Project Overview: Financial Markets Platform


With the rise of Fintech in the past couple of decades, high-frequency trading exploits have become more and more problematic. HFT traders spend a lot of money on making their trading algorithms better and faster by a negligible amount. Our current financial market system, CDA, allows for this “HFT arms race” (here are a couple papers that describe the problem more in depth: see Aldrich and Budish). Since HFT isn’t illegal, my college’s economics research lab (LEEPS) wanted to test different stock market rules with HFT to see which set of rules would allow for the least amount of exploitation.

So, for my last two quarters in college, I worked with our economics research lab and a team of 5 other CS majors to develop a platform for the lab to run financial market experiments in support of their research paper mentioned earlier. With our platform, researchers are able to see graphical changes in the market due to the effects of HFT and other exploits on the market after the simulation is run with CDA, the current market system, and FBA, the proposed market system (to learn more about these systems, check out the “Aldrich” paper linked above). These market systems were pre-built by the lab.


A simulation will consist of different types of traders representing the types of trading that take place in real life (to learn more about the strategies, click on the GitHub button at the top!). The algorithmic trading strategy represents non-HFT algorithms, the smart trading strategy mimics HFT, and the random trading strategy generates random orders to represent the traders who don’t use any algorithms. Due to the limited amount of time we had to develop this platform and our limited financial knowledge then, we were only able to complete a few trading strategies. All the stock orders from these traders will be sent to the broker component.

The broker is a router between the traders and the stock exchange. It sends the orders made by the traders to the stock exchange, which then executes these orders. After execution, the stock exchange sends the confirmation messages to the broker, which then sends these messages back to the traders to let them know that their orders have been executed.

Additionally, there is an external feed that connects to the stock exchange and creates noise to simulate the real changes in the actual market prices. The stock exchange updates the prices and sends this information to the broker. The broker then relays this information to the traders so that the traders know what the current fundamental value of a stock is and can change the price of their orders accordingly.

The resulting graph made by the simulation will show three lines representing CDA, FBA, and the fundamental value of the stock that was being traded. The difference in prices from the fundamental value represents the amount of exploitation that happens from HFT. In other words, the farther away a line is from the fundamental value, the more exploits occur. As you can see from the graph at the beginning of this post, FBA prevents more exploits from happening than CDA.


Our platform was able to prove that FBA is indeed a better market system to reduce the amount of HFT-caused exploitation. Will this research change the financial market system we have in place today? That is up to our government to decide, but for now, I’ll stick with safe stocks to invest in on Robinhood.

Published by Mary Chern

First gen living in the Bay Area, software engineer, animal lover, and story teller. I love eating, traveling, and connecting!

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