How to Evaluate Trading Strategies: Single Agent Market Replay or Multiple Agent Interactive Simulation?
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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.
Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). First posted on arXiv 28 Jun 2019.
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Citing papers per year: 2021: 4; 2022: 6; 2023: 2; 2024: 1; 2025: 2.
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- 2021ABIDES-Gym: Gym Environments for Multi-Agent Discrete Event Simulation and Application to Financial Marketscited by 34
- 2021Towards Realistic Market Simulations: a Generative Adversarial Networks Approachcited by 29
- 2022Learning to simulate realistic limit order book markets from data as a World Agentcited by 27
- 2021Deep Reinforcement Learning for Active High Frequency Tradingcited by 21
- 2023Conditional Generators for Limit Order Book Environments: Explainability, Challenges, and Robustnesscited by 14
- 2022DSLOB: A Synthetic Limit Order Book Dataset for Benchmarking Forecasting Algorithms under Distributional Shiftcited by 6
- 2022Market Making with Scaled Beta Policiescited by 5
- 2024Optimal Execution with Reinforcement Learningcited by 5
- 2021Robust Risk-Sensitive Reinforcement Learning Agents for Trading Marketscited by 3
- 2022Model-based gym environments for limit order book tradingcited by 3
- 2022Pearl: Parallel Evolutionary and Reinforcement Learning Librarycited by 2
- 2022Limited or Biased: Modeling Sub-Rational Human Investors in Financial Marketscited by 2
- 2025DiffVolume: Diffusion Models for Volume Generation in Limit Order Books12 Aug 2025
- 2025Representation Learning of Limit Order Book: A Comprehensive Study and Benchmarking4 May 2025
- 2024Optimal Execution with Reinforcement Learning10 Nov 2024
- 2023INTAGS: Interactive Agent-Guided Simulation4 Sep 2023
- 2023Conditional Generators for Limit Order Book Environments: Explainability, Challenges, and Robustness22 Jun 2023
- 2022DSLOB: A Synthetic Limit Order Book Dataset for Benchmarking Forecasting Algorithms under Distributional Shift17 Nov 2022
- 2022Limited or Biased: Modeling Sub-Rational Human Investors in Financial Markets16 Oct 2022
- 2022Learning to simulate realistic limit order book markets from data as a World Agent26 Sep 2022
- 2022Model-based gym environments for limit order book trading16 Sep 2022
- 2022Market Making with Scaled Beta Policies7 Jul 2022
- 2022Pearl: Parallel Evolutionary and Reinforcement Learning Library24 Jan 2022
- 2021ABIDES-Gym: Gym Environments for Multi-Agent Discrete Event Simulation and Application to Financial Markets27 Oct 2021
- 2021ABIDES-Gym: Gym Environments for Multi-Agent Discrete Event Simulation and Application to Financial Marketscited by 34
- 2021Towards Realistic Market Simulations: a Generative Adversarial Networks Approachcited by 29
- 2022Learning to simulate realistic limit order book markets from data as a World Agentcited by 27
- 2021Deep Reinforcement Learning for Active High Frequency Tradingcited by 21
- 2023Conditional Generators for Limit Order Book Environments: Explainability, Challenges, and Robustnesscited by 14
- 2022DSLOB: A Synthetic Limit Order Book Dataset for Benchmarking Forecasting Algorithms under Distributional Shiftcited by 6
- 2022Market Making with Scaled Beta Policiescited by 5
- 2024Optimal Execution with Reinforcement Learningcited by 5
- 2021Robust Risk-Sensitive Reinforcement Learning Agents for Trading Marketscited by 3
- 2022Model-based gym environments for limit order book tradingcited by 3
- 2022Pearl: Parallel Evolutionary and Reinforcement Learning Librarycited by 2
- 2022Limited or Biased: Modeling Sub-Rational Human Investors in Financial Marketscited by 2
12 most cited of 14 All 14 (JSON)
- 2025Representation Learning of Limit Order Book: A Comprehensive Study and Benchmarking4 May 2025
- 2024Optimal Execution with Reinforcement Learning10 Nov 2024
- 2023INTAGS: Interactive Agent-Guided Simulation4 Sep 2023
- 2023Conditional Generators for Limit Order Book Environments: Explainability, Challenges, and Robustness22 Jun 2023
- 2022DSLOB: A Synthetic Limit Order Book Dataset for Benchmarking Forecasting Algorithms under Distributional Shift17 Nov 2022
- 2022Limited or Biased: Modeling Sub-Rational Human Investors in Financial Markets16 Oct 2022
- 2022Learning to simulate realistic limit order book markets from data as a World Agent26 Sep 2022
- 2022Model-based gym environments for limit order book trading16 Sep 2022
- 2022Market Making with Scaled Beta Policies7 Jul 2022
- 2022Pearl: Parallel Evolutionary and Reinforcement Learning Library24 Jan 2022
- 2021ABIDES-Gym: Gym Environments for Multi-Agent Discrete Event Simulation and Application to Financial Markets27 Oct 2021
- 2021Towards Realistic Market Simulations: a Generative Adversarial Networks Approach25 Oct 2021
12 newest of 14 All 14 (JSON)