CQF Learning Pathway(三)--高级选修课

文章目录

• 概述
• Advanced Electives
  • Algorithmic Trading
  • Advanced Computational Methods
  • Advanced Risk Management
  • Advanced Volatility Modeling
  • Machine Learning with Python
  • Advanced Portfolio Management
  • Counterparty Credit Risk Modeling
  • Behavioural Finance for Quants
  • R for Quant Finance
  • Risk Budgeting
  • Fintech
  • C++
• 结束语

概述

前两篇文章中介绍了CQF项目的核心课程，感兴趣的同学可以阅读:

• CQF项目课程学习介绍(一) -- 主要是投资组合原理，股票和货币
• CQF项目课程学习介绍(二) -- 主要是机器学习和固定收益

这篇文章将继续介绍CQF项目学习内容中高级选修课部分。

CQF核心课程是Model 1 - Model 6, 在Model 6模块学习完后，还有12门高级选修课，每位学员可以选择2门自己感兴趣的课程内容进行学习，高级选修课的内容和CQF的Final Project考试课题是相关的，因为Final Project的多个考试课题中，大部分是来自高级选修的课题，如果你想在Final Project考试中做一个你擅长的课题，那么在高级选修课中就选择相关课题进行学习，就一举两得了。

高级选修课会在Exam 3考试成绩结果出来之后，给学员开放，我记得是有2周的选择时间，2周的选择时间内12门课程都可以观看(高级课程是录制好的视频)，所以可以大概看看这些课程哪些是适合自己的，自己感兴趣的。最后会让学员进行选择其中的两门，然后关闭其他课程，CQF学习网站主页高级选修课中就只有选择的两门选修课了。

高级选修课的学习内容覆盖的细分领域非常全面了，涉及: 算法交易，编程语言，金融科技，学术前沿，行业前瞻等等，想在量化行业内某一专业领域内深耕，高级选修课是一个不错的开端。下面就来看看高级选修课有哪些内容吧！

Advanced Electives

In this module you choose two from the following online electives to specialize in your area of interest. You will be required to complete a practical project relating to the electives you have chosen.

在本模块中，您可以从以下在线选修课中选择两门，以专攻您感兴趣的领域。你将被要求完成一个与你选择的选修课相关的实践项目。

Algorithmic Trading

算法交易

The use of algorithms has become an important element of modern-day financial markets, used by both the buy side and sell side. This elective will look into the techniques used by quantitative professionals who work within the area.

算法的使用已经成为现代金融市场的一个重要元素，买方和卖方都在使用。这门选修课将研究在该领域工作的定量专家使用的技术。

• What is Algorithmic Trading
• Preparing data; Back testing, analysing results and optimisation
• Build your own algorithm
• Alternative approaches: Paris trading Options; New Analytics
• A career in Algorithmic trading

Advanced Computational Methods

高级计算方法

One key skill for anyone who works within quantitative finance is how to use technology to solve complex mathematical problems. This elective will look into advanced computational techniques for solving and implementing math in an efficient and succinct manner, ensuring that the right techniques are used for the right problems.

对于任何从事量化金融工作的人来说，一个关键技能是如何使用技术解决复杂的数学问题。这门选修课将研究先进的计算技术，以高效和简洁的方式解决和实施数学，确保正确的技术用于正确的问题。

• Finite Difference Methods (algebraic approach) and application to BVP
• Root finding
• Interpolation
• Numerical Integration

Advanced Risk Management

高级风险管理

In this elective, we will explore some of the recent developments in Quantitative Risk Management. We take as a point of departure the paradigms on how market risk is conceived and measured, both in the banking industry (Expected Shortfall) and under the new Basel regulatory frameworks (Fundamentals Review of the Trading Book, New Minimum, Capital of Market Risk).

在这门选修课中，我们将探讨量化风险管理的一些最新发展。我们以如何在银行业(预期亏空)和新的巴塞尔监管框架(交易账簿基本回顾，新的最小值，市场风险资本)下构思和衡量市场风险的范例为出发点。

• Review of new developments on market risk management and measurement
• Explore the use of extreme value of theory (EVT)
• Explore adjoint automatic differentiation

Advanced Volatility Modeling

高级波动率模型

Volatility and being able to model volatility is a key element to any quant model. This elective will look into the common techniques used to model volatility throughout the industry. It will provide the mathematics and numerical methods for solving problems in stochastic volatility.

波动率和能够对波动率进行建模是任何量化模型的关键要素。本选修课将研究用于模拟整个行业的波动率的常用技术。它将提供解决随机波动率问题的数学和数值方法。

• Fourier Transforms
• Functions of a Complex Variable
• Stochastic Volatility
• Jump Diffusion

Machine Learning with Python

基于Python的机器学习

This elective will focus on Machine Learning and deep learning with Python applied to Finance. We will focus on techniques to retrieve financial data from open data sources.

这门选修课将侧重于使用Python在机器学习和深度学习在金融中的应用。我们将重点介绍从开源数据中检索财务数据的技术。

• Using linear OLS regression to predict financial prices & returns
• Using scikit-learn for machine learning with Python
• Application to the pricing of the American options by Monte Carlo simulation
• Applying logistic regression to classification problems
• Predicting stock market returns as a classification problem
• Using TensorFlow for deep learning with Python
• Using deep learning for predicting stock market returns

Advanced Portfolio Management

高级投资组合管理

As quantitative finance becomes more important in today’s financial markets, many buyside firms are using quantitative techniques to improve their returns and better manage client capital. This elective will look into the latest techniques used by the buy side in order to achieve these goals.

随着量化金融在当今的金融市场中变得越来越重要，许多买方公司正在使用量化技术来提高回报并更好地管理客户资本。该选修课将研究买方为实现这些目标而使用的最新技术。

• Perform a dynamic portfolio optimization, using stochastic control
• Combine views with market data using filtering to determine the necessary parameters
• Understand the importance of behavioural biases and be able to address them
• Understand the implementation issues
• Develop new insights into portfolio risk management

Counterparty Credit Risk Modeling

交易对手风险模型

Post-global financial crisis, counterparty credit risk and other related risks have become much more pronounced and need to be taken into account during the pricing and modeling stages. This elective will go through all the risks associated with the counterparty and how they are included in any modeling frameworks.

后全球金融危机、交易对手信用风险和其他相关风险变得更加明显，需要在定价和建模阶段加以考虑。该选修课将介绍与交易对手相关的所有风险，以及它们如何包含在任何建模框架中。

• Credit Risk to Credit Derivatives
• Counterparty Credit Risk: CVA, DVA, FVA
• Interest Rates for Counterparty Risk – dynamic models and modeling
• Interest Rate Swap CVA and implementation of dynamic model

Behavioural Finance for Quants

量化中的行为经济学

Behavioural finance and how human psychology affects our perception of the world, impacts our quantitative models and drives our financial decisions. This elective will equip delegates with tools to identify the key psychological pitfalls, use their mathematical skills to address these pitfalls and build better financial models.

行为金融学以及人类心理学如何影响我们对世界的感知，影响我们的定量模型并推动我们的财务决策。该选修课将为学员提供工具，以识别关键的心理陷阱，利用他们的数学技能来解决这些陷阱并建立更好的财务模型。

• S ystem 1 Vs System 2
• Behavioural Biases; Heuristic processes; Framing effects and Group processes
• Loss aversion Vs Risk aversion; Loss aversion; SP/A theory
• Linearity and Nonlinearity
• Game theory

R for Quant Finance

基于R语言的量化金融分析

R is a powerful statistical programming language, with numerous tricks up its sleeves making it an ideal environment to code quant finance and data analytics applications.

R 是一种强大的统计编程语言，拥有众多技巧，使其成为编写量化金融和数据分析应用程序的理想环境。

• Intro to R and R Studio
• Navigate and understand packages
• Understand data structures and data types
• Plot charts, read and write data files
• Write your own scripts and code

Risk Budgeting

风险预算

Rather than solving the risk-return optimization problem as in the classic (Markowitz) approach, risk budgeting focuses on risk and its limits (budgets). This elective will focus on the quant aspects of risk budgeting and how it can be applied to portfolio management.

风险预算不是像经典（Markowitz）方法那样解决风险回报优化问题，而是专注于风险及其极限（预算）。本选修课将侧重于风险预算的量化方面以及如何将其应用于投资组合管理。

• Portfolio Construction and Measurement
• Value at Risk in Portfolio Management
• Risk Budgeting in Theory
• Risk Budgeting in Practice

Fintech

金融科技

Financial technology, also known as fintech, is an economic industry composed of companies that use technology to make financial services more efficient. This elective gives an insight into the financial technology revolution and the disruption, innovation and opportunity therein.

金融技术，也称为金融科技，是一个利用技术使金融服务更有效率的公司组成的经济产业。这门选修课让你深入了解金融科技革命带来的变革，创新和机遇。

• Intro to and History of Fintech
• Fintech – Breaking the Financial Services Value Chain
• FinTech Hubs
• Technology – Blockchain; Cryptocurrencies; Big Data 102; AI 102
• Fintech Solutions
• The Future of Fintech

C++

C++编程

Starting with the basics of simple input via keyboard and output to screen, this elective will work through a number of topics, finishing with simple OOP.

从简单的键盘输入和屏幕输出开始学习C++的基础知识，该选修课将会涉及许多主题，最后将会以C++面向对象编程的简单示例结束。

• Getting Started with the C++ Environment – First Program; Data Types; Simple Debugging
• Control Flow and Formatting – Decision Making; File Management; Formatting Output
• Functions – Writing User Defined Functions; Headers and Source Files
• Intro to OOP – Simple Classes and Objects
• Arrays and Strings

结束语

虽然我的CQF项目学习课程结束了，自己考试也通过了，但是对我来说学习并没有结束。

CQF项目课程带来的学习内容我还需要不断的去吸收和发掘，对我来说持证不是唯一的目的，整个项目思维的训练，认知的提升，职业生涯更多可能的选择等等，都是这个项目给我带来的益处，这些都让受益匪浅！

最后欢迎大家点赞、收藏、评论，转发！

欢迎大家关注我的微信公众号！微信搜索：进击的Matrix