Gain an edge in financial trading through deploying Machine Learning techniques to financial data using Python. In this course, you will:

• Discover hidden market states and regimes using Hidden Markov Models.

• Objectively group like-for-like ETF's for pairs trading using K-Means Clustering and understand how to capitalise on this using statistical methods like Cointegration and Zscore.

• Make predictions on the VIX by including a vast amount of technical indicators and distilling just the useful information via Principle Component Analysis (PCA).

• Use one of the most advanced Machine Learning algorithms, XGBOOST, to make predictions on Bitcoin price data regarding the future.

• Evaluate performance of models to gain confidence in the predictions being made.

• Quantify objectively the accuracy, precision, recall and F1 score on test data to infer your likely percentage edge.

• Develop an AI model to trade a simple sine wave and then move on to learning to trade the Apple stock completely by itself without any prompt for selection positions whatsoever.

• Build a Deep Learning neural network for both Classification and receive the code for using an LSTM neural network to make predictions on sequential data.

• Use Python libraries such as Pandas, PyTorch (for deep learning), sklearn and more.

   

This course does not cover much in-depth theory. It is purely a hands-on course, with theory at a high level made for anyone to easily grasp the basic concepts, but more importantly, to understand the application and put this to use immediately.

If you are looking for a course with a lot of math, this is not the course for you.

If you are looking for a course to experience what machine learning is like using financial data in a fun, exciting and potentially profitable way, then you will likely very much enjoy this course.