Discussion of article "Data Science and Machine Learning (Part 13): Improve your financial market analysis with Principal Component Analysis (PCA)"
Doubts: 1 - What are the 3 indicators? They are inversely correlated with each other. Correct? 2 - Can you change the program to insert more indicators? Trend indicators like moving average and volume indicator in the program ? 3 - Does this result of the 3 indicators change for each asset, time frame and respective parameters?
Doubts: 1 - What are the 3 indicators? They are inversely correlated with each other. Correct? 2 - Can you change the program to insert more indicators? Trend indicators like moving average and volume indicator in the program ? 3 - Does this result of the 3 indicators change for each asset, time frame and respective parameters?
thanks for reading my article, there are plenty of ideas and indicators to play with in this program. I can't explore all of them tbh I would suggest downloading the program and play with it as I believe the article is very clear. No body can do the work for you especially for you, especially for free.
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New article Data Science and Machine Learning (Part 13): Improve your financial market analysis with Principal Component Analysis (PCA) has been published:
Revolutionize your financial market analysis with Principal Component Analysis (PCA)! Discover how this powerful technique can unlock hidden patterns in your data, uncover latent market trends, and optimize your investment strategies. In this article, we explore how PCA can provide a new lens for analyzing complex financial data, revealing insights that would be missed by traditional approaches. Find out how applying PCA to financial market data can give you a competitive edge and help you stay ahead of the curve.
Principal component analysis(PCA), Is a dimensionality-reduction method that is often used to reduce the dimensionality of large data sets, by transforming a large set of variables into a smaller one that still contains most of the information in the large set.
Reducing the number of variables in the dataset usually comes at the expense of accuracy, but the trick in dimensionality reduction is to trade little accuracy for simplicity, You and I both know that a few variables in the dataset are easier to explore, visualize and make analyzing data much easier and faster for machine learning algorithms. I personally don't think trading simplicity for accuracy is a bad thing at all because we are in the trading space, Accuracy doesn't necessarily mean profits.
The main idea of PCA is very simple at the core -- Reduce the number of variables in a data set, while preserving as much information as possible. Let's look at the steps involved in the Principal Component Analysis algorithm.
Author: Omega J Msigwa