The data science platform for historical market data The data science platform for historical market data

Access years of the highest quality historical market data, built-in analytics, enterprise-grade features and scalable compute in one place. Built for research, analytics, and data science

Designed by quants. Trusted by the market.

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BMLL Data Lab Factsheet

The Solution How BMLL Data Lab solves market data challenges

Every challenge, from messy datasets to scalable workflows, is solved with BMLL Data Lab - built by quants for research, execution analysis, backtesting and market surveillance.

Full-depth, harmonised market data

Access full-depth historical market data built direct from source, that preserves all information from the exchange, ready for research, back-testing, and analysis. View the data in the way you need it - either with the raw fields preserved or in a normalised format.

Built for scale

Process petabyte scale historical datasets without infrastructure bottlenecks or performance constraints in a completely managed service, leveraging the latest compute and technologies.

Easy to use libraries

Fully customisable environment containing easy-to-use APIs and built in analytics.

Automation as standard

Move from ad-hoc experiments to daily pipelines using automated scheduled jobs and version controlled notebooks. Seamlessly connect to your own cloud storage and SFTP servers.

World class support

Utilise dedicated expert quant support, pre-configured notebooks and robust documentation on every feature of the BMLL Data Lab to begin deriving insights immediately.

Cloud-native, reproducible environment

Enterprise-grade solution with built in version control, SSO and security features.

Use Cases How BMLL Data Lab is used

For Quant Researchers

Back-test strategies without weeks of data cleaning

The challenge

Testing and refining trading strategies depends on clean, full-depth historical data, but poor normalisation, changing protocols and inaccurate data mean teams can spend weeks pre-processing before they can run a single back-test.

How BMLL Data Lab helps

Work with full-depth historical market data built direct from source, available raw or normalised
Run back-tests on petabyte-scale datasets in a fully managed service, with no infrastructure to maintain
Build and refine models using easy-to-use APIs and built-in analytics
Promote ad-hoc experiments into repeatable workflows with scheduled jobs and version-controlled notebooks

What you achieve

Spend your time testing and refining strategies instead of cleaning data, and move from experiment to repeatable back-test with confidence.

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For Execution & TCA Teams

Prove execution quality across venues

The challenge

Analysing execution performance and meeting Best Execution obligations requires consistent data across venues, but inconsistent schemas and manual data wrangling make reproducible, defensible analysis hard.

How BMLL Data Lab helps

Analyse execution across venues using harmonised data that is consistent across markets
Build reproducible TCA and Best Execution workflows with programmatic access
Measure execution against real market conditions using full-depth order book data
Automate recurring analysis with scheduled jobs and version-controlled notebooks

What you achieve

Produce consistent, reproducible execution and TCA analysis you can stand behind, without rebuilding the data pipeline each time.

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For Market Structure Researchers

Study liquidity and price formation at scale

The challenge

Studying liquidity, order book dynamics and price formation means working across enormous historical datasets, processing petabyte-scale data normally demands significant infrastructure and dedicated resources.

How BMLL Data Lab helps

Study order book dynamics and price formation with full-depth, harmonised historical data
Process petabyte-scale datasets without infrastructure bottlenecks or performance constraints
Explore the data raw or normalised, whichever your research requires
Work in a customisable environment with easy-to-use APIs and built-in analytics

What you achieve

Research liquidity and market structure across markets and history at scale, without building or maintaining the underlying infrastructure.

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For SOR & Algo Teams

Optimise routing with normalised cross-market data

The challenge

Improving smart order router performance depends on high-quality data normalised across many markets. Assembling and maintaining that data, and the computer to model on it, is a major engineering burden.

How BMLL Data Lab helps

Use high-quality historical data normalised across multiple markets to identify trends and test routing logic
Back-test SOR models in a scalable, fully managed compute environment
Model against realistic conditions using full-depth order book data direct from source
Re-run model tests reliably with version-controlled notebooks and scheduled jobs

What you achieve

Identify market trends and back-test SOR models on normalised cross-market data, all within one scalable environment.

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For Strategy & Market Entry Teams

Explore new markets without the engineering cost

The challenge

Evaluating new venues and asset classes usually means buying, sourcing and engineering new datasets before you can even begin, a slow, expensive commitment made before you know whether the market is worth it.

How BMLL Data Lab helps

Explore new venues and asset classes without buying, sourcing or engineering the data yourself
Work with harmonised historical data that is consistent across markets from day one
Scale analysis up or down in a fully managed environment with no infrastructure to maintain
Get started quickly with pre-configured notebooks, documentation and expert quant support

What you achieve

Assess unfamiliar markets and asset classes quickly and cost-effectively, without committing engineering resources up front.

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Technical overview Trial vs Full Version

Explore the technical capabilities of the BMLL Data Lab — sign up for a trial and see what’s available before moving to the full version.

	Trial Explore the environment Start trial	Enterprise For Scaling teams Full production capabilities Contact us
Instance Sizes	Up to 16 GB	Up to 1.5TB
Datasets	All available*	Custom selection
Scheduling		Scheduled by Market Scheduled by Time Triggered by API
Shared Areas Secure, partitioned workspaces for cross-functional quant and research teams to collaborate on code and datasets.		Multiple shared directories allowed
Clusters Elastic MapReduce (EMR) allows you to run distributed data processing frameworks like Apache Spark for massive-scale historical analytics.		Run large scale EMR clusters (64 cores per node)
Support	Standard	Dedicated
GPUs		Multiple available for selection
SFTP
Cloud Connection		S3 Azure Blob Storage GCP Storage
* Some markets not available in trial due to exchange licensing.

Ready to see the market differently?

Discover how BMLL helps trading desks optimise execution and market insight

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