ViperQ: Order Flow Pattern Recognition via Auction Market Theory for Reinforcement Learning Trading

2026-09-12

AI Summary

本文通过基于拍卖市场理论的状态表示方法，利用强化学习解决交易问题，提出ViperQ系统，并在实际数据上验证了其有效性。

Abstract

Reinforcement learning trading systems published in the academic literature overwhelmingly rely on price-aggregate state representations (OHLCV bars) or limit-order-book depth features, leaving microstructure pattern theories from the practitioner literature, namely Auction Market Theory and Market Profile, without a peer-reviewed computational instantiation. We present ViperQ, a reinforcement learning system whose state representation is built explicitly from Auction Market Theory primitives: Volume Point of Control, Value Area position, Low Volume Node flags, Cumulative Volume Delta divergence, and tape-velocity signatures, assembled into a 20-dimensional Z-normalised vector. Two Proximal Policy Optimisation agents are trained with a prospect theory-grounded asymmetric reward function that penalises losing holds at a magnitude consistent with Kahneman and Tversky's loss-aversion coefficient. Evaluated on a held-out twelve-month partition of institutional tick data the agents have never seen, ViperQ achieves +163.6% ROI on TSLA (-27.5% max drawdown, 27,019 trades) and +116.5% ROI on NVDA (-47.8% max drawdown, 12,892 trades) under zero leverage. The results establish Auction Market Theory features as a tractable structured input modality for sequential decision-making on financial time series and motivate further work on microstructure-aware policy learning.

Problem

• Auction Market Theory
• Reinforcement Learning
• Trading Systems
• Microstructure Pattern Theories
• State Representation

Innovation

• Auction Market Theory
• Reinforcement Learning
• Volume Point of Control
• Prospect Theory
• Proximal Policy Optimisation