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An Adaptive Multi Agent Bitcoin Trading System

Authors: Aadi Singhi

Published: 2025-10-09

arXiv ID: 2510.08068v1

Added to Library: 2025-11-14 23:14 UTC

📄 Abstract

This paper presents a Multi Agent Bitcoin Trading system that utilizes Large Language Models (LLMs) for alpha generation and portfolio management in the cryptocurrencies market. Unlike equities, cryptocurrencies exhibit extreme volatility and are heavily influenced by rapidly shifting market sentiments and regulatory announcements, making them difficult to model using static regression models or neural networks trained solely on historical data [53]. The proposed framework overcomes this by structuring LLMs into specialised agents for technical analysis, sentiment evaluation, decision-making, and performance reflection. The system improves over time through a novel verbal feedback mechanism where a Reflect agent provides daily and weekly natural-language critiques of trading decisions. These textual evaluations are then injected into future prompts, allowing the system to adjust indicator priorities, sentiment weights, and allocation logic without parameter updates or finetuning. Back-testing on Bitcoin price data from July 2024 to April 2025 shows consistent outperformance across market regimes: the Quantitative agent delivered over 30% higher returns in bullish phases and 15% overall gains versus buy-and-hold, while the sentiment-driven agent turned sideways markets from a small loss into a gain of over 100%. Adding weekly feedback further improved total performance by 31% and reduced bearish losses by 10%. The results demonstrate that verbal feedback represents a new, scalable, and low-cost method of tuning LLMs for financial goals.

🔍 Key Points

  • Systematic evaluation of indirect prompt injection attacks on large language models (LLMs), highlighting vulnerabilities across various models and implementations.
  • Identification of key factors influencing model susceptibility including size and architecture, revealing persistent weaknesses even in advanced models.
  • Development of novel obfuscation techniques utilized in the attack scenarios, allowing adversaries to exploit models through hidden instructions embedded in seemingly benign inputs.
  • Empirical evidence showcasing varying degrees of resilience among different LLMs, with some models exhibiting alarming rates of successful attacks despite advanced security mechanisms.
  • Recommendations for improving LLM defenses, including a centralized database of attack vectors and the integration of security into model training processes.

💡 Why This Paper Matters

This paper is critically relevant in addressing the emerging threats posed by indirect prompt injection attacks on LLMs, underscoring the necessity for enhanced security frameworks in AI applications. The findings not only highlight significant vulnerabilities in existing models but also provide a structured approach for future developments in AI security protocols, making it a pivotal resource for safeguarding corporate data against unprecedented threats.

🎯 Why It's Interesting for AI Security Researchers

For AI security researchers, this paper offers crucial insights into the evolving landscape of vulnerabilities associated with LLMs, particularly in the context of their integration with external data sources. The empirical data on attack success rates, coupled with a comprehensive analysis of obfuscation techniques, serves as a foundational study for understanding and mitigating security threats in generative AI systems. Furthermore, the proposed frameworks and defensive strategies can guide researchers in developing robust countermeasures against increasingly sophisticated adversarial tactics.

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