Matt's a machine learning expert.

Jason's a quant.

Spoiler alert: That's why we teamed up to build Quant Science.

In today's issue, we're going to combine Matt and Jason's experience and build a state of the art stock portfolio using an advanced technique.

The good news?

You don't need a Ph.D. to do it.

In today's issue of the QS Newsletter (get the code), we are going to use machine learning to build an advanced portfolio using the riskfolio package.

What You’ll Learn:

1. Build the optimal portfolio and visualize it with a dendogram chart

2. Optimize the portfolio and visualize the optimal weights

3. Understand how each asset contributes to the risk of the portfolio

BONUS: Get the Python Code for EVERYTHING you see in this post

Disclaimer:

The information and educational material provided by Quant Science, LLC are for educational purposes only and should not be considered as financial advice or recommendations to purchase, hold, or sell any securities or other financial instruments. Before you proceed, please review our full disclaimer here.

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Now on to the show...

Use machine learning to build portfolios

A cutting edge technique called Hierarchical Risk Parity (HRP) uses graph theory and machine learning to build a hierarchical structure of the investments.

First, make sure to sign up for our Newsletter to get all of the code you see today.

Imports and set up

In the first step, we’ll use the excellent RiskFolio-Lib to build our HRP portfolio and yfinance for market data.

Run this code:

Next, grab historic data and compute returns.

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Build the optimal portfolio

We can plot the dendrogram to visualize which ETFs are clustered together.

The plot visualizes the hierarchical clustering of assets based on their historical return correlations. It illustrates how clusters of assets are merged at each hierarchical level and can give us insight into the correlation structure within a portfolio.

Building the optimal portfolio based on the hierarchy is one line of code.

Additional parameters like linkage, max_k, and leaf_order are specified to fine-tune the clustering and dendrogram construction process.

The result is a pandas Series with the optimal weight for each of the assets.

Visualize the results

RiskFolio-Lib makes it easy to visualize the results of the optimization. The result of running the last few lines of code is the portfolio weights.

We can also visualize the risk contribution of each asset.

The risk contribution of each asset in a portfolio quantifies how much individual assets contribute to the total risk, considering both their own volatility and their correlation with other assets.

We can see the highest risk contribution is from OIH which is an oil ETF.

Risk contribution is important for identifying assets that disproportionately increase portfolio risk.

Congratulations!

You just learned how to use machine learning to build an optimal stock portfolio.

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