Machine Learning Applied to Stock & Crypto Trading - Python (by Udemy)
Gain an edge in financial trading through deploying Machine Learning techniques to financial data using Python. In this course, I will:
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Discover hidden market states and regimes using Hidden Markov Models.
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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.
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Make predictions on the VIX by including a vast amount of technical indicators and distilling just the useful information via Principle Component Analysis (PCA).
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Use one of the most advanced Machine Learning algorithms, XGBOOST, to make predictions on Bitcoin price data regarding the future.
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Evaluate performance of models to gain confidence in the predictions being made.
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Quantify objectively the accuracy, precision, recall and F1 score on test data to infer your likely percentage edge.
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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.
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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.
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Use Python libraries such as Pandas, PyTorch (for deep learning), sklearn and more.