F2Agent: Financial Fusion of Agentic Intelligence for Multimodal Trading
C. Liu, Y. Jin, S. Cai, P. Fang, X. Xiao, B. C. Ooi · 2026
What this means for traders
How much each data source counts toward a verdict should be an explicit, inspectable number, not something buried inside a block of text.
Abstract
Most LLM trading systems merge market data, indicators, news and sentiment by concatenating them into a single prompt, which can let the textual signal dominate and loses cross-modal dependencies. F2Agent instead assigns one specialist encoder per modality and fuses them through learned modality-aware attention, regularised for prior diversity and for stability under single-modality perturbation. In a six-month backtest (April to September 2025) on five US stocks and Bitcoin, it ranks first on annualized return on all six assets against sixteen baselines, and its ablation shows the fusion design carries much of the gain: with the same specialist agents, replacing it with plain concatenation drops annualized return from 50.1% to 19.2% on AAPL.