EvoQuantLab: Training-Free Dual-Loop Evolution for Auditable Quantitative Research

Haolin Yang; Zhiqiang Qian; Chutian Chen; Jipeng Zhang; Zhenghao Zhu; Tangjia Deng; Jian Xie; Shuaishuai Gong

cs.AIOct 12, 2026

EvoQuantLab: Training-Free Dual-Loop Evolution for Auditable Quantitative Research

Authors: Haolin Yang, Zhiqiang Qian, Chutian Chen, Jipeng Zhang, Zhenghao Zhu, Tangjia Deng, Jian Xie, Shuaishuai Gong,

Organizations: The Hong Kong University of Science and Technology · Microsoft · Tsinghua University · University of Macau

Abstract

Automated alpha factor discovery is critical for modern quantitative research. While large language models (LLMs) have increasingly been integrated into quantitative workflows, existing agent-based approaches remain highly susceptible to severe backtest overfitting. To address this challenge, we propose EvoQuantLab, a training-free, dual-loop self-evolving framework for robust factor discovery. Instead of relying entirely on LLM generation, EvoQuantLab deploys an inner genetic-programming (GP) loop that dramatically improves token efficiency while evolving typed factor expressions under structural and diversity constraints. An outer diagnosis loop then systematically audits evidence across cycles to drive protocol-level improvements. Furthermore, our evaluation pipeline applies multi-metric validation to rigorously filter out spurious factors before deployment. Extensive out-of-sample experiments across both daily and hourly frequencies -- covering equities and cryptocurrency -- demonstrate the robustness of EvoQuantLab. Specifically, on daily CSI 500 and S&P 500 tasks, EvoQuantLab achieves leading annualized returns of 18.34% and 21.30%, consistently outperforming existing agent baselines. On high-turnover hourly cryptocurrency data, EvoQuantLab delivers a 37.91% annualized return while effectively avoiding execution collapse.

Figures & tables

Figure 1: A conventional quantitative-research workflow. Iteration over development backtests is necessary, but repeated inspection makes the complete search history part of the statistical experiment. The held-out test must remain outside this feedback path.

Figure 2: Overview of EvoQuantLab . A frozen L0–L6 evidence pipeline evaluates every candidate under a fixed data and evaluation contract. The fast inner loop evolves factor expressions and reallocates search capacity using development-only evidence, while the slow outer loop diagnoses overfitting after each completed research cycle and proposes a versioned protocol for the next cycle. Information from held-out evaluation never writes back into the current cycle.

Dataset	Frequency	Market	Train / Dev Period	Prediction target
CSI 500	Daily	China equities	2025-03-31–2026-04-30	Next-day cross-sectional return
S&P 500	Daily	U.S. equities	2025-03-31–2026-04-30	Next-day cross-sectional return
Crypto	Hourly	Digital assets	2025-04-01–2026-03-31	Next-hour cross-sectional return

Table 1: Summary of the three evaluation tasks.

	CSI 500 (2026-05-06–2026-08-17)						S&P 500 (2026-05-01–2026-08-14)
Method	IC $\uparrow$	ICIR $\uparrow$	IR $\uparrow$	AR $\uparrow$	Ret. $\uparrow$	MDD $\downarrow$	IC $\uparrow$	ICIR $\uparrow$	IR $\uparrow$	AR $\uparrow$	Ret. $\uparrow$	MDD $\downarrow$
AlphaAgent	0.0177	0.1072	1.340	14.02%	4.06%	-7.09%	0.0070	0.0573	1.490	13.67%	3.96%	-3.48%
QuantaAlpha	0.0139	0.0893	0.939	9.41%	2.73%	-9.88%	0.0095	0.0663	0.796	7.59%	2.20%	-5.92%
FactorMiner	0.0187	0.0746	1.047	4.11%	1.19%	-3.26%	0.0173	0.0954	1.582	15.23%	4.41%	-4.18%
RD-Agent	0.0080	0.0438	0.546	6.31%	1.83%	-7.36%	0.0136	0.0580	0.902	13.07%	3.79%	-7.76%
EvoQuantLab	0.0281	0.1211	1.613	18.34%	5.31%	-4.78%	0.0233	0.1029	1.614	21.30%	6.17%	-5.82%

Table 2: Main experimental results on CSI 500 and S&P 500. Bold indicates the best result.

Figure 3: Cumulative excess returns of different approaches on CSI 500 and S&P 500.

Figure 4: Performance comparison on Crypto. Left: predictive and portfolio metrics of each method. Right: cumulative excess-return trajectories over the OOS period.

Method	Crypto	CSI 500	S&P 500	Mean
EvoQuantLab	4.055	1.041	2.466	2.521
RD-Agent-Quant	-0.229	1.002	-0.082	0.230
AlphaAgent	-2.385	0.798	-0.490	-0.692
QuantaAlpha	-4.999	-0.079	0.819	-1.420
FactorMiner †	0.952	0.547	1.382	0.960

Table 3: Out-of-sample IR of the single development-selected factor. Means reflect equal weighting across markets.

Variant	IC $\uparrow$	ICIR $\uparrow$	IR $\uparrow$	AR $\uparrow$	Ret. $\uparrow$
Full EvoQuantLab	0.0089	0.0299	3.709	37.91%	7.17%
Cycle 0 only	0.0067	0.0343	1.570	19.93%	3.77%
Cycle 0 w/o GP updates	–	–	–	–	–
No Loop B	0.0055	0.0281	0.752	6.58%	1.54%
No L1 search	0.0039	0.0120	2.331	27.44%	5.19%
No diversity controller	-0.0029	-0.0156	1.712	10.51%	2.58%

Table 4: Component ablation on the dual-loop framework. Metrics report out-of-sample performance of the top L5-approved factor chosen via a predeclared selection rule. Values marked with “–” indicate that no sampled candidates passed the validation filters to reach L5 approval.

Figure 5: Retrospective trajectory of discovered factor families and the cumulative L5 portfolio across outer cycles on Crypto. Crucially, all OOS IR and Return values shown are computed strictly post-hoc for retrospective analysis; no OOS signal is exposed to the search agent.

Method	Wall time	Time to first valid	Input tokens	Output tokens	LLM calls	API cost	L5 factors / hour
R&D-Agent-Quant	18m39s	1m47s	91745	18007	26	0.909	45
AlphaAgent	25m39s	5m39s	149870	67721	23	2.442	9.36
QuantaAlpha	2h4m	2m29s	767897	455379	94	15.224	10.2
FactorMiner	24m36s	50s	102771	11040	21	0.790	156
EvoQuantLab	22h08m	2h45m	520780	256000	50	9.000	0.95

Table 5: Efficiency and resource consumption comparison across factor discovery methods. API costs are computed using a standardized pricing schedule frozen across all runs.

Figure 6: Comparison of EvoQuantLab using different models (11 cycles each). Axes are normalized, with outer points indicating better performance.

Appendix figures & tables3 assets

Supplementary material from the paper’s appendix.

Appendix

Dimension	Required fields	Typical red flag
Generalization	train, inner validation, outer development in one metric	sign reversal or small outer/train ratio
Fold/seed stability	worst fold, dispersion, same-sign rate, rank consistency	winner identity changes with seed
Complexity	feature/parameter count, simple-model score, one-SE loss	simpler model is statistically tied
Selection	total and family trials, matched-budget permutation winners	real winner is ordinary under null search
Concentration	date/entity contribution and leave-group-out results	a few dates or assets explain the score
Placebo	shifted or block-permuted labels	placebo remains comparable to real score

Appendix

Table 6: Minimum Gate 1 evidence. Thresholds are calibrated per task before OOS access.

Candidate	gp-87c5cf72fc0e	gp-5f24c12b824c
Worst-fold score	5.487	5.298
Gate 1	Overfit-Model	Pass
Story / falsification	Fail	Pass
Residual validation	Pass	Pass
Final blocking evidence	unnecessary complexity	redundancy ( $\rho=0.726$ )
L5 decision	Reject	Reject

Appendix

Table 7: Two high-development candidates rejected by different validation stages. The worst-fold score is computed entirely on development data.

Market	Method	$M$	Dev-best IR	OOS IR	$\Delta$ IR	Sign Transition	Winner OOS Pct.	Positive Sign Survival
Crypto	EvoQuantLab	12	7.679	4.055	-3.624	$+\to+$	80v %	80.0%
	RD-Agent-Quant	7	-4.019	-0.229	+3.790	$-\to-$	100.0%	—
	AlphaAgent	1	-3.711	-2.385	+1.326	$-\to-$	100.0%	—
	QuantaAlpha	3	-1.683	-4.999	-3.316	$-\to-$	33.3%	—
	FactorMiner	1	-3.710	0.952	+4.662	$-\to+$	100.0%	—
CSI 500	EvoQuantLab	36	2.524	1.041	-1.483	$+\to+$	36.1%	95.5%

Appendix

Table 8: Comprehensive Dev-to-OOS overfitting and factor-transfer audit. All single factors are pre-registered strictly on development data without any OOS inspection. Positive sign survival measures the fraction of factors with

\text{Dev IR}>0

that remain positive in OOS.

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Open paper AskPDF arXiv

Modern quantitative trading increasingly relies on systematic models to extract predictive signals from large-scale financial data, where alpha factor discovery plays a central role in transforming market observations into tradable signals. Recent LLM-based methods have shown promise in automating factor generation, but most of them still rely on prompt-level generation--evaluation--feedback loops for iterative optimization. As the loop becomes longer, repeatedly appended historical candidates and feedback can cause context explosion, increase inference cost, dilute useful information, and introduce feedback drift. Moreover, these methods often depend on very large LLMs whose stable generation preferences may lead to structurally similar expressions, redundant candidates, and search stagnation. To address these limitations, we propose QuantEvolver, a self-evolving alpha factor discovery framework based on reinforcement fine-tuning. Instead of accumulating feedback in the prompt, QuantEvolver converts executable quantitative evaluation into policy updates, enabling a Miner LLM to internalize historical optimization experience through parameter learning. Specifically, QuantEvolver constructs high-quality seed factors, builds diverse seed--time-window training tasks, generates executable Factor DSL expressions, evaluates them through Regime Backtest, and optimizes the Miner LLM with Diversity-Complementarity Reward. During training, high-quality factors are continuously accumulated in a Mined Factor Database, which serves as the final discovered factor library. Extensive experiments on three realistic market benchmarks demonstrate the effectiveness of QuantEvolver, which consistently improves the primary evaluation metric of each task over existing LLM-based alpha factor discovery baselines, produces higher-quality and more complementary factor pools.

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