AutoHypothesis
Agents self-improve their stock portfolio strategy.
1 follower
Agents self-improve their stock portfolio strategy.
1 follower
I built a framework where ai agents search for market inefficiencies and optimize risk-adjusted returns through an agentic loop: (1) propose a hypothesis with economic justification, (2) iterate on historical data (2010–2016), (3) validate on out-of-sample periods (2017–2021). No hyperparameter tuning is allowed once a strategy enters validation, forcing the system to behave like a researcher. Holdout results (2022–2025): Sharpe 0.86 vs. 0.67 benchmark, 28.1% turnover, 11.4% max drawdown.
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