---
title: Limit Order Book Simulations: A Review
url: https://www.ml-quant.com/papers/ssrn/4745587/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-09-26
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: SSRN 4745587
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4745587
featured: 2024-03-06
citations: 19
topic: Trading, Microstructure & Execution
---


# Limit Order Book Simulations: A Review

The piece reviews models of Limit Order Books simulations, emphasizing the role of AI in improving these models and the significance of price impacts in algorithmic trading.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4745587
- Identifier: SSRN 4745587
- Released: 2024-03-01
- First featured: Quant Letter No. 39 (2024-03-06): https://www.ml-quant.com/issues/2024-03-06/
- Citations (Semantic Scholar): 19
- Published in: not yet
- Topic: Trading, Microstructure & Execution

## Related

- [Microstructure Modes -- Disentangling the Joint Dynamics of Prices & Order Flow](https://www.ml-quant.com/papers/arxiv/2405.10654/): Research using a double coarse-graining procedure and Principal Component Analysis on electronic order books reveals stable parameters in a Vector Auto-Regressive model, but fails to account for the square-root law of price impact.
- [Non-uniformly sampled simulated price impact of an order-book](https://www.ml-quant.com/papers/arxiv/2310.06079/): Order Book Simulation: The paper expands a numerical method to simulate the spread of financial market orders, showing the price impact of flash limit-orders and market orders, and advocates for non-uniform sampling in diffusive dynamics simulations.
- [Robust Market Making with Hawkes Order Flow and Price Impact via Adversarial Reinforcement Learning](https://www.ml-quant.com/papers/arxiv/2609.22785/): The research extends adversarial reinforcement learning for market making to handle self-exciting order arrivals and price impact, using an LSTM module to improve robustness in complex microstructure environments.
- [Fill Probabilities in a Limit Order Book with State-Dependent Stochastic Order Flows](https://www.ml-quant.com/papers/arxiv/2403.02572/): A new stochastic model accurately calculates fill probabilities for limit orders at different price levels in the order book, effectively capturing its dynamics.
- [Reinforcement Learning for Optimal Execution When Liquidity Is Time-Varying](https://www.ml-quant.com/papers/arxiv/2402.12049/): Research shows Double Deep Q-learning, a Reinforcement Learning technique, can effectively learn optimal trading strategies in fluctuating liquidity conditions.
- [Deep limit order book forecasting: a microstructural guide](https://www.ml-quant.com/papers/arxiv/2403.09267/): The research applies deep learning techniques to predict mid-price changes for NASDAQ-traded stocks, providing a framework to evaluate the feasibility of these predictions.