Portfolio optimization using Python

What´s portfolio optimization?

Optimization can be roughly defined as a quantitative approach for decision making, where we seek to determine a “best” decision from a “set” of possible decisions.

Goal

With this project my main objective was to find the optimal portfolio with my favorite companies in the stock market, namely Tesla, Amazon, Apple, Google, Nvidia, Qualcomm and Microsoft.

Tools

import pandas as pd

import numpy as np

import matplotlib.pyplot as plt

import statsmodels.api as sm

import yfinance as yf

Data

stocks = ["AAPL", "AMZ", "MSFT", "TSLA", "GOOGL", "QCOM", "NVDA"]

data = yf.download(stocks, start="2018-01-01")

data = data["Close"]

x = data.pct_change()

Then I precided to create empty lists for weights, returns, risk and for the sharp ratio.

Generate random portfolios

count = 10000

for k in range(0, count):

wts = np.random.uniform(size = len(x.columns))

wts = wts/np.sum(wts)

p_weights.append(wts)

Then I did the same process for the other parameters and preceded to find the max values for the sharp ratio and the weights to each stock in the portfolio, using np.argmax.

Optimal portfolio

1.4270321023413632 [0.3264971 0.01088931 0.01399479 0.22366134 0.03132052 0.020364 0.37327295]

Using the scatter() function with the volatility and return lists as parameters:

plt.scatter(p_risk, p_returns, c=p_sharpe, cmap="plasma") plt.colorbar(label="Sharpe Ratio") plt.scatter(p_risk[max_ind], p_returns[max_ind], color="r", marker="*", s=100) plt.show()