Quantitative trading is not new. Its roots stretch back decades, to a handful of mathematicians and scientists who saw markets differently than their contemporaries.
The Pioneers
Edward O. Thorp demonstrated in the 1960s that mathematical methods could beat both casinos and markets. His work on option pricing predated Black-Scholes by years. Jim Simons, a former codebreaker, founded Renaissance Technologies and built what may be the most successful hedge fund in history.
The Democratization of Data
What was once the province of a few well-funded institutions is now accessible to smaller players. Computing power that cost millions in the 1990s fits in a laptop today. Data that was closely guarded is now widely available.
The Alpha Decay Problem
As more capital pursues quantitative strategies, edges become harder to find and faster to decay. The half-life of alpha shrinks. This reality demands continuous research and adaptation—what worked yesterday may not work tomorrow.
The Future
Machine learning and alternative data offer new frontiers, but the fundamental challenge remains unchanged: distinguishing signal from noise, managing risk, and executing with discipline. The tools evolve, but the principles endure.
We stand on the shoulders of giants, and we continue the work they started.
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