Algorithmic Trading Model for Manifold Learning in FX

11 Pages Posted: 26 Nov 2014

Date Written: June 26, 2008

Abstract

This paper will give a brief overview of the work of introducing machine learning intelligence in the Kineta e-markets system, to facilitate auto-hedging, smart price engine algorithms and proprietary automatic positioning within the foreign exchange market. In this paper we will give a brief overview of the steps taken in the project. A number of quantitative techniques have been implemented in the system and evaluated. As of late we have investigated the use of manifold learning; a class of geometrically motivated nonlinear data mining methods, to predict movements in the foreign exchange market. Financial time series are often correlated over time; and may contain valuable customer specific proprietary information. In principle, such relationships may be exploited for forecasting. However, they may be noisy, nonlinear and changing over time, making this a challenging task. Hence, robust methods for detection and exploitation of such correlations are of high interest for model trading and quantitative strategies. To this end, we study the application of a proposed method for nonlinear regression on manifolds. The approach involves dimensionality reduction through Laplacian Eigenmaps and optimization of cross-covariance operators in the kernel feature space induced by the normalized graph Laplacian.

Keywords: FX Manifold Learning, NLDR, KDR, Kernel Dimension, Machine Learning, Data Mining, Financial Analytics, Algorithmic Trading

JEL Classification: G10, C53, C88, D58, F17, G14, G24, C45, C63, C20

Suggested Citation

Fernandez, Steve, Algorithmic Trading Model for Manifold Learning in FX (June 26, 2008). Available at SSRN: https://ssrn.com/abstract=2530845 or http://dx.doi.org/10.2139/ssrn.2530845

Steve Fernandez (Contact Author)

Modus Operandi, Inc. ( email )

709 S. Harbor City Blvd.
Melbourne, FL 32901-1936
United States

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