Systematic trading firms rely on BMLL for historical market… | BMLL

Systematic trading firms rely on BMLL for historical market data

BMLL’s high-quality datasets remove the cost and labour-intensive burden of data collection, management, storage, and analytics generation, enabling firms to focus on value-additive tasks such as generating trading strategies, optimising executions, and market structure research.

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How BMLL Datasets Enhance Your Workflows

  • Develop and optimise trading strategies: Full-depth order book data containing trade information, including trade flags, classification, and market state information, enable traders to generate new or improve existing trading ideas.
  • Independently validate market data feeds: Coverage across Global Equities, ETFs, Futures, and Options combined with a harmonised data schema enables quant research teams to independently validate their internal market data feeds.
  • Conduct insightful market microstructure research: Granular market datasets and analytics combined with a rich documentation library and robust research tooling enable researchers to conduct insightful research efficiently.
  • Enhance execution analysis: A normalised view of all trades across venues and mechanisms with trade IDs enables traders to benchmark trade performance effectively.
  • Improve surveillance workflows: A harmonised view of market activity and a record of every order, with order IDs, enables surveillance teams to contextualise spurious orders with prevailing market conditions.

Resources

A new approach to Level 3 market data

13 May, 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.

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Normalisation: a dirty word or a differentiator for success?

9 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.

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Garbage In, Losses Out - What defines high-quality historical market data, and why it matters

17 September, 2025

Garbage In, Losses Out - What defines high-quality historical market data, and why it matters

In this article, Dr Elliot Banks explores what makes good quality, usable historical market data, and the key questions to ask when sourcing it.

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Find out more about how high quality historical market data can revolutionise your trading strategies

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