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CalBarraVolatility.py
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"""
Created on Wed Nov 13 2019
@author: RosemaryHe
"""
import pandas as pd
import numpy as np
import math
from sklearn import datasets, linear_model
import sqlalchemy
import urllib
def linear_regression(X,y):
reg = linear_model.LinearRegression()
reg.fit(X,y)
return reg.coef_, reg.intercept_
def cal_Z(T, stock_rate, rf_rate):
z = 0
for i in range(T):
z += math.log(stock_rate.iloc[i]) - math.log(rf_rate.iloc[i])
return z
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 = 'BarraMomentum.h5'
def CalBarraVolatility(dates):
Volatility = ['Beta','HSIGMA','DASTD','CMRA']
statemap = {'Beta':['sec_return'],'HSIGMA':['sec_return'],'DASTD':['sec_return'],'CMRA':['sec_return']}
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)
##to save time, we will use the regression results directly from Beta
Beta_res_error = pd.DataFrame()
for factor in Volatility:
fcl = ['date'] + statemap[factor]
#数据读入,市场,财务数据
st = pd.HDFStore(inFilename)
state = st.select('mkt',"columns="+str(fcl))
if factor in ['Beta','HSIGMA']:
lists = ['date','cash']
tempa = st.select('sheet', "columns=" + str(lists))
mkt = st.select('mkt', "columns=['close_price', 'total_share']")
st.close()
dates = state.index.get_level_values('date')
stocks = state.index.get_level_values('sec_code')
state['secID'] = stocks
state['dates'] = dates
dates = dates.drop_duplicates()
stocks = stocks.drop_duplicates()
##setting risk_free to 0.1 for now, will change later
if factor in ['Beta','HSIGMA','CMRA']:
temp_arr = [0.1] * len(state['sec_return'])
state['rf_return'] = temp_arr
if factor == 'Beta':
##if factor in ['Beta','HSIGMA']:
# 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'])
# 4. 因子计算
# 财务数据对齐
tempa = tempa.unstack()
tempa = tempa[(tempa.index.month.isin([3, 6, 9, 12]))].stack()
nf = tempa.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.unstack()
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
total_weight = pd.DataFrame(dates, columns=['date'])
total_weight['Rt'] = pd.Series()
for i in range(len(dates)):
temp_return = state[state.dates == dates[i]]
temp_tcap = pd.DataFrame(tcap.iloc[i, :])
temp_tcap['secID'] = temp_tcap.index.values
weights = temp_tcap[temp_tcap['secID'].isin(temp_return.secID.values)]
temp_weight = weights.iloc[:, 0]
temp_weight = temp_weight / temp_weight.sum()
total_weight.iloc[i, 1] = (temp_weight * temp_return['sec_return']).sum()
total_weight = total_weight.dropna()
state = state[state.dates.isin(total_weight.date.values)]
new_state = pd.merge(state, total_weight, left_on='dates', right_on='date', how='outer').fillna(0)
for s in range(len(stocks)):
each_stock = new_state[new_state.secID == stocks[s]]
each_stock = each_stock.set_index(['dates', 'secID'])
each_fact = abs(each_stock.iloc[:, 0] * 0)
each_fact_res = abs(each_stock.iloc[:, 0] * 0)
factor_arr = []
res_arr = []
for l in range(len(each_stock) - 1, 251, -1):
short_y = each_stock['sec_return'].iloc[l - 252:l] - each_stock['rf_return'].iloc[l - 252:l].values
short_x = each_stock['Rt'].iloc[l - 252:l].values
short_x = short_x * ((0.5 ** (1 / 63)) ** (252 - l)) ##指数加权
short_y = short_y * ((0.5 ** (1 / 63)) ** (252 - l))
x = np.asmatrix(short_x).transpose()
y = np.asmatrix(short_y).transpose()
b,a = linear_regression(x, y)
each_fact.iloc[l] = b
intercept_arr = [a] * len(x)
array = y - np.multiply(b,x) - intercept_arr
each_fact_res.iloc[l] = np.std(array)
factor_arr.append(each_fact)
res_arr.append(each_fact_res)
factorvalue = pd.concat(factor_arr)
Beta_res_error = pd.concat(res_arr)
elif factor == 'HSIGMA': ##must run Beta so only regress once,
factorvalue = Beta_res_error
elif factor == 'DASTD':
weights_arr = [0.0] * 252
for w in range(len(weights_arr)):
weights_arr[w] = (0.5**(1/42))**(252-w)
factor_arr = []
for s in stocks:
each_return = state[state.secID==s]
each_return = each_return.set_index(['dates', 'secID'])
each_factor = abs(each_return.iloc[:, 0] * 0)
for i in range(len(each_return)-1, 251, -1):
temp_slice = each_return.iloc[i-252:i,0]
#temp_slice['weights'] = weights
temp_r = temp_slice * weights_arr
each_factor.iloc[i] = temp_r.std()
factor_arr.append(each_factor)
factorvalue = pd.concat(factor_arr)
elif factor == 'CMRA':
fact_array = []
for s in stocks:
each_stock = state[state.secID==s]
fact = abs(each_stock.iloc[:,0] * 0)
for k in range(len(each_stock)-1, 251, -1):
monthly_stock = pd.Series([0.0] * 12)
monthly_rf = pd.Series([0.0] * 12)
##find the return of each month
for m in range(12):
each_month = each_stock.iloc[k - 252:k, :]
monthly_compound_stock = 1
monthly_compound_rf = 1
for i in range(21):
monthly_compound_stock = monthly_compound_stock * (1 + each_month.iloc[21*m+i,0] / 100)
monthly_compound_rf = monthly_compound_rf * (1 + each_month.iloc[21*m+i,3] / 100)
monthly_stock.iloc[m] = monthly_compound_stock - 1
monthly_rf.iloc[m] = monthly_compound_rf - 1
monthly_stock[monthly_stock <= -1] = 0
monthly_rf[monthly_rf <= -1] = 0
each_arr = [math.log(1 + monthly_stock.iloc[x]) - math.log(1 + monthly_rf.iloc[x]) for x in np.arange(12)]
each_Z = pd.Series(each_arr)
Z = []
for j in range(len(each_Z)):
if j == 0:
Z.append(each_Z[j])
else:
Z.append(Z[j-1] + each_Z[j])
fact.iloc[k] = np.max(Z) - np.min(Z)
fact_array.append(fact)
factorvalue = pd.concat(fact_array)
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)