Python is insane for finance! In this QS Newsletter (get the code), we are showing how to do algorithmic trading and quantitative finance data manipulations in Python 10X faster using a new library called Polars. Today, you learn:

• What is Polars (and how does is make algorithmic trading signals 10X faster)?

• Where can I use Polars in my workflow (and how can I screen 100X more stocks with it)

• 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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What is Polars (And Why Is It So Fast)?

According to the polars documentation:

The polars package for R gives users access to a lightning fast Data Frame library written in Rust. Polars’ embarrassingly parallel execution, cache efficient algorithms and expressive API makes it perfect for efficient data wrangling, data pipelines, snappy APIs, and much more besides. Polars also supports “streaming mode” for out-of-memory operations. This allows users to analyze datasets many times larger than RAM.

Why this is important to quants and algorithmic traders:

1. Polars is designed for fast data manipulation including grouped aggregations, rolling calculations, and expanding calculations, which are common in time series and financial analysis

2. Polars is super fast because it's built on top of Rust (a language that is as fast and in some cases faster than C++)

3. Polars is built for scale (it uses parallel execution, and handles data that is larger than memory)

4. You don't need anything other than Python to run it (Polars Rust implementation is transportable with a compiler so no extra build tools are required)

But how can we use Polars for algorithmic trading and quant finance?

That's where this tutorial will help.

Full Tutorial: How to do algorithmic trading signals and Quant Finance Analysis 10X Faster with Polars

Ok, let's dive in and see how to use Polars for algo trading signals and quant finance analysis. First, make sure to sign up for our Newsletter to get all of the code you see today.

Part 1: Download Stock Data from Yfinance for Stanley Druckenmiller's Portfolio (Top 25 Assets)

The code below downloads stock price data for the 25 top holdings from Stanley Druckenmiller's Portfolio (Duquesne Capital Management, LLC) valued at $4.39 Billion Assets Under Management (AUM) as of when this article was produced.

Run this code from the "QS018-polars" Folder to download stock data from the 13F filing:

This produces a Polars data frame. Here's what the data looks like:

Key Points:

1. This Polars Data Frame is in the "wide" format. We will normally pivot the data into the "long format" to perform data analysis by group.

2. This looks like Pandas, but it's not. It's Polars. You can tell because the data types are listed along with the data shape. Also there is no index with polars data frames.

Part 2: 10X Faster Algorithmic Trading Signals (Rolling Calculations)

Rolling calculations are a staple for algorithmic trading for signal development. Unfortunately, they take a long time to process especially for many time series groups (in finance, each stock symbol is a group).

Fortunately, Polars is extremely fast. In fact, it's between 10X and 3500X faster than Pandas.

Wide to Long Format

Run this code to convert the wide data into long format. We need long format so we can do grouped analysis (i.e. by stock symbol).

Visualize Prices Over Time

We can visualize the Stock Prices Over Time using 1 line of code in polars. Run this code:

Fast Algorithmic Trading Signals (10-Day and 50-Day Moving Averages)

Next, run this code to perform 10-day and 50-day moving averages for 25 stocks in a few milliseconds (about 10X faster than Pandas):

Visualize the Trading Signals

We can quickly visualize the trading signals for one of the stocks. Run this code to visualize NVDA:

You can see how fast the rolling averages were performed, and how quickly we can then develop trading signals with Polars.

Next, let's cover a common Quant Finance task...

Part 3: Rolling Sharpe Ratio for 10X Larger Quantitive Finance Stock Screening and Feature Engineering

A common task in Quant Finance is investigating the rolling Sharpe Ratio for tens of thousands of stocks. The rolling sharpe can be used as a screening tool or as a feature in financial machine learning models.

50-Day Rolling Sharpe Ratio by Stock

We can do this fast with polars. Run this code to calculate the returns by stock and then the 50-Day rolling Sharpe Ratio by stock:

50-Day Rolling Sharpe Plot (25 Stocks)

Next, run this code to visualize the 50-Day rolling Sharpe Ratio for each of the 25 stocks.

There you have it-- An easy way to screen stocks by rolling Sharpe Ratio and add a rolling Sharpe Ratio to your Algorithmic Machine Learning models. With Polars we can scale this to 100,000s of assets.

Conclusion: Polars is insane for Finance

Polars is insane for finance. We've used Polars to perform fast rolling calculations for algorithmic trading signals and to create scalable rolling sharpe ratios for screening and machine learning trading models. The best part-- We can scale this to 100,000+ stocks.

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