The compute-ready data platform for global equities, ETFs & derivatives
Compute-Ready Research Data
Compute-Ready Research Data
For quant research teams, the gap between a new trading hypothesis and a validated model comes down to one thing: data quality and availability. Researchers spend much time dealing with data friction, aligning mismatched exchange timestamps, parsing different execution formats, and hitting storage limits when they could be focused on building features and tuning models.
While larger firms often throw data engineering resources at this problem, keeping data consistent across global venues remains a major challenge. The effort required to ingest and clean massive market datasets strains internal systems, slowing backtesting and research.
BMLL removes this operational overhead. We provide clean, harmonised, and normalised Level 1, 2, and 3 historical market data and analytics through two products: the BMLL Data Feed and the BMLL Data Lab.
The BMLL Data Feed delivers BMLL’s historical (T+1) Level 1, 2, and 3 market data, as well as daily analytics, directly into your internal systems.
Scaling strategies into new regions or asset classes is highly engineering-intensive. Engineers typically have to build a fresh ingestion pipeline for each venue's unique raw format. BMLL handles all venue-specific parsing and data engineering across 148+ Equity, ETFs, Futures, and Options venues. You access harmonised, normalised datasets immediately, with zero per-exchange parsers to write or maintain.
Market data is inherently fragmented. Exchanges use different protocols and update their matching engines on their own schedules. BMLL maps every market event, including order placements, modifications, executions, and cancellations, to a single, globally consistent schema. A signal or feature you emerge in one market can easily be tested in others with very little code change.
Calculating standard order-book metrics from raw data takes massive compute power. The BMLL Data Feed includes 500+ pre-computed daily analytics. You access the metrics you need, freeing up your compute for proprietary feature discovery.
The BMLL Data Feed integrates with the infrastructure you already use, supporting cloud-to-cloud transfers (AWS, Azure, GCP, Snowflake, Databricks), APIs, and SFTP. You can onboard data without building custom plumbing for every new feed.
When local storage and large file transfers slow you down, the BMLL Data Lab puts compute power right beside the historical data, so you stop moving files across networks.
The BMLL Data Lab is a scalable, cloud-hosted research environment. Instead of downloading large datasets, researchers use simple APIs to retrieve the exact order book data or historical window they need, loading it directly into high-performance dataframes.
The platform includes JupyterLab and VS Code, pre-configured notebooks, standard data science libraries, remote API connectivity, scheduled notebooks and tasks, and access to scalable compute clusters. There is no infrastructure or connection code to write before you start researching market microstructure.
Compute scales with your workload, from 16 GB to 1.5 TB of RAM, with CPU and GPU options available. Every workspace is fully isolated, ensuring your code, data, and research remain completely private.
Moving from fragmented exchange feeds to a managed data infrastructure improves the cost and speed of quantitative research by removing key data-engineering bottlenecks. With globally structured data and co-located compute, researchers can move more quickly from idea to backtesting and deployment. This shift frees teams to focus on feature development and model iteration, enabling a leaner, more scalable path to alpha generation.
26 June 2026
Measuring ICE Futures Liquidity Beyond the Lit Book
When 39% of cotton futures trading occurs via ICE implied order book, building an exhaustive view of executable liquidity becomes critical.
16 June 2026
BMLL Market Lens: European Liquidity Maps
European Real economic interest ADV up 53% YoY
16 June 2026
Beyond the Headlines: The Market Mechanics Behind SpaceX's First Day
SpaceX (SPCX) traded US$85.3 billion on 522 million shares, or 94% of float, during its first trading session. It closed 19.2% above its US$135 offer price and 7.3% above the opening cross. BMLL level 3 data shows offer side passive liquidity recovered slower than bid side during aggressive trading burst in its first trading day.
11 June 2026
BMLL Market Lens: European Liquidity Maps - A Real Economic Interest Viewpoint
How you read the European tape depends on which viewpoint you take. This month only and in the context of ESMA current equity market structure call for evidence, we provide a lens built upon the notion of Real Economic Interest.
