# Towards Realistic Market Simulations: a Generative Adversarial Networks Approach

arXiv: https://arxiv.org/abs/2110.13287 · HTML: https://mlsanity.com/paper/2110.13287/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 35 references that are in the map.
- First posted on arXiv 25 Oct 2021.

## Builds on (most cited first, 14 of 14)

- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [World Models](https://mlsanity.com/paper/1803.10122/) (2018)
- [Are GANs Created Equal? A Large-Scale Study](https://mlsanity.com/paper/1711.10337/) (2017)
- [Deep Hedging](https://mlsanity.com/paper/1802.03042/) (2018)
- [Market Making via Reinforcement Learning](https://mlsanity.com/paper/1804.04216/) (2018)
- [Get Real: Realism Metrics for Robust Limit Order Book Market Simulations](https://mlsanity.com/paper/1912.04941/) (2019)
- [Generating Realistic Stock Market Order Streams](https://mlsanity.com/paper/2006.04212/) (2020)
- [How to Evaluate Trading Strategies: Single Agent Market Replay or Multiple Agent Interactive Simulation?](https://mlsanity.com/paper/1906.12010/) (2019)
- [Model-based Reinforcement Learning for Predictions and Control for Limit Order Books](https://mlsanity.com/paper/1910.03743/) (2019)
- [Explaining Agent-Based Financial Market Simulation](https://mlsanity.com/paper/1909.11650/) (2019)
- [Using generative adversarial networks to synthesize artificial financial datasets](https://mlsanity.com/paper/2002.02271/) (2020)
- [Simulating and classifying behavior in adversarial environments based on action-state traces: an application to money laundering](https://mlsanity.com/paper/2011.01826/) (2020)
- [Analysis of the impact of maker-taker fees on the stock market using agent-based simulation](https://mlsanity.com/paper/2010.08992/) (2020)

## Built on by (most cited first, 29 of 29)

- [On the Constrained Time-Series Generation Problem](https://mlsanity.com/paper/2307.01717/) (2023)
- [Non-adversarial training of Neural SDEs with signature kernel scores](https://mlsanity.com/paper/2305.16274/) (2023)
- [MarS: a Financial Market Simulation Engine Powered by Generative Foundation Model](https://mlsanity.com/paper/2409.07486/) (2024)
- [Learning to simulate realistic limit order book markets from data as a World Agent](https://mlsanity.com/paper/2210.09897/) (2022)
- [HyperTime: Implicit Neural Representation for Time Series](https://mlsanity.com/paper/2208.05836/) (2022)
- [TimeDP: Learning to Generate Multi-Domain Time Series with Domain Prompts](https://mlsanity.com/paper/2501.05403/) (2025)
- [Generative AI for End-to-End Limit Order Book Modelling: A Token-Level Autoregressive Generative Model of Message Flow Using a Deep State Space Network](https://mlsanity.com/paper/2309.00638/) (2023)
- [Dynamic Datasets and Market Environments for Financial Reinforcement Learning](https://mlsanity.com/paper/2304.13174/) (2023)
- [Conditional Generators for Limit Order Book Environments: Explainability, Challenges, and Robustness](https://mlsanity.com/paper/2306.12806/) (2023)
- [LOB-Based Deep Learning Models for Stock Price Trend Prediction: A Benchmark Study](https://mlsanity.com/paper/2308.01915/) (2023)
- [Time-Causal VAE: Robust Financial Time Series Generator](https://mlsanity.com/paper/2411.02947/) (2024)
- [Quant 4.0: Engineering Quantitative Investment with Automated, Explainable and Knowledge-driven Artificial Intelligence](https://mlsanity.com/paper/2301.04020/) (2022)
- [K-SHAP: Policy Clustering Algorithm for Anonymous Multi-Agent State-Action Pairs](https://mlsanity.com/paper/2302.11996/) (2023)
- [TRADES: Generating Realistic Market Simulations with Diffusion Models](https://mlsanity.com/paper/2502.07071/) (2025)
- [New Money: A Systematic Review of Synthetic Data Generation for Finance](https://mlsanity.com/paper/2510.26076/) (2025)
- [Deep Calibration of Market Simulations using Neural Density Estimators and Embedding Networks](https://mlsanity.com/paper/2311.11913/) (2023)
- [Universal randomised signatures for generative time series modelling](https://mlsanity.com/paper/2406.10214/) (2024)
- [TLOB: A Novel Transformer Model with Dual Attention for Price Trend Prediction with Limit Order Book Data](https://mlsanity.com/paper/2502.15757/) (2025)
- [Asynchronous Deep Double Duelling Q-Learning for Trading-Signal Execution in Limit Order Book Markets](https://mlsanity.com/paper/2301.08688/) (2023)
- [CNN-DRL with Shuffled Features in Finance](https://mlsanity.com/paper/2402.03338/) (2024)
- [DiffLOB: Diffusion Models for Counterfactual Generation in Limit Order Books](https://mlsanity.com/paper/2602.03776/) (2026)
- [A Modular Framework for Reinforcement Learning Optimal Execution](https://mlsanity.com/paper/2208.06244/) (2022)
- [INTAGS: Interactive Agent-Guided Simulation](https://mlsanity.com/paper/2309.01784/) (2023)
- [Financial Wind Tunnel: A Retrieval-Augmented Market Simulator](https://mlsanity.com/paper/2503.17909/) (2025)
- [DiffVolume: Diffusion Models for Volume Generation in Limit Order Books](https://mlsanity.com/paper/2508.08698/) (2025)
- [Diffusion Models for Adaptive Sequential Data Generation](https://mlsanity.com/paper/2606.06007/) (2026)
- [High-Quality Synthetic Financial Time-Series using a GAN-Diffusion Framework](https://mlsanity.com/paper/2605.27113/) (2026)
- [LOB-ID: Evaluating Synthetic Market Data by Inception Distances](https://mlsanity.com/paper/2608.13082/) (2026)
- [FlowLOB: Efficient and Controllable Limit Order Book Generation with Flow Matching](https://mlsanity.com/paper/2608.13096/) (2026)

## Abstract (arXiv)

Simulated environments are increasingly used by trading firms and investment banks to evaluate trading strategies before approaching real markets. Backtesting, a widely used approach, consists of simulating experimental strategies while replaying historical market scenarios. Unfortunately, this approach does not capture the market response to the experimental agents' actions. In contrast, multi-agent simulation presents a natural bottom-up approach to emulating agent interaction in financial markets. It allows to set up pools of traders with diverse strategies to mimic the financial market trader population, and test the performance of new experimental strategies. Since individual agent-level historical data is typically proprietary and not available for public use, it is difficult to calibrate multiple market agents to obtain the realism required for testing trading strategies. To addresses this challenge we propose a synthetic market generator based on Conditional Generative Adversarial Networks (CGANs) trained on real aggregate-level historical data. A CGAN-based "world" agent can generate meaningful orders in response to an experimental agent. We integrate our synthetic market generator into ABIDES, an open source simulator of financial markets. By means of extensive simulations we show that our proposal outperforms previous work in terms of stylized facts reflecting market responsiveness and realism.