Four Eras of Quantitative Trading: Models, Speed, Alternative Data, and AI

Summary

This article presents a four-stage account of quantitative trading. It describes early strategies built on financial theory, multi-factor stock selection, and linear regression; a later high-frequency phase centered on low-latency hardware and algorithms; an alternative-data phase using sources such as satellite imagery, spending data, and text sentiment; and a recent AI phase combining multiple data types with simulated market interactions and iterative strategy training.

The examples are illustrative claims, including accounts of LTCM, alleged closing-price manipulation, and funds using alternative data. The article supplies no citations, independent verification, or comparative performance analysis, so its historical claims and descriptions of current institutional practice should be treated cautiously. Its broad thesis is that sources of advantage shift as methods become crowded, but it does not provide a reproducible strategy, data, or evidence that autonomous AI agents can reliably predict or trade future markets.

Key ideas

• The article divides quantitative trading into model-driven, low-latency, alternative-data, and AI-focused phases.
• Early approaches used financial factors and regression to identify market relationships.
• High-frequency competition shifted attention toward hardware, connectivity, and execution speed.
• Alternative data can include imagery, spending flows, and textual sentiment.
• The AI discussion emphasizes multimodal inputs and simulated market behavior, but offers no validation of its claims.

Tags

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.