BMLL Vantage Wins ‘Best Cloud-Based Market Data Delivery Solution’ at the Trading Tech Insight Awards Europe 2024

London, 1st March 2024: We are delighted to announce that BMLL has won ‘Best Cloud-Based Market Data Delivery Solution’ at the Trading Tech Insight Awards Europe 2024. These awards recognise excellence in trading solutions, services and consultancy for capital markets, and focus on vendors providing exceptional trading infrastructure, trading technology, and data solutions.

BMLL was awarded “Best Cloud-Based Market Data Delivery Solution” for BMLL Vantage, an intuitive no-code data visualisation tool for US and European equities and ETFs, that brings data science and quant analytics capabilities to all market participants.

BMLL Vantage users include:

  • Sales traders & market structure specialists - to understand market fragmentation, liquidity profiles, the evolution of different trading mechanisms and available liquidity

  • Exchange analysts - to understand how their venue compares to their peers, analyse market share moves, order resting times and fill probabilities

  • ETF issuers - to understand how their products are competing across different markets

  • Quant researchers who need to derive valuable insights quickly and efficiently

In 2023 over 20 datasets were added to BMLL’s coverage, including APAC, Americas and EMEA, all now available within BMLL Vantage.

Paul Humphrey, CEO of BMLL, said: “We are delighted to have been recognised for the Best Cloud-Based Market Data Delivery Solution at the Trading Tech Insight Awards Europe 2024. BMLL Vantage is an out-of-the-box data science and visualisation tool which continues our mission of democratising access to the most granular Level 3 market data and analytics by removing the need to code in order to derive meaningful insights.”

BMLL Vantage covers Equities and ETFs from over 100+ venues and 485 metrics that are categorised into 14 groups (auction analysis; volatility; execution market impact; trade quality; trade prices; liquidation cost; OHLC bars; depth liquidity; spread; liquidity classification; venue price quality; order placement statistics; order fill behaviour; and liquidity around BBO).

Users can look at daily, monthly or annually aggregated data sets and correlations between them; compare how stocks performed on different markets; sort and explore the data via simple, easy-to-use dashboards without requiring a quant analyst, data science or cloud computing resource.

About BMLL Technologies

BMLL Technologies is the leading, independent provider of harmonised, Level 3, 2 and 1 historical data and analytics to the world’s most sophisticated capital market participants, covering global equities, ETFs and futures.

BMLL offers banks, brokers, asset managers, hedge funds, global exchange groups, academic institutions and regulators immediate and flexible access to the most granular Level 3, 2 and 1 T+1 order book data and advanced pre and post-trade analytics. BMLL gives users the ability to understand market behaviour, accelerate research, optimise trading strategies and generate alpha more predictably.

Founded in 2014 in the machine learning laboratories of the University of Cambridge, the platform enables researchers and quants across global financial services firms to apply complex statistical techniques to BMLL’s unique big-data sets with applications such as market impact, pre and post trade analytics, order book simulation and compliance. Users no longer need to buy, curate and harmonise data. With BMLL, they gain cost-effective, instant access to a cloud-native Data Science environment via a single web portal, with a long history of the most granular, full order book data across global equities, futures and ETFs for back-testing and simulation, delivered directly into their workflows.

BMLL secured $26 million in their Series B funding round in Q4 2022, led by Nasdaq Ventures, FactSet and IQ Capital’s Growth Fund to fuel further product and geographic expansion. Snowflake Ventures joined the investment round in September 2023.

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