How to apply modern Machine Learning on Volume Spread Analysis (VSA).Following up the previous posts in these series, this time we are going to explore a real Technical Analysis (TA) in the financial market. For a very long time, I have been fascinated by the inner logic of TA called Volume Spread Analysis (VSA). I have found no articles on applying modern Machine learning on this time proving long-lasting technique.
Warning: There is no magical formula or Holy Grail here, though a new world might open the door for you.
Following up the previous posts in these series, this time we are going to explore a real Technical Analysis (TA) in the financial market. For a very long time, I have been fascinated by the inner logic of TA called Volume Spread Analysis (VSA). I have found no articles on applying modern Machine learning on this time proving long-lasting technique. Here I am trying to throw out a minnow to catch a whale. If I could make some noise in this field, it was worth the time I spent on this article.
Especially, after I read David H. Weis’s Trades About to Happen, in his book he described:
“Instead of analyzing an array of indicators or algorithms, you should be able to listen to what any market says about itself.”¹
To closely listen to the market, as also well said from this quote below, just as it may not be possible to predict the future, it is also hard to neglect things about to happen. The key is to capture what is about to happen and follow the flow.
But how to perceive things about to happen, a statement made long ago by Richard Wyckoff gives some clues:
“Successful tape reading [chart reading] is a study of Force. It requires ability to judge which side has the greatest pulling power and one must have the courage to go with that side. There are critical points which occur in each swing just as in the life of a business or of an individual. At these junctures it seems as though a feather’s weight on either side would determine the immediate trend. Any one who can spot these points has much to win and little to lose.”²
How to select the right predictors using statistical measures. This article will give you a brief walkthrough on what feature selection is all about, accompanied by some practical examples in Python.
We discussed what feature selection is about and provided some walkthroughs using the statistical method. This article follow-ups on the original article by further explaining the other two common approaches in feature selection for Machine Learning (ML) — namely the wrapper and embedded methods. Explanations will be accompanied by sample coding in Python.
3 Ways to Select Features Using Machine Learning Algorithms in Python. In this article, take a look at three ways to select features using machine learning learning algorithms in Python.
Use OptimalFlow’s autoFS module to implement ensemble feature selection, which simplifies this process easily. Why we use OptimalFlow? You could read another story of its introduction: “An Omni-ensemble Automated Machine Learning — OptimalFlow”.
Feature Selection in Python. We will provide a walk-through example of how you can choose the most important features. For this example, we will work with a classification problem but can be extended to regression cases too by adjusting the parameters of the function.