arXiv AI
Aug 21
Concentrated Liquidity Provision: a Reinforcement Learning Perspective
arXiv:2608. 19389v1 Announce Type: cross Abstract: Automated market makers (AMMs) are a cornerstone of decentralised finance (DeFi).
SAiFE-gym: Model-based Environments for Automated Market Making with Concentrated Liquidity
SAiFE-gym is a Python module that offers simulation environments for studying trading in Constant Product Markets with Concentrated Liquidity. It decomposes the microstructure of these markets into interactive components, allowing researchers to model various economic settings. The environments are vectorized for scalability in high‑dimensional reinforcement learning workflows, and the paper demonstrates their usefulness by evaluating RL agents under uncertain market parameters.
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arXiv AI
Jul 9
Can Reinforcement Learning Efficiently Discover Price Manipulation?
arXiv:2607. 06121v1 Announce Type: cross Abstract: In this paper, we investigate whether a model-free RL agent can identify and exploit price manipulation opportunities more effectively than a traditional model-based approach that assumes correct specification of the data-generating process but relies on noisy parameter estimates.
arXiv AI
Jul 8
Beyond Static Evaluation: Building Simulation Environments for Scalable Agentic Reinforcement Learning
arXiv:2607. 05773v1 Announce Type: new Abstract: As Large Language Models (LLMs) evolve into autonomous agents, traditional static evaluation fails to capture multi-step decision-making.
arXiv AI
Jun 8
InvEvolve: Evolving White-Box Inventory Policies via Large Language Models with Performance Guarantees
arXiv:2605. 00369v4 Announce Type: replace-cross Abstract: We study how large language models can be used to generate inventory policies in online settings with non-stationary demand.
arXiv AI
Jul 29
AlphaCrafter: Harnessing Multi-Agent Workflows for Cross-Sectional Quantitative Trading
arXiv:2605. 05580v2 Announce Type: replace Abstract: Quantitative trading agents have demonstrated substantial promise in automating factor discovery, signal aggregation, and portfolio execution.
arXiv AI
Aug 24
AI-driven Prices for Externalities and Sustainability in Production Markets
arXiv:2106.06060v4 Announce Type: replace-cross Abstract: Traditional competitive markets do not account for negative externalities; indirect costs that some participants impose on others, such as th...
arXiv AI
Jun 26
OpenFinGym: A Verifiable Multi-Task Gym Environment for Evaluating Quant Agents
arXiv:2606. 26350v1 Announce Type: new Abstract: 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.
arXiv Machine Learning
Jul 31
FinSMART: Financial Sentiment Analysis for Algorithmic Trading through Market-Aligned Reinforcement Learning
arXiv:2607. 28127v1 Announce Type: cross Abstract: Recent advances in Generative AI have substantially improved financial sentiment analysis through post-trained financial large language models (LLMs).
arXiv AI
Jun 10
A Unified Multi-Modal Framework for Intelligent Financial Systems: Integrating Reinforcement Learning, High-Frequency Trading, and Game-Theoretic Approaches with Cross-Modal Sentiment Analysis
arXiv:2606. 10412v1 Announce Type: new Abstract: The rapid evolution of financial technology demands sophisticated artificial intelligence systems capable of handling diverse challenges across multiple domains simultaneously.
arXiv Machine Learning
Jul 14
Reinforcement Learning for Execution under Dynamic Fees in a Closed-Loop DEX Simulator
arXiv:2607. 10960v1 Announce Type: new Abstract: Trader-facing dynamic fees are increasingly proposed for automated market makers (AMMs), but historical data do not identify how order flow would respond: trader-facing fees do not vary, trader types are latent, and a replayed tape is not a sequential decision environment.
arXiv AI
Sep 15
Are LLMs Good Financial User Simulators? A Preliminary Study
Large language models (LLMs) are being tested as simulators of individual financial decision-making. In a controlled paper‑trading experiment with 120 volunteers, the study evaluated whether an LLM could predict a participant’s next‑day trading action, chosen security, and transaction size using only pre‑cutoff information. Results showed that including market context improved predictions of actions and tickers, but sizing remained challenging, and the models tended to over‑predict hold actions, under‑predict sells, and simplify multi‑security trades.
arXiv Machine Learning
Aug 18
Learning Stock Trading Policies via Barycenter-Based Adversarial Inverse Reinforcement Learning
arXiv:2608. 15770v1 Announce Type: new Abstract: Designing effective trading strategies using reinforcement learning remains challenging due to delayed and noisy rewards, poor exploration, and the difficulty of enforcing explicit risk constraints.