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Classification-Based Machine Learning for Finance
Introduction
Introduction (3:07)
Feedback (4:26)
Obtaining the Course Resources (3:28)
How to Succeed In This Course (6:03)
Introduction To Machine Learning For Algorithmic Trading
Brief Introduction to Machine Learning (5:38)
Machine Learning Project Check List Part 1 (12:43)
Machine Learning Project Check List Part 2 (10:00)
Model Selection and Quant Workflow (8:24)
Financial Time Series Characteristics (5:58)
Logistic Regression
Understanding Logistic Regression (11:16)
Logistic Regression and Scikit Learn (13:55)
Classification - A Walk Through Tutorial
Understanding Classification ML and Data Exploration (9:12)
Building a Simple Classifier and Performing Cross Validation (15:23)
Confusion Matrix (15:51)
Precision/Recall Tradeoff (14:25)
The Receiver Operating Characteristics (ROC) Curve (8:49)
Default Prediction
Template (10:27)
Default prediction with LDA, KNN and Random Forest (12:10)
Predicting Next Day's Returns
Background to Returns Prediction (9:14)
Predicting Next Day's Returns Using Logistic Regression (10:01)
Predicting Next Day's Returns Using LDA and QDA (7:33)
Price Prediction Using Real Market Data from Quantopian (7:10)
Back Test and Tear Sheet (16:03)
Ideas
Ideas (18:29)
Global Stock Selection Strategy
Introduction to Alpha Factors (8:32)
Global Stock Selection Strategy (7:13)
Bonus Section
Bonus Lecture (1:46)
Model Selection and Quant Workflow
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