This is a summary of links recently featured on Quantocracy as of Saturday, 04/18/2026. To see our most recent links, visit the Quant Mashup. Read on readers!
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Exploiting Mean-Reversion in Decentralized Prediction Markets: Evidence from Polymarket [Quantpedia]This study examines the profitability of mean-reversion trading strategies applied to binary outcome contracts on Polymarket, the worlds largest decentralized prediction market platform. We analyze three distinct contracts representing varying risk profiles: a quasi-risk-free instrument (No to Will Jesus Christ return in 2025?) and two high-yield speculative contracts (No to Will China
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Inflation as a trading signal [Macrosynergy]Simple inflation-based trading factors have proven their predictive power in global financial markets over the past decades. Excess inflation ratios measure the average difference between CPI growth and a countrys effective inflation target (relative to that target). Inflation pressure ratios combine excess inflation ratios with recent CPI growth surprises. Both can be calculated for headline
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The AutoTune filter [Financial Hacker]By the Fourier theorem, any price curve is a mix of many long-term and short-term cycles. Once in a while a dominant market cycle emerges and can be exploited for trading. In his TASC 5/2026 article, John Ehlers described an algorithm for detecting such dominant cycles, using them to tune a bandpass filter, and creating a profitable trading system. Heres how to do it. Ehlers Easylanguage
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Looking Inside The Black Box [Vertox Quant]People often criticise how ML models are just black boxes that take in some features and spit out a prediction. While some models (like linear regression) are naturally a lot more interpretable than others (like neural networks), its wrong that you cant figure out why a model made a certain prediction and how the different features affect the prediction. In this article, we will look at some