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/sH = 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,ceilfrom scipy import statsimport datetimeimport tushare as tsimport pandas as pdimport numpy as npimport randomimport time as timessimport osdef 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 codesdef 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_cdef 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 sigmadef get_mu(sigma,r): mu = (r-pow(sigma,2)/2)/pow(sigma,2) return mudef get_lambda(mu,r,sigma): lam = sqrt(mu*mu+2*r/pow(sigma,2)) return lamdef 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,zdef 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,f6def 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)

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