Five years of full depth US order book data now available via BMLL

Systematic hedge funds, quants and algo traders now able to benefit from five years of historic Level 3 data for insights, backtesting and alpha generation, thanks to data and analytics company BMLL Technologies.

As the demand for data driven insights to improve trading decisions is continuously increasing, so is the need for new, alternative and granular data sets that help industry participants navigate market volatility while maintaining a competitive advantage. However, the technical lift and scalable compute power required to ingest huge amounts of data and derive meaningful insights is significant. To achieve these insights, a large proportion of quants spend 80% of their time cleansing and harmonising data, before they can use it to improve trading decisions and backtest strategies for alpha generation.

BMLL is now able to offer five years of Level 3 data for US markets and European funds trading US stocks. These data sets enable participants to understand how markets behave and unlock the full potential of the predictive power of pricing data over a sufficiently long time horizon to capture a wide spectrum of market scenarios, without the need for complex in-house data cleansing and engineering capabilities. This ability has previously been the preserve of large systematic funds with in-house capabilities to generate, analyse and ingest these levels of data. 

Paul Humphrey, CEO of BMLL Technologies, says: “The race for speed is over and the industry is waking up to the predictive power of historic data. We all understand the value of pre-trade or real-time data and the costs associated with it. But predictability only comes from a deep understanding of how the market behaves; it comes from historic Level 3 order book data that includes every single order sent to an exchange over the last five years, the fill probability as well as average resting time of an order.” 

Dr Elliot Banks, CPO of BMLL Technologies, adds: “The primary use for our data is to find alternative sources of alpha generation. Having five years of Level 3 data means you have the ability to look beyond what happened in the market over the last 18 months. You can analyse long term cycles and trends, back test your strategies and look at the decision making processes that every market participant made to actually trade. That is absolutely critical if you want to find inferences and go beyond the top of the order book.”

BMLL is the only firm that offers granular historic order book data in a completely harmonised and information rich format alongside a comprehensive suite of analytics and the cloud resources needed to calculate these insights, all easily delivered and accessible in daily workflows. 

BMLL’s five years of data is available to market participants in two different formats. Python-native quants can access BMLL’s Data Lab, our data science platform that allows users to access Level 3 harmonised order book data, process it at scale, and find inferences by drilling down into every single message. 

Alternatively, our BMLL Data Feed can be easily consumed via API or FTP delivery straight into users’ research and production systems, delivering metrics such as average resting time of an order or fill probability at a particular level of the order book. 

The immediate outcome of using BMLL’s Data Lab and Data Feed is that data scientists no longer need to spend 80% of their time cleaning and harmonising the data. That time can now be spent actually creating value.

Ben Collins, Head of Sales, says: “The ability to derive meaningful insights from Level 3 data requires a dedicated, scalable environment and compute power.  To date, only a handful of high frequency trading firms have had the infrastructure to curate this level of data and analytics. At BMLL, we have done the heavy lifting for our clients. We bring together the full depth, historic order book data, the scalable, cloud-based, compute power and unrivalled data science to deliver unique analytics in a cost-effective manner.”