Machine Learning Applied to Stock & Crypto Trading - Python

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Shaun McDonogh

17:23:58

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1. Introduction

1. Welcome and Course Introduction.mp4

07:42

2. Where to Ask Questions.mp4

03:49

3.1 MLTrading.zip

3.2 SlideDeck ML.pdf

3. Resources Folder Overview.mp4

02:17

4. Plan of Attack - Course Structure.mp4

09:54

2. Resources and Disclaimer

1.1 MLTrading.zip

1.2 SlideDeck ML.pdf

1. Resources and Disclaimer.html

3. Primer Theory

1. What is Machine Learning.mp4

06:55

2. A Brief Overview of Machine Learning.mp4

09:30

3. Stage 1 - Data Ingestion.mp4

03:37

4. Stage 2 - Feature Engineering.mp4

06:03

5. Stage 3 - Model Selection and Training.mp4

09:48

6. Stage 4 - Performance Evaluation.mp4

12:41

7. Stage 5 - Model Deployment.mp4

04:39

4. Environment Setup and Data Retrieval

1. Option 1 - Google Colab.mp4

05:26

2. Option 1 - Google Colab Reading Existing Notebooks.mp4

00:38

3. Option 1 - Google Colab Solving for Pandas Datareader (with YFinance).mp4

03:08

4. Option 2 - Notebooks Installing Python and Anaconda.mp4

03:01

5. Option 2 - Notebooks Creating a Conda Environment.mp4

12:28

6. Where to Get Data.mp4

14:20

5. Primer Practical

1. Python 101 - Variables and Arrays.mp4

23:59

2. Python 101 - Dictionaries.mp4

06:32

3. Python 101 - If Statements and Loops.mp4

19:25

4. Python 101 - Functions and Classes.mp4

14:12

5. Pandas 101 - Retrieve Data and Calculate Returns.mp4

16:01

6. Pandas 101 - Structure Conditions and Iterations.mp4

08:04

7. Pandas 101 - Value Extraction, Multiple Adj, Save and Load.mp4

09:47

8. Backtesting 101 - Calculations and Strategy Returns.mp4

17:08

9. Backtesting 101 - Metrics and Equity Curve.mp4

09:46

10. Feature Engineering 101 - Data Preprocessing Part I.mp4

29:49

11. Feature Engineering 101 - Data Preprocessing Part II.mp4

12:39

12. Feature Engineering 101 - Applied Machine Learning.mp4

10:38

13. Statistics - Testing for Market Efficiency Code Walkthrough.mp4

06:20

6. Unsupervised Machine Learning - Hidden Markov Models

1. Theory - Unsupervised Machine Learning Introduction.mp4

04:33

2. Theory - Hidden Markov Models Intuition.mp4

12:08

3. HMM - Initial Data Structuring.mp4

12:42

4. HMM - Model Training.mp4

06:13

5. HMM - Viewing Hidden States.mp4

10:56

6. HMM II - Data Structuring.mp4

09:07

7. HMM lI - Model Predictions.mp4

04:20

8. HMM II - Structuring Backtest.mp4

16:06

9. HMM II - Initial Metrics.mp4

08:29

10. HMM II - Making Use of Hidden States.mp4

04:49

11. HMM II - Saving Outputs.mp4

01:41

7. Unsupervised Machine Learning - K-Means Clustering

1. Theory - K-Means Clustering Intuition.mp4

04:18

2. K-Means Setup.mp4

09:42

3. K-Means Data Extraction.mp4

07:58

4. K-Means Feature Engineering.mp4

05:34

5. K-Means Applied and Visualized.mp4

11:22

6. K-Means Removing Outliers.mp4

06:14

7. Pairs Trading - Calculating Cointegrated Pairs.mp4

19:45

8. K-Means - (Optional) - Visualizing TSNE Plot.mp4

12:07

9. Pairs Trading - Calculating Spread and ZScore.mp4

12:00

8. Unsupervised Learning - Principle Component Analysis

1. Theory - Principle Component Analysis.mp4

05:52

2. PCA - Data Extraction.mp4

11:38

3. PCA - Data Preprocessing - Handling Stationarity.mp4

07:46

4. PCA - Train Test Split.mp4

06:24

5. PCA - Completion with Visualization.mp4

15:25

6. Random Forest Classification - Results.mp4

13:47

7. Unsupervised Learning - Summary.mp4

03:44

9. Supervised Machine Learning

1. Theory - Random Forests vs XGBOOST.mp4

10:18

2. XGB Preprocessing - Data Ingestion.mp4

13:39

3. XGB Preprocessing - Feature Expansion.mp4

10:09

4. XGB Preprocessing - Stationarity.mp4

09:56

5. XGB Preprocessing - Train Test Split.mp4

12:25

6. XGB - Hyperparameter Optimization.mp4

18:13

7. XGB - Initial Model Training.mp4

12:44

8. XGB - Feature Selection.mp4

09:11

9. XGB II - Train Test Split.mp4

12:47

10. XGB II - Model Fitting.mp4

11:53

11. XGB II - Model Evaluation - Measuring Loss and ROC.mp4

09:56

12. XGB II - Model Evaluation - Performance Comparison.mp4

07:35

13. XGB II - Model Evaluation - Summary Report.mp4

03:31

14. XGB II - Model Evaluation - Confusion Matrix.mp4

04:19

15. XGB II - Model Evaluation - View Tree.mp4

08:04

10. Supervised Deep Learning - Basic Introduction

1. Theory - Deep Learning Neural Network Anatomy.mp4

13:01

2. Deep Learning - Feature Engineering Part I.mp4

14:53

3. Deep Learning - Feature Engineering Part II.mp4

03:08

4. Deep Learning - Neural Net and Data Build.mp4

11:15

5. Deep Learning - Model Training.mp4

11:07

6. Deep Learning - (Optional Code Walkthrough) - LSTM Sequential Model.mp4

15:48

11. Reinforcement Learning

1. Theory - Reinforcement Learning Complete Basics.mp4

20:14

2. Theory - Proximal Policy Optimisation (PPO) Overview.mp4

16:58

3. RL - First Steps.mp4

07:26

4. RL - Sine Wave Construction.mp4

08:47

5. RL - Environment Variables.mp4

09:07

6. RL - Environment Reward Structure.mp4

04:56

7. RL - Environment Observation Structure.mp4

07:03

8. RL - Environment Action Helper Functions.mp4

05:53

9. RL - Environment Action Function.mp4

11:47

10. RL - Environment Step Function.mp4

03:30

11. RL - Environment Reset and Render.mp4

03:50

12. RL - Environment Testing.mp4

09:06

13. RL - Utilities.mp4

03:03

14. RL - PPO Memory.mp4

09:15

15. RL - PPO Actor and Critic Neural Networks Construction.mp4

12:47

16. RL - PPO Agent Construction.mp4

23:48

17. RL - PPO Agent Testing.mp4

08:53

18. RL - PPO Training Agent.mp4

12:42

19. RL - Load and Structure Predictions.mp4

11:24

20. RL - View Results and Summarize Sine Trading.mp4

06:45

21. RL II - Modify Environment and Data for Trading AAPL Stock.mp4

14:08

12. Course Summary and Next Steps

1. Congratulations and Next Steps.mp4

05:25

13. APPENDIX - General Trading Principles

1. The Biggest Illusion in Trading.mp4

12:31

2. Probability Odds - The Math Does Not Lie.mp4

09:43

3. Gaining An Edge With Statistical Arbitrage.mp4

04:49

4. Position Sizing with Kelly Criterion.mp4

15:08

5. An Edge from Making Markets.mp4

04:38

6. Profiting in Up Down and Sideways Markets.mp4

08:23

7. Managing Exchange and Volatility Risk.mp4

03:11

Description

Use Unsupervised, Supervised and Reinforcement Learning techniques to gain an edge in trading Stocks, Crypto, Forex...

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

Who is this 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

What You Need to Know?

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

More details

Description
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 AIÂ 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 networkÂ 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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"I care about having an idea that is unusual, finding hidden gems and exposing them for those who have left the world of hype and are looking for something more real. Rinse and repeat. My teaching is an outlet for myself to put ideas into action, helping others effectively along the way and sharing for those without the same level of experience. I keep falling into other ventures, but am always happiest and most content when returning back to the teaching initiatives."

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