Artificial Intelligence for Algorithmic Trading Digital Assets: Evidence from the Counter-Strike 2 Skin Market

Federico Guede-Fernández^1,2,3*Yash Sham Wagle¹Pedro Dias^1,3Ana Paula Giordano^1,4Lúcio Henriques⁵Gonçalo Costa⁵Salome Azevedo^1,3,6

• ¹Value for Health.CoLAB, Lisboa, Portugal
• ²Universidade de Lisboa Laboratorio de Instrumentacao Engenharia Biomedica e Fisica da Radiacao, Lisbon, Portugal
• ³Universidade NOVA de Lisboa Comprehensive Health Research Centre, Lisbon, Portugal
• ⁴CUBE – CATÓLICA-LISBON Research Unit in Business and Economics, Lisboa, Portugal
• ⁵Exeedme, Braga, Portugal
• ⁶Instituto Superior Tecnico Centro de Estudos de Gestao, Lisbon, Portugal

The Counter-Strike 2 skin market has developed into a multi-billion-dollar digital asset ecosystem, characterized by high volatility, low liquidity, and pricing inefficiencies that differ substantially from traditional financial markets. Despite the growing economic relevance of virtual items, no previous study has systematically examined the use of artificial intelligence for skin trading. This work designs and evaluates an automated trading system that applies deep learning models, specifically Long Short-Term Memory networks and Neural Hierarchical Interpolation for Time Series, to forecast skin prices and guide trading decisions. A dataset of 12,000 unique skins from the Steam Market, covering the period from May 2024 to April 2025, was collected using the CSGOskins.gg application programming interface. To reflect real market conditions, the trading strategy incorporated the Steam Market restrictions of a seven-day minimum holding period and a ten percent transaction cost, and was benchmarked against a traditional buy-and-hold strategy. Backtesting was performed multiple time horizons of two, three, and six months. Portfolio selection was based on risk and return criteria, including a Sharpe ratio greater than one, a Sortino ratio greater than two, and a return on investment above five percent. The results demonstrate that artificial intelligence consistently outperforms buy-and-hold, particularly in smaller, more concentrated portfolios and over longer time horizons. For example, in six-month simulations, artificial intelligence portfolios achieved returns approaching 20%, compared to 5% to 10% for buy-and-hold, with excess returns as high as 75% in small portfolios. Larger portfolios reduced absolute returns but improved risk-adjusted performance, confirming that diversification enhances stability while diluting raw profitability. Analysis of portfolio composition by rarity further revealed that artificial intelligence favors moderately rare and liquid skins such as Mil-Spec, resembling mid-cap equity investment strategies, while buy-and-hold accumulates rarer skins, analogous to small-cap holdings that rely on scarcity premiums. These findings highlight that even in virtual goods markets, the trade-offs between return, risk, and diversification reflect established principles of modern portfolio theory. The study demonstrates both the feasibility and the potential of artificial intelligence-based trading systems in the Counter-Strike 2 skin economy, contributing methodological advances and practical insights for participants in this emerging digital asset market.