Enhancement of trading rules on stock markets using Genetic Network Programming with Sarsa Learning

Yan Chen, Shingo Mabu, Kotaro Hirasawa, Jinglu Hu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

In this paper, the enhancement of trading rules on stock markets using Genetic Network Programming (GNP) with Sarsa Learning is described. There are three important points in this paper: First, we use GNP with Sarsa learning as the basic algorithm while importance Indices and Candlestick Charts are introduced for efficient stock trading decision-making. Importance indices have been proposed to tell GNP the timing of buying and selling stocks. Second, to improve the performance of the proposed GNP-Sarsa algorithm, we develop a new method that can learn appropriate function describing the relation between the value of each technical index and the output of the importance index (IMX). This is an important point that devotes to the enhancement of the proposed GNP-Sarsa algorithm.Third, in order to create more efficient judgment functions to judge the current stock price appropriately, we develop a new way of classifying the candlestick chart body type. To confirm the effectiveness of the proposed method, we also compare the simulation results using GNP-Sarsa with other methods like traditional GNP and Buy&Hold method.

Original languageEnglish
Title of host publicationSICE Annual Conference, SICE 2007
Pages2700-2707
Number of pages8
DOIs
Publication statusPublished - 2007 Dec 1
EventSICE(Society of Instrument and Control Engineers)Annual Conference, SICE 2007 - Takamatsu, Japan
Duration: 2007 Sep 172007 Sep 20

Publication series

NameProceedings of the SICE Annual Conference

Conference

ConferenceSICE(Society of Instrument and Control Engineers)Annual Conference, SICE 2007
CountryJapan
CityTakamatsu
Period07/9/1707/9/20

Keywords

  • Candlestick chart
  • Genetic network programming
  • Reinforcement learning, sarsa
  • Stock trading model
  • Technical index

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering

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