Optimal Execution with Reinforcement Learning

2024-11-10 · Yadh Hafsi, Edoardo Vittori

This study investigates the development of an optimal execution strategy through reinforcement learning, aiming to determine the most effective approach for traders to buy and sell inventory within a limited time frame. Our proposed model leverages input features derived from the current state of the limit order book. To simulate this environment and overcome the limitations associated with relying on historical data, we utilize the multi-agent market simulator ABIDES, which provides a diverse range of depth levels within the limit order book. We present a custom MDP formulation followed by the results of our methodology and benchmark the performance against standard execution strategies. Our findings suggest that the reinforcement learning-based approach demonstrates significant potential.

📄 PDF Abstract BibTeX arXiv:2411.06389

Code (0)

등록된 구현이 없습니다.

Tasks

reinforcement-learning Reinforcement Learning

Deep Reinforcement Learning for Online Optimal Execution Strategies

2024-10-17 · Alessandro Micheli, Mélodie Monod

This paper tackles the challenge of learning non-Markovian optimal execution strategies in dynamic financial markets. We introduce a novel actor-critic algorithm based on Deep Deterministic Policy Gradient (DDPG) to addr…

Deep Reinforcement Learning reinforcement-learning Reinforcement Learning

Optimal Execution Using Reinforcement Learning

2023-06-19 · Cong Zheng, Jiafa He, Can Yang

This work is about optimal order execution, where a large order is split into several small orders to maximize the implementation shortfall. Based on the diversity of cryptocurrency exchanges, we attempt to extract cross…

Decision Making Diversity reinforcement-learning Reinforcement Learning

Universal Trading for Order Execution with Oracle Policy Distillation

2021-01-28 · Yuchen Fang, Kan Ren, Weiqing Liu, Dong Zhou 외

As a fundamental problem in algorithmic trading, order execution aims at fulfilling a specific trading order, either liquidation or acquirement, for a given instrument. Towards effective execution strategy, recent years …

Algorithmic Trading reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Efficient Action Robust Reinforcement Learning with Probabilistic Policy Execution Uncertainty

2023-07-15 · Guanlin Liu, Zhihan Zhou, Han Liu, Lifeng Lai

Robust reinforcement learning (RL) aims to find a policy that optimizes the worst-case performance in the face of uncertainties. In this paper, we focus on action robust RL with the probabilistic policy execution uncerta…

reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Multi-Agent Reinforcement Learning in a Realistic Limit Order Book Market Simulation

2020-06-10 · Michaël Karpe, Jin Fang, Zhongyao Ma, Chen Wang

Optimal order execution is widely studied by industry practitioners and academic researchers because it determines the profitability of investment decisions and high-level trading strategies, particularly those involving…

Multi-agent Reinforcement Learning Q-Learning reinforcement-learning Reinforcement Learning (RL)