Modules

Course Length: 11 Hours

Chapter 01: You, This Course & Us

Lesson 01: Introduction – You, This Course & Us!

Chapter 02: Developing Trading Strategies in Excel

Lesson 01: Are markets efficient or inefficient?
Lesson 02: Momentum Investing
Lesson 03: Mean Reversion
Lesson 04: Evaluating Trading Strategies – Risk & Return
Lesson 05: Evaluating Trading Strategies – The Sharpe Ratio
Lesson 06: The 2 Step process – Modeling & Backtesting
Lesson 07: Developing a Trading Strategy in Excel

Chapter 03: Setting up your Development Environment

Lesson 01: Installing Anaconda for Python
Lesson 02: Installing Pycharm – a Python IDE
Lesson 03: MySQL Introduced & Installed (Mac OS X)
Lesson 04: MySQL Server Configuration & MySQL Workbench (Mac OS X)
Lesson 05: MySQL Installation (Windows)
Lesson 06: [For Linux/Mac OS Shell Newbies] Path & other Environment Variables

Chapter 04: Setting up a Price Database

Lesson 01: Programmatically Downloading Historical Price Data
Lesson 02: Code Along – Downloading Price data from Yahoo Finance
Lesson 03: Code Along – Downloading a URL in Python
Lesson 04: Code Along – Downloading Price data from the NSE
Lesson 05: Code Along – Unzip & process the downloaded files
Lesson 06: Manually download data for 10 years
Lesson 07: Code Along – Download Historical Data for 10 years
Lesson 08: Inserting the Downloaded files into a Database
Lesson 09: Code Along – Bulk loading downloaded files into MySQL tables
Lesson 10: Data Preparation
Lesson 11: Code Along – Data Preparation
Lesson 12: Adjusting for Corporate Actions
Lesson 13: Code Along – Adjusting for Corporate Actions 1
Lesson 14: Code Along – Adjusting for Corporate Actions 2
Lesson 15: Code Along – Inserting Index prices into MySQL
Lesson 16: Code Along – Constructing a Calendar Features table in MySQL

Chapter 05: Decision Trees, Ensemble Learning & Random Forests

Lesson 01: Planting the seed – What are Decision Trees?
Lesson 02: Growing the Tree – Decision Tree Learning
Lesson 03: Branching out – Information Gain
Lesson 04: Decision Tree Algorithms
Lesson 05: Overfitting – The Bane of Machine Learning
Lesson 06: Overfitting Continued
Lesson 07: Cross-Validation
Lesson 08: Regularization
Lesson 09: The Wisdom Of Crowds – Ensemble Learning
Lesson 10: Ensemble Learning continued – Bagging, Boosting & Stacking
Lesson 11: Random Forests – Much more than trees

Chapter 06: A Trading Strategy as Machine Learning Classification

Lesson 01: Defining the problem – Machine Learning Classification

Chapter 07: Feature Engineering

Lesson 01: Know the basics – A Pandas tutorial
Lesson 02: Code Along – Fetching Data from MySQL
Lesson 03: Code Along – Constructing some simple features
Lesson 04: Code Along – Constructing a Momentum Feature
Lesson 05: Code Along – Constructing a Jump Feature
Lesson 06: Code Along – Assigning Labels
Lesson 07: Code Along – Putting it all together
Lesson 08: Code Along – Include support features from other tickers

Chapter 08: Engineering a Complex Feature – A Categorical Variable with Past Trends

Lesson 01: Engineering a Categorical Variable
Lesson 02: Code Along – Engineering a Categorical Variable

Chapter 09: Building a Machine Learning Classifier in Python

Lesson 01: Introducing Scikit-Learn
Lesson 02: Introducing RandomForestClassifier
Lesson 03: Training & Testing a Machine Learning Classifier
Lesson 04: Compare Results from different Strategies
Lesson 05: Using Class probabilities for predictions

Chapter 10: Nearest Neighbors Classifier

Lesson 01: A Nearest Neighbors Classifier
Lesson 02: Code Along – A nearest neighbors Classifier

Chapter 11: Gradient Boosted Trees

Lesson 01: What are Gradient Boosted Trees?
Lesson 02: Introducing XGBoost – A Python library for GBT
Lesson 03: Code Along – Parameter Tuning for Gradient Boosted Classifiers

Chapter 12: Introduction to Quant Trading

Lesson 01: Financial Markets – Who are the players?
Lesson 02: What is a Stock Market Index?
Lesson 03: The Mechanics of Trading – Long Vs Short positions
Lesson 04: Futures Contracts