Hi, This is Ernest Chan. I am the managing director of QTS Capital Management, a commodity pool operator. I am launching this course for the first time in Python exclusively on Quantra’s website in collaboration with the Quantinsti team. This is a self-paced course that offers a mixture of videos, reading documents, multiple choice questions, and lots of interactive coding exercises. Through the course, we will introduce you to some of the widely used mean reversion strategies in the past few decades, mixing them with insights I have gained from actually exploiting some of those theories in live trading over the last decades! This course consists of nine sections. Each of the sections will provide you answers to some of the most important questions and issues related to the implementation of mean reversion strategies in live markets. In the first section, you will be introduced to the concept of stationarity of time series. In the second section, we will talk about cointegration and hedge ratio, and implement these concepts to create a mean reversion strategy. In the third section, you will learn mean reversion trading of triplets. In the fourth, you will understand the concept of half-life along with its practical application. In the fifth and sixth sections, you will understand the importance of stop loss and discuss the best markets to trade mean reversion strategies. In the seventh section, you will learn about Index Arbitrage, which is an extension of pairs and triplets mean reversal strategies. In the eighth section, you will learn a long-short portfolio strategy and also discover the different techniques to optimize that strategy. In the final section, you will be provided with a summary of the complete course. There are different statistical techniques which you will learn throughout the course. Some of these are Augmented Dickey Fuller or (ADF) Test, Cointegrated Augmented Dickey Fuller or (CADF) test, Johansen test, Linear Regression, Half-Life, Autoregressive models, etc. All these concepts will be dealt in a careful and structured manner so that you can understand the strategies intuitively. Along with the theoretical understanding, Python codes will be provided for different strategies and you will also get an opportunity to have a hands-on-coding experience through the Interactive Exercises and iPython notebook modules. You will also get your own Python coding environment where you can experiment and manipulate the code. By the end of this course, you will get to know the concept and mathematics behind Mean Reversion Strategies, but more importantly, you will also understand the limitations of this theory in practice and how to handle that. You will learn four Mean Reversion strategies coded in Python with detailed explanation. This course offers a practical application based learning, which will enable you to design and refine your own Mean Reversion strategies. I hope you’ll have a lot of fun learning. Good Luck!