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master's thesis
Model dubokog učenja za upravljanje novcem
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2022. urn:nbn:hr:168:582084
Šimek, Filip
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

Cite this document

Šimek, F. (2022). Model dubokog učenja za upravljanje novcem (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:582084

Šimek, Filip. "Model dubokog učenja za upravljanje novcem." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2022. https://urn.nsk.hr/urn:nbn:hr:168:582084

Šimek, Filip. "Model dubokog učenja za upravljanje novcem." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2022. https://urn.nsk.hr/urn:nbn:hr:168:582084

Šimek, F. (2022). 'Model dubokog učenja za upravljanje novcem', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 19 December 2024, https://urn.nsk.hr/urn:nbn:hr:168:582084

Šimek F. Model dubokog učenja za upravljanje novcem [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2022 [cited 2024 December 19] Available at: https://urn.nsk.hr/urn:nbn:hr:168:582084

F. Šimek, "Model dubokog učenja za upravljanje novcem", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2022. Available at: https://urn.nsk.hr/urn:nbn:hr:168:582084

Metadata
TitleModel dubokog učenja za upravljanje novcem
Title (english)Deep Learning Model for Money Management
AuthorFilip Šimek
MentorZvonko Kostanjčar (mentor)
Committee memberZvonko Kostanjčar (predsjednik povjerenstva)
Committee memberDražen Jurišić (član povjerenstva)
Committee memberStjepan Begušić (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2022-07-15, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Široka primjena modela strojnog učenja u raznim znanstvenim disciplinama dovela je do sve češće upotrebe takvih modela u financijama, a osobito u algoritamskom trgovanju. Osim problema određivanja najboljih trenutaka za zauzimanje pozicije u financijskoj imovini, aktualan je i problem određivanja veličine pozicije. Jednostavni all-or-nothing pristup ne upravlja rizikom, dok je nedostatak često korištene Kelly strategije temeljenje na dobro procijenjenom očekivanom povratu investicije što može rezultirati sugeriranjem prevelikih i riskantnih pozicija. U ovom radu, razvijena je strategija trgovanja utemeljena na modelu ćelije s dugoročnim pamćenjem za određivanje dobrih trenutaka ulaska i veličine pozicije (utemeljena na vjerojatnosnoj interpretaciji izlaza modela). Rezultati pokazuju da takva strategija u nekim slučajevima ostvaruje najbolje rezultate prema Sharpe omjeru uz dobro upravljanje rizikom.
Abstract (english)
The widespread application of machine learning models in various scientific disciplines has led to the increasing use of such models in finance and, in particular, algorithmic trading. In addition to the problem of determining the best times to take positions in financial assets, the problem of determining the size of the positions is also important. A simple all-or-nothing approach cannot be used to manage risk, while the commonly used Kelly strategy has a disadvantage that it is based on a well – estimated expected return of investment which could result in oversizing and taking risks. In this paper, we developed a trading strategy based on a long short-term memory network model to determine good entry points and position sizes (based on a probabilistic interpretation of the model's output ). The results show that such a strategy can achieve the best results according to the Sharpe ratio along with good risk management.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:582084
Study programmeTitle: Computing
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra inženjer/inženjerka računarstva (magistar/magistra inženjer/inženjerka računarstva)
Type of resourceText
File originBorn digital
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Created on2023-01-13 16:16:19