2018
@author: admin
"""
import os
import warnings
import calendar
import numpy as np
import pandas as pd
import statsmodels.api as sm
import pandas.tseries.offsets as toffsets
from datetime import datetime, time
from functools import reduce
from itertools import takewhile, dropwhile
from collections import Iterable
from WindPy import w
warnings.filterwarnings('ignore')
WORK_PATH = os.path.dirname(os.path.dirname(__file__))
class WindQueryFailError(Exception):
pass
class FileAlreadyExistError(Exception):
pass
class lazyproperty:
def __init__(self, func):
self.func = func
def __get__(self, instance, cls):
if instance is None:
return self
else:
value = self.func(instance)
setattr(instance, self.func.__name__, value)
return value
class Data:
startday = "20060101"
endday = pd.tseries.offsets.datetime.now().strftime("%Y%m%d")
freq = "M"
root = WORK_PATH
metafile = 'all_stocks.xlsx'
mmapfile = 'month_map.xlsx'
month_group_file = 'month_group.xlsx'
tradedays_file = 'tradedays.xlsx'
tdays_be_m_file = 'trade_days_begin_end_of_month.xlsx'
value_indicators = [
'pe_ttm', 'val_pe_deducted_ttm', 'pb_lf', 'ps_ttm',
'pcf_ncf_ttm', 'pcf_ocf_ttm', 'dividendyield2', 'profit_ttm'
]
value_target_indicators = [
"EP", "EPcut", "BP", "SP",
"NCFP", "OCFP", "DP", "G/PE"
]
growth_indicators = [
"qfa_yoysales", "qfa_yoyprofit", "qfa_yoyocf", "qfa_roe_G_m"
]
growth_target_indicators = [
"Sales_G_q", "Profit_G_q", "OCF_G_q", "ROE_G_q"
]
finance_indicators = [
"roe_ttm2_m", "qfa_roe_m",
"roa2_ttm2_m", "qfa_roa_m",
"grossprofitmargin_ttm2_m", "qfa_grossprofitmargin_m",
"deductedprofit_ttm", "qfa_deductedprofit_m", "or_ttm", "qfa_oper_rev_m",
"turnover_ttm_m", "qfa_netprofitmargin_m",
"ocfps_ttm", "eps_ttm", "qfa_net_profit_is_m", "qfa_net_cash_flows_oper_act_m"
]
finance_target_indicators = [
"ROE_q", "ROE_ttm",
"ROA_q", "ROA_ttm",
"grossprofitmargin_q", "grossprofitmargin_ttm",
"profitmargin_q", "profitmargin_ttm",
"assetturnover_q", "assetturnover_ttm",
"operationcashflowratio_q", "operationcashflowratio_ttm"
]
leverage_indicators = [
"assetstoequity_m", "longdebttoequity_m",
"cashtocurrentdebt_m", "current_m"
]
leverage_target_indicators = [
"financial_leverage", "debtequityratio",
"cashratio", "currentratio"
]
cal_indicators = ["mkt_cap_float", "holder_avgpct", "holder_num"]
cal_target_indicators = [
"ln_capital",
"HAlpha", "return_1m", "return_3m", "return_6m", "return_12m",
"wgt_return_1m", "wgt_return_3m", "wgt_return_6m", "wgt_return_12m",
"exp_wgt_return_1m", "exp_wgt_return_3m", "exp_wgt_return_6m", "exp_wgt_return_12m",
"std_1m", "std_3m", "std_6m", "std_12m",
"beta",
"turn_1m", "turn_3m", "turn_6m", "turn_12m",
"bias_turn_1m", "bias_turn_3m", "bias_turn_6m", "bias_turn_12m",
"holder_avgpctchange",
]
tech_indicators = [
"MACD", "RSI", "PSY", "BIAS"
]
tech_target_indicators = [
"MACD", "DEA", "DIF", "RSI", "PSY", "BIAS"
]
barra_quote_indicators = [
"mkt_cap_float", "pct_chg", "amt"
]
barra_quote_target_indicators = [
"LNCAP_barra", "MIDCAP_barra",
"BETA_barra", "HSIGMA_barra", "HALPHA_barra",
"DASTD_barra", "CMRA_barra",
"STOM_barra", "STOQ_barra", "STOA_barra",
"RSTR_barra"
]
barra_finance_indicators = [
"mkt_cap_ard", "longdebttodebt", "other_equity_instruments_PRE",
"tot_equity", "tot_liab", "tot_assets", "pb_lf",
"pe_ttm", "pcf_ocf_ttm", "eps_ttm", "orps"
]
barra_finance_target_indicators = [
"MLEV_barra", "BLEV_barra", "DTOA_barra", "BTOP_barra",
"ETOP_barra", "CETOP_barra", "EGRO_barra", "SGRO_barra"
]
_tech_params = {
"BIAS": [20],
"MACD": [10, 30, 15],
"PSY": [20],
"RSI": [20],
}
freqmap = {}
ind_wsscond = {
#日频
'close': 'tradeDate={date};priceAdj=U;cycle=D', #不复权收盘价/技术
'adjfactor': "tradeDate={date}", #后复权因子/技术
'amt': "tradeDate={date};cycle=D", #成交额/新反转
'dealnum': "tradeDate={date}", #成交笔数/新反转
'turn': "tradeDate={date};cycle=D", #换手率/动量反转；换手率
'trade_status': "tradeDate={date}", #交易状态/basic; 换手率
'maxupordown': "tradeDate={date}", #涨跌停状态/换手率
'pct_chg': "tradeDate={date};cycle=D", #日收益率/动量反转；换手率；beta
'mkt_cap_ard': "unit=1;tradeDate={date}", #总市值2/barra_finance
#月频
'pe_ttm': 'tradeDate={date}', #PE_ttm/估值
'val_pe_deducted_ttm': "tradeDate={date}", #扣非PE_ttm/估值
'pb_lf': "tradeDate={date}", #PB_lf/估值
'ps_ttm': "tradeDate={date}", #PS_ttm/估值
'pcf_ncf_ttm': "tradeDate={date}", #PCF(现金净流量)/估值
'pcf_ocf_ttm': "tradeDate={date}", #PCF(经营净流量)/估值
'dividendyield2': "tradeDate={date}", #股息率(ttm)/估值
'profit_ttm': "unit=1;tradeDate={date}", #净利润(ttm)/估值
'deductedprofit_ttm': "unit=1;tradeDate={date}", #扣非净利润/财务质量
'or_ttm': "unit=1;tradeDate={date}", #营业收入(ttm)/财务质量
'ocfps_ttm': "tradeDate={date}", #每股经营净现金流(ttm)/财务质量
'eps_ttm': "tradeDate={date}", #每股收益(ttm)/财务质量
'industry_citic': "industryType=3;industryStandard=1;tradeDate={date}", #中信一级行业/basic
'industry_citic_level2': "industryType=3;industryStandard=2;tradeDate={date}", #中信二级行业/basic
'mkt_cap_float': "unit=1;tradeDate={date};currencyType=", #流通市值/市值；basic
'sec_name1': "tradeDate={date}", #证券简称/basic
#季频
"qfa_yoysales": "rptDate={date}", #营业收入(单季同比%)/成长
"qfa_yoyprofit": "rptDate={date}", #净利润(单季同比%)/成长
"qfa_yoyocf": "rptDate={date}", #经营现金流(单季同比%)/成长
"qfa_roe": "rptDate={date}", #ROE(单季)/成长；财务质量
'roe_ttm2': "rptDate={date}", #ROE_ttm/财务质量
"qfa_roa": "rptDate={date}", #ROA(单季)/财务质量
'roa2_ttm2': "rptDate={date}", #ROA_ttm/财务质量
"qfa_grossprofitmargin": "rptDate={date}", #毛利率(单季)/财务质量
'grossprofitmargin_ttm2': "rptDate={date}", #毛利率(ttm)/财务质量
"qfa_deductedprofit": "unit=1;rptDate={date}", #扣非净利润(单季)/率财务质量
"qfa_oper_rev": "unit=1;rptDate={date};rptType=1", #营业收入(单季)/财务质量
"qfa_netprofitmargin": "rptDate={date}", #销售净利率(单季)/财务质量
'turnover_ttm': "rptDate={date}", #总资产周转率(ttm)/财务质量
"qfa_net_cash_flows_oper_act": \
"unit=1;rptDate={date};rptType=1", #经营活动净现金流(单季)/财务质量
"qfa_net_profit_is": "unit=1;rptDate={date};rptType=1", #净利润(单季)/财务质量
'assetstoequity': "rptDate={date}", #权益乘数/杠杆
'longdebttoequity': "rptDate={date}", #非流动负债权益比/杠杆
'cashtocurrentdebt': "rptDate={date}", #现金比率/杠杆
'current': "rptDate={date}", #流动比率/杠杆
'holder_avgpct': "rptDate={date};shareType=1", #户均持股比例/股东
'holder_num': "unit=1;rptDate={date}", #持股数量(户)/股东
'stm_issuingdate': "rptDate={date}", #定期报告实际披露日期/meta
"tot_equity": "unit=1;rptDate={date};rptType=1", #所有者权益总计/barra_finance
"tot_liab": "unit=1;rptDate={date};rptType=1", #负责合计/barra_finance
"tot_assets": "unit=1;rptDate={date};rptType=1", #资产总计/barra_finance
"other_equity_instruments_PRE": "unit=1;rptDate={date};rptType=1", #其他权益工具：优先股（合并报表）/barra_finance
"longdebttodebt": "rptDate={date}", #长期负债占比/barra_finance
"orps": "rptDate={date};currencyType=", #每股营业收入/barra_finance
"eps_diluted2": "rptDate={date}", #eps-期末股本摊薄/barra_finance
}
ind_wsdcond = {
#日频
'close': "{date};{date};", #不复权收盘价/技术
'adjfactor': "{date};{date};", #后复权因子/技术
'amt': "{date};{date};", #成交额/新反转
'dealnum': "{date};{date};", #成交笔数/新反转
'turn': "{date};{date};", #换手率/动量反转；换手率
'trade_status': "{date};{date};", #交易状态/basic; 换手率
'maxupordown': "{date};{date};", #涨跌停状态/换手率
'pct_chg': "{date};{date};", #日收益率/动量反转；换手率；beta
'mkt_cap_ard': "{date};{date};unit=1;Days=Alldays", #总市值2/barra_finance
#月频
'pe_ttm': "{date};{date};Days=Alldays", #PE_ttm/估值
'val_pe_deducted_ttm': "{date};{date};Days=Alldays", #扣非PE_ttm/估值
'pb_lf': "{date};{date};Days=Alldays", #PB_lf/估值
'ps_ttm': "{date};{date};Days=Alldays", #PS_ttm/估值
'pcf_ncf_ttm': "{date};{date};Days=Alldays", #PCF(现金净流量)/估值
'pcf_ocf_ttm': "{date};{date};Days=Alldays", #PCF(经营净流量)/估值
'dividendyield2': "{date};{date};Days=Alldays", #股息率(ttm)/估值
'profit_ttm': "{date};{date};unit=1;Days=Alldays", #净利润(ttm)/估值
'deductedprofit_ttm': "{date};{date};unit=1;Days=Alldays", #扣非净利润/财务质量
'or_ttm': "{date};{date};unit=1;Days=Alldays", #营业收入(ttm)/财务质量
'ocfps_ttm': "{date};{date};Days=Alldays", #每股经营净现金流(ttm)/财务质量
'eps_ttm': "{date};{date};Days=Alldays", #每股收益(ttm)/财务质量
'industry_citic': "{date};{date};industryType=3;industryStandard=1", #中信一级行业/basic
'industry_citic_level2': "{date};{date};industryType=3;industryStandard=2", #中信二级行业/basic
'mkt_cap_float': "{date};{date};unit=1;currencyType=;Days=Alldays", #流通市值/市值；basic
'sec_name1': "{date};{date};Days=Alldays", #证券简称/basic
#季频
"qfa_yoysales": "{date};{date};Days=Alldays", #营业收入(单季同比%)/成长
"qfa_yoyprofit": "{date};{date};Days=Alldays", #净利润(单季同比%)/成长
"qfa_yoyocf": "{date};{date};Days=Alldays", #经营现金流(单季同比%)/成长
"qfa_roe": "{date};{date};Days=Alldays", #ROE(单季)/成长；财务质量
'roe_ttm2': "{date};{date};Days=Alldays", #ROE_ttm/财务质量
"qfa_roa": "{date};{date};Days=Alldays", #ROA(单季)/财务质量
'roa2_ttm2': "{date};{date};Days=Alldays", #ROA_ttm/财务质量
"qfa_grossprofitmargin": "{date};{date};Days=Alldays", #毛利率(单季)/财务质量
'grossprofitmargin_ttm2': "{date};{date};Days=Alldays", #毛利率(ttm)/财务质量
"qfa_deductedprofit": "{date};{date};unit=1;Days=Alldays", #扣非净利润(单季)/率财务质量
"qfa_oper_rev": "{date};{date};unit=1;rptType=1;Days=Alldays", #营业收入(单季)/财务质量
"qfa_netprofitmargin": "{date};{date};Days=Alldays", #销售净利率(单季)/财务质量
'turnover_ttm': "{date};{date};Days=Alldays", #总资产周转率(ttm)/财务质量
"qfa_net_cash_flows_oper_act": \
"{date};{date};unit=1;rptType=1;Days=Alldays", #经营活动净现金流(单季)/财务质量
"qfa_net_profit_is": \
"{date};{date};unit=1;rptType=1;Days=Alldays", #净利润(单季)/财务质量
'assetstoequity': "{date};{date};Days=Alldays", #权益乘数/杠杆
'longdebttoequity': "{date};{date};Days=Alldays", #非流动负债权益比/杠杆
'cashtocurrentdebt': "{date};{date};Days=Alldays", #现金比率/杠杆
'current': "{date};{date};Days=Alldays", #流动比率/杠杆
'holder_avgpct': "{date};{date};shareType=1;Days=Alldays", #户均持股比例/股东
'holder_num': "{date};{date};unit=1;Days=Alldays", #持股数量(户)/股东
'stm_issuingdate': "{date};{date};Days=Alldays", #定期报告实际披露日期/meta
"tot_equity": "{date};{date};unit=1;rptType=1;Days=Alldays", #所有者权益合计
"tot_liab": "{date};{date};unit=1;rptType=1;Days=Alldays", #负责合计/barra_finance
"tot_assets": "{date};{date};unit=1;rptType=1;Days=Alldays", #资产总计/barra_finance
"other_equity_instruments_PRE": "{date};{date};unit=1;rptType=1;Days=Alldays", #其他权益工具：优先股（合并报表）/barra_finance
"longdebttodebt": "{date};{date};Days=Alldays", #长期负债占比/barra_finance
"orps": "{date};{date};currencyType=;Days=Alldays", #每股营业收入/barra_finance
"eps_diluted2": "{date};{date};Days=Alldays", #eps-期末股本摊薄/barra_finance
}
def __init__(self):
self.dpath = os.path.join(self.root, "daily_data")
self.mpath = os.path.join(self.root, "monthly_data")
self.qpath = os.path.join(self.root, "quarterly_data")
self.save_path = os.path.join(self.root, "factor_data")
self.__update_frepmap()
def __update_frepmap(self):
self.freqmap.update({name.split(".")[0]: self.dpath for name in os.listdir(self.dpath)})
self.freqmap.update({name.split(".")[0]: self.mpath for name in os.listdir(self.mpath)})
self.freqmap.update({name.split(".")[0]: self.qpath for name in os.listdir(self.qpath)})
def open_file(self, name):
if name == 'meta':
return pd.read_excel(os.path.join(self.root, 'src', self.metafile), index_col=[0],
parse_dates=['ipo_date', "delist_date"], encoding='gbk')
elif name == 'month_map':
return pd.read_excel(os.path.join(self.root, 'src', self.mmapfile), index_col=[0],
parse_dates=[0, 1], encoding='gbk')['calendar_date']
elif name == 'trade_days_begin_end_of_month':
return pd.read_excel(os.path.join(self.root, 'src', self.tdays_be_m_file), index_col=[1],
parse_dates=[0, 1], encoding='gbk')
elif name == 'month_group':
return pd.read_excel(os.path.join(self.root, 'src', self.month_group_file), index_col=[0],
parse_dates=True, encoding='gbk')
elif name == 'tradedays':
return pd.read_excel(os.path.join(self.root, 'src', self.tradedays_file), index_col=[0],
parse_dates=True, encoding='gbk').index.tolist()
path = self.freqmap.get(name, None)
if path is None:
raise Exception(f'{name} is unrecognisable or not in file dir, please check and retry.')
try:
dat = pd.read_csv(os.path.join(path, name+'.csv'),
index_col=[0], engine='python', encoding='gbk')
except TypeError:
print(name, path)
raise
dat.columns = pd.to_datetime(dat.columns)
if name in ('stm_issuingdate', 'applied_rpt_date_M'):
dat = dat.replace('0', np.nan)
dat = dat.applymap(pd.to_datetime)
return dat
def close_file(self, df, name, **kwargs):
if name == 'meta':
df.to_excel(os.path.join(self.root, 'src', self.metafile),
encoding='gbk', **kwargs)
elif name == 'month_map':
df.to_excel(os.path.join(self.root, 'src', self.mmapfile),
encoding='gbk', **kwargs)
elif name == 'trade_days_begin_end_of_month':
df.to_excel(os.path.join(self.root, 'src', self.tdays_be_m_file),
encoding='gbk', **kwargs)
elif name == 'tradedays':
df.to_excel(os.path.join(self.root, 'src', self.tradedays_file),
encoding='gbk', **kwargs)
else:
path = self.freqmap.get(name, None)
if path is None:
if 'lyr' in name or '_m' in name:
path = self.mpath
elif '_d' in name:
path = self.dpath
elif 'qfa' in name:
path = self.qpath
else:
path = self.root
if name in ['stm_issuingdate', 'applied_rpt_date_M']:
df = df.replace(0, pd.NaT)
df.to_csv(os.path.join(path, name+'.csv'), encoding='gbk', **kwargs)
self.__update_frepmap()
self.__update_attr(name)
# @staticmethod
# def _fill_nan(series, value=0, ffill=False):
# if ffill:
# series = series.fillna(method='ffill')
# else:
# if value:
# start_valid_idx = np.where(pd.notna(series))[0][0]
# series.loc[start_valid_idx:] = series.loc[start_valid_idx:].fillna(0)
# return series
def __update_attr(self, name):
if name in self.__dict__:
del self.__dict__[name]
self.__dict__[name] = getattr(self, name, None)
def __getattr__(self, name):
if name not in self.__dict__:
self.__dict__[name] = self.open_file(name)
return self.__dict__[name]
class FactorProcess:
def __init__(self, updatefreq, sentinel=1000, update_only=False):
self.data = Data()
self.sentinel = sentinel
if not update_only:
self._turnover_preprocessed = self.__preprocess_turn_data()
self.dates_d = sorted(self.adjfactor.columns)
self.dates_m = sorted(self.pct_chg_M.columns)
if updatefreq not in ('M','w'):
raise TypeError(f'Unsupported update frequency {updatefreq}.')
self.updatefreq = updatefreq
def __getattr__(self, name):
return getattr(self.data, name, None)
def __preprocess_turn_data(self):
turnover = self.turn.copy()
labeled = turnover.replace(np.nan, self.sentinel)
row_index, col_index = turnover.index, turnover.columns
status = self.trade_status.loc[row_index, col_index]
tolimit = self.maxupordown.loc[row_index, col_index]
liststatus = self.listday_matrix.loc[row_index, col_index]
cond = (status==1) & (tolimit==0)
turnover = turnover.where(cond) #将停牌和涨跌停日的数值换为nan
cond2 = (liststatus==1) & pd.isnull(turnover)
turnover = turnover.where(~cond2, 0) #将上市但停牌或涨跌停的日期值设为0
turnover = labeled.where(lambda s: s==self.sentinel, turnover)
return turnover
def __update_tradedays(self):
startday = self.tradedays[-1] + toffsets.DateOffset(1)
endday = datetime.combine(toffsets.datetime.now(), time.min)
startday, endday = str(startday)[:10], str(endday)[:10]
res = w.tdays(startday, endday, "")
if res.ErrorCode != 0:
raise WindQueryFailError("Get tradedays list from WindPy failed, errorcode={}.".format(res.ErrorCode))
try:
new_tdays = res.Data[0]
except IndexError:
new_tdays = []
self.tradedays.extend(new_tdays)
tdays_series = pd.Series(index=self.tradedays)
tdays_series.index.name = 'tradedays'
self.close_file(tdays_series, 'tradedays')
def _get_trade_days(self, startday, endday, freq=None):
if freq is None:
freq = self.freq
startday, endday = pd.to_datetime((startday, endday))
try:
self.__update_tradedays()
except WindQueryFailError:
print("Update tradedays list from wind failed...trying continue")
pass
if freq == 'd':
try:
start_idx = self._get_date_idx(startday, self.tradedays)
except IndexError:
return []
else:
try:
end_idx = self._get_date_idx(endday, self.tradedays)
except IndexError:
return self.tradedays[start_idx:]
else:
return self.tradedays[start_idx:end_idx+1]
else:
new_cdays_curfreq = pd.Series(index=self.tradedays).resample(freq).asfreq().index
c_to_t_dict = {cday:tday for tday, cday in self.month_map.to_dict().items()}
try:
new_tdays_curfreq = [c_to_t_dict[cday] for cday in new_cdays_curfreq]
except KeyError:
new_tdays_curfreq = [c_to_t_dict[cday] for cday in new_cdays_curfreq[:-1]]
start_idx = self._get_date_idx(c_to_t_dict.get(startday, startday), new_tdays_curfreq) + 1
try:
end_idx = self._get_date_idx(c_to_t_dict.get(endday, endday), new_tdays_curfreq)
except IndexError:
end_idx = len(new_tdays_curfreq) - 1
return new_tdays_curfreq[start_idx:end_idx+1]
@lazyproperty
def trade_days(self):
self.__trade_days = self._get_trade_days(self.startday, self.endday)
return self.__trade_days
def generate_factor_file(self, datepath):
try:
date = pd.to_datetime(datepath.split(".")[0])
except ValueError:
raise ValueError("Must pass in a string path in time format [YYYY-mm-dd]")
fpath = os.path.join(self.savepath, datepath)
self.create_factor_file(date, fpath)
def open_file(self, path):
if path.endswith('.csv'):
return pd.read_csv(path, encoding='gbk', index_col=[1], engine='python')
else:
raise TypeError("Unsupportted type {}, only support csv currently.".format(path.split('.')[-1]))
def save_file(self, datdf, path):
global save_codes
datdf = datdf.loc[~pd.isnull(datdf['is_open1']), :]
for col in ['name', 'industry_sw']:
datdf[col] = datdf[col].apply(str)
datdf = datdf.loc[~datdf['name'].str.contains('0')]
save_cond1 = (~datdf['name'].str.contains('ST')) #剔除ST股票
save_cond2 = (~pd.isnull(datdf['industry_sw'])) & \
(~datdf['industry_sw'].str.contains('0')) #剔除行业值为0或为空的股票
save_cond3 = (~pd.isnull(datdf['MKT_CAP_FLOAT'])) #剔除市值为空的股票
save_cond = save_cond1 & save_cond2 & save_cond3
datdf = datdf.loc[save_cond]
datdf = datdf.reset_index()
datdf.index = range(1, len(datdf)+1)
datdf.index.name = 'No'
datdf = datdf.rename(columns={"index":"code"})
if path.endswith('.csv'):
return datdf.to_csv(path, encoding='gbk')
else:
raise TypeError("Unsupportted type {}, only support csv currently.".format(path.split('.')[-1]))
@staticmethod
def concat_df(left, right, *, how="outer", left_index=True,
right_index=True, **kwargs):
return pd.merge(left, right, how=how, left_index=left_index,
right_index=right_index, **kwargs)
def create_factor_file(self, date, savepath):
if os.path.exists(savepath):
raise FileAlreadyExistError(f"{date}'s data already exist, please try calling update method.")
stklist, dat0 = self.get_basic_data(date)
dat1 = self.get_factor_data(date, stklist)
res = self.concat_df(dat0, dat1)
self.save_file(res, savepath)
def _ind_map(self, inds_lv1, inds_lv2):
inds_lv1 = inds_lv1.values
inds_lv2 = inds_lv2.values
return np.where(inds_lv1 == '非银行金融', inds_lv2, inds_lv1)
def _get_stock_list(self, tdate):
df = self.meta[self.meta['ipo_date'] <= tdate]
cond = (pd.isnull(df['delist_date'])) | (df['delist_date'] >= tdate)
df = df[cond]
return df.index.tolist()
def get_basic_data(self, tdate):
df0 = self.meta[self.meta['ipo_date'] <= tdate]
cond = (pd.isnull(df0['delist_date'])) | (df0['delist_date'] >= tdate)
df0 = df0[cond]
df0 = df0.rename(columns={'sec_name':'name'})
del df0['delist_date']
stocklist = df0.index.tolist()
if self.updatefreq == 'w':
df0['name'] = self.sec_name1_d.loc[stocklist, tdate]
cur_citic_level1 = self.industry_citic_d.loc[stocklist, tdate]
cur_citic_level2 = self.industry_citic_level2_d.loc[stocklist, tdate]
cur_citic_level = self._ind_map(cur_citic_level1, cur_citic_level2)
df0["industry_sw"] = cur_citic_level
df0['MKT_CAP_FLOAT'] = self.mkt_cap_float_d.loc[stocklist, tdate] #最新日频
else:
caldate = self.month_map[tdate]
df0['name'] = self.sec_name1.loc[stocklist, caldate] #
cur_citic_level1 = self.industry_citic.loc[stocklist, caldate]
cur_citic_level2 = self.industry_citic_level2.loc[stocklist, caldate]
cur_citic_level = self._ind_map(cur_citic_level1, cur_citic_level2)
df0["industry_sw"] = cur_citic_level1
df0['MKT_CAP_FLOAT'] = self.mkt_cap_float.loc[stocklist, caldate] #最新日频
if self.updatefreq == 'M':
try:
tdate = self._get_next_month_first_trade_date(tdate)
except IndexError:
df0["is_open1"] = None
df0["PCT_CHG_NM"] = None
return stocklist, df0
df0["is_open1"] = self.trade_status.loc[stocklist, tdate].map({1:"TRUE", 0:"FALSE"})
df0["PCT_CHG_NM"] = self.get_next_pctchg(stocklist, tdate) if self.updatefreq == 'M' else np.nan
return stocklist, df0
def _get_next_month_first_trade_date(self, date):
date = pd.to_datetime(date)
tdates = self.trade_status.columns.tolist()
def _if_same_month(x):
nonlocal date
if date.month != 12:
return (x.year != date.year) or (x.month - 1 != date.month)
else:
return (x.year - 1 != date.year) or (x.month != 1)
daterange = dropwhile(_if_same_month, tdates)
return list(daterange)[0]
def get_next_pctchg(self, stocklist, tdate):
try:
nextdate = tdate + toffsets.MonthEnd(1)
dat = self.pct_chg_M.loc[stocklist, nextdate]
except Exception as e:
print("Get next month data failed. msg: {}".format(e))
dat = [np.nan] * len(stocklist)
return dat
def get_last_month_end(self, date):
if date.month == 1:
lstyear = date.year - 1
lstmonth = 12
else:
lstyear = date.year
lstmonth = date.month - 1
return datetime(lstyear, lstmonth, 1) + toffsets.MonthEnd(n=1)
def get_factor_data(self, tdate, stocklist=None):
if stocklist is None:
stocklist = self._get_stock_list(tdate)
if self.updatefreq == 'M':
caldate = self.month_map[tdate]
else:
caldate = tdate
dat1 = self._get_value_data(stocklist, caldate)
lstcaldate_cm = caldate - toffsets.timedelta(days=1) + toffsets.MonthEnd(n=1)
if self.updatefreq == 'w' and caldate != lstcaldate_cm:
caldate = self.get_last_month_end(caldate)
dat2 = self._get_growth_data(stocklist, caldate)
dat3 = self._get_finance_data(stocklist, caldate)
dat4 = self._get_leverage_data(stocklist, caldate)
dat5 = self._get_cal_data(stocklist, tdate)
dat6 = self._get_tech_data(stocklist, tdate)
res = reduce(self.concat_df, [dat1, dat2, dat3, dat4, dat5, dat6])
if self.updatefreq == 'M':
dat7 = self._get_barra_quote_data(stocklist, tdate)
dat8 = self._get_barra_finance_data(stocklist, tdate)
res = reduce(self.concat_df, [res, dat7, dat8])
return res
def _get_value_data(self, stocks, caldate):
"""
Default value indicators getted from windpy:
'pe_ttm', 'val_pe_deducted_ttm', 'pb_lf', 'ps_ttm',
'pcf_ncf_ttm', 'pcf_ocf_ttm', 'dividendyield2', 'profit_ttm'
Default target value indicators:
'EP', 'EPcut', 'BP', 'SP',
'NCFP', 'OCFP', 'DP', 'G/PE'
"""
date = pd.to_datetime(caldate)
dat = pd.DataFrame(index=stocks)
if self.updatefreq == 'w':
dat['EP'] = 1 / self.pe_ttm_d.loc[stocks, date]
dat['EPcut'] = 1 / self.val_pe_deducted_ttm_d.loc[stocks, date]
dat['BP'] = 1 / self.pb_lf_d.loc[stocks, date]
dat['SP'] = 1 / self.ps_ttm_d.loc[stocks, date]
dat['NCFP'] = 1 / self.pcf_ncf_ttm_d.loc[stocks, date]
dat['OCFP'] = 1 / self.pcf_ocf_ttm_d.loc[stocks, date]
dat['DP'] = self.dividendyield2_d.loc[stocks, date]
dat['G/PE'] = self.profit_ttm_G_d.loc[stocks, date] * dat['EP']
else:
dat['EP'] = 1 / self.pe_ttm.loc[stocks, date]
dat['EPcut'] = 1 / self.val_pe_deducted_ttm.loc[stocks, date]
dat['BP'] = 1 / self.pb_lf.loc[stocks, date]
dat['SP'] = 1 / self.ps_ttm.loc[stocks, date]
dat['NCFP'] = 1 / self.pcf_ncf_ttm.loc[stocks, date]
dat['OCFP'] = 1 / self.pcf_ocf_ttm.loc[stocks, date]
dat['DP'] = self.dividendyield2.loc[stocks, date]
dat['G/PE'] = self.profit_ttm_G.loc[stocks, date] * dat['EP']
dat = dat[self.value_target_indicators]
return dat
def _get_growth_data(self, stocks, caldate):
"""
Default growth indicators getted from windpy:
"qfa_yoysales", "qfa_yoyprofit", "qfa_yoyocf", "qfa_roe"
Default target growth indicators:
"Sales_G_q","Profit_G_q", "OCF_G_q", "ROE_G_q",
"""
date = pd.to_datetime(caldate)
dat = pd.DataFrame(index=stocks)
dat["Sales_G_q"] = self.qfa_yoysales_m.loc[stocks, date]
dat["Profit_G_q"] = self.qfa_yoyprofit_m.loc[stocks, date]
dat["OCF_G_q"] = self.qfa_yoyocf_m.loc[stocks, date]
dat['ROE_G_q'] = self.qfa_roe_G_m.loc[stocks, date]
dat = dat[self.growth_target_indicators]
return dat
def _get_finance_data(self, stocks, caldate):
"""
Default finance indicators getted from windpy:
"roe_ttm2_m", "qfa_roe_m",
"roa2_ttm2_m", "qfa_roa_m",
"grossprofitmargin_ttm2_m", "qfa_grossprofitmargin_m",
"deductedprofit_ttm", "qfa_deductedprofit_m", "or_ttm", "qfa_oper_rev_m",
"turnover_ttm_m", "qfa_netprofitmargin_m",
"ocfps_ttm", "eps_ttm", "qfa_net_profit_is_m", "qfa_net_cash_flows_oper_act_m"
Default target finance indicators:
"ROE_q", "ROE_ttm",
"ROA_q", "ROA_ttm",
"grossprofitmargin_q", "grossprofitmargin_ttm",
"profitmargin_q", "profitmargin_ttm",
"assetturnover_q", "assetturnover_ttm",
"operationcashflowratio_q", "operationcashflowratio_ttm"
"""
date = pd.to_datetime(caldate)
dat = pd.DataFrame(index=stocks)
dat["ROE_q"] = self.qfa_roe_m.loc[stocks, date]
dat["ROE_ttm"] = self.roe_ttm2_m.loc[stocks, date]
dat["ROA_q"] = self.qfa_roa_m.loc[stocks, date]
dat["ROA_ttm"] = self.roa2_ttm2_m.loc[stocks, date]
dat["grossprofitmargin_q"] = self.qfa_grossprofitmargin_m.loc[stocks, date]
dat["grossprofitmargin_ttm"] = self.grossprofitmargin_ttm2_m.loc[stocks, date]
dat["profitmargin_q"] = self.qfa_deductedprofit_m.loc[stocks, date] / \
self.qfa_oper_rev_m.loc[stocks, date]
dat["profitmargin_ttm"] = self.deductedprofit_ttm.loc[stocks, date] / \
self.or_ttm.loc[stocks, date]
dat["assetturnover_q"] = self.qfa_roa_m.loc[stocks, date] / \
self.qfa_netprofitmargin_m.loc[stocks, date]
dat['assetturnover_ttm'] = self.turnover_ttm_m.loc[stocks, date]
dat["operationcashflowratio_q"] = self.qfa_net_cash_flows_oper_act_m.loc[stocks, date] / \
self.qfa_net_profit_is_m.loc[stocks, date]
dat["operationcashflowratio_ttm"] = self.ocfps_ttm.loc[stocks, date] / \
self.eps_ttm.loc[stocks, date]
dat = dat[self.finance_target_indicators]
return dat
def _get_leverage_data(self, stocks, caldate):
"""
Default leverage indicators getted from windpy:
"assetstoequity_m", "longdebttoequity_m", "cashtocurrentdebt_m", "current_m"
Default target leverage indicators:
"financial_leverage", "debtequityratio", "cashratio", "currentratio"
"""
date = pd.to_datetime(caldate)
dat = pd.DataFrame(index=stocks)
dat["financial_leverage"] = self.assetstoequity_m.loc[stocks, date]
dat["debtequityratio"] = self.longdebttoequity_m.loc[stocks, date]
dat["cashratio"] = self.cashtocurrentdebt_m.loc[stocks, date]
dat["currentratio"] = self.current_m.loc[stocks, date]
dat = dat[self.leverage_target_indicators]
return dat
def _get_cal_data(self, stocks, tdate):
"""
Default calculated indicators getted from windpy:
"mkt_cap_float", "holder_avgpct", "holder_num"
Default target growth indicators:
"ln_capital",
"HAlpha",
"return_1m", "return_3m", "return_6m", "return_12m",
"wgt_return_1m", "wgt_return_3m", "wgt_return_6m", "wgt_return_12m",
"exp_wgt_return_1m", "exp_wgt_return_3m", "exp_wgt_return_6m", "exp_wgt_return_12m",
"std_1m", "std_3m", "std_6m", "std_12m",
"beta",
"turn_1m", "turn_3m", "turn_6m", "turn_12m",
"bias_turn_1m", "bias_turn_3m", "bias_turn_6m", "bias_turn_12m",
"holder_avgpctchange"
"""
tdate = pd.to_datetime(tdate)
dat = pd.DataFrame(index=stocks)
if self.updatefreq == 'w':
dat['ln_capital'] = np.log(self.mkt_cap_float_d.loc[stocks, tdate])
caldate = self.get_last_month_end(tdate)
else:
caldate = self.month_map[tdate]
dat['ln_capital'] = np.log(self.mkt_cap_float.loc[stocks, caldate])
dat['holder_avgpctchange'] = self.holder_avgpctchg.loc[stocks, caldate]
dat1 = self._get_mom_vol_data(stocks, tdate, self.dates_d, params=[1,3,6,12])
dat2 = self._get_turnover_data(stocks, tdate, self.dates_d, params=[1,3,6,12])
dat3 = self._get_regress_data(stocks, tdate, self.dates_m, params=["000001.SH", 60])
dat = reduce(self.concat_df, [dat, dat1, dat2, dat3])
dat = dat[self.cal_target_indicators]
return dat
def _get_tech_data(self, stocks, tdate):
"""
Default source data loaded from local file:
"close(freq=d)"
Default target technique indicators:
"MACD", "DEA", "DIF", "RSI", "PSY", "BIAS"
"""
dat = pd.DataFrame(index=stocks)
for tname in self.tech_indicators:
calfunc = getattr(self, 'cal_'+tname, None)
if calfunc is None:
msg = "Please define property:'{}' first.".format("cal_"+tname)
raise NotImplementedError(msg)
else:
if tname == "MACD":
dat["DIF"], dat["DEA"], dat["MACD"] = calfunc(stocks, tdate, self._tech_params[tname])
else:
dat[tname] = calfunc(stocks, tdate, self._tech_params[tname])
return dat
def _get_mom_vol_data(self, stocks, qdate, dates, params=(1,3,6,12)):
pct_chg = self.pct_chg
turnover = self.turn
if self.updatefreq == 'M':
caldate = self.month_map[qdate]
else:
caldate = qdate
res = pd.DataFrame(index=stocks)
for offset in params:
period_d = self._get_period_d(qdate, offset=-offset, freq="M", datelist=dates)
cur_pct_chg_d = pct_chg.loc[stocks, period_d]
if self.updatefreq == 'M':
cur_pct_chg_m = getattr(self, f"pctchg_{offset}M", None)
else:
cur_pct_chg_m = getattr(self, f"pctchg_{offset}M_d", None)
cur_turnover = turnover.loc[cur_pct_chg_d.index, period_d]
days_wgt = cur_pct_chg_d.expanding(axis=1).\
apply(lambda df: np.exp(-(len(period_d) - len(df))/4/offset))
wgt_pct_chg = cur_pct_chg_d * cur_turnover
exp_wgt_pct_chg = wgt_pct_chg * days_wgt
res[f"return_{offset}m"] = cur_pct_chg_m.loc[stocks, caldate]
res[f"wgt_return_{offset}m"] = wgt_pct_chg.apply(self._cal_func, axis=1, args=(np.nanmean,))
res[f"exp_wgt_return_{offset}m"] = exp_wgt_pct_chg.apply(self._cal_func, axis=1, args=(np.nanmean,))
res[f"std_{offset}m"] = cur_pct_chg_d.apply(self._cal_func, axis=1, args=(np.nanstd,))
return res
def _get_turnover_data(self, stocks, qdate, dates, params=(1,3,6,12)):
base_period_d = self._get_period_d(qdate, offset=-2, freq="y", datelist=dates)
cur_turnover_base = self._turnover_preprocessed.loc[stocks, base_period_d]
turnover_davg_base = cur_turnover_base.apply(self._cal_func, axis=1, args=(np.nanmean, self.sentinel))
res = pd.DataFrame(index=stocks)
for offset in params:
period_d = self._get_period_d(qdate, offset=-offset, freq="M", datelist=dates)
cur_turnover = self._turnover_preprocessed.loc[stocks, period_d]
turnover_davg = cur_turnover.apply(self._cal_func, axis=1, args=(np.nanmean, self.sentinel))
res[f"turn_{offset}m"] = turnover_davg
res[f"bias_turn_{offset}m"] = turnover_davg / turnover_davg_base - 1
return res
@staticmethod
def _cal_func(df, func=np.nanmean, sentinel=1000):
val = list(takewhile(lambda x: x < sentinel or pd.isnull(x), df.values))
if len(val) == len(df):
return func(val, axis=0)
else:
return np.nan
def _get_regress_data(self, stocks, qdate, dates, params=("000001.SH", 60)):
"""
return value contains:
HAlpha --intercept
beta --slope
"""
index_code, period = params
if self.updatefreq == 'w':
qdate = self.get_last_month_end(qdate)
col_index = self._get_period(qdate, offset=-period, freq="M", datelist=dates,
resample=False)
pct_chg_idx = self.pct_chg_M.loc[index_code, col_index]
pct_chg_m = self.pct_chg_M.loc[stocks, col_index].dropna(how='any', axis=0).T
x, y = pct_chg_idx.values.reshape(-1,1), pct_chg_m.values
valid_stocks = pct_chg_m.columns.tolist()
try:
beta, Halpha = self.regress(x, y)
except ValueError as e:
print(e)
# raise
beta, Halpha = np.empty((len(valid_stocks),1)), np.empty((1, len(valid_stocks)))
beta = pd.DataFrame(beta, index=valid_stocks, columns=['beta'])
Halpha = pd.DataFrame(Halpha.T, index=valid_stocks, columns=['HAlpha'])
res = self.concat_df(beta, Halpha)
return res
def _get_barra_quote_data(self, stocks, tdate):
"""
Default source data loaded from local file:
"mkt_cap_float", "pct_chg", "amt"
Default target barra_quote indicators:
"LNCAP_barra", "MIDCAP_barra",
"BETA_barra", "HSIGMA_barra", "HALPHA_barra",
"DASTD_barra", "CMRA_barra",
"STOM_barra", "STOQ_barra", "STOA_barra",
"RSTR_barra"
"""
tdate = pd.to_datetime(tdate)
caldate = self.month_map[tdate]
dat = pd.DataFrame(index=stocks)
dat1 = self._get_size_barra(stocks, caldate, self.dates_d, params=[True,True,True])
dat2 = self._get_regress_barra(stocks, tdate, self.dates_d, params=[4,504,252,True,'000300.SH'])
dat3 = self._get_dastd_barra(stocks, tdate, self.dates_d, params=[252,42])
dat4 = self._get_cmra_barra(stocks, tdate, self.dates_d, params=[12, 21])
dat5 = self._get_liquidity_barra(stocks, tdate, params=[21,1,3,12])
dat6 = self._get_rstr_barra(stocks, tdate, self.dates_d, params=[252,126,11,'000300.SH'])
dat = reduce(self.concat_df, [dat, dat1, dat2, dat3, dat4, dat5, dat6])
dat = dat[self.barra_quote_target_indicators]
return dat
def _get_size_barra(self, stocks, caldate, dates, params=(True,True,True)):
intercept, standardize, wls = params
res = pd.DataFrame(index=stocks)
lncap = self.mkt_cap_float.loc[stocks, caldate].apply(np.log)
lncap_3 = lncap ** 3
if wls:
w = lncap.apply(np.sqrt)
x_y_w = pd.concat([lncap, lncap_3, w], axis=1).dropna(how='any', axis=0)
x, y, w = x_y_w.iloc[:,0], x_y_w.iloc[:,1], x_y_w.iloc[:,-1]
x, y, w = x.values, y.values, w.values
else:
w = 1
x_and_y = pd.concat([lncap, lncap_3], axis=1).dropna(how='any', axis=0)
x, y = x_and_y.iloc[:,0], x_and_y.iloc[:,-1]
x, y = x.values, y.values
intercept, coef = self.regress(x, y, intercept, w)
resid = lncap_3 - (coef * lncap + intercept)
if standardize:
resid = self.standardize(self.winsorize(resid))
res['MIDCAP_barra'] = resid
res['LNCAP_barra'] = lncap
return res
def _get_regress_barra(self, stocks, tdate, dates_d, params=(4,504,252,True,'000300.SH')):
shift, window, half_life, if_intercept, index_code = params
res = pd.DataFrame(index=stocks)
w = self.get_exponential_weights(window, half_life)
idx = self._get_date_idx(tdate, dates_d)
date_period = dates_d[idx-window+1-shift:idx+1]
pct_chgs = self.pct_chg.T.loc[date_period,:]
for i in range(1,shift+1):
pct_chg = pct_chgs.iloc[i:i+window,:]
ys = pct_chg.loc[:, stocks].dropna(how='any', axis=1)
x = pct_chg.loc[:, index_code]
X, Ys = x.values, ys.values
try:
intercept, coef = self.regress(X, Ys, if_intercept, w)
except:
print(X)
print(Ys)
raise
alpha = pd.Series(intercept, index=ys.columns)
beta = pd.Series(coef[0], index=ys.columns)
alpha.name = f'alpha_{i}'; beta.name = f'beta_{i}'
res = pd.concat([res, alpha, beta], axis=1)
if i == shift:
resid = Ys - (intercept + X.reshape(-1,1) @ coef)
sigma = pd.Series(np.std(resid, axis=0), index=ys.columns)
sigma.name = 'HSIGMA_barra'
res = pd.concat([res, sigma], axis=1)
res['HALPHA_barra'] = np.sum((res[f'alpha_{i}'] for i in range(1,shift+1)), axis=0)
res['BETA_barra'] = np.sum((res[f'beta_{i}'] for i in range(1,shift+1)), axis=0)
res = res[['BETA_barra', 'HALPHA_barra', 'HSIGMA_barra']]
return res
def _get_dastd_barra(self, stocks, tdate, dates_d, params=(252,42)):
window, half_life = params
res = pd.DataFrame(index=stocks)
w = self.get_exponential_weights(window, half_life)
pct_chg = self._get_daily_data("pct_chg", stocks, tdate, window, dates_d)
pct_chg = pct_chg.dropna(how='any', axis=1)
res['DASTD_barra'] = pct_chg.apply(self._std_dev, args=(w,))
return res
@staticmethod
def _std_dev(series, weight=1):
mean = np.mean(series)
std_dev = np.sqrt(np.sum((series - mean)**2 * weight))
return std_dev
def _get_cmra_barra(self, stocks, tdate, dates_d, params=(12,21)):
months, days_pm = params
window = months * days_pm
res = pd.DataFrame(index=stocks)
pct_chg = self._get_daily_data("pct_chg", stocks, tdate, window, dates_d)
pct_chg = pct_chg.dropna(how='any', axis=1)
res['CMRA_barra'] = np.log(1 + pct_chg).apply(self._cal_cmra, args=(months, days_pm))
return res
@staticmethod
def _cal_cmra(series, months=12, days_per_month=21):
z = sorted(series[-i * days_per_month:].sum() for i in range(1, months+1))
return z[-1] - z[0]
def _get_liquidity_barra(self, stocks, tdate, params=(21,1,3,12)):
days_pm, freq1, freq2, freq3 = params
window = freq3 * days_pm
stocks = self._get_stock_list(tdate)
res = pd.DataFrame(index=stocks)
amt = self._get_daily_data('amt', stocks, tdate, window)
mkt_cap_float = self._get_daily_data('mkt_cap_float_d', stocks, tdate,
window)
share_turnover = amt / mkt_cap_float