Foreign Exchange Trading With Support Vector Machines

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Foreign Exchange Trading with Support Vector Machines ...

    https://link.springer.com/chapter/10.1007/978-3-540-70981-7_62
    This paper analyzes and examines the general ability of Support Vector Machine (SVM) models to correctly predict and trade daily EUR exchange rate directions. Seven models with varying kernel...Cited by: 11

Foreign Exchange Trading with Support Vector Machines ...

    https://www.researchgate.net/publication/221649462_Foreign_Exchange_Trading_with_Support_Vector_Machines
    Request PDF on ResearchGate Foreign Exchange Trading with Support Vector Machines This paper analyzes and examines the general ability of Support Vector Machine (SVM) models to …

Machine Learning: How Support Vector Machines can be used ...

    https://www.mql5.com/en/articles/584
    Dec 17, 2012 · This opens up the opportunity to use support vector machines in any aspect of your trading. It is not only possible to use support vector machines to signal new trades, but it can also be used to signal the closing of trades, money management, new advanced indicators etc.

How To Use A Support Vector Machine (SVM) In Daily Trading ...

    http://forex.tradingninja.com/how-to-use-a-support-vector-machine-svm-in-daily-trading/
    Jun 12, 2016 · Support Vector Machine is an important Machine Learning Algorithm. Now you don’t need to master how to do the modelling with an SVM. You should just have a broad understanding what is this algorithm and how you can use to cut your losses in your daily trading.

Foreign Exchange Trading with Support Vector Machines

    https://freepaper.me/downloads/abstract/10.1007/978-3-540-70981-7_62
    Foreign Exchange Trading with Support Vector Machines This paper analyzes and examines the general ability of Support Vector Machine (SVM) models to correctly predict and trade daily EUR exchange rate directions. Seven models with varying kernel functions are considered.

Introduction - pdfs.semanticscholar.org

    https://pdfs.semanticscholar.org/f3ab/04d3e1494858f5ae3dff09391827b17fe4fe.pdf
    Foreign exchange rates are notoriously di cult to predict. Deciding which of thousands of market signals to ignore and which to act on poses a challenging problem which potentially lends itself well to techniques in machine learning. Support Vector Machines have been shown to outperform a number of learning algorithms in FX prediction [2], [7].



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