Deep Reinforcement Learning for Online Optimal Execution Strategies
Deep Reinforcement Learning for Online Optimal Execution Strategies
This paper addresses the challenge of learning optimal execution strategies in financial markets with transient price impact, which introduces non-Markovian dynamics. The authors propose a novel model-free reinforcement learning algorithm based on Deep Deterministic Policy Gradient (DDPG) that operates in a continuous action space. The state representation includes time, current inventory, past actions, and current price, enabling the agent to capture the memory effect of transient impact. An auxiliary Q-function is introduced to enhance convergence. The algorithm is designed for online learning, allowing it to adapt to changing market conditions without frequent recalibration.
Through numerical experiments with various decay kernels (e.g., exponential, power-law), the authors demonstrate that the DDPG algorithm successfully approximates the optimal execution strategy, closely matching the theoretical benchmark. The method also shows robustness to parameter shifts, adapting its strategy in real-time as market conditions evolve. The results highlight the potential of deep reinforcement learning to reduce reliance on human intervention and improve efficiency in algorithmic trading.
Highlights
- 1Introduces a novel actor-critic algorithm based on DDPG for optimal execution with transient price impact.
- 2Handles non-Markovian dynamics via a state representation including past actions, enabling learning under general decay kernels.
- 3Enables online learning within and across episodes for real-time adaptation to changing market conditions.
- 4Uses a continuous action space, allowing flexible execution strategies beyond discrete actions.
- 5Demonstrates successful approximation of optimal strategies for various decay kernels and adaptability to parameter shifts.
Methods
- MDeep Deterministic Policy Gradient (DDPG) with an auxiliary Q-function for optimal execution.
- MMarkov Decision Process (MDP) formulation with state including time, inventory, past actions, and price.
- MModel-free reinforcement learning: no explicit assumption on price impact model, learns from interactions.
- MOnline learning: algorithm updates policy continuously within and across episodes.
Results
- RThe DDPG algorithm successfully approximates the optimal execution strategy for various decay kernels (e.g., exponential, power-law).
- RThe learned strategy adapts to evolving market conditions where parameters (e.g., volatility, impact) fluctuate over time.
- RThe algorithm reduces the need for frequent human intervention and recalibration compared to traditional model-based methods.
- RContinuous action space allows more flexible strategies than discrete-action methods like DDQN.
- RThe auxiliary Q-function improves convergence and stability of the learning process.
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