arXiv Machine Learning July 7, 2026 By Lin Li, Li Rong Wang, Hsuan Fu, Xiuyi Fan

Trading Confidence: Comprehensive Uncertainty Estimation in Algorithmic Trading

Read the original on arXiv Machine Learning →

arXiv:2607. 02864v1 Announce Type: new Abstract: Reinforcement Learning (RL) has emerged as a powerful approach in financial trading, enabling agents to learn optimal strategies through direct market interaction.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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.

By Damian Lebied\'z, Robert \'Slepaczuk

agents reinforcement-learning

arXiv AI

Jul 9

Can Reinforcement Learning Efficiently Discover Price Manipulation?

arXiv:2607. 06121v1 Announce Type: cross Abstract: In this paper, we investigate whether a model-free RL agent can identify and exploit price manipulation opportunities more effectively than a traditional model-based approach that assumes correct specification of the data-generating process but relies on noisy parameter estimates.

By Ioanna-Yvonni Tsaknaki, Andrea Macr\`i, Fabrizio Lillo

agents reinforcement-learning robotics benchmarks

arXiv Machine Learning

Aug 18

Learning Stock Trading Policies via Barycenter-Based Adversarial Inverse Reinforcement Learning

arXiv:2608. 15770v1 Announce Type: new Abstract: Designing effective trading strategies using reinforcement learning remains challenging due to delayed and noisy rewards, poor exploration, and the difficulty of enforcing explicit risk constraints.

By Arishi Orra, Himanshu Choudhary, Manoj Thakur

reinforcement-learning safety

arXiv AI

Aug 21

Concentrated Liquidity Provision: a Reinforcement Learning Perspective

arXiv:2608. 19389v1 Announce Type: cross Abstract: Automated market makers (AMMs) are a cornerstone of decentralised finance (DeFi).

By Georgios Chionas, Charalampos Kleitsikas, Stefanos Leonardos, Leandro S\'anchez-Betancourt, Carmine Ventre

agents reinforcement-learning benchmarks

arXiv AI

Jul 9

Deep Reinforcement Learning for Reliability Based Bi-Objective Portfolio Optimization

arXiv:2607. 06610v1 Announce Type: cross Abstract: Portfolio optimization under uncertainty is inherently a multi-objective decision problem involving complex interactions among return, risk, market dynamics, and practical investment constraints.

By Sounaq Das, Tanmay Sen, Raghu Nandan Sengupta, Aditya Gupta

reinforcement-learning

arXiv AI

Jun 17

Ensemble RL through Classifier Models: Enhancing Risk-Return Trade-offs in Trading Strategies

arXiv:2502. 17518v3 Announce Type: replace-cross Abstract: This paper presents a comprehensive study on the use of ensemble Reinforcement Learning (RL) models in financial trading strategies, leveraging classifier models to enhance performance.

By Zheli Xiong

agents reinforcement-learning robotics

One email a morning, machine-written

Related stories

Dynamic Multi-Pair Trading Strategy in Cryptocurrency Markets with Deep Reinforcement Learning

Can Reinforcement Learning Efficiently Discover Price Manipulation?

Learning Stock Trading Policies via Barycenter-Based Adversarial Inverse Reinforcement Learning

Concentrated Liquidity Provision: a Reinforcement Learning Perspective

Deep Reinforcement Learning for Reliability Based Bi-Objective Portfolio Optimization

Ensemble RL through Classifier Models: Enhancing Risk-Return Trade-offs in Trading Strategies