Smart Beta Strategies: Creating Enhanced Index Funds with Machine Learning

Index funds have gained significant popularity due to their low costs and passive investment approach. However, investors are always looking for ways to enhance the performance of these index funds. One way to achieve this is through Smart Beta Strategies, which aim to improve returns by systematically selecting and weighting securities within an index.

In this tutorial, we will explore how to create enhanced index funds using Machine Learning techniques. We will download real financial data using the yfinance library, preprocess the data, implement Smart Beta Strategies and evaluate the performance of our enhanced index fund.

Photo by Joshua Mayo on Unsplash

Getting Started: Importing Libraries and Downloading Data

First, let’s import the necessary libraries and download the financial data for our analysis. We will use the yfinance library to download historical stock price data for a diverse set of securities until the end of February 2024.

import yfinance as yf
import numpy as np

# Downloading stock price data for a diverse set of securities
securities = ['GOOG', 'TSLA', 'NFLX', 'AMZN', 'MSFT']
start_date = '2019-01-01'
end_date = '2024-02-29'

data = yf.download(securities, start=start_date, end=end_date)['Adj Close']…