Building crypto portfolios with agentic AI

ArXiv ID: 2507.20468 “View on arXiv”

Authors: Antonino Castelli, Paolo Giudici, Alessandro Piergallini

Abstract

The rapid growth of crypto markets has opened new opportunities for investors, but at the same time exposed them to high volatility. To address the challenge of managing dynamic portfolios in such an environment, this paper presents a practical application of a multi-agent system designed to autonomously construct and evaluate crypto-asset allocations. Using data on daily frequencies of the ten most capitalized cryptocurrencies from 2020 to 2025, we compare two automated investment strategies. These are a static equal weighting strategy and a rolling-window optimization strategy, both implemented to maximize the evaluation metrics of the Modern Portfolio Theory (MPT), such as Expected Return, Sharpe and Sortino ratios, while minimizing volatility. Each step of the process is handled by dedicated agents, integrated through a collaborative architecture in Crew AI. The results show that the dynamic optimization strategy achieves significantly better performance in terms of risk-adjusted returns, both in-sample and out-of-sample. This highlights the benefits of adaptive techniques in portfolio management, particularly in volatile markets such as cryptocurrency markets. The following methodology proposed also demonstrates how multi-agent systems can provide scalable, auditable, and flexible solutions in financial automation.

Keywords: Multi-Agent Systems, Portfolio Optimization, Cryptocurrency, Modern Portfolio Theory, Rolling-Window Optimization, Cryptocurrency

Complexity vs Empirical Score

  • Math Complexity: 6.0/10
  • Empirical Rigor: 7.5/10
  • Quadrant: Holy Grail
  • Why: The paper employs advanced mathematics like Modern Portfolio Theory (MPT), mean-variance optimization, and rolling Sharpe maximization, which are conceptually dense. It demonstrates high empirical rigor by using real 5-year daily data on 10 assets, conducting in-sample and out-of-sample backtests, and implementing a full multi-agent system for automation.
  flowchart TD
    A["Research Goal<br>Optimize crypto portfolios in volatile markets"] --> B["Data Input<br>Top 10 crypto assets (2020-2025)"]
    B --> C["Methodology<br>Multi-Agent System (Crew AI)"]
    C --> D["Strategy 1<br>Static Equal Weighting"]
    C --> E["Strategy 2<br>Rolling-Window Optimization"]
    D --> F["Evaluation & Comparison<br>Modern Portfolio Theory MPT<br>Sharpe & Sortino Ratios"]
    E --> F
    F --> G["Key Outcomes<br>Dynamic Optimization wins<br>Risk-adjusted returns improved<br>Scalable & Auditable solution"]