CQF的高級選修課有：算法交易、高級計算方法、高級風(fēng)險管理、高級波動率模型、基于Python的機(jī)器學(xué)習(xí)、高級投資組合管理、交易對手風(fēng)險模型、量化中的行為經(jīng)濟(jì)學(xué)、基于R語言的量化金融分析、風(fēng)險預(yù)算、金融科技、C++編程。

　　CQF整個項(xiàng)目的主要包含核心課程和高級選修課程，核心課程是Model 1-Model 6,在Model 6模塊學(xué)習(xí)完后，還有上述的12門高級選修課，每位學(xué)員可以選擇2門自己感興趣的課程內(nèi)容進(jìn)行學(xué)習(xí)，高級選修課的內(nèi)容和CQF的Final Project考試課題是相關(guān)的，因?yàn)镕inal Project的多個考試課題中，大部分是來自高級選修的課題，如果你想在Final Project考試中做一個你擅長的課題，那么在高級選修課中就選擇相關(guān)課題進(jìn)行學(xué)習(xí)，就一舉兩得了。

CQF高級選修課的課程介紹

　　CQF的高級選修課的課程介紹如下：

　　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.

　　算法的使用已經(jīng)成為現(xiàn)代金融市場的一個重要元素，買方和賣方都在使用。這門選修課將研究在該領(lǐng)域工作的定量專家使用的技術(shù)。

　　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、高級計算方法（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.

　　對于任何從事量化金融工作的人來說，一個關(guān)鍵技能是如何使用技術(shù)解決復(fù)雜的數(shù)學(xué)問題。這門選修課將研究先進(jìn)的計算技術(shù)，以高效和簡潔的方式解決和實(shí)施數(shù)學(xué)，確保正確的技術(shù)用于正確的問題。

　　Finite Difference Methods(algebraic approach)and application to BVP

　　Root finding

　　Interpolation

　　Numerical Integration

　　3、高級風(fēng)險管理（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).

　　在這門選修課中，我們將探討量化風(fēng)險管理的一些最新發(fā)展。我們以如何在銀行業(yè)(預(yù)期虧空)和新的巴塞爾監(jiān)管框架(交易賬簿基本回顧，新的最小值，市場風(fēng)險資本)下構(gòu)思和衡量市場風(fēng)險的范例為出發(fā)點(diǎn)。

　　Review of new developments on market risk management and measurement

　　Explore the use of extreme value of theory(EVT)

　　Explore adjoint automatic differentiation

　　4、高級波動率模型（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.

　　波動率和能夠?qū)Σ▌勇蔬M(jìn)行建模是任何量化模型的關(guān)鍵要素。本選修課將研究用于模擬整個行業(yè)的波動率的常用技術(shù)。它將提供解決隨機(jī)波動率問題的數(shù)學(xué)和數(shù)值方法。

　　Fourier Transforms

　　Functions of a Complex Variable

　　Stochastic Volatility

　　Jump Diffusion

　　5、基于Python的機(jī)器學(xué)習(xí)（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.

　　這門選修課將側(cè)重于使用Python在機(jī)器學(xué)習(xí)和深度學(xué)習(xí)在金融中的應(yīng)用。我們將重點(diǎn)介紹從開源數(shù)據(jù)中檢索財務(wù)數(shù)據(jù)的技術(shù)。

　　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、高級投資組合管理（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.

　　隨著量化金融在當(dāng)今的金融市場中變得越來越重要，許多買方公司正在使用量化技術(shù)來提高回報并更好地管理客戶資本。該選修課將研究買方為實(shí)現(xiàn)這些目標(biāo)而使用的最新技術(shù)。

　　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、交易對手風(fēng)險模型（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.

　　后全球金融危機(jī)、交易對手信用風(fēng)險和其他相關(guān)風(fēng)險變得更加明顯，需要在定價和建模階段加以考慮。該選修課將介紹與交易對手相關(guān)的所有風(fēng)險，以及它們?nèi)绾伟谌魏谓？蚣苤小?/p>

　　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、量化中的行為經(jīng)濟(jì)學(xué)（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.

　　行為金融學(xué)以及人類心理學(xué)如何影響我們對世界的感知，影響我們的定量模型并推動我們的財務(wù)決策。該選修課將為學(xué)員提供工具，以識別關(guān)鍵的心理陷阱，利用他們的數(shù)學(xué)技能來解決這些陷阱并建立更好的財務(wù)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語言的量化金融分析（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是一種強(qiáng)大的統(tǒng)計編程語言，擁有眾多技巧，使其成為編寫量化金融和數(shù)據(jù)分析應(yīng)用程序的理想環(huán)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、風(fēng)險預(yù)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.

　　風(fēng)險預(yù)算不是像經(jīng)典（Markowitz）方法那樣解決風(fēng)險回報優(yōu)化問題，而是專注于風(fēng)險及其極限（預(yù)算）。本選修課將側(cè)重于風(fēng)險預(yù)算的量化方面以及如何將其應(yīng)用于投資組合管理。

　　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.

　　金融技術(shù)，也稱為金融科技，是一個利用技術(shù)使金融服務(wù)更有效率的公司組成的經(jīng)濟(jì)產(chǎn)業(yè)。這門選修課讓你深入了解金融科技革命帶來的變革，創(chuàng)新和機(jī)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++編程（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.

　　從簡單的鍵盤輸入和屏幕輸出開始學(xué)習(xí)C++的基礎(chǔ)知識，該選修課將會涉及許多主題，最后將會以C++面向?qū)ο缶幊痰暮唵问纠Y(jié)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