Towards Generalizable Reinforcement Learning for Trade Execution

ArXiv ID: 2307.11685 “View on arXiv”

Authors: Unknown

Abstract

Optimized trade execution is to sell (or buy) a given amount of assets in a given time with the lowest possible trading cost. Recently, reinforcement learning (RL) has been applied to optimized trade execution to learn smarter policies from market data. However, we find that many existing RL methods exhibit considerable overfitting which prevents them from real deployment. In this paper, we provide an extensive study on the overfitting problem in optimized trade execution. First, we model the optimized trade execution as offline RL with dynamic context (ORDC), where the context represents market variables that cannot be influenced by the trading policy and are collected in an offline manner. Under this framework, we derive the generalization bound and find that the overfitting issue is caused by large context space and limited context samples in the offline setting. Accordingly, we propose to learn compact representations for context to address the overfitting problem, either by leveraging prior knowledge or in an end-to-end manner. To evaluate our algorithms, we also implement a carefully designed simulator based on historical limit order book (LOB) data to provide a high-fidelity benchmark for different algorithms. Our experiments on the high-fidelity simulator demonstrate that our algorithms can effectively alleviate overfitting and achieve better performance.

Keywords: Reinforcement Learning (RL), Trade Execution, Limit Order Book (LOB), Offline RL, Overfitting Mitigation, Equities

Complexity vs Empirical Score

• Math Complexity: 7.5/10
• Empirical Rigor: 6.0/10
• Quadrant: Holy Grail
• Why: The paper presents a theoretical framework (ORDC) and derives generalization bounds, indicating high mathematical complexity, while also implementing a high-fidelity simulator and evaluating algorithms, demonstrating strong empirical rigor.

  flowchart TD
    A["Research Goal<br>Address Overfitting in<br>RL for Trade Execution"] --> B["Methodology<br>Model as Offline RL with<br>Dynamic Context (ORDC)"]
    B --> C["Input Data<br>Historical Limit Order Book<br>LOB Data"]
    C --> D["Computational Process<br>Learn Compact Context<br>Representations"]
    D --> E{"Key Finding 1<br>Generalization Bound<br>Limited Samples cause Overfitting"}
    D --> F{"Key Finding 2<br>Proposed Algorithms<br>Effective Overfitting Mitigation"}
    E --> G["Outcome<br>High-Fidelity Simulator<br>Benchmark"]
    F --> G