This is a summary of links recently featured on Quantocracy as of Thursday, 07/23/2026. To see our most recent links, visit the Quant Mashup. Read on readers!
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What Should You Change First in a Crypto Backtest? 99.75 Million Tests [Rulyfi]Key takeaways Neither entries nor exits controlled every result. Win rate was more sensitive to exits in all ten market-direction jobs, while maximum drawdown was more sensitive to entries in all ten. Changing an entry indicator was a much larger move than nudging that indicator's period. Treating both as "entry tuning" hides the useful distinction. For the study's
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Getting the Target Right in Return Prediction [Quantpedia]Recent interesting research from Cakici and Zaremba, highlights an often-overlooked aspect of machine learning for equity return prediction: the choice of prediction target. Rather than focusing on increasingly sophisticated model architectures or feature engineering, the authors show that how returns are represented during training has a much larger impact on predictive performance. In
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Algorithmic Trading, HFT, and Market Stability [Relative Value Arbitrage]Advances in computing power, declining hardware costs, and the rapid rise of machine learning and algorithmic trading have fundamentally transformed modern financial markets. While these technologies have improved market efficiency and execution, they have also introduced new challenges and risks. In this post, we examine research on the impact of algorithmic trading, from its influence on