This is a summary of links recently featured on Quantocracy as of Monday, 03/02/2026. To see our most recent links, visit the Quant Mashup. Read on readers!
-
The Winter of our Pairs Trading Discontent: Problems, limitations, frustrations [Robot Wealth]In the last article, we built up a conceptual understanding of universe selection: how to find pairs that diverge and converge in a tradeable way. We talked about measuring the thing you actually care about directly, rather than reaching for statistical tests like cointegration that sound perfect but turn out to be unstable in practice. The natural next step is to start trading them the
-
Systematic FX trading with point-in-time GDP growth estimates [Macrosynergy]Even a single basic macroeconomic factor applied to one derivatives market can generate material and consistent long-term risk-adjusted returns. This is illustrated using point-in-time GDP nowcasts in global FX forward markets. The deployment is based on a simple premise: relatively strong economic growth positively affects local-currency FX returns, owing to its support for higher real interest
-
Fund Selection When Borrowing Is Restricted [Alpha Architect]Selecting mutual funds is one of the most important jobs investors face. Yet the tool everyone reaches for, the Sharpe ratio, quietly assumes something most real people do not have: the ability, and willingness, to borrow at the risk free rate to lever the best fund up or down to their preferred risk level. Once borrowing is realistically restricted, the Sharpe ratio can stop lining up with
-
Backtesting course from Rob Carver, March 7 and 8, in person and remote [Investment Idiocy]No, it's not one of those 'make $$$ easy by trading' courses, it's a dull and tedious one about robust fitting and backtesting. This is the first* time I've taught outside of a university. * and possibly last, we'll see. This could be a one-off opportunity. In person and remote:
-
State-Space Models for Market Microstructure [Jonathan Kinlay]n 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 new class of models State-Space Models