3S-Trader: A Multi-LLM Framework for Adaptive Stock Scoring, Strategy, and Selection in Portfolio Optimization
Kefan Chen, Hussain Ahmad, Diksha Goel, Claudia Szabo

TL;DR
3S-Trader is a novel, training-free multi-LLM framework for adaptive stock portfolio construction that effectively scores, strategizes, and selects stocks, outperforming existing methods across multiple stock universes.
Contribution
It introduces a flexible, multi-module framework that enables adaptive portfolio management using large language models without additional training.
Findings
Achieves 131.83% return on DJIA stocks
Outperforms existing multi-LLM and time-series baselines
Demonstrates strong results across various stock sectors
Abstract
Large Language Models (LLMs) have recently gained popularity in stock trading for their ability to process multimodal financial data. However, most existing methods focus on single-stock trading and lack the capacity to reason over multiple candidates for portfolio construction. Moreover, they typically lack the flexibility to revise their strategies in response to market shifts, limiting their adaptability in real-world trading. To address these challenges, we propose 3S-Trader, a training-free framework that incorporates scoring, strategy, and selection modules for stock portfolio construction. The scoring module summarizes each stock's recent signals into a concise report covering multiple scoring dimensions, enabling efficient comparison across candidates. The strategy module analyzes historical strategies and overall market conditions to iteratively generate an optimized selection…
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Taxonomy
TopicsStock Market Forecasting Methods · Financial Markets and Investment Strategies · Advanced Bandit Algorithms Research
