BMLL Now Has 5 Years of Order Book Data Available
Quants and data scientists can now access five years of Level 3 data through the vendor's Data Lab platform for use in alpha generation.
BMLL Technologies now has five years of Level 3 order book data available for US customers and for European funds trading US stocks. The data engineering-as-a-service company ingests the data from trading venues, then harmonizes it to create datasets to help data scientists and quants derive insights for alpha generation.
“As of this month, we have five years of Level 3 data—that is the full order book with every individual order that has been sent to trading venues—on the US equity markets. Many of our clients have told us that five years of history is imperative for them to be able to do the backtesting they require, at a level of depth and granularity they require,” says BMLL CEO Paul Humphrey.
BMLL has a back-end system that ingests end-of-day files directly from multiple trading venues. The company then performs proprietary normalizations on the data, which is stored in its data lake that runs on Amazon Web Services. Quants can access the harmonized datasets via BMLL’s Data Lab platform, either in the platform’s Python environment, or by taking in the data through an API or FTP into their own research systems.
Because Level 3 is every single order on a limit order book (as opposed to Level 2, which is orders aggregated at a particular price level), a researcher can see not only the price level at what orders are being submitted, but also individual orders. Elliot Banks, chief product officer at BMLL, says this allows the user to trace orders and look at metrics such as the average resting time of an order or fill probability at a particular price level of the order book.
“Quants and data scientists want this data for backtesting, to look at historical performance, and to help them in decision-making. They feed it into smart order routers, or use it to derive different features and datasets to help in their alpha discovery process. But they need clean data. So we capture it, and we make it consistent,” Banks says.
These datasets are large and complex, and every venue records data in different ways, following different standards and formats. Many firms don’t have the computing infrastructure to derive meaningful analytics from the data, Banks says. BMLL aims to free up time for quants by taking on the technological lift of normalizing the data, so they can focus on their value-add activities, he adds.
“We are taking Level 2 and 3 data, and turning it into datasets that quants can use in the alpha discovery process. They need enough history to be able to tally that with some of their other datasets,” Banks says.
Five years is a milestone in the collection of the Level 3 data because it’s necessary to have a sufficiently long time horizon to capture a wide spectrum of market scenarios, says BMLL COO William L’Heveder.
“When you are looking at microsecond granularity, in order to get a full cycle of trading events that are relevant to you, you probably don’t need that much history. When you start thinking about consolidating that microsecond granularity into intraday summaries or end-of-day summaries, you need to capture a longer time horizon in order to capture as many events cycles as possible,” L’Heveder says.
Only users who have a paid subscription or are part of a corporate subscription are able to print or copy content.
To access these options, along with all other subscription benefits, please contact info@waterstechnology.com or view our subscription options here: http://subscriptions.waterstechnology.com/subscribe
You are currently unable to print this content. Please contact info@waterstechnology.com to find out more.
You are currently unable to copy this content. Please contact info@waterstechnology.com to find out more.
Copyright Infopro Digital Limited. All rights reserved.
As outlined in our terms and conditions, https://www.infopro-digital.com/terms-and-conditions/subscriptions/ (point 2.4), printing is limited to a single copy.
If you would like to purchase additional rights please email info@waterstechnology.com
Copyright Infopro Digital Limited. All rights reserved.
You may share this content using our article tools. As outlined in our terms and conditions, https://www.infopro-digital.com/terms-and-conditions/subscriptions/ (clause 2.4), an Authorised User may only make one copy of the materials for their own personal use. You must also comply with the restrictions in clause 2.5.
If you would like to purchase additional rights please email info@waterstechnology.com
More on Data Management
New working group to create open framework for managing rising market data costs
Substantive Research is putting together a working group of market data-consuming firms with the aim of crafting quantitative metrics for market data cost avoidance.
Off-channel messaging (and regulators) still a massive headache for banks
Waters Wrap: Anthony wonders why US regulators are waging a war using fines, while European regulators have chosen a less draconian path.
Back to basics: Data management woes continue for the buy side
Data management platform Fencore helps investment managers resolve symptoms of not having a central data layer.
‘Feature, not a bug’: Bloomberg makes the case for Figi
Bloomberg created the Figi identifier, but ceded all its rights to the Object Management Group 10 years ago. Here, Bloomberg’s Richard Robinson and Steve Meizanis write to dispel what they believe to be misconceptions about Figi and the FDTA.
SS&C builds data mesh to unite acquired platforms
The vendor is using GenAI and APIs as part of the ongoing project.
Aussie asset managers struggle to meet ‘bank-like’ collateral, margin obligations
New margin and collateral requirements imposed by UMR and its regulator, Apra, are forcing buy-side firms to find tools to help.
Where have all the exchange platform providers gone?
The IMD Wrap: Running an exchange is a profitable business. The margins on market data sales alone can be staggering. And since every exchange needs a reliable and efficient exchange technology stack, Max asks why more vendors aren’t diving into this space.
Reading the bones: Citi, BNY, Morgan Stanley invest in AI, alt data, & private markets
Investment arms at large US banks are taken with emerging technologies such as generative AI, alternative and unstructured data, and private markets as they look to partner with, acquire, and invest in leading startups.