# ABIDES: Towards High-Fidelity Market Simulation for AI Research

arXiv: https://arxiv.org/abs/1904.12066 · HTML: https://mlsanity.com/paper/1904.12066/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Apr 2019.

## Built on by (most cited first, 30 of 32)

- [ABIDES-Gym: Gym Environments for Multi-Agent Discrete Event Simulation and Application to Financial Markets](https://mlsanity.com/paper/2110.14771/) (2021)
- [Reinforcement Learning for Quantitative Trading](https://mlsanity.com/paper/2109.13851/) (2021)
- [Deep Reinforcement Learning for Active High Frequency Trading](https://mlsanity.com/paper/2101.07107/) (2021)
- [Deep Learning for Market by Order Data](https://mlsanity.com/paper/2102.08811/) (2021)
- [DSLOB: A Synthetic Limit Order Book Dataset for Benchmarking Forecasting Algorithms under Distributional Shift](https://mlsanity.com/paper/2211.11513/) (2022)
- [K-SHAP: Policy Clustering Algorithm for Anonymous Multi-Agent State-Action Pairs](https://mlsanity.com/paper/2302.11996/) (2023)
- [A Financial Time Series Denoiser Based on Diffusion Model](https://mlsanity.com/paper/2409.02138/) (2024)
- [Phantom -- A RL-driven multi-agent framework to model complex systems](https://mlsanity.com/paper/2210.06012/) (2022)
- [Towards Generalizable Reinforcement Learning for Trade Execution](https://mlsanity.com/paper/2307.11685/) (2023)
- [Fox in the Henhouse: Supply-Chain Backdoor Attacks Against Reinforcement Learning](https://mlsanity.com/paper/2505.19532/) (2025)
- [Deep Calibration of Market Simulations using Neural Density Estimators and Embedding Networks](https://mlsanity.com/paper/2311.11913/) (2023)
- [Robust Risk-Sensitive Reinforcement Learning Agents for Trading Markets](https://mlsanity.com/paper/2107.08083/) (2021)
- [Asynchronous Deep Double Duelling Q-Learning for Trading-Signal Execution in Limit Order Book Markets](https://mlsanity.com/paper/2301.08688/) (2023)
- [Limited or Biased: Modeling Sub-Rational Human Investors in Financial Markets](https://mlsanity.com/paper/2210.08569/) (2022)
- [Safe and Compliant Cross-Market Trade Execution via Constrained RL and Zero-Knowledge Audits](https://mlsanity.com/paper/2510.04952/) (2025)
- [PredictionMarketBench: A SWE-bench-Style Framework for Backtesting Trading Agents on Prediction Markets](https://mlsanity.com/paper/2602.00133/) (2026)
- [Deep Q-Learning Market Makers in a Multi-Agent Simulated Stock Market](https://mlsanity.com/paper/2112.04494/) (2021)
- [Empirical Equilibria in Agent-based Economic systems with Learning agents](https://mlsanity.com/paper/2408.12038/) (2024)
- [Limit Order Book Simulation and Trade Evaluation with K-Nearest-Neighbor Resampling](https://mlsanity.com/paper/2409.06514/) (2024)
- [DiffVolume: Diffusion Models for Volume Generation in Limit Order Books](https://mlsanity.com/paper/2508.08698/) (2025)
- [Similarity metrics for Different Market Scenarios in Abides](https://mlsanity.com/paper/2107.09352/) (2021)
- [CTMSTOU driven markets: simulated environment for regime-awareness in trading policies](https://mlsanity.com/paper/2202.00941/) (2022)
- [Intelligent Systematic Investment Agent: an ensemble of deep learning and evolutionary strategies](https://mlsanity.com/paper/2203.13125/) (2022)
- [Dropout-Robust Mechanisms for Differentially Private and Fully Decentralized Mean Estimation](https://mlsanity.com/paper/2506.03746/) (2025)
- [Tail-Safe Hedging: Explainable Risk-Sensitive Reinforcement Learning with a White-Box CBF--QP Safety Layer in Arbitrage-Free Markets](https://mlsanity.com/paper/2510.04555/) (2025)
- [When Quotes Crumble: Detecting Transient Mechanical Liquidity Erosion in Limit Order Books](https://mlsanity.com/paper/2604.21993/) (2026)
- [TT-DAC-PS: Twin-Target Deterministic Actor-Critic with Policy Smoothing for Optimal Trade Execution](https://mlsanity.com/paper/2606.08379/) (2026)
- [CARD: Controlled Agentic Reddit Discussions for Credit Card Simulation](https://mlsanity.com/paper/2608.09790/) (2026)
- [M3: A State-Event Generative Foundation Model for Market Microstructure Dynamics](https://mlsanity.com/paper/2608.19227/) (2026)
- [WolfSociety: Understanding Collective Risk from Harmful-Agent Scaling in Financial Agent Societies](https://mlsanity.com/paper/2609.05591/) (2026)

## Abstract (arXiv)

We introduce ABIDES, an Agent-Based Interactive Discrete Event Simulation environment. ABIDES is designed from the ground up to support AI agent research in market applications. While simulations are certainly available within trading firms for their own internal use, there are no broadly available high-fidelity market simulation environments. We hope that the availability of such a platform will facilitate AI research in this important area. ABIDES currently enables the simulation of tens of thousands of trading agents interacting with an exchange agent to facilitate transactions. It supports configurable pairwise network latencies between each individual agent as well as the exchange. Our simulator's message-based design is modeled after NASDAQ's published equity trading protocols ITCH and OUCH. We introduce the design of the simulator and illustrate its use and configuration with sample code, validating the environment with example trading scenarios. The utility of ABIDES is illustrated through experiments to develop a market impact model. We close with discussion of future experimental problems it can be used to explore, such as the development of ML-based trading algorithms.