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Artificial intelligence for algorithmic trading digital assets: evidence from the Counter-Strike 2 skin market

2025-11-11Artigo de investigação Acesso aberto
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dc.contributor.authorGuede-Fernández, Federico
dc.contributor.authorWagle, Yash
dc.contributor.authorDias, Pedro
dc.contributor.authorGiordano, Ana Paula
dc.contributor.authorHenriques, Lúcio
dc.contributor.authorCosta, Gonçalo
dc.contributor.authorAzevedo, Salomé
dc.date.accessioned2025-12-15T11:42:14Z
dc.date.available2025-12-15T11:42:14Z
dc.date.issued2025-11-11
dc.description.abstractIntroduction: 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. Methods: 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 6 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. Results: Artificial intelligence consistently outperforms buy-and-hold, particularly in smaller, more concentrated portfolios and over longer time horizons. For example, in 6-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. Discussion: 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.eng
dc.identifier.citationGuede-Fernández, F., Wagle, Y., Dias, P., & Giordano, A. P. et al. (2025). Artificial intelligence for algorithmic trading digital assets: evidence from the Counter-Strike 2 skin market. Frontiers in Artificial Intelligence, 8, Article 1702924. https://doi.org/10.3389/frai.2025.1702924
dc.identifier.doi10.3389/frai.2025.1702924
dc.identifier.eid105023584135
dc.identifier.issn2624-8212
dc.identifier.other76c57721-e7bb-46a5-b673-d6e21a0866d1
dc.identifier.pmcPMC12643999
dc.identifier.pmid41306520
dc.identifier.urihttp://hdl.handle.net/10400.14/55892
dc.language.isoeng
dc.peerreviewedyes
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectAlgorithmic trading
dc.subjectArtificial intelligence
dc.subjectCounter-Strike 2
dc.subjectDeep learning
dc.subjectDigital assets
dc.subjectSkins market
dc.subjectVirtual economy
dc.titleArtificial intelligence for algorithmic trading digital assets: evidence from the Counter-Strike 2 skin marketeng
dc.typeresearch article
dspace.entity.typePublication
oaire.citation.titleFrontiers in Artificial Intelligence
oaire.citation.volume8
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85

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