MacroHFT: Memory Augmented Context-aware Reinforcement Learning On High Frequency Trading

TL;DR

MacroHFT introduces a memory-augmented, context-aware reinforcement learning framework for high-frequency cryptocurrency trading, addressing overfitting and market volatility issues to improve decision-making and profitability.

Contribution

It proposes a novel two-phase training approach with sub-agents conditioned on financial indicators and a hyper-agent with memory, enhancing HFT performance over existing RL methods.

Findings

Achieves state-of-the-art results on minute-level cryptocurrency trading tasks.

Effectively handles rapid market fluctuations with a memory-augmented hyper-agent.

Improves robustness and adaptability of RL-based HFT strategies.

Abstract

High-frequency trading (HFT) that executes algorithmic trading in short time scales, has recently occupied the majority of cryptocurrency market. Besides traditional quantitative trading methods, reinforcement learning (RL) has become another appealing approach for HFT due to its terrific ability of handling high-dimensional financial data and solving sophisticated sequential decision-making problems, \emph{e.g.,} hierarchical reinforcement learning (HRL) has shown its promising performance on second-level HFT by training a router to select only one sub-agent from the agent pool to execute the current transaction. However, existing RL methods for HFT still have some defects: 1) standard RL-based trading agents suffer from the overfitting issue, preventing them from making effective policy adjustments based on financial context; 2) due to the rapid changes in market conditions, investment decisions made by an individual agent are usually one-sided and highly biased, which might lead to significant loss in extreme markets. To tackle these problems, we propose a novel Memory Augmented Context-aware Reinforcement learning method On HFT, \emph{a.k.a.} MacroHFT, which consists of two training phases: 1) we first train multiple types of sub-agents with the market data decomposed according to various financial indicators, specifically market trend and volatility, where each agent owns a conditional adapter to adjust its trading policy according to market conditions; 2) then we train a hyper-agent to mix the decisions from these sub-agents and output a consistently profitable meta-policy to handle rapid market fluctuations, equipped with a memory mechanism to enhance the capability of decision-making. Extensive experiments on various cryptocurrency markets demonstrate that MacroHFT can achieve state-of-the-art performance on minute-level trading tasks.