Can post-trade TCA data strengthen your portfolio decisions?

KEY TAKEAWAYS

• Post-trade TCA becomes more powerful when viewed through a portfolio lens. Linking execution costs and momentum with portfolio returns reveals whether trading reinforced or eroded alpha.
• As market volatility and momentum shifts persist across sectors and market capitalizations, combining momentum and trading costs can guide better execution decisions—helping traders and portfolio managers know when to accelerate, slow down, or adjust strategy to protect or enhance performance.
• APIs turn post-trade TCA data into a decision-support tool, enabling scalable, on-demand integration of costs, momentum, and portfolio data to improve monitoring, attribution, data-driven decisions, and outcomes. 

This was written by David Sobolewski and Mike Googe, BTCA product managers at Bloomberg.

The industry is signaling that post-trade TCA matters more than ever. According to Bloomberg Intelligence research, “Traders increasingly rely on third parties for post-trade TCA, signaling a desire for independent, credible assessment,” making the case that portfolio performance and execution quality can no longer live in silos.

Can post-trade TCA data strengthen portfolio decisions?

Portfolio performance and trading results are often viewed separately. While portfolio managers focus on returns, attribution and risk, traders and execution teams track implied costs and market impact. When bringing portfolio and execution data together, it is possible to uncover deeper insights that can illustrate the combined outcomes that can make a competitive difference. The key is framing execution data, including post-trade Transaction Cost Analysis (TCA) data, through a portfolio lens. 

How can TCA data help inform portfolio and trading decisions?

When a factor or holding outperforms or underperforms, how much do trading costs and momentum contribute? Did slippage quietly erode alpha, or did favorable momentum amplify results?  By overlaying portfolio returns with TCA data, these questions can begin to be answered.  Combined views can reveal whether strong performance was reinforced, or quietly offset, by execution costs. Take a simple example: suppose there’s a need evaluate whether monthly technology portfolio returns were supported by momentum or undermined by trading costs. With an API, traders could: 

• Pull all technology trades, grouped by cap size

• Overlay portfolio returns with average arrival cost per month

• Add a momentum curve for each subgroup

The results might, for example, show that large caps contributed positively with low costs and sustained momentum, while small caps underperformed, weighed down by higher costs and fading momentum. If these patterns align with expectations, they can point to specific trading strategy adjustments that may improve portfolio performance going forward.

How can managing momentum while trading improve investment outcomes?

When momentum remains strong and costs are contained, it reinforces confidence in both timing and execution. In these situations, accelerating trades may create opportunities to enhance performance. Portfolio managers can share this feedback with traders, confirming that their approach is effective and encouraging them to maintain or even increase their pace when momentum signals remain strong for specific names or factors. 

Conversely, when momentum remains neutral or weak while trading costs run high, it may suggest that execution was too fast. In these cases, slowing the pace can create opportunities to improve results. Portfolio managers can share this feedback with traders, highlighting that when momentum signals stay soft for certain names or factors, a more measured approach may help reduce costs and protect returns.

What if you could integrate trading costs and momentum with your portfolio returns? 

Traditionally these two disciplines have existed in separate silos, whether in systems operational responsibility. However, demand to demonstrate portfolio performance that showcases all aspects of the investment process is becoming a key differentiator. That’s where an API comes in.  

By linking TCA data with portfolio returns, individuals can get a fuller picture of how trading influenced performance. APIs let users weave costs and momentum into their portfolio views, so they can see how they shaped outcomes, across the factors and timeframes that matter most to them. This integration isn’t just about efficiency; it’s about transforming TCA data into a practical decision-making tool.

Why use an API?

Traditionally, TCA was seen as a compliance requirement or a quarterly check on trading costs. But when accessed through an API and connected to portfolio returns, it becomes something far more powerful: a continuous feedback loop. 

TCA datasets already contain the execution and momentum details needed for deep analysis and more. The real challenge is scale. These datasets span weeks, months, or even years of trades across sectors, market caps, and geographies and they tend to reside in distinct systems. 

That’s where an API can prove valuable. Instead of moving entire datasets, users can pull only the data points needed, exactly when needed, to enable: 

• Merging TCA data with portfolio returns or holdings 

• Custom calculations, such as attribution models 

• Extraction of insights tailored to your strategy and time horizon 

• Portfolio managers to test whether alpha forecasts hold up after trading costs 

• Traders to spot when low slippage hides rising portfolio risk, or when high costs were justified by joining a favorable trend 

• Risk teams to track whether execution patterns consistently support or undermine outcomes in certain sectors or cap sizes 

This feedback loop makes TCA data a living part of the investment process, ensuring portfolio outcomes reflect not just returns but also the contribution to total returns from the execution process. Most importantly, an API makes it easy to deliver results directly into a user’s applications or dashboards. When TCA data no longer sits in static reports, it becomes an active driver of portfolio monitoring, attribution, and decision-making. And if there’s a need to pull and store large historical TCA datasets, the same API can support both on-demand workflows and long-term data management to deliver scalability.

Conclusion

Connecting portfolio returns with TCA data can help you see and demonstrate to your customers the total performance outcomes of your business. An API can transform TCA from a static report into a dynamic decision-making tool, allowing users to pull the right data at the right time, merge it with portfolio insights, and deliver it in a format that prompts action. 

In short: APIs help connect execution quality, momentum, and portfolio outcomes—so you can make faster, sharper, and better-informed investment decisions and improve outcomes.