Elektronsko trgovanje na valutnem trgu s pomočjo Twitterja

magistrsko delo

Anže Brvar (Author), Polona Oblak (Mentor), Blaž Zupan (Co-mentor)

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.

Keywords

valutno trgovanje;forex;twitter;strojno učenje;word2vec;napovedovanje;simulacija;računalništvo;računalništvo in informatika;magisteriji;

Data

Language:	Slovenian
Year of publishing:	2015
Typology:	2.09 - Master's Thesis
Organization:	UL FRI - Faculty of Computer and Information Science
Publisher:	[A. Brvar]
UDC:	004.85(043.2)
COBISS:	1536667331
Views:	974
Downloads:	207
Average score:	0 (0 votes)
Metadata:

Other data

Secondary language:	English
Secondary title:	Algorithmic trading on Forex market with help of a Twitter
Secondary 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.
Secondary keywords:	foreign exchange;forex;twitter;machine learning;word2vec;prediction;simulation;computer science;computer and information science;master's degree;
File type:	application/pdf
Type (COBISS):	Master's thesis/paper
Study programme:	1000471
Embargo end date (OpenAIRE):	1970-01-01
Thesis comment:	Univ. v Ljubljani, Fak. za računalništvo in informatiko
Pages:	82 str.
ID:	9064664