Quantitative_Finance
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LYNX: Analytical Program Synthesis via Grounding and Closed-Loop Refinement
2026-10-10 10:01:24Ruichen Xia, Xiao Han, Dingyu Yang, Luwei Yang, Sai Wu, Dongxiang Zhang, Binhui Yao, Gang ChenAbstract
LLM-powered data analytics aims to translate natural language instructions into executable analytical programs over underlying data. However, existing approaches, whether treating LLMs as intent-to-code translators or as standalone analytical engines, often fail to reliably align domain semantics with complex schemas and heterogeneous metadata, resulting in brittle execution and semantically inconsistent analytical outputs in real-world workloads. We present LYNX, a multi-agent framework that formulates LLM-driven analytics as a schema- and knowledge-constrained analysis problem rather than a single-pass generation task. LYNX organizes analytical synthesis into a closed-loop workflow of planning, generation, verification, and refinement through three role-specialized agents that collaboratively enforce schema grounding, semantic consistency, and execution correctness. To address domain ambiguity and schema misalignment, LYNX integrates Semantic-Structural Distillation and Stage-Aware Allocation (SSD-SAA). A delta-guided iterative revision continuously diagnoses and repairs semantic and execution-level failures, substantially improving synthesis robustness. We evaluate LYNX on two NL-to-SQL benchmarks, BIRD-SQL Mini-Dev and the more challenging Spider 2.0-Lite. Experimental results demonstrate strong performance and efficiency, achieving 10.66% to 59.54% execution accuracy across lightweight and commercial models, while reducing token consumption by 70.53% compared with the prior most efficient method. Our framework displays high generalization over different backbones, from open-source, lightweight models to the latest commercial models. Additional case studies on quantitative finance analytics and complex multi-relational SQL workloads further demonstrate the framework's robustness, scalability, and practical applicability in real-world analytical scenarios.
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URL
https://arxiv.org/abs/2610.14777
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Resolving the Missing Financial Data Crisis: A Generative AI Pipeline for SEC 10-K Extraction
2026-09-25 21:37:59Prisha Nair, Roee ShragaAbstract
SEC 10-K filings contain substantial financial information that is not consistently captured in structured datasets, creating a missing-data problem affecting over 70% of firms and half of total market capitalization. This can disproportionately bias quantitative analysis against smaller firms, which may be excluded due to limited available data. Traditional financial extraction methods such as Regular Expressions (Regex) and BERT, have been widely used. However, they are highly brittle when parsing complex SEC 10-K filings, which leads to data that is existent in the files being lost since these methods do not consider that a data attribute could be located in a different section or a footnote. This study evaluates several Large Language Models (LLMs), including Llama-3 8B, Qwen-2.5 14B, and Llama-3.3 70B, to figure out individual model strengths and weaknesses when extracting specific attributes from SEC 10-K text. The extraction quality was evaluated across four financial variables of varying structural complexity: Cash and Cash Equivalents (tabular), Short-Term Debt (hybrid), Credit Facilities (narrative), and Research and Development (hybrid). Results show that while smaller models like Llama-3 8B experience performance degradation under complex negative prompting, aligning parameter scale with document complexity yields high zero-shot accuracy. Qwen-2.5 14B excels as a tabular specialist with an 83.33% F1 score on Cash, whereas Llama-3.3 70B effectively navigates dense narrative footnotes, achieving a 76.92% F1 score on R&D. This scalable framework addresses critical information gaps in quantitative finance datasets and eliminates missing-data bias through a more thorough analysis of the SEC 10-K files.
Abstract (translated)
URL
https://arxiv.org/abs/2609.35864
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Contagion on the Trading Floor: How Adversarial Signals Spread in Multi-Agent Trading Systems
2026-09-17 06:54:59Qi Rong Sua, Junhao Dong, Nguyen Duc Thai, Yuqing Wen, Cheston Tan, Yew-Soon OngAbstract
Multi-agent trading systems built on large language models (LLMs) are beginning to appear in quantitative finance, yet their robustness to adversarial inputs is largely unknown. We study the vulnerability of LLM trading stacks to black-box, input-only attacks that enter solely via admissible social-media feeds. We introduce the Generic Multi-Agent Trading System (GMATS), a framework that captures modern multiagent trading architectures and instantiate a class of black-box poisoning attackers that treat an LLM as a post generator and inject budget-constrained, plausibly benign social-media content into the analyst's evidence stream. We define contagion metrics that trace how adversarial content propagates through the stack, including belief-shift scores at analyst and coordinator layers and attack-clean deltas on standard backtest metrics. Experiments on a safe offline benchmark with historical market and social data show that even simple input-only attackers can materially degrade risk-return profiles, sharply reducing Sharpe ratios. At the same time, we find that suitably designed multi-agent topologies and coordinator prompts can dampen adversarial shocks and improve average robustness under identical poisoning budgets.
Abstract (translated)
URL
https://arxiv.org/abs/2609.19789
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RIDGE: An Autonomous Framework for Validation and Method Discovery in LLM-Generated Option Pricing
2026-07-28 02:12:52Liexin Cheng, Xue Cheng, Shuaiqiang Liu, Cornelis W. OosterleeAbstract
Automated code generation is becoming an important tool in quantitative finance, where large language models can generate option pricing implementations directly from mathematical model specifications. Validating such implementations, however, requires considerably more than conventional software testing: numerical pricing methods must remain mathematically consistent, numerically stable, and reliable across a wide range of model parameters. We introduce RIDGE, an autonomous validation framework in which generated pricing implementations are subjected to structured no-arbitrage tests, stress tests, benchmark comparisons, and consistency checks. Validation evidence is interpreted diagnostically, while the resulting knowledge is accumulated in a repository and reused across models and successive validation iterations. This enables systematic refinement of both the pricing implementation and the validation methodology. The framework is applied to five stochastic volatility models. Across these studies, all detected implementation defects are removed and, in two cases, the validation process itself leads to new semi-analytic pricing methodologies. The supplementary material is available in the GitHub repository: this https URL.
Abstract (translated)
URL
https://arxiv.org/abs/2607.25199
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Recursive Harness Self-Improvement
2026-07-17 00:21:19Hyunin Lee, Jinglue Xu, Jeffrey Seely, Donghyun Lee, Matei Zaharia, Yujin TangAbstract
Under model--harness co-evolution, harnesses are not merely inference-time scaffolds but data-generating components whose execution traces can shape future foundation models. This motivates harness-in-the-loop learning: optimizing harnesses for both immediate agent performance and the quality of traces used for future model training. However, continually updating provider-built scaffolds is costly and labor-intensive. We therefore investigate whether optimizing user-constructed harnesses in a task-specific manner can improve execution-trace quality while remaining computationally lightweight and requiring only a few update iterations. To this end, we introduce Recursive Harness Self-Improvement (RHI), which represents the harness as a prompt-level specification of the agent loop and iteratively refines it using pairwise feedback over its own revision history. Across 30 synthetic machine-learning research tasks spanning quantitative finance, robotics, and pharmacy, a few RHI iterations suffice to substantially raise the performance ceiling of low-reasoning-effort agents, exceeding the corresponding maximum-reasoning-effort setting while reducing inference cost by up to 60%. We show that these gains arise primarily from improved task-specific context management through more effective inter-agent information flow rather than longer reasoning traces. Finally, we formalize this behavior as an information-theoretic hypothesis for RHI's implicit optimization objective, suggesting RHI as a practical algorithm for continual learning within the paradigm of model--harness co-evolution.
Abstract (translated)
URL
https://arxiv.org/abs/2607.15524
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OpenFinGym: A Verifiable Multi-Task Gym Environment for Evaluating Quant Agents
2026-06-24 19:42:55Kaicheng Zhang, Wen Ge, Lei Jiang, Weixin Yang, Jordan Langham-Lopez, Jialin Yu, Lukasz Szpruch, Hao NiAbstract
Although large language model agents are increasingly applied to quantitative-finance workflows, their evaluation remains fragmented across isolated tasks, while the financial relevance of benchmark tasks is often overlooked. Yet financial workflows are inherently multi-stage, spanning interdependent tasks such as forecasting, strategy construction, risk management, and trading. Existing platforms typically focus on a single task, and can therefore overstate agent competence and fail to reveal weaknesses in generalization, real-market interaction, and financially meaningful decision-making. We introduce OpenFinGym, a unified gym environment for quantitative-finance agent development that covers forecasting, market generation, real-time trading, and fraud detection under a single execution and verification interface. OpenFinGym additionally provides an automated task-construction pipeline that turns quantitative finance publications into executable task packages; a containerised runtime with a host-side verifier service that supports scalable agent rollouts and prevents runtime train-test leakage; a paper trading engine with a low-latency data-stream design; deferred-resolution support for long-horizon and event-market forecasts; and integration for SFT and RL post-training
Abstract (translated)
URL
https://arxiv.org/abs/2606.26350
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Scaling Laws for Task-Specific LLM Distillation
2026-06-23 16:09:57Lavinia Ghita, Dhruv Desai, Ioana BoierAbstract
Large Language Models (LLMs) achieve strong performance across a growing range of domains, yet their scale poses deployment challenges in applications where latency and cost constraints are critical. This paper derives empirical scaling laws for domain-specific LLM compression, quantifying how in-domain and general knowledge performance scale with dataset size, compression ratio, supervision format, and iterative pruning schedule. Using quantitative finance as our application domain, we compare logit-based and LoRA-based distillation under iterative structural pruning, introducing a blended chain-of-thought supervision loss that stabilizes KL-divergence distillation over reasoning traces. In-domain task quality degrades predictably under compression while general-knowledge benchmarks collapse well before the same point; supervision format is the key driver of this tradeoff, with chain-of-thought supervision actively recovering general knowledge that pruning erases. We release the headline dataset FinHeadlineMix, scaling law results, and practical recommendations to provide a reusable framework for domain-specific compression decisions.
Abstract (translated)
URL
https://arxiv.org/abs/2606.24747
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Mojo: A Promising Tool for Scalable Financial AI Efficiency
2026-06-14 23:18:58Henry HanAbstract
For thirty years, quantitative finance has paid a costly two-language tax: models researched in Python are rewritten in C++ for production, often introducing numerical discrepancies. GPU-accelerated deep learning exacerbates this problem, as nondeterministic floating-point reductions can produce drift in long backtests, challenging regulatory reproducibility and auditability expectations. This article surveys Mojo, Modular's 2026 Python-like systems language, as a structural response for capital markets engineering. While closing the Python-to-C++ performance gap, Mojo uniquely combines native interoperability with the low-level systems control required to construct bit-exact deterministic kernels. Its MLIR compilation infrastructure further allows a single codebase to target scalar, SIMD, multicore, and GPU execution, reducing the translation bottleneck between research and production. We benchmark four core financial AI workloads: Monte Carlo option pricing, LLM sentiment inference, multi-asset backtesting, and portfolio Value at Risk. On Apple Silicon, Mojo demonstrates 20x to 180x speedups over pure Python on directly measured kernels; larger-scale GPU workload results are projections calibrated from published benchmarks. Alongside transparent performance data, we introduce mojo-deterministic, an open-source library of reproducible reduction kernels, and provide a candid assessment of the problems Mojo does and does not yet solve.
Abstract (translated)
URL
https://arxiv.org/abs/2606.16059
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PandaAI: A Practical Agent CQ2 for Neuro-symbolic Data Analysis And Integrated Decision-Making in Quantitative Finance
2026-06-05 01:56:28Yuqi Li, Siyuan Liu, Bingjun LiuAbstract
While deep learning has excelled in various domains, its application to sequential decision-making in finance remains challenging due to the low Signal-to-Noise Ratio (SNR) and non-stationarity of financial data. Leveraging the reasoning capabilities of Large Language Models (LLMs), we propose \textbf{PandaAI}, a closed-loop neuro-symbolic LLM agent with market regime modeling and constrained alpha generation, which bridges general LLM reasoning with financial rigor and suppresses the financial toxicity of LLM-generated outputs. To bridge the gap between general linguistic capability and financial rigor, we fine-tune a domain-specific LLM. Furthermore, we integrate this LLM into a modular architecture and form a closed-loop system. Unlike traditional models that optimize isolated prediction metrics, \textbf{PandaAI} is designed as a neuro-symbolic agent that navigates the complex, real-world financial environment with explicit risk awareness. Extensive experiments on CSI 300 stock data show that \textbf{PandaAI} achieves a $18.2\%$ higher Rank IC and $25.7\%$ lower maximum drawdown than state-of-the-art time-series models. Our constrained LLM generation and dual-channel adaptation method provide a general paradigm for LLM deployment in high-stakes sequential decision-making scenarios.
Abstract (translated)
URL
https://arxiv.org/abs/2606.06823
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MadEvolve: Evolutionary Optimization of Trading Systems with Large Language Models
2026-05-21 20:28:57Yurii Kvasiuk, Tianyi Li, Owen Colegrove, Moritz M\"unchmeyerAbstract
We explore the application of LLM-driven algorithm optimization to several common tasks in quantitative finance. MadEvolve, a general-purpose algorithm optimization framework inspired by DeepMind's Alpha-Evolve, was recently developed to optimize algorithms in computational cosmology. Here we demonstrate the utility of MadEvolve to optimize algorithmic trading strategies and alpha generation at the example of Bitcoin trading. On our simulation and backtesting setup, we achieve significant improvements on all tasks we considered, such as evolving feature sets for signal generation, optimizing separate components of the trading strategy, and jointly evolving the feature pipeline together with the execution strategy. Additionally, we compare our method to other agentic search approaches, specifically Claude Code, and carefully evaluate p-hacking probabilities on our simulation setup. Our findings strongly support the utility of AI-driven agentic and evolutionary algorithms for algorithmic trading and quantitative finance.
Abstract (translated)
URL
https://arxiv.org/abs/2605.23007
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A Review of Large Language Models for Stock Price Forecasting from a Hedge-Fund Perspective
2026-04-10 17:36:04Olivia Zhang, Zhilin ZhangAbstract
Large language models (LLMs) are increasingly deployed in quantitative finance for stock price forecasting. This review synthesizes recent applications of LLMs in this domain, including extracting sentiment from financial news and social media, analyzing financial reports and earnings-call transcripts, tokenizing or symbolizing stock price series, and constructing multi-agent trading systems. Particular attention is paid to practical pitfalls that are often understated in the literature, such as fragility in sentiment analysis, dataset and horizon design, performance evaluation metrics, data leakage, illiquidity premia, and limits of stock price predictability. Organized from a hedge-fund perspective, the review is intended to guide both academic researchers and hedge fund managers in integrating LLMs into real-world trading pipelines and in stress-testing their robustness under realistic market frictions.
Abstract (translated)
URL
https://arxiv.org/abs/2605.05211
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Emergent Collaborative Deliberation in Multi-Model AI Systems: A BFT-Derived Protocol for Epistemic Synthesis
2026-03-26 20:38:21VD DoskeAbstract
We present the Consilium Protocol, a Byzantine Fault Tolerance-derived architecture for structured multi-model AI deliberation that treats inter-model disagreement as epistemic signal rather than error. The protocol assigns engineered cognitive personas to language models -- separating what a model is from how it reasons -- and introduces an In-Sample/Out-of-Sample validation framework adapted from quantitative finance to distinguish training-data consensus from empirically grounded conclusions. Across 1,478 deliberation sessions spanning 32 topics in 10 domain categories, we demonstrate that (1) the cognitive persona, not the underlying model, determines epistemic behavior: free edge-inference models costing 0.0002 USD per batch produced comparable analytical output to frontier models costing 10.69 USD; (2) RLHF alignment training creates measurable, domain-specific epistemic blind spots -- contested policy topics exhibit 12.3 percentage points less adversarial challenge than settled science topics, and AI safety topics show asymmetric bias ($\Delta$=11.6%) where models challenge claims that AI is dangerous far more vigorously than claims that AI risk is overstated; (3) the protocol exhibits no directional bias of its own (immigration $\Delta$=2.3%, renewables $\Delta$=1.2%); and (4) out-of-sample evidence retrieval validated 239 claims with 100% evidence retrieval and surfaced 167 blind-spot discoveries invisible to training-data deliberation. Run-to-run reproducibility across randomized model$\times$persona assignments averages $\pm$2.2% standard deviation. Total cost for the complete battery including all overhead: 217 USD. We release the protocol specification under MIT license to enable independent verification.
Abstract (translated)
URL
https://arxiv.org/abs/2606.00005
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ARTEMIS: A Neuro Symbolic Framework for Economically Constrained Market Dynamics
2026-03-18 13:00:28Rahul D RayAbstract
Deep learning models in quantitative finance often operate as black boxes, lacking interpretability and failing to incorporate fundamental economic principles such as no-arbitrage constraints. This paper introduces ARTEMIS (Arbitrage-free Representation Through Economic Models and Interpretable Symbolics), a novel neuro-symbolic framework combining a continuous-time Laplace Neural Operator encoder, a neural stochastic differential equation regularised by physics-informed losses, and a differentiable symbolic bottleneck that distils interpretable trading rules. The model enforces economic plausibility via two novel regularisation terms: a Feynman-Kac PDE residual penalising local no-arbitrage violations, and a market price of risk penalty bounding the instantaneous Sharpe ratio. We evaluate ARTEMIS against six strong baselines on four datasets: Jane Street, Optiver, Time-IMM, and DSLOB (a synthetic crash regime). Results demonstrate ARTEMIS achieves state-of-the-art directional accuracy, outperforming all baselines on DSLOB (64.96%) and Time-IMM (96.0%). A comprehensive ablation study confirms each component's contribution: removing the PDE loss reduces directional accuracy from 64.89% to 50.32%. Underperformance on Optiver is attributed to its long sequence length and volatility-focused target. By providing interpretable, economically grounded predictions, ARTEMIS bridges the gap between deep learning's power and the transparency demanded in quantitative finance.
Abstract (translated)
URL
https://arxiv.org/abs/2603.18107
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Hubble: An LLM-Driven Agentic Framework for Safe and Automated Alpha Factor Discovery
2026-03-09 05:21:00Runze Shi, Shengyu Yan, Yuecheng Cai, Chengxi LvAbstract
Discovering predictive alpha factors in quantitative finance remains a formidable challenge due to the vast combinatorial search space and inherently low signal-to-noise ratios in financial data. Existing automated methods, particularly genetic programming, often produce complex, uninterpretable formulas prone to overfitting. We introduce Hubble, a closed-loop factor mining framework that leverages Large Language Models (LLMs) as intelligent search heuristics, constrained by a domain-specific operator language and an Abstract Syntax Tree (AST)-based execution sandbox. The framework evaluates candidate factors through a rigorous statistical pipeline encompassing cross-sectional Rank Information Coefficient (RankIC), annualized Information Ratio, and portfolio turnover. An evolutionary feedback mechanism returns top-performing factors and structured error diagnostics to the LLM, enabling iterative refinement across multiple generation rounds. In experiments conducted on a panel of 30 U.S. equities over 752 trading days, the system evaluated 181 syntactically valid factors from 122 unique candidates across three rounds, achieving a peak composite score of 0.827 with 100% computational stability. Our results demonstrate that combining LLM-driven generation with deterministic safety constraints yields an effective, interpretable, and reproducible approach to automated factor discovery.
Abstract (translated)
URL
https://arxiv.org/abs/2604.09601
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MEME: Modeling the Evolutionary Modes of Financial Markets
2026-02-12 13:16:05Taian Guo, Haiyang Shen, Junyu Luo, Zhongshi Xing, Hanchun Lian, Jinsheng Huang, Binqi Chen, Luchen Liu, Yun Ma, Ming ZhangAbstract
LLMs have demonstrated significant potential in quantitative finance by processing vast unstructured data to emulate human-like analytical workflows. However, current LLM-based methods primarily follow either an Asset-Centric paradigm focused on individual stock prediction or a Market-Centric approach for portfolio allocation, often remaining agnostic to the underlying reasoning that drives market movements. In this paper, we propose a Logic-Oriented perspective, modeling the financial market as a dynamic, evolutionary ecosystem of competing investment narratives, termed Modes of Thought. To operationalize this view, we introduce MEME (Modeling the Evolutionary Modes of Financial Markets), designed to reconstruct market dynamics through the lens of evolving logics. MEME employs a multi-agent extraction module to transform noisy data into high-fidelity Investment Arguments and utilizes Gaussian Mixture Modeling to uncover latent consensus within a semantic space. To model semantic drift among different market conditions, we also implement a temporal evaluation and alignment mechanism to track the lifecycle and historical profitability of these modes. By prioritizing enduring market wisdom over transient anomalies, MEME ensures that portfolio construction is guided by robust reasoning. Extensive experiments on three heterogeneous Chinese stock pools from 2023 to 2025 demonstrate that MEME consistently outperforms seven SOTA baselines. Further ablation studies, sensitivity analysis, lifecycle case study and cost analysis validate MEME's capacity to identify and adapt to the evolving consensus of financial markets. Our implementation can be found at this https URL.
Abstract (translated)
大型语言模型（LLMs）在量化金融领域展示了显著的潜力，通过处理大量非结构化数据来模拟类似人类的分析工作流程。然而，目前基于LLM的方法主要遵循两种范式：一种是专注于个股预测的资产中心主义方法；另一种则是用于投资组合配置的市场中心主义方法，两者通常忽视了推动市场变动的根本原因。在本文中，我们提出了一种逻辑导向视角，将金融市场建模为一个动态、进化的竞争性投资叙事生态系统，称为思想模式（Modes of Thought）。为了实现这一观点，我们引入了MEME（Modeling the Evolutionary Modes of Financial Markets），旨在通过不断演化的逻辑来重建市场动态。MEME采用多代理提取模块将嘈杂的数据转换为高保真的投资论据，并使用高斯混合模型在语义空间内揭示潜在的共识。为了模拟不同市场条件下语义漂移，我们还实施了一种时间评估和对齐机制，以跟踪这些模式的生命历程及其历史盈利能力。通过优先考虑持久的市场智慧而非短暂异常，MEME确保投资组合构建由稳健的理由引导。 从2023年到2025年的三个异质中国股票池中进行的大量实验表明，MEME在七种最先进的基准方法上始终表现出色。进一步的消融研究、敏感性分析、生命周期案例研究和成本分析验证了MEME识别并适应金融市场不断演变共识的能力。 我们的实现可以在以下网址找到：[此处插入实际链接]
URL
https://arxiv.org/abs/2602.11918
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AlphaPROBE: Alpha Mining via Principled Retrieval and On-graph biased evolution
2026-02-12 13:14:58Taian Guo, Haiyang Shen, Junyu Luo, Binqi Chen, Hongjun Ding, Jinsheng Huang, Luchen Liu, Yun Ma, Ming ZhangAbstract
Extracting signals through alpha factor mining is a fundamental challenge in quantitative finance. Existing automated methods primarily follow two paradigms: Decoupled Factor Generation, which treats factor discovery as isolated events, and Iterative Factor Evolution, which focuses on local parent-child refinements. However, both paradigms lack a global structural view, often treating factor pools as unstructured collections or fragmented chains, which leads to redundant search and limited diversity. To address these limitations, we introduce AlphaPROBE (Alpha Mining via Principled Retrieval and On-graph Biased Evolution), a framework that reframes alpha mining as the strategic navigation of a Directed Acyclic Graph (DAG). By modeling factors as nodes and evolutionary links as edges, AlphaPROBE treats the factor pool as a dynamic, interconnected ecosystem. The framework consists of two core components: a Bayesian Factor Retriever that identifies high-potential seeds by balancing exploitation and exploration through a posterior probability model, and a DAG-aware Factor Generator that leverages the full ancestral trace of factors to produce context-aware, nonredundant optimizations. Extensive experiments on three major Chinese stock market datasets against 8 competitive baselines demonstrate that AlphaPROBE significantly gains enhanced performance in predictive accuracy, return stability and training efficiency. Our results confirm that leveraging global evolutionary topology is essential for efficient and robust automated alpha discovery. We have open-sourced our implementation at this https URL.
Abstract (translated)
通过阿尔法因子挖掘提取信号是量化金融中的一个基本挑战。现有的自动化方法主要遵循两种范式：解耦因素生成，这种方法将因子发现视为孤立事件；以及迭代因素进化，侧重于局部的父子层次细化。然而，这两种范式都缺乏全局结构视角，往往将因子池视作无结构集合或碎片化链条，导致冗余搜索和多样性受限。 为了克服这些限制，我们引入了AlphaPROBE（通过原则性检索和图上偏置演化进行阿尔法挖掘），这是一个框架，它重新定义阿尔法挖掘为有向无环图(DAG)的战略导航。AlphaPROBE将因子视为节点，并将进化链接视作边，从而将因子池视为一个动态的、相互关联的生态系统。该框架由两个核心组件组成：贝叶斯因子检索器，通过后验概率模型平衡利用和探索来识别高潜力种子；以及DAG感知型因子生成器，它利用因素的完整先祖追踪以产生上下文相关且非冗余优化。 在三个主要中国股票市场数据集上进行的大量实验表明，AlphaPROBE相较于8个竞争基线，在预测准确性、收益稳定性和训练效率方面显著提升了性能。我们的研究结果证实了借助全局进化拓扑对于有效和鲁棒自动阿尔法发现的重要性。 我们已经开源了此实现，请访问[此处](https://URL)查看。
URL
https://arxiv.org/abs/2602.11917
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History Is Not Enough: An Adaptive Dataflow System for Financial Time-Series Synthesis
2026-01-15 07:38:59Haochong Xia, Yao Long Teng, Regan Tan, Molei Qin, Xinrun Wang, Bo AnAbstract
In quantitative finance, the gap between training and real-world performance-driven by concept drift and distributional non-stationarity-remains a critical obstacle for building reliable data-driven systems. Models trained on static historical data often overfit, resulting in poor generalization in dynamic markets. The mantra "History Is Not Enough" underscores the need for adaptive data generation that learns to evolve with the market rather than relying solely on past observations. We present a drift-aware dataflow system that integrates machine learning-based adaptive control into the data curation process. The system couples a parameterized data manipulation module comprising single-stock transformations, multi-stock mix-ups, and curation operations, with an adaptive planner-scheduler that employs gradient-based bi-level optimization to control the system. This design unifies data augmentation, curriculum learning, and data workflow management under a single differentiable framework, enabling provenance-aware replay and continuous data quality monitoring. Extensive experiments on forecasting and reinforcement learning trading tasks demonstrate that our framework enhances model robustness and improves risk-adjusted returns. The system provides a generalizable approach to adaptive data management and learning-guided workflow automation for financial data.
Abstract (translated)
在量化金融领域，由于概念漂移（concept drift）和分布非平稳性（distributional non-stationarity），训练数据与实际世界性能之间的差距仍然是构建可靠的数据驱动系统的关键障碍。基于静态历史数据进行训练的模型往往过度拟合，在动态市场中表现不佳。口号“历史不够”强调了需要自适应数据生成，以学习随着市场变化而演变，而不是仅仅依赖于过去的观察结果。 我们提出了一种概念漂移感知的数据流系统，该系统将机器学习基础的自适应控制集成到了数据管理过程中。该系统结合了一个参数化的数据操作模块（包括单股票转换、多股票混合和数据管理操作）与一个采用基于梯度的双层优化方法进行自我调节的规划调度器。这种设计统一了数据增强、课程学习以及数据工作流管理在一个单一可微分框架内，使得来源追踪感知重放和持续的数据质量监控成为可能。 在预测任务和强化学习交易任务上的广泛实验表明，我们的框架能够提升模型鲁棒性并改善风险调整后的回报率。该系统为适应性数据管理和由学习引导的工作流程自动化提供了通用的方法论，适用于金融数据处理。
URL
https://arxiv.org/abs/2601.10143
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QuantEval: A Benchmark for Financial Quantitative Tasks in Large Language Models
2026-01-13 16:14:23Zhaolu Kang, Junhao Gong, Wenqing Hu, Shuo Yin, Kehan Jiang, Zhicheng Fang, Yingjie He, Chunlei Meng, Rong Fu, Dongyang Chen, Leqi Zheng, Eric Hanchen Jiang, Yunfei Feng, Yitong Leng, Junfan Zhu, Xiaoyou Chen, Xi Yang, Richeng XuanAbstract
Large Language Models (LLMs) have shown strong capabilities across many domains, yet their evaluation in financial quantitative tasks remains fragmented and mostly limited to knowledge-centric question answering. We introduce QuantEval, a benchmark that evaluates LLMs across three essential dimensions of quantitative finance: knowledge-based QA, quantitative mathematical reasoning, and quantitative strategy coding. Unlike prior financial benchmarks, QuantEval integrates a CTA-style backtesting framework that executes model-generated strategies and evaluates them using financial performance metrics, enabling a more realistic assessment of quantitative coding ability. We evaluate some state-of-the-art open-source and proprietary LLMs and observe substantial gaps to human experts, particularly in reasoning and strategy coding. Finally, we conduct large-scale supervised fine-tuning and reinforcement learning experiments on domain-aligned data, demonstrating consistent improvements. We hope QuantEval will facilitate research on LLMs' quantitative finance capabilities and accelerate their practical adoption in real-world trading workflows. We additionally release the full deterministic backtesting configuration (asset universe, cost model, and metric definitions) to ensure strict reproducibility.
Abstract (translated)
大型语言模型（LLMs）在多个领域展现了强大的能力，然而它们在金融量化任务中的评估仍然碎片化，并且主要局限于知识为中心的问题回答。我们引入了QuantEval基准测试，它从定量金融的三个方面来评价LLMs：基于知识的问答、数量化的数学推理以及量化的策略编码。 与之前的财务基准不同，QuantEval整合了一个CTA风格的回测框架，该框架可以执行模型生成的策略，并使用财务绩效指标进行评估，从而能够更真实地衡量量化代码编写能力。我们对一些最先进的开源和专有LLMs进行了评价，观察到在推理和策略编码方面与人类专家存在显著差距。 最后，我们在领域内对齐的数据上进行了大规模监督微调和强化学习实验，显示出了持续的改进效果。我们希望QuantEval能促进对LLMs量化金融能力的研究，并加速它们在现实世界交易工作流程中的实际应用。此外，为了确保严格的可重复性，我们将完整的确定性回测配置（资产组合、成本模型及指标定义）一并发布。
URL
https://arxiv.org/abs/2601.08689
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Generative AI-enhanced Sector-based Investment Portfolio Construction
2025-12-31 00:19:41Alina Voronina, Oleksandr Romanko, Ruiwen Cao, Roy H. Kwon, Rafael Mendoza-ArriagaAbstract
This paper investigates how Large Language Models (LLMs) from leading providers (OpenAI, Google, Anthropic, DeepSeek, and xAI) can be applied to quantitative sector-based portfolio construction. We use LLMs to identify investable universes of stocks within S&P 500 sector indices and evaluate how their selections perform when combined with classical portfolio optimization methods. Each model was prompted to select and weight 20 stocks per sector, and the resulting portfolios were compared with their respective sector indices across two distinct out-of-sample periods: a stable market phase (January-March 2025) and a volatile phase (April-June 2025). Our results reveal a strong temporal dependence in LLM portfolio performance. During stable market conditions, LLM-weighted portfolios frequently outperformed sector indices on both cumulative return and risk-adjusted (Sharpe ratio) measures. However, during the volatile period, many LLM portfolios underperformed, suggesting that current models may struggle to adapt to regime shifts or high-volatility environments underrepresented in their training data. Importantly, when LLM-based stock selection is combined with traditional optimization techniques, portfolio outcomes improve in both performance and consistency. This study contributes one of the first multi-model, cross-provider evaluations of generative AI algorithms in investment management. It highlights that while LLMs can effectively complement quantitative finance by enhancing stock selection and interpretability, their reliability remains market-dependent. The findings underscore the potential of hybrid AI-quantitative frameworks, integrating LLM reasoning with established optimization techniques, to produce more robust and adaptive investment strategies.
Abstract (translated)
本文研究了领先供应商（OpenAI、Google、Anthropic、DeepSeek 和 xAI）提供的大型语言模型（LLMs）在基于量化行业的投资组合构建中的应用。我们使用这些模型来识别标普500指数中各行业成分股的投资范围，并评估它们的选择与经典投资组合理论方法结合后的表现。每个模型被提示选择并加权各行业中20只股票，然后我们将生成的投资组合与其他同类市场指数在两个不同的样本外时间段进行了比较：一个稳定的市场时期（2025年1月至3月）和一个动荡的市场时期（2025年4月至6月）。我们的研究结果揭示了LLM投资组合绩效具有明显的时变特性。在稳定市场的条件下，通过累积回报和风险调整后收益（夏普比率）衡量，LLM加权的投资组合常常优于行业指数表现。然而，在动荡的市场期间，许多由LLM构建的投资组合的表现不佳，这表明当前模型可能难以适应其训练数据中代表性不足的制度转变或高波动性环境。值得注意的是，当基于LLM的选择与传统优化技术相结合时，投资组合在性能和一致性方面都有所提升。 这项研究提供了对生成式AI算法在资产管理中的多模态、跨供应商评估的一个早期示例。研究表明，虽然LLMs可以通过增强选股能力和可解释性有效地补充量化金融，但其可靠性仍然依赖于市场条件。这些发现强调了混合AI-量化框架的潜力，即结合LLM推理和成熟的优化技术来生成更加稳健且适应性强的投资策略。
URL
https://arxiv.org/abs/2512.24526
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MASFIN: A Multi-Agent System for Decomposed Financial Reasoning and Forecasting
2025-12-26 06:01:55Marc S. Montalvo, Hamed YaghoobianAbstract
Recent advances in large language models (LLMs) are transforming data-intensive domains, with finance representing a high-stakes environment where transparent and reproducible analysis of heterogeneous signals is essential. Traditional quantitative methods remain vulnerable to survivorship bias, while many AI-driven approaches struggle with signal integration, reproducibility, and computational efficiency. We introduce MASFIN, a modular multi-agent framework that integrates LLMs with structured financial metrics and unstructured news, while embedding explicit bias-mitigation protocols. The system leverages GPT-4.1-nano for reproducability and cost-efficient inference and generates weekly portfolios of 15-30 equities with allocation weights optimized for short-term performance. In an eight-week evaluation, MASFIN delivered a 7.33% cumulative return, outperforming the S&P 500, NASDAQ-100, and Dow Jones benchmarks in six of eight weeks, albeit with higher volatility. These findings demonstrate the promise of bias-aware, generative AI frameworks for financial forecasting and highlight opportunities for modular multi-agent design to advance practical, transparent, and reproducible approaches in quantitative finance.
Abstract (translated)
最近在大型语言模型（LLMs）方面取得的进展正在改变数据密集型领域，尤其是在金融行业这样一个高风险环境中，透明且可重复地分析异构信号至关重要。传统的量化方法仍然容易受到幸存者偏差的影响，而许多基于AI的方法则难以整合信号、确保可重复性和提高计算效率。我们推出了MASFIN，这是一个模块化的多代理框架，它将LLMs与结构化金融指标和非结构化新闻相结合，并嵌入了明确的偏见缓解协议。该系统利用GPT-4.1-nano来实现可重复性并进行成本效益高的推理，生成包含15至30个股票的每周投资组合，其分配权重经过优化以提高短期表现。在为期八周的评估中，MASFIN实现了7.33%的累计收益，在八个周期中有六次超过了标准普尔500指数、纳斯达克-100和道琼斯基准的表现，尽管波动性较高。这些发现展示了具有偏见意识的生成式AI框架在金融预测中的潜力，并强调了模块化多代理设计在量化金融中推进实用、透明和可重复方法的机会。
URL
https://arxiv.org/abs/2512.21878
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