Algorithmic Trading with Python – Free 4-hour Course With Example Code Repos

Algorithmic trading, also known as algo trading, is a method of executing orders using automated pre-programmed trading instructions accounting for variables such as time, price, and volume. This allows traders to trade quickly, accurately and at high frequencies that would be impossible to achieve manually.

In recent years, algorithmic trading has become increasingly popular due to advances in technology and the availability of powerful tools like Python. In this article, we‘ll explore the world of algorithmic trading with Python and take an in-depth look at a free 4-hour course (with code repositories) that will teach you how to build your own algo trading systems from scratch.

Why Python for Algorithmic Trading?

Python has become the go-to programming language for algorithmic traders for several reasons:

1. Extensive Libraries: Python offers powerful libraries for data analysis (pandas), numerical computing (NumPy), scientific computing (SciPy), and machine learning (scikit-learn). These enable rapid development of sophisticated trading algorithms.

2. Simple Syntax: Python has a clean and expressive syntax that allows developers to write concise, maintainable code. This is especially valuable in trading where speed and efficiency are critical.

3. Wide Adoption: Python is widely used in finance and trading, with a large ecosystem of tools, frameworks, and educational resources. Whatever trading workflow you need, chances are there‘s a Python package for it.

4. Performance: While Python itself is a relatively slow language, it integrates well with high-performance languages like C++. Many Python trading libraries are actually Python wrappers around optimized C/C++ code, giving you the best of both worlds – an expressive high-level interface with fast low-level execution.

Algorithmic Trading Course Overview

The free course "Algorithmic Trading with Python" is a 4-hour video tutorial developed by investment analyst and software developer Nick McCullum. It‘s a practical, hands-on introduction to building algorithmic trading strategies using real market data.

By the end of the course, you‘ll have built three complete trading algorithms:

1. An equal-weight S&P 500 index fund
2. A quantitative momentum investing strategy
3. A quantitative value investing strategy

Each project introduces key concepts and guides you step-by-step through the implementation in Python. Full source code is provided in a linked GitHub repository, with both starter files and final versions.

Let‘s take a closer look at each of these projects and the skills you‘ll learn along the way.

Project 1: Equal-Weight S&P 500 Index Fund

The first project tackles a common algorithmic trading task – building an index fund. An index fund is an investment that attempts to track the performance of a market index like the S&P 500.

The S&P 500 is a market-capitalization-weighted index of the 500 largest U.S. publicly traded companies. In a market-cap-weighted index, larger companies account for a greater portion of the index.

In this project, you‘ll build an equal-weight version of the S&P 500 index, where every company has the same weighting regardless of size. Here are the key steps:

1. Import the current constituent stocks of the S&P 500 index
2. Pull historical stock price data for each constituent
3. Calculate the equal weights for each stock
4. Generate an output file with the final stock weightings

Along the way, you‘ll learn how to:

• Import constituent data from a file
• Make API calls to retrieve financial data
• Calculate equal weights for a portfolio
• Export results to an output file

This project provides a foundation in pulling, processing and outputting financial data with Python. These skills are essential for any algorithmic trading system.

Project 2: Quantitative Momentum Investing Strategy

The second project dives into a powerful quantitative investing technique called momentum investing. Momentum investors believe that stocks that have outperformed recently (i.e. have "momentum") will continue to outperform in the near future.

You‘ll build a complete momentum investing algorithm in two stages:

1. A basic strategy using a single momentum metric
2. An advanced strategy combining multiple momentum metrics

The key steps are similar to the index fund project:

1. Import the current constituents of the S&P 500
2. Pull relevant data for each stock (e.g. historical prices, trading volume)
3. Calculate momentum scores for each stock based on the chosen metrics
4. Generate an output file with the top momentum stocks and their weightings

In addition to the data pulling and processing skills from the first project, you‘ll learn:

• The theory behind momentum investing and why it works
• How to calculate common momentum metrics like price returns and average volume
• Techniques for combining multiple factors into a single momentum score
• How to identify and weight the top momentum stocks in a portfolio

By the end of this project, you‘ll have a working momentum investing algorithm that you can enhance and adapt to your own needs.

Project 3: Quantitative Value Investing Strategy

The final project explores another cornerstone of quantitative investing – value investing. Value investors seek to buy stocks that are trading below their intrinsic value. They believe that these undervalued stocks will eventually rise in price to reflect their true worth.

Like the momentum project, you‘ll build the value investing algorithm in two phases:

1. A basic strategy using a single valuation metric
2. A more advanced strategy combining multiple valuation metrics

The implementation will follow a similar pattern:

1. Import the current S&P 500 constituents
2. Pull relevant financial data for each stock (e.g. earnings, book value, cash flow)
3. Calculate valuation scores based on the chosen value metrics
4. Generate an output file with the top value stocks for investment

Key concepts covered in this project include:

• The theory and rationale behind value investing
• Important valuation metrics like Price-to-Earnings (P/E), Price-to-Book (P/B), and Price-to-Cash-Flow (P/CF) ratios
• Techniques for normalizing and combining valuation metrics
• Identifying and weighting the top value stocks

After completing this project, you‘ll have a robust framework for value investing that you can extend and customize.

Accessing the Course and Code

The "Algorithmic Trading with Python" course is completely free and available on the freeCodeCamp.org YouTube channel. You can watch the full 4-hour video at your own pace and follow along with the code.

All of the code for the course projects is available in a public GitHub repository. The repo includes both starter code files and final versions, so you can check your work or skip ahead if you get stuck.

To get the most out of the course, I recommend that you:

1. Watch the video from start to finish to get a high-level overview
2. Follow along with the code and try to implement each project yourself
3. Experiment with the code and test your own ideas
4. When you‘re done, review the final code files and compare them to your own implementations

Remember, the goal isn‘t just to copy the code, but to understand the underlying concepts and techniques so you can apply them to your own trading ideas.

Challenges and Limitations

While the course projects provide an excellent introduction to algorithmic trading, it‘s important to understand their limitations:

1. Simplification: The algorithms in the course are simplified versions of real-world trading strategies. They don‘t account for important factors like transaction costs, slippage, and market impact.

2. Historical Data: The projects use historical stock data, which may not reflect future market conditions. Always test your strategies on out-of-sample data before deploying them with real money.

3. Risk Management: The projects don‘t include explicit risk management controls like stop-losses and position sizing. These are essential for live trading and should be added to any real-world algorithm.

4. Execution: The course focuses on generating trading signals, not executing them in the market. Executing trades involves additional challenges like order routing, latency, and liquidity that aren‘t covered.

Despite these limitations, the course provides a solid foundation for further learning and exploration. Once you‘ve mastered the basic techniques, you can start to layer on more advanced concepts.

Next Steps and Additional Resources

Algorithmic trading is a vast and complex field, and a 4-hour course can only scratch the surface. To continue your learning journey, here are some recommended next steps and resources:

1. Learn More Python: Deepen your Python skills with courses like "Python for Data Science and Machine Learning" and "Advanced Python Programming." The better you know the language, the easier it will be to implement your trading ideas.

2. Dive into Data: Learn how to work with large financial datasets using tools like pandas, SQLite, and Apache Spark. Understanding data is key to building robust trading algorithms.

3. Study Machine Learning: Many modern trading algorithms use machine learning techniques to identify patterns and make predictions. A course like "Machine Learning for Trading" can show you how to apply these powerful tools to trading.

4. Explore Advanced Techniques: Once you‘ve mastered the basics, explore advanced algorithmic trading concepts like market microstructure, high-frequency trading, and statistical arbitrage. Resources like "Algorithmic Trading: Winning Strategies and Their Rationale" can guide you.

5. Join a Community: Connect with other algo traders through online forums, meetups, and conferences. The QuantConnect and Quantopian communities are great places to start.

Remember, algorithmic trading is a journey, not a destination. The field is constantly evolving, with new techniques, tools, and challenges emerging all the time. The key is to stay curious, keep learning, and enjoy the ride.