XALPHA: A Memory-Driven AI Quant Researcher for Hypothesis-to-Code Alpha Discovery
Financial markets are noisy, non-stationary, and high-dimensional, making it difficult to discover predictive and robust trading signals. Alpha discovery has evolved from manual factor design to machine learning, evolutionary search, and recent LLM-based frameworks, improving the efficiency of factor generation, search, and evaluation. However, existing methods still mostly automate isolated steps, rather than functioning as end-to-end quant researchers that can absorb external knowledge, close the hypothesis-to-code validation loop, and learn from accumulated discovery feedback. To fill this
Lineage graph
Paper → model → repo connections mined from source citations (Tier-1 exact match).
Why these links exist
Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- PossiblePossibly related (embedding) · 69%leoncuhk/awesome-quant-ai →
- PossiblePossibly related (embedding) · 61%ZhuLinsen/alphasift →
- PossiblePossibly related (embedding) · 57%qrak/LLM_trader →
- PossiblePossibly related (embedding) · 57%ryanfrigo/kalshi-ai-trading-bot →
- PossiblePossibly related (embedding) · 56%AI gold trading bots and the data revolution: How machine learning is transforming XAUUSD automation in 2026 - Dataconomy →
- LinkedLinked via arxiv author · 85%Fengyuan Liu →
“XALPHA: A Memory-Driven AI Quant Researcher for Hypothesis-to-Code Alpha Discovery”
- LinkedLinked via arxiv author · 85%Yuchen Fu →
“XALPHA: A Memory-Driven AI Quant Researcher for Hypothesis-to-Code Alpha Discovery”
- LinkedLinked via arxiv author · 85%Yuqi Wang →
“XALPHA: A Memory-Driven AI Quant Researcher for Hypothesis-to-Code Alpha Discovery”