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Regression-Based Machine Learning for Algorithmic Trading
Introduction
Introduction (5:20)
How to Succeed in This Course (3:47)
Introduction to Machine Learning for Algorithmic Trading and Investing
Introduction and Classification of Machine Learning (10:31)
Introduction to Machine Learning development Work Flow using Linear Regression (9:39)
Characteristic of Financial Time Series and Linear Regression Assumptions (10:26)
Effects of Outliers on Machine Learning Model (5:19)
Model Selection and Quant Workflow (8:25)
Machine Learning and Pairs Trading
Pairs Trading and Machine Learning (6:37)
Understanding the Data (Data Exploration) (14:10)
Python statsmodel Library (9:07)
Python scikit-learn Library (6:46)
Cointegration Test (3:56)
Trading Logic (13:09)
Backtesting Pairs Trading
Pairs Trading Code Walk Through (17:10)
Backtest and Performance Analysis (15:20)
Penalized Regression for Investing
Rationale for Penalized Regression (3:01)
Application of Penalized Regression to Investing (11:45)
Kalman Filter
Kalman Filter Introduction (14:24)
Backtesting Kalman Filter Based Investing Strategy (13:21)
Machine Learning and Multi-Assets Trend Following Strategies
Introduction to Multi-Assets Trend Following Strategies (10:48)
Machine Learning and Multi-Assets Trend Following Strategies (14:36)
Backtesting Multi-Assets Trend Following Machine Learning Strategies (13:46)
Bonus Section
Bonus Lecture (1:46)
Effects of Outliers on Machine Learning Model
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