# AlphaForge: A Framework to Mine and Dynamically Combine Formulaic Alpha Factors

arXiv: https://arxiv.org/abs/2406.18394 · HTML: https://mlsanity.com/paper/2406.18394/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 52 references that are in the map.
- First posted on arXiv 26 Jun 2024.

## Builds on (most cited first, 19 of 19)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model](https://mlsanity.com/paper/1911.08265/) (2019)
- [Deep symbolic regression: Recovering mathematical expressions from data via risk-seeking policy gradients](https://mlsanity.com/paper/1912.04871/) (2019)
- [Neural Symbolic Regression that Scales](https://mlsanity.com/paper/2106.06427/) (2021)
- [A Closer Look at Invalid Action Masking in Policy Gradient Algorithms](https://mlsanity.com/paper/2006.14171/) (2020)
- [Learning Equations for Extrapolation and Control](https://mlsanity.com/paper/1806.07259/) (2018)
- [SymbolicGPT: A Generative Transformer Model for Symbolic Regression](https://mlsanity.com/paper/2106.14131/) (2021)
- [Symbolic Regression via Neural-Guided Genetic Programming Population Seeding](https://mlsanity.com/paper/2111.00053/) (2021)
- [You Impress Me: Dialogue Generation via Mutual Persona Perception](https://mlsanity.com/paper/2004.05388/) (2020)
- [Qlib: An AI-oriented Quantitative Investment Platform](https://mlsanity.com/paper/2009.11189/) (2020)
- [Spatio-Temporal Self-Supervised Learning for Traffic Flow Prediction](https://mlsanity.com/paper/2212.04475/) (2022)
- [Alpha-GPT: Human-AI Interactive Alpha Mining for Quantitative Investment](https://mlsanity.com/paper/2308.00016/) (2023)
- [HIST: A Graph-based Framework for Stock Trend Forecasting via Mining Concept-Oriented Shared Information](https://mlsanity.com/paper/2110.13716/) (2021)
- [Where are we now? A large benchmark study of recent symbolic regression methods](https://mlsanity.com/paper/1804.09331/) (2018)
- [AlphaEvolve: A Learning Framework to Discover Novel Alphas in Quantitative Investment](https://mlsanity.com/paper/2103.16196/) (2021)
- [Epsilon-Lexicase Selection for Regression](https://mlsanity.com/paper/1905.13266/) (2019)
- [REST: Relational Event-driven Stock Trend Forecasting](https://mlsanity.com/paper/2102.07372/) (2021)

## Built on by (most cited first, 27 of 27)

- [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)
- [Navigating the Alpha Jungle: An LLM-Powered MCTS Framework for Formulaic Factor Mining](https://mlsanity.com/paper/2505.11122/) (2025)
- [QuantaAlpha: An Evolutionary Framework for LLM-Driven Alpha Mining](https://mlsanity.com/paper/2602.07085/) (2026)
- [Cognitive Alpha Mining via LLM-Driven Code-Based Evolution](https://mlsanity.com/paper/2511.18850/) (2025)
- [AlphaEval: A Comprehensive and Efficient Evaluation Framework for Formula Alpha Mining](https://mlsanity.com/paper/2508.13174/) (2025)
- [MASS: Muli-agent simulation scaling for portfolio construction](https://mlsanity.com/paper/2505.10278/) (2025)
- [Alpha-R1: Alpha Screening with LLM Reasoning via Reinforcement Learning](https://mlsanity.com/paper/2512.23515/) (2025)
- [LLM-Meta-SR: In-Context Learning for Evolving Selection Operators in Symbolic Regression](https://mlsanity.com/paper/2505.18602/) (2025)
- [From Feedback Loops to Policy Updates: Reinforcement Fine-Tuning for LLM-Based Alpha Factor Discovery](https://mlsanity.com/paper/2605.15412/) (2026)
- [LLM-Based Scientific Equation Discovery via Physics-Informed Token-Regularized Policy Optimization](https://mlsanity.com/paper/2602.10576/) (2026)
- [AlphaPROBE: Alpha Mining via Principled Retrieval and On-graph biased evolution](https://mlsanity.com/paper/2602.11917/) (2026)
- [FactorEngine: A Program-level Knowledge-Infused Factor Mining Framework for Quantitative Investment](https://mlsanity.com/paper/2603.16365/) (2026)
- [AlphaMemo: Structured Search-Process Memory for Self-Evolving Alpha Mining Agents](https://mlsanity.com/paper/2606.20625/) (2026)
- [AlphaSchema: Exploring the Space of Trading Semantics for LLM-Based Alpha Mining](https://mlsanity.com/paper/2607.26642/) (2026)
- [MEME: Modeling the Evolutionary Modes of Financial Markets](https://mlsanity.com/paper/2602.11918/) (2026)
- [MOT-SR: Multi-Objective Tool-Augmented Scientific Equation Discovery with Large Language Models](https://mlsanity.com/paper/2607.29561/) (2026)
- [The Label Horizon Paradox: Rethinking Supervision Targets in Financial Forecasting](https://mlsanity.com/paper/2602.03395/) (2026)
- [EVOQUANT: Self-Evolving Verifier-Guided Strategy Optimization for Robust Quantitative Trading](https://mlsanity.com/paper/2607.12455/) (2026)
- [FinHardBench: Can LLMs Generate Latency-Aware Hardware for Financial Computing?](https://mlsanity.com/paper/2608.00909/) (2026)
- [AlphaG-OPD: Reliability-Gated Sibling Counterfactuals for On-Policy Distillation in Symbolic Alpha Factor Discovery](https://mlsanity.com/paper/2608.01303/) (2026)
- [AgonAlpha: Autonomous Alpha Discovery via Prompt Economy and Scalable Agentic Search](https://mlsanity.com/paper/2608.11250/) (2026)
- [AlphaDiverse: Post-Training Local Quantitative Research Agents for Diverse Exploration in Alpha Factor Mining](https://mlsanity.com/paper/2609.29014/) (2026)
- [RICE-Alpha: Reliability-Informed Correction with Event Graphs for LLM-Agent Stock Forecasting](https://mlsanity.com/paper/2609.34004/) (2026)
- [AlphaPareto: Formulaic Alpha Discovery with LLM-Guided Multi-Objective Reinforcement Learning](https://mlsanity.com/paper/2609.34188/) (2026)
- [FactorBench: A Portfolio-Aware Benchmark for Automated Factor Mining](https://mlsanity.com/paper/2610.06947/) (2026)
- [AlphaRLM: Aligning Language Models for Literature-Grounded Symbolic Factor Discovery](https://mlsanity.com/paper/2610.14959/) (2026)

## Abstract (arXiv)

The complexity of financial data, characterized by its variability and low signal-to-noise ratio, necessitates advanced methods in quantitative investment that prioritize both performance and interpretability.Transitioning from early manual extraction to genetic programming, the most advanced approach in the alpha factor mining domain currently employs reinforcement learning to mine a set of combination factors with fixed weights. However, the performance of resultant alpha factors exhibits inconsistency, and the inflexibility of fixed factor weights proves insufficient in adapting to the dynamic nature of financial markets. To address this issue, this paper proposes a two-stage formulaic alpha generating framework AlphaForge, for alpha factor mining and factor combination. This framework employs a generative-predictive neural network to generate factors, leveraging the robust spatial exploration capabilities inherent in deep learning while concurrently preserving diversity. The combination model within the framework incorporates the temporal performance of factors for selection and dynamically adjusts the weights assigned to each component alpha factor. Experiments conducted on real-world datasets demonstrate that our proposed model outperforms contemporary benchmarks in formulaic alpha factor mining. Furthermore, our model exhibits a notable enhancement in portfolio returns within the realm of quantitative investment and real money investment.