Machine Learning Applied to Stock & Crypto Trading – Python

Course Curriculum

Chapter 1: Introduction

Lecture 1: Welcome and Course Introduction

Lecture 2: Where to Ask Questions

Lecture 3: Resources Folder Overview

Lecture 4: Plan of Attack – Course Structure

Chapter 2: Resources and Disclaimer

Lecture 1: Resources and Disclaimer

Lecture 2: Updated Resources (2023)

Lecture 3: 2024 Update: StratManager

Chapter 3: Primer Theory

Lecture 1: What is Machine Learning?

Lecture 2: A Brief Overview of Machine Learning

Lecture 3: Stage 1 – Data Ingestion

Lecture 4: Stage 2 – Feature Engineering

Lecture 5: Stage 3 – Model Selection and Training

Lecture 6: Stage 4 – Performance Evaluation

Lecture 7: Stage 5 – Model Deployment

Chapter 4: Environment Setup and Data Retrieval

Lecture 1: Option 1 – Google Colab

Lecture 2: Option 1 – Google Colab Reading Existing Notebooks

Lecture 3: Option 1 – Google Colab Solving for Pandas Datareader (with YFinance)

Lecture 4: Option 2 – Notebooks Installing Python and Anaconda

Lecture 5: Option 2 – Notebooks Creating a Conda Environment

Lecture 6: Where to Get Data

Lecture 7: Bonus: Getting Poloniex and Binance Data

Chapter 5: Primer Practical

Lecture 1: Python 101 – Variables and Arrays

Lecture 2: Python 101 – Dictionaries

Lecture 3: Python 101 – If Statements and Loops

Lecture 4: Python 101 – Functions and Classes

Lecture 5: Pandas 101 – Retrieve Data and Calculate Returns

Lecture 6: Pandas 101 – Structure Conditions and Iterations

Lecture 7: Pandas 101 – Value Extraction, Multiple Adj, Save and Load

Lecture 8: Backtesting 101 – Calculations and Strategy Returns

Lecture 9: Backtesting 101 – Metrics and Equity Curve

Lecture 10: Feature Engineering 101 – Data Preprocessing Part I

Lecture 11: Feature Engineering 101 – Data Preprocessing Part II

Lecture 12: Feature Engineering 101 – Applied Machine Learning

Lecture 13: Statistics – Testing for Market Efficiency Code Walkthrough

Chapter 6: Unsupervised Machine Learning – Hidden Markov Models

Lecture 1: Theory – Unsupervised Machine Learning Introduction

Lecture 2: Theory – Hidden Markov Models Intuition

Lecture 3: HMM – Initial Data Structuring

Lecture 4: HMM – Model Training

Lecture 5: HMM – Viewing Hidden States

Lecture 6: HMM II – Data Structuring

Lecture 7: HMM lI – Model Predictions

Lecture 8: HMM II – Structuring Backtest

Lecture 9: HMM II – Initial Metrics

Lecture 10: HMM II – Making Use of Hidden States

Lecture 11: HMM II – Saving Outputs

Chapter 7: Unsupervised Machine Learning – K-Means Clustering

Lecture 1: Theory – K-Means Clustering Intuition

Lecture 2: K-Means Setup

Lecture 3: K-Means Data Extraction

Lecture 4: K-Means Feature Engineering

Lecture 5: K-Means Applied and Visualized

Lecture 6: K-Means Removing Outliers

Lecture 7: Pairs Trading – Calculating Cointegrated Pairs

Lecture 8: K-Means – (Optional) – Visualizing TSNE Plot

Lecture 9: Pairs Trading – Calculating Spread and ZScore

Chapter 8: Unsupervised Learning – Principle Component Analysis

Lecture 1: Theory – Principle Component Analysis

Lecture 2: PCA – Data Extraction

Lecture 3: PCA – Data Preprocessing – Handling Stationarity

Lecture 4: PCA – Train Test Split

Lecture 5: PCA – Completion with Visualization

Lecture 6: Random Forest Classification – Results

Lecture 7: Unsupervised Learning – Summary

Chapter 9: Supervised Machine Learning

Lecture 1: Theory – Random Forests vs XGBOOST

Lecture 2: XGB Preprocessing – Data Ingestion

Lecture 3: XGB Preprocessing – Feature Expansion

Lecture 4: XGB Preprocessing – Stationarity

Lecture 5: XGB Preprocessing – Train Test Split

Lecture 6: XGB – Hyperparameter Optimization

Lecture 7: XGB – Initial Model Training

Lecture 8: XGB – Feature Selection

Lecture 9: XGB II – Train Test Split

Lecture 10: XGB II – Model Fitting

Lecture 11: XGB II – Model Evaluation – Measuring Loss and ROC

Lecture 12: XGB II – Model Evaluation – Performance Comparison

Lecture 13: XGB II – Model Evaluation – Summary Report

Lecture 14: XGB II – Model Evaluation – Confusion Matrix

Lecture 15: XGB II – Model Evaluation – View Tree

Chapter 10: Supervised Deep Learning – Basic Introduction

Lecture 1: Theory – Deep Learning Neural Network Anatomy

Lecture 2: Deep Learning – Feature Engineering Part I

Lecture 3: Deep Learning – Feature Engineering Part II

Lecture 4: Deep Learning – Neural Net and Data Build

Lecture 5: Deep Learning – Model Training

Lecture 6: Deep Learning – (Optional Code Walkthrough) – LSTM Sequential Model

Chapter 11: Reinforcement Learning

Lecture 1: Theory – Reinforcement Learning Complete Basics

Lecture 2: Theory – Proximal Policy Optimisation (PPO) Overview

Lecture 3: RL – First Steps

Lecture 4: RL – Sine Wave Construction

Lecture 5: RL – Environment Variables

Lecture 6: RL – Environment Reward Structure

Lecture 7: RL – Environment Observation Structure