This is a summary of links recently featured on Quantocracy as of Wednesday, 08/12/2026. To see our most recent links, visit the Quant Mashup. Read on readers!
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How to Spot a Fake ML Trading Paper [Aligrithm]Three papers crossed my desk this month. One detects market manipulation with graph neural networks and reports 98.7% accuracy at 8.3 milliseconds per decision. A second, same author cluster, detects anomalies in high-frequency trading and reports a 15% accuracy improvement with an F1 of 0.915. A third uses XGBoost on Bitcoin technical indicators and reports 92.40% accuracy with a ROC AUC of
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I Scaled Out to Raise My Win Rate. It Didn’t Move and It Cost Me $115,000 [Jan Heger]You lock in gains, you stop giving winners back, and, the part everyone repeats, your win rate goes up. I tested it on 3,966 of my own trades. Two of those three claims are false, and the third one cost me a fortune. The setup I run an automated futures system with a fixed exit: each strategy takes its full target or its full stop. No partials, no breakeven moves; the trade rides to one of two
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Autopsy No.005: The congressional-trading ETF that beats the market [Morgue Labs]Where this came from. A reader replied to Autopsy 001 with a Morningstar page showing NANC ahead of the index and a fair question: what am I missing? They were not missing anything. The fund really has outperformed. This is the answer to what that outperformance is made of. Unlike our other case files this is not a pre-registered test of our own hypothesis. It is a descriptive analysis of
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The Rise of CTA ETFs [Concretum Group]Over the past few years, the liquid alternatives space has moved decisively into a new ETF era. What was once a narrow category now stretches from CTA to hedge-fund replication strategies, sometimes pairing those return streams directly with core stocks and bonds exposures. And thanks to their low-cost, daily liquidity and ease of access, these vehicles are attracting progressively more inflows.