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CalBarraQuality.py
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"""
Created on Wed Nov 13 2019
@author: RosemaryHe
"""
import pandas as pd
import numpy as np
import math
import sqlalchemy
import urllib
from sklearn import datasets, linear_model
def linear_regression_coef(X,y):
reg = linear_model.LinearRegression()
reg.fit(X,y)
return reg.coef_
DB_CONN = 0 #1-有数据库连接，从数据库取数据；0-无连接，从csv文件取数据
inFilename = 'A.h5'
outFilename = 'BarraQuality.h5'
def CalBarraQuality(dates):
Quality = ['MLEV','BLEV','DTOA','VSAL','VERN','VFLO','SPIBS','CETOE','ACBS','ACCF','ATO','GP','GM','ROA','AGRO','IGRO','CXGRO']
statemap = {'MLEV':['total_long_liab'],'BLEV':['total_asset','total_liab'],'DTOA':['total_long_liab','total_equity'],
'VSAL':['operate_in_total'],'VERN':['net_profit0','operate_in_total'],'VFLO':['operate_net_cash','operate_in_total'],
'SPIBS':['未来12个月每股收益预测值标准差'], ##暂无数据
'CETOE':['depreciation_amortization'], ##暂无数据
'ACBS':['total_cur_asset','cash','total_cur_liab','short_borrow','total_asset'], ##'depreciation_amortization'暂无数据
'ACCF':['inventory','total_cur_liab','total_asset'], ##'account_rec','depreciation_amortization'暂无数据
'ATO':['operate_profit','operate_expense','total_asset'], #operate_income = operate_profit + operate_exp
'GP':['operate_profit','total_asset'],
'GM':['operate_profit','operate_expense'],'ROA':['ROA'],
'AGRO':['total_asset'],'IGRO':['total_equity'],
'CXGRO':['capital_expenditure']} ##capital expenditure = 购置各种长期资产支出 - 无息长期负债 暂无数据
if DB_CONN == 1:
#函数以数据库连接
conn_params = urllib.parse.quote_plus("""DRIVER={SQL Server Native Client 10.0};
SERVER=quant;DATABASE=tbas;UID=*****;PWD=********""")
conn = sqlalchemy.create_engine("mssql+pyodbc:///?odbc_connect=%s" % conn_params)
conn_params=urllib.parse.quote_plus("""DRIVER={SQL Server Native Client 10.0};
SERVER=10.130.14.41;DATABASE=fcdb;UID=*****;PWD=********""")
conn2 = sqlalchemy.create_engine("mssql+pyodbc:///?odbc_connect=%s" % conn_params)
for factor in Quality:
fcl = ['date'] + statemap[factor]
#数据读入,市场,财务数据
st = pd.HDFStore(inFilename)
mkt = st.select('mkt',"columns=['close_price','total_share']")
state = st.select('sheet',"columns="+str(fcl))
st.close()
#分红数据，处理市值用.
if DB_CONN==1:
dividend = pd.read_sql("""select sec_code,cast(ex_date as varchar) as date,cast(reg_date as varchar) as reg_date,bonus_ratio
from tbas..tCOM_dividend where div_type = 1""",
con=conn, index_col=['date', 'sec_code'], parse_dates=['date', 'reg_date'])
else:
dividend = pd.read_csv(r'data/datadividend.csv', index_col=None, parse_dates=['date', 'reg_date'], encoding='gbk')
dividend['sec_code'] = [('00000'+str(x))[-6:] for x in dividend['sec_code']]
dividend = dividend.set_index(['date', 'sec_code'])
dividend = dividend.reset_index()
dividend = dividend.drop_duplicates(['date', 'sec_code']) ###the date and bonus ratio for each divident for each stock
dividend = dividend.set_index(['reg_date', 'sec_code'])
dividend['share'] = mkt['total_share']
dividend = dividend.dropna()
dividend['dividend'] = dividend['bonus_ratio'] * dividend['share'] / 10.0
dividend = dividend.reset_index()
dividend = dividend.set_index(['date', 'sec_code'])
#因子计算,财务数据对齐
##财务数据对齐
state = state.unstack()
state = state[(state.index.month.isin([3,6,9,12]))].stack()
for x in statemap[factor]:
if x in ['total_cur_asset','cash','total_cur_liab','short_borrow','inventory']: ##'depreciation_amortization', 先不算
dat = state[x].unstack()
cdat = dat - dat.shift(4)
state['delta'+x] = cdat.stack()
nf = state.reset_index()
tnf = nf['pdate'].groupby([nf['date'],nf['sec_code']]).max()
nf = nf.set_index(['date', 'sec_code', 'pdate'])
tnf = tnf.reset_index()
tnf = tnf.set_index(['date', 'sec_code', 'pdate'])
nf = nf[nf.index.isin(tnf.index)]
nf = nf.reset_index('pdate')
##财报数据发布后，剔除分红
dividend = dividend['dividend'].reindex(nf.index.union(dividend.index)).fillna(0)
##日度对齐
nf = nf.drop(['pdate'], axis=1)
nf = nf.unstack()
if factor in ['AGRO', 'IGRO', 'CXGRO']:
nf = nf.reindex(nf.index.union(dates))
else:
nf = nf.reindex(nf.index.union(dates)).ffill()
nf = nf.reindex(dates)
dividend = dividend.unstack()
dividend = dividend.reindex(dividend.index.union(dates)).ffill()
dividend = dividend.reindex(dates)
#估值因子
mkt = mkt.unstack()
mkt = mkt.reindex(mkt.index.union(dates)).ffill()
mkt = mkt.reindex(dates)
tcap = mkt['close_price'] * mkt['total_share']
tcap[tcap == 0] = np.nan
if factor == 'MLEV':
factorvalue = (nf['total_long_liab'] + tcap + dividend) / (tcap + dividend)
elif factor == 'BLEV':
factorvalue = nf['total_liab'] / nf['total_asset']
elif factor == 'DTOA':
factorvalue = (nf['total_long_liab'] + nf['total_equity']) / nf['total_equity']
elif factor == 'VSAL':
factorvalue = nf['operate_in_total'].rolling(756).std() / nf['operate_in_total'].rolling(756).mean()
elif factor == 'VERN':
factorvalue = (nf['net_profit0'] / nf['operate_in_total']).rolling(756).std()
elif factor == 'VFLO':
factorvalue = (nf['operate_net_cash'] / nf['operate_in_total']).rolling(756).std()
elif factor == 'SPIBS':
factorvalue = nf['未来12个月每股收益预测值标准差'] / nf['stock_price']
elif factor == 'CETOE':
factorvalue = (nf['depreciation_amortization'].rolling(252).sum() / 63) / mkt['close_price']
elif factor == 'ACBS':
factorvalue = (nf['delta'+'total_cur_asset'] - nf['delta'+'cash'] - nf['delta'+'total_cur_liab'] +
nf['delta'+'short_borrow']) / nf['total_asset'] ##暂时缺失数据,有数据后可用下一行
##factorvalue = (nf['delta'+'total_cur_asset'] - nf['delta'+'cash'] - nf['delta'+'total_cur_liab'] + nf['delta'+'short_borrow'] - nf['depreciation_amortization']) / nf['total_asset']
elif factor == 'ACCF':
factorvalue = (nf['delta' + 'inventory'] - nf['delta' + 'total_cur_liab']) / nf['total_asset'] ##暂时缺失数据,有数据后可用下一行
##factorvalue = (nf['delta'+'account_rec'] + nf['delta'+'inventory'] - nf['delta'+'account_pay'] - nf['delta'+'tax_pay'] - nf['delta'+'other_current_liab_asset'] - nf['depreciation_amortization']) / nf['total_asset']
elif factor == 'ATO':
factorvalue = ((nf['operate_profit'] + nf['operate_expense']).rolling(252).sum() / 63) / nf['total_asset']
elif factor == 'GP':
factorvalue = (nf['operate_profit'].rolling(252).sum() / 63) / nf['total_asset']
elif factor == 'GM':
factorvalue = (nf['operate_profit'].rolling(252).sum() / 63) / ((nf['operate_profit'] + nf['operate_expense']).rolling(252).sum() / 63)
elif factor == 'ROA':
factorvalue = nf['ROA']
elif factor in ['AGRO','IGRO','CXGRO']:
temp = nf[statemap[factor][0]]
factorvalue = abs(temp.copy(deep=True) * 0)
##回归
for i in range(len(temp.iloc[0, :])):
stock_val = temp.iloc[:, i]
stock_val = stock_val.dropna(how='all')
stock_fval = abs(stock_val.copy(deep=True) * 0)
for j in range(len(stock_val) - 1, 19, -1):
temp_y = stock_val.iloc[j - 20:j].fillna(0)
temp_x = np.arange(20)
x = np.asmatrix(temp_x).transpose()
y = np.asmatrix(temp_y).transpose()
B = linear_regression_coef(x, y)
mean = y.mean()
if mean == 0:
stock_fval.iloc[j] = B
else:
stock_fval.iloc[j] = B / mean
stock_fval = stock_fval.reindex(stock_fval.index.union(dates)).ffill()
factorvalue.iloc[:, i] = stock_fval
st = pd.HDFStore(outFilename)
if factor in [x[1:] for x in st.keys()]:
existday = st.select_column(factor, 'index')
st.append(factor, factorvalue.loc[factorvalue.index.difference(existday)], format='t')
else:
st.append(factor, factorvalue, format='t')
st.close()
print(factor)