Algorithmic Trading

Follow this topic Ask about this topic

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.

Can Large Language Models Execute Parent Orders?
Jul 30, 2026Zane Shen, Xinli Xu, Guangyi Zhang +7Language Model-Based Control Large Language Model-Guided Optimization
Independent Researcher · HKUST(GZ) · ZJU +1
FinSMART: Financial Sentiment Analysis for Algorithmic Trading through Market-Aligned Reinforcement Learning
Jul 30, 2026Giorgos Iacovides, Wuyang Zhou, Danilo MandicRL for Language Models Reinforcement Learning
Imperial College London · London, UK
AlphaSchema: Exploring the Space of Trading Semantics for LLM-Based Alpha Mining
Jul 29, 2026Jingyang Yi, Jian Yang, Yifei Jin +2Automated Algorithm Discovery Alpha Factor Discovery
1X-Tech · 2Xtech-PandaAI-Waton Joint Lab · 3Monash University +2
Predictive Extrema, Unprofitable Policies: An AI-Assisted Audit of Candle-Based Binance Spot Timing Models
Jul 21, 2026Ayoub JadouliQuantitative Finance Algorithmic Auditing
Computer Science and Smart Systems, Faculty of Sciences and Technology Abdelmalek Essaâdi University, Tangier, Morocco
CLaC@FinMMEval 2026 Task 3: Sentiment-Augmented Deep Reinforcement Learning for Active Trading -- An Alpha-Reward Approach
Jul 17, 2026Andrei Neagu, Eeham Khan, Leila KosseimReinforcement Learning Deep Q-Learning
Department of Computer Science and Software Engineering, Concordia University, Montréal, Canada
EVOQUANT: Self-Evolving Verifier-Guided Strategy Optimization for Robust Quantitative Trading
Jul 14, 2026Jie Mao, Changlun Li, Xiang Li +6Large Language Model-Guided Optimization Language Model Self-Improvement
The Hong Kong University of Science and Technology (Guangzhou) · Paradoox AI Research
Fin-Analyst at FinMMEval 2026 Task 3: A Live Hybrid Trading Agent with LLM Specialists and Rule-Based Signals
Jul 14, 2026Mohotarema Rashid, Lingzi Hong, Junhua Ding +1Multi-Agent LLM Systems LLM Agent Evaluation
Department of Information Science, University of North Texas, Denton, TX, United States · Department of Data Science, University of North Texas, Denton, TX, United States
NextFund: A Unified Performance Tracking Platform for Agentic Portfolio Management
Jul 13, 2026Changlun Li, Peixian Ma, Qiqi Duan +2Quantitative Finance LLM Agent Evaluation
Paradoox AI Research
Reinforcement Learning for Execution under Dynamic Fees in a Closed-Loop DEX Simulator
Jul 12, 2026Wen-Ting WangDecentralized Finance Finance
Can Agentic Trading Systems Pay for Their Own Intelligence?
Jul 11, 2026Qiqi Duan, Changlun Li, Chen Wang +10LLM Agent Evaluation Agent Evaluation
HKUST(GZ) · Paradoox AI Research · MBZUAI +1
X-Alpha: An AI Quant Researcher with Memory-Driven Self-Evolution
Jul 9, 2026Fengyuan Liu, Yuchen Fu, Yuqi Wang +3Automated Algorithm Discovery Memory-Augmented Agents
School of Computing and Data Science, The University of Hong Kong · Grace Investment Machine
When do prophets profit in prediction markets?
Jul 7, 2026Anri Gu, Nicole Kagan, Alec Sun +2Probabilistic Forecasting Proper Scoring Rules
University of Chicago · Kalshi Research · New York University, Shanghai
Can Reinforcement Learning Efficiently Discover Price Manipulation?
Jul 7, 2026Ioanna-Yvonni Tsaknaki, Andrea Macrì, Fabrizio LilloModel-Free RL Algorithmic Trading
Scuola Normale Superiore, Piazza dei Cavalieri 7, Pisa 56126, Italy.
Trading Confidence: Comprehensive Uncertainty Estimation in Algorithmic Trading
Jul 3, 2026Lin Li, Li Rong Wang, Hsuan Fu +1Reinforcement Learning Uncertainty Quantification
Nanyang Technological University 50 Nanyang Avenue, Singapore · Nanyang Technological University Centre for Frontier AI Research, A*STAR 50 Nanyang Avenue, Singapore · Laval University Quebec, Canada
CSTrader: A Testbed for Language-Grounded Trading in a Community-Driven Virtual Asset Market
Jun 30, 2026Yao Shi, Kingfung Luo, Nan Tang +1Multi-Agent LLM Systems Finance
(Guangzhou). · Hong Kong University of Science and Technology
CLQT: A Closed-Loop, Cost-Aware, Strategy-Consistent Benchmark for Diagnostic Evaluation of LLM Portfolio-Management Agents
Jun 29, 2026Bo Qu, Mingguang ChenQuantitative Finance LLM Agent Evaluation
Illinois Institute of Technology (IIT). · University of California, Riverside (UCR).
Persona-Trained Monte Carlo: Estimating Market-Outcome Distributions via Swarms of Persona-Conditioned Neural Policy Bots in a Limit Order Book
Jun 28, 2026Salavat IshbulatovQuantitative Finance Agent-Based Modeling
Independent researcher
AI Trading's Alpha Singularity: Emergent Market Reasoning through Agent-to-Agent Self-Evolution
Jun 28, 2026Yuqi Li, Siyuan Liu, Bingjun LiuFinance Emergent Behavior in Multi-Agent Systems
Panda AI
Liquidity-Based Audit of Algorithmic Trading Strategies
Jun 27, 2026Irene AldridgeFinancial Services Quantitative Finance
The Inference-Compute Frontier and a Latency-Efficient Architecture for Limit Order Book Prediction
Jun 24, 2026C. Evans HedgesLimit Order Book Forecasting Efficient Neural Network Inference
Independent Researcher New York, USA
Hierarchical Graph Learning for Calendar Spread Strategies in Commodity Futures Markets
Jun 24, 2026Yoonsik Hong, Diego KlabjanQuantitative Finance Hierarchical GNNs
Department of Industrial Engineering and Management Sciences, Northwestern University, Evanston, 60208, IL, USA
AlgoEvolve: LLM-driven Meta-evolution of Algorithmic Trading Programs
Jun 24, 2026Dhruv Sharma, Gautam ShroffAutomated Algorithm Discovery Genetic Programming
Indraprastha Institute of Information Technology, Delhi
Quant Convergence: Bridging Classical Value Investing and Modern Factor Models for Systematic Equity Selection
Jun 23, 2026Augusto Eiji Yamazaki, Hugo Garrido-Lestache BelinchonQuantitative Finance Algorithmic Trading
Milwaukee School of Engineering Milwaukee, WI US
MetaPS: Adaptive Programmatic Strategy Selection for Market Agents
Jun 21, 2026Jiaxiang Chen, Aotian Luo, Zhouyi Zheng +3Algorithm Selection LLM Agent Training
1Fudan University · 2TensorPacific
A Unified Multi-Modal Framework for Intelligent Financial Systems: Integrating Reinforcement Learning, High-Frequency Trading, and Game-Theoretic Approaches with Cross-Modal Sentiment Analysis
Jun 9, 2026Fanrong Liu, Zhang Yuwei, Mingni LuoReinforcement Learning Quantitative Finance
Henan University, International Eurasia College, Kaifeng, Henan, China · City University of Hong Kong, College of Business, Hong Kong, China · Northeastern University, School of Electronic and Information Engineering, Seattle, United States
TT-DAC-PS: Twin-Target Deterministic Actor-Critic with Policy Smoothing for Optimal Trade Execution
Jun 7, 2026Ilia Zaznov, Atta Badii, Julian Kunkel +1Reinforcement Learning Algorithmic Trading
Department of Computer Science University of Reading Reading, UK · Department of Computer Science/GWDG University of Göttingen Goettingen, Germany · ICMA Centre, Henley Business School University of Reading Reading, UK
Execution Realism and Reproducibility in LLM-Based Trading Systems: A Systematic Scoping Review and Evidence Audit
Jun 6, 2026Junyi Yao, Zihao Zheng, Baichuan Li +1ML Reproducibility Finance
Washington University in St. Louis, St. Louis, MO 63130 USA. · Department of Operations Research and Engineering Management, Southern Methodist University, Dallas, TX 75275 USA.
Post-Rejection Follow-up Sampling: Measuring Outcomes of Rejected Decisions in Algorithmic DEX Trading
Jun 6, 2026Arati Uday KamatCounterfactual Evaluation Decentralized Finance
Independent researcher, United States
PandaAI: A Practical Agent CQ2 for Neuro-symbolic Data Analysis And Integrated Decision-Making in Quantitative Finance
Jun 5, 2026Yuqi Li, Siyuan Liu, Bingjun LiuQuantitative Finance LLM Decision-Making
Panda AI
Dynamic Multi-Pair Trading Strategy in Cryptocurrency Markets with Deep Reinforcement Learning
Jun 3, 2026Damian Lebiedź, Robert ŚlepaczukSafe RL Algorithmic Trading
Faculty of Economic Sciences, University of Warsaw, ul. Długa 44/50, Warsaw, 00-241, Poland · Quantitative Finance Research Group, Department of Quantitative Finance and Machine Learning, Faculty of Economic Sciences, University of Warsaw, ul. Długa 44/50, Warsaw, 00-241, Poland

Jul 30, 2026J/K move · Enter open · S save

Can Large Language Models Execute Parent Orders?

Zane Shen, Xinli Xu, Guangyi Zhang, Jialong Chen+6

Independent Researcher · HKUST(GZ) · ZJU+1

Language Model-Based Control Large Language Model-Guided Optimization Algorithmic Trading

Open paper AskPDF arXiv

Parent-order execution is a core problem in algorithmic trading, where the goal is to split a large order into smaller orders while reducing execution costs. Existing approaches either rely on pre-specified market assumptions that may not hold in practice, or require task-specific training that limits adaptability to new settings. To overcome these limitations, we present the first systematic study of large language models (LLMs) for parent-order execution. This extends the use of LLMs in finance from what to trade to how to execute. We propose PACE (Plan-Ahead Controlled Execution), a hierarchical framework that decomposes parent-order execution into long-horizon planning and short-horizon execution, requiring neither explicit market assumptions nor task-specific training. Experiments on Shenzhen Stock Exchange Level-1 data show that PACE outperforms TWAP, Almgren-Chriss, and learning-based baselines, exceeding the strongest baseline by 0.65 bps. Behavioral analysis reveals that LLMs make execution decisions differently from human investors: higher model confidence predicts better performance rather than worse returns, and the model trades earlier rather than procrastinating toward the deadline. These findings suggest that LLMs can complement human traders in execution decisions.