For years, the challenge for pre-trade TCA has been not only developing high-quality analytics, but making them practical to apply consistently across the full breadth of trading activity, according to Vlad Rashkovich, Global Head of Quantitative Trading Research and Analytics at Bloomberg.

“Traders manage hundreds of orders in fast-moving markets, so even valuable analytics have often been used selectively. API delivery changes that by giving firms the flexibility to integrate best-in-class execution analytics directly into their proprietary trading environments alongside the tools that best fit their workflows,” he told Traders Magazine.

On July 8, Bloomberg announced the launch of its multi-asset Pre-Trade Transaction Cost Analysis (TCA) API, allowing clients to programmatically access Bloomberg’s pre-trade transaction cost analytics at scale and integrate them directly into their proprietary fixed income and equities trading and investment workflows.

Bloomberg said the API is designed to help firms apply pre-trade analytics more consistently across their trading activity. While pre-trade TCA is widely used to support execution decisions, practical workflow considerations have meant analytics have often been applied selectively.

Through the API, firms can embed execution analytics directly into their existing trading systems, allowing them to access Bloomberg’s pre-trade transaction cost analytics across a broader range of trading activity while reducing manual processes and operational friction.

“Rather than asking traders to leave their workflow to access decision-support tools, analytics become an integral part of the execution process, helping firms apply a more consistent analytical framework across a much broader share of the order book,” Rashkovich said.

“Markets are becoming faster, more automated and increasingly multi-asset, raising the bar for execution decision-making across the trading lifecycle,” Rashkovich said.

Rashkovich said firms are looking for flexibility in how they build trading solutions, including the ability to combine capabilities from different sources.

“Firms are looking for the flexibility to build trading solutions using best-in-class components, which may come from multiple sources, rather than relying on a single technology stack,” he said.

“That’s increasing demand for APIs that allow sophisticated execution analytics to be integrated directly into proprietary workflows,” he added.

Rashkovich also highlighted the importance of connecting analytics across different stages of the trading process.

“Equally important, firms are increasingly looking to connect pre-trade and post-trade analytics through a common analytical framework, creating a stronger feedback loop to measure execution outcomes, refine trading strategies and continuously improve decision-making,” he said.

As firms automate more of their order flow, Rashkovich said embedding analytics directly into execution systems is becoming increasingly important.

“It’s becoming increasingly important. As firms automate more of their order flow, they need full confidence in the tools powering rules engines,” he said.

“That means leveraging trusted pre-trade analytics to automate trade flow and provide the necessary guardrails,” Rashkovich added.

Rashkovich said APIs give clients flexibility in how they integrate analytics into their existing workflows.

“There are different ways for firms to consume trade analytics, but APIs give clients the flexibility to build those capabilities into their own workflows,” he said.

Rashkovich said adoption of multi-asset pre-trade TCA is unlikely to happen uniformly across all asset classes. Equities remain the most established asset class for pre-trade TCA, while fixed income and FX are evolving as data quality, market structure and analytical models continue to develop.

“I think adoption will continue to expand, but it’s unlikely to happen uniformly across all asset classes,” Rashkovich said.

“Today, pre-trade TCA is most established in equities, while fixed income and FX are rapidly evolving as data quality, market structure and analytical models continue to evolve,” he said.

“True cross-asset pre-trade TCA is still burgeoning and the adoption may vary by maturity of each market,” he added.

Looking ahead, Rashkovich said advances in data, analytics and artificial intelligence could further influence the development of transaction cost analysis and execution optimization.

“The next evolution of execution optimization is the creation of a connected and scalable analytical framework across the trade lifecycle, where firms have the flexibility to select best-in-class analytics for their workflows,” he said.

Rashkovich said improvements in data, modeling and AI could help firms identify opportunities to improve execution quality.

“Better data, more insightful models and AI deployed at scale have the potential to significantly optimize execution quality by surfacing high impact orders and solutions that drive stronger trading outcomes,” he said.

However, he emphasized that AI-driven workflows depend on the quality of the underlying data, methodologies and expertise.

“AI workflows are only as effective as the quality of the underlying data, methodologies and domain expertise behind it,” Rashkovich said.

“Many of the building blocks of TCA involve many decades of market experience, and those human insights will continue to play an essential role in developing the models that power AI-driven analytics,” he added.