V1

2022/05/08阅读：90主题：自定义主题1

# CQF Learning Pathway(三)--高级选修课

## 概述

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。

## 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.

### 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.

• 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

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.

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

### 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).

• 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

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.

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

### 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.

• 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

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.

• 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

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.

• 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

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.

• 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

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

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.

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

### 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.

• 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.

• 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

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！

V1