Reinforcement Learning for Trade Execution with Market Impact

ArXiv ID: 2507.06345 “View on arXiv”

Authors: Patrick Cheridito, Moritz Weiss

Abstract

In this paper, we introduce a novel reinforcement learning framework for optimal trade execution in a limit order book. We formulate the trade execution problem as a dynamic allocation task whose objective is the optimal placement of market and limit orders to maximize expected revenue. By employing multivariate logistic-normal distributions to model random allocations, the framework enables efficient training of the reinforcement learning algorithm. Numerical experiments show that the proposed method outperforms traditional benchmark strategies in simulated limit order book environments featuring noise traders submitting random orders, tactical traders responding to order book imbalances, and a strategic trader seeking to acquire or liquidate an asset position.

Keywords: Trade execution, Limit order book, Reinforcement learning, Market and limit orders, Multivariate logistic-normal distributions, Equities

Complexity vs Empirical Score

  • Math Complexity: 8.5/10
  • Empirical Rigor: 6.0/10
  • Quadrant: Holy Grail
  • Why: The paper employs advanced mathematics including multivariate logistic-normal distributions, stochastic calculus for limit order book modeling, and policy gradient derivations for RL, scoring high on math complexity. It features substantial empirical rigor through simulated market environments with multiple agent types (noise, tactical, strategic) and comparison to benchmark strategies, though it lacks real-world backtests or datasets.
  flowchart TD
    A["Research Goal: Optimal Trade Execution<br>Maximize revenue, minimize market impact"] --> B["Data/Inputs:<br>Simulated Limit Order Book (LOB)"]
    B --> C["Methodology: RL Framework<br>with Logistic-Normal Distributions"]
    C --> D["Action:<br>Market & Limit Order Placement"]
    D --> E["Process: Dynamic Allocation<br>via Reinforcement Learning"]
    E --> F["Findings: Outperformance vs. Benchmarks<br>(Noise/Tactical/Strategic Traders)"]