MadEvolve: Evolutionary Optimization of Trading Systems with Large Language Models
“Here we demonstrate the utility of MadEvolve to optimize algorithmic trading strategies and alpha generation at the example of Bitcoin trading.”
AssetCryptospot crypto
StrategyNews sentiment
TechniqueLarge language modelsymbolic search
ClaimAlpha
Also stated: Text and news
Missing from abstract: benchmark, out-of-sample test, costs, multiple testing, universe
Institutional-grade audit of this strategy.
£5 — Request the audit- Audit of the paper claims accounting for costs, luck, crises, originality, geography, etc.
- Reproduction code integratable into Bloomberg, IBKR, etc.
- Bias-free datasets for backtesting included
- Delivered in 2 working days
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.
| Similar papers | citation | same method | same asset | same authors | |
|---|---|---|---|---|---|
| More Strategies, Same Zero: Multiple Testing Against LLM-Scale Alpha Search by Mehndiratta | 2026 | ||||
| From Hypotheses to Portfolios: NeuraQuant for Auditable Crypto-Alpha Discovery with Large Language Model by Zheng and Wang | 2026 | ||||
| Large Language Models and Generative Factor Discovery in Crypto Markets by Sun et al. | 2026 | ||||
| EVOQUANT: Self-Evolving Verifier-Guided Strategy Optimization for Robust Quantitative Trading by Mao et al. | 2026 | ||||
| QuantHarness: Price-Driven Multi-Agent LLMs for High-Frequency Trading by Xiong et al. | 2026 |