Archive: 2025年9月21日

python障碍式期权定价公式

python障碍式期权定价公式

更新时间：2019年07月19日 09:44:11   作者：王大锤95

这篇文章主要为大家详细介绍了python障碍式期权定价公式，具有一定的参考价值，感兴趣的小伙伴们可以参考一下

早期写的python障碍式期权的定价脚本，供大家参考，具体内容如下

#coding:utf-8
'''
障碍期权
q=x/s
H = h/x H 障碍价格
[1] Down-and-in call cdi
[2] Up-and-in call cui
[3] Down-and-in put pdi
[4] Up-and-in put pui
[5] Down-and-out call cdo
[6] Up-and-out call cuo
[7] Down-and-out put pdo
[8] Up-and-out put puo
'''
from math import log,sqrt,exp,ceil
from scipy import stats
import datetime
import tushare as ts
import pandas as pd
import numpy as np
import random
import time as timess
import os
def get_codes(path='D:\code\20180313.xlsx'):     #从代码表格从获取代码
 codes = pd.read_excel(path)
 codes = codes.iloc[:,1]    
 return codes
def get_datas(code,N=1,path='D:\data\'):        #获取数据N=1当天数据
 datas = pd.read_csv(path+eval(code)+'.csv',encoding='gbk',skiprows=2,header=None,skipfooter=N,engine='python').dropna() #读取CSV文件 名称为股票代码 解gbk skiprows跳过前两行文字 第一行不做为表头
 date_c = datas.iloc[:,[0,4,5]]     #只用第0 列代码数据和第4列收盘价数据
 date_c.index = datas[0]
 return date_c
def get_sigma(close,std_th):
 x_i = np.log(close/close.shift(1)).dropna()
 sigma = x_i.rolling(window=std_th).std().dropna()*sqrt(244)
 return sigma
def get_mu(sigma,r):
 mu = (r-pow(sigma,2)/2)/pow(sigma,2)
 return mu
def get_lambda(mu,r,sigma):
 lam = sqrt(mu*mu+2*r/pow(sigma,2))
 return lam
def x_y(sigma,T,mu,H,lam,q=1):
 x1 = log(1/q)/(sigma*sqrt(T))+(1+mu)*sigma*sqrt(T)
 x2 = log(1/(q*H))/(sigma*sqrt(T))+(1+mu)*sigma*sqrt(T)
 y1 = log(H*H/q)/(sigma*sqrt(T))+(1+mu)*sigma*sqrt(T)
 y2 = log(q*H)/(sigma*sqrt(T))+(1+mu)*sigma*sqrt(T)
 z = log(q*H)/(sigma*sqrt(T))+lam*sigma*sqrt(T)
 return x1,x2,y1,y2,z
def get_standardBarrier(eta,phi,mu,sigma,r,T,H,lam,x1,x2,y1,y2,z,q=1):
 f1 = phi*1*stats.norm.cdf(phi*x1,0.0,1.0)-phi*q*exp(-r*T)*stats.norm.cdf(phi*x1-phi*sigma*sqrt(T),0.0,1.0)
 f2 = phi*1*stats.norm.cdf(phi*x2,0.0,1.0)-phi*q*exp(-r*T)*stats.norm.cdf(phi*x2-phi*sigma*sqrt(T),0.0,1.0)
 f3 = phi*1*pow(H*q,2*(mu+1))*stats.norm.cdf(eta*y1,0.0,1.0)-phi*q*exp(-r*T)*pow(H*q,2*mu)*stats.norm.cdf(eta*y1-eta*sigma*sqrt(T),0.0,1.0)
 f4 = phi*1*pow(H*q,2*(mu+1))*stats.norm.cdf(eta*y2,0.0,1.0)-phi*q*exp(-r*T)*pow(H*q,2*mu)*stats.norm.cdf(eta*y2-eta*sigma*sqrt(T),0.0,1.0)
 f5 = (H-1)*exp(-r*T)*(stats.norm.cdf(eta*x2-eta*sigma*sqrt(T),0.0,1.0)-pow(H*q,2*mu)*stats.norm.cdf(eta*y2-eta*sigma*sqrt(T),0.0,1.0))
 f6 = (H-1)*(pow(H*q,(mu+lam))*stats.norm.cdf(eta*z,0.0,1.0)+pow(H*q,(mu-lam))*stats.norm.cdf(eta*z-2*eta*lam*sigma*sqrt(T),0.0,1.0))
 return f1,f2,f3,f4,f5,f6
def main(param,t,r=0.065):
 typeflag = ['cdi','cdo','cui','cuo','pdi','pdo','pui','puo']
 r = log(1+r)
 T = t/365
 codes = get_codes()
 H = 1.2
 for i in range(len(codes)):
 sdbs = []
 for j in typeflag:
 code = codes.iloc[i]
 datas = get_datas(code)
 close = datas[4]
 sigma = get_sigma(close,40)[-1]
 mu = get_mu(sigma,r)
 lam = get_lambda(mu,r,sigma)
 x1,x2,y1,y2,z = x_y(sigma,T,mu,H,lam)
 eta = param[j]['eta']
 phi = param[j]['phi']
 f1,f2,f3,f4,f5,f6 = get_standardBarrier(eta,phi,mu,sigma,r,T,H,lam,x1,x2,y1,y2,z)
 if j=='cdi':
 sdb = f1-f2+f4+f5
 if j=='cui':
 sdb = f2-f3+f4+f5
 if j=='pdi':
 sdb = f1+f5
 if j=='pui':
 sdb = f3+f5
 if j=='cdo':
 sdb = f2+f6-f4
 if j=='cuo':
 sdb = f1-f2+f3-f4+f6
 if j=='pdo':
 sdb = f6
 if j=='puo':
 sdb = f1-f3+f6
 sdbs.append(sdb)
 print(T,r,sigma,H,sdbs)
if __name__ == '__main__':
 param = {'cdi':{'eta':1,'phi':1},'cdo':{'eta':1,'phi':1},'cui':{'eta':-1,'phi':1},'cuo':{'eta':-1,'phi':1},
 'pdi':{'eta':1,'phi':-1},'pdo':{'eta':1,'phi':-1},'pui':{'eta':-1,'phi':-1},'puo':{'eta':-1,'phi':-1}}
 t = 30
 main(param,t)

以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持脚本之家。