CQF项目课程学习介绍（三）—— 高级选修课

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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

小编CQF课程学习中的一张图片

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