AlphaAgentEvo: Evolution-Oriented Alpha Mining via Self-Evolving Agentic Reinforcement Learning
Authors: Ziyi Tang
Xuexiong Yin
Weixing Chen
Zechuan Chen
Yongsen Zheng
Wenxuan Ye
Keze Wang
Liang Lin
Venue: ICLR 2026 Poster (2026)
Keywords: Alpha Mining
Agentic AI
Quantitative Investment
Self-evolving Agent
Primary area: foundation or frontier models, including LLMs
TL;DR: AlphaAgentEvo introduces a new evolution-oriented paradigm for alpha mining via self-evolving agentic reinforcement learning, outperforming traditional and LLM baselines—even surpassing state-of-the-art LLMs with only 1.7B–4B parameters.

Abstract: Alpha mining seeks to identify predictive alpha factors that generate excess returns relative to the market from a vast and noisy search space; however, existing evolution-based approaches struggle to facilitate the systematic evolution of alphas. Traditional methods, such as Genetic Programming (GP), cannot interpret natural language instructions and often fail to extract valuable insights from unsuccessful attempts, leading to low interpretability and inefficient exploration. Analogously, without mechanisms for systematic evolution, e.g., long-term planning and reflection, existing multi-agent approaches may easily fall into repetitive evolutionary routines, resulting in inefficient evolution. To overcome these limitations, we introduce AlphaAgentEvo, a self-evolving Agentic Reinforcement Learning (ARL) framework for alpha mining, which moves alpha mining beyond the brittle search-backtest-restart cycle toward a continuous trajectory of evolution. Guided by a hierarchical reward function, our agent engages in self-exploration of the search space, progressively learning basic requirements (e.g., valid tool calls) and then harder objectives (e.g., continuous performance improvements). Through this process, the agent acquires advanced behaviors such as long-horizon planning and reflective reasoning, which enable it to actively react to the underlying state (e.g., market regime shifts) and realize a self-evolving agent, marking a step toward more principled and scalable alpha mining. Extensive experiments demonstrate that AlphaAgentEvo achieves more efficient alpha evolution and generates diverse and transferable alphas, consistently surpassing a wide range of baselines. Notably, with only 4B parameters, it outperforms LLM-driven evolution methods configured with state-of-the-art closed-source reasoning models, highlighting the promise of ARL for next-generation alpha mining.

Published as a conference paper at ICLR 2026
ALPHAAGENTEVO: EVOLUTION-ORIENTER ALPHA MINING VIA SELF-EVOLVING AGENTIC REINFORCE-
MENT LEARNING
Ziyi Tang-
Xuexiong Yin-
Weixin Chen'
Zechuan Chen Liang Lin
Yongsen Zheng2.
Wenxuan Ye!
Keze Wang
Schooo of Computer Science and Engineering, Sun Yat-Sen University
"College of Computing and Data Science, Nanyang Technological University, Singapore DDigital Trust Centre, Nanyang Technological University, Singapore
*The Chinese University of Hong Kong
[tangzy27, yinxxx7, chenwx228 chenzch6) sysu. edu. on,
yongsen. zheng@ntu. edu sg, abwincenteliteeg ucla. ucla.edu, kezewange gma illil.... com, liningemail....mm.. sysu. et
ABSTRACT
Alpha mining seeks to identify predictive alpha factors that generate excess re- turns relative the market from vast and noisy search space; however, exist- ing evolution-based approaches struggle to facilitate the systematic evolution of alphas. Traditional methods, such as Genetic Programming (GP), cannot inter- pret natural language instructions and often fail to extract valuable insights from unsuccessful attempts, leading to low interpretability and inefficient exploration. Analogously, without mechanisms for systematic evolution, e.g., long-term plan- ning and reflection, existing multi-agent approaches may easily fall into repeti- tive evolutionary routines, resulting in inefficient evolution. To overcome these limitations, we introduce AlphaAgentEvo, self-evolving Agentic Reinforce- ment Learning (ARL) framework for alpha mining, which moves alpha mining wescch-bbbbtettttresttte cycle toward a continuous trajectory of evolution. Guided by hierarchical reward function, our agent engages in self- exploration of the search space, progressively learning basic requirements (e.g.. valid tool calls) and then more complex objectives (c.g., continuous performance improvements). Through this process, the agent acquires advanced behaviors such as long-horizon planning and reflective reasoning, which enable it to actively re- act to the underlying state (e.g., market regime shifts) and realize self-evolving agent, marking a step toward more principled and scalable alpha mining. Exten- sive experiments demonstrate that AlphaAgentEvo achieves more efficient alpha evolution and generates diverse and transferable alphas, consistently surpassing a wide range of baselines. Notably, with only 4B parameters, it outperforms LLM- driven evolution methods configured with state-of-the-an closed-source reasoning models, highlighting the promise of ARL for next-generation alpha mining.
beyond the brittle
INTRODUCTION
Alpha mining, which refers to uncovering quantitative signals that generate excess returns rela- tive to the market, remains highly challenging due to its vast search space, high-variance feedback signals, spurious correlations, and high computational cost. These issues make the process both computationally intensive and prone to false discoveries, highlighting the need for more system- atic approaches. In this context, the notion of alpha evolution becomes central: instead of viewing each candidate alpha an initial alpha into a superior version through multi-turn interactions that incorporate evaluative feedback, refine its structure, and enhance its performance. This evolution-oriented perspective not only increases the likelihood of uncovering genuinely effective alpha factors (hereafter referred to
independent trial, the process emphasizes progressively transforming
"Corresponding author.

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Published as a conference paper at ICLR 2026
factors
idea-sioned factor's
di weciiig dingh
evaluation
Random
toal call
tocl resp
how byg
foodback
tne fir stores
BACK
sood sipha
SuccesiTully
(a) Genetic programing
(b) Multi agent approach
(c) AlphaAgentEve: self evolvinv alpha mining
Figure I: Comparison of evolution-oriented approaches for alpha mining.
as alphas) but also helps preserve their internal logic and interpretability across interaction turns, offering principled alternative to ad hoc trial-and-error exploration. Despite this promise, existing approaches struggle to effectively realize such systematic alpha evolution.
Traditional evolution-oriented approaches, such Genetic Programming (GP) (Lin al., 2019 Schmidt & Lipson 2010; ZZhooaan er al.) (022), rely heavily on heuristic search and random muta- tion (Ren al. (222). ) without leveraging valuable insights from negative feedback, thereby missing opportunities to learn from failures and refine alpha design. Moreover, these methods cannot inter- pret their evolution process with human-readaby language, which not only limits their usability but also increases the risk of generating alphas that capture spurious correlations (Shi et al. 2025b).
Emerging Large Language Models (LLMs) (Yang al., 2025 DeepSeek-AL et al. 2025 OpenAl 2025) and multi-agent frameworks (Tang et al., 2025 Li et al. 2025) accommodate more nuanced human instructions often lack mechanisms for self-evolution, such as long-term planning and reflective reasoning from past outcomes. As result, they tend to get trapped in repetitive local modifications, leading to inefficient exploration. Consequently, current alpha mining workflows remain myopic: they search, backtest, and restart rather than systematically evolve alphas. This gap calls for an evolution-orientee paradigm that couples deliberate planning with reflective reasoning to refine alphas over multi-turn trajectories.
To overcome these limitations, we introduce AlphaAgentEvo, the first self-evolving Agentic Rein- forcement Learning (ARL) framework for alpha mining. First, AlphaAgentEvo aims move alpha mining beyond the brittle "search-backtest--e termed evolution, encouraging progressively refined alphas by searching diverse alphas grounded in given seed. During the ARL process, an LLM-driven agent engages in large-scale exploration of the alpha search space, interacting with an evaluation tool under the guidance of hierarchical reward structure. With this reward, the agent explicitly learns to satisfy basic requirements (e.g., constructing valid alphas) and then moves on to harder objectives (e.g., performance and streak improvements). More importantly, the agent acquires advanced behaviors, such as long-horizon planning and reflective reasoning, that enable it to actively react to the underlying state (e.g.. market regime shifting). Ultimately, AlphaAgentEvo gives rise to a self-evolving agent, marking step toward more principled and scalable alpha mining.
cycle toward a continuous interaction trajectory,
Results show that AlphaAgemlEvo, at relatively lightweight scale of 4B parameters, consis- tently outperforms GP, multi-agent, tool-use RL, and LLM-driven evolution baselines by a sig- nificant margin. Moreover, AlphaAgentEvo generates diverse and transferable alphas, avoiding over-exploitation of specific patterns and showing stronger out-of-sample robustness. These results underscore the superiority of our self-evolving ARL paradigm for evolution-oriented alpha mining.
Our key contributions are summarized as follows:
We propose a novel framework, AlphaAgentEva, that reformulates alpha mining from a brittle
backtest-restart loop into an volution-orienter paradigm. To the best of our knowledge, this is the first self-evolving agentic reinforcement learning framework for quantitative alpha mining.
We design hierarchical reward mechanism that guides an LLM-driven agent to perform multi-turn exploration of the alpha search space. This enables the agent to gradually ac- quire advanced behaviors, such as long-ho long-horizon planning and reflective reasoning, allowing it to adapt to changing market regimes and overcome performance bottlenecks in dynamic environments.
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Extensive experiments confirm that method demonstrates strong evolution efficiency and validity, diversity, and transferability of the generated alphas, achieving strong gener- alization in the self-evolving agent. Remarkably, with only 4B parameters, AlphaA- gentEvo surpasses strong baselines powered by state-of-the-art LLMs on several metrics.
2 METHOD
2.1 PROBLEM DEFINITION
alpha factor (or simply alpha) a quantitative signal designed to predict future stock returns. We consider stock universe S (s1,...,SN) over time horizon H. feature matrix X R Nxxxx where d denotes the number raw features. An alpha is mapping X Mh+1, where X, represents the market data observed up to and including time h and Th+1 is the subsequent return. Beyond static alpha mining, we view alpha evolution as learning an evolution policy instead of directly optimizing single alpha. Let Deed denote distribution over expert-designed seed alphas. For given seed fwent Diedd the policy π interacts with the backtesting tool for T turns and produces evolved alphas F (Seca)) We evaluate each evolved factor on two market distributions: Devo, which corresponds to the in-distribution regimes observed during the agent's multi-turn evolution, and Piest, which captures -ittt---iitriittionn market regimes. Formally, we learn the evolution policy by
hLL, with
(Bx-D. o(f;)) + AEx D santea//sss::: s(:x)) (
π arg max 13
max
7
(I)
s.t. sim(f, faea) 5 for all Fx
where s(; X) is the performance scoring function on market features X, distribution fitness and out-of-distribution generalization, and sim(-..) -) is an AST-based structural similarity between alphas. The constraint sim [f fseed) S leads the policy to search in local neighborhood of each seed, producing evolved alphas that are both stronger and still interpretable, rather than overfitting to noise via unconstrained global optimization.
0 trades off in-
2.2 SELF-EVOLVING AGENTIC REINFOR LEARNING
Existing RL-based fine-tuning approaches typically designed for single-turn language modeling or reasoning, where evaluation is per response and cross-turn coupling is weak. In contrast, alpha evolution is inherently multi-turn tool-in-the- loop process. To realize a self-evolving agent, extend GRPO (Shao et al. 2024; DeepSeek-A1 et al. 2025), into an Agentic Reinforcement Learning (ARL) formulation that directly optimizes the policy LLM in the cill---rooo---valllee loop with an external evaluation tool U.
In our formulation, each turn consists of trigger the tool, followed by tool response tokens (tool resp in Fig22;; all are appended the trajec- tory, but only policy-generatee tokens (indicated by M,,e) contribute to gradients. To broaden explo- ration, at turn t, the policy LLM produces a set of k, parallel offspring F+() as candidates, which are jointly evaluated by U. Note that when generating turn t's tokens, the policy LLM conditions on the entire past trajectory 71:-1 to refine alphas, enabling reflective reasoning across previous attempts.
reasoning tokens and tool call tokens that
For each input x sampled from the dataset D, a group of trajectories T {TI.... TG} are rolled out by the old policy Tola- Their rewards are normalized within the group to estimate their relative advantages {Á Ào} using the group average reward baseline, i.c., A, A(Ta)_HT where (uT and of denote the mean and standard deviation of [R(T;)]{=1 The optimization objective is
of
Σ, Mi, Σ Mi. min Te(T..c (d)
(0) = By-D, T (1
As,t ToalTTa 1 T.,,,
(2)
clip
Te(TE. ToalTT
Tc. U))
1 - 1 Ar.o) BD (TT Teed]
where t; is a complete evolution trajectory, Tye its f-th token, and To TTaa()) are the curren/old policies conditioned on o, past tokens Ti,ct, and the tool U. The mask Mi. excludes tool-emitted
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Reference LLM
traedoy's +p turn-
KL
Lask description
Evall Tool ju
Evolving
traeectories
tool call
tool resp
think
Polic
Reward Functon
next turn evolution
Rewards
update
Advantage
araizee X reflect plan...
tool call
tool
resp
A
Ag
Ag
Advantage
Toal
tum
score
(a) An alpha evolving trajectory
(b) AlphaAgenEEvo's agentic RL training process
Figure 2: Overview of the AlphaAgentEvo framework. (a) An alpha-evolving trajectory. In each turn of the trajectory, the agent (policy LLM) generates multiple tool calls (i.e., alpha proposals) after analyzing and reflecting the figure). (b) The multi-turn on-policy RL training process: each evolving trajectory is assigned reward through the reward function. Trajectories that originate from the same seed alpha are grouped, and their rewards are jointly utilized for advantage estimation and policy LLM update.
previous alphas and their feedback (labeled "tool resp") in
tokens from gradients, Σ, Mr., normalizes for effective length, clip(-) is ratio clipping with param- eter e, and DKL a KL penalty to a reference policy Tee with weight 3.
1
In summary, our ARL formulation adapts GRPO from single-turn text optimization to interactive, multi-turn alpha evolution. This enables the model to plan, analyze, and reflect throughout a long trajectory that progressively evolves alphas beyond the heuristic
backtest- restartt cycle.
2.3 ALPHA EVOLUTION REWARD FUNCTION
Unlike domains such as mathematics or generic tool use, alpha mining central to quantitative investment but remains highly challenging due its vast search space, noisy data with spurious correlations, and high computational cost, making it infeasible to rely on single scalar reward. Moreover, the logical semantics of alphas and the need for efficient search are critical for effective alpha mining but rarely considered in other reasoning tasks. These challenges motivate a hierarchical reward function that enables principled alpha evolution throughout multiple turns, where AlphaA- gentEvo integrates multiple objectives: ensuring valid alpha candidates, preventing excessive devi- ation from the seed alpha, promoting diversity exploration, rewarding performance improvements, and sustaining progress across turns.
Starting from an individual component, Tool Call Reward (Rioa) provides feedback on correct tool usage and penalizes failed attempts, defined as (To O'uuce Nowee Ofail Nua. where Nance and Nia denote the number of successful and failed tool calls, respectively. Here, each a denotes the corresponding weighting coefficient (e.g., Osucce.. Next. for direction-ane -aece alpha generation, Consistency Reward (Reous) prevents excessive deviation from the seed alpha by penalizing can- didates whose structural similarity sim(fe, fxeed falls below lower threshold how (we set to 0. in our experiments). i.e., Reon((T) = -E f(.EF...()) Occons 1 [sim fweed) > how], where [.] is an indicator function. This serves as a soft constraint, preventing random drifts that may harm inter- pretability. Exploration Reward (Resp)) encourages diversified exploration by rewarding alphas whose similarity to previously proposed ones remains low:
(T)
Oeep
(1- max sim(fi, fim(fi.fi)) fi)
(3)
1gEFeil))
where (T) is the of successfully runnable offspring alphas in trajectory T, Fc)(T) denotes all factors proposed before f; (including the seed). The structural similarity score sim (:.:) measured by Abstract Syntax Tree (AST) overlap (Tang al. 2025) is written as:
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(AST(A) n AST(A))
fim(ft,
(4)
To encourage performance improvement while handling noisy metrics, Performance Reward (Rpert) uses logarithmic scaling Opert' log(1 + exp(s(f*) (0 Finally, Streak Reward streak) provides an additional bonus Ostreak Natrcak where streak denotes the length of the longest sequence of progressive performance improvements within a trajectory, serving as a booster towards efficient alpha evolution.
Totally, the hierarchical reward trajectory T is
R(r) min( ( (T), Com) + min (Rea(r)) Cau)) min( (aaeeR) Geat) min (uaa(T); Cuea)) min (T Cuaa))
($)
where each component reward (T( is capped by its corresponding C (e.g., Cwol)) avoid any single term from overwhelming the total reward. The tool-use term treats each tool call as cost, preventing brute-force search through frequent tool calls and encouraging meaningful and efficient alpha evolution. This hierarchical reward structure transforms the sparse and noisy feedback from financial backtesting into dense, multi-dimmnsional signals. By balancing direction-awann consis- tency and exploration (normalized by tool usage) and integrating performance with sustained im- provement through multiplicative term, it progressively guides the agent from basic compliance to higher-level objectives, ultimately preventing collapse into repetitive patterns and enabling efficient alpha evolution (Wang et al., 2025
EXPERIMENTS
In this section, we present the main experimental results, while we strongly encourage readers to refer to Sec. Sec.Eiin the Appendix for detailed analysis that provides deeper understanding of the agent's 's self-evolution process. For details on the training statistics, please refer to Sec. C for training configurations and the evaluation tool, please refer Sec. Dnd Sec. Fin the Appendix.
3.1 EXPERIMENT SETTINGS
Datasets. To systematically evaluate alpha evolution ability, we construct an expert-curated dataset, referred to as AlphaEvo5000, which serves as an alpha evolution benchmark in this study. consists of 350 seed alphas for training, 50 for validation, and 100 for testing, enabling controlled yet diverse setting for evolutionary experiments. To further assess the generalization ability, we additionally incorporate Alpha158 (Yang et al. 2020
extra test set.
Backtesting settings. Backtesting is conducted on the HS300 and CSI500 markets, spanning from January 2023 November 2025, and covering both bearish and bullish market conditions. For model training, only one year of market data (2023-01-01 to 2024-01-01) is used to accelerate iterations. For evaluation, alpha evolution experiments are performed on two distinct periods: 2023- 01-01 to 2024-01-01 (bearish) and 2024-01-01 to 2025-01-01 (bullish), with test split's alphas as the seeds. It should be noted that no data information is disclosed from the evaluation tool or the prompt, covering both the market and the time range. We adopt single-factor evaluation protocol, where the cross-sectional values of each alpha are treated as signals, without extra processing. In each rebalancing period, long most the top 10% of stocks in the universe. The rebalancing frequency is set to every trading days. Unless otherwise specified, other experimental settings are described at the beginning of each subsection. For detailed data and operators that we use in this paper, please see Sec. G in the Appendix.
Evaluation metrics. We evaluate the capability of AlphaAgentEvo to evolve alphas by com- puting the pass rate at the third and fifth turns, denoted as @ and pass @5, respectively. For performance measurement, we adopt the Information Ratio (IR) as s(f), which quantifies risk- adjusted excess return. Given seed alpha freed with score s(fsee), generated alpha is regarded as successful its score is higher than the seed and non-negative. Formally, the pass rate at turn T is defined
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max s(f) > max(0, s s(Dl)))) N j=1 SEU Flo)
1
pass @T
(6)
Here N is the number of test cases, _F(() is the set of evolved alphas turn t, and the indicator 1[-] equals if the success condition satisfied and otherwise. In addition, we report the valid ratio (VR), which measures the percentage of generated alphas that syntactically valid and executable in backtesting, reflecting the reliability of the generation process.
For alpha performance measurement, we adopt the Annualized Excess Return (AER) quantify the yearly excess investment return relative to the benchmark index and the Information Ratio (IR) measure the risk-adjusted performance, which calculates the ratio between the AER and the an- nualized standard deviation of an alpha's ' daily excess returns. With these evaluation metrics, we can comprehensively assess an alpha's profitability and risk- bearing capacity. We do not rely on cross-sectional correlation- -aased metrics (such as information coefficient or IC), since some of the seed alphas are designed as stoc-seelcction signals in the form of Boolean expressions, where their values for unselected stocks are set to NaN, making these measures unreliable.
Compared methods. We compare our method against four categories of baselines: (i) ge- netic programming (GP) (Lin al., 2019 Schmidt Lipson 2010: Zhaofan et al.) 2022 Patil
2023) with 4, 20, and 50 offspring per generation, representing traditional heuristic search; (
LLM-driven evolutionary frameworks, AlphaAgent (Tang et al. 2025) (multi-agent evolution)
and GEPA (Agrawal et al. (025) (reflective prompt evolution); (iti) series of reasoning LLMs that evolve alphas via our unified pipeline, incorporating our base models (i.c., Qwen3-1.7D and
Owen3-4B-thinking ( (Yang et al. 2025)) and state-of-the-art models (GPT-5-mini (OpenAl
2025
DeepSeek-R1 (Guo et al., 2025 ); and (iv) a tool-use RL approach ToolRL (Qian et al. 2025
All methods utilize the same backtesting tool and adhere to identical tool call budgets for training and inference (4 offspring per turn, unless otherwise noted) to ensure a fair comparison. We also
compare multi-factor portfolio performance with global optimization approaches (Zhu & Zhu
2025
Fan & Shen 2024
Ke et al. 2017) (see Appendix Sec. B
3.2 ALPHA EVOLUTION PERFORMANCE
To evaluate whether AlphaAgentEvo can consistently outperform existing evolution-oriented base- lines in terms of alpha evolution capability, we first foeus VR and the pass rate, as illustrated previously. By evaluating on AlphaEvo500 and Alpha158 alpha libraries across two periods, we further test the generalization ability of our approach under distinct market regimes. Note that Al- phaAgentEvo models are trained with at most 3 turns.
Table Performance comparison on AlphaEvo500 across two markets during 2024-2025.
HS300
CS1500
Method
VR
Pass@P
Puss
VR
Pass@
Pass@P
Qwen3-1.78 Yang et al
2025
0.676
0.08
0.11
0.657
U.35
U.43
Qwen3 thinking Yany
0.942
0.47
0.951
GPT-5-mini
OpenAA 2045
0.970
0.75
0.88
0.972
0.73
0.82
DeepSeck R
Luu al
0025
0.872
0.68
0.71
0.836
0.71
0.86
TonlRI.
Quin
2115
0.864
0.74
0.78
0.851
0.66
0.74
ToolRL-4B (Qian al
2025
0.954
0.75
0.81
0.961
0.73
0.76
GEPA Agrowal
GPT:--minn)
0,992
0.87
0.90
0.971
GEPA
Agrawal et a.. 2015
(DeepSeck RI)
0.977
0.83
0.87
0.978
0.82
0.88
Alpha Meaba//enilvxx......
0,940
0.77
0.90
0.76
0.78
AlphaAgemtExo-4B (ours)
0.979
0.97
0.97
0.977
0.93
0.95
Results on AlphaEvo500. Table I shows the results in the HS300 and CS1500 markets. While GP's expression system is incompatible with AlphaEvo500, we are unable to test GP here. In terms of pass rates, Qwen3 and GPT-S-mini offer only limited improvements, and DeepSeek-RI performs more strongly but inconsistently. For an agentic RL baseline, ToolRL, its pass@3 remains the same level as GPT-5-mini, but fails to generalize to longer horizon due to the shortcomings in multi-turn planning. By contrast, AlphaAgentEvo achieves clear superiority: even the 1.7B version surpasses GPT-5-mini, while the 4B model outperforms the strongest baseline GEPA and attains

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Published as a conference paper at ICLR 2026
the best overall results. These findings demonstrate that self-evolving agent not only effectively generalizes to different market regimes but also to longer evolution trajectories.
Table 2: Performance comparison on Alpha 158 across two periods.
Method
2023-01
2024-01
2024-01 2025-00
VR
Pass
P
VR
Pass
Pass
GP (4 offspring)
0.766
0.000
0.074
0.823
0,003
0,003
GP (20 ollspring)
0.714
0.000
0.058
0.713
0.125
0.132
(50 offspring)
0.619
0.022
0.024
0.633
0.094
0.107
AlphaAgent [Tang el
2025
(GPT-3.5-tubbo
0.905
0.236
0.495
0,900
0.643
0.783
AlphaAgeat (Tang
(DopSeek-RI)
0.294
0.848
Qwen3-1.7B 1,70 Tang cl sl.
2025
0.714
0.100
0.113
0.674
0,500
0,543
Qwen3.4BB thinking Tang
0.792
DeepSeck- R1 (Guo el
0.889
0.327
0.519
0.874
0.872
0,943
GPT:5 mini (OpenA] A12h
0.988
0.156
0.293
0.975
0.828
0.903
AlphaAgenEvv-11777
0.952
0.506
0.613
0.917
0.909
0.926
AlphaAgontEvor (owss)
0.982
0,581
0.725
0.982
0.963
0.994
Results on Alpha158. Table 2 reports the results on the external Alpha158 library, which serves as an additional test set. GP reveals poor results with limited offspring size, while the 4-offspring setting is aligned with all other approaches. This highlights the inefficiency of purely heuristic search. The multi-agent framework AlphaAgent improves upon GP, yielding higher VR and rea- sonable pass rates, particularly with stronger backbone models. However, AlphaAgentEvo again VR remains above 0,91 across
achieves the most consistent improvements. AlphaAgen(Evo-1122 both market periods, while the 4B version shows striking advantage in pass rates, with pass @5 exceeding 0.72 in the bearish period and reaching 0.994 in the bullish period, nearly saturating the success rate. This demonstrates not only superior evolutionary efficiency but also strong adaptability to different market styles and factor libraries.
Summary. Across both datasets and market conditions, AlphaAgentEvo exhibits clear superiority over wide range of strong baselines. Its ability to sustain both high valid ratios and high pass rates underscores the effectiveness of our self-evolving agent paradigm. Importantly, these results validate that the proposed framework not only accelerates the discovery of profitable alphas but also reduces invalid generations and enhances robustness under dynamic market environments.
3.3 EVOLUTION ANALYSIS.
(a) Alpha Evolution Curve
(b) Std. & Consistency Curves
Mean
1445
Curs
Tuo R, Mean
TuuiR) Std.
Diffarence
0.43
Standard Deviation
0.70
Curs consistoncy with Seed
0.
AST Similarity Score
5.2
coreistency with Seedi
Difference
0.30
1.4
1120
6.75
6,70
5
Turn
Turn
Figure 3: Comparison between AlphaAgentEvo and ToolRL's evolution trajectories.
To probe into how AlphaAgentEvo's 's agent and its generated alphas evolve across turns, in Fig. 3 we visualize its IR trajectory and per-turn standard deviation (Std.) along with average consistency score against TooIRL (Qian et al., 2025) on AlphaEvo5000. For better visual clarity, we display the uncer- tainty band as +0.25xstd. In Fig. (a), our approach's mean IR increases more rapidly than ToolRL, leading to continually widening gap. Fig. (b) shows that AlphaAgentEvo more aggressively explores in the search space as the agent continues to mine alphas, resulting in a higher standard deviation. Morcover, AiphaAgentEvo stays anchored seed alphas, ensuring that improvements accumulate progressively rather than degenerating into uncontrolled search. These patterns cannot be explained by alpha-level evolution alone. The accelerating IR gains across turns, together with
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the simultaneous rise in exploration and stabilized consisteney, indicate that the agent's strategy in each turn is evolving (improving over time). These superience-dependenee behaviors constitute clear evidence of agent-level self-evolution, rather than merely evolving individual alphas.
3.4 ABLATION STUDY
To verify the effectiveness of our ARL training and reward design, we compare validity rates before and after training and ablate two key reward components. As shown Fig. a training markedly improves validity, confirming the model's ability to generate well-formed alphas. In Fig. (c)- removing either the exploration or direction-aware reward lowers pass rates on both datasets, with the largest drops at p 3 (AlphaEvo500: 0.65-20.54/0.51:: Alpha158: 0.581-0.511/0.5511 are both critical and complementary
These results show that exploration and for efficient alpha evolution.
(a) Va Validity Rates
(b) AlphaEvo500 Pass Rates
(c) Alpha158 Pass Rates
1.02
Ballow tran
After traming
0.6
w/ corsisiency reners
wib etp cration reward
w/b explorat on meard w/o consiiercy reward ApruAgunitro
3.90
08
AppakgunnEo
3,952
0.53
0.923
154
0.5
0A-
0.4
1.0
03
0.3
AlphaEvo500
Alpha158
Pass@3
Pass@5
Pass@3
Pass
Figure 4: Ablation study on reward components.
3.5 DIVERSITY AND TRANSFERABILITY OF ALPHAS
To investigate whether models suffer from reward hacking (Wang al. 2025) by overexploiting specific patterns, we analyze the structural similarity distribution of the top 20 generated alphas (evaluated on the alpha evolution period) using Eq. Note that for GP, Alpha 158 158 serves as seeds due to an incompatible alpha calculation system; therefore, its results provided solely for reference. For AlphaAgent, we reset its alpha zoo for each seed alpha.
In Fig. 5 the seed alpha library (AlphaEEvo500 test split) exhibits a broad similarity distribution, with an average pairwise similarity of 0.043 and a relatively high maximum similarity of 0.722, re- flecting the presence of clusters of closely related alphas despite overall diversity. When comparing different models, our method achieves both a low average similarity (0.039) and a low maximum diverse. By contrast, models such
similarity (0.263), indicating that the generated alphas are as DeepSeek-RI and Qwen3-4B tend to produce alphas with higher maximum similarity (0.583 and 0.600, respectively), suggesting partial over-concentration on specific patterns. AlphaAgent (GPT- 4) also shows an elevated average similarity (0.058), suggesting that for different seed alphas, it may repeatedly fall into local optima with limited diversity. These statistics highlight a key advantage of our approach: it does not overexploit narrow or spurious patterns and instead learns genuinely generalizable cvolutionary strategies.
AlphaEva500 Test Split Avg: 0.043. Max: 0.722
Ours
GPT-S-mini
DeepSeek-R]
Aur: 0.039, Max: 0.263
Avg: 0.040. Max: 0.219
Aug: 0.043, Max: 0.583
Pactor B1OKCC
Fectorr Indlxx
16
35
3.4
OwenSS-BB
AlphaAgent (GPT-4)
for:: 0.039, Max: 0. 5.000
Avg: 0 0,050, Max: 0.333
Aug: 0.
Max: 0.010
Tartor B1dee
5.2
SSECE
R H R Factor Index
S
3.0
Figure 5: Similarity scores of top 20 alphas generated by different models.
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To assess the out-of-sample performance of alphas from different LLMs, collect two groups of evolved alphas from each LLM. While one group uses market data from 2023-01-00 to 2024-01-01 evolution period, the other uses 2024-01-01 to 2025-01-01 evolve alphas. Then, the first group undergoes backtesting from 2024-01-01 to 2025-01-01 (test period 1), and the second group from 2025-01-01 -0 2025-06-01 (until the datasets are created), noted test period 2. These evolved alphas are sampled from each model's s top-20 candidates without altering their original order such that the selected subsets share the same average IR during the evolution period. Specifically, the average evolution-perion IR is 1.05 for test period 1 and 2.72 for test period 2.
period
Test period
Test period
period
0.3
Mean
Mean
Mewn
Mean
0.2
0.0
422
-0.3
Ours GPT-S-mini
R1
Ours GPT-S-mini
R1
Ours GPT-S-mini
R1
Ours GPT-S-mini R1
Figure 6: Alpha performance comparison with state-of-the-art LLMs out-of-sample periods. Compared to two state-of-the-art LLMs, GPT-5-mini and DeepSeek-R1 (R1), our AlphaAgentEvo- 4B demonstrates highly competitive performance, consistently achieving higher average AER and average IR across test periods I and 2. This indicates that its evolution strategy exhibits favorable out-of-sample generalization and superiority under diverse market conditions. Meanwhile, its violin plots of test period 2 reveal a more evident top-heavy distribution, with wider upper and narrower lower tail, suggesting that a greater share of its evolved alphas achieve positive predictive power.
RELATED WORK
Due to the vast number of available operators and features, the search space for alphas is astro- nomically large. A traditional category is Genetic Programming (GP), which generate candidates through random mutation and crossover (Lin al. 2019 Schmidt Lipson 2010 /Zhaofan et al.) 2022 Patil 2023). or introduce hierarchical mechanisms to identify reusable components for effi cient search (Zhang et al. 2020). While GP can recycle partial structures from existing alphas, they are largely heuristic, fail to exploit feedback from failed candidates. and offer limited interpretability.
Some Reinforcement Learning (RL)-based attempts Yu et al.) 2023 Shi et al. 2025a Zhu & Zhu 2025) further guide alpha mining with reward signals, but they still operate at the operator level and rely heavily on trial-and-en -nd-roo- non-stinn -ta-ttationary markets, such incremental search is easily misled by spurious correlations and struggles discover robust alphas.
Unlike conventional AI applications Zheng et al. 2026 2024
2023) Large language models
(LLMs) provide promising alternative by leveraging semantic reasoning and domain knowledge to construct more interpretable alphas (Wang et al., 2023 Haluptzok et al. 2023 Weng, 2023 Sumers)
al.
2024 Shi et al.) 2025b). Several recent studies (Luo et al. 2025 Wang al. 2024) inte-
grate LLMs into alpha mining. such as FAMA for dynamic factor combination (Li et al. (0224) and
AlphaAgent (Tang al. 2025) for multi aulti-agent architecture for decay-resistann alphas. However, these approaches remain essentially prompt-driven, lacking mechanisms for long-horizon planning, systematic reflection, and self-evolution.
parallel, RI.-based L.LM post-training (Wang et al,, 2025 Jin et al.) 2025 Chen et al.] 2025) has made progress in mathematics, games, and tool use. Self-evolving LLM systems (ang Gao et al.) 2025 Agrawal et al. 2025 Porera-Paredes et al.) 2024 Chen et al. 2023) demonstrate great potential solving complex problems through progres- sive refinement, but their application to alpha mining remains problematic, due to their vulnerability to market regime shifts the inherent inability of text-based experience to fully encode desired evolutionary patterns. In this work, we bridge this gap with a self-evolving agentic RL paradigm.
5 CONCLUSION
In this work, we introduced AlphaAgentEvo, a novel agentic reinforcement learning paradigm for alpha mining. By reformulating alpha mining from brute-force searching problem into multi-turn

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Published as a conference paper at ICLR 2026
evolution-driven paradigm, framework endows LLM-driven agents with stronger self-evolution capabilities, enabling them to dynamically extract structure from noisy and high-variance variance financial tool feedback through hierarchical reward signals. Extensive experiments on AlphaEvo500 and Al- pha/58 confirm that our method not only delivers consistently higher valid ratios and pass rates, but also generalizes effectively across market regimes and longer evolutionary trajectories, surpassing modern self-evolution approaches associated with state-of-the-art closed-source LLMs with only 4B parameters. These results highlight self-evolving ARL principled and generalizable paradigm for next generatientin quantitative investment.
6 ACKNOWLEDGMENT
This work was supported in part by the National Natural Science Foundation of China (NSFC) under Grant 62276283, part by the China Meteorological Administration's 's Science and Tech- nology Project under Grant CMAJBGS202517, in part by Guangdong- Kong-Macao Greater Bay Area Meteorological Technology Collaborative Research Project under Grant GHMA2024704,, in part by Fundamental Research Funds for the Central Universities, Sun Yat-sen University under Grant 23hytd006, in part by Guangdong Provincial High-Level Young Talent Program under Grant 1L2024-151-2-1 in part by the Key Development Project of the Artificial Intelligence Institute, Sun Yat-sen University, and in part by The Major Key Project of PCL (Grant No. PCL2025A17).
REPRODUCIBILITY STATEMENT
In this submission, we have made extensive efforts to ensure the reproducibility of our work. Specif- ically, the dataset files used in this paper enclosed in the supplementary materials, including training, validation, and test splits. The evaluation tool's parameters are described in Sec. [.1 and the tool schema in Sec. F Available data variables and representative functions are listed in Sec. Appendix. Training configurations are documented Sec. of the Appendix.
In addition, we provide the full source code (including training pipelines, evaluation scripts, and evaluation tools) as supplementary files. We will publicly release the source code associated with the used data once the paper is accepted to facilitate further research.
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A DISCLOSURE OF LARGE LANGUAGE MODEL USAGE
In this paper, Large Language Models (LLMs) only used for polishing paragraph content, check- ing and revising grammar, and writing visualization code for some experimental results during the paper writing process. All other parts of this paper were completed by human authors. in particular, the conception of research ideas, the creation of figures in the paper, the conduct of experiments, and the recording of experimental data.
B MULTI-FACTOR PERFORMANCE COMPARISON.
While some baseline approaches cannot be compared directly, to evaluate AlphaAgentEvo's per- formance with them, we evaluate AlphaAgentEvo based the multi-factor strategy where group of alphas are weighted to generate an meta alpha. We report the performance comparison results against three categories of baselines, including time series models (TS model), non-LLM RL framework, and LLMM-ggent-based frameworks. For faireess, the top-10 mined alphas from each LLM-agent- framework are evenly combined for backtesting.
Table 3: Multi-factor portfolio performance comparison from 2024-01 to 2025-11.
Method
Category
Trainable
AER
MDD
LightGBM Ke al. 2017
TS model model
-0,009
1.192
-0.195
Stock-Mixer
Fan Shen
2024)
0.013
1.977
-0.182
AlphaQCM
Zhu & Zhu
2025
RL Framework
0,027
1.815
-0.192
AlphaAgent Tang et al.
2025
Multi Litti-LLM-aa LLM--geet
X
0.064
2.046
-0.196
GPT-5-mini
OpenAl
2025)
Single-LLMMage
X
-0.158
0.587
-0.213
TooIRL-4B
QQia et al.
20025
Migle-LLMMaagen
-0.027
1.532
-0.215
AlphaAgentF-vo-Bl (0UrS)
Single-I.LM-ageett
0.129
2.442
-0.176
C TRAINING ANALYSIS
To analyze the policy LLM's changes during the reinforcement learning process, we present the rewards in training and validation sets, average response length, and the output entropy during Al- phaAAgenEvo's training.
(5) Training reward vs, va lidiaation reward
(b) Training reward and response length
Training reward and entropy
840
0.62
5500
0.40
0.3
Thaining raainingrwwer roward
0.35
0000
Resporse lmngth
0.35
050
0.90
7200
7000
Toanning Validation
0229
Tranning
Training roward
0.15
Resporss sngth
0.15
Entropy
Step
Figure 7: Training statistics of AlphaAgentEvo.
Training reward vs. validation reward. Fig. [a), both training and validation reward curves rise steadily from ~0.16 to -0..8-0.39 over 150 steps, with the validation reward closely tracking the training reward and maintaining a small generalization gap (visually < 0.02 throughout). The improvement is fastest in the first 50 steps and then saturates gradually, with minor jitter but sign of overfitting: validation continues to trend upward in tandem with training.
Response length. Fig. 7b shows clear stepwise increase in average response length. Early on (0-50 steps), the model rapidly adapts to task requirements and learns to produce stable tool calls. Between 50-100 steps, growth slows it improves tool-call quality and forms its evolution strategy. After 100 steps, response length stabilizes at relatively high level, reflecting the transition from basic adaptation more complex reasoning for alpha mining.
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Output entropy. As shown in Fig. 7c), output entropy first decreases the agent masters consis- tent reasoning, then remains stable, and finally rises again after 1225 steps. This rebound indicates renewed exploration, where the model diversifies its reasoning to generate richer alphas.
Fig.
Taken together, these results verify that our training can converge stably with good generalization, and our method is capable of effective complex reasoning to mine richer and more effective alphas.
D TRAINING CONFIGURATIONS
We train Qwen3 Yang et al. Verl framework (Sheng et al. 2024). The 4B model provides sufficient capability while maintaining favorable
2025)
(1.7B and 4B) 10 x RTX4090 GPUs for 150 steps using the
cearmmannc--ffcciene trade-off for large-scale alpha mining, with the 1.7B variant serving as a lighter comparison. Each batch samples 20 seed alphas, with 3 rollouts per seed, up 3 turns per trajectory, and up tool calls per turn. The coefficient / for KL loss is set to 0.001. The 80th step checkpoint is used for testing. Reward caps are set as Cooo
1. Coons = 0.2,
Cespl = 0.3, Opert 0.5, and Catreak = 0.6. The weighting coefficients are O'suce = 0.1, Otaiil 0.2, Ocons 0.02, Ceexp 0.02, Operf 0.1, and Ostreak 0.15. We set the learning rate to 1 10-6 with a warmup ratio of 0.1, and use the Adam optimizer for training. The policy LLM is updated with mini-batch size 20.
E CASE ANALYSIS
We take sample from A1pha158 as a case study (only first two turns) showcase why our model outperforms other baselines in alpha evolution, as shown in Fig. 8
In the first turn, two models demonstrate fundamentally different approaches to evaluating the seed alpha. The baseline model immediately suggests horizontal adjustments such as extending the look- back window or applying Z-score normalization, e.g., "maybe using a longer window... or ZS- CORE..." This reflects a focus on technical re-scaling while leaving the underlying semantics of the alpha unchanged. By contrast, AlphaAgentEvo-44B begins with a critical examination of the seed alpha: "I should check if the current alpha has any issues ... perhaps we look at the cumulative effect of down days...". This reasoning explicitly questions the behavioral implication of the alpha- whethhr higher value actually aligns with profitable trading logic. By identifying the misalignment between signal direction and trading outcome, AlphaAgentEEvo-44 opens the path toward semantic reconstruction. This critical stance directly motivates subsequent refinements such as combining with RSI, normalizing by volatility, and embedding structural signals (e.g.. ZigZag), which collectively transform a weak predictor into an alpha with strong positive alpha.
In the second turn, this divergence becomes even clearer. The baseline model reflects only on param- eter choices from its previous trial: "Looking at the previous attempts: the first new alpha SIGNIFI- CANT DOWN DAYS 90 But its IR is worse (-0.3529), so maybe the threshold is too strict or the window too long." " The analysis remains confined to surface-level tuning, without questioning the semantic suitability of the alpha design. In contrast, Alpha BEI.DOWN_COUN ... giving a high IR of 0.9417 ... the key
demonstrates a stronger
capacity for reflective reasoning: here is that the previous factors improved by incorporating other indicators (RSI, volatility, swing points) with the down count..." Rather than merely mhe-tunnng thresholds, AlphaAgenEEvo-4B dis- tills general patterns from past attempts and reasons about why certain designs succeed. Building on this reflection, further proposes new factor constructions, such as adding industry neutrality volatility adjustment, showing ability to transform statistical feedback into structured insights that guide subsequent evolution.
Overall, the baseline model remains locked in mechanical parameter tweaks, while AlphaAgeenEEve 4B demonstrates a dialectical process: integrating empirical outcomes with theoretical reasoning to refine the alpha semanties. This difference explains why our model converges successful, high- performance alphas while the baseline model fails.
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Tum1
Think
Think
percentage days pr evvoouu day's
days
Looking previous attempts: the first
(-0. 3522,,,, long. The second factor, DOWN DAY ZSCORE, standardizes the count aallo...aabbee standardia dardizinng doesn't help here because the underlying signal is weak.
SGNFICANT DOWN DAYS, Bu.
measur days.
momentum
maybe esredd
the
...Let me think about possible ovemnntts.
1. Use combination of down days and the magnitude of the decline.
Let me br ainsstorm some Ideas.
Use the Z-score e the down day percentage capture e extr emne values...
Combine down days with high sell volume.
2. Use
the down day relative the
industry...
Tool Call
Tool Call
Tool Call
Tool Call
NAME DAvr
NAME EXPR: R8cORE (200Nt (go
20000 J0Na RATI ED
AnE WADE 60
EXPR 1aaLowee,. UaquA (sclowe, +)> 60) /90
1E5AYDELAY 6017 601
EXPR EXER, INCOSTRY NECTRAL COBRT (So1 (ooo--DELAY 15clese, 10 601 763, Find aetry!
EXPR BAAT 11ehhp
9011 (0OUNT 1 or6-DBLA Y19c1cee, 1) /80)
Tool Response
Tool Response
Tool Response
Tool Response
Success:
Success: Prup
Success: Drue IR: -0.4712 2
Success:
IR: 3529
IR: -0.07 07
IR: -0.8117
NAME: CNTN60
Owen3-4B-thinkiin
DESC: down days
EXPR: COINT ( ARYAA (3close, 11, 601/63
IR: 00.0793
AlphaAgentEvo-4B
Turn
Turn
Think
Think
First, I need to understand what 'ONNN60' does. A higher value would mean more down days, which might
Looking at the pr previous results: RSI DOWN COUNT multiplied the down count by RSI(14), giving high IR 0. 0.9417, That's good. RSI momentum indicator, so combining might capture both trend str ength and downward movement... SWING DOWN DAYS YS uses zigzag to find the days since the last swing low, which might moves.
bearish signal.
with the down count
should check the current factor has any issues... If the factor is high (more down days), the strategy would have been declining. which might be bad...
stocks that
more robust measure downside
To Impr ove this, perhaps we can look the cumulative effect down days...
So the key here that the previous factors improved by incorporating other indicators (RSI, volatility, swing points) with down count. I need to think of new combinations. Let me br brainstorm some ideas.
...
specific expr expressions...
2. Mameenttm-AAjustt Down Days: Combine the down count with momentum indicator.
First idea: Add industry neutrality to RSL DOWN COUNT. The previous RSI DOWN. COUNT had high IR. If neutr alize it by Industry, it might better...
3. Swing Point Analysis: Use zigzag functions to find swing highs/lows and then count how many days since the last swing low...
Another idea: measures es volability. Maybe when high, down count more significant...
adjust
count.
Tool Call
Tool Call
Tool Call
Tool Call
5891, DOMN 000
KAAEE LOSENN NG BOWN
INDUSTRY RSIDOME C
NAME: ATME 8,3 BaIDOKC COUNT EXPR: (cotleT 19c1 ooe-tSa 15close, 11 631/601 /61 201*FR *E3 (dcloge, 18T5, gh, $104, Sclose,
EXPRA L0CONT 166105
ASS
STGERG BOTTO
EXPR TNDUSTRY (CCUNT DE-DELLY 155 Lose, 1: 801 660 AST 19c13
60) 261 FR8T ( (falos
e<DELAM
DAYE 19ce, 11
e, 2,14
28,2 21,-411-1ne00e @ineastryy eyl
Tool Response
Tool Response
Tool Response
Tool Response
Success: ue
Success: True
Success: TERe
Sucoess: 2r00
0.9417
IR: 0.6339
1.1863
Figure 8: Case study: AlphaAgentEvv-44B our base model Qwen3-44--hinkinng
F EVALUATION TOOL
As part of AlphaAgentEvo, we design dedicated tool interface, evaluate_factox support the evaluation of alphas with backtesting. The simplified tool schema is shown in Listing I which illustrates the core arguments (factor.name) factor.expr, and metric). For clarity, some auxiliary parameters such as time range and market are omitted here.
16

---

Published as a conference paper at ICLR 2026
Listing 1: Schema of the evaluate-factor tool used in this paper.
"type": "function",
"function":
"name": arrauuate_factor
5
"description"; "A tool for evaluating factors with backtesting",
"parameters":
"type": "object",
"properties":
9
"factor_name";
"type": "string", "description": "The name of the factor"
1,
"factor_eee _ectoor_eppr=111
14
"typo": "string",
"descriptien": "The expression of the factor" b
"metric":
"type": "string",
"description": "The metric to evaluate (default: Information_Rate Ratio_with cast' "default": "Information Ratio_with_ Rot.ooottttotoottsst cost
22
"required": ("factor_name", "factor_expr")
G AVAILABLE DATA AND FUNCTIONS
Data variables used construct alphas are shown in Table 5 all sourced from Tushare (Wang) A representative set of functions to operate these data variables are displayed in Table
2024
Table 4: Representative functions used in our alpha expressions. Deseription
Function Name
A. Cruss-Sectional Operations
RANK (var)
Cross-sectional percentile rank of variable. Standardizes variable (z-score) cross-sectionally. Neutralizes the variable's exposure within industries.
ZSCORE (var)
INDUSTRY INDUSTTRYNEETRRALEE (var,
sindustry)
B. Time-Series Rolling Window
TS TS_MEAN (var, p)
Rolling
the past periads. Rolling maximum and minimum.
TS_MAX (var, p), (var,,
p)
TS BANK (var, p)
Time-series percentile rank over window. Percentage change over 'p' periods.
TS PCTCHANGE (var, p)
DELTA (var, p)
Difference from 'p" periods ago (æ; It-p).
EMA (var, p). SMA (var, p)
Exponential and Simple Moving Average.
C. Mathematical & Logical
LOG (var), PCW (var, exp)
Natural logarithm power:
DELAY (var, p) COUNT (cond, p)
Value the variable 'p' periods ago (lag). Count of times condition is true over 'p' periods. Ternary operator (if condition A then B, else C).
A B
Advanced & Technical
TS_CORR (varl, var2, p] RECBETA (varl, var2, p)
Rolling correlation between two variables. Rolling beta from regressing varl on var2. Relative Strength Index. Moving Average Convergence Divergence.
RSI (var, p)
MACD (var, p_short, p-long)
17

---

Benchmark turnover ratio Published as a conference paper at ICLR 2026
Table Available data variables. Description
Variable Name
Price & Market Data
Sopen
Opening price
Shigh
Highest price of the day
$low
Lowest price of the day Closing price
Sclose
Svolume
Trading volume (shares)
Samount
Trading amount (CNY)
$change
Price change previous close
Sreturn
day's return
Chip-Distribation Data
Shis_low
Historical low price since listing Historical high price since listing
$his-high
$cost_5pct
Cost where 5% chips lie below
$cost_15pct
Cost where 15% of chips lie helow Median cost of chips
Scost 50pct
Scost 85pet
Cost where 85% of chips lie below
$cost.95pct
Cost where 95% of chips below Average cost across all chips
$weight.avg
$winner.rate
The chip win rate
Schip.conet.90
Chip concentration within the densest 90% Chip concentration within the densest 70%
Schip.conet.70
Mider-Flow//Mogee: Data
Soay_sm..voi.$sell..mm vell
Small-lot buy/sell volume Small-lot buy/sell turnover
Sbuy.sm.amount,
Ssell.sm.anouat
fouy.md.voi..scll. vol
Medium-lot buy/sell volume Medium-lot buy/sell turnover
$buy.md_amount,
$scll.dd.amont
$buy:1g.v0i,8ae11.1g..0
Large-lot buy/sell volume Large-lot buy/sell turnover
Sbuy.1g_amount,
$sell 1g amount
$buy eig vol. $sel1 eig vol Sbuy.elg.amount.
Extra-large-lor buy/sell volume Extra-large-lot buy/sell turnover
9sell.elg.amount
$net.mf_vo1
Net inflow volume (buy sell) Net inflow amount
Snet.mf_amount
Benchmark & Industry
Sbench.open, brench_bigh
Benchmark index OHLC prices
9bench_low, www.:/eeeehhlossseeeee
$pench.preclose
Benchmark previous close
Sbench.volume
Benchmark trading volume
sbench.amount
Benchmark trading amount
Spench.turn
Spench.retura
Benchmark last day's 's return
Sindustry
Categorical industry label
18


Reviews:
[{"review_id": "qXI2L6mHkB", "reviewer": "Reviewer_MHr3", "summary": "The paper introduces AlphaAgentEvo, a self-evolving Agentic Reinforcement Learning (ARL) framework designed for alpha mining—the process of discovering quantitative trading signals (“alphas”) that predict stock returns. This is the first work to propose a self-evolving agentic reinforcement learning framework for training LLMs for quantitative alpha mining. The performance of training Qwen3-1.7B with the proposed method is significantly better than prompting stronger models including GPT5, and GPT3.5 with the AlphaAgent framework.", "questions": "See weaknesses.", "limitations": "", "rating": 6, "confidence": 2, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. This is the first work to propose a self-evolving agentic reinforcement learning framework for training LLMs for quantitative alpha mining.\n\n2. The training algorithm is modified on top of GRPO and the rewards are carefully designed with ablation study showing their effectiveness.\n\n3. The performance of training Qwen3-1.7B with the proposed method is significantly better than prompting stronger models including GPT5, and GPT3.5 with the AlphaAgent framework.", "weaknesses": "1. Please define alpha mining in the abstract/introduction.\n\n2. As I'm not familiar with alpha mining, not sure if there are any related training-based LLM baseline for alpha mining (seems to be none). If there is any, should be used as a baseline as well.\n\n3. There is only one training set and two test set used for experiments. More evaluation benchmarks would make the results more convincing.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "i5nl6YfchX", "reviewer": "Reviewer_68ro", "summary": "The paper proposes AlphaAgentEvo, a self-evolving Agentic RL (ARL) framework that turns alpha mining from a brittle search–backtest–restart loop into a multi-turn evolution process guided by a hierarchical reward (tool-use validity, direction-aware consistency to the seed, exploration diversity, performance, and improvement streak). The policy LLM plans, proposes multiple offspring factors per turn, queries a backtesting tool, reflects on outcomes, and updates via a GRPO-style objective adapted to multi-turn, tool-in-the-loop trajectories. Experiments on AlphaEvo500 (new) and Alpha158 across bearish/bullish periods report higher valid ratios and pass rates than GP, multi-agent, and strong LLM baselines, with 1.7B–4B models outperforming larger closed models on several metrics.", "questions": "1.\tReport compute per trajectory, backtest latency, and throughput relative to baselines at equal offspring budgets.\n2.\tWhen do direction-aware and exploration rewards conflict? Please show concrete failure trajectories and mitigations.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "1.\tWell-motivated paradigm: Precise problem framing of alpha evolution and limitations of GP / prompt-only methods; clear move from single-shot search to multi-turn evolution with reflection. \n2.\tMethodical ARL design: A GRPO-style objective extended to multi-turn trajectories and masked tool tokens; multiple offspring per turn with group-normalized advantages. \n3.\tHierarchical reward that encodes domain priors: direction-aware similarity, exploration via AST-based structural diversity, performance and streak bonuses, with caps to prevent domination. \n4.\tStrong empirical evidence across two libraries and two regimes, with consistent gains in VR and pass@T; AlphaAgentEvo-4B reaches pass@5 of 0.76/0.94 on AlphaEvo500 and 0.725/0.994 on Alpha158.", "weaknesses": "1.\tMany hand-set caps/weights and thresholds (e.g., similarity thresholds, reward caps C*, α*)—no sensitivity study is shown; robustness to these knobs is unclear. \n2.\tHeavy use of pass@T and VR; fewer statistics on effect sizes with uncertainty (CI/std) and transaction-cost / turnover analyses. Some violin plots are helpful but more rigorous statistical testing is desirable. \n3.\tGP incompatibility with AlphaEvo500 and varying offspring budgets complicate strict apples-to-apples comparisons; stronger head-to-head with exactly matched toolchains would isolate the contribution of ARL vs. retrieval/evaluation differences. \n4.\tThe method is framed as broadly agentic, but all experiments remain alpha-formula evolution; no results on portfolio construction, risk models, or cross-market transfer.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "2CAiHcYg3R", "reviewer": "Reviewer_5QHQ", "summary": "The paper addresses the problem of _alpha mining_ — optimising financial factors (alphas) that demonstrate strong predictive power in markets. To this end, the authors propose _AlphaAgentEvo_, a reinforcement learning–based framework that learns an evolutionary process, essentially mapping an initial seed factor $f_{{seed}}$ to an optimal factor $f^*$, a process they term _alpha evolution_. Through experiments on real-world financial market data (CSI500), the authors report that AlphaAgentEvo outperforms existing baselines. Further analysis indicates that AlphaAgentEvo produces more diverse solutions and demonstrates stronger generalization to unseen data.", "questions": "1. How important is the number of turns in the training of AlphaAgentEvo?\n2. See Weakness 2 b,c, 4, 7 and 8.\n3. How are the datasets constructed?\n4. What is the training budget used for AlphaAgentEvo and the baselines?", "limitations": "", "rating": 2, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. The paper is clearly written and accessible. Although I am not deeply familiar with the field of alpha mining, it effectively defines both the problem and the proposed solution methodology.\n\n2. The application of RL + LLM evolution to the field of alpha-mining is interesting and novel.\n\n3. The results are promising, particularly given that the evaluation was conducted on real-world, previously unseen financial data.", "weaknesses": "1. The paper argues that alpha evolution is a more effective approach for discovering \\( f^* \\) than static methods, as it “looks into past feedback.” It further claims that alternative “search–test–restart” strategies (which includes RL)—are “brittle.” However, RL methods, particularly off-policy methods, do in fact leverage prior experience to improve over time. Thus, the characterization of these methods as “naive” seems unconvincing. Further, the paper does not provide any evidence to support the claim of their “brittleness”.\n\n    a. Further, in practice, AlphaAgentEvo uses 3 turns (which is quite low), so I’m curious as to how important the “evolution” is. For this, I suggest an ablation on the number of steps with t = 0 (predict f^* directly), t=1 (f_init -> f^*) and maybe longer horizons, to validate the effectiveness of “evolution”.\n\n\n2. It is unclear how the reward function is “hierarchical.” From its definition in Eq. 5, it appears to simply combine multiple objectives. Moreover, the use of fractional forms for the first three rewards is not well justified—why not employ a simpler linear combination instead?\n\n    a. (Minor) The reward function introduces several additional hyperparameters. An analysis of their robustness, along with a complete ablation study on all components of the reward function, would strengthen the practical validity of the proposed algorithm.\n\n    b. What is the rationale for enforcing proximity to \\( f_{\\text{init}} \\)? If the objective is solely to identify the optimal alpha \\( f^* \\), why constrain the search to remain close to the initial factor?\n\n    c. How are the initial factors generated, and what is their quality or performance baseline?\n\n3. (Datasets) The paper provides no details on how the dataset was constructed, which makes reproducibility and thorough analysis of the results difficult.\n\n4. (Evaluation) Why do you use score > f_seed as the main evaluation and not score directly? \n\n5. While I am not deeply familiar with the specifics of alpha mining, evaluating the model on training data (2023-01-01 to 2024-01-01) is a very poor choice for assessment. Moreover, dividing “seeds” into train, validation, and test sets is unconventional. The standard practice is to perform such splits on the data itself (i.e., train/validation/test data partitions). Therefore, I believe the results presented in Tables 1 and 2 (left), as well as Figure 3(a), should be interpreted with caution or potentially excluded from the evaluation.\n\n6. (Unfair evaluation of baselines) I am concerned that the baseline methods may not have been allocated a comparable training budget. What is the training budget for your proposed method (e.g., 150 steps × k_t)? If so, what value of k_t was used? Was an equivalent budget applied to the baselines? From my understanding, it appears that the baselines have been given a significantly smaller budget (only three turns)\n\n7. The paper lacks a baseline comparison with state-of-the-art LLM-based evolutionary methods, such as FunSearch, AlphaEvolve and EvoTune. Additionally, such work has not been discussed at all.\n\n8. Figure 4: what is direction aware reward?\n\nFunSearch: Romera-Paredes, B., Barekatain, M., Novikov, A. et al. Mathematical discoveries from program search with large language models. Nature\n\nAlphaEvolve: Novikov, Alexander, et al. \"AlphaEvolve: A coding agent for scientific and algorithmic discovery.\" arXiv preprint arXiv:2506.13131.\n\nEvoTune: Šurina, A. et al. Algorithm discovery with large language models: Evolutionary search meets reinforcement learning. Second Conference on Language Modelling", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "0BeayXIu9H", "reviewer": "Reviewer_znBA", "summary": "This paper introduces AlphaAgentEvo, a framework for quantitative alpha mining that uses Agentic Reinforcement Learning. This agent interacts with a financial backtesting tool over a multi-turn trajectory to progressively refine an initial \"seed\" alpha. The learning process is guided by a sophisticated, hierarchical reward function that balances tool usage correctness, structural consistency, exploration, performance improvement, and sustained progress. Experiments show that AlphaAgentEvo outperforms traditional Genetic Programming methods and state-of-the-art LLMs in generating alphas with higher success rates.", "questions": "1. Why design this method just for alpha mining? Can it be performed on general tasks? What part of the work (except the reward) is specifically designed for alpha mining?\n2. Why do you call your method \"self\"-evolving? It seems that the authors just use RL to train the LLM on the alpha mining task. There's no highlighted \"self\" part in the method. And the performance increase is evolving?", "limitations": "", "rating": 2, "confidence": 5, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. The hierarchical reward function is a key strength. Instead of relying on a single, sparse performance metric, it provides the agent with dense, multi-faceted feedback.\n2. AlphaAgentEvo demonstrates superiority over a range of baselines, including GP and large reasoning models like DeepSeek-R1.", "weaknesses": "1. The core technical method is an adaptation of an existing RL algorithm (GRPO) to an agentic setting. The primary innovation lies in the problem formulation and reward engineering. This makes the contribution more of an applied nature than a fundamental advance.\n2. True self-evolution might imply an agent that modifies its own learning algorithm or core reasoning architecture, whereas this work uses RL to improve performance on a task. The \"evolution\" observed is in the alpha's quality, not a fundamental evolution of the agent itself.\n3. The experiment is not sufficient. It should be considered to compare models trained on this task. Currently, all the baselines are training-free.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)