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Real Time Trading Infrastructure (Read 3001 times)
RichardMantell
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Real Time Trading Infrastructure
10/17/08 at 15:28:01
 
London Hedge Fund, is seeking to recruit an exceptional Senior Developer to participate in the development of algorithmic trading strategies in worldwide financial markets by taking over the management and improvement of the real-time trading system with a low level of supervision. This will involve taking over the real-time infrastructure and research infrastructure from both a design and a ‘maintenance’ or ‘feature addition’ point of view.
The core technologies used are Java, Python, Linux and PostgreSQL and various distributed computing frameworks. Suitable candidates will have strong design and implementation experience with Linux to the point of being able to self-administer a basic network. Experience of the industry is a benefit but not a pre-requisite. Outstanding Infrastructure Developers from companies such as British Telecom are encouraged to apply.
Key areas you should excel in are; Market Data Infrastructure, Real Time Data, Interface to Database, Exchanged Connectivity, Systems Information, FIX, Unit Testing, Deployment, Connectivity, Reuters, Compute Pool, Database Replication, Distributed Code, Multithreaded Code.
You will have a high aptitude and strong intrinsic motivation to learn. This can be evidenced by multiple means such as academic achievement, extensive participation in an open-source project, knowledge of an extensive range of programming languages and a wide range of quantitative experience.
You will be working for a proprietary trading firm with a track record of producing extraordinary returns using automated, statistically-based trading strategies in financial markets.
The firm has two core competencies:
(a) Experience in proprietary trading and academic finance, leading to formulation of strong priors about likely sources of inefficiencies in financial markets and (b) design and implementation of infrastructure to enable efficient testing of hypotheses and the inefficiencies over massive data.
email; rmantell@execuzen.com
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