arXiv AI
ViperQ: Order Flow Pattern Recognition via Auction Market Theory for Reinforcement Learning Trading
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv Machine Learning
Jul 20
CLaC@FinMMEval 2026 Task 3: Sentiment-Augmented Deep Reinforcement Learning for Active Trading -- An Alpha-Reward Approach
arXiv:2607. 16028v1 Announce Type: new Abstract: This paper presents our system for Task 3 of the CLEF 2026 FinMMEval Lab, which requires daily long, flat, or short trading decisions for Bitcoin (BTC) and Tesla (TSLA) using news and historical market data.
arXiv AI
Jun 26
State Representation Matters in Deep Reinforcement Learning: Application to Energy Trading
arXiv:2606. 27032v1 Announce Type: cross Abstract: Energy trading decisions depend not only on current market prices, but also on expected future market conditions, and operational constraints.
arXiv AI
Aug 21
The Bidding Games: Reinforcement Learning for MEV Extraction on Polygon Blockchain
arXiv:2510. 14642v2 Announce Type: replace-cross Abstract: In blockchain networks, the strategic ordering of transactions within blocks has emerged as a significant source of profit extraction, known as Maximal Extractable Value (MEV).
arXiv Machine Learning
Jun 4
Dynamic Multi-Pair Trading Strategy in Cryptocurrency Markets with Deep Reinforcement Learning
arXiv:2606. 04574v1 Announce Type: new Abstract: This study aims to determine whether the application of Deep Reinforcement Learning (DRL) as a specialized execution overlay can enhance pair trading in highly volatile cryptocurrency markets.
arXiv Machine Learning
Sep 22
Robust Market Making with Hawkes Order Flow and Price Impact via Adversarial Reinforcement Learning
The paper proposes an adversarial reinforcement‑learning framework for market making that incorporates Hawkes‑process driven order arrivals and trade‑induced price impact, addressing limitations of prior Poisson‑based models. An LSTM module captures temporal dependencies in recent observations to handle increased non‑stationarity, and the authors analyze equilibrium properties and introduce a robustness evaluation protocol focused on the left tail of returns. Experiments across diverse market regimes demonstrate that the method improves left‑tail performance, especially under strong Hawkes excitation and moderate price impact, without relying on a terminal inventory bias.
arXiv Machine Learning
Jun 30
A3M: Adaptive, Adversarial and Multi-Objective Learning for Strategic Bidding in Repeated Auctions
arXiv:2606. 28943v1 Announce Type: cross Abstract: Learning to bid in repeated multi-unit auctions with bandit feedback poses a fundamental challenge.