AI Trading Engine

Title

AI Trading Engine: Exploring the capabilities of AI in digital asset trading

 Author

van Gurp, Ralph (TU Delft Electrical Engineering, Mathematics and Computer Science)
Hu, Jasper (TU Delft Electrical Engineering, Mathematics and Computer Science)
Kooijman, Hugo (TU Delft Electrical Engineering, Mathematics and Computer Science)
Somai, Ashay (TU Delft Electrical Engineering, Mathematics and Computer Science)

Contributor

Rellermeyer, Jan S. (mentor) ORCID 0000-0003-3791-7114

 Degree granting institution

Delft University of Technology

 Programme

Computer Science

 Date

2019-02-01

 Abstract

Scientific advances in the field of artificial intelligence, and the ever increasing processing power of computers, have opened up opportunities to use artificial in- telligence for new industries and applications. Blockrise foresaw opportunities in using artificial intelligence for digital asset management. Blockrise’s founders and the development team formalised the problem and drew up a project pro- posal, which was accepted by the TU Delft Bachelor End Project coordinators and overseen by Prof. Dr. Jan Rellermeyer in the role of TU Coach.
The development team first reserved roughly two weeks to spend on research- ing the problem and possible solutions. A research proposal was formalised, in which details of the problem were explained and abstract solutions were given. During the next six weeks, the team started the concrete implementation of the solution. The team created a neural network to use as core functionality in the product. Supporting functionality was simultaneously developed to allow re- trieval and processing of necessary data. With enough useful data on hand, the neural network could be trained to make predictions based on an asset’s closing price, opening price, the highest price and the lowest price for each next minute, hour and day.
Extensions to the product were made in the form live-data processing, and validation and visualisation of predictions. Trading strategies were included to allow fully automated decision on placing market orders. The final stage of the development period was spent tweaking the neural network parameters in order to minimise prediction error.

 To reference this document use:

http://resolver.tudelft.nl/uuid:77e9e442-5de2-4355-bee9-0d670eebe738

 Embargo date

2022-01-31

 Part of collection

Student theses

 Document type

bachelor thesis

 Rights

© 2019 Ralph van Gurp, Jasper Hu, Hugo Kooijman, Ashay Somai
Files
PDF BEP_Final_thesis_report.pdf 4.68 MB
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