If you’re diving deep into the waters of algorithmic trading, Pairs Trading is a strategy that might have caught your attention. In this article, we will take a detailed look at how to explore three different approaches for performing Pairs Trading based on our Final Year Project at HKUST. Let’s get started!

Understanding Pairs Trading: Approaches Explained

Imagine you’re in a garden filled with different plants. You notice that some plants grow harmoniously together, while others seem to compete for nutrients and sunlight. In the financial realm, Pairs Trading operates similarly; it’s about identifying two assets that generally move in tandem. Here’s a breakdown of the three main approaches we’ve tested:

• Distance Method: Think of this as measuring the distance between two plants in the garden based on size. A smaller distance indicates that they’re moving together, while an increased distance signifies a potential trading opportunity.
• Cointegration Method: This approach is like closely observing the water and sunlight needs of two plants to determine whether they can live together harmoniously over time. Two assets can be cointegrated if they maintain a stable relationship despite transient deviations.
• Reinforcement Learning Agent: Picture a gardener using trial and error to find the best conditions for plants to thrive. By continuously learning from past experiences and adapting practices, the agent seeks to optimize trading strategies.

How to Get Started with Pairs Trading

Ready to jump in? Here’s a step-by-step guide to kick off your Pairs Trading journey:

1. Start by cloning our repository from GitHub: My GitHub
2. Run the setup script to install all necessary dependencies:

bash setup.sh

3. Gather your financial data. Please note that our experiments used financial data from the Interactive Brokers platform, which is not publicly available. You’re encouraged to use your own price data.

Important Notes to Consider

While experimenting with Pairs Trading strategies, it’s essential to keep the following in mind:

• The strategies implemented in our project have not been proven profitable in live trading scenarios.
• Reported returns are based strictly on backtesting and might be subjected to lookahead bias.

Troubleshooting Tips

If you encounter issues when running Pairs Trading, here are some troubleshooting steps you can take:

• Make sure all dependencies are correctly installed by checking your setup script output.
• If the data does not load, double-check the file paths and ensure your dataset is properly formatted.
• Consider reaching out to the community for support. For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Updates and Future Directions

As of now, our team is no longer actively developing this project. However, we encourage you to check out Yuri’s findings regarding the Reinforcement Learning agent.

Final Thoughts

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.

Embrace the nuances of Pairs Trading, explore the various methods, and you may just cultivate a fruitful trading strategy!