Using Probabilistic Machine Learning to improve your Stock Trading
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Probabilistic Machine Learning comes hand in hand with Stock Trading: Probabilistic Machine Learning uses past instances to predict probabilities of certain events happening in future instances. This can be directly applied to stock trading, to predict future stock prices.
The Concept:
This program will use Gaussian Naive Bayes to classify data into increasing stock price, or decreasing stock price.
Because of the volatility of the stocks, I will not be using the closing price of the stock to predict it, but rather be using the ratio between the past and current closing prices. To understand how the program works, we must first understand the underling algorithm at play:
What is Gaussian Naive Bayes Classifier?
Gaussian Naive Bayes is an algorithm that classifies data by extrapolating data using Gaussian Distribution (identical to Normal Distribution) as well as Bayes theorem.
