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所在平台: Udemy |
课程主页: https://www.udemy.com/course/foundations-of-alpha-the-theory-behind-algorithmic-trading/
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课程名称：阿尔法基础：算法交易背后的理论 课程概述：本课程旨在向您介绍算法交易和量化交易的领域。课程采用以科学为导向的框架结构，所有讲座基于学术和科学原则，而非个人主观的观点。您将学习算法和量化交易的基本知识，为进入这些领域打下基础，并通过科学方法来导航。 课程内容包括： 1. 什么是量化分析师（Quant）：了解量化专业人士的工作，以及您需要具备的技能以成为一名成功的量化分析师。 2. 量化和算法交易模型：学习构建交易“黑匣子”的基本要素，涵盖运作所需的主要模型及其理论元素。 3. 阿尔法的理解：了解阿尔法的来源以及如何将其整合进交易系统，这是量化分析师必须清楚理解的关键要素。 4. 风险元素：学习量化金融和算法系统中各种风险的基本知识，讨论算法策略中的主要固有风险，以及开发强健量化策略的推荐风险模型基础。 5. 交易成本：通常被低估的话题。有时在回测阿尔法策略时表现出色，但在实际交易中表现不佳，这往往是由于直接或隐含的交易成本所致。本课程将教您如何理解和妥善处理与交易相关的各种成本，特别是在算法和量化策略中。 6. 数据、研究与执行：了解数据的各个方面，包括类型、来源、清洗和存储方法；研究过程如创意生成、测试，以及利用指标和工具；执行算法如TWAP和VWAP的详细内容，以及市场和限价订单的复杂性。 7. 错误识别与避免：学习识别并避免导致错误的量化策略和算法系统开发的常见错误，了解基于学术和科学原则的模型结构。 额外内容：参与额外材料，深入探讨高级主题和学术研究。 通过参加本课程，您将为进入算法交易和量化交易领域做好充分准备，并掌握成功交易所需的核心知识。
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.
