arXiv:2501.00826v3 Announce Type: replace-cross Abstract: Cryptocurrency portfolio management requires the fusion of heterogeneous multi-modal signals, including structured price and on-chain time series, unstructured news text, and technical indicators, under high-volatility and real-time constraints. While deep learning approaches show predictive capability, their opacity limits practical adoption, and single large language model (LLM) agents struggle to process the breadth of modality-specific inputs needed for robust decision-making. We propose a multi-agent system (MAS) framework in which
The rapid advancement of large language models (LLMs) and the increasing complexity of crypto markets make autonomous, multi-agent systems a logical next step for sophisticated portfolio management.
This development indicates a move towards more autonomous and sophisticated financial management systems, reducing human intervention and potentially democratizing advanced trading strategies.
Traditional, human-led crypto portfolio management will be challenged by AI systems capable of processing diverse data streams and executing decisions in real-time.
- · AI-driven asset managers
- · Sophisticated retail investors
- · AI developers
- · Traditional crypto fund managers
- · Single-modality trading algorithms
- · Less technologically advanced investors
More efficient and potentially higher-return crypto portfolios managed by AI.
Increased competition in crypto asset management, potentially driving down fees and increasing market volatility due to algorithmic interactions.
The integration of such AI agents into broader financial sectors, leading to a significant automation of investment decision-making beyond crypto.
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Read at arXiv cs.AI