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Title:Avtomatizirano trgovanje z uporabo pametnega predikcijskega modela : magistrsko delo
Authors:Štrakl, Luka (Author)
Kokol, Peter (Mentor) More about this mentor... New window
Files:.pdf MAG_Strakl_Luka_2021.pdf (3,75 MB)
MD5: 4076042A3EED8600C499E63EA32C624F
 
Language:Slovenian
Work type:Master's thesis/paper (mb22)
Typology:2.09 - Master's Thesis
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:V magistrskem delu je opisano področje avtomatiziranega trgovanja z algoritmičnim pristopom, ki temelji na odločitvah napovednih modelov, katerih znanje je pridobljeno s pomočjo strojnega učenja. Opisane je delo z podatki, metode strojnega učenja in izdelava napovednih modelov v programskem jeziku Python. Poudarek je na pridobivanju, manipulaciji in uporabi vhodnih podatkov, ter optimizaciji napovednega modela za dosego boljših odločitev na še ne videnih podatkih. V sklopu magistrske naloge smo izdelali programsko opremo algoritmične narave, ki uporablja sožitje pogojev, ki jih trgovalni instrument mora zadovoljiti, ter odločitve dveh napovednih modelov za odpiranje ali zapiranje trgovalnih pozicij.
Keywords:Trgovanje, Forex, delnice, strojno učenje, napovedni model
Year of publishing:2021
Place of performance:Maribor
Publisher:[L. Štrakl]
Number of pages:VII, 66 f.
Source:Maribor
UDC:004.85:004.777(043.2)
COBISS_ID:63069955 New window
NUK URN:URN:SI:UM:DK:CKODAVUN
Views:58
Downloads:23
Metadata:XML RDF-CHPDL DC-XML DC-RDF
Categories:KTFMB - FERI
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Licences

License:CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Link:http://creativecommons.org/licenses/by-nc-nd/4.0/
Description:The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.
Licensing start date:05.03.2021

Secondary language

Language:English
Title:Automated trading with a smart prediction model
Abstract:This master's thesis describes the topic of automated trading with the use of an algorithmic approach based on decisions made by smart prediction models. The models possess the knowledge derived from machine learning techniques. We describe the used data set and techniques used for machine learning and implementation of a smart prediction model using the Python programming language. The emphasis is on the gathering, transformation and usage of input data as well as on the optimization of the prediction model to get the best results possible from new data sets. The product of the thesis is a software based on the algorithm that requires that certain market place conditions are met with a conjunction of smart model predictions to open or close the trading positions
Keywords:Trading, Forex, Stocks, machine learning, prediction model


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