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"LLM-Powered Multi-Agent System for Automated Crypto Portfolio Management"

Generated below podcast on this paper with Google's Illuminate.

Team of specialized AI agents collaborate to manage crypto portfolios with explainable decisions.

It integrates multi-modal data and expert knowledge to make explainable investment decisions and outperform market benchmarks.

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https://arxiv.org/abs/2501.00826

🤔 Original Problem:

→ Single LLMs struggle with complex crypto investment tasks due to limited domain knowledge and inability to process multi-modal data effectively

→ Traditional deep learning approaches lack transparency in decision-making, reducing investor trust

→ Existing solutions focus only on major cryptocurrencies like Bitcoin and Ethereum, using limited data types

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🛠️ Solution in this Paper:

→ The framework uses specialized agent teams for data collection, literature analysis, and investment decisions

→ Expert training module fine-tunes agents using historical data and academic research

→ Market team analyzes trends and determines cash-crypto allocation using news and market factors

→ Crypto team selects individual cryptocurrencies based on technical and fundamental analysis

→ Unique intrateam collaboration combines confidence scores within agent groups

→ Interteam collaboration enables information sharing between market and crypto teams

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💡 Key Insights:

→ Multi-agent approach outperforms single LLM models in both accuracy and portfolio returns

→ Fine-tuning with domain-specific knowledge significantly improves prediction quality

→ Collaboration mechanisms reduce prediction errors and enhance decision quality

→ The framework generates professional, consistent explanations for investment decisions

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📊 Results:

→ Achieved highest classification accuracy of 0.5248 and MCC score of 0.0428

→ Portfolio performance exceeded market benchmarks with 0.8347 cumulative return

→ Generated 0.0172 weekly return mean with 1.5425 Sharpe ratio

→ Outperformed in both boom (3.8430 Sharpe) and bust (-2.7125 Sharpe) market periods

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