Portfolio & Risk Management for Traders

The objective of this course is to provide you with knowledge on creating a portfolio and managing the same according to your needs. From the introduction you have understood the importance of portfolio management. You will start learning the basics such as calculation of portfolio returns, covariance and portfolio standard deviation that are required in further sections of the course. Then, you will learn to allocate capital to different securities in your portfolio. This is covered through three different methods namely modern portfolio theory, kelly criterion and risk parity. You will also learn how the risk parity method performs when compared with a traditional portfolio. Then, you will learn about the factors that explain the price movements of securities. The first factor you will learn is Beta(𝛃). And then you will learn to calculate the expected returns of an asset using the Capital Asset Pricing Model (CAPM). After that, you will learn the Fama-French three-factor and five-factor models which improvises the CAPM framework. Then you will be introduced to the Factor investing. And you will learn to create a strategy using momentum and short-term reversal factors. The important part of the portfolio management is analyzing the performance of your portfolio. You will learn different performance measures to evaluate your portfolio. The codes that you created in the course can be extended further and used for paper trading. You can refer to the guide on how to automate trades which is provided at the end of the course. This self-paced course is designed to comprehensively cover quantitative portfolio management through a mixture of videos, reading documents, multiple choice questions, and lots of interactive coding exercises. Python codes created in different sections are available in a single downloadable folder at the end of the course. You are advised to tweak and use it in your own trading after thorough backtesting and analysis. Before running the codes in your local machine, refer to the python packages versions mentioned in the readme.pdf file in the downloadables folder. You can post all the doubts and clarifications regarding the course on the Quantra Community. We hope you enjoy the interactive learning throughout this course. Good Luck.

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