Accelerating Market Insight with BMLL Trades Plus
Linking trades and quotes together to provide a deeper understanding of market microstructure
By Anantya Bhatnagar, Quantitative Analyst, BMLL
Execution quality has always been a core performance driver for traders, brokers, and venues. Yet, until now, the granularity required to truly understand intraday execution behaviour has often been fragmented or incomplete. To truly understand intraday behaviour, it is necessary to examine when trading activity clusters, where liquidity resides, and how different mechanisms interact to move the market.
While prices and volumes provide the starting point, they only tell part of the story. Every trade takes place against a backdrop of quoted bids and offers, and the relationship between the two is what reveals real market dynamics. In practice, trades and quotes are rarely analysed in isolation. The informational content of a trade can only be properly understood in the context of the prevailing quotes at the time it occurred, while quotes themselves gain significance from how they shape or respond to executed trades. Understanding markets therefore requires linking trades and quotes together, a task that demands careful alignment and significant processing power, but one that is essential for an accurate view of liquidity and intraday behaviour.
Removing the need for complex coding, reducing computation time
BMLL’s Trades Plus dataset brings these dimensions together, enriching historical trades and quotes data with market context, enabling practitioners to build comprehensive intraday execution profiles. With granular trades and quotes data enriched by BMLL’s Level 3 analytics, Trades Plus removes the need for complex coding or processing, giving users immediate access to insights that accelerate research. The efficiency gains relative to combining datasets represent a step change. Tasks such as markout calculations, which previously consumed hours of cluster time, can now be completed in just minutes. This reduction in computation time allows researchers to test more ideas, refine strategies faster, and reduce costs.
Gaining a holistic view of execution quality and market impact
Trading today involves navigating a complex web of venues, execution mechanisms, and trade types. Traditional datasets often make it hard to join the dots and fall short in showing when, where, and how trades take place, or what their impact is on prices. Trades Plus fills this gap by combining detailed trade-by-trade data with standardised classifications, enriched flags, order book analytics, and integrated quote-based metrics, enabling a holistic view of execution quality and market impact.
The Trades Plus dataset unlocks insights such as:
Intraday trading patterns: How activity is distributed throughout the day, and which classifications dominate at different times (e.g., opening auction, midday, closing session).
Block liquidity sourcing: When and where large block trades are executed, helping traders identify venues best suited for sourcing liquidity.
Market impact profiling: How trades of different classifications move the market before and after execution, showing distinct impact curves and patterns of mean reversion.
Venue quality assessment: Which venues tend to supply liquidity at the midpoint, at the quoted touch, or within the spread.
The examples above highlight just a glimpse of what the Trades Plus dataset makes possible. Beyond these use cases, Trades Plus unlocks a wide spectrum of powerful analyses, giving users the flexibility to address their unique requirements and uncover new insights. To demonstrate its capabilities, the following exhibits, drawn from UK stocks on 22 August 2025, provide an illustration of the types of insights Trades Plus can reveal.
Analysing multiple different signals into a complete execution picture.
The first exhibit plots trading activity by standardised classification over the course of the day. Read left to right, it shows how certain mechanisms dominate at the open, subside through the midday and intensify again as the close approaches. For a portfolio manager or trader, this intraday pattern informs when to schedule participation to maximise spread capture or minimise footprint, depending on their objective.
The second exhibit turns to block-sized prints and, crucially, splits them by execution venue. The picture that emerges is not just how much block liquidity is available, but where and when it can be sourced with the least signalling. Venues display distinct profiles: some are reliable hosts of size around the close; others see block activity cresting earlier in the afternoon. This becomes an insightful criterion for venue selection and routing rules, so that traders can route larger orders at the times and places where it is optimal to do so.
With the third exhibit, the focus shifts from where and when to what happens next. By measuring the primary midpoint before and after trades, the chart shows market impact curves by venue. You can see the immediate move against the trader, the extent of any mean reversion, and how that profile differs across venues. This gives practitioners a way to tune urgency, adjust participation rates and choose venues that consistently align with their tolerance for slippage.
Finally, the fourth exhibit examines venue quality through the lens of spread capture. Using a simple PricePoint measure, where 0.5 denotes midpoint trades, values between 0 and 1 indicate executions inside the spread, and values above 1 or below 0 reflect trades outside the spread, the distribution makes clear which venues deliver genuine price improvement and which skew toward at-touch or outside-spread outcomes. Armed with this, a trading desk can tier venues in a way that reflects realised execution quality.
Taken together, these views show how Trades Plus enables users to analyse multiple different signals into a complete execution picture.
For buy-side firms, that means discovering where to route size without information leakage, scheduling slices for times and mechanisms that deliver the desired outcome, and evidencing choices.
For sell-side brokers, it enables benchmarking venues and algos on markouts and spread capture, and codifying rules that adapt intraday as liquidity shifts.
For exchanges, trading venues and market operators, the same metrics provide a feedback loop to evaluate matching rules, sessions and incentives against realised price improvement and market impact, creating a framework to monitor and enhance market quality.
A deeper and faster understanding of how and where liquidity is formed
Trades Plus is a unique dataset that enables faster analysis by bringing together enriched historical trade data, standardised classifications, and market context in one place. Instead of stitching together multiple sources, users can immediately benchmark execution, analyse spread capture and market impact, and identify different trade types such as dark, block, or retail. This saves time and computation resources and reduces complexity. It also enables a deeper understanding of how and where liquidity is formed, allowing traders, brokers, and venues to make better decisions, refine strategies, and uncover real patterns. If execution is the sum of when you trade, where you source, and how your orders move prices, Trades Plus helps market participants trade better and prove it.
