The paper studies Limit Order Books (LOBs) that are populated only by autonomous reinforcement‑learning agents. It shows that such agentic LOBs exhibit clear phase boundaries between orderly price discovery and hyper‑volatile cascade states, determined by critical thresholds in agent number and market depth. Additionally, it finds that market impact in these systems departs from the classic square‑root law, revealing distinct dissipative, balanced, and non‑dissipative regimes driven by nonlinear feedback loops.
arXiv:2608. 19227v1 Announce Type: cross Abstract: Market microstructure simulation aims to model how liquidity, prices, and order flow evolve in electronic financial markets.
The paper proposes an adversarial reinforcement‑learning framework for market making that incorporates Hawkes‑process driven order arrivals and trade‑induced price impact, addressing limitations of prior Poisson‑based models. An LSTM module captures temporal dependencies in recent observations to handle increased non‑stationarity, and the authors analyze equilibrium properties and introduce a robustness evaluation protocol focused on the left tail of returns. Experiments across diverse market regimes demonstrate that the method improves left‑tail performance, especially under strong Hawkes excitation and moderate price impact, without relying on a terminal inventory bias.
arXiv:2608. 19389v1 Announce Type: cross Abstract: Automated market makers (AMMs) are a cornerstone of decentralised finance (DeFi).
arXiv:2609.13825v1 Announce Type: new Abstract: Reinforcement learning trading systems published in the academic literature overwhelmingly rely on price-aggregate state representations (OHLCV bars) o...
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:2606. 05363v1 Announce Type: cross Abstract: On a platform with many sellers, should a pricing algorithm explicitly model competitors' prices when learning demand?
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:2606. 31522v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly deployed as autonomous financial agents initialized with explicit behavioral mandates such as "preserve capital" or "avoid speculative bets" that are meant to govern every decision throughout deployment.
arXiv:2609.07675v1 Announce Type: cross Abstract: Transaction-local controls answer whether one financial request may proceed, but market behavior can be distributed across messages, agents, assets,...
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.
arXiv:2609.05591v1 Announce Type: cross Abstract: Safety evaluations typically focus on individual agents, but interacting agents can spread harmful information and influence the environment in which...
arXiv:2606. 16613v1 Announce Type: new Abstract: As LLM agents become capable of increasingly long-horizon tasks, evaluating their performance in economic systems is becoming increasingly important.