Machine Learning Applied to Stock & Crypto Trading – Python
Machine Learning Applied to Stock & Crypto Trading – Python
Use Unsupervised, Supervised and Reinforcement Learning techniques to gain an edge in trading Stocks, Crypto, Forex.
Created by Shaun McDonogh | 17.5 hours on-demand video course
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.
What you’ll learn
- Understand hidden states and regimes for any market or asset using Hidden Markov Models
- Discover optimum assets for pairs trading in ETF’s, Stocks, Forex or Crypto using K-Means Clustering
- Condense information from a vast array of indicators with PCA
- Make objective future predictions on financial data with XGBOOST
- Train an AI Reinforcement Learning agent to trade stocks with PPO
- Test for market efficiency on any given asset
- Become familiar with Python Libraries including Pandas, PyTorch (for deep learning) and sklearn
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