AI-powered digital intelligence market scanner for smarter cryptocurrency trading decisions
Mohamoud Dayib, Fowsi (2025)
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-2025120231664
https://urn.fi/URN:NBN:fi:amk-2025120231664
Tiivistelmä
The purpose of this thesis was to design and develop an AI-powered Digital Intelligence Market Scanner (DIMS) to enhance cryptocurrency trading decisions through data integration, automation, and intelligent analysis. The study addressed a key problem faced by traders: fragmented information across multiple platforms that makes it difficult to form timely and objective decisions.
The research followed a design science approach, where the DIMS prototype was built as a functional artifact consisting of four modular layers: data collection, deterministic analytics, AI-assisted interpretation, and a user-friendly dashboard. Data were primarily retrieved through the CryptoRank API and cross-verified using CoinMarketCap and TradingView. A market validation survey with 71 participants provided insights into trader needs, confirming that difficulty in finding entry/exit points, risk management, and excessive manual research were the most common challenges.
The evaluation demonstrated that the DIMS prototype successfully unified multi source data into clear, reliable insights. Test results confirmed a high level of consistency between DIMS outputs and reference sources. The platform’s integration of AI-assisted analytics showed potential to improve decision accuracy and research efficiency while reducing human bias.
The study concludes that AI-driven market intelligence can significantly support swing traders and short-term investors by improving data quality, interpretation, and automation in a volatile and complex trading environment. Future work will Turku University of Applied Sciences Thesis | Fowsi Mohamoud Dayib focus on expanding predictive AI features, integrating vesting-data analytics, and deploying the platform on AWS as a scalable SaaS solution.
The research followed a design science approach, where the DIMS prototype was built as a functional artifact consisting of four modular layers: data collection, deterministic analytics, AI-assisted interpretation, and a user-friendly dashboard. Data were primarily retrieved through the CryptoRank API and cross-verified using CoinMarketCap and TradingView. A market validation survey with 71 participants provided insights into trader needs, confirming that difficulty in finding entry/exit points, risk management, and excessive manual research were the most common challenges.
The evaluation demonstrated that the DIMS prototype successfully unified multi source data into clear, reliable insights. Test results confirmed a high level of consistency between DIMS outputs and reference sources. The platform’s integration of AI-assisted analytics showed potential to improve decision accuracy and research efficiency while reducing human bias.
The study concludes that AI-driven market intelligence can significantly support swing traders and short-term investors by improving data quality, interpretation, and automation in a volatile and complex trading environment. Future work will Turku University of Applied Sciences Thesis | Fowsi Mohamoud Dayib focus on expanding predictive AI features, integrating vesting-data analytics, and deploying the platform on AWS as a scalable SaaS solution.