The financial markets are complex. Over 150 different trading venues, over 10,000 liquid tickers. Different exchange feeds, different protocols, different data structures, different trading rules. All changing over time. Reference data, derived data, missing data, dirty data, poor documentation. Latencies, bandwidths, conflation rates, jitter. Technical complexity of working with petabytes, the constantly changing suite of ‘big-data’ tools, the rate of evolution of the cloud and the data science arms race that the financial services industry is now engaged in. For companies and their researchers this is a lot to take in and manage before they can assert their true value-add – generating insights from the data.
BMLL’s offering provides a utility in the form of a platform whereby research scientists can immediately lever their statistical skills to quickly and efficiently investigate the problems their organization faces.
The product is a RESTful API within a web portal. BMLL exposes the API through the Jupyter IDE. The primary computational kernel for Jupyter is Python, followed by MATLAB and R. The API has three core components; data, hardware and analytics. The data API acts on data objects including limit order book data, reference data, unstructured data and metadata. The hardware API allows the user to perform calculations both in a local environment and in a distributed environment. The analytics API allows the user to apply code to the data in a pre-defined hardware environment. This includes order-book specific libraries that BMLL researchers have built to address pressing problems in the space, proprietary libraries written by the user, libraries written by third parties such as ISVs and open-source libraries. The USP of BMLL is the tight integration of these three APIs. In summary we offer a research platform dedicated to the complexities of working with this data in order to enable users to focus on addressing the problems of interest to the end user – not the plumbing problems associated with provisioning the data science environment.
Global historical limit order book data on a per ticker, per trading day basis, with hourly access charges.
Unlimited processing power through the AWS cloud with up to 80% reduction over usual cloud costs.
Research at BMLL means being able to apply the latest advances in machine learning, statistics and data analytics to one of the largest and most complex datasets available. Our researchers come from academia and industry. From signal processing to image recognition, from machine intelligence to natural language processing. Experience working with web-scale datasets is desirable. A passion to understand data is essential.
Software developers at BMLL include recent graduates and experienced professionals. We are looking for expertise in areas such as Python, cloud computing platforms such as Amazon Web Services, front-end development, container platforms such as Docker, and distributed computing.
Sales at BMLL is about building relationships with leading institutions working in the financial services, including investment banks, hedge funds, financial regulators and government agencies. These relationships enable customers to access and analyse the highest quality financial dataset in the world.
Operations at BMLL is the lifeblood of the company. Operations staff maintain business relationships with data owners, cloud platforms and customers. They enable efficient and timely accounting and facilitate the essential daily mechanics of the company.
BMLL Technologies Ltd
Portland House, Bressenden Place
London SW1E 5RS
Charles Russell Speechlys LLP
5 Fleet Place
London EC4M 7RD