Researchers from Princeton, Ant Group and Stanford Introduce AQuA: A Two-Part Agentic Framework for Autonomous Factor Discovery and Model Development in Quantitative Finance

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Part I is a six-agent pipeline: Data Steward, Visual Analyst, Idea Miner, Factor Evaluator, Backtest Engineer and Research Librarian — orchestrated by an AI Manager. Agents never call one another; every handoff goes through the Manager, keeping runs auditable.

A factor enters as a falsifiable proposal, not an expression: hypothesis, mechanism, predicted direction, and refutation conditions. Only then is it assembled from the standard formulaic-alpha operator registry. Because every time-series operator reads only a trailing window and every cross-sectional operator reads only the current timestamp, causality is closed under composition. Three feedback loops run: direction calibration inside a backtest, falsification-driven belief update inside a run, and cross-run memory that steers the next search.

On a crypto five-minute universe the combined validation Spearman IC climbs across 20 research epochs to approximately 0.190, against 0.171 for an adapted AlphaMemo, 0.151 for an adapted AlphaGen, 0.137 for LSTM, 0.106 for LightGBM and 0.075 for an Alpha158-style baseline. Individual mechanisms stay weak — single-factor ICs of 0.026 to 0.037. The claim is about the harness, not one expression.

Part II: Config-Driven Model Development

Part II predicts each stock’s forward return over the next thirty minutes on intraday US equities. Training runs on 2010–2019, 2020 is an embargo gap nothing touches, and 2021–2025 is untouched test data. Selection uses an inner-validation slice from the end of the training window only.

A hypothesis here is one config diff — architecture, loss, sampler or optimizer — and one diff produces exactly one variant, keeping variants comparable. The predictor is a hybrid: a multi-scale 1-D convolutional front-end, a configurable backbone spanning LSTM, Mamba and attention (attention in the reported run), a cross-sectional stage that mixes across the panel, gated fusion and a pooled per-stock readout.

No single price-volume feature carries the signal: the strongest is a 5-minute return at −0.031, and a ridge combination reaches only +0.025. Across model families on identical data and the same evaluator, per-stock raw IC runs +0.0251 (ridge), +0.0397 (LGB), +0.0434 (xLSTM), +0.0535 (LSTM), +0.0613 (GRU) and +0.0843 for the hybrid — +0.0230 absolute over the best baseline, 37.5% relative. The two parts’ ICs use different conventions and the paper states plainly they should not be compared.

From Signal to Strategy

The per-stock score becomes a dollar-neutral threshold long/short book at a two-leg cost of 2 bps. Sector-neutralizing raises the held-out Sharpe to +2.15, with training and held-out values nearly equal. A causal volatility-targeting overlay lifts it to +2.50, and a fully causal walk-forward choosing every parameter from past data alone still reaches +2.00. Per-stock R² is 1.20%. Sharpe by year runs +1.7, +3.5, +1.9, +1.8 and +2.7 for 2021 through 2025 — positive in every year, including the 2022 drawdown.

Key Takeaways

• Two independent research loops: factor discovery and model development, share no agents, memory or state.
• Freedom is asymmetric: the agent explores inside a DSL, but splits, features, labels and evaluator are sealed.
• Part I hits ~0.190 combined IC on crypto; Part II hits +0.0843 per-stock IC versus +0.0613 for a GRU.
• The equity book holds a +2.50 Sharpe at 2 bps and is positive in all five years, 2021–2025.

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_{Note:Thanks to the Ant Research team for the thought leadership/ Resources for this article. Ant Research team has supported this content/article for promotion.}