http://archive.is/QtGCI Regression-Based Machine Learning is Archive: for Algorithmic TradingHands on Python guide to develop investing strategies using regression based Machine Learning techniquesFinally, 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..
Archive: http://archive.is/QtGCI Regression-Based Machine Learning for Algorithmic TradingHands on Python guide to develop investing strategies using regression based Machine Learning techniquesFinally, 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.
information is However, on and application of machine learning to investment are scarce..
However, information on and application of machine learning to investment are scarce.
This course is has been designed to address that..
This course has been designed to address that.
It is is meant to spark your creative juices.In this course, we are first going to provide some background information to machine learning..
It is meant to spark your creative juices.In this course, we are first going to provide some background information to machine learning.
ease is To you into the machine lingo, we start will something that most people are familiar with - Linear Regression..
To ease you into the machine lingo, we start will something that most people are familiar with - Linear Regression.
The assumptions is of financial time series as well as the stylized facts are introduced and explained at length due to its importance..
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 is 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 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 is in the series includes:Regression-Based Machine Learning for Algorithmic TradingClassification-Based Machine Learning for Algorithmic TradingEnsemble Machine Learning for Algorithmic TradingUnsupervised Machine Learning: Hidden Markov for Algorithmic TradingClustering and PCA for InvestingIf 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..
The courses in the series includes:Regression-Based Machine Learning for Algorithmic TradingClassification-Based Machine Learning for Algorithmic TradingEnsemble Machine Learning for Algorithmic TradingUnsupervised Machine Learning: Hidden Markov for Algorithmic TradingClustering and PCA for InvestingIf 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.
over 30 machine learning techniques is With 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.Course CurriculumIntroductionIntroduction (5:20)How to Succeed in This Course (3:47)Introduction to Machine Learning for Algorithmic Trading and InvestingIntroduction 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 TradingPairs 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 TradingPairs Trading Code Walk Through (17:10)Backtest and Performance Analysis (15:20)Penalized Regression for InvestingRationale for Penalized Regression (3:01)Application of Penalized Regression to Investing (11:45)Kalman FilterKalman Filter Introduction (14:24)Backtesting Kalman Filter Based Investing Strategy (13:21)Machine Learning and Multi-Assets Trend Following StrategiesIntroduction 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 SectionBonus Lecture (1:46).
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.Course CurriculumIntroductionIntroduction (5:20)How to Succeed in This Course (3:47)Introduction to Machine Learning for Algorithmic Trading and InvestingIntroduction 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 TradingPairs 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 TradingPairs Trading Code Walk Through (17:10)Backtest and Performance Analysis (15:20)Penalized Regression for InvestingRationale for Penalized Regression (3:01)Application of Penalized Regression to Investing (11:45)Kalman FilterKalman Filter Introduction (14:24)Backtesting Kalman Filter Based Investing Strategy (13:21)Machine Learning and Multi-Assets Trend Following StrategiesIntroduction 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 SectionBonus Lecture (1:46)