Foundations of Alpha: The Theory Behind Algorithmic Trading
An In-depth Exploration of Systematic Trading Principles
Foundations of Alpha: The Theory Behind Algorithmic Trading udemy course free download
An In-depth Exploration of Systematic Trading Principles
This course is designed to introduce you to the fields of algorithmic and quantitative trading. The course is structured under a scientific-oriented framework, meaning that all the lectures are based on academic and scientific principles rather than discretionary and subjective perspectives.
Learn the essentials of algorithmic and quantitative trading. This is your starting point to enter these fields and learn how to navigate them using the scientific method.
Understand what a Quant is. Gain a broader knowledge of what Quant professionals do and what you need to become one.
Quantitative and Algorithmic Trading Models: Learn the essentials of building a trading “Black Box”. We cover the main models and their theoretical elements necessary to understand how an algorithmic quantitative system, also known as a “Black Box”, operates.
Alphas: Understanding what alphas are, where to obtain them, and how to integrate them into the trading system is a vital element that a Quant should clearly understand.
The 'Risk' Element in the Field: You will learn about the essentials of various types of risks present in quantitative finance and algorithmic systems. We discuss the primary inherent risks in algorithmic strategies and cover the fundamentals of recommended risk models for developing a robust quantitative strategy.
Transaction Cost, often an underrated topic: Sometimes when backtesting Alpha strategies the outcomes are outstanding, but when the Alpha goes live it performs poorly. In some occasions, this is due to direct or hidden transaction costs. In the course, we show how to understand and adequately address some of the most relevant types of costs involved in trading, especially in Algorithmic and Quantitative strategies. This topic is a cornerstone of any successful trading system.
Data, research and execution, key elements of any Algorithmic System: In this course, students will learn about various data aspects including types, sources, cleaning, and storage methods; research processes such as idea generation, testing, and the use of metrics and tools; as well as execution algorithms like TWAP and VWAP, and the intricacies of market and limit orders.
What Not to Do!: Learn to identify and avoid mistakes that can result in the development of inaccurate quantitative strategies and algorithmic systems by understanding the correct structure of these models, based on academic and scientific principles.
Extra: Engage with additional content that delves into advanced topics and academic studies.
