Advanced Electives

Your advanced electives are the final element in our core program. These give you the opportunity to explore an area that’s most relevant or interesting to you. You need to select two electives from the extensive choice below to complete the CQF qualification. Struggling to choose just two electives? Don’t worry, you will have access to every advance elective as part of the CQF Lifelong Learning Library

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Advanced Computational Methods

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

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  • Finite Difference Methods (algebraic approach) and Application to BVP
  • Root Finding
  • Interpolation
  • Numerical Integration
     

Who is it for: IT, quant analytics, derivatives, valuation, actuarial, model validation professionals

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Advanced Portfolio Management

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

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  • Perform a Dynamic Portfolio Optimization, Using Stochastic Control
  • Combine Views with Market Data Using Filtering to Determine the Necessary Parameters
  • Understand the Importance of Behavioral Biases and Be Able to Address Them
  • Understand the Implementation Issues
  • Develop New Insights Into Portfolio Risk Management


Who is it for: Trading, fund management, asset management professionals

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Advanced Machine Learning

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The Machine Learning (ML) elective will focus on the practical consideration of deep sequential modeling. From gaining an understanding of the Machine Learning framework to feature engineering and selection, the elective teaches essential skills required to build and tune Neural Networks.

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  • Definition, Trends, and Landscape
  • Seven Steps to model an ML problem
  • Understanding Learning and Data Representation
  • Working of Learning Algorithms 
  • Exploratory Data Analysis
  • Feature Engineering on Date - Time Data
  • Feature Engineering on Numeric Data
  • Addressing Class Imbalances
  • Overview of Feature Selection Methods
  • Feature Selection using Boruta Algorithm
  • Understanding Sequences 
  • Sequence-data Generation
  • Getting started with TensorFlow and Keras API
  • Building & Training a Multivariate LSTM Model
  • Hyperparameter Optimization and Tuning
  • Evaluation of ML model 
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Advanced Risk Management

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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 (Fundamental Review of the Trading Book, New Minimum, Capital of Market Risk).

One of the consequences of these changes is the dramatic increase in the need for efficient and accurate computation of sensitivities. To cover this topic we will explore adjoint automatic differentiation (AAD) techniques from computational finance. We will see how, when compared to finite difference approximations, this approach can potentially reduce the computational cost by several orders of magnitude, with sensitivities accurate up to machine precision.

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  • Review of New Developments on Market Risk Management and Measurement
  • Explore the Use of Extreme Value Theory (EVT)
  • Explore Adjoint Automatic Differentiation (AAD)
     

Who is it for: Risk management, trading, fund management professionals

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Advanced Volatility Modeling

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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.

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


Who is it for: Derivatives, structuring, trading, valuations, actuarial, model validation professionals

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Algorithmic Trading I

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The algorithmic trading elective is a Do-It-Yourself (DIY) guide that enables you to start your quantitative trading from scratch. From gaining an understanding of data science workflow to retrieving data using public/private APIs and storing it in SQL, the elective teaches essential skills required for different quant applications.

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  • Overview of Data Science workflow
  • Getting familiar with data sources
  • Overview of Data API 
  • Data retrieval using opensource APIs
  • Getting started with IBKR, Alpaca
  • TWS vs IB Gateway
  • Using Python Wrapper for IB API
  • Installation and Connection
  • Specifying Contracts
  • Retrieving Historical EOD & Intraday Data
  • Retrieving Real-Time Tick Data
  • Retrieving Option Chains
  • Introduction to Database with SQLite 
  • Scheduling Cron Jobs
  • Email & Telegram notifications 
  • Storing & retrieval of data in/from database

Who is it for: Traders and quants who want to learn and use Python in trading.

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Algorithmic Trading II

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The algorithmic trading elective is a Do-It-Yourself (DIY) guide that enables you to start your quantitative trading from scratch. This elective is an extension of Algorithmic Trading I and covers some of the best software practices in developing quant applications including automatic data ingestion using CRON, backtesting, and live programmatic execution of trades using Alpaca and Zipline APIs

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  • Machine Learning Web-apps with Python
  • Review of Data Science workflow
  • Getting started with backtesting
  • Vectorized vs Event based backtesting
  • Getting started with Zipline 
  • Installation & Data Ingestion
  • Alpaca API and Custom Data Bundles
  • Custom data ingestion for Indian stocks
  • Backtesting strategies using Zipline

Who is it for: Traders and quants who want to learn and use Python in trading.

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Behavioural Finance for Quants

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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.

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  • System 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


Who is it for: Trading, Fund Management, Asset Management professionals

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C++

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Intended for those who are completely new to C++ or have very little exposure to the language.

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.

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  • 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


Who is it for: IT, Quant analytics, Valuation, Derivatives, Model Valuation

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Counterparty Credit Risk Modeling

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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.

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  • 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


Who is it for: Risk management, structuring, valuations, actuarial, model validation professionals

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Fintech

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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.

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  • 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


Who is it for: IT, quant analytics, trading, derivatives, valuation, Actuarial, Model Validation professionals

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Numerical Methods

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Any study in mathematics is incomplete without treatment of numerical analysis. When a closed form solution is not available or the problem to be solved is too complex to make amenable to explicit methods, a numerical/computational solution is sought. The resulting solution is an example of an approximate solution. 

This one-day elective will present several basic numerical methods for solving some of the most classic problems in mathematics.

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  • Basic iteration and convergence
  • Bisection method
  • Newton-Raphson
  • Rates of convergence
  • Taylor series and the error term
  • Numerical differentiation
  • Trapezoidal method
  • Simpson’s rule
  • Error analysis
  • Euler
  • Runge-Kutta
  • Lagrange interpolation
  • Cubic splines
  • LU decomposition
  • SOR methods
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R for Quant Finance

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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.

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  • Install R and R Studio
  • Navigate R Studio to Unleash the Power of R and Stay Organised
  • Use Packages
  • Understand Data Structures and Data Types
  • Use Some of R's Most Useful Functions
  • Plot Charts
  • Read and Write Data Files
  • Write your Own Scripts and Code
  • Know how to Deal with some of R's "Loveable Quirks"


Who is it for: IT, Quant Analytics, Valuation, Actuarial, Model Validation,Trading and Asset Management Professionals

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Risk Budgeting: Risk-Based Approaches to Asset Allocation

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Risk budgeting is the name of the last-generation approach to portfolio management.

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.

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


Who is it for: Risk Management, Trading, Fund Management Professionals

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Fixed Income & Credit