QUANTITATIVE RESEARCH AND TRADING

First, a correction The panel in my September post was supposed to have zero alpha.

September 2026 I gave a research agent four years of prices with no predictable structure in them — none, by construction — and it came back with a long/short book, an in-sample Sharpe of 2.1, and a paragraph explaining the economics of an effect that does

In May I published a case study on running alpha research through a team of LLM agents: four roles, instrumented handoffs, and roughly a 2× lift in hypotheses tested per week.

A 12-Week Practitioner Case Study There is by now a small mountain of vendor material claiming that AI agents will run hedge funds.

Beating TWAP on a LOBSTER Replay Optimal execution is the part of the trading stack where small percentages compound into real money.

A self-contained synthetic benchmark of a small mask-conditional CNN against calendar-projected linear interpolation and a per-slice SVI fit.

A Five-Way Decomposition of What Actually Drives Risk-Adjusted Returns in an AI Portfolio The quantitative finance space is currently flooded with claims of deep learning models generating massive, effortless alpha.

A Practical Guide to Attention Mechanisms in Quantitative Trading Introduction Quantitative researchers have always sought new methods to extract meaningful signals from noisy financial data.

The Quest for Portfolio Optimization The quest for optimal portfolio allocation has occupied quantitative researchers for decades.

In my recent piece on Kronos, I explored how foundation models trained on K-line data are reshaping time series forecasting in finance.

Time Series Foundation Models for Financial Markets: Kronos and the Rise of Pre-Trained Market Models The quant finance industry has spent decades building specialized models for every conceivable forecasting task: GARCH variants for volatility, ARIMA for…