Are you an investor looking to gain an edge in stock and cryptocurrency trading using machine learning techniques? Look no further than the Machine Learning Applied to Stock & Crypto Trading - Python course. With a course rating aggregate of 4.66769 and 247 reviews, this course can help you discover hidden market states and regimes using Hidden Markov Models.
What You Will Learn
This hands-on course is designed to help investors use machine learning techniques to gain an edge in trading. Through deploying Machine Learning techniques to financial data using Python, you'll learn several trading techniques based on a number of methods. Firstly, you'll discover hidden market states and regimes through the application of Hidden Markov Models. Secondly, you'll objectively group like-for-like ETF's for pairs trading using K-Means Clustering. Finally, the course will teach you how to capitalize on your newfound skills in Z-score and Cointegration.
How the Course Works
This course is not designed for math experts, but is created for those who want to learn by applying concepts through real-life data. The course is hands-on and theory is kept at a high level, making this course user-friendly and easy to follow, but still effective in teaching you the necessary skills.
The course utilizes Python libraries such as Pandas, PyTorch, and Sklearn, to make prediction on Bitcoin price data, allowing investors to make informed predictions regarding the future of stock and cryptocurrency investments.
What You'll Create
The final assignment for this course is to create an AI model to trade a simple sine wave without any prompt for position selection, and subsequently to learn to trade the Apple stock on your own. This will allow you to test and practice your newfound skills, and build up your confidence in your trading abilities
Theoretical Content
This course does not cover much in-depth theory. As previously mentioned, the course is designed to be hands-on, with theory presented at a high level, making it easy for anyone to grasp the basic concepts, while also being able to apply them to the real world.
Course Structure
The course is centered around practical applications of machine learning techniques that can be applied to stock and cryptocurrency trading. These techniques involve unsupervised, supervised, and reinforcement learning. The course starts with an introduction to Machine learning and finance and then dives into techniques for trend following, analysis, and prediction.
Along the way, you'll learn about Hidden Markov Models, K-Means Clustering, and Principle Component Analysis (PCA). You'll then move on to more advanced techniques such as XGBOOST. Plus, you'll learn how to build a Deep Learning neural network for both classification and make predictions on sequential data.
Benefits of the Course
If you are looking for a course that is hands-on and practical, this is the course for you. This course is great for investors who want to gain an edge in stock and cryptocurrency trading, and who want to leverage machine learning techniques to predict market trends.
This course does not demand any high-level math or coding skills, making it easily accessible for a wide audience. The course is perfect for anyone who wants to experience what machine learning is like using financial data in a fun, exciting and potentially profitable way, without having to deal with complex math concepts.
Are You Ready to Learn?
Investors looking to gain an edge in stock and cryptocurrency trading often turn to machine learning techniques. The Machine Learning Applied to Stock & Crypto Trading - Python course offers practical experience in the field of machine learning and finance. You'll learn how to utilize machine learning techniques for stock and cryptocurrency trading, and predict market trends based on in-depth analysis of financial data using Python libraries such as Pandas, PyTorch, Sklearn, and more.
Get ready to change your investment game with the Machine Learning Applied to Stock & Crypto Trading - Python course.
