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This paper presents a unified multi-modal framework integrating reinforcement learning, high-frequency trading, game-theoretic approaches, and cross-modal sentiment analysis for intelligent financial systems, claiming significant improvements over single-domain systems.
QuantAgent is a multi-agent LLM framework designed specifically for high-frequency trading, using four specialized agents (Indicator, Pattern, Trend, Risk) to make rapid, risk-aware decisions based on short-horizon signals. In zero-shot evaluations across ten financial instruments including Bitcoin and Nasdaq futures, it outperforms existing neural and rule-based baselines in predictive accuracy and cumulative return.