Quantocracy

Recent Quant Links from Quantocracy as of 03/08/2026

Reinforcement Learning for Portfolio Optimization: From Theory to Implementation [Jonathan Kinlay] The quest for optimal portfolio allocation has occupied quantitative researchers for decades. Markowitz gave us mean-variance optimization in 1952, and since then weve 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 struggle to adapt. What if we

Reinforcement Learning for Portfolio Optimization: From Theory to Implementation [Jonathan Kinlay]

AI Will Create Millions of Quants [Kris Longmore] AI makes it easier than ever to build trading strategies. Prompt a model, run a backtest, optimise some parameters, and suddenly youve got a beautiful equity curve staring back at you. It feels like progress. It feels like research. I wrote recently about how AI coding assistants tend to prescribe more of the disease, faster, skipping the learning that makes trading research actually

AI Will Create Millions of Quants [Kris Longmore]

Macro trading signals with regression-based machine learning [Macrosynergy]

Recent Quant Links from Quantocracy as of 03/05/2026

New Contributor: Scaling Python Financial Models on AWS [Quantt] How to take a Python financial model from running 150 scenarios in a Lambda function to processing over a million using AWS Step Functions, Batch, and Fargate without managing a single server. From Laptop to a Million Scenarios You've built a financial model in Python. It runs beautifully on your laptop perhaps a portfolio stress-testing model that grinds through a few hundred

New Contributor: Scaling Python Financial Models on AWS [Quantt]

2-Year Notes Momentum: Extracting Term Structure Anomalies from FOMC Cycles from @quantpedia.bsky.social

The Market Rank Indicator: Measuring Financial Risk, Part 3 [Portfolio Optimizer] In the previous post of this series on measuring financial risk, I described the absorption ratio, a measure of financial market fragility based on principal components analysis, introduced in Kritzman et al.1. In this new blog post, I will describe another measure of financial distress called the market rank indicator (MRI), this time related to the notion of condition number2 of a matrix,

The Market Rank Indicator: Measuring Financial Risk, Part 3 [Portfolio Optimizer]

Correlated Time Series Generation using Object Oriented Python [Quant Start]

Sentiment Analysis Series Part 3: Three Ways the Sentiment Model Can Fail from @tommijohnsen.bsky.social Every day, financial news outlets publish thousands of articles about publicly traded companies. For investors, the obvious question is: does any of it actually matter? If a headline says a company just signed a major contract or passed a clinical trial, should you expect the stock to move the next day? Thanks for reading! Subscribe for free to receive new posts and support my work. This article

Sentiment Analysis Series Part 3: Three Ways the Sentiment Model Can Fail from @tommijohnsen.bsky.social

Recent Quant Links from Quantocracy as of 03/02/2026

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

The Winter of our Pairs Trading Discontent: Problems, limitations, frustrations [Robot Wealth]

Systematic FX trading with point-in-time GDP growth estimates [Macrosynergy]

Fund Selection When Borrowing Is Restricted [Alpha Architect]

Backtesting course from Rob Carver, March 7 and 8, in person and remote [Investment Idiocy]

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

State-Space Models for Market Microstructure [Jonathan Kinlay]

Recent Quant Links from Quantocracy as of 02/26/2026

Systematic Allocation in International Equity Regimes from @quantpedia.bsky.social

Time Series Models using Object Oriented Python [Quant Start]

More Bets, Better Bets [Quantitativo] Casino gambling with a system where you have the edge is a wonderful teacher for elementary money management. Ed Thorp Ed Thorp is the money manager I admire most. Many people have never heard of him. They should have. In 1961, with Claude Shannon the father of information theory he built the first wearable computer to beat roulette. He wrote Beat the Dealer and proved blackjack

More Bets, Better Bets [Quantitativo]

Recent Quant Links from Quantocracy as of 02/23/2026

Evaluating Reversal Potential in Niche Alternative ETFs from @quantpedia.bsky.social

Infra: Scraping financial data [Trading the Breaking] In fund research, the input that matters most is simple: what stocks are inside the funds right now, and in what weight. Without that look-through layer, fund momentum, category rotation, or risk exposure becomes a label-driven proxy. We are going to construct and execute a systematic fund scraping operation. The explicit objective of this architecture is to expose what underlying stocks these

Infra: Scraping financial data [Trading the Breaking]

Do Options Exhibit Momentum? from @harbourfrontquant.substack.com

Recent Quant Links from Quantocracy as of 02/22/2026

Kronos and the Rise of Pre-Trained Market Models [Jonathan Kinlay] 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. Weve become remarkably good at squeezing insights from limited data, optimizing hyperparameters on in-sample windows, and

Kronos and the Rise of Pre-Trained Market Models [Jonathan Kinlay]

Multivariate Break-Even Correlation Tresholds [Yannick Kalber]

Break-Even Correlation Thresholds for Linear Predictive Signals [Yannick Kalber]

Research Review| 20 February 2026 | Forecasting Returns [Capital Spectator]

Recent Quant Links from Quantocracy as of 02/20/2026