NDLI: FX trading via recurrent reinforcement learning

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FX trading via recurrent reinforcement learning

Content Provider	IEEE Xplore Digital Library
Author	Gold, C.
Copyright Year	2003
Description	Author affiliation: Comput. & Neural Syst., California Inst. of Technol., Pasadena, CA, USA (Gold, C.)
Abstract	This study investigates high frequency currency trading with neural networks trained via recurrent reinforcement learning (RRL). We compare the performance of single layer networks with networks having a hidden layer and examine the impact of the fixed system parameters on performance. In general, we conclude that the trading systems may be effective, but the performance varies widely for different currency markets and this variability cannot be explained by simple statistics of the markets. Also we find that the single layer network outperforms the two layer network in this application.
Sponsorship	IEEE Neural Networks Soc. (NNS)
Starting Page	363
Ending Page	370
File Size	783434
Page Count	8
File Format	PDF
ISBN	0780376544
DOI	10.1109/CIFER.2003.1196283
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2003-03-20
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Learning Neural networks Recurrent neural networks Neurons Gold Computer networks Frequency Statistics System testing History
Content Type	Text
Resource Type	Article

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