It uses the research module of rice basket , From the results , Mean variance model is highly sensitive to parameters , Many parameters are not as good as random weights , It's hard to apply to actual combat .
import pandas as pd import numpy as np from scipy import linalg import
matplotlib.pyplot as plt stockslist = ['000001.XSHE','000002.XSHE','600004.XSHG'
,'600033.XSHG','000651.XSHE'] data = get_price_change_rate(stockslist,start_date
= '20100101',end_date = '20200420') data.to_csv('MeanVar.csv') print(data.head(
20)) train_set = data[data.index<'20171231'] #print(train_set.tail(20)) test_set
= data[data.index>'20180101'] #print(test_set.head(20))

# to glance at 5 Historical performance of three stocks cumreturn = (1 + data).cumprod() cumreturn.plot() plt.title(
'Cumulative Return of Stocks') plt.show()

# correlation analysis data.corr()

# Core module , Realize the calculation of mean variance model class MeanVariance: # Incoming yield data def __init__(self,returns): self
.returns = returns # Define the minimum variance function , That is to solve quadratic programming def minVar(self,goalRet): covs = np.array(
self.returns.cov()) means = np.array(self.returns.mean()) L1 = np.append(np.
append(covs.swapaxes(0,1),[means],0), [np.ones(len(means))],0).swapaxes(0,1) L2
= list(np.ones(len(means))) L2.extend([0,0]) L3 = list(means) L3.extend([0,0])
L4= np.array([L2,L3]) L = np.append(L1,L4,0) results = linalg.solve(L,np.append(
np.zeros(len(means)),[1,goalRet],0)) return np.array([list(self.returns.columns)
,results[:-2]]) # Define the function of drawing minimum variance leading edge curve def frontierCurve(self): goals = [x/500000 for x
in range(-100,4000)] variances = list(map(lambda x: self.calVar(self.minVar(x)[1
,:].astype(np.float)),goals)) plt.plot(variances,goals) # Define the proportion of each asset , Calculate the average yield def
meanRet(self,fracs): meanRisky = ffn.to_returns(self.returns).mean() #assert
(len(meanRisky == len(fracs),'Length of fractions must be equal to number of
assets') return np.sum(np.multiply(meanRisky,np.array(fracs))) # Define the proportion of each asset , Calculate the variance of yield
def calVar(self,fracs): return np.dot(np.dot(fracs,self.returns.cov()),fracs)
# Calculate effective leading edge minVar = MeanVariance(data) minVar.frontierCurve()

# Calculate the weight of training set varMinimizer = MeanVariance(train_set) goal_return = 0.003
portfolio_weight= varMinimizer.minVar(goal_return) portfolio_weight # Calculate test set yield
test_return= np.dot(test_set,np.array([portfolio_weight[1,:].astype(np.float)]).
swapaxes(0,1)) test_return = pd.DataFrame(test_return,index = test_set.index)
test_cum_return= (1+test_return).cumprod() # Calculate random weight combination sim_weight = np.random.
uniform(0,1,(100,len(stockslist))) sim_weight = np.apply_along_axis(lambda x: x/
sum(x),1,sim_weight) sim_return = np.dot(test_set,sim_weight.swapaxes(0,1))
sim_return= pd.DataFrame(sim_return,index = test_cum_return.index)
sim_cum_return= (1+sim_return).cumprod() plt.plot(sim_cum_return.index,
sim_cum_return,color = 'green') plt.plot(test_cum_return.index,test_cum_return,
label= 'MeanVar') plt.legend() plt.title('MeanVar & Random')

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