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data.py
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data.py
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#encoding=utf-8
import re
#import os
import scipy.stats as sc
import pandas as pd
import numpy as np
import datetime
import random
import datetime
from QuantLib import *
import arch
#导入原始数据,进行数据处理
#生成所需的sheet 以及sheet_names。
class dataProcess:
def __init__(self):
self.data()
def data(self):
print '开始导入数据'
tempExcel=pd.read_excel('option.xlsx')
print '数据导入完毕'
num=tempExcel.columns.shape[0]
underlying=tempExcel.iloc[:,-1:]
new_underlying_index=pd.DatetimeIndex(underlying[8:].index)
new_underlying_value=np.matrix(underlying[8:])
spot_=pd.DataFrame(new_underlying_value,index=new_underlying_index,columns=['spot'])
self.spot_=spot_
self.sheet=[]
self.sheet_names=[]
self.sheet_num=[]
for i in range(2,num,4):
new_sheet=tempExcel.iloc[:,i-2:i]
#sheet_names.append(new_sheet.columns[0])
new_index=pd.DatetimeIndex(new_sheet[8:].index)
new_value=np.matrix(new_sheet[8:])
temp=pd.DataFrame(new_value,index=new_index,columns=['mktprice','ptmtradeday'])
temp['ptmtradeday'][temp['ptmtradeday']<2]=np.nan
#print new_sheet.columns[0],pd.concat([temp,spot_],axis=1).dropna(axis=0).index,pd.concat([temp,spot_],axis=1)
if len(pd.concat([temp,spot_],axis=1).dropna(axis=0).index)>=10:
min_=pd.concat([temp,spot_],axis=1).dropna(axis=0).index[0]
max_=pd.concat([temp,spot_],axis=1).dropna(axis=0).index[-1]
temp=pd.concat([temp,spot_],axis=1)[min_:max_].fillna(method='ffill')
else:
temp=pd.concat([temp,spot_],axis=1)[min_:max_].dropna()
#交易数据小于10个交易日的，剔除。
if len(temp.index)>=10:
self.sheet.append(temp)
self.sheet_names.append(new_sheet.columns[0])
self.sheet_num.append(new_sheet.iloc[0][0])
else:
pass
self.code_index=pd.Series(self.sheet_num,index=self.sheet_names)
rate_index=pd.read_excel('option.xlsx',sheetname='NoRiskRate')[2:].iloc[:,0]
rate_index=pd.DatetimeIndex(rate_index.values)
rate_values=pd.read_excel('option.xlsx',sheetname='NoRiskRate')[2:].iloc[:,1:3]
self.rate=pd.DataFrame(np.matrix(rate_values),index=rate_index,columns=['shibor','10年期国债'])/100
#生成数据
class simulation:
def __init__(self,data,strike,option_type,preSpot_,contractUnit,costrate):
self.preSpot_=preSpot_
self.cost_rate=costrate
self.option_type=option_type
self.data=data
self.contractUnit=contractUnit
self.date_index=[]
for i in data.index:
self.date_index.append((re.sub(r"\D","",str(i)[:10])))
self.strike=strike
self.mktprice=list(data['mktprice'])
self.mktprice_=pd.DataFrame(self.mktprice,index=data.index,columns=['mktprice'])
self.spot=list(data['spot'])
self.spot_=pd.DataFrame(self.spot,index=data.index,columns=['spot'])
self.ptmtradeday=list(data['ptmtradeday'])
self.ptmtradeday_=pd.DataFrame(self.ptmtradeday,index=data.index,columns=['ptmtradeday'])
self.impliedVolatility()
self.greeks()
#self.capital_account()
#self.WW_capital_account()
#self.Zakamouline_capital_account()
#self.WW_band(10)
#self.Zakamouline_band(10)
self.margin_account()
self.ContractUnit()
#求delta
def impliedVolatility(self):
self.implied=[]
i=0
u=SimpleQuote(self.strike)
r=SimpleQuote(0.03)
sigma=SimpleQuote(0)
for date in self.date_index:
year=int(date[:4])
month=int(date[4:6])
day=int(date[6:])
today=Date(day,month,year)
china_calendar=China()
period=Period(self.ptmtradeday[i]-1,Days)
exerciseday=china_calendar.advance(today,period)
#exerciseday=today+period
Settings.instance().evaluationDate=today
#r.setValue(self.r[i])
u.setValue(self.spot[i])
calloption=EuropeanOption(PlainVanillaPayoff(Option.Call,self.strike),EuropeanExercise(exerciseday))
putoption=EuropeanOption(PlainVanillaPayoff(Option.Put,self.strike),EuropeanExercise(exerciseday))
riskFreeCurve=FlatForward(0,TARGET(),QuoteHandle(r),Actual360())
volatility=BlackConstantVol(0,TARGET(),QuoteHandle(sigma),Actual360())
process=BlackScholesProcess(QuoteHandle(u),YieldTermStructureHandle(riskFreeCurve),BlackVolTermStructureHandle(volatility))
engine=AnalyticEuropeanEngine(process)
if self.option_type==1:
calloption.setPricingEngine(engine)
#当隐含波动率不存在时，令隐含波动率为0.0001，否则计算no-trade region会报错
if calloption.NPV()>=self.mktprice[i]:
vol=0.0000
#print 'call',date,'theoryvalue:',calloption.NPV(),'mktprice:',self.mktprice[i],self.spot[i]
else:
vol=calloption.impliedVolatility(self.mktprice[i],process,1.0e-3,1000,0.0,50)
else:
putoption.setPricingEngine(engine)
if putoption.NPV()>self.mktprice[i]:
vol=0.0000
#print 'put',date,'theoryvalue:',putoption.NPV(),'mktprice',self.mktprice[i],self.spot[i]
else:
vol=putoption.impliedVolatility(self.mktprice[i],process,1.0e-3,1000,0.0,50)
self.implied.append(vol)
i=i+1
self.implied_=pd.DataFrame(self.implied,index=self.data.index,columns=['implied'])
def greeks(self):
self.delta=[]
self.gamma=[]
self.vega=[]
self.theta=[]
self.theoryvalue=[]
i=0
self.u=SimpleQuote(self.strike)
r=SimpleQuote(0.03)
self.sigma=SimpleQuote(0.2)
for date in self.date_index:
year=int(date[:4])
month=int(date[4:6])
day=int(date[6:])
today=Date(day,month,year)
china_calendar=China()
period=Period(self.ptmtradeday[i]-1,Days)
exerciseday=china_calendar.advance(today,period)
Settings.instance().evaluationDate=today
#r.setValue(self.r[i])
self.u.setValue(self.spot[i])
self.sigma.setValue(self.implied[i])
self.calloption=EuropeanOption(PlainVanillaPayoff(Option.Call,self.strike),EuropeanExercise(exerciseday))
self.putoption=EuropeanOption(PlainVanillaPayoff(Option.Put,self.strike),EuropeanExercise(exerciseday))
riskFreeCurve=FlatForward(0,TARGET(),QuoteHandle(r),Actual360())
volatility=BlackConstantVol(0,TARGET(),QuoteHandle(self.sigma),Actual360())
process=BlackScholesProcess(QuoteHandle(self.u),YieldTermStructureHandle(riskFreeCurve),BlackVolTermStructureHandle(volatility))
engine=AnalyticEuropeanEngine(process)
if self.option_type==1:
self.calloption.setPricingEngine(engine)
delta=self.calloption.delta()
gamma=self.calloption.gamma()
vega=self.calloption.vega()
theta=self.calloption.theta()
theoryvalue=self.calloption.NPV()
else:
self.putoption.setPricingEngine(engine)
delta=self.putoption.delta()
gamma=self.putoption.gamma()
vega=self.putoption.vega()
theta=self.putoption.theta()
theoryvalue=self.putoption.NPV()
self.delta.append(delta)
self.gamma.append(gamma)
self.vega.append(vega)
self.theta.append(theta)
self.theoryvalue.append(theoryvalue)
i=i+1
self.delta_=pd.DataFrame(self.delta,index=self.data.index,columns=['delta'])
self.gamma_=pd.DataFrame(self.gamma,index=self.data.index,columns=['gamma'])
self.vega_=pd.DataFrame(self.vega,index=self.data.index,columns=['vega'])
self.theta_=pd.DataFrame(self.theta,index=self.data.index,columns=['theta'])
self.theoryvalue_=pd.DataFrame(self.theoryvalue,index=self.data.index,columns=['theoryvalue'])
#risk_aversion 为风险厌恶系数 \lambda_为交易成本
def WW_band(self,risk_aversion):
j=0
self.WWHedgeFreq=0
self.WWDeltaHold=[]
self.WW_band_sup=[]
self.WW_band_inf=[]
for i in self.data.index:
_ptmtradeday=self.ptmtradeday[j]-1
_lambda=self.cost_rate
_spot=self.spot[j]
_gamma=self.gamma[j]
r=0.03
H0=((3.0/2.0)*np.exp(-r*_ptmtradeday)*_lambda*_spot*(_gamma**2)/risk_aversion)**(1.0/3)
self.WW_band_sup.append(self.delta[j]+H0)
self.WW_band_inf.append(self.delta[j]-H0)
if j==0:
self.WWDeltaHold.append(self.delta[j])
else:
if self.WWDeltaHold[-1]<self.WW_band_sup[-1] and self.WWDeltaHold[-1]>self.WW_band_inf[-1]:
self.WWDeltaHold.append(self.WWDeltaHold[-1])
elif self.WWDeltaHold[-1]<=self.WW_band_inf[-1]:
self.WWDeltaHold.append(self.WW_band_inf[-1])
self.WWHedgeFreq+=1
else:
self.WWDeltaHold.append(self.WW_band_sup[-1])
self.WWHedgeFreq+=1
j=j+1
self.WW_band_sup_=pd.DataFrame(self.WW_band_sup,index=self.data.index,columns=['WW_band_sup'])
self.WW_band_inf_=pd.DataFrame(self.WW_band_inf,index=self.data.index,columns=['WW_band_inf'])
self.WWDeltaHold_=pd.DataFrame(self.WWDeltaHold,index=self.data.index,columns=['WWDeltaHold'])
def Zakamouline_band(self,risk_aversion):
j=0
self.ZakamoulineHedgeFreq=0
self.Zakamouline_band_sup=[]
self.Zakamouline_band_inf=[]
self.Zakamouline_delta=[]
self.ZakamoulineDeltaHold=[]
r=0.03
for date in self.date_index:
_ptmtradeday=self.ptmtradeday[j]-1
_lambda=self.cost_rate
_spot=self.spot[j]
_gamma=self.gamma[j]
_sigma=self.implied[j]
#print _sigma
year=int(date[:4])
month=int(date[4:6])
day=int(date[6:])
today=Date(day,month,year)
china_calendar=China()
period=Period(_ptmtradeday,Days)
exerciseday=china_calendar.advance(today,period)
Settings.instance().evaluationDate=today
#_ptmtradeday=float(_ptmtradeday)
H0=_lambda/(risk_aversion*_spot*(_sigma**2)*_ptmtradeday)
H1=1.12*(_lambda**0.31)*(_ptmtradeday**0.05)*((np.exp(-r*_ptmtradeday)/_sigma)**0.25)*((abs(_gamma)/risk_aversion)**0.5)
K=-4.76*((_lambda**0.78)/(_ptmtradeday**0.02))*((np.exp(-r*_ptmtradeday)/_sigma)**0.25)*((risk_aversion*(_spot**2)*abs(_gamma))**0.15)
#print K
modified_sigma=_sigma*np.sqrt(1-K)
#self.r.setValue(self.r[j])
self.u.setValue(self.spot[j])
self.sigma.setValue(modified_sigma)
#print modified_sigma
if self.option_type==1:
modified_delta=self.calloption.delta()
else:
modified_delta=self.putoption.delta()
self.Zakamouline_delta.append(modified_delta)
self.Zakamouline_band_sup.append(modified_delta+H1+H0)
self.Zakamouline_band_inf.append(modified_delta-H1-H0)
if j==0:
self.ZakamoulineDeltaHold.append(modified_delta)
else:
if self.ZakamoulineDeltaHold[-1]<self.Zakamouline_band_sup[-1] and self.ZakamoulineDeltaHold[-1]>self.Zakamouline_band_inf[-1]:
self.ZakamoulineDeltaHold.append(self.ZakamoulineDeltaHold[-1])
elif self.ZakamoulineDeltaHold[-1]<=self.Zakamouline_band_inf[-1]:
self.ZakamoulineDeltaHold.append(self.Zakamouline_band_inf[-1])
self.ZakamoulineHedgeFreq=self.ZakamoulineHedgeFreq+1
else:
self.ZakamoulineDeltaHold.append(self.Zakamouline_band_sup[-1])
self.ZakamoulineHedgeFreq=self.ZakamoulineHedgeFreq+1
j=j+1
self.Zakamouline_delta_=pd.DataFrame(self.Zakamouline_delta,index=self.data.index,columns=['Zakamouline_delta'])
self.Zakamouline_band_sup_=pd.DataFrame(self.Zakamouline_band_sup,index=self.data.index,columns=['Zakamouline_band_sup'])
self.Zakamouline_band_inf_=pd.DataFrame(self.Zakamouline_band_inf,index=self.data.index,columns=['Zakamouline_band_inf'])
self.ZakamoulineDeltaHold_=pd.DataFrame(self.ZakamoulineDeltaHold,index=self.data.index,columns=['ZakamoulineDeltaHold'])
#资金账户
def capital_account(self):
#self.cost_rate=0.0025
delta_diff_=self.delta_-self.delta_.shift(1)
capital_account=[a*b*self.contractUnit for a,b in zip(delta_diff_['delta'],self.spot_['spot'])]
capital_account_=pd.DataFrame(capital_account,index=self.data.index,columns=['capital_account'])
capital_account_cost=[]
for i in capital_account:
if i>0:
capital_account_cost.append(i*(1-self.cost_rate))
else:
capital_account_cost.append(i*(1+self.cost_rate))
self.capital_account_cost_=pd.DataFrame(capital_account_cost,index=self.data.index,columns=['capital_account'])
self.gross_return=self.capital_account_cost_.sum()
self.gross_cost=np.array(-self.capital_account_cost_.sum())+np.array(capital_account_.sum())
def WW_capital_account(self):
#self.cost_rate=0.0025
delta_diff_=self.WWDeltaHold_-self.WWDeltaHold_.shift(1)
WW_capital_account=[a*b*self.contractUnit for a,b in zip(delta_diff_['WWDeltaHold'],self.spot_['spot'])]
WW_capital_account_=pd.DataFrame(WW_capital_account,index=self.data.index,columns=['WW_capital_account'])
WW_capital_account_cost=[]
for i in WW_capital_account:
if i>0:
WW_capital_account_cost.append(i*(1-self.cost_rate))
else:
WW_capital_account_cost.append(i*(1+self.cost_rate))
self.WW_capital_account_cost_=pd.DataFrame(WW_capital_account_cost,index=self.data.index,columns=['WW_capital_account'])
self.WW_gross_return=self.WW_capital_account_cost_.sum()
self.WW_gross_cost=np.array(-self.WW_capital_account_cost_.sum())+np.array(WW_capital_account_.sum())
def Zakamouline_capital_account(self):
#self.cost_rate=0.0025
delta_diff_=self.ZakamoulineDeltaHold_-self.ZakamoulineDeltaHold_.shift(1)
Zakamouline_capital_account=[a*b*self.contractUnit for a,b in zip(delta_diff_['ZakamoulineDeltaHold'],self.spot_['spot'])]
Zakamouline_capital_account_=pd.DataFrame(Zakamouline_capital_account,index=self.data.index,columns=['Zakamouline_capital_account'])
Zakamouline_capital_account_cost=[]
for i in Zakamouline_capital_account:
if i>0:
Zakamouline_capital_account_cost.append(i*(1-self.cost_rate))
else:
Zakamouline_capital_account_cost.append(i*(1+self.cost_rate))
self.Zakamouline_capital_account_cost_=pd.DataFrame(Zakamouline_capital_account_cost,index=self.data.index,columns=['Zakamouline_capital_account'])
self.Zakamouline_gross_return=self.Zakamouline_capital_account_cost_.sum()
self.Zakamouline_gross_cost=np.array(-self.Zakamouline_capital_account_cost_.sum())+np.array(Zakamouline_capital_account_.sum())
#def option_account(self):
def margin_account(self):
self.margin_deposit=[]
self.initial_margin=[]
j=0
for i in self.data.index:
_mktprice=self.mktprice[j]
_premktprice=self.mktprice_.shift(1).ix[j]['mktprice']
_spot=self.spot[j]
_preSpot=self.preSpot_.shift(1)['spot'][i]#50ETF前一天收盘价
if self.option_type==1:
call_out_option=max(self.strike-_preSpot,0)
self.margin_deposit.append((_mktprice+max(0.12*_spot-call_out_option,0.07*_preSpot))*self.contractUnit)
self.initial_margin.append((_premktprice+max(0.12*_spot-call_out_option,0.07*_preSpot))*self.contractUnit)
else:
put_out_option=max(_preSpot-self.strike,0)
self.margin_deposit.append((min(_mktprice+max(0.12*_spot-put_out_option,0.07*self.strike),self.strike))*self.contractUnit)
self.initial_margin.append((min(_premktprice+max(0.12*_spot-put_out_option,0.07*self.strike),self.strike))*self.contractUnit)
j=j+1
self.margin_deposit_=pd.DataFrame(self.margin_deposit,index=self.data.index,columns=['margin_deposit'])
self.initial_margin_=pd.DataFrame(self.initial_margin,index=self.data.index,columns=['initial_margin'])
def ContractUnit(self):
unit=[]
partcipationday=datetime.datetime(2016,11,28)
for i in self.data.index:
if i<partcipationday:
unit.append(10000)
else:
unit.append(self.contractUnit)
self.unit_=pd.DataFrame(unit,index=self.data.index,columns=['contractunit'])
class sheetData:
def __init__(self,costrate):
self.costrate=costrate
self.CreateSheet()
def CreateSheet(self):
delta_sheet=pd.DataFrame()
gamma_sheet=pd.DataFrame()
vega_sheet=pd.DataFrame()
theta_sheet=pd.DataFrame()
#WWBandInf_sheet=pd.DataFrame()
#WWBandSup_sheet=pd.DataFrame()
#WWDeltaHold_sheet=pd.DataFrame()
#ZakamoulineBandInf_sheet=pd.DataFrame()
#ZakamoulineBandSup_sheet=pd.DataFrame()
#ZakamoulineDeltaHold_sheet=pd.DataFrame()
#ZakamoulineDelta_sheet=pd.DataFrame()
impliedVolatility_sheet=pd.DataFrame()
theoryvalue_sheet=pd.DataFrame()
mktprice_sheet=pd.DataFrame()
ptmtradeday_sheet=pd.DataFrame()
MarginAccount_sheet=pd.DataFrame()
InitialAccount_sheet=pd.DataFrame()
ContractUnit_sheet=pd.DataFrame()
j=0
self.dataprocess=dataProcess()
option_startdate=[]
self.per_option_startdate={}
print '开始生成数据'
for i in self.dataprocess.sheet:
if self.dataprocess.sheet_names[j][-1]=='A':
strike=self.dataprocess.sheet_names[j][-6:-1]
contractUnit=10220
else:
strike=self.dataprocess.sheet_names[j][-4:]
contractUnit=10000
strike=float(strike)
if self.dataprocess.sheet_names[j][5]==u'购':
optiontype=1
else:
optiontype=0
print self.dataprocess.sheet_names[j]
print self.dataprocess.sheet_num[j]
temp=simulation(self.dataprocess.sheet[j],strike,optiontype,self.dataprocess.spot_,contractUnit,self.costrate)
option_startdate.append(str(temp.delta_.index[0])[:10])
self.per_option_startdate[self.dataprocess.sheet_names[j]]=str(temp.delta_.index[0])[:10]
delta_sheet=pd.concat([delta_sheet,temp.delta_],axis=1)
gamma_sheet=pd.concat([gamma_sheet,temp.gamma_],axis=1)
vega_sheet=pd.concat([vega_sheet,temp.vega_],axis=1)
theta_sheet=pd.concat([theta_sheet,temp.theta_],axis=1)
#WWBandInf_sheet=pd.concat([WWBandInf_sheet,temp.WW_band_inf_],axis=1)
#WWBandSup_sheet=pd.concat([WWBandSup_sheet,temp.WW_band_sup_],axis=1)
#WWDeltaHold_sheet=pd.concat([WWDeltaHold_sheet,temp.WWDeltaHold_],axis=1)
MarginAccount_sheet=pd.concat([MarginAccount_sheet,temp.margin_deposit_],axis=1)
InitialAccount_sheet=pd.concat([InitialAccount_sheet,temp.initial_margin_],axis=1)
ContractUnit_sheet=pd.concat([ContractUnit_sheet,temp.unit_],axis=1)
#ZakamoulineBandInf_sheet=pd.concat([ZakamoulineBandInf_sheet,temp.Zakamouline_band_inf_],axis=1)
#ZakamoulineBandSup_sheet=pd.concat([ZakamoulineBandSup_sheet,temp.Zakamouline_band_sup_],axis=1)
#ZakamoulineDeltaHold_sheet=pd.concat([ZakamoulineDeltaHold_sheet,temp.ZakamoulineDeltaHold_],axis=1)
#ZakamoulineDelta_sheet=pd.concat([ZakamoulineDelta_sheet,temp.Zakamouline_delta_],axis=1)
impliedVolatility_sheet=pd.concat([impliedVolatility_sheet,temp.implied_],axis=1)
theoryvalue_sheet=pd.concat([theoryvalue_sheet,temp.theoryvalue_],axis=1)
mktprice_sheet=pd.concat([mktprice_sheet,temp.mktprice_],axis=1)
ptmtradeday_sheet=pd.concat([ptmtradeday_sheet,temp.ptmtradeday_],axis=1)
j=j+1
self.delta_sheet_=pd.DataFrame(np.matrix(delta_sheet),index=delta_sheet.index,columns=self.dataprocess.sheet_names)
self.gamma_sheet_=pd.DataFrame(np.matrix(gamma_sheet),index=gamma_sheet.index,columns=self.dataprocess.sheet_names)
self.vega_sheet_=pd.DataFrame(np.matrix(vega_sheet),index=vega_sheet.index,columns=self.dataprocess.sheet_names)
self.theta_sheet_=pd.DataFrame(np.matrix(theta_sheet),index=theta_sheet.index,columns=self.dataprocess.sheet_names)
#self.WWBandInf_sheet_=pd.DataFrame(np.matrix(WWBandInf_sheet),index=WWBandInf_sheet.index,columns=self.dataprocess.sheet_names)
#self.WWBandSup_sheet_=pd.DataFrame(np.matrix(WWBandSup_sheet),index=WWBandSup_sheet.index,columns=self.dataprocess.sheet_names)
#self.WWDeltaHold_sheet_=pd.DataFrame(np.matrix(WWDeltaHold_sheet),index=WWDeltaHold_sheet.index,columns=self.dataprocess.sheet_names)
#self.ZakamoulineBandInf_sheet_=pd.DataFrame(np.matrix(ZakamoulineBandInf_sheet),index=ZakamoulineBandInf_sheet.index,columns=self.dataprocess.sheet_names)
#self.ZakamoulineBandSup_sheet_=pd.DataFrame(np.matrix(ZakamoulineBandSup_sheet),index=ZakamoulineBandSup_sheet.index,columns=self.dataprocess.sheet_names)
#self.ZakamoulineDeltaHold_sheet_=pd.DataFrame(np.matrix(ZakamoulineDeltaHold_sheet),index=ZakamoulineDeltaHold_sheet.index,columns=self.dataprocess.sheet_names)
#self.ZakamoulineDelta_sheet_=pd.DataFrame(np.matrix(ZakamoulineDelta_sheet),index=ZakamoulineDelta_sheet.index,columns=self.dataprocess.sheet_names)
self.impliedVolatility_sheet_=pd.DataFrame(np.matrix(impliedVolatility_sheet),index=impliedVolatility_sheet.index,columns=self.dataprocess.sheet_names)
self.theoryvalue_sheet_=pd.DataFrame(np.matrix(theoryvalue_sheet),index=theoryvalue_sheet.index,columns=self.dataprocess.sheet_names)
self.mktprice_sheet_=pd.DataFrame(np.matrix(mktprice_sheet),index=mktprice_sheet.index,columns=self.dataprocess.sheet_names)
self.ptmtradeday_sheet_=pd.DataFrame(np.matrix(ptmtradeday_sheet),index=ptmtradeday_sheet.index,columns=self.dataprocess.sheet_names)
self.MarginAccount_sheet_=pd.DataFrame(np.matrix(MarginAccount_sheet),index=MarginAccount_sheet.index,columns=self.dataprocess.sheet_names)
self.InitialAccount_sheet_=pd.DataFrame(np.matrix(InitialAccount_sheet),index=InitialAccount_sheet.index,columns=self.dataprocess.sheet_names)
self.ContractUnit_sheet_=pd.DataFrame(np.matrix(ContractUnit_sheet),index=ContractUnit_sheet.index,columns=self.dataprocess.sheet_names)
self.option_names=self.dataprocess.sheet_names
self.option_startdate=set(option_startdate)
temp=[]
for i in self.option_startdate:
temp.append(i)
temp=pd.Series(temp)
self.option_startdate=temp.sort_values()
self.options_in_startdate={}
for i in self.option_startdate:
self.options_in_startdate[i]=[]
for num,j in enumerate(option_startdate):
self.options_in_startdate[j].append(self.option_names[num])
print '数据生成完毕'
class realizedVolatility:
def __init__(self):
self.dataCreate()
self.Forecast()
self.factor()
self.function()
self.corr()
self.forecast()
def dataCreate(self):
#sheet_temp=pd.read_excel('option.xlsx',sheetname='underlying')
sheet_temp=pd.read_excel('temp.xlsx')
index_temp=pd.DatetimeIndex(sheet_temp[8:].index)
self.sheet_temp=pd.DataFrame(np.matrix(sheet_temp[8:]),index=index_temp,columns=['open','high','low','close','volume','total_turnover'])
self.underlying=pd.DataFrame(np.matrix(self.sheet_temp.loc[:,'close']),columns=self.sheet_temp.index,index=['spot']).T
self.underlyingYieldRate=np.log(self.underlying.pct_change(1)+1)
self.underlyingYieldRate_5=np.log(self.underlying.pct_change(5)+1)
self.underlyingYieldRate_10=np.log(self.underlying.pct_change(10)+1)
self.underlyingYieldRate_20=np.log(self.underlying.pct_change(20)+1)
self.underlyingYieldRate_30=np.log(self.underlying.pct_change(30)+1)
self.underlyingYieldRate_60=np.log(self.underlying.pct_change(60)+1)
self.realizedVol_90=self.underlyingYieldRate.rolling(window=90,center=False).std()*np.sqrt(252)
self.realizedVol_60=self.underlyingYieldRate.rolling(window=60,center=False).std()*np.sqrt(252)
self.realizedVol_30=self.underlyingYieldRate.rolling(window=30,center=False).std()*np.sqrt(252)
self.realizedVol_20=self.underlyingYieldRate.rolling(window=20,center=False).std()*np.sqrt(252)
self.realizedVol_10=self.underlyingYieldRate.rolling(window=10,center=False).std()*np.sqrt(252)
self.realizedVol_day_90=self.underlyingYieldRate.rolling(window=90,center=False).std()
self.realizedVol_day_60=self.underlyingYieldRate.rolling(window=60,center=False).std()
self.realizedVol_day_30=self.underlyingYieldRate.rolling(window=30,center=False).std()
self.realizedVol_day_20=self.underlyingYieldRate.rolling(window=20,center=False).std()
self.realizedVol_day_10=self.underlyingYieldRate.rolling(window=10,center=False).std()
temp=pd.concat([self.realizedVol_90,self.realizedVol_60,self.realizedVol_30,self.realizedVol_20,self.realizedVol_10],axis=1)
self.realizedVol=pd.DataFrame(np.matrix(temp),index=temp.index,columns=['realizedVol_90','realizedVol_60','realizedVol_30','realizedVol_20','realizedVol_10'])
def Forecast(self):
vol_fore=[]
test=self.underlyingYieldRate[30:]
for i,j in enumerate(test.index):
train_=test.loc[:j].iloc[-30:]*100
am=arch.arch_model(train_)
res=am.fit()
vol_fore.append(res.params['omega']+res.params['alpha[1]']*np.mean(res.resid[-10:])**2+res.params['beta[1]']*np.mean(res.conditional_volatility[-10:])**2)
self.VolForecast=0.01*np.sqrt(pd.DataFrame(vol_fore,index=test.index,columns=['vol_fore']))*np.sqrt(252)
#因子指标计算
def factor(self):
self.P=pd.DataFrame(self.sheet_temp.loc[:,'open'],columns=['open'])
self.H=pd.DataFrame(self.sheet_temp.loc[:,'high'],columns=['high'])
self.L=pd.DataFrame(self.sheet_temp.loc[:,'low'],columns=['low'])
self.C=pd.DataFrame(self.sheet_temp.loc[:,'close'],columns=['close'])
self.V=pd.DataFrame(self.sheet_temp.loc[:,'volume'],columns=['volume'])
self.TurnOver=pd.DataFrame(np.matrix(self.sheet_temp.loc[:,'total_turnover']),columns=self.sheet_temp.index,index=['TurnOver']).T
self.yield_rate=self.C['close']/self.P['open']-1
self.yield_rate=pd.DataFrame(np.matrix(self.yield_rate),index=['yield_rate'],columns=self.C.index).T
def ADV_20(self,n):
mean=pd.rolling_mean(self.V,n)
ADV=self.V/mean
return pd.DataFrame(ADV.values,index=ADV.index,columns=['ADV'])
def TurnOver_20(self,n):
TurnOver_20=pd.rolling_mean(self.TurnOver,5)/pd.rolling_mean(self.TurnOver,n)
TurnOver_20=TurnOver_20
return pd.DataFrame(TurnOver_20.values,index=TurnOver_20.index,columns=['TurnOver'])
def RSI_20(self,n):
p=pd.DataFrame((self.C['close']-self.P['open'])/self.P['open'],index=self.C.index,columns=['yieldrate'])
p_max=pd.DataFrame(np.zeros(p.shape),index=p.index,columns=p.columns)
p_abs=p
for i in range(len(p)):
j=np.logical_and(p.ix[i]>0,p.ix[i]>0)
p_max.ix[i,j]=p.ix[i,j]
for i in range(len(p)):
j=np.logical_and(p.ix[i]<0,p.ix[i]<0)
p_abs.ix[i,j]=abs(p.ix[i,j])
RSI=pd.rolling_mean(p_max,n)/pd.rolling_mean(p_abs,n)*100
return pd.DataFrame(RSI.values,index=RSI.index,columns=['RSI'])
def RE_20(self,n):
return pd.DataFrame(self.C.pct_change(n).values,index=self.C.index,columns=['RE'])
def VMC(self):
return pd.DataFrame((self.TurnOver['TurnOver']/self.V['volume']-self.C['close']),columns=['VMC'])
def LDECC_5(self):
p=pd.DataFrame((self.C['close']-self.P['open'])/self.P['open'],columns=['yieldrate'])
def rolling_5(A):#A为dataFrame 返回Series index=A.columns
return 1*A.iloc[-5]+2*A.iloc[-4]+3*A.iloc[-3]+4*A.iloc[-2]+5*A.iloc[-1]
MM=pd.DataFrame(rolling_5(p.iloc[-len(self.H)+1:-len(self.H)+6,:])).T
for i in range(6,len(self.H)-1):
MM=np.r_[MM,pd.DataFrame(rolling_5(p.iloc[(-len(self.H)+i-4):(-len(self.H)+i+1),:])).T]
MM=np.r_[MM,pd.DataFrame(rolling_5(p.iloc[-5:,:])).T]
return pd.DataFrame(MM,columns=['LDECC'],index=self.H.index[5:])
def function(self):
#self.func=pd.Series([self.ADV_20(20).dropna(),self.TurnOver_20(20).dropna(),self.RSI_20(20).dropna(),self.RE_20(20).dropna(),self.VMC().dropna(),self.LDECC_5().dropna()],index=['ADV','TurnOver','RSI','RE','VMC','LDECC'])
self.func_name=['ADV_20(20)','ADV_20(15)','ADV_20(10)','ADV_20(5)','TurnOver_20(20)','TurnOver_20(15)','TurnOver_20(10)','RSI_20(20)','RSI_20(15)','RSI_20(10)','RSI_20(5)','RE_20(20)','RE_20(15)','RE_20(10)','RE_20(5)','VMC()','LDECC_5()']
self.func=pd.Series([self.ADV_20(20).dropna(),self.ADV_20(15).dropna(),self.ADV_20(10).dropna(),self.ADV_20(5).dropna(),self.TurnOver_20(20).dropna(),self.TurnOver_20(15).dropna(),self.TurnOver_20(10).dropna(),self.RSI_20(20).dropna(),self.RSI_20(15).dropna(),self.RSI_20(10).dropna(),self.RSI_20(5).dropna(),self.RE_20(20).dropna(),self.RE_20(15).dropna(),self.RE_20(10).dropna(),self.RE_20(5).dropna(),self.VMC().dropna(),self.LDECC_5().dropna()],index=self.func_name)
def corr(self):
temp=pd.concat([self.underlyingYieldRate_5,self.ADV_20(20),self.ADV_20(15),self.ADV_20(10),self.ADV_20(5),self.TurnOver_20(20),self.TurnOver_20(15),self.TurnOver_20(10),self.RSI_20(20),self.RSI_20(15),self.RSI_20(10),self.RSI_20(5),self.RE_20(20),self.RE_20(15),self.RE_20(10),self.RE_20(5),self.VMC(),self.LDECC_5()],axis=1)
temp=pd.DataFrame(np.matrix(temp),index=temp.index,columns=['underlyingYieldRate_5','ADV_20(20)','ADV_20(15)','ADV_20(10)','ADV_20(5)','TurnOver_20(20)','TurnOver_20(15)','TurnOver_20(10)','RSI_20(20)','RSI_20(15)','RSI_20(10)','RSI_20(5)','RE_20(20)','RE_20(15)','RE_20(10)','RE_20(5)','VMC()','LDECC_5()'])
#temp=pd.concat([self.yield_rate,self.ADV_20(20),self.TurnOver_20(20),self.RSI_20(20),self.RE_20(20),self.VMC(),self.LDECC_5()],axis=1)
ADV_20_corr=pd.rolling_corr(temp['underlyingYieldRate_5'],temp['ADV_20(20)'],10)
ADV_15_corr=pd.rolling_corr(temp['underlyingYieldRate_5'],temp['ADV_20(15)'],10)
ADV_10_corr=pd.rolling_corr(temp['underlyingYieldRate_5'],temp['ADV_20(10)'],10)
ADV_5_corr=pd.rolling_corr(temp['underlyingYieldRate_5'],temp['ADV_20(5)'],10)
TurnOver_20_corr=pd.rolling_corr(temp['underlyingYieldRate_5'],temp['TurnOver_20(20)'],10)
TurnOver_15_corr=pd.rolling_corr(temp['underlyingYieldRate_5'],temp['TurnOver_20(15)'],10)
TurnOver_10_corr=pd.rolling_corr(temp['underlyingYieldRate_5'],temp['TurnOver_20(10)'],10)
RSI_20_corr=pd.rolling_corr(temp['underlyingYieldRate_5'],temp['RSI_20(20)'],10)
RSI_15_corr=pd.rolling_corr(temp['underlyingYieldRate_5'],temp['RSI_20(15)'],10)
RSI_10_corr=pd.rolling_corr(temp['underlyingYieldRate_5'],temp['RSI_20(10)'],10)
RSI_5_corr=pd.rolling_corr(temp['underlyingYieldRate_5'],temp['RSI_20(5)'],10)
RE_20_corr=pd.rolling_corr(temp['underlyingYieldRate_5'],temp['RE_20(20)'],10)
RE_15_corr=pd.rolling_corr(temp['underlyingYieldRate_5'],temp['RE_20(15)'],10)
RE_10_corr=pd.rolling_corr(temp['underlyingYieldRate_5'],temp['RE_20(10)'],10)
RE_5_corr=pd.rolling_corr(temp['underlyingYieldRate_5'],temp['RE_20(5)'],10)
VMC_corr=pd.rolling_corr(temp['underlyingYieldRate_5'],temp['VMC()'],10)
LDECC_corr=pd.rolling_corr(temp['underlyingYieldRate_5'],temp['LDECC_5()'],10)
corr=pd.concat([ADV_20_corr,ADV_15_corr,ADV_10_corr,ADV_5_corr,TurnOver_20_corr,TurnOver_15_corr,TurnOver_10_corr,RSI_20_corr,RSI_15_corr,RSI_10_corr,RSI_5_corr,RE_20_corr,RE_15_corr,RE_10_corr,RE_5_corr,VMC_corr,LDECC_corr],axis=1).dropna()
self.corr=pd.DataFrame(np.matrix(corr),index=corr.index,columns=self.func_name)
def unique(self,A):
D=[]
temp1_2=[]
temp2=[]
for i in range(len(A)):
if A[i][:2] not in temp1_2:
temp1_2.append(A[i][:2])
temp2.append(A[i])
return temp2
def fore_(self,n,m):
fore=[]
for num,i in enumerate(self.corr.index[n:]):
pp=[]
temp_=self.unique(self.corr[n:].iloc[num].abs().sort_values(ascending=False).index)[:3]
for j in temp_:
pp.append(self.func[j].loc[:i].iloc[-n:])
constant=np.ones(len(pp[0]))
constant=pd.DataFrame(constant,index=pp[0].index,columns=['constant'])
ppp=pd.concat([constant,pp[0],pp[1],pp[2]],axis=1)
X=ppp.dropna()
Y=self.underlyingYieldRate_5.loc[:i].iloc[-n:]
temp1=np.array(X.T.dot(X).values,dtype='float')
temp2=np.array(X.T.dot(Y).values,dtype='float')
beta=np.linalg.inv(temp1).dot(temp2)
X_=np.mean(X[-m:])
Y_=X_.dot(beta)
fore.append(Y_)
fore=pd.DataFrame(fore,index=self.corr[n:].index,columns=['fore'])
return fore
def forecast(self):
self.fore_30=self.fore_(30,10)
self.fore_10=self.fore_(10,5)
self.fore_5=self.fore_(5,3)
class winddata:
def __init__(self):
self.tempExcel=pd.read_excel('option.xlsx',sheetname='Greeks')
self.realizedVoldata=pd.read_excel('vol.xlsx',sheetname='Sheet1')
self.dataCreate()
def dataCreate(self):
new_index=pd.DatetimeIndex(self.realizedVoldata[8:].index)######
start=new_index[0]
end=new_index[-1]
print 'winddata'
self.tempExcel_=self.tempExcel[start:end]
num=self.tempExcel_.columns.shape[0]
self.wind_theoryValue_sheet=pd.DataFrame()
self.wind_delta_sheet=pd.DataFrame()
self.wind_gamma_sheet=pd.DataFrame()
self.wind_vega_sheet=pd.DataFrame()
self.wind_theta_sheet=pd.DataFrame()
self.wind_impliedVolatility_sheet=pd.DataFrame()
names=[]
for i in range(0,num,8):
names.append(self.tempExcel_.columns[i])
for i in range(0,num,8):
theoryvalue=self.tempExcel_.iloc[:,i]
theoryvalue=pd.DataFrame(np.matrix(theoryvalue),columns=new_index,index=['theoryvalue']).T
self.wind_theoryValue_sheet=pd.concat([self.wind_theoryValue_sheet,theoryvalue],axis=1)
delta=self.tempExcel_.iloc[:,i+1]
delta=pd.DataFrame(np.matrix(delta),columns=new_index,index=['delta']).T
self.wind_delta_sheet=pd.concat([self.wind_delta_sheet,delta],axis=1)
gamma=self.tempExcel_.iloc[:,i+2]
gamma=pd.DataFrame(np.matrix(gamma),columns=new_index,index=['gamma']).T
self.wind_gamma_sheet=pd.concat([self.wind_gamma_sheet,gamma],axis=1)
vega=self.tempExcel_.iloc[:,i+3]
vega=pd.DataFrame(np.matrix(vega),columns=new_index,index=['vega']).T
self.wind_vega_sheet=pd.concat([self.wind_vega_sheet,vega],axis=1)
theta=self.tempExcel_.iloc[:,i+4]
theta=pd.DataFrame(np.matrix(theta),columns=new_index,index=['theta']).T
self.wind_theta_sheet=pd.concat([self.wind_theta_sheet,theta],axis=1)
impliedVolatility=self.tempExcel_.iloc[:,i+5]
impliedVolatility=pd.DataFrame(np.matrix(impliedVolatility),columns=new_index,index=['impliedVolatility']).T
self.wind_impliedVolatility_sheet=pd.concat([self.wind_impliedVolatility_sheet,impliedVolatility],axis=1)
self.wind_theoryValue_sheet_=pd.DataFrame(np.matrix(self.wind_theoryValue_sheet),index=new_index,columns=names)
self.wind_delta_sheet_=pd.DataFrame(np.matrix(self.wind_delta_sheet),index=new_index,columns=names)
self.wind_gamma_sheet_=pd.DataFrame(np.matrix(self.wind_gamma_sheet),index=new_index,columns=names)
self.wind_vega_sheet_=pd.DataFrame(np.matrix(self.wind_vega_sheet),index=new_index,columns=names)
self.wind_theta_sheet_=pd.DataFrame(np.matrix(self.wind_theta_sheet),index=new_index,columns=names)
self.wind_impliedVolatility_sheet_=pd.DataFrame(np.matrix(self.wind_impliedVolatility_sheet),index=new_index,columns=names)
self.wind_realizedVol_30=pd.DataFrame(np.matrix(self.realizedVoldata[8:].iloc[:,0]),index=['wind_realizedVol_30'],columns=new_index).T/100
self.wind_realizedVol_60=pd.DataFrame(np.matrix(self.realizedVoldata[8:].iloc[:,1]),index=['wind_realizedVol_60'],columns=new_index).T/100
self.wind_realizedVol_90=pd.DataFrame(np.matrix(self.realizedVoldata[8:].iloc[:,2]),index=['wind_realizedVol_90'],columns=new_index).T/100