# AlphaEvolve: A Learning Framework to Discover Novel Alphas in Quantitative Investment

arXiv: https://arxiv.org/abs/2103.16196 · HTML: https://mlsanity.com/paper/2103.16196/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 25 references that are in the map.
- First posted on arXiv 30 Mar 2021.

## Builds on (most cited first, 11 of 11)

- [Discovering Symbolic Models from Deep Learning with Inductive Biases](https://mlsanity.com/paper/2006.11287/) (2020)
- [A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction](https://mlsanity.com/paper/1704.02971/) (2017)
- [AutoML-Zero: Evolving Machine Learning Algorithms From Scratch](https://mlsanity.com/paper/2003.03384/) (2020)
- [Temporal Relational Ranking for Stock Prediction](https://mlsanity.com/paper/1809.09441/) (2018)
- [Evolving Deep Neural Networks](https://mlsanity.com/paper/1703.00548/) (2017)
- [Applications of deep learning in stock market prediction: recent progress](https://mlsanity.com/paper/2003.01859/) (2020)
- [Database Meets Deep Learning: Challenges and Opportunities](https://mlsanity.com/paper/1906.08986/) (2019)
- [MLCask: Efficient Management of Component Evolution in Collaborative Data Analytics Pipelines](https://mlsanity.com/paper/2010.10246/) (2020)
- [TRACER: A Framework for Facilitating Accurate and Interpretable Analytics for High Stakes Applications](https://mlsanity.com/paper/2003.12012/) (2020)
- [Trading via Image Classification](https://mlsanity.com/paper/1907.10046/) (2019)
- [Discovering Language of the Stocks](https://mlsanity.com/paper/1902.08684/) (2019)

## Built on by (most cited first, 30 of 32)

- [Memento: Fine-tuning LLM Agents without Fine-tuning LLMs](https://mlsanity.com/paper/2508.16153/) (2025)
- [Alpha-GPT: Human-AI Interactive Alpha Mining for Quantitative Investment](https://mlsanity.com/paper/2308.00016/) (2023)
- [R&D-Agent-Quant: A Multi-Agent Framework for Data-Centric Factors and Model Joint Optimization](https://mlsanity.com/paper/2505.15155/) (2025)
- [AlphaAgent: LLM-Driven Alpha Mining with Regularized Exploration to Counteract Alpha Decay](https://mlsanity.com/paper/2502.16789/) (2025)
- [Generating Synergistic Formulaic Alpha Collections via Reinforcement Learning](https://mlsanity.com/paper/2306.12964/) (2023)
- [AlphaForge: A Framework to Mine and Dynamically Combine Formulaic Alpha Factors](https://mlsanity.com/paper/2406.18394/) (2024)
- [Automate Strategy Finding with LLM in Quant Investment](https://mlsanity.com/paper/2409.06289/) (2024)
- [Navigating the Alpha Jungle: An LLM-Powered MCTS Framework for Formulaic Factor Mining](https://mlsanity.com/paper/2505.11122/) (2025)
- [Cognitive Alpha Mining via LLM-Driven Code-Based Evolution](https://mlsanity.com/paper/2511.18850/) (2025)
- [QuantFactor REINFORCE: Mining Steady Formulaic Alpha Factors with Variance-bounded REINFORCE](https://mlsanity.com/paper/2409.05144/) (2024)
- [miniF2F-Lean Revisited: Reviewing Limitations and Charting a Path Forward](https://mlsanity.com/paper/2511.03108/) (2025)
- [AlphaEval: A Comprehensive and Efficient Evaluation Framework for Formula Alpha Mining](https://mlsanity.com/paper/2508.13174/) (2025)
- [From Deep Learning to LLMs: A survey of AI in Quantitative Investment](https://mlsanity.com/paper/2503.21422/) (2025)
- [Learning Game-Playing Agents with Generative Code Optimization](https://mlsanity.com/paper/2508.19506/) (2025)
- [FactorEngine: A Program-level Knowledge-Infused Factor Mining Framework for Quantitative Investment](https://mlsanity.com/paper/2603.16365/) (2026)
- [SciNav: A General Agent Framework for Scientific Coding Tasks](https://mlsanity.com/paper/2603.20256/) (2026)
- [AI Trading's Alpha Singularity: Emergent Market Reasoning through Agent-to-Agent Self-Evolution](https://mlsanity.com/paper/2606.29194/) (2026)
- [AlphaSchema: Exploring the Space of Trading Semantics for LLM-Based Alpha Mining](https://mlsanity.com/paper/2607.26642/) (2026)
- [AQuA: Recursively Self-Improving Quantitative Trading Research Agents](https://mlsanity.com/paper/2608.12841/) (2026)
- [Alpha2: Discovering Logical Formulaic Alphas using Deep Reinforcement Learning](https://mlsanity.com/paper/2406.16505/) (2024)
- [QuantBench: Benchmarking AI Methods for Quantitative Investment](https://mlsanity.com/paper/2504.18600/) (2025)
- [Advocate for Complete Benchmarks for Formal Reasoning with Formal/Informal Statements and Formal/Informal Proofs](https://mlsanity.com/paper/2507.04719/) (2025)
- [Synergistic Formulaic Alpha Generation for Quantitative Trading based on Reinforcement Learning](https://mlsanity.com/paper/2401.02710/) (2024)
- [Ludax: A GPU-Accelerated Domain Specific Language for Board Games](https://mlsanity.com/paper/2506.22609/) (2025)
- [An open dataset of neural networks for hypernetwork research](https://mlsanity.com/paper/2507.15869/) (2025)
- [A Theory of LLM Information Susceptibility](https://mlsanity.com/paper/2603.23626/) (2026)
- [EVOQUANT: Self-Evolving Verifier-Guided Strategy Optimization for Robust Quantitative Trading](https://mlsanity.com/paper/2607.12455/) (2026)
- [AlphaG-OPD: Reliability-Gated Sibling Counterfactuals for On-Policy Distillation in Symbolic Alpha Factor Discovery](https://mlsanity.com/paper/2608.01303/) (2026)
- [Agentic Empirical Asset Pricing: Methodological Foundations](https://mlsanity.com/paper/2609.00731/) (2026)
- [GoAnt: Quality-Diversity Multi-Agent Search for Alpha Factor Discovery in Market Microstructure Data](https://mlsanity.com/paper/2609.08719/) (2026)

## Abstract (arXiv)

Alphas are stock prediction models capturing trading signals in a stock market. A set of effective alphas can generate weakly correlated high returns to diversify the risk. Existing alphas can be categorized into two classes: Formulaic alphas are simple algebraic expressions of scalar features, and thus can generalize well and be mined into a weakly correlated set. Machine learning alphas are data-driven models over vector and matrix features. They are more predictive than formulaic alphas, but are too complex to mine into a weakly correlated set. In this paper, we introduce a new class of alphas to model scalar, vector, and matrix features which possess the strengths of these two existing classes. The new alphas predict returns with high accuracy and can be mined into a weakly correlated set. In addition, we propose a novel alpha mining framework based on AutoML, called AlphaEvolve, to generate the new alphas. To this end, we first propose operators for generating the new alphas and selectively injecting relational domain knowledge to model the relations between stocks. We then accelerate the alpha mining by proposing a pruning technique for redundant alphas. Experiments show that AlphaEvolve can evolve initial alphas into the new alphas with high returns and weak correlations.