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Title:Avtomatiziran sistem za borzno trgovanje : diplomsko delo
Authors:Celcer, Matevž (Author)
Korže, Danilo (Mentor) More about this mentor... New window
Borovič, Mladen (Co-mentor)
Files:.pdf UN_Celcer_Matevz_2019.pdf (1,38 MB)
Work type:Bachelor thesis/paper (mb11)
Typology:2.11 - Undergraduate Thesis
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:Namen diplomskega dela je bila raziskava in implementacija sodobnih načinov predvidevanja prihodnjih vrednosti delnic. Razloženi so koncepti borznega in avtomatiziranega trgovanja in japonske svečke. Uporabljeni so bili algoritmi RNN, AR, MA in ARIMA. Izdelek je napisan v celoti v programskem jeziku Python, ključni moduli za razvoj so bili Numpy, Pandas, Statsmodels in Keras. Uporabljena je bila verzija Python 3.7.1.
Keywords:časovne vrste, avtoregresivna časovna vrsta, AR, tekoče povprečje MA, ARIMA, ponavljajoče se nevronske mreže, RNN, avtomatizirano borzno trgovanje, japonske svečke
Year of publishing:2019
Place of performance:Maribor
Publisher:[M. Celcer]
Number of pages:VII, 40 f.
COBISS_ID:22792726 Link is opened in a new window
License:CC BY-NC 4.0
This work is available under this license: Creative Commons Attribution Non-Commercial 4.0 International
Categories:KTFMB - FERI
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Secondary language

Title:System for automated stock trading
Abstract:The purpose of the diploma thesis was to research and implement modern ways of predicting future stock values. The concepts of stock exchange, automated trading and Japanese candles are explained. The algorithms used were RNN, AR, MA and ARIMA. The product is written entirely in Python and key modules for development were Numpy, Pandas, Statsmodels and Keras. The Python version used was Python 3.7.1.
Keywords:Time series, Autoregressive time series, AR, Moving average MA, ARIMA, Recurrent neural networks, RNN, Automated stock trading, candlestick charts


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