EVOQUANT: Self-Evolving Verifier-Guided Strategy Optimization for Robust Quantitative Trading
A 2026 paper by Mao and co-authors on news sentiment and text signals in crypto, using a large language model: the abstract mentions a Sharpe ratio and leaves out a benchmark, an out-of-sample test, transaction costs, multiple testing and the size of the universe.
“Experimental results show that our method significantly improves the Sharpe ratio across all tested strategies: the average test Sharpe increases from -0.298 to 0.538, and the best-performing strategy achieves a 199% relative improvement.”
AssetCrypto
StrategyNews sentiment
TechniqueLarge language model
GeographyChina
Also stated: Sharpe ratio, Text and news
Missing from abstract: benchmark, out-of-sample test, costs, multiple testing, universe
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The paper's neighbourhoodNews sentiment and text signals by year
The family is counted across the whole universe. Asset, method and authors are counted inside the family.
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