# Conditional Generators for Limit Order Book Environments: Explainability, Challenges, and Robustness

arXiv: https://arxiv.org/abs/2306.12806 · HTML: https://mlsanity.com/paper/2306.12806/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 71 references that are in the map.
- First posted on arXiv 22 Jun 2023.

## Builds on (most cited first, 30 of 30)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [Diffusion Models in Vision: A Survey](https://mlsanity.com/paper/2209.04747/) (2022)
- [Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images](https://mlsanity.com/paper/1412.1897/) (2014)
- [Modeling Tabular data using Conditional GAN](https://mlsanity.com/paper/1907.00503/) (2019)
- [Evasion Attacks against Machine Learning at Test Time](https://mlsanity.com/paper/1708.06131/) (2017)
- [Robust Adversarial Reinforcement Learning](https://mlsanity.com/paper/1703.02702/) (2017)
- [Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecasting](https://mlsanity.com/paper/2101.12072/) (2021)
- [Real-valued (Medical) Time Series Generation with Recurrent Conditional GANs](https://mlsanity.com/paper/1706.02633/) (2017)
- [Pros and Cons of GAN Evaluation Measures](https://mlsanity.com/paper/1802.03446/) (2018)
- [TrafficSim: Learning to Simulate Realistic Multi-Agent Behaviors](https://mlsanity.com/paper/2101.06557/) (2021)
- [Multivariate Probabilistic Time Series Forecasting via Conditioned Normalizing Flows](https://mlsanity.com/paper/2002.06103/) (2020)
- [Market Making via Reinforcement Learning](https://mlsanity.com/paper/1804.04216/) (2018)
- [Learning To Simulate](https://mlsanity.com/paper/1810.02513/) (2018)
- [Double Deep Q-Learning for Optimal Execution](https://mlsanity.com/paper/1812.06600/) (2018)
- [Get Real: Realism Metrics for Robust Limit Order Book Market Simulations](https://mlsanity.com/paper/1912.04941/) (2019)
- [Deep Latent State Space Models for Time-Series Generation](https://mlsanity.com/paper/2212.12749/) (2022)
- [A Data-driven Market Simulator for Small Data Environments](https://mlsanity.com/paper/2006.14498/) (2020)
- [Generating Realistic Stock Market Order Streams](https://mlsanity.com/paper/2006.04212/) (2020)
- [Towards Realistic Market Simulations: a Generative Adversarial Networks Approach](https://mlsanity.com/paper/2110.13287/) (2021)
- [Learning to simulate realistic limit order book markets from data as a World Agent](https://mlsanity.com/paper/2210.09897/) (2022)
- [How to Evaluate Trading Strategies: Single Agent Market Replay or Multiple Agent Interactive Simulation?](https://mlsanity.com/paper/1906.12010/) (2019)
- [Neural Stochastic Agent-Based Limit Order Book Simulation: A Hybrid Methodology](https://mlsanity.com/paper/2303.00080/) (2023)
- [K-SHAP: Policy Clustering Algorithm for Anonymous Multi-Agent State-Action Pairs](https://mlsanity.com/paper/2302.11996/) (2023)
- [Efficient Calibration of Multi-Agent Simulation Models from Output Series with Bayesian Optimization](https://mlsanity.com/paper/2112.03874/) (2021)
- [Market Making with Scaled Beta Policies](https://mlsanity.com/paper/2207.03352/) (2022)

## Built on by (most cited first, 14 of 14)

- [MarS: a Financial Market Simulation Engine Powered by Generative Foundation Model](https://mlsanity.com/paper/2409.07486/) (2024)
- [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)
- [From Deep Learning to LLMs: A survey of AI in Quantitative Investment](https://mlsanity.com/paper/2503.21422/) (2025)
- [Time-Causal VAE: Robust Financial Time Series Generator](https://mlsanity.com/paper/2411.02947/) (2024)
- [TRADES: Generating Realistic Market Simulations with Diffusion Models](https://mlsanity.com/paper/2502.07071/) (2025)
- [LOB-Bench: Benchmarking Generative AI for Finance -- an Application to Limit Order Book Data](https://mlsanity.com/paper/2502.09172/) (2025)
- [ByteGen: A Tokenizer-Free Generative Model for Orderbook Events in Byte Space](https://mlsanity.com/paper/2508.02247/) (2025)
- [INTAGS: Interactive Agent-Guided Simulation](https://mlsanity.com/paper/2309.01784/) (2023)
- [Limit Order Book Simulation and Trade Evaluation with K-Nearest-Neighbor Resampling](https://mlsanity.com/paper/2409.06514/) (2024)
- [Financial Wind Tunnel: A Retrieval-Augmented Market Simulator](https://mlsanity.com/paper/2503.17909/) (2025)
- [Large Investment Model](https://mlsanity.com/paper/2408.10255/) (2024)
- [Factor-Based Conditional Diffusion Model for Contextual Portfolio Optimization](https://mlsanity.com/paper/2509.22088/) (2025)
- [LOB-ID: Evaluating Synthetic Market Data by Inception Distances](https://mlsanity.com/paper/2608.13082/) (2026)
- [ViperQ: Order Flow Pattern Recognition via Auction Market Theory for Reinforcement Learning Trading](https://mlsanity.com/paper/2609.13825/) (2026)

## Abstract (arXiv)

Limit order books are a fundamental and widespread market mechanism. This paper investigates the use of conditional generative models for order book simulation. For developing a trading agent, this approach has drawn recent attention as an alternative to traditional backtesting due to its ability to react to the presence of the trading agent. Using a state-of-the-art CGAN (from Coletta et al. (2022)), we explore its dependence upon input features, which highlights both strengths and weaknesses. To do this, we use "adversarial attacks" on the model's features and its mechanism. We then show how these insights can be used to improve the CGAN, both in terms of its realism and robustness. We finish by laying out a roadmap for future work.