Code:
#Import relevant libraries
import pandas as pd
import numpy as np
import pandas_datareader.data as web
import matplotlib.pyplot as plt
import scipy.optimize as spo
import pyfolio as pf
import math
#Fetch data from yahoo and save under DataFrame named 'data'
stock = ['ERIS.NS', 'CROMPTON.NS', 'GNFC.NS', 'GRAPHITE.NS','BEPL.NS','KMCSHIL.BO']
data = web.DataReader(stock,data_source="yahoo",start='07/01/2017',end='today')['Adj Close']
#Arrange the data in ascending order
data=data.iloc[::-1]
print(data.round(2))
#Compute stock returns and print the returns in percentage format
stock_ret = data.pct_change()*252
print (stock_ret.round(4)*100)
mybenchmark_return=web.DataReader('^NSEI',data_source="yahoo",start='07/01/2016',end='today')
#Calculate mean returns and covariances of all four the stocks
mean_returns = stock_ret.mean()
cov_matrix = stock_ret.cov()
print (mean_returns)
print (cov_matrix)
#Set the number of iterations to 10000 and define an array to hold the simulation results; initially set to all zeros
num_iterations = 1000000
simulation_res = np.zeros((4+len(stock)-1,num_iterations))
for i in range(num_iterations):
#Select random weights and normalize to set the sum to 1
weights = np.array(np.random.random(6))
weights /= np.sum(weights)
#Calculate the return and standard deviation for every step
portfolio_return = np.sum(mean_returns * weights)
portfolio_std_dev = np.sqrt(np.dot(weights.T,np.dot(cov_matrix, weights)))
#Store all the results in a defined array
simulation_res[0,i] = portfolio_return
simulation_res[1,i] = portfolio_std_dev
#Calculate Sharpe ratio and store it in the array
simulation_res[2,i] = simulation_res[0,i] / simulation_res[1,i]
#Save the weights in the array
for j in range(len(weights)):
simulation_res[j+3,i] = weights[j]
sim_frame = pd.DataFrame(simulation_res.T,columns=['ret','stdev','sharpe',stock[0],stock[1],stock[2],stock[3],stock[4],
stock[5]])
print (sim_frame.head (5))
print (sim_frame.tail (5))
#Spot the position of the portfolio with highest Sharpe Ratio
max_sharpe = sim_frame.iloc[sim_frame['sharpe'].idxmax()]
#Spot the position of the portfolio with minimum Standard Deviation
min_std = sim_frame.iloc[sim_frame['stdev'].idxmin()]
print ("The portfolio for max Sharpe Ratio:\n", max_sharpe.round(3)*100)
print ("The portfolio for min risk:\n", min_std.round(3)*100)
#Create a scatter plot coloured by various Sharpe Ratios with standard deviation on the x-axis and returns on the y-axis
plt.scatter(sim_frame.stdev,sim_frame.ret,c=sim_frame.sharpe,cmap='RdYlBu')
plt.xlabel('Standard Deviation')
plt.ylabel('Returns')
plt.ylim(0,.003)
plt.xlim(0.0075,0.012)
#Plot a red star to highlight position of the portfolio with highest Sharpe Ratio
plt.scatter(max_sharpe[1],max_sharpe[0],marker=(5,1,0),color='r',s=600)
#Plot a blue star to highlight position of the portfolio with minimum Variance
plt.scatter(min_std[1],min_std[0],marker=(5,1,0),color='b',s=600)
plt.show()
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