Over the past few weeks / months, I've been looking at possibilities of using Machine Learning techniques for trading. This has mainly been research, reading, watching some stuff, messing around with Python, etc. Also, a few experiments on MarketScope. Let's call that Phase 1 - it wasn't very directed and nothing really concrete has come out of it, it was just meant to be a learning activity to discover some of the possibilities.
I'm now at a stage where I want to focus a little more, and put some more effort into this activity. With more direction, and some actual goals too. I'll try to cover as much as possible on the blog over the next few weeks and months.
What is Machine Learning?
"Machine learning is a method of data analysis that automates analytical model building. Using algorithms that iteratively learn from data, machine learning allows computers to find hidden insights without being explicitly programmed where to look"
Well, that's one definition at least. I guess the essence is that there is some kind of "engine" which will analyse the data (inputs) and produce / deduce some kind of result (output) without having to be explicitly programmed how to do it - the learning algorithm itself works out the "how" from the data.
Machine Learning is typically used for either "classification" or "regression". Probably most people are familiar with regression, such as linear regression, where a straight line is fitted to the observed data, and this line can be used to predict values that have not yet been observed. Classification is where the data is classified into 2 or more categories and the learning algorithm learns how to classify new points based on its learnt model. These categories (or classes) can be given (e.g. as labels in the training data) or even determined by the learning algorithm itself.
Machine Learning is used increasingly more in many tasks these days, everything from search engines to computer vision and speech recognition. I will of course be focusing on the application of trading.
Wiki page on Machine Learning.
Read more ...
I'm now at a stage where I want to focus a little more, and put some more effort into this activity. With more direction, and some actual goals too. I'll try to cover as much as possible on the blog over the next few weeks and months.
What is Machine Learning?
"Machine learning is a method of data analysis that automates analytical model building. Using algorithms that iteratively learn from data, machine learning allows computers to find hidden insights without being explicitly programmed where to look"
Well, that's one definition at least. I guess the essence is that there is some kind of "engine" which will analyse the data (inputs) and produce / deduce some kind of result (output) without having to be explicitly programmed how to do it - the learning algorithm itself works out the "how" from the data.
Machine Learning is typically used for either "classification" or "regression". Probably most people are familiar with regression, such as linear regression, where a straight line is fitted to the observed data, and this line can be used to predict values that have not yet been observed. Classification is where the data is classified into 2 or more categories and the learning algorithm learns how to classify new points based on its learnt model. These categories (or classes) can be given (e.g. as labels in the training data) or even determined by the learning algorithm itself.
Machine Learning is used increasingly more in many tasks these days, everything from search engines to computer vision and speech recognition. I will of course be focusing on the application of trading.
Wiki page on Machine Learning.
