# 克隆自聚宽文章：https://www.joinquant.com/post/14348
# 标题：场外期权对冲策略回测框架-（以Whally-Wilmott为例）
# 作者：颖硕

import datetime
import time
from scipy.stats import norm
import jqdata
import numpy

def initialize(context):
    set_options(context)
    set_params()
    
# 设置期权参数
def set_options(context):  
    g.securities='002724.XSHE'
    g.K=1
    g.T=30
    g.rf=0.09
    g.sigma=secstd(context)
    g.S0=secinitialprice(context)
    g.startdate=context.run_params.start_date
    g.maturity=maturity(context)
    g.NP=context.portfolio.cash/1.05
    g.YearDay=float(365)
    g.secname=get_security_info(g.securities).display_name
    log.info('##################################基本信息####################################')
    log.info('标的代码->',g.securities,'标的名称->',g.secname,'名义本金->',g.NP)
    log.info('起始日期->',g.startdate,'到期日期->',g.maturity)
    log.info('期初价格->',g.S0,'执行价格->',g.K,'合约期限->',g.T,\
    '无风险利率->',g.rf,'波动率->',round(g.sigma,2))
    log.info('##############################################################################')

# 设置参数    
def set_params():
    set_order_cost(OrderCost(close_tax=0.001, open_commission=0.0003, \
    close_commission=0.0003, min_commission=5), type='stock') 
    set_option('use_real_price', True)
    set_benchmark(g.securities)
    set_slippage(FixedSlippage(0))

def before_trading_start(context):
# 获取当日日期
    current_date=context.current_dt.date()
# 第一天建仓
    if current_date==g.startdate:
        delta=DeltaCalculator(context,g.S0)
        DeltaPosition=round(g.NP/g.S0*delta/100)*100
        g.lastdelta=delta
        order_target(g.securities,DeltaPosition)
# 第二天到倒数第二天判断分红拆股
    if current_date>g.startdate and current_date<g.maturity:
       checkadj=get_price(g.securities,end_date=current_date,\
       fields=['close','factor'],fq='pre',count=2)
       if checkadj.iloc[0,1]!=checkadj.iloc[1,1]:
          temp=g.S0
          g.S0=round(g.S0*checkadj.iloc[0,1]/checkadj.iloc[1,1],2)
          log.info('期初价格调整',temp,'-->',g.S0)
# 到期日清仓
    if current_date==g.maturity:
       order_target(g.securities,0)
       log.info('今天清仓日->',str(current_date))
# 到期日之后就没事了
    if current_date>g.maturity:
       pass
  
def handle_data(context, data):
# 获取时间并判断
    current_time=context.current_dt
    current_date=current_time.date()

# 只有第二天到倒数第二天才会有对冲
# 9点30会取到昨天的收盘价,所以我们跳过这一分钟。
    if current_date>g.startdate and current_date<g.maturity and \
       (current_time.hour>9 or current_time.minute>30): 

# 中间交易日的触发对冲条件
       price=history(1,'1m', field='close',security_list=g.securities,\
          fq=None)
       currentdelta=DeltaCalculator(context,price.iloc[0,0])
       threshold=WhalleyWilmottThrehold(context,price.iloc[0,0])
       
       if abs(g.lastdelta-currentdelta)>threshold:
          delta=DeltaCalculator(context,price.iloc[0,0])
          DeltaPosition=round(g.NP/g.S0*delta/100)*100
          g.lastdelta=delta
          order_target(g.securities,DeltaPosition)

def secstd(context):
    end_date=context.current_dt.date()
    start_date=end_date-datetime.timedelta(days=365)
    price=get_price(g.securities,start_date=start_date,end_date=end_date,frequency='daily',fields='close',skip_paused=False,fq='pre')
    rets=np.diff(np.log(price),axis=0)
    std=rets.std()*sqrt(250)
    return std

def secinitialprice(context):
    start_date=context.current_dt.date()
    S0=get_price(g.securities,start_date=start_date,end_date=start_date,frequency='daily',fields='open',fq=None)
    return S0.values[0][0]

def maturity(context):
    matday=g.startdate+datetime.timedelta(days=g.T)
    array=jqdata.get_all_trade_days()
    index=numpy.where(array>=matday)[0][0]  #datetime.date(matday.year,matday.month,matday.day)
    truematday=array[index]
    return truematday

def DeltaCalculator(context,S):
    current_date=context.current_dt.date()
    Tau=(g.maturity-current_date).days+1
    d1=(np.log(S/(g.S0*g.K))+(g.rf+g.sigma**2/2)*(Tau/g.YearDay))\
    /(g.sigma*np.sqrt(Tau/g.YearDay))
    delta=norm.cdf(d1)
    return delta
    
def GammaCalculator(context,S):
    current_date=context.current_dt.date()
    Tau=(g.maturity-current_date).days+1
    d1=(np.log(S/(g.S0*g.K))+(g.rf+g.sigma**2/2)*(Tau/g.YearDay))\
    /(g.sigma*np.sqrt(Tau/g.YearDay))
    gamma=norm.pdf(d1)/(S*g.sigma*np.sqrt(Tau/g.YearDay))
    return gamma

def WhalleyWilmottThrehold(context,S):
    current_date=context.current_dt.date()
    risktolerance=5
    tradingcost=0.00055
    Tau=(g.maturity-current_date).days+1
    gamma=GammaCalculator(context,S)
    a=exp(-g.rf*Tau/g.YearDay)*tradingcost*S*gamma**2
    wwt=(3.0/2.0*a/risktolerance)**(1.0/3.0)
    return wwt