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ArtikelComputational Learning Techniques for Intraday FX Trading Using Popular Technical Indicators  
Oleh: Thompson, G. W. P. ; Romahi, Y. ; Dempster, M. A. H. ; Payne, T. W.
Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 12 no. 4 (2001), page 744-754.
Topik: computational; computational learning; techniques; intraday; FX trading; technical indicators
Ketersediaan
  • Perpustakaan Pusat (Semanggi)
    • Nomor Panggil: II36.5
    • Non-tandon: 1 (dapat dipinjam: 0)
    • Tandon: tidak ada
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Isi artikelWe 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.
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