The latest theories, models and investment strategies in quantitative research and trading
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. The post was careful about what it measured. It was silent about a risk I had not thought to price. Here is the risk. Continue reading "Your Research Agent Is an Undisclosed Factor Exposure — And So Is Everyone Else s" The…
A 12-Week Practitioner Case Study There is by now a small mountain of vendor material claiming that AI agents will run hedge funds. The reality on the ground — for those of us who actually do the work — is more interesting and more useful. Agentic workflows, properly constructed, materially accelerate the parts of quant Continue reading "Agentic Workflows for Alpha Research" The post Agentic…
Beating TWAP on a LOBSTER Replay Optimal execution is the part of the trading stack where small percentages compound into real money. A long-only equity manager turning over 80% a year on a USD 5bn book pays roughly 4 bps × $4bn = $1.6m for every basis point of slippage. The textbook approach — Almgren–Chriss Continue reading "Reinforcement Learning for Optimal Execution" The post Reinforcement…
A self-contained synthetic benchmark of a small mask-conditional CNN against calendar-projected linear interpolation and a per-slice SVI fit. A volatility surface marker is rarely a clean rectangle of quotes. Strikes go unobserved during illiquid hours, wings get crossed and then erased, broker stripes drop out across an entire maturity, and weeklies arrive at the desk Continue reading "Deep…
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. As practitioners, we know that raw returns are easy to simulate but risk-adjusted outperformance out-of-sample is exceptionally hard to achieve. In this post, we build Continue reading…
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. Over the past decade, the field has progressed from linear factor models through gradient-boosting ensembles to recurrent architectures such as LSTMs and GRUs. This article explores the next step in Continue…
The Quest for Portfolio Optimization The quest for optimal portfolio allocation has occupied quantitative researchers for decades. Markowitz gave us mean-variance optimization in 1952,¹ and since then we ve seen Black-Litterman, risk parity, hierarchical risk parity, and countless variations. Yet the fundamental challenge remains: markets are dynamic, regimes shift, and static optimization methods…
In my recent piece on Kronos, I explored how foundation models trained on K-line data are reshaping time series forecasting in finance. That discussion naturally raises a follow-up question that several readers have asked: what about the architecture itself? The Transformer has dominated deep learning for sequence modeling over the past seven years, but a Continue reading "State-Space Models for…
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 mean reversion, Kalman filters for state estimation, and countless proprietary approaches for statistical arbitrage. We’ve become remarkably…
Introduction If you ve been trading anything other than cash over the past eighteen months, you ve noticed something peculiar: periods of calm tend to persist, but so do periods of chaos. A quiet Tuesday in January rarely suddenly explodes into volatility on Wednesday—market turbulence comes in clusters. This isn t market inefficiency; it s a fundamental stylized fact Continue reading "Volatility…