mlsanityData to 7 Oct 2026

Qlib: An AI-oriented Quantitative Investment Platform

arXiv 2009.11189 ↗First posted 22 Sep 2020q-fin.GN cs.LG q-fin.PMcited by 77

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Abstract (arXiv)

Quantitative investment aims to maximize the return and minimize the risk in a sequential trading period over a set of financial instruments. Recently, inspired by rapid development and great potential of AI technologies in generating remarkable innovation in quantitative investment, there has been increasing adoption of AI-driven workflow for quantitative research and practical investment. In the meantime of enriching the quantitative investment methodology, AI technologies have raised new challenges to the quantitative investment system. Particularly, the new learning paradigms for quantitative investment call for an infrastructure upgrade to accommodate the renovated workflow; moreover, the data-driven nature of AI technologies indeed indicates a requirement of the infrastructure with more powerful performance; additionally, there exist some unique challenges for applying AI technologies to solve different tasks in the financial scenarios. To address these challenges and bridge the gap between AI technologies and quantitative investment, we design and develop Qlib that aims to realize the potential, empower the research, and create the value of AI technologies in quantitative investment.

Key facts. Cited by 77 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Sep 2020.

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Citing papers per year: 2021: 3; 2022: 6; 2023: 8; 2024: 10; 2025: 22; 2026: 28.

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  1. 2024A Multimodal Foundation Agent for Financial Trading: Tool-Augmented, Diversified, and Generalistcited by 137
  2. 2021HIST: A Graph-based Framework for Stock Trend Forecasting via Mining Concept-Oriented Shared Informationcited by 43
  3. 2025R&D-Agent-Quant: A Multi-Agent Framework for Data-Centric Factors and Model Joint Optimizationcited by 39
  4. 2023Generating Synergistic Formulaic Alpha Collections via Reinforcement Learningcited by 37
  5. 2022DDG-DA: Data Distribution Generation for Predictable Concept Drift Adaptationcited by 36
  6. 2025AlphaAgent: LLM-Driven Alpha Mining with Regularized Exploration to Counteract Alpha Decaycited by 36
  7. 2024AlphaForge: A Framework to Mine and Dynamically Combine Formulaic Alpha Factorscited by 25
  8. 2025Kronos: A Foundation Model for the Language of Financial Marketscited by 25
  9. 2025FinTSB: A Comprehensive and Practical Benchmark for Financial Time Series Forecastingcited by 23
  10. 2023DoubleAdapt: A Meta-learning Approach to Incremental Learning for Stock Trend Forecastingcited by 21
  11. 2021Automated Deep Learning: Neural Architecture Search Is Not the Endcited by 13
  12. 2024QuantFactor REINFORCE: Mining Steady Formulaic Alpha Factors with Variance-bounded REINFORCEcited by 13
38 more
  1. 2025Navigating the Alpha Jungle: An LLM-Powered MCTS Framework for Formulaic Factor Miningcited by 13
  2. 2022A Novel Deep Reinforcement Learning Based Automated Stock Trading System Using Cascaded LSTM Networkscited by 12
  3. 2025Cognitive Alpha Mining via LLM-Driven Code-Based Evolutioncited by 12
  4. 2024BPQP: A Differentiable Convex Optimization Framework for Efficient End-to-End Learningcited by 10
  5. 2023A Versatile Multi-Agent Reinforcement Learning Benchmark for Inventory Managementcited by 9
  6. 2024DiffsFormer: A Diffusion Transformer on Stock Factor Augmentationcited by 9
  7. 2025MASS: Muli-agent simulation scaling for portfolio constructioncited by 9
  8. 2025AlphaEval: A Comprehensive and Efficient Evaluation Framework for Formula Alpha Miningcited by 9
  9. 2025QuantAgents: Towards Multi-agent Financial System via Simulated Tradingcited by 8
  10. 2023Learning Multi-Agent Intention-Aware Communication for Optimal Multi-Order Execution in Financecited by 7
  11. 2023Reinforcement Learning with Maskable Stock Representation for Portfolio Management in Customizable Stock Poolscited by 7
  12. 2026From Knowing to Doing: A Memory-Controlled Benchmark for LLM Trading Agents on Stock Marketscited by 7
  13. 2021Trade When Opportunity Comes: Price Movement Forecasting via Locality-Aware Attention and Iterative Refinement Labelingcited by 6
  14. 2026Hubble: An LLM-Driven Agentic Framework for Safe, Diverse, and Reproducible Alpha Factor Discoverycited by 5
  15. 2025Learning Universal Multi-level Market Irrationality Factors to Improve Stock Return Forecastingcited by 4
  16. 2025CSPO: Cross-Market Synergistic Stock Price Movement Forecasting with Pseudo-volatility Optimizationcited by 4
  17. 2025Learning from Expert Factors: Trajectory-level Reward Shaping for Formulaic Alpha Miningcited by 4
  18. 2025FinWorld: An All-in-One Open-Source Platform for End-to-End Financial AI Research and Deploymentcited by 4
  19. 2025Exploring Accuracy Law for Deep Time Series Forecasters: An Empirical Studycited by 4
  20. 2026AlphaMemo: Structured Search-Process Memory for Self-Evolving Alpha Mining Agentscited by 3
  21. 2024Alpha2: Discovering Logical Formulaic Alphas using Deep Reinforcement Learningcited by 2
  22. 2024STORM: A Spatio-Temporal Factor Model Based on Dual Vector Quantized Variational Autoencoders for Financial Tradingcited by 2
  23. 2026Alpha Discovery via Grammar-Guided Learning and Searchcited by 2
  24. 2026AlphaPROBE: Alpha Mining via Principled Retrieval and On-graph biased evolutioncited by 2
  25. 2026Representation Signatures and Risk-Feedback Alignment in LLM Trading Agentscited by 2
  26. 2026OpenFinGym: A Verifiable Multi-Task Gym Environment for Evaluating Quant Agentscited by 2
  27. 2026AlphaSchema: Exploring the Space of Trading Semantics for LLM-Based Alpha Miningcited by 2
  28. 2026AutoScientist-Quant: Self-Evolving Coding Agents for Automatic Research in Quantitative Investmentcited by 2
  29. 2022Quantitative Stock Investment by Routing Uncertainty-Aware Trading Experts: A Multi-Task Learning Approachcited by 1
  30. 2023Discovering Predictable Latent Factors for Time Series Forecastingcited by 1
  31. 2024Hierarchical Reinforced Trader (HRT): A Bi-Level Approach for Optimizing Stock Selection and Executioncited by 1
  32. 2025Perforated Backpropagation: A Neuroscience Inspired Extension to Artificial Neural Networkscited by 1
  33. 2025QuantBench: Benchmarking AI Methods for Quantitative Investmentcited by 1
  34. 2025FineFT: Efficient and Risk-Aware Ensemble Reinforcement Learning for Futures Tradingcited by 1
  35. 2026TF-CoDiT: Conditional Time Series Synthesis with Diffusion Transformers for Treasury Futurescited by 1
  36. 2026AlphaForgeBench: Benchmarking End-to-End Trading Strategy Design with Large Language Modelscited by 1
  37. 2026AlphaCrafter: Harnessing Multi-Agent Workflows for Cross-Sectional Quantitative Tradingcited by 1
  38. 2026Forecast Collapse in Time-Series Foundation Modelscited by 1

50 most cited of 77 All 77 (JSON)