Anda belum login :: 23 Jul 2025 11:20 WIB
Home
|
Logon
Hidden
»
Administration
»
Collection Detail
Detail
Computational 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
Lihat Detail Induk
Isi artikel
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.
Opini Anda
Klik untuk menuliskan opini Anda tentang koleksi ini!
Kembali
Process time: 0 second(s)