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Elektronsko trgovanje na valutnem trgu s pomočjo Twitterja : magistrsko delo
ID Brvar, Anže (Author), ID Oblak, Polona (Mentor) More about this mentor... This link opens in a new window, ID Zupan, Blaž (Comentor)

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MD5: 7C35670EDE1984B4B2B4196D707B60D7
PID: 20.500.12556/rul/961dddaf-b5bb-4ffb-b0ec-e08ef6b317a6

Abstract
V magistrskem delu smo raziskovali uspešnost elektronskega trgovanja na valutnem trgu z metodami strojnega učenja.Primerjali smo uspešnost razvitih algoritmov, ki trgujejo s pomočjo objav (tvitov) na Twitterju, in takih, ki za učne podatke uporabijo pretekle vrednosti valutnih tečajev in tehničnih indikatorjev. Za transformacijo besedil v atributni zapis smo poleg znanih metod preizkusili tudi vektorje besed word2vec. Razvite metode transformacije besedil in njihove parametre smo najprej ovrednotili na sorodnem problemu zaznavanja sentimenta tvitov, nato pa jih preizkusili v trgovanju v simulacijskem okolju. Napovedi razvitih metod smo izboljšali z metodami za združevanje napovedi in tako dosegli do 250% vrednost začetnih sredstev pri simulaciji v obdobju zadnjih petih let. V delu poročamo o najprimernejši izbiri parametrov, ki imajo velik vpliv na uspešnost elektronskega trgovanja. Ugotovili smo, da je Twitter bolj primeren vir informacij za uspešno elektronsko trgovanje kot pretekle vrednosti valutnih tečajev.

Language:Slovenian
Keywords:valutno trgovanje, forex, twitter, strojno učenje, word2vec, napovedovanje, simulacija
Work type:Master's thesis/paper
Typology:2.09 - Master's Thesis
Organization:FRI - Faculty of Computer and Information Science
Publisher:[A. Brvar]
Year:2015
Number of pages:82 str.
PID:20.500.12556/RUL-73440 This link opens in a new window
COBISS.SI-ID:1536667331 This link opens in a new window
Publication date in RUL:13.11.2015
Views:1946
Downloads:523
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Secondary language

Language:English
Title:Algorithmic trading on Forex market with help of a Twitter
Abstract:
In this thesis we study the performance of electronic trading algorithms with a help of machine learning methods. We compare the performance of developed trading algorithms that trade based on posts (tweets) on Twitter with those that trade based on historic foreign exchange values and technical indicators. Besides the well known methods for text transformation to attribute notation we also use word2vec word vectors. We evaluate all the developed text transformation methods and their parameters, first on simpler but related tweet sentiment detection problem and later with trading in simulation environment. We improve developed models' predictions with the prediction combining techniques and we achieve up to 250% of initial funds at simulation in the period of last five years. The results show that Twitter is a better source of trading information than foreign exchange rates and technical indicators.

Keywords:foreign exchange, forex, twitter, machine learning, word2vec, prediction, simulation

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