# How to Evaluate Trading Strategies: Single Agent Market Replay or Multiple Agent Interactive Simulation?

arXiv: https://arxiv.org/abs/1906.12010 · HTML: https://mlsanity.com/paper/1906.12010/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jun 2019.

## Built on by (most cited first, 15 of 15)

- [ABIDES-Gym: Gym Environments for Multi-Agent Discrete Event Simulation and Application to Financial Markets](https://mlsanity.com/paper/2110.14771/) (2021)
- [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)
- [Deep Reinforcement Learning for Active High Frequency Trading](https://mlsanity.com/paper/2101.07107/) (2021)
- [Conditional Generators for Limit Order Book Environments: Explainability, Challenges, and Robustness](https://mlsanity.com/paper/2306.12806/) (2023)
- [DSLOB: A Synthetic Limit Order Book Dataset for Benchmarking Forecasting Algorithms under Distributional Shift](https://mlsanity.com/paper/2211.11513/) (2022)
- [Market Making with Scaled Beta Policies](https://mlsanity.com/paper/2207.03352/) (2022)
- [Optimal Execution with Reinforcement Learning](https://mlsanity.com/paper/2411.06389/) (2024)
- [Robust Risk-Sensitive Reinforcement Learning Agents for Trading Markets](https://mlsanity.com/paper/2107.08083/) (2021)
- [Pearl: Parallel Evolutionary and Reinforcement Learning Library](https://mlsanity.com/paper/2201.09568/) (2022)
- [Model-based gym environments for limit order book trading](https://mlsanity.com/paper/2209.07823/) (2022)
- [Limited or Biased: Modeling Sub-Rational Human Investors in Financial Markets](https://mlsanity.com/paper/2210.08569/) (2022)
- [Representation Learning of Limit Order Book: A Comprehensive Study and Benchmarking](https://mlsanity.com/paper/2505.02139/) (2025)
- [INTAGS: Interactive Agent-Guided Simulation](https://mlsanity.com/paper/2309.01784/) (2023)
- [DiffVolume: Diffusion Models for Volume Generation in Limit Order Books](https://mlsanity.com/paper/2508.08698/) (2025)

## Abstract (arXiv)

We show how a multi-agent simulator can support two important but distinct methods for assessing a trading strategy: Market Replay and Interactive Agent-Based Simulation (IABS). Our solution is important because each method offers strengths and weaknesses that expose or conceal flaws in the subject strategy. A key weakness of Market Replay is that the simulated market does not substantially adapt to or respond to the presence of the experimental strategy. IABS methods provide an artificial market for the experimental strategy using a population of background trading agents. Because the background agents attend to market conditions and current price as part of their strategy, the overall market is responsive to the presence of the experimental strategy. Even so, IABS methods have their own weaknesses, primarily that it is unclear if the market environment they provide is realistic. We describe our approach in detail, and illustrate its use in an example application: The evaluation of market impact for various size orders.