Download Machine Learning Applied to Stock & Crypto Trading - Python

What you would learn in Machine Learning Applied to Stock & Crypto Trading - Python course?

Get an edge in financial trading by deploying Machine Learning techniques to financial data using Python. This course will teach you'll:

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

• Find a way to objectively group ETFs similar to trade pairs with K-Means Clustering and learn how you can benefit from this by using statistical techniques like cointegration and Zscore.

• Create predictions about the VIX by incorporating many technical indicators and separating the relevant details using Principle Component Analysis (PCA).

• Make use of one of the most sophisticated Machine Learning algorithms, XGBOOST, to predict Bitcoin price information for the future.

• Review the model's performance to verify the predictions made.

• Determine the accuracy, precision-recall objectively, and F1 score of test data to calculate your probabilities of having an edge.

• Create an AI model that can trade just a sine wave. Then begin to master how to market the Apple stock entirely on its own without prompts for selecting positions.

• Create a Deep Learning neural network for both Classifications and receive the code to use the LSTM neural system to predict the sequential data.

• Use Python libraries, such as Pandas, PyTorch (for deep learning), Sklearn, learn, and others.

The course does not provide any in-depth theoretical concepts. It's an experiential course that focuses on theoretical concepts at a high-level designed to help anyone grasp the fundamentals as well as to grasp the concept and implement this immediately.

If you're seeking a course that involves a lot of maths, this course is not the class for you.

If you're looking for an opportunity to learn more about what machine learning can do with financial data in an enjoyable, fascinating, stimulating, and possibly lucrative way, then you'll probably like this course.

Course Content:

• Learn about the hidden states and regimes of any asset or market with the help of Hidden Markov Models
• Find the most suitable assets for pairs trading in ETF's, stocks, or Crypto with Forex, K-Means Clustering.
• Condense information from a vast range of indicators using PCA
• Make accurate forecasts for the future based on financial data using XGBOOST
• Create the AI Reinforcement Learning agent to trade stocks using PPO
• Test the market efficiency of any particular asset
• Learn about Python Libraries, including Pandas, PyTorch (deep learning), and Sklearn.

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