What quantitative trading is, and why humans still matter
- Article
- Finance
Buy low, sell high. Follow the news. Trust your gut. These are the ideas most people attach to trading. Quantitative trading replaces them with something else. This explainer, written by Artemio Arcega, Miranda Samson, and George Tobias for the society’s DataBytes series, is based on Callum McDougall’s Quant Trading Guide from 2020.
The definition
Quantitative trading uses mathematical models, data analysis, and computer algorithms to make decisions in financial markets. The point is to turn a trading idea into a rule that can be written down, tested on past data, refined, and executed by a program, rather than relying on intuition or on reading charts by eye. Math plus data plus programming, applied in a systematic way.
Everything under that heading depends on statistics and data science: parametric and non-parametric distributions, regression, and, more recently, machine learning models. The research side of the job is mostly building and testing those models.
What makes a good quant
According to the guide, the core requirements are strong quantitative skills (mathematics, logical reasoning, problem solving) and being comfortable making decisions under uncertainty. It also pushes back on some common fears. The work culture tends to be informal and people-oriented. You do not need to be a math genius; strong math helps, but olympiad-level math is not required. You do not need to be an expert coder, and you do not need prior finance experience.
Humans still matter
Despite the automation, traditional trading desks still exist, and the guide explains why. Not everything can be automated. Some markets lack data, and some are too illiquid for an algorithm to trade well. Someone has to supervise the models and step in when the market does something unusual. Traders often add the most value exactly when the systems behave unpredictably. The most important skill can be knowing when to trust the model and when to override it.
A small example
The guide does not include code, so the explainer adds a short script. It uses the yfinance library to download six months of Apple (AAPL) prices, computes 20-day and 50-day simple moving averages, computes a 14-day relative strength index, and plots both with Matplotlib. In the resulting chart, AAPL had been in a gradual downtrend over the six months, with the RSI dipping into oversold territory twice, in January and March 2026. What that pattern means for the next six months is left as a question for the reader.
For practice, the explainer points to open-source projects such as QuantLib, quantstats, Qlib, OpenBB, awesome-quant, awesome-systematic-trading, mlfinlab, and skfolio.
Source: this post is an explainer. It summarizes the Quant Trading Guide by Callum McDougall (2020), a self-published PDF that circulates as a shared document; no canonical hosted copy was found.