- author = "M. A. H. Dempster and Tom W. Payne and Yazann Romahi and G. W. P. Thompson",
- title = "Computational learning techniques for intraday {FX} trading using popular technical indicators",
- journal = "IEEE Transactions on Neural Networks",
- year = "2001",
- volume = "12",
- number = "4",
- pages = "744--754",
- month = jul,
- keywords = "genetic algorithms, genetic programming, Markov processes, foreign exchange trading, genetic algorithms, learning (artificial intelligence), Markov decision, computational learning, foreign exchange trading, heuristic, reinforcement learning, technical trading, transaction costs",
- ISSN = "1045-9227",
-
URL = "
http://mahd-pc.jbs.cam.ac.uk/archive/PAPERS/2000/ieeetrading.pdf",
-
DOI = "
doi:10.1109/72.935088",
- abstract = "We consider strategies which use a collection of popular technical indicators as input and seek a profitable trading rule defined in terms of them. We consider two popular computational learning approaches, reinforcement learning and genetic programming, and compare them to a pair of simpler methods: the exact solution of an appropriate Markov decision problem, and a simple heuristic. We find that although all methods are able to generate significant in-sample and out-of-sample profits when transaction costs are zero, the genetic algorithm approach is superior for non-zero transaction costs, although none of the methods produce significant profits at realistic transaction costs. We also find that there is a substantial danger of overfitting if in-sample learning is not constrained",
-
notes = "CODEN: ITNNEP. INSPEC Accession Number:6997298
Location: technical report WP30/2000
",
