Author
Listed:
- Alberto Garinei
(Department of Engineering Sciences, Guglielmo Marconi University, 00193 Rome, Italy
Idea-Re S.r.l., 06128 Perugia, Italy)
- Massimiliano Proietti
(Idea-Re S.r.l., 06128 Perugia, Italy)
- Alessandro Vispa
(Idea-Re S.r.l., 06128 Perugia, Italy)
- Stefano Speziali
(Idea-Re S.r.l., 06128 Perugia, Italy)
- Giovanni Bartolini
(Idea-Re S.r.l., 06128 Perugia, Italy)
- Marcello Marconi
(Department of Engineering Sciences, Guglielmo Marconi University, 00193 Rome, Italy
Idea-Re S.r.l., 06128 Perugia, Italy)
- Emanuele Piccioni
(Idea-Re S.r.l., 06128 Perugia, Italy)
- Matteo Martini
(Department of Engineering Sciences, Guglielmo Marconi University, 00193 Rome, Italy)
- Francesca Fallucchi
(Department of Engineering Sciences, Guglielmo Marconi University, 00193 Rome, Italy)
- Romeo Giuliano
(Department of Engineering Sciences, Guglielmo Marconi University, 00193 Rome, Italy)
- Ernesto William De Luca
(Department of Engineering Sciences, Guglielmo Marconi University, 00193 Rome, Italy)
- Umberto Di Matteo
(Department of Engineering Sciences, Guglielmo Marconi University, 00193 Rome, Italy)
- Valerio Lemma
(Department of Engineering Sciences, Guglielmo Marconi University, 00193 Rome, Italy)
Abstract
The SAIFIN project (Satellite data and Artificial Intelligence for FINtech) develops a novel algorithmic trading system that integrates satellite imagery, financial data, and advanced artificial intelligence to enhance decision-making, particularly in commodity and agricultural markets. This paper presents the motivation, design, implementation, and validation of the SAIFIN framework. Leveraging alternative data and modular multi-agent architectures, SAIFIN aims to deliver robust, context-aware trading signals in diverse market conditions.
Suggested Citation
Alberto Garinei & Massimiliano Proietti & Alessandro Vispa & Stefano Speziali & Giovanni Bartolini & Marcello Marconi & Emanuele Piccioni & Matteo Martini & Francesca Fallucchi & Romeo Giuliano & Erne, 2026.
"Satellite Data and Artificial Intelligence for FINtech,"
Forecasting, MDPI, vol. 8(1), pages 1-20, February.
Handle:
RePEc:gam:jforec:v:8:y:2026:i:1:p:17-:d:1863903
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