Building crypto portfolios with agentic AI

• URL: http://arxiv.org/abs/2507.20468v1
• Date: Fri, 11 Jul 2025 18:03:51 GMT
• Title: Building crypto portfolios with agentic AI
• Authors: Antonino Castelli, Paolo Giudici, Alessandro Piergallini,
• Abstract summary: The rapid growth of crypto markets has opened new opportunities for investors, but at the same time exposed them to high volatility.<n>This paper presents a practical application of a multi-agent system designed to autonomously construct and evaluate crypto-asset allocations.
• Score: 46.348283638884425
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Deep Learning Enhanced Multi-Day Turnover Quantitative Trading Algorithm for Chinese A-Share Market [0.0]
  Algorithm is trained on comprehensive A-share data from 2010-2020 and rigorously backtested on 2021-2024 data.<n>It achieves remarkable performance with 15.2% annualized returns, maximum drawdown constrained below 5%, and a Sharpe ratio of 1.87.
  arXiv  Detail & Related papers  (2025-06-03T01:59:55Z)
• Deep Reinforcement Learning for Investor-Specific Portfolio Optimization: A Volatility-Guided Asset Selection Approach [2.2835610890984164]
  This study proposes a volatility-guided portfolio optimization framework that dynamically constructs portfolios based on investors' risk profiles.<n>The efficacy of the proposed methodology is established using stocks from the Dow $30$ index.
  arXiv  Detail & Related papers  (2025-04-20T10:17:37Z)
• Dynamic Hedging Strategies in Derivatives Markets with LLM-Driven Sentiment and News Analytics [30.815524322885754]
  This paper introduces a novel framework that leverages large language models (LLMs) for sentiment analysis and news analytics to inform hedging decisions.<n>The framework allows for real-time adjustments to hedging strategies, adapting positions based on continuous sentiment signals.
  arXiv  Detail & Related papers  (2025-04-05T22:35:06Z)
• Collab: Controlled Decoding using Mixture of Agents for LLM Alignment [90.6117569025754]
  Reinforcement learning from human feedback has emerged as an effective technique to align Large Language models.<n>Controlled Decoding provides a mechanism for aligning a model at inference time without retraining.<n>We propose a mixture of agent-based decoding strategies leveraging the existing off-the-shelf aligned LLM policies.
  arXiv  Detail & Related papers  (2025-03-27T17:34:25Z)
• MTS: A Deep Reinforcement Learning Portfolio Management Framework with Time-Awareness and Short-Selling [0.8642326601683299]
  This paper introduces a Deep Reinforcement Learning Portfolio Management Framework with Time-Awareness and Short-Selling.<n>It addresses limitations in dynamic risk management, exploitation of temporal markets, and incorporation of complex trading strategies such as short-selling.<n>It consistently achieves higher cumulative returns, Sharpe, Omega, and Sortino ratios, underscoring its effectiveness in balancing risk and return.
  arXiv  Detail & Related papers  (2025-03-06T06:41:17Z)
• Composing Ensembles of Instrument-Model Pairs for Optimizing Profitability in Algorithmic Trading [0.0]
  This paper describes a daily price directional predictive system of financial instruments, addressing the difficulty of predicting short-term price movements. Strategy was back-tested on a wide range of financial instruments and time frames, demonstrating an improvement of 20% over the benchmark.
  arXiv  Detail & Related papers  (2024-11-06T18:17:26Z)
• Automate Strategy Finding with LLM in Quant Investment [15.475504003134787]
  We present a novel three-stage framework leveraging Large Language Models (LLMs) within a risk-aware multi-agent system for automate strategy finding in quantitative finance.<n>Our approach addresses the brittleness of traditional deep learning models in financial applications by: employing prompt-engineered LLMs to generate executable alpha factor candidates across diverse financial data.<n> Experimental results demonstrate the robust performance of the strategy in Chinese & US market regimes compared to established benchmarks.
  arXiv  Detail & Related papers  (2024-09-10T07:42:28Z)
• Explainable Post hoc Portfolio Management Financial Policy of a Deep Reinforcement Learning agent [44.99833362998488]
  We develop a novel Explainable Deep Reinforcement Learning (XDRL) approach for portfolio management. By executing our methodology, we can interpret in prediction time the actions of the agent to assess whether they follow the requisites of an investment policy.
  arXiv  Detail & Related papers  (2024-07-19T17:40:39Z)
• Deep Reinforcement Learning and Mean-Variance Strategies for Responsible Portfolio Optimization [49.396692286192206]
  We study the use of deep reinforcement learning for responsible portfolio optimization by incorporating ESG states and objectives. Our results show that deep reinforcement learning policies can provide competitive performance against mean-variance approaches for responsible portfolio allocation.
  arXiv  Detail & Related papers  (2024-03-25T12:04:03Z)
• Cryptocurrency Portfolio Optimization by Neural Networks [81.20955733184398]
  This paper proposes an effective algorithm based on neural networks to take advantage of these investment products. A deep neural network, which outputs the allocation weight of each asset at a time interval, is trained to maximize the Sharpe ratio. A novel loss term is proposed to regulate the network's bias towards a specific asset, thus enforcing the network to learn an allocation strategy that is close to a minimum variance strategy.
  arXiv  Detail & Related papers  (2023-10-02T12:33:28Z)
• Diffusion Variational Autoencoder for Tackling Stochasticity in Multi-Step Regression Stock Price Prediction [54.21695754082441]
  Multi-step stock price prediction over a long-term horizon is crucial for forecasting its volatility. Current solutions to multi-step stock price prediction are mostly designed for single-step, classification-based predictions. We combine a deep hierarchical variational-autoencoder (VAE) and diffusion probabilistic techniques to do seq2seq stock prediction. Our model is shown to outperform state-of-the-art solutions in terms of its prediction accuracy and variance.
  arXiv  Detail & Related papers  (2023-08-18T16:21:15Z)
• IMM: An Imitative Reinforcement Learning Approach with Predictive Representation Learning for Automatic Market Making [33.23156884634365]
  Reinforcement Learning technology has achieved remarkable success in quantitative trading. Most existing RL-based market making methods focus on optimizing single-price level strategies. We propose Imitative Market Maker (IMM), a novel RL framework leveraging both knowledge from suboptimal signal-based experts and direct policy interactions.
  arXiv  Detail & Related papers  (2023-08-17T11:04:09Z)
• Data-Scarce Identification of Game Dynamics via Sum-of-Squares Optimization [29.568222003322344]
  We introduce the Side-Information Assisted Regression (SIAR) framework, designed to identify game dynamics in multiplayer normal-form games. SIAR is solved using sum-of-squares (SOS) optimization, resulting in a hierarchy of approximations that provably converge to the true dynamics of the system. We showcase that the SIAR framework accurately predicts player behavior across a spectrum of normal-form games, widely-known families of game dynamics, and strong benchmarks, even if the unknown system is chaotic.
  arXiv  Detail & Related papers  (2023-07-13T09:14:48Z)
• Quantitative Stock Investment by Routing Uncertainty-Aware Trading Experts: A Multi-Task Learning Approach [29.706515133374193]
  We show that existing deep learning methods are sensitive to random seeds and network routers. We propose a novel two-stage mixture-of-experts (MoE) framework for quantitative investment to mimic the efficient bottom-up trading strategy design workflow of successful trading firms. AlphaMix significantly outperforms many state-of-the-art baselines in terms of four financial criteria.
  arXiv  Detail & Related papers  (2022-06-07T08:58:00Z)
• Bitcoin Transaction Strategy Construction Based on Deep Reinforcement Learning [8.431365407963629]
  This study proposes a framework for automatic high-frequency bitcoin transactions based on a deep reinforcement learning algorithm-proximal policy optimization (PPO) The proposed framework can earn excess returns through both the period of volatility and surge, which opens the door to research on building a single cryptocurrency trading strategy based on deep learning.
  arXiv  Detail & Related papers  (2021-09-30T01:24:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.