This is a summary of links recently featured on Quantocracy as of Sunday, 03/08/2026. To see our most recent links, visit the Quant Mashup. Read on readers!
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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
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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
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Macro trading signals with regression-based machine learning [Macrosynergy]Regression-based machine learning is suitable for optimizing macro trading signals, particularly for combining multiple trading factors within a strategy. However, due to the low frequency of macroeconomic events and trends, the bias-variance trade-off in machine learning is very steep, meaning model flexibility comes at a high cost of instability. To improve the trade-off, regression-based