Course Information
Registration period
Year-round Recruitment
Course Level
Study Mode
Duration
17 Hour(s) 49 Minute(s)
Language
English
Taught by
Shaun McDonogh
Certificate
- Available
- *The delivery and distribution of the certificate are subject to the policies and arrangements of the course provider.
Rating
3 views
Course Overview
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.
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.
Course Content
- 13 section(s)
- 111 lecture(s)
- Section 1 Introduction
- Section 2 Resources and Disclaimer
- Section 3 Primer Theory
- Section 4 Environment Setup and Data Retrieval
- Section 5 Primer Practical
- Section 6 Unsupervised Machine Learning - Hidden Markov Models
- Section 7 Unsupervised Machine Learning - K-Means Clustering
- Section 8 Unsupervised Learning - Principle Component Analysis
- Section 9 Supervised Machine Learning
- Section 10 Supervised Deep Learning - Basic Introduction
- Section 11 Reinforcement Learning
- Section 12 Course Summary and Next Steps
- Section 13 APPENDIX - General Trading Principles
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
Reviews
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AAlex Hughes
A good primer for getting a basic understanding of some machine learning concepts. I personally do not like to watch code being written and would rather have more examples with time spent fine tuning and improving models. Some of the libraries used here are deprecated, like gym, or do not work like DataReader with yahoo finance. There is a bug the code in the final workbook (action variable is never set) which is not picked up and the results are not as expected which is explained away incorrectly. This is picked up by another student in the Q&A but there is no response from the teacher or subsequent correction of the course material which leads me to conclude that the course is no longer actively monitored or updated.
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RRuben Araujo
From the first video, before even 3 minutes in, I knew I was going to like this course. The memes, the honesty of the words and thank God, perfect English diction and use. It's always refreshing to hear a native speaking, it's so much easier to learn that way. Having said that, this is NOT a course for beginners and I would wager, not even for intermediate individuals. You SHOULD use have a good enough grasp of both economics and programming in Python. I did reinforce a few things I already knew and agree though: reinforcement learning and deep learning more so are useless for trading (whatever asset). I do recommend the course though, though much of it it's never explained just shown. Which is why I recommend a high level of programming and economics. On that note I recommend two of the greatest teachers in Udemy: Angela Yu (100 days of Code...) and pretty much all courses from Alexander Hagmann. In this order. First, master Python, then with Alex master finance and programming applied to it.
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HHAO YANG
Good stuff, thank you! Please continue
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PPaulman
Good Explaination
