Maximize Your Returns: Portfolio Optimization for the Nasdaq 100 with Python

Imagine stepping into the world of high finance, not as a Wall Street mogul (yet!), but as a sharp investor equipped with Python. Our mission? To build a portfolio of Nasdaq 100 stocks that squeezes out the maximum return for every drop of risk we’re willing to take. It’s like finding the perfect recipe, but instead of flour and sugar, we’re dealing with stock tickers and algorithms.

Think of them as our trusty sidekicks: yfinance will fetch stock data, Beautiful Soup will help us scrape data from the web and pandas will be our data crunching machine. We'll visualize our findings with the artistry of matplotlib and seaborn, because, hey, even financial wizards appreciate a good chart.

Photo by Kari Shea on Unsplash

Table of Contents

• Data Acquisition and Preprocessing: We’ll scrape a list of Nasdaq 100 components straight from Wikipedia and wrangle that data into a usable format. Then, it’s off to the races as we download historical stock prices using yfinance.
• Exploratory Data Analysis: Before diving into complex calculations, we’ll get a feel for our data. Think of it as checking the ingredients before baking a cake. We’ll calculate descriptive statistics and create some eye-catching visuals.
• Modern Portfolio Theory and the Efficient Frontier…