Machine Learning Applied to Stock & Crypto Trading – Python

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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.

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.

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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