This is the fourth and final chapter of my series on an introduction to quantitative trading. Be sure to start with the first post, and check back soon for more quantitative trading news!
While quantitative trading may initially seem like a complex and foreign topic, hopefully through these last three posts, I’ve cleared up any confusion you may have and given you a solid understanding of this specialized area of the stock market. In truth, quantitative trading has a mystique or element of secrecy to it simply because the use of automated trading platforms is relatively new and it relies on sophisticated systems and algorithms. I hope to demonstrate that quant trading seems more complex than it actually is (for a basic understanding, anyway…actually becoming a quant trader requires an analytical, creative mind and a love of data and analysis- it’s not for everyone, but it was certainly the right path for me!).
When it comes to risk management, a key factor of quant trading, there are two primary considerations: compliance and money management. Because quant trading relies on automatic machines that run throughout the day in order to consume information electronically and respond, within a matter of microseconds, there is a margin of error that needs to be accounted for. Roughly 70-80 percent of U.S. equities are traded by machines automatically, so it’s a vast majority of the market, but a degree of human interaction is still required to optimize performance and manage risk.
The upside of using low-latency machines to process data electronically is that thousands of symbols can be traded at a time, with multiple machines operating simultaneously. The downside is the lack of human intervention, so that is where risk management comes in.
Any market is regulated, but the United States stock market is arguably regulated more than any other market. Therefore, quantitative traders need to adhere to strict compliance risk checks and take measures to avoid violating various regulations.
The second element of managing risk in quant trading is managing money. These low-latency machines potentially handle large sums of money and can enter large transactions before humans have time to respond. As a result, quantitative traders or the risk pieces they use need to be more prudent when managing money than they would have been before automated trading existed. There are a lot of large-scale considerations at stake with quant trading, the ultimate goal being to limit realized losses.
I could go into a lot more about quant trading, and I plan to delve deeper into more avenues of this industry in the future, but this is where I’ll leave you for the time being. I hope I’ve given you a good introduction to the topic of quantitative trading, but don’t hesitate to reach out if you have any questions!