In Vietnam, Quantitative trading has become increasingly popular, particularly due to WorldQuant’s influence. Many consider all traders who apply data analytics and computational power in trading different from manual trading as quant trading. However, there are distinct differences between quantitative trading, algorithmic trading and high-frequency trading. Understanding these disciplines can help traders choose the right career path. This article aims to clarify these distinctions.
One Term to Rule Them All: Applied Computational Finance
Currently, there is still significant overlap among these related disciplines. However, “applied computational finance” serves as the overarching term for all these fields.
Computational finance, a branch of applied computer science, deals with problems with practical interest in finance. It emphasizes approximations of the target rather than exact solutions.
Three main applications within computational finance are quantitative trading, algorithmic trading and high-frequency trading, each with distinct approaches.
Quantitative trading focuses on developing models that predict target instrument values. It requires statistical methods, mathematical modeling, and complex mathematical skills. Artificial intelligence and machine learning also play a significant role in this field. To be qualified as a quant, a complex quantitative model is the criterion.
Algorithmic trading aims to fully automate trading processes, including data input, signal generation, executing, reporting, and risk management. Advanced algorithmic trading involves automating capital allocation, beta optimization, and multi-algorithm utilization. An algorithmic trader aims to automate all required processes to minimize human intervention.
High-frequency trading (HFT) prioritizes speed in the target market as its core competitive advantage. A vivid example of high-frequency trading is front running a sent order. When a retail client sends an order, HFT may catch this information, analyzes whether this order can generate profits or not. In case of positive return possibility, a HFT will send its order to monetize this information. And, the order of HFT will surely arrive much sooner that the original order of the retail client. In developed markets, a retail client order may take milliseconds to transfer but HFT will only need around 700 nanoseconds. So if one order is half way through the destination, a HFT even takes a lot of time to process data and sending another order will arrive much sooner than the original order. Another simple use case is latency arbitrage that exploits price differences across trading platforms. This strategy is ultra simple and the faster will be the winner.
An Intuitive Way of Trader Development
An experienced discretionary trader eventually becomes weary of time-consuming tasks like monitoring the market, placing orders, and managing their portfolio. Consequently, there’s a growing demand for automating these processes. When a trader is able to minimize human intervention in the process and relies on algorithms, they officially become an algorithmic trader.
Another trader believes in the utilization of modern technology and mathematical skills to discover superior alpha that can beat the market. He starts to focus on statistical arbitrage or applying machine learning and artificial intelligence aiming for great performance models. Achieving this goal brings them to the status of a quant trader.
After fully automating the process, the algorithmic trader tries to find other complex models that may enhance performance. After discovering working models, the quant trader comes to solve the problem of fully automation. When one achieves the target, they become a so-called “quantomation”.
A “quantomation” if ever thinking about investing in speed will invest intensively on hardware such as Field-programmable gate arrays (FPGAs). Successfully achieving this target will ultimately elevate them to a final title “Iron Trader”. This term draws inspiration from the well-known “Iron Man'' who utilizes all technological power to achieve superiority.
As it gets extremely hard for any individual to achieve “quantomation” or “Iron Trader” status, a collaboration between talent members is encouraged as a well known standard in the globe.
Final Thought
Despite the distinct names and differences among Quantitative trading, Algorithmic trading, and High-frequency trading, they actually share many similarities during their development. Traders need not worry about finding the absolute best approach; instead they should explore any viable path. The more expertise was gained, the closer all these fields will seem to be. And, best of luck!