I’ve just finished the course “Analyze Financial Data with Python”. I’m still struggling with importing data from APIs and editing a data frame (dropping columns/rows). Anyway it’s just so great to finish a simple project like this. I’ll try to improve my skills continuously. Would love to hear any comments/suggestion/recommendations or you can even share your projects for reference.
-- coding: utf-8 --
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Created on Thu Jul 22 15:00:17 2021
@author: ASUS
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import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from datetime import datetime
import time
import yfinance as yf
from scipy.optimize import minimize
import cvxopt as opt
from cvxopt import blas, solvers
def return_portfolios(expected_returns, cov_matrix):
port_returns =
port_volatility =
stock_weights =
selected = (expected_returns.axes)[0]
num_assets = len(selected)
num_portfolios = 10000
for single_portfolio in range(num_portfolios):
weights = np.random.random(num_assets)
weights /= np.sum(weights)
returns = np.dot(weights, expected_returns)
volatility = np.sqrt(np.dot(weights.T, np.dot(cov_matrix, weights)))
port_returns.append(returns)
port_volatility.append(volatility)
stock_weights.append(weights)
portfolio = {'Returns': port_returns,
'Volatility': port_volatility}
for counter,symbol in enumerate(selected):
portfolio[symbol +' Weight'] = [Weight[counter] for Weight in stock_weights]
df = pd.DataFrame(portfolio)
column_order = ['Returns', 'Volatility'] + [stock+' Weight' for stock in selected]
df = df[column_order]
return df
def optimal_portfolio(returns):
n = returns.shape[1]
returns = np.transpose(returns.to_numpy())
N = 100
mus = [10**(5.0 * t/N - 1.0) for t in range(N)]
# Convert to cvxopt matrices
S = opt.matrix(np.cov(returns))
pbar = opt.matrix(np.mean(returns, axis=1))
# Create constraint matrices
G = -opt.matrix(np.eye(n)) # negative n x n identity matrix
h = opt.matrix(0.0, (n ,1))
A = opt.matrix(1.0, (1, n))
b = opt.matrix(1.0)
# Calculate efficient frontier weights using quadratic programming
portfolios = [solvers.qp(mu*S, -pbar, G, h, A, b)['x']
for mu in mus]
## CALCULATE RISKS AND RETURNS FOR FRONTIER
returns = [blas.dot(pbar, x) for x in portfolios]
risks = [np.sqrt(blas.dot(x, S*x)) for x in portfolios]
## CALCULATE THE 2ND DEGREE POLYNOMIAL OF THE FRONTIER CURVE
m1 = np.polyfit(returns, risks, 2)
x1 = np.sqrt(m1[2] / m1[0])
# CALCULATE THE OPTIMAL PORTFOLIO
wt = solvers.qp(opt.matrix(x1 * S), -pbar, G, h, A, b)['x']
return np.asarray(wt), returns, risks
tickers = yf.Tickers([‘GOOG’, ‘JPM’, ‘UL’, ‘JNJ’])
start = datetime(2016,1,1)
end = datetime(2021,1,1)
#weekly_prices = tickers.history(period = ‘5Y’, interval = ‘1wk’)
weekly_prices = pd.read_csv(‘weekly_prices.csv’)
print(weekly_prices.head())
selected = list(weekly_prices.columns[1:])
weekly_returns = weekly_prices[selected].pct_change()
print(weekly_returns.head())
mean_returns = weekly_returns.mean()
cov_matrix = weekly_returns.cov()
port_return = return_portfolios(mean_returns, cov_matrix)
print(port_return.head())
weights, returns, risks = optimal_portfolio(weekly_returns[1:])
plt.scatter(port_return.Volatility, port_return.Returns)
plt.plot(risks,returns,‘y-o’)
ax = plt.subplot()
ax.set_xlabel(‘Risk’)
ax.set_ylabel(‘Expected Return’)
plt.title(‘Risks vs Returns of Random portfolios’)
