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 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 I

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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 Machine Learning II

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This elective is an extension of Advanced Machine Learning that focuses on the practical consideration of machine learning. The elective teaches essential skills required to build, evaluate and track various machine learning models.

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  • Understanding Machine Learning Lifecycle
  • Optimizing Models with Experiment Trackers
  • Building Data / ML Apps in Python
  • Understanding Ensemble Learning
  • Building Ensemble Models for Trend Prediction
  • Customizing TensorBoard for ML Experiments 

Who is it for: IT, Data Science, Risk Management, Trading, Fund Management, and Machine Learning Professionals

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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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Introduction to Algorithmic Trading

  • Definitions, Trends and Landscape
  • Overview of Quant Workflow
  • Application and System Schematic
  • Building Blocks of a Quantitative System
  • Overview of Data API
  • Overview of Database

 

Opensource Data APIs

  • Getting Started with Data APIs
  • Getting Familiar with Data Formats
  • Handling Streaming Data
  • Data Retrieval and Storage

 

Getting Started with Interactive Brokers

  • Python Wrapper for IB API
  • Specifying Contracts
  • Retrieving Historical EOD and Intraday Data
  • Retrieving Real-Time Tick Data

 

Getting Started with Alpaca - Part 1

  • Handling Trade & Market Data API
  • Data Storage and Retrieval

 

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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Getting started with Alpaca - Part II
  • Automated Data Ingestion
  • Data Updation Alerts using Gmail
  • Setting up Notification using Telegram

 

Getting started with Zipline
  • Overview of Vectorized & Event-driven Backtesting
  • Alpaca Data Ingestion
  • Backtesting strategies with Alpaca ingested data

 

Trading Live with Alpaca
  • Streaming Real-time Data
  • Live Trading with Alpaca
  • Trading Live using Zipline*

 

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

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Behavioral Finance for Quants

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Behavioral 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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Decentralized Finance Technologies

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Blockchain technology is one of the biggest innovations of the 21st Century. While this technology dates back to the early 1990s, it gained popularity after the launch of Bitcoin in 2009. As the number of applications that were built on it grew rapidly, such technologies have the power to shape the future from finance to manufacturing.

This elective gives an insight into the financial technology revolution as we demystify the concepts surrounding these new-age technologies.

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  • Blockchain Basics
  • Prototyping Bitcoin Mining in Python
  • Demystifying Decentralized Finance [DeFi]
  • Ethereum Basics & Smart Contracts 
  • Programming with Solidity
  • Developing Smart Contracts on Ethereum Network


Who is it for: IT, quant analytics, trading, derivatives, valuation, Actuarial, Model Validation professionals, and anyone who wants to learn these new-age technologies.

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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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Quantum Computing in Finance

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Quantum Computing is about the application of the principles of quantum mechanics to computer science. In this Advanced Elective we will:

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  • Define quantum computing and its relevance in finance
  • Review the three key ingredients of quantum computing: qubits, quantum gates and quantum circuits
  • Enumerate some of the applications of quantum computing in various fields
  • Construct ourselves a simple quantum circuit online using the IBM Quantum 
  • Learn how to write our own quantum program using the Python module Qiskit
  • Explore examples of quantum algorithms in finance, including pricing European options, interest rate products and credit risk
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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 Data Science & Machine Learning

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R is a powerful programming language and software environment for statistical computing. It is one of the favorite tools among academicians and is widely used among statisticians and data miners for their data analysis. In this workshop, we'll revisit R programming from scratch to solve quant finance and machine learning problems that help in understanding mathematical and computational tools from a quant’s perspective.

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  • Introduction & Installation
  • Getting Started with R & RStudio
  • Working with Data
  • Writing your own Custom Functions
  • Visualization & Charting
  • Statistics and Probability
  • Machine Learning Applications in R


Who is it for: IT, Data Science, Risk Management, Trading, Fund Management, and Machine Learning 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