Three Year PhD Studentship jointly funded by Royal Holloway University of London (RHUL) and AlgoLabs in Online Machine Learning for Effective Market Making Pricing Strategies
Royal Holloway University of London (RHUL) and Algorithmic Laboratories Ltd (‘AlgoLabs’)
Generous tax-free stipend of £20,000 per year, equipment and research expenses, plus all tuition fees paid at UK/EU rates
BackgroundAlgoLabs is a global FinTech start-up, born as an R&D subsidiary to its parent companies Divisa UK Ltd and Equiti Global. The primary objective of AlgoLabs is to research, build and provide support for in-house/proprietary high-level trading software for financial brokers. AlgoLabs has unique links to academic institutions, in particular Royal Holloway University of London (RHUL), to help drive research into novel trading ideas that build on its software, hence its tagline ‘Science of Trading’.
AlgoLabs is seeking to expand its team by offering a fully-funded and full-time PhD project based at its RHUL office in Egham. The project outlined below will be co-supervised in conjunction with Professor Chris Watkins and Dr Yuri Kalnishkan from the Department of Computer Science at RHUL. This project will offer a bright and talented graduate the unique opportunity to learn about the fast-paced and exciting world of finance in an exceptional research setting.
For information about working at AlgoLabs, RHUL and the Computer Science Department, please see:
Brief Project DescriptionAlgoLabs has developed its own proprietary software suite which are fully automated data driven models designed to run the full lifecycle of the business, from pricing to clients (the pricing model), to managing the resulting risk that accumulates (the hedging model) and execution of desired hedges to the wider market (the execution model).
Working within this lifecycle, a PhD project focused on the pricing model has been designed. This will investigate the use of online machine learning for effective market making pricing strategies. This project will allow the candidate to have access to large amounts of real historic FX data acquired by AlgoLabs (including time-series and price data), so will suit candidates interested in Big Data applications and analysis.
Entry Requirements and SalaryWe are looking for an outstanding candidate with an interest and/or expertise in some of the following fields:
• Machine Learning
• Time series Analysis
• Backtesting Trading Models
• Online Predictive Analytics
• Complex Event Processing
• Quantitative Finance
• Deep Q-Learning (especially applications building agents to play games)
• Recurrent Neural Networks, LTSM
• Reinforcement learning
• High Frequency Trading
• Market Making
• Real Time Systems
• Portfolio Risk Optimisation
• Knowledge of Foreign Exchange and CFD markets
• Prediction with expert advice
• Prediction with confidence
Applicants should possess or be expected to receive a good (1 or 2.1) honours degree in Computer Science/Mathematics or other relevant STEM discipline. It is essential that applicants are strong programmers in a suitable object-oriented programming language.
Candidates must also be able show that their English language proficiency is at a level which allows them to successfully complete the PhD. All applicants require an English language qualification, typically a GCSE or an IELTS test (a score of 7 or above is required, with a minimum of 6 in each component).
The award provides the PhD student with a generous stipend of £20,000 p/a and includes a top-spec laptop for data analysis and an allowance of £3,000 p/a for research/travel expenses.
Note: due to RHUL sponsorship requirements, these projects are open only to UK and EU applicants.
Start date: March 2019
Duration: 3 years
Applications deadline: 18th February 2019
How to ApplyTo apply, please send in a covering letter and your CV to [Email Address Removed]
In your covering letter please explain how you meet some or all of the eligibility criteria as outlined above and what interests you about the PhD project. Please also direct any questions to [Email Address Removed]