Algorithmic Trading

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Algorithmic trading studies how to turn market data and other information into rules for selecting assets, managing portfolios, and placing orders. Common approaches include statistical and machine-learning factors, deep reinforcement learning for portfolio choice or execution, market-making models, and newer language-model agents for research and strategy generation. Researchers test these systems with historical walk-forward backtests, limit-order-book simulations, and increasingly prospective or live evaluations, measuring returns alongside risk and trading costs. A central challenge is showing that apparent gains survive data leakage controls, changing market regimes, execution frictions, and the feedback effects of deploying similar strategies.

Latest papers

149 papers27 papers in the last four weeks, up 59% on the four weeks before. 0.2% of all new papers.

Deep Reinforcement Learning for Equity Trading: Benchmarking Actor-Critic Methods with Forward Retraining
Sep 25, 2026Bicheng Wang, Xinyi ZhangAlgorithmic Trading Reinforcement Learning
Stanford University
Agentic Limit Order Books: Phase Transitions and Market Impact
Sep 25, 2026Jan RosenzweigLimit Order Book Modeling Phase Transitions
Functional Architecture of European Electricity Trading Markets: Requirements for AI Supported Trading Systems under Regulatory Constraints
Sep 24, 2026Walter Kurz, Wojtek StrickerAI Control Runtime Enforcement for AI Agents
Swissi Institute for AI
Agent Memory with Episodic Retrieval for Financial Decision-Making
Sep 23, 2026Nuoyue Xu, Jiang Liu, Wenxuan Huang +3Multi-Agent LLM Systems LLM Agent Memory
University of British Columbia · University of Science and Technology of China · Fudan University +1
Financial Language Models as Applied Artificial Intelligence Systems for News-Based Trading under Market Frictions
Sep 20, 2026Kemal KirtacFinancial Language Model Evaluation LLM Evaluation
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
Robust Market Making with Hawkes Order Flow and Price Impact via Adversarial Reinforcement Learning
Sep 19, 2026Hao Yang, Zhenguo XuAdversarial MARL Hawkes Processes
North China Institute of Computer System Engineering, Beijing, China · University of Science and Technology of China, Hefei, China
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 LLM Systems Adversarial Robustness of Language Models
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 +2Reinforcement Learning Decentralized Finance
University of Oxford Oxford, UK
EvolveTrade: Experience-Driven Policy Refinement for Self-Evolving LLM Trading Agents
Sep 15, 2026Sehee Kim, Yumin Choi, Minki Kang +1LLM Agent Self-Improvement Prompt Optimization
KAIST
Repurposing Deep Limit Order Book Forecasting for Scenario-Conditioned Market Impact Modeling
Sep 15, 2026Eljas Linna, Kestutis Baltakys, Derrick Manoharan +2Limit Order Book Forecasting Financial Forecasting
Data Science Research Center, Tampere University, Finland
ViperQ: Order Flow Pattern Recognition via Auction Market Theory for Reinforcement Learning Trading
Sep 12, 2026Asser Moustafa, Rares-Mihail Neagu, Jugal KalitaAlgorithmic Trading Quantitative Finance
Department of Computer Science, University of Colorado, Colorado Springs, 1420 Austin Bluffs Pkwy, Colorado Springs, CO 80918, USA · Department of Computer Science, University of Iowa, 101 Jessup Hall, Iowa City, IA 52242, USA
GoAnt: Quality-Diversity Multi-Agent Search for Alpha Factor Discovery in Market Microstructure Data
Sep 8, 2026Stella Zhao, Tommy ShaMulti-Agent System Optimization Quantitative Finance
University of Minnesota, Minneapolis, MN, USA · Stony Brook University, Stony Brook, NY, USA
What LLM Trading Agents Actually Do in Production: A Six-Month, Population-Scale Record from Two Fleets
Sep 4, 2026T. J. Barton, Chris Constantakis, Patti Hauseman +4Algorithmic Trading LLM Agent Evaluation
DX Research Group (DXRG)
Artificial Intelligence in Equity and Crypto Markets: Progress, Profitability Evidence, and the Limits of Automated Investing
Sep 4, 2026Linsen Zhu, Mengqing CaiAlgorithmic Trading Portfolio Optimization
FORESIGHT-9: Prospective and Process-Aware Evaluation of Adaptive Trading Agents
Aug 29, 2026Xiangxin Luo, Chengtian Hong, Haohua Li +1Long-Horizon Agent Evaluation AI Agent Benchmarks
University of Science and Technology of China · University of Hong Kong · Shanghai Jiao Tong University
RetailAgent: Structured Adverse Timing in Self-Conditioned Multimodal LLM Trading Agents
Aug 28, 2026Yupeng Zhang, Liuyuan Jiang, Hongyi Huang +2Algorithmic Trading Quantitative Finance
University of Wisconsin–Madison · University of Rochester · Michigan State University
Regime-Aware Portfolio Management via Retrieval-Augmented LLM-Guided Expert Switching
Aug 28, 2026Ahmad Asadi, Reza SafabakhshAdaptive Model Routing Portfolio Optimization
Deep Learning Lab, Computer Engineering Department, Amirkabur University of Technology, Hafez Avenue, Tehran, Iran
Tabular Deep Learning for Algorithmic Trading: Cross-Regime Bayesian Optimisation for Equity Signal Generation
Aug 27, 2026Joshua Le GriceAlgorithmic Trading Tabular ML
Department of Computer Science University of Exeter
The Axiomatic Trader: Latent Regularity, Information Budgets, and the Canonical Form of a Quantitative Investment System
Aug 24, 2026Jiayu LiQuantitative Finance Algorithmic Trading
FIDES: A Concordance Protocol for LLM-Generated Trading Strategies
Aug 24, 2026Arther Tian, Alex Ding, Simon Wu +1Look-Ahead Bias in Language Models Language Model Generation Evaluation
DGrid AI
Concentrated Liquidity Provision: a Reinforcement Learning Perspective
Aug 19, 2026Georgios Chionas, Charalampos Kleitsikas, Stefanos Leonardos +2Decentralized Finance Algorithmic Trading
University of Oxford Oxford, UK
REFLEX: Reflexive Equilibrium Fixed-point Learning for Endogenous eXchanges
Aug 17, 2026Vignesh Nagarajan, Shriraghav AshokFinance Algorithmic Trading
Texas A&M University Brown Foundation Scholar College Station, TX, USA · University of California, Berkeley Haas School of Business Berkeley, CA, USA
Resource-Efficient QUBO Formulation for Anchored Currency Arbitrage
Aug 16, 2026Eric A. F. Reinhardt, Adam J. HauserQuadratic Unconstrained Binary Optimization Algorithmic Trading
Department of Physics and Astronomy University of Alabama, Tuscaloosa, AL 35487, USA
AQuA: Recursively Self-Improving Quantitative Trading Research Agents
Aug 13, 2026Jiacheng Guo, Suozhi Huang, Yunlong Gao +5Quantitative Finance Automated Algorithm Discovery
Princeton University · Ant Group · Stanford University
Large Language Model-Driven Small-Capitalization Trading: Integrating Financial News Sentiment, Macroeconomic Indicators, and Technical Signals
Aug 12, 2026Alireza Kargarzadeh, Nariman Khaledian, Navid Parvini +1Portfolio Optimization Finance
Tailstate Intelligence Ltd. · Independent Researcher · Zanista AI Ltd.
TradingMoE: Routing the Right Experts in Evolving Markets
Aug 12, 2026Chang Zhou, Xingtong Yu, Minbin Huang +4Mixture-of-Experts Language Models Sparse Mixture-of-Experts
University of Science and Technology of China · The Chinese University of Hong Kong · The University of Tokyo +1
F $^2$ Agent: Financial Fusion of Agentic Intelligence for Multimodal Trading
Aug 6, 2026Changshuo Liu, Yanzheng Jin, Shangfeng Cai +3Multimodal Agents Multimodal Fusion
National University of Singapore · Huazhong University of Science and Technology · Zhejiang University
AutoScientist-Quant: Self-Evolving Coding Agents for Automatic Research in Quantitative Investment
Aug 5, 2026Zongqian Li, Yaoyiran Li, Yaohui Guo +3Automated Algorithm Discovery Autonomous Research Agents
1Google · University of Cambridge
Towards Autonomous Formulaic Alpha Discovery: An Evolutionary Computation Perspective
Aug 3, 2026Xinwei Yu, Yiyang Fu, Mingcheng Fan +3Automated Algorithm Discovery Evolutionary Optimization
Xi’an Jiaotong-Liverpool University, Suzhou 215123, China · Shanghai University, Shanghai 200444, China
Backtrader-Bench: Benchmarking LLM Agents on Algorithmic Trading with Self-Generated MCQs
Jul 31, 2026Ruoxi Zhao, Maziar RaissiAI Coding Agents LLM Agent Evaluation
University of California, Riverside

Sep 25, 2026J/K move · Enter open · S save

Deep Reinforcement Learning for Equity Trading: Benchmarking Actor-Critic Methods with Forward Retraining

Bicheng Wang, Xinyi Zhang

Stanford University

Algorithmic Trading Reinforcement Learning Actor-Critic Methods RL Benchmarks

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Consistently profitable trading is difficult because equity markets are noisy, non-stationary, and only partially predictable from historical data. We benchmark five deep reinforcement learning (DRL) actor-critic methods: A2C, PPO, DDPG, TD3, and SAC, that learn trading actions end-to-end from market states, and compare them with a supervised price-forecasting baseline. Using daily data for 20 large-capitalization S&P 500 stocks from 2000 to 2020, enriched with trend-following technical indicators and log min-max scaling, we train on 2000-2018 and backtest on 2019-2020. Each agent is evaluated both when trained once and under forward retraining, in which it is retrained on all data available before each successive test window. DDPG achieves the highest annual return (55.5%), Sharpe ratio (1.38), and alpha (0.22), but also the highest market beta (1.24). TD3 and SAC offer a better risk-return balance, with Sharpe ratios of 1.37 and 1.33 and maximum drawdowns of about 25%. Forward retraining improves A2C, PPO, and SAC, leaves TD3 essentially unchanged, and reduces DDPG's annual return from 55.5% to 29.8%, consistent with TD3's greater robustness to hyperparameters. The forecasting baseline has the smallest maximum drawdown (9.6%) and the lowest beta (0.31), underscoring a trade-off between the higher returns of end-to-end DRL and the lower risk of forecast-driven strategies.