Discussion of article "Machine Learning: How Support Vector Machines can be used in Trading"
New article Machine Learning: How Support Vector Machines can be used in Trading is published:
Author: Josh Readhead
Thank you very much for you article.
alsu:
Вот я бы все таки отметил, что метод не бует работать, если хотя бы один из классов не связный, то есть состоит из 2 и более непересекающихся подгрупп. Например, если Шняки (на самом деле - компьютер перед анализом этого не знает!) бывают 2 видов - зеленоватые, весом 100 кг и которые любят морковку и радужно-блестящие весом 30 кг, которые морковку не переносят, но зато хавают селедку, то провести гиперплоскость между "шняками" и "не шняками" будет довольно проблематично. А такая ситуация на рынке, да еще и в многомерном случае, типична вполне.
... And that you can use this algorithm to solve this problem. Thank you very much for it by the way!
P.S: Sorry, but I couldn't resist ... :)
1) You see a creature with 9 legs (!) and 4 eyes. That's not a glitch!!! It's a SNACK!
2) The mating frequency of animals is 14000 Hz (14,000 times per second). 0_o
Very useful for implementing SVM in trading!
Great work!
- Free trading apps
- Over 8,000 signals for copying
- Economic news for exploring financial markets
You agree to website policy and terms of use
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.
What is a Support Vector Machine?
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