Workflow and technical layers
Our workflow has three practical layers. The data layer focuses on what you already collect, not on buying new feeds by default. We catalogue venue feeds, reference data, and archives, then decide which elements are stable enough to support cross venue liquidity mapping. We favour transformations that are reversible and explainable so that later audits are possible. The modelling layer applies deterministic rules where structure is clear and AI methods where inference adds value. For example, we might align instruments across venues using rules, then use models to detect shifts in where liquidity clusters over time. Each model is versioned, with inputs, outputs, and intended use documented. The integration layer delivers outputs into your research environment. We expose liquidity maps, surfaces, and anomaly markers in formats your analysts already use, such as time series, snapshots, or graph metrics. We then observe actual usage and prune components that do not support real research questions. This keeps cost in line with value and avoids a build up of unused complexity. Past performance does not guarantee future results, and results may vary.
Our internal methods give structure to how we analyse market structure, build liquidity maps, and connect them to research workflows.
Core internal methods we rely on
To make our approach usable, we rely on a small set of internal methods that we apply consistently across projects.
The Market Structure Baseline method describes how we analyse your existing view of venues and instruments. We inventory feeds, mappings, and routing rules, then compare them with how trading can actually occur. This often reveals inconsistencies that need resolution before AI based mapping makes sense. By addressing these first, we reduce the risk of building sophisticated models on top of unstable structure.
The Liquidity Mapping Layer method defines how we combine deterministic rules and AI components. We use rules for structural relationships that are known, such as instrument listings and venue affiliations. We use AI for inference tasks, such as estimating cross venue depth or identifying anomalous shifts. Each component has a clear role, and we avoid adding models where simple rules suffice. This keeps the system understandable and maintainable.
The Research Integration Loop method governs how we connect outputs to your workflow. We track which metrics analysts actually use, which drive changes in research driven trading design decisions, and which remain ignored. We then adjust or remove components accordingly. This keeps the mapping aligned with real usage and prevents long term accumulation of unused features. Past performance does not guarantee future results, and results may vary.
We explain how our AI driven liquidity mapping works, where it helps, and where its limits sit.
How we approach AI for cross venue liquidity research
We created this information page for teams that want a deeper view of how we apply AI to cross venue liquidity mapping before they commit time or budget. We outline our methods, constraints, and governance so you can decide whether our approach fits your research driven trading design process. We describe the mechanics rather than promising outcomes, because complex markets do not reward vague assurances.
We treat market microstructure as the fixed boundary for any AI liquidity mapping work.
We use AI as a hypothesis engine for liquidity behaviour, not as an opaque authority.
We document every transformation so researchers can audit and challenge outputs.
We design around budget constraints, reusing existing data and infrastructure first.
What this information page covers and what it does not
We explain the mechanics, constraints, and review processes behind our AI driven liquidity mapping so you can assess fit without marketing noise.
This page is for teams that want a precise explanation of our methods before deciding whether to start a deeper discussion.
We treat AI for cross venue liquidity mapping as infrastructure, not as a shortcut to trading decisions. That means we focus on data structures, model governance, and integration details rather than promising outcomes. We describe how we map venues, estimate liquidity surfaces, and compare them with realised behaviour. We also state where our approach does not apply, such as personal financial decisions or training programmes. This clarity helps you decide early whether our work aligns with your objectives and constraints.
Our process is iterative and evidence driven. We run the Liquidity Topology Cycle repeatedly, updating assumptions when the market structure changes or when execution data shows that our mapping no longer reflects reality. We log these changes and make them visible to your team, so you can see when and why the view of liquidity has shifted. This reduces the risk of relying on stale models and keeps researchers aware of the moving parts behind their inputs.
We also acknowledge limits. AI based liquidity mapping depends on data quality, venue coverage, and infrastructure. If those foundations are weak, we will say so and recommend a smaller scope or preliminary data work instead of pushing ahead. Past performance does not guarantee future results, and results may vary. Use the information here as one input alongside your own analysis and independent professional advice before committing resources.
We design our AI liquidity mapping so that it can be explained to risk, compliance, and technology stakeholders in concrete terms. We document data sources, model roles, validation routines, and monitoring thresholds. This documentation is not a marketing summary. It is a working reference that shows how each view of liquidity is produced and where its limits sit. This helps internal teams assess whether and how to allow the outputs into their research workflows.
We also recognise that not every environment is suitable for our approach. Constraints on data access, infrastructure, or governance may make AI based cross venue mapping impractical or disproportionate to the potential benefit. In those cases we prefer to say so clearly rather than stretch the method to fit. This avoids misaligned expectations and reduces the risk of partially implemented systems that nobody owns or trusts.
Throughout, we emphasise that our work supports research, not decision making on its own. Any use of our material in connection with trading, risk, or resource allocation should be combined with independent professional advice and internal approvals. Past performance does not guarantee future results, and results may vary. You remain responsible for ensuring that your use of any insights derived from our methods complies with applicable law and internal policies.
Examples of how teams use our liquidity mapping work
This section shows how AI driven cross venue liquidity mapping can support research driven trading design in different contexts without turning into a black box or an all or nothing platform change.
Event focused liquidity analysis
A research team wants to understand how liquidity shifts between venues around scheduled events. We start by mapping the relevant venues and instruments, then build a liquidity surface that highlights where depth clusters before and after those events. Analysts use this map as a backdrop when designing and reviewing their own event driven ideas. The AI does not propose trades. It provides a structured view of how liquidity behaves so the team can adjust their assumptions. Past performance does not guarantee future results, and results may vary.
Rationalising internal tooling
A technology lead needs to rationalise multiple internal scripts that each approximate liquidity across venues in different ways. We document the current pipelines, identify common steps, and replace overlapping logic with a single, documented liquidity mapping layer. AI components handle inference tasks, such as aligning fragmented trades, while deterministic rules cover structural relationships. This reduces maintenance load and gives researchers a consistent reference point.
Supporting oversight reviews
A governance team wants clarity on how AI based liquidity views are produced before approving their use in research workflows. We provide documentation that traces data sources, transformations, model assumptions, and validation checks. We explain where human judgement enters the process and how monitoring flags model drift. This lets oversight functions evaluate the approach on its merits instead of relying on opaque assurances.
Method overview
From venue graph to testable liquidity surface
We structure our AI work around a method we call the Liquidity Topology Cycle. First, we map the venue graph. We identify the venues, instruments, and routing constraints that matter for your research, then encode them into a structure the models must respect. This prevents the AI from inventing relationships that cannot exist in practice. Second, we estimate the liquidity surface over that graph. We combine direct observations from your existing feeds with model based inference to describe where depth concentrates, fragments, and shifts across venues. We treat every estimate as provisional and attach diagnostics that show confidence levels and data gaps. Third, we pressure test the surface against realised execution outcomes and other empirical checks. Where the map and reality diverge, we adjust assumptions, retrain, or retire components. This keeps models in the role of testable hypotheses, not fixed rules. Throughout this cycle we log transformations in plain language so researchers, technology teams, and oversight functions can review how each view of liquidity was produced. Past performance does not guarantee future results, and results may vary.
Key questions about our AI liquidity mapping work
This section answers common questions we receive from research, technology, and oversight teams that are evaluating AI for cross venue liquidity mapping but want clear boundaries and practical details before moving further.
- We work with research and technology teams that already have venue data and a need to understand how liquidity behaves across instruments and venues. Typical counterparts include heads of research, quantitative leads, and market structure specialists who want a consistent liquidity layer without committing to a full platform replacement.
- How we scope work
- What we do not provide
- We do not provide trading advice, personal financial guidance, or training programmes. Our focus is on analytical infrastructure for liquidity research. Any examples or case studies are illustrative only. Past performance does not guarantee future results, and results may vary. You should always combine our material with independent professional advice before making decisions.
- How we support governance
Who we work with
How integration works
We design our approach so that it can sit alongside existing research tools rather than displacing them. We integrate with your current data stores and analytics stack, then deliver outputs such as liquidity surfaces and venue metrics in compatible formats. This reduces integration time and makes it easier to trial the approach in a limited scope.
We use a simple, staged scoping method. First we define the venue set, instruments, and research questions that matter. Then we estimate effort for data preparation, model configuration, and integration. Finally, we agree on a narrow initial scope so that you can evaluate usefulness before expanding. We treat budget and operational constraints as design inputs, not afterthoughts.
We document model assumptions, data sources, and validation steps so that oversight and governance teams can understand how each view of liquidity is produced. We support reviews related to risk, compliance, and technology standards by providing clear descriptions of methods, limitations, and monitoring routines.