Regression-Based Machine Learning for Algorithmic Trading – Anthony Ng

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Regression-Based Machine Learning for Algorithmic Trading

Hands on Python guide to develop investing strategies using regression based Machine Learning techniques

Finally, a comprehensive hands-on machine learning course with specific focus on regression based models for the investment community and any passionate investors.

In the past few years, there has been a massive adoption and growth in the use of data science, artificial intelligence and machine learning to find alpha. However, information on and application of machine learning to investment are scarce. This course has been designed to address that. It is meant to spark your creative juices.

In this course, we are first going to provide some background information to machine learning. To ease you into the machine lingo, we start will something that most people are familiar with – Linear Regression. The assumptions of financial time series as well as the stylized facts are introduced and explained at length due to its importance. The assumptions of linear regression are also highlighted to demonstrate the challenges and danger of blindly applying machine learning to investment without proper care and considerations to the nuances of financial time series.

More advanced topics of cross-validation, model validation, penalized regression – Lasso, Ridge, and ElasticNet, Kalman Filter, back test, professional Quant work flow, cross-sectional and time-series momentum are also explain in details.

This course not only covers machine learning techniques, it also covers in depth the rationale of investing strategy development.

This course is the first of the Machine Learning for Finance and Algorithmic Trading & Investing Series. The courses in the series includes:

• Regression-Based Machine Learning for Algorithmic Trading
• Classification-Based Machine Learning for Algorithmic Trading
• Ensemble Machine Learning for Algorithmic Trading
• Unsupervised Machine Learning: Hidden Markov for Algorithmic Trading
• Clustering and PCA for Investing

If you are looking for a course on applying machine learning to investing, the Machine Learning for Finance and Algorithmic Trading & Investing Series is for you. With over 30 machine learning techniques test cases, which included popular techniques such as Lasso regression, Ridge regression, SVM, XGBoost, random forest, Hidden Markov Model, common clustering techniques and many more, to get you started with applying Machine Learning to investing quickly.

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Course Curriculum

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)

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