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3 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

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PPO-HRAP: Proximal Policy Optimization with a Hybrid Regime-Aware Policy for Risk-Controlled Trading
Oct 1, 2026Duong Hien Chi Kien, Thanh Trung HuynhProximal Policy Optimization Trading
College of Engineering and Computer Science, VinUniversity Ha Noi, Viet Nam
Can AI Make Money in Crypto? Measuring the Gap from Backtests to Real Markets
Sep 28, 2026Xingtong Yu, Jiarun Zhou, Guanlin Ding +12Trading
The Chinese University of Hong Kong · University of Science and Technology of China · Jilin University +3
Financial Language Models as Applied Artificial Intelligence Systems for News-Based Trading under Market Frictions
Sep 20, 2026Kemal KirtacFinancial Sentiment Analysis Trading
Department of Computer Science, University College London, 66–72 Gower Street, London WC1E 6EA, United Kingdom · Department of Computer Science, University of Warwick, 6 Lord Bhattacharyya Way, Coventry CV4 7EZ, United Kingdom
Contagion on the Trading Floor: How Adversarial Signals Spread in Multi-Agent Trading Systems
Sep 17, 2026Qi Rong Sua, Junhao Dong, Nguyen Duc Thai +3Multi-Agent Large Language Model Systems Trading
Nanyang Technological University, Singapore · Centre for Frontier AI Research, IHPC, A*STAR, Singapore · National University of Singapore, Singapore
SAiFE-gym: Model-based Environments for Automated Market Making with Concentrated Liquidity
Sep 15, 2026Georgios Chionas, Charalampos Kleitsikas, Stefanos Leonardos +2Trading Matching Markets
University of Oxford Oxford, UK
AQuA: Recursively Self-Improving Quantitative Trading Research Agents
Aug 13, 2026Jiacheng Guo, Suozhi Huang, Yunlong Gao +5Algorithmic Trading Recursive Self-Improvement
Princeton University · Ant Group · Stanford University
Reinforcement Learning for Execution under Dynamic Fees in a Closed-Loop DEX Simulator
Jul 12, 2026Wen-Ting WangTrading Order
When do prophets profit in prediction markets?
Jul 7, 2026Anri Gu, Nicole Kagan, Alec Sun +2Trading Betting
University of Chicago · Kalshi Research · New York University, Shanghai
AlgoEvolve: LLM-driven Meta-evolution of Algorithmic Trading Programs
Jun 24, 2026Dhruv Sharma, Gautam ShroffAlgorithmic Trading Evolutionary Search
Indraprastha Institute of Information Technology, Delhi
Trading Utility for Dynamic Fairness in Multiple Resource Division with Sequential Demand
Jun 9, 2026Kaiqi Jiang, Karim El Husseini, Wenzhe Fan +1Algorithmic Fairness Pareto Frontier
Computer Science Dept., University of Illinois Chicago, Chicago, Illinois, USA
Beyond Agent Architecture: Execution Assumptions and Reproducibility in LLM-Based Trading Systems
Jun 6, 2026Junyi Yao, Zihao Zheng, Baichuan LiTrading Reproducibility
Absorbing Complexity: An Interaction-Native Knowledge Harness for Financial LLM Agents
Jun 1, 2026Ailiya Borjigin, Igor Stadnyk, Ben Bilski +4Personalized Large Language Model Agents Trading
True Trading
Trading Complexity for Expressivity Through Structured Generalized Linear Token Mixing
May 29, 2026Erwan Fagnou, Paul Caillon, Blaise Delattre +1Expressivity Long-Range Temporal Dependencies
Miles Team, LAMSADE, Université Paris Dauphine-PSL, Paris, France · Department of Computer Science, School of Computing, Institute of Science Tokyo, Tokyo, Japan · ESPCI PSL, Paris, France
Regime-Adaptive Continual Learning for Portfolio Management
May 29, 2026Chaofan Pan, Lingfei Ren, Linbo Xiong +3Continual Learning Portfolio Construction
Southwestern University of Finance and Economics · Shanxi University
Zero-shot adaptation to order book dynamics
May 20, 2026Arip AsadulaevTrading Matching Markets
Machine Learning-Based Bitcoin Trading Under Transaction Costs: Evidence From Walk-Forward Forecasting
May 19, 2026Andrei Bysik, Robert ŚlepaczukTrading Xgboost Model
Quantitative Finance Research Group, Faculty of Economic Sciences, University of Warsaw, ul. Długa 44/50, 00-241 Warsaw, Poland · Quantitative Finance Research Group, Department of Quantitative Finance and Machine Learning, Faculty of Economic Sciences, University of Warsaw, ul. Długa 44/50, 00-241 Warsaw, Poland
BacktestBench: Benchmarking Large Language Models for Automated Quantitative Strategy Backtesting
May 18, 2026Zhensheng Wang, Wenmian Yang, Qingtai Wu +3Algorithmic Trading Large Language Model Benchmarks
Beijing Normal University Beijing, China · Beijing Normal University Zhuhai, China · Elmleaf Ltd. Shanghai, China
The Alpha Illusion: Reported Alpha from LLM Trading Agents Should Not Be Treated as Deployment Evidence
May 16, 2026Yuxuan Ye, Jun Han, Ao Hu +7Trading Finance Benchmarks
Fudan University · Shanghai University of Finance and Economics · Southwest University of Finance and Economics +3
OpenFinGym: A Verifiable Multi-Task Gym Environment for Evaluating Quant Agents
Date pendingKaicheng Zhang, Wen Ge, Lei Jiang +5Skillgym Trading
University of Edinburgh · University College London · Alan Turing Institute +1

Oct 1, 2026, cs.AIJ/K move · Enter open · S save

PPO-HRAP: Proximal Policy Optimization with a Hybrid Regime-Aware Policy for Risk-Controlled Trading

Duong Hien Chi Kien, Thanh Trung Huynh

College of Engineering and Computer Science, VinUniversity Ha Noi, Viet Nam

Proximal Policy Optimization Trading Volatility

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Reinforcement learning for trading often struggles to balance upside participation with drawdown control. Profit-only policies can collapse toward passive long exposure on upward-drifting assets, while aggressively risk-penalized rewards can become too defensive during volatile periods. This paper proposes PPO-HRAP, a hybrid regime-aware policy that combines Proximal Policy Optimization with an interpretable regime prior. The agent observes both market features and portfolio-state variables, receives a reward combining portfolio log return, VIX-conditioned drawdown-increase penalty, target-exposure deviation, and turnover cost, and executes a blended action between the PPO actor output and a regime-derived target exposure. On the held-out 2020-2022 SPY test window, PPO-HRAP achieves 27.62% total return, 8.48% annualized return, 0.6447 Sharpe ratio, 0.8588 Sortino ratio, and 0.4592 Calmar ratio, while reducing maximum drawdown from 34.10% for Buy and Hold to 18.47%. Across five SPY seeds, PPO-HRAP remains stable with mean total return

0.2725 \pm 0.0109

and mean Sharpe ratio

0.6219 \pm 0.0565

. Single-run cross-asset tests on QQQ and DIA further show that the proposed method ranks first on total return and Sharpe ratio for all three reported assets. These results suggest that blending learned actions with a volatility-aware regime prior is a practical way to improve risk-adjusted trading behavior, although the current policy still incurs high turnover and cross-asset robustness beyond SPY remains limited to single-run evidence.

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