{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T19:27:34Z","timestamp":1764962854226,"version":"3.46.0"},"reference-count":39,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2025,11,29]],"date-time":"2025-11-29T00:00:00Z","timestamp":1764374400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Blockchain-based financial ecosystems generate unprecedented volumes of multi-temporal data streams requiring sophisticated analytical frameworks that leverage both on-chain transaction patterns and off-chain market microstructure dynamics. This study presents an empirical evaluation of a two-class confidence-threshold framework for cryptocurrency direction prediction, systematically integrating macro momentum indicators with microstructure dynamics through unified feature engineering. Building on established selective classification principles, the framework separates directional prediction from execution decisions through confidence-based thresholds, enabling explicit optimization of precision\u2013recall trade-offs for decentralized financial applications. Unlike traditional three-class approaches that simultaneously learn direction and execution timing, our framework uses post-hoc confidence thresholds to separate these decisions. This enables systematic optimization of the accuracy-coverage trade-off for blockchain-integrated trading systems. We conduct comprehensive experiments across 11 major cryptocurrency pairs representing diverse blockchain protocols, evaluating prediction horizons from 10 to 600 min, deadband thresholds from 2 to 20 basis points, and confidence levels of 0.6 and 0.8. The experimental design employs rigorous temporal validation with symbol-wise splitting to prevent data leakage while maintaining realistic conditions for blockchain-integrated trading systems. High confidence regimes achieve peak profits of 167.64 basis points per trade with directional accuracies of 82\u201395% on executed trades, suggesting potential applicability for automated decentralized finance (DeFi) protocols and smart contract-based trading strategies on similar liquid cryptocurrency pairs. The systematic parameter optimization reveals fundamental trade-offs between trading frequency and signal quality in blockchain financial ecosystems, with high confidence strategies reducing median coverage while substantially improving per-trade profitability suitable for gas-optimized on-chain execution.<\/jats:p>","DOI":"10.3390\/a18120758","type":"journal-article","created":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T18:42:02Z","timestamp":1764960122000},"page":"758","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Blockchain-Native Asset Direction Prediction: A Confidence-Threshold Approach to Decentralized Financial Analytics Using Multi-Scale Feature Integration"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2331-6326","authenticated-orcid":false,"given":"Oleksandr","family":"Kuznetsov","sequence":"first","affiliation":[{"name":"Department of Theoretical and Applied Sciences, eCampus University, Via Isimbardi 10, 22060 Novedrate, Italy"},{"name":"Department of Intelligent Software Systems and Technologies, School of Computer Science and Artificial Intelligence, V.N. Karazin Kharkiv National University, 61022 Kharkiv, Ukraine"}]},{"given":"Dmytro","family":"Prokopovych-Tkachenko","sequence":"additional","affiliation":[{"name":"Department of Cybersecurity and Information Technologies, University of Customs and Finance, 49000 Dnipro, Ukraine"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-5875-4950","authenticated-orcid":false,"given":"Maksym","family":"Bilan","sequence":"additional","affiliation":[{"name":"Department of Cybersecurity and Information Technologies, University of Customs and Finance, 49000 Dnipro, Ukraine"}]},{"given":"Borys","family":"Khruskov","sequence":"additional","affiliation":[{"name":"Department of Cybersecurity and Information Technologies, University of Customs and Finance, 49000 Dnipro, Ukraine"}]},{"given":"Oleksandr","family":"Cherkaskyi","sequence":"additional","affiliation":[{"name":"Department of Cybersecurity and Information Technologies, National Technical University \u201cDnipro Polytechnic\u201d, 49005 Dnipro, Ukraine"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"102524","DOI":"10.1016\/j.najef.2025.102524","article-title":"Risk Spillover and Hedging Effects between Stock Markets and Cryptocurrency Markets Depending upon Network Analysis","volume":"80","author":"Guo","year":"2025","journal-title":"N. 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