Regression-Based Machine Learning for Algorithmic Trading
4.3 (33 ratings)
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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
Best Seller
4.3 (33 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
429 students enrolled
Created by Anthony NG
Last updated 9/2017
English
Current price: $12 Original price: $100 Discount: 88% off
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Includes:
  • 3.5 hours on-demand video
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion

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What Will I Learn?
  • Understand and implement linear regression, Lasso, Ridge, ElasticNet in Python for pairs trading, and trend-following strategies.
  • Design investment strategies following professional Quant work flow
  • Understanding and implement machine learning techniques utilizing Python scikit-learn library.
  • Gain a deeper appreciation of challenges of modelling financial time series
  • Understand the different categories of machine learning
  • Understand hyperparameters and how to validate machine learning model
  • Understand the limitations of various machine learning techniques
View Curriculum
Requirements
  • Good understanding of Python data science stack.
  • Deep appreciation of finance, algorithmic trading, and hedge fund investment strategies
Description

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. 

Who is the target audience?
  • Investment professionals who is interested to learn to apply regression-based machine learning techniques to investing strategies
  • Investment professionals and students who would are sourcing innovative investment ideas
  • Anyone looking for ideas and gentle introduction to how artificial intelligence (AI) can be applied to investing
Compare to Other Machine Learning Courses
Curriculum For This Course
23 Lectures
03:43:09
+
Introduction
2 Lectures 09:07
+
Introduction to Machine Learning for Algorithmic Trading and Investing
5 Lectures 44:20

Introduction to Machine Learning development Work Flow using Linear Regression
09:39

Characteristic of Financial Time Series and Linear Regression Assumptions
10:26

Effects of Outliers on Machine Learning Model
05:19

Model Selection and Quant Workflow
08:25
+
Machine Learning and Pairs Trading
6 Lectures 53:45
Pairs Trading and Machine Learning
06:37

Understanding the Data (Data Exploration)
14:10

Python statsmodel Library
09:07

Python scikit-learn Library
06:46

Cointegration Test
03:56

Trading Logic
13:09
+
Backtesting Pairs Trading
2 Lectures 32:30
Pairs Trading Code Walk Through
17:10

Backtest and Performance Analysis
15:20
+
Penalized Regression for Investing
2 Lectures 14:46
Rationale for Penalized Regression
03:01

Application of Penalized Regression to Investing
11:45
+
Kalman Filter
2 Lectures 27:45
Kalman Filter Introduction
14:24

Backtesting Kalman Filter Based Investing Strategy
13:21
+
Machine Learning and Multi-Assets Trend Following Strategies
3 Lectures 39:10
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
1 Lecture 01:46
Bonus Lecture
01:46
About the Instructor
Anthony NG
4.2 Average rating
162 Reviews
2,240 Students
5 Courses
Algorithmic Trading Workshop Researcher and Conductor

Anthony Ng has spent the last seven years as a Senior Lecturer teaching algorithmic trading, financial data analysis, banking, finance, investment and portfolio management. He assists Quantopian, a Boston-based Hedge Fund, to conduct Algorithmic Trading Workshops in Singapore and has presented in the recent QuantCon Singapore 2016. You can find his Algorithmic Trading tutorials on his YouTube channel. Just click the YouTube icon to visit his channel.

Passionate with finance, data science and python, Anthony enjoyed researching, teaching and sharing on these topics. Anthony studied Masters of Science in Financial Engineering at NUS Singapore.