QuantHarness: Price-Driven Multi-Agent LLMs for High-Frequency Trading
A 2026 paper by Xiong and co-authors on news sentiment and text signals in crypto, using a large language model: the abstract mentions excess returns and leaves out a benchmark, an out-of-sample test, transaction costs, multiple testing and the size of the universe.
“Extensive experiments across nine financial instruments, including Bitcoin and Nasdaq futures, demonstrate that QuantHarness consistently outperforms baseline methods, achieving higher predictive accuracy at both 1-hour and 4-hour trading intervals across multiple evaluation metrics.”
AssetCryptofutures, spot crypto
StrategyNews sentiment
TechniqueLarge language model
FrequencyIntraday
Also stated: Excess return, Price and volume, Fundamentals, 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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