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Machine Learning Applied to Stock & Crypto Trading – Python

Machine Learning Applied to Stock & Crypto Trading - Python

Published 07/2022
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 108 lectures (17h 24m) | Size: 8.2 GB

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
You should have some basic experience with Python
You should be aware of trading related concepts like Pairs Trading
You should have awareness of assets like ETF's, the VIX, Stocks and Crypto
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.
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.
Who this course is for
Retail traders who are looking to gain an objective edge in the financial markets
Enthusiasts who are looking for a practical and fun application of Machine Learning


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