Advanced C++ Design And Implementation In Quantitative Finance By Nick Webber

The goal of this hands-on course is to introduce the new language features in C++11 and apply them to the design and implementation of frameworks to approximate the solution of partial differential equations (PDE) using the finite difference method (FDM).

This course is a practical introduction to the f# language, functional programming and their use in the financial domain. You will learn about f#, fundamental functional concepts as well as libraries for numeric computing, data analysis. You will also become familiar with best practices for using f# tools and integrating f# with larger systems.

Understand the mechanics and implementation challenges of the new regulatory framework for market risk (Fundamental Review of the Trading Book). The course links conceptual requirements to implementation consequences. There are no academic prerequisites for this course. It would be however helpful, if participants have some knowledge on today’s market risk regulation. Furthermore, participants should know the concept of Value-at-Risk and its current application for risk measurement.

In each lecture the presented examples are out of the quant finance field. For example in the lecture dynamic interactivity and mma the audience will be guided to develop a viewer for different copula functions with different marginal distributions.

The course gives an overview of the matlab system with a view towards financial engineering. As a beginners course we start by giving an introduction to the basic functionality like plotting, handling of matrices, using m-files and running scripts. All examples are based on financial problems. Thus, we aim to implement the black-scholes pricing formula, calculate Greeks. Furthermore, we consider writing programs. To this end we develop basic programming skills and show how to transform algorithms to working matlab code and how to arrange the code. We wish to use the binomial model as an example. Finally, we cover useful functionality for everyday life such as interpolation, integration or special functions.

Understand the mechanics of standard implementations of the single asset and portfolio based risk-premia trading strategies, the basis for CTAs and Quant funds, Equities Quant funds, position taking by e-traders/market-makers and a standard set of strategies in HFT. Recognize pros and cons of various approaches to designing strategies and the common pitfalls encountered by algorithmic traders. Be able to devise new and improved algorithmic strategies.

Python has established itself as a real contender in the quant finance world to implement efficient analytics workflows and performant applications. Although being an interpreted language, quantative analysts and developers can draw on the powerful (scientific) ecosystem that has grown around python. This comprises libraries such as numpy that allows array management and operations both in a highly vectorised fashion and at the speed of c code. Using the panda library makes the management and analysis of financial time series both convenient and efficient. In such a context, data visualization is also easily accomplished with python. The workshop also illustrates how to achieve hardware-bound, input-output operations with python/numpy and libraries such as pytables

This workshop introduces the statistical programming language R. The workshop focuses on the aspects of the R language useful for financial analyses, with emphasis on techniques for automation and visualisation.

This course is a structured study that will enable students to develop an advanced level of understanding of the core process of banking, that of asset-liability management (alm), capital and liquidity risk management. Students will be able to apply market standard techniques to measure alm risks and formulate strategies for management of these risks at any bank.

Quantitative finance is moving into a new era. The Machine Learning Institute Certificate offers candidates the chance to upgrade their skill set by combining academic rigour with practical industry insight. The Machine Learning Institute Certificate in Finance (MLI) is a comprehensive six-month part-time course, with weekly live lectures in London or globally online. The MLI is comprised of 2 levels, 6 modules, 24 lecture weeks, lab assignments, a practical final project and a final sit-down examination using our global network of examination centres. Designed to empower individuals who work in or are seeking a career in machine learning in finance. Start date, 23rd April 2019 Level39 London and Online (WebEx and Moodle) One Canada Square Canary Wharf London E14 5AB.

The objective of the course is to develop fundamental skills of quantitative developer role. The course is of an introductory level and does not require programming experience. The course is designed by practitioners from quantitative finance with experience in model development for derivative pricing and systematic trading. The primary coding languages of the course are Python and C++. As it is essential in finance to work with time series data we introduce database KDB and the language q, which are the leading solutions for storing the time series.