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Quant Trading Using Machine Learning
Course Intro
You, This Course and Us (2:00)
Developing Trading Strategies in Excel
Are markets efficient or inefficient (10:27)
Momentum Investing (11:31)
Mean Reversion (6:30)
Evaluating Trading Strategies - Risk And Return (16:22)
Evaluating Trading Strategies - The Sharpe Ratio (10:16)
Evaluating Trading Strategies - The Sharpe Ratio (10:16)
The 2 Step process - Modeling and Backtesting (3:48)
Developing a Trading Strategy in Excel (11:42)
Setting up your Development Environment
Installing Anaconda for Python (9:00)
Installing Pycharm - a Python IDE (3:55)
MySQL Introduced and Installed (Mac OS X) (7:03)
MySQL Server Configuration and MySQL Workbench (Mac OS X) (17:26)
MySQL Installation (Windows) (6:31)
[For LinuxMac OS Shell Newbies] Path and other Environment Variables (8:25)
Setting up a Price Database
Programmatically Downloading Historical Price Data (6:23)
CodeAlong - Downloading a URL in Python (5:49)
CodeAlong - Downloading Price data from the NSE (13:49)
CodeAlong - Unzip and process the downloaded files (5:21)
CodeAlong - Download Historical Data for 10 years (6:26)
Inserting the Downloaded files into a Database (10:10)
CodeAlong - Bulk loading downloaded files into MySQL tables (15:12)
CodeAlong - Data Preparation (12:43)
Data Preparation (4:16)
Adjusting for Corporate Actions (8:41)
CodeAlong - Adjusting for Corporate Actions 1 (15:29)
CodeAlong - Adjusting for Corporate Actions 2 (8:47)
CodeAlong - Inserting Index prices into MySQL (5:40)
CodeAlong = Constructing a Calendar Features table in MySQL (6:53)
Decision Trees, Ensemble Learning and Random Forests
Planting the seed - What are Decision Trees (17:00)
Growing the Tree - Decision Tree Learning (18:03)
Branching out - Information Gain (18:51)
Decision Tree Algorithms (7:50)
Overfitting - The Bane of Machine Learning (19:03)
Overfitting Continued (11:19)
Cross Validation (4:15)
Regularization (7:18)
The Wisdom Of Crowds - Ensemble Learning (0:25)
10 Ensemble Learning continued - Bagging, Boosting and Stacking (18:02)
Random Forests - Much more than trees (12:28)
A Trading Strategy as Machine Learning Classification
Defining the problem - Machine Learning Classification (15:51)
Feature Engineering
Know the basics - A Pandas tutorial (11:41)
CodeAlong - Fetching Data from MySQL (18:34)
CodeAlong - Constructing some simple features (7:27)
CodeAlong - Constructing a Momentum Feature (8:42)
CodeAlong - Constructing a Jump Feature (5:52)
CodeAlong - Assigning Labels (3:12)
CodeAlong - Putting it all together (18:08)
CodeAlong - Include support features from other tickers (6:34)
Engineering a Complex Feature - A Categorical Variable with Past Trends
Engineering a Categorical Variable (3:49)
CodeAlong - Engineering a Categorical Variable (6:46)
Building a Machine Learning Classifier in Python
Introducing Scikit-Learn (3:33)
Introducing RandomForestClassifier (9:25)
Training and Testing a Machine Learning Classifier (15:01)
Compare Results from different Strategies (4:08)
Using Class probabilities for predictions (3:11)
Nearest Neighbors Classifier
A Nearest Neighbors Classifier (6:49)
CodeAlong - A nearest neighbors Classifier (4:16)
Introduction to Quant Trading
Financial Markets - Who are the players (16:38)
What is a Stock Market Index (3:13)
The Mechanics of Trading - Long vs Short positions (11:56)
Futures Contracts (14:25)
[For LinuxMac OS Shell Newbies] Path and other Environment Variables
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