首页 技术,金融

投资学是我上个学期学的东西了,不过当时并没有完全弄懂整个流程。近几天我的一个朋友(无中生友)找我帮忙做课设,我就尝试做了一番。

背景知识

投资组合理论,简单解释就是研究两只及以上股票(或其他资产),每只股票买多少才能最赚钱,或者效用最大,抑或是风险最小,这三种情况就对应着最大化夏普比率最大化效用函数最小化风险,有这些公式,其中$$w_i$$是第i只股票购买的权重(权重可小于0,代表做空):

收益:E(R_p)=\sum_{i}w_iE(R_i)
风险:\sigma_p^2=\sum_{i}\sum_{j}w_i w_j Cov(i,j)
夏普比率:S=\frac{E(R_p)}{\sigma_p}
效用:U=E(R_p)-\frac{1}{2}A\sigma_p^2

获取数据

看了下baostock的api参考,写了如下代码:

def bs_login():
    lg = bs.login()
    return [lg.error_code, lg.error_msg]
def getKdata(codes, sdate, edate, f):
    result = pd.DataFrame()
    for i in range(len(codes)):
        code = codes[i]
        rs = bs.query_history_k_data_plus(code,
                                          "date,pctChg",
                                          start_date=sdate, end_date=edate, frequency=f, adjustflag='2')
        data_list = []
        while (rs.error_code == '0') & rs.next():
            data_list.append(rs.get_row_data())
        if i == 0:
            result = pd.DataFrame(data_list, columns=rs.fields)
            result.rename(columns={'pctChg': code}, inplace=True)
        else:
            temp = pd.DataFrame(data_list, columns=rs.fields)
            temp.rename(columns={'pctChg': code}, inplace=True)
            result = pd.merge(result, temp, left_on='date', right_on='date')
    resultList = [[float(s) for s in l] for l in result.values.T.tolist()[1:]]
    print(resultList)
    x = {}
    for i in range(len(codes)):
        code = codes[i]
        x[code] = resultList[i]
    tempResult = pd.DataFrame(x, columns=codes)
    return tempResult

在其他地方执行这两个函数:

bs_login()
# args = sys.argv
args = ['', 'sz.399300,sz.300657,sh.603533,sz.300391,sh.600130,sh.600519', '2019-01-01', '2020-01-01', 'm']

最小方差组合

顾名思义,最小方差组合就是能使方差最小的投资组合,首先写出求方差的函数:

def varP(w):
    global data
    covariance = np.cov(data.values.T)
    return w.T.dot(covariance).dot(w)

然后用scipy的optimize模块优化

def solveMinVariance():
    global n_stocks, ers
    w0 = np.zeros(n_stocks)
    w0[0] = 1
    cons = ({'type': 'eq', 'fun': lambda x: np.sum(x) - 1})
    optv = sp.optimize.minimize(varP, w0, method='SLSQP', constraints=cons)
    w = optv.x
    erp = 0
    for i in range(n_stocks):
        er = ers[i]
        erp += er*w[i]
    sigmaP2 = varP(w)
    sharpeP = sP(w)
    return [w.tolist(), erp, sigmaP2, -sharpeP]

最优风险组合

顾名思义,最优风险组合意思就是最优秀的风险组合,什么是“优秀”呢?在这里的“优秀”,指的其实就是夏普比率高,也就是高收益低风险。

全协方差模型

类似的思路,先写出求夏普比率的函数

def sP(w):
    global data, n_stocks, rf
    erp = 0
    for i in range(n_stocks):
        er = ers[i]
        erp += er*w[i]
    covariance = np.cov(data.values.T)
    return -(erp-rf)/np.sqrt(w.T.dot(covariance).dot(w))

同样地使用scipy的optimize优化,这里为了避免弄成局部最优解,使用了$$E(R_i)/\sigma_i$$作初值

def solveMaxSharpe():
    global n_stocks
    w0 = np.zeros(n_stocks)
    w0[0] = 0.01
    for i in range(1, n_stocks):
        w0[i] = was[i - 1]
    wbeforeSum = np.sum(w0)
    for i in range(n_stocks):
        w0[i] = w0[i]/wbeforeSum
    cons = ({'type': 'eq', 'fun': lambda x: np.sum(x) - 1})
    #bnds = tuple((0, 1) for x in range(n_stocks))
    optv = sp.optimize.minimize(sP, w0, method='SLSQP', constraints=cons)
    w = optv.x
    erp = 0
    for i in range(n_stocks):
        er = ers[i]
        erp += er*w[i]
    sigmaP2 = varP(w)
    sharpeP = sP(w)
    return [w.tolist(), erp, sigmaP2, -sharpeP]

单指数模型

大家可能都发现了,上面写了个词叫“全协方差模型”,这个模型的步骤太少了,为了帮朋友完成课设,这么点字估计不行。因此我就盯上了基于CAPM模型的单指数模型的最优化过程,这个步骤就多了,但计算量没全协方差模型大。不过最终优化得出的夏普比率没有它高,但也十分接近了。

def singleIndex():
    print('单指数模型计算过程:')

    print('    E(RM)=', erm)
    print('    sigma(M)=', sigmaM)
    print('    模型估计alpha值:', alphas)
    print('    模型估计beta值:', betas)
    print('    模型估计残差方差:', sigmas)
    print('    计算原始头寸:', was)
    sumwas = np.sum(was)
    for i in range(len(was)):
        was[i] = was[i]/sumwas
    print('    调整组合权重:', was)
    alphaA = np.array(was).T.dot(np.array(alphas))
    print('    计算积极组合的alpha值:', alphaA)
    sigmaA = (np.array(was)**2).T.dot(np.array(sigmas))
    print('    计算积极组合的残差的方差:', sigmaA)
    wA = alphaA/sigmaA/(erm/sigmaM)
    print('    计算积极组合的原始头寸:',wA)
    betaA = np.array(was).T.dot(np.array(betas))
    print('    计算积极组合的beta值:',betaA)
    wAstar = abs(wA/(1+(1-betaA)*wA))
    print('    调整积极组合的原始头寸:',wAstar)
    wMstar = 1 - wAstar
    print('    此时最优风险组合的权重:',wMstar)
    ws = [wMstar]
    for s in np.array(was).dot(wAstar):
        ws.append(s)
    ws = np.array(ws)
    sharpeRatio = -sP(ws)
    varianceAll = varP(ws)
    er = 0
    for i in range(n_stocks):
        e = ers[i] - rf
        er += e*ws[i]
    print('    计算最优风险组合的风险溢价:',er/100)
    print('    计算最优风险组合的收益率:', (er+rf)/100)
    print('    计算最优风险组合的收益率(换算为年):', (1+(er+rf)/100)**dataTimetoYear-1)
    print('    计算最优风险组合的组合方差:',varianceAll)
    print('    计算夏普比率:', sharpeRatio*np.sqrt(dataTimetoYear))
    yStarS = (er + rf)/(A*varianceAll)
    print('    最优完全资产组合中最优风险组合的比重(风险厌恶系数A=3,无风险利率rf=',rf,'):', yStarS)
    print(' ')

全部代码

import baostock as bs
import pandas as pd
import numpy as np
import scipy as sp
import scipy.optimize as opt
import sys
import statsmodels.api as sm

def bs_login():
    lg = bs.login()
    return [lg.error_code, lg.error_msg]

def getKdata(codes, sdate, edate, f):
    result = pd.DataFrame()
    for i in range(len(codes)):
        code = codes[i]
        rs = bs.query_history_k_data_plus(code,
                                          "date,pctChg",
                                          start_date=sdate, end_date=edate, frequency=f, adjustflag='2')
        data_list = []
        while (rs.error_code == '0') & rs.next():
            data_list.append(rs.get_row_data())
        if i == 0:
            result = pd.DataFrame(data_list, columns=rs.fields)
            result.rename(columns={'pctChg': code}, inplace=True)
        else:
            temp = pd.DataFrame(data_list, columns=rs.fields)
            temp.rename(columns={'pctChg': code}, inplace=True)
            result = pd.merge(result, temp, left_on='date', right_on='date')
    resultList = [[float(s) for s in l] for l in result.values.T.tolist()[1:]]
    print(resultList)
    x = {}
    for i in range(len(codes)):
        code = codes[i]
        x[code] = resultList[i]
    tempResult = pd.DataFrame(x, columns=codes)
    return tempResult

def varP(w):
    global data
    covariance = np.cov(data.values.T)
    return w.T.dot(covariance).dot(w)

def sP(w):
    global data, n_stocks, rf
    erp = 0
    for i in range(n_stocks):
        er = ers[i]
        erp += er*w[i]
    covariance = np.cov(data.values.T)
    return -(erp-rf)/np.sqrt(w.T.dot(covariance).dot(w))

# minimum variance portfolio
def solveMinVariance():
    global n_stocks, ers
    w0 = np.zeros(n_stocks)
    w0[0] = 1
    cons = ({'type': 'eq', 'fun': lambda x: np.sum(x) - 1})
    optv = sp.optimize.minimize(varP, w0, method='SLSQP', constraints=cons)
    w = optv.x
    erp = 0
    for i in range(n_stocks):
        er = ers[i]
        erp += er*w[i]
    sigmaP2 = varP(w)
    sharpeP = sP(w)
    return [w.tolist(), erp, sigmaP2, -sharpeP]

# maximum sharpe ratio
def solveMaxSharpe():
    global n_stocks
    w0 = np.zeros(n_stocks)
    w0[0] = 0.01
    for i in range(1, n_stocks):
        w0[i] = was[i - 1]
    wbeforeSum = np.sum(w0)
    for i in range(n_stocks):
        w0[i] = w0[i]/wbeforeSum
    cons = ({'type': 'eq', 'fun': lambda x: np.sum(x) - 1})
    #bnds = tuple((0, 1) for x in range(n_stocks))
    optv = sp.optimize.minimize(sP, w0, method='SLSQP', constraints=cons)
    w = optv.x
    erp = 0
    for i in range(n_stocks):
        er = ers[i]
        erp += er*w[i]
    sigmaP2 = varP(w)
    sharpeP = sP(w)
    return [w.tolist(), erp, sigmaP2, -sharpeP]

def singleIndex():
    print('单指数模型计算过程:')

    print('    E(RM)=', erm)
    print('    sigma(M)=', sigmaM)
    print('    模型估计alpha值:', alphas)
    print('    模型估计beta值:', betas)
    print('    模型估计残差方差:', sigmas)
    print('    计算原始头寸:', was)
    sumwas = np.sum(was)
    for i in range(len(was)):
        was[i] = was[i]/sumwas
    print('    调整组合权重:', was)
    alphaA = np.array(was).T.dot(np.array(alphas))
    print('    计算积极组合的alpha值:', alphaA)
    sigmaA = (np.array(was)**2).T.dot(np.array(sigmas))
    print('    计算积极组合的残差的方差:', sigmaA)
    wA = alphaA/sigmaA/(erm/sigmaM)
    print('    计算积极组合的原始头寸:',wA)
    betaA = np.array(was).T.dot(np.array(betas))
    print('    计算积极组合的beta值:',betaA)
    wAstar = abs(wA/(1+(1-betaA)*wA))
    print('    调整积极组合的原始头寸:',wAstar)
    wMstar = 1 - wAstar
    print('    此时最优风险组合的权重:',wMstar)
    ws = [wMstar]
    for s in np.array(was).dot(wAstar):
        ws.append(s)
    ws = np.array(ws)
    sharpeRatio = -sP(ws)
    varianceAll = varP(ws)
    er = 0
    for i in range(n_stocks):
        e = ers[i] - rf
        er += e*ws[i]
    print('    计算最优风险组合的风险溢价:',er/100)
    print('    计算最优风险组合的收益率:', (er+rf)/100)
    print('    计算最优风险组合的收益率(换算为年):', (1+(er+rf)/100)**dataTimetoYear-1)
    print('    计算最优风险组合的组合方差:',varianceAll)
    print('    计算夏普比率:', sharpeRatio*np.sqrt(dataTimetoYear))
    yStarS = (er + rf)/(A*varianceAll)
    print('    最优完全资产组合中最优风险组合的比重(风险厌恶系数A=3,无风险利率rf=',rf,'):', yStarS)
    print(' ')

bs_login()
# args = sys.argv
args = ['', 'sz.399300,sz.300657,sh.603533,sz.300391,sh.600130,sh.600519', '2019-01-01', '2020-01-01', 'm']
dataTimetoYear = 365
if args[4] == 'd':
    dataTimetoYear = 365
if args[4] == 'm':
    dataTimetoYear = 12
if args[4] == 'w':
    dataTimetoYear = 48
n_stocks = len(args[1].split(','))
data = getKdata(args[1].split(','), args[2], args[3], args[4])
ers = [np.mean(data.values.T[i]) for i in range(n_stocks)]
A = 3
rf = 3/dataTimetoYear

x = data.values.T[0] - rf
X = sm.add_constant(x)
erm = np.mean(x)
sigmaM = np.cov(x)
alphas = []
betas = []
sigmas = []
was = []
for i in range(len(data.values.T) - 1):
    y = data.values.T[i+1] - rf
    model = sm.OLS(y, X)
    results = model.fit()
    alphas.append(results.params[0])
    betas.append(results.params[1])
    sigmas.append(np.cov(results.resid).tolist())
    was.append(alphas[i]/sigmas[i])

resultMV = solveMinVariance()
resultMS = solveMaxSharpe()
yStar = (resultMS[1] - rf)/(A*resultMS[2])

print('全协方差模型(最小化方差)')
print('    计算最小方差组合权重:', resultMV[0])
print('    计算最小方差组合收益率:', resultMV[1]/100)
print('    计算最小方差组合收益率(换算为年):', (1+resultMV[1]/100)**dataTimetoYear-1)
print('    计算最小方差组合方差:', resultMV[2])
print('    计算最小方差组合夏普比率:', resultMV[3]*np.sqrt(dataTimetoYear))
print(' ')

print('全协方差模型(最大化夏普比率)')
print('    计算最优风险组合权重:', resultMS[0])
print('    计算最优风险组合收益率:', resultMS[1]/100)
print('    计算最优风险组合收益率(换算为年):', (1+resultMS[1]/100)**dataTimetoYear-1)
print('    计算最优风险组合方差:', resultMS[2])
print('    计算最优风险组合夏普比率:', resultMS[3]*np.sqrt(dataTimetoYear))
print('    计算最优完全资产组合中最优风险组合的比重(风险厌恶系数A=3,无风险利率rf=',rf,'):', yStar)
print(' ')

singleIndex()
data.to_excel('test.xlsx')



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