CQF的高级选修课有哪些？附课程介绍

　　CQF的高级选修课有：算法交易、高级计算方法、高级风险管理、高级波动率模型、基于Python的机器学习、高级投资组合管理、交易对手风险模型、量化中的行为经济学、基于R语言的量化金融分析、风险预算、金融科技、C++编程。

　　CQF整个项目的主要包含核心课程和高级选修课程，核心课程是Model 1-Model 6,在Model 6模块学习完后，还有上述的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。

CQF高级选修课的课程介绍

　　CQF的高级选修课的课程介绍如下：

　　1、算法交易（Algorithmic Trading）

　　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

　　2、高级计算方法（Advanced Computational Methods）

　　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

　　3、高级风险管理（Advanced Risk Management）

　　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

　　4、高级波动率模型（Advanced Volatility Modeling）

　　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

　　5、基于Python的机器学习（Machine Learning with Python）

　　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

　　6、高级投资组合管理（Advanced Portfolio Management）

　　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

　　7、交易对手风险模型（Counterparty Credit Risk Modeling）

　　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

　　8、量化中的行为经济学（Behavioural Finance for Quants）

　　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

　　9、基于R语言的量化金融分析（R for Quant Finance）

　　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

　　10、风险预算（Risk Budgeting）

　　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

　　11、金融科技（Fintech）

　　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

　　12、C++编程（C++）

　　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