# Building crypto portfolios with agentic AI

arXiv: https://arxiv.org/abs/2507.20468 · HTML: https://mlsanity.com/paper/2507.20468/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 13 references that are in the map.
- First posted on arXiv 11 Jul 2025.

## Builds on (most cited first, 6 of 6)

- [TradingAgents: Multi-Agents LLM Financial Trading Framework](https://mlsanity.com/paper/2412.20138/) (2024)
- [The Landscape of Emerging AI Agent Architectures for Reasoning, Planning, and Tool Calling: A Survey](https://mlsanity.com/paper/2404.11584/) (2024)
- [When AI Meets Finance (StockAgent): Large Language Model-based Stock Trading in Simulated Real-world Environments](https://mlsanity.com/paper/2407.18957/) (2024)
- [Explainable AI in Credit Risk Management](https://mlsanity.com/paper/2103.00949/) (2021)
- [MAPS: Multi-agent Reinforcement Learning-based Portfolio Management System](https://mlsanity.com/paper/2007.05402/) (2020)
- [Best Practices for Responsible Machine Learning in Credit Scoring](https://mlsanity.com/paper/2409.20536/) (2024)

## Built on by (most cited first, 1 of 1)

- [Agentic Trading: When LLM Agents Meet Financial Markets](https://mlsanity.com/paper/2605.19337/) (2026)

## Abstract (arXiv)

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.