New article Machine Learning: How Support Vector Machines can be used in Trading is published:
Support Vector Machines have long been used in fields such as bioinformatics and applied mathematics to assess complex data sets and extract useful patterns that can be used to classify data. This article looks at what a support vector machine is, how they work and why they can be so useful in extracting complex patterns. We then investigate how they can be applied to the market and potentially used to advise on trades. Using the Support Vector Machine Learning Tool, the article provides worked examples that allow readers to experiment with their own trading.
A support vector machine is a method of
machine learning that attempts to take input data and classify into one
of two categories. In order for a support vector machine to be
effective, it is necessary to first use a set of training input and
output data to build the support vector machine model that can be used
for classifying new data.
A support vector machine develops this
model by taking the training inputs, mapping them into multidimensional
space, then using regression to find a hyperplane
(a hyperplane is a surface in n-dimensional space that it separates the
space into two half spaces) that best separates the two classes of
inputs. Once the support vector machine has been trained, it is able to
assess new inputs with respect to the separating hyperplane and classify
it into one of the two categories.
A support vector machine is essentially
an input/output machine. A user is able to put in an input, and based on
the model developed through training, it will return an output. The
number of inputs for any given support vector machine theoretically
ranges from one to infinity, however in practical terms computing power
does limit how many inputs can be used. If for example, N inputs are
used for a particular support vector machine (the integer value of N can
range from one to infinity), the support vector machine must map each
set of inputs into N-dimensional space and find a (N-1)-dimensional
hyperplane that best separates the training data.
Author: Josh Readhead
Thank you very much for you article.
Very useful for implementing SVM in trading!