# Assistance in Encoding an AI High-Frequency Trading Algorithm

I have built a AI High-Frequency Trading system whose logic is very similar to how a quantum computer operates. The trading system enables one to manage large (500 to more than 2000 symbols simultaneously) baskets of stocks in real time. The software is causing some problems when operated on a classical computer: the complexity and speed of the system is such that both hardware and the Linux operating system tend to fail on a regular basis. I am hoping that converting the system to run on a quantum computer might solve the problem.

I am looking for assistance/guidance in rewriting logic that uses AI and digital signal processing techniques to process probabilities related to optimizing the value of a basket of stocks in real time. This is a purely Alpha (profit) seeking algorithm that is stable across time and market conditions. Technical problems with hardware and software not designed to handle the processing load is the primary issue at this time.

If anyone is interested in being of assistance, please leave a comment below. I believe I need a means of encoding probability statements that can be solved in parallel, which is effectively what I am doing now with a large server. A quantum computer seems to be an ideal tool for this problem. However, the available programming tools are somewhat primitive with respect to the complexity of solving a large number of probability logic equations in real time.

• Hi Daro,

This sounds like a really interesting problem!

It seems like you're approaching encoding your application onto the QPU from the right frame of mind - you will need to translate the problem to a binary quadratic model (QUBO/Ising) or constraint satisfaction problem in order to run it on the QPU. Have you taken a look at this guide on formulating a problem or this guide on solving problems on the D-Wave system

Given the size and complexity of your problem I think the Hybrid framework may be a good candidate. It decomposes a problem and runs the sub-problems in parallel on classical and quantum resources. It might be similar to what you're doing with a large server.

Here are a few papers on using quantum computing in portfolio optimization. I don't think any of them use a purely alpha seeking algorithm, but there may be some other similarities or approaches that could help.

Let me know if you have any questions!

• Alex,