Dynamic Adaptive Algorithm Selection: Profit Maximization for Online Trading

  • Iftikhar Ahmad
  • Javeria Iqbal
  • Günter Schmidt
Conference paper

DOI: 10.1007/978-3-642-30359-3_21

Volume 117 of the book series Lecture Notes in Business Information Processing (LNBIP)
Cite this paper as:
Ahmad I., Iqbal J., Schmidt G. (2012) Dynamic Adaptive Algorithm Selection: Profit Maximization for Online Trading. In: Abramowicz W., Kriksciuniene D., Sakalauskas V. (eds) Business Information Systems. BIS 2012. Lecture Notes in Business Information Processing, vol 117. Springer, Berlin, Heidelberg

Abstract

Online trading algorithms can facilitate the investment decisions in financial markets. This paper presents a dynamic adaptive algorithm selection framework for online trading with the goal of maximizing overall revenue (lower competitive ratio). We integrate the algorithm selection ([1]) and probabilistic graphs to transform the algorithm selection model of offline algorithms for a dynamically adaptive model of online trading. Unlike the traditional static approach of algorithm selection, where a single algorithm is executed over the whole investment horizon, we dynamically select and update the trading algorithm by analyzing the time series features. The time series is partitioned in different windows and each window’s features are extracted sequentially using real time hybrid pattern matching approach. The extracted features of current window wi are analyzed and the decision making module determines the best suitable algorithm for next window wi + 1 on the basis of underlying probabilistic graphical model. The process is repeated until all windows in a time series are processed. Our dynamic adaptive algorithm selection model outperforms the static model on real world datasets of DAX30 and S&P500.

Keywords

Business applications of artificial intelligence Business Intelligence Online Trading Adaptive Algorithms 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Iftikhar Ahmad
    • 1
  • Javeria Iqbal
    • 1
  • Günter Schmidt
    • 1
    • 2
  1. 1.Saarland UniversitySaarbrückenGermany
  2. 2.Department of Statistical SciencesUniversity of Cape TownSouth Africa