Artificial intelligence is changing the financial industry in extraordinary ways. DataMotion projects that the fintech sector will spend over $26 billion on AI by 2026.
There are many great applications of AI in the financial sector. One of the best is the promise of taking advantage of high-frequency trading.
There are a lot of great AI tools that help traders engage in high-frequency trading. Keep reading to learn more.
AI Technology Makes High Frequency Trading Possible
Despite going through fluctuations over the last decade, high-frequency algorithmic trading (HFT) remains popular on the market. In the beginning of the 2010s, it accounted for more than 70% of all equity trades in the US alone. With constant advances in computing power, more people have been exploring the option on their own. This is a change in course compared to the status quo which had the market dominated by trading firms, often using their own purpose-built solutions.
Prakhar Ganesh has talked in depth about the benefits of using AI in high-frequency trading. He wrote a post in Towards Data Science that addressed the benefits of using artificial intelligence to take advantage of these opportunities.
What Is High-Frequency Algorithmic Trading and How Does AI Help?
HFT takes the human component out of trading operations almost entirely. It utilizes programs that constantly monitor the market and make split-second trading decisions based on an algorithmic approach. With the scene for those algorithmic solutions moving towards a relatively stable state in the last few years, focus has shifted to sheer processing power and network speed.
AI algorithms are the basis for high-frequency trading. This is a prime example of how big data impacts stock trading. They use machine learning technology to better anticipate the best times to place trades. They use a variety of historic data to identify the situations when prices are most likely to increase. They then have automated processes to place trades accordingly.
Despite that, choosing an appropriate HFT solution is an important decision for anyone starting out in this field. Different products on the market have their advantages and disadvantages, and sometimes the best solution is to combine multiple tools and fine-tune them to one’s needs.
How to Pick a Good HFT Solution that Uses AI
Choosing a suitable AI-driven HFT solution comes down to finding a balanced mix between several factors:
- Market connectivity: different markets provide their data in different formats and protocols. An advanced HFT tool should allow for maximum connectivity to various platforms with minimal additional interaction on the user’s side.
- Latency: good HFT solutions should be designed with minimal latency in mind. While reaching zero latency is practically impossible, reducing each operation by as little as 0.1 seconds can have a noticeable effect on the trading solution’s performance.
- Data access: ideally, you should be able to run an HFT tool without having to specifically set up its access to trading data. HFT algorithms are created specifically for working with real-time data and should be able to retrieve current market information without any special setup. An added bonus would be the ability to set up access to your own training data so you can evaluate the performance of the algorithm under certain conditions and parameters.
- Customization options: you should be able to experiment with different parameters to fine-tune your HFT solution for optimal performance. The availability of customization options and advanced settings varies from product to product. In some solutions, almost everything is hard-coded.
Popular AI Tools on the Market
Here are some of the currently popular tools on the market. They all use sophisticated AI algorithms to make the best trades. More than a thousand different solutions are available at places like the MQL5.community Market, so make sure to do your own research if you have specific functionality in mind.
Prime Gold HFT Pro
Prime Gold HFT Pro is a relatively cheap package with impressive performance across the board. The latest version is very new on the market and uses complex machine learning algorithms, and the developer is highly communicative, including advising users on the optimal parameters for their use of the tool. It supports back-testing, allowing you to verify its performance before deploying it in a live environment.
HFT Hunter faced some initial criticism, but the developer has been responsive to user inquiries and has provided advice on adjusting parameters for the best results. They have tweaked their AI to be more responsive to market conditions. It comes at a very low price compared to other HFT tools, which can make it a good starting point for traders looking to test the waters before committing to a more expensive solution.
MQL5 language is an established programming language specifically aimed at the development of trading robots and similar solutions. It’s highly compatible with C++ (around 90% direct compatibility) and supported by an active community of developers who regularly release new solutions and support others.
Several books have been written on the subject, and various online resources are available for free as well. “High-Frequency Trading” by Maureen O’Hara, Marcos Lopez de Prado, and David Easley is a good starting point if you want an in-depth look into the field. Investopedia also has an extensive article on the subject, featuring links to various external resources that can allow you to deepen your studies.
At What Point Does It Make Sense to Build Your Own Tool?
Using pre-made HFT software can only get you that far. At some point, it’s advisable to look into building your trading robot according to your own specifications. This can be a time-consuming task, especially if you don’t have any programming experience. Even for experienced developers, it can take over a year to create the first iteration of a trading robot from scratch. You should look into contracting a freelance developer to implement your specifications if you don’t feel confident tackling this task yourself.
AI Tools Create More Opportunities for High-Frequency Trading
Finding an AI-driven HFT solution that fits all of your requirements is a difficult task that often takes a lot of time. You should be prepared to spend several weeks analyzing the market and testing different solutions before you find one that fits the bill. If you have a more advanced strategy in mind, there may be nothing available that matches all your criteria. You should be prepared to contract an experienced freelance software developer who also understands the financial market in depth to build a tool for you, or at least have sufficient AI programming knowledge in languages like C++, Python, Java, and MQL5 language to apply modifications to existing solutions if you want to get the most of what they have to offer.