An algorithm for Elliott Waves pattern detection

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F18%3A10241920" target="_blank" >RIV/61989100:27240/18:10241920 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61989100:27740/18:10241920

Výsledek na webu

<a href="https://content.iospress.com/articles/intelligent-decision-technologies/idt319" target="_blank" >https://content.iospress.com/articles/intelligent-decision-technologies/idt319</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3233/IDT-170319" target="_blank" >10.3233/IDT-170319</a>

Jazyk výsledku

angličtina

Název v původním jazyce

An algorithm for Elliott Waves pattern detection

Popis výsledku v původním jazyce

The examination of the ElliottWave theory is the main motivation of this contribution. All of the fundamental features of an proper ElliottWave pattern (EW pattern) are reviewed and explained. Based on this knowledge, an algorithm for detection of these patterns is designed, developed and tested. Under several different algorithm settings, several EW pattern sets are obtained. They differ in amount of found EW patterns, quality and size. The following application of the developed detection algorithm was based on recognition of an incomplete EW patterns with aim of the prediction of the following progress of the time set. The Random Decision Forest and the Support Vector Machine are the machine learning algorithms employed for this task. The accuracy of trend prediction above 70% proves the relevancy of EW patterns on stock market data as well as the validity of the algorithm as a tool for detection of such patterns.

Název v anglickém jazyce

An algorithm for Elliott Waves pattern detection

Popis výsledku anglicky

The examination of the ElliottWave theory is the main motivation of this contribution. All of the fundamental features of an proper ElliottWave pattern (EW pattern) are reviewed and explained. Based on this knowledge, an algorithm for detection of these patterns is designed, developed and tested. Under several different algorithm settings, several EW pattern sets are obtained. They differ in amount of found EW patterns, quality and size. The following application of the developed detection algorithm was based on recognition of an incomplete EW patterns with aim of the prediction of the following progress of the time set. The Random Decision Forest and the Support Vector Machine are the machine learning algorithms employed for this task. The accuracy of trend prediction above 70% proves the relevancy of EW patterns on stock market data as well as the validity of the algorithm as a tool for detection of such patterns.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/GA15-06700S" target="_blank" >GA15-06700S: Nekonvenční řízení komplexních systémů</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2018

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Intelligent Decision Technologies

ISSN

1872-4981

e-ISSN

—

Svazek periodika

12

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

10

Strana od-do

15-24

Kód UT WoS článku

000445796700003

EID výsledku v databázi Scopus

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