Quant Research & Backtesting

For quantitative researchers, data engineering is often the biggest obstacle to alpha generation. Cleaning, normalising, and stitching together fragmented venue data consumes valuable research time and delays strategy development.

BMLL removes this operational burden by delivering harmonised, nanosecond-precision historical order book data and analytics. We enable quantitative teams to focus directly on alpha discovery, rigorous backtesting, and execution modelling across multiple market regimes without managing complex data infrastructure.

Key Capabilities Accelerate Alpha Discovery and Strategy Validation

BMLL enables quantitative teams to test, refine, and validate strategies using consistent datasets across global markets and time periods.

Alpha Research

Rapidly explore market dynamics and identify new trading signals using clean, research-ready datasets.

Backtesting

Simulate systematic strategies using high-fidelity historical order book data to validate model robustness.

Execution Modelling

Quantify slippage, market impact, and queue dynamics before deploying algorithms into live markets.

Strategy Optimisation

Continuously refine and calibrate models using structured, harmonised market data and analytics.

Essential Data & Metrics

Full order book data harmonised across global equities, ETFs, and futures venues.

Nanosecond-precision timestamps for accurate sequencing and latency modelling.

Granular queue dynamics, cancel/replace message tracking, and hidden liquidity indicators.

Consistent cross-venue liquidity and execution metrics for robust comparative analysis.

BENEFITS AND USE CASES How BMLL Supports Quant Research

Platform	Benefits	Use Cases
	A secure cloud-based research environment that enables deep, scalable analysis of harmonised historical market data and pre-computed analytics without infrastructure overhead.	• Interactive alpha research • Execution model prototyping • Deep-dive strategy backtesting
	Delivers normalised datasets and pre-computed analytics directly into your environment for seamless integration into existing workflows.	• Automated backtesting engines • Systematic signal generation • Large-scale research workflows
	A no-code analytics platform that provides instant visibility into market quality and liquidity dynamics.	• Strategy comparison • Market regime analysis • Visual performance dashboards

BMLL ECOSYSTEM Explore the BMLL Platform

Access to our full range historical harmonised and normalised level 3, equities, futures, options and prediction markets market data, built in analytics and compute that scales across CPU and GPU. Built for alpha, insights, research, development and data science. Designed by quants.

Petabytes of historical market data and derived analytics, securely delivered to your environment by API, files, cloud warehouse or data platform. Level 3 order book granularity, normalised across venues and geographies, research-ready schema.

Intuitive no code visual application bringing all market participants exceptional granular insights over 500 metrics derived from BMLL best in class Level 3.

LATEST INSIGHTS Case Studies and Client Insights

20 October 2025

Would you hire Picasso to paint your living room?

I’m sure we’d all want to say yes, knowing that this wouldn’t be the best use of his time and skills. By the same token, every day, some of the industry’s most experienced quants, hired at great expense, spend 80% of their valuable time scrubbing data before they can start using it. But is that the best use of their skills? That debate is now over. And here is why.

Read the Article

26 September 2025

A new approach to Level 3 market data

This article is the story of how Optiver became, first, customers of BMLL’S Level 3 historical data, and then investors. It’s also the story of how companies like BMLL, with the help of Optiver, are putting the ‘buy’ into the age-old ‘build versus buy’ question when it comes to historical market data.

Read the Case study

3 September 2024

Normalisation: a dirty word or a differentiator for success?

Trading practitioners and quants need access to high quality data that captures all information, but importantly is also consistent and easy to use. Dr Elliot Banks takes a deeper look at what good data normalisation really means, and why it matters.