Electronic & Algorithmic Trading
Design and improve electronic execution across order and execution management, routing, market access and connectivity.

Markets & Trading
Develop models and analytics around trading, pricing and risk decisions.
Overview
Quantitative models support execution, pricing, risk, liquidity analysis and trading decisions. Their usefulness depends on the data, testing, workflow and governance around them.
Capmark helps clients design, prototype, backtest and implement models and analytical tools. We work with business, quant, technology, risk and operations teams to connect research with practical use.
Our role is to help clients develop and evaluate the tools they need, not to promise trading performance or investment returns.
What we do
We help define the model purpose, inputs, assumptions, outputs and intended use. The work connects the quant logic to the trading workflow, control requirements and decisions the model is meant to support.
We build prototypes and backtests that help teams test execution logic, signals, routing choices, pricing assumptions or portfolio rules. The emphasis is on disciplined testing, clean data, clear assumptions and evidence that can be reviewed.
We develop analytics for execution quality, slippage, spread capture, fill rates, venue performance, market impact and routing outcomes. These tools support algorithm selection, broker review, best execution and post-trade improvement.
We help move quant logic from spreadsheet, notebook or research code into controlled implementation. That can include Python services, APIs, data pipelines, model libraries, testing, release control, monitoring and integration with trading or risk platforms.
We document the model purpose, assumptions, limitations, testing, approval path and monitoring approach. Controls are designed around the way the model is used, whether it supports execution, pricing, risk, reporting or decision support.
We define the monitoring needed once models are live: performance drift, data-quality issues, exceptions, threshold breaches, user overrides and control evidence. Monitoring gives the desk and control functions a shared view of whether the model is behaving as expected.
What to expect
A senior practitioner leads the work with input from trading, quant, technology, risk, compliance and operations. The first stage defines the model purpose, users, data sources, decisions supported, control requirements and delivery constraints.
The work can then move through prototype, backtest, validation, implementation, testing, release control and monitoring.
Where the model affects client orders, execution decisions, pricing, risk or regulatory obligations, governance and evidence are built into the delivery path from the start.
Engagements can focus on model design, a prototype, validation support or integration into a trading workflow.
Establish the current state, the constraints, the risks and the value at stake.
Shape the target model and the business case with the executives who own the outcome.
Stand up the team, the plan and the governance around the outcome.
Design, build and test the change, with the business alongside.
Cutover, hypercare and handover, so the business runs it under its own control.
The same five stages on every engagement, led by senior practitioners end to end. How we work
Client result

Capital Markets · Trading Technology
A US Investment Bank · eFX & Equities Algorithmic Trading
The bank partnered with us to enhance its eFX platform, built on the third-party Integral solution, and its internal equities algorithmic trading platform: engineering back-to-back trades into the US book to manage risk, enhancing the Calypso-integrated middle office and updating order management systems including OPICS and Fenics.
Read the case study
Insights
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