Autonomous Trading

A few weeks ago while I was at a quantitative finance conference, I had the pleasure of meeting a young man named William Mok, one of the co-founders of Tech Trader fund. At the ripe young age of 22, he had managed to develop and implement a fully autonomous trading system. What makes his system so unique compared to the thousands of other systematic trading systems in the markets is the inherent human element he claims it possesses.

A simple way to understand this is by looking at the process of walking across the street. A quantitative system would crunch thousands of numbers assessing the probability of a car crossing the street; the time of day, the temperature, the color of the grass etc... His system, Tech Trader, would simply look down the street to see there is no car, making it physically impossible to get hit. It leverages technology to do what the best traders do at scale and at maximum efficiency. But how exactly does it do this?

He claims the system contains three layers of artificial intelligence. The first, a layer of machine learning which acts like an extra pair of senses for a human like system. The second, a layer of reinforcement learning, abstracts the majority of the data and analyses it. This layer also helps the system learn from its past experiences (but only from its past experiences). This takes the form of Q-learning, a form of reinforcement learning used to find an optimal solution for a given set of decisions. The missing piece that ties the system together is the third layer. This layer helps the computer simulate human like qualities of dreaming, imagination and reflecting, storing the data like memories. It doesn't only consider what has happened but what could happen.

What I found most surprising are the stages of evolution of the system. He had told me that when the system first rolled out, it had a tendency to take a very high risk strategy approach, trading multiple times a day in and out of large positions. But as it gained data and information, it began taking a much more conservative trading approach, flattening out its overall variance over time.

The scope computer science has on finance is boundless in its limitations. As long as there are people like William Mok who are willing to continue to innovate and think outside the box, finance will continue to move forward in more exciting ways than any could even imagine.

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