Auction market theory features improve reinforcement learning trading performance
ViperQ: Order Flow Pattern Recognition via Auction Market Theory for Reinforcement Learning Trading
Artificial Intelligence
Summary
Trading systems that use reinforcement learning often rely on simple price summaries or detailed order book data. This paper presents a trading system called ViperQ that uses specific features from a trader’s theory, Auction Market Theory, to better understand market patterns. The authors trained ViperQ on real, detailed trading data and found it made strong profits on Tesla and Nvidia stocks without borrowing money. This shows that these specialized market features can help machines make better trading decisions.
What this means in practice
- •For quantitative trading teams: Integrate auction market theory features into reinforcement learning models to improve trading strategies on institutional tick data.
- •For financial data engineers: Develop data pipelines that extract structured Auction Market Theory signals for use in automated trading systems.
Authors
Asser Moustafa, Rares-Mihail Neagu, Jugal Kalita
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.