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#llm-trading News & Analysis

4 articles tagged with #llm-trading. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

4 articles
AI × CryptoNeutralarXiv – CS AI · Jun 97/10
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Beyond Agent Architecture: Execution Assumptions and Reproducibility in LLM-Based Trading Systems

A new arXiv paper audits 30 LLM-based trading studies and finds that while agent architectures are well-documented, evaluation methodologies—including execution timing, transaction costs, and data splits—lack standardization, making performance claims difficult to compare or reproduce. The authors argue that LLM trading research needs clearer reporting standards for execution realism before architectural improvements can be meaningfully assessed.

AINeutralarXiv – CS AI · May 286/10
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AlphaForgeBench: Benchmarking End-to-End Trading Strategy Design with Large Language Models

Researchers introduce AlphaForgeBench, a new evaluation framework that addresses critical instability issues in Large Language Models deployed as trading agents. Rather than having LLMs generate discrete trading actions, the framework redefines their role as quantitative researchers producing alpha factors and strategies, enabling deterministic, reproducible evaluation aligned with real-world financial workflows.

AINeutralarXiv – CS AI · May 96/10
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Strat-LLM: Stratified Strategy Alignment for LLM-based Stock Trading with Real-time Multi-Source Signals

Researchers introduce Strat-LLM, a framework that aligns large language models for stock trading by matching model architecture to operational modes (Free, Guided, Strict), finding that reasoning-heavy models excel with minimal constraints while standard models benefit from strict guardrails. Live-forward testing across 2025 on A-share and U.S. markets reveals that optimal performance depends on market regime and model scale, with mid-size models (35B) showing superior risk-adjusted returns under constraints.