What Are the Current Best Practices for Demonstrating Best Execution after RTS 28? ▴ Question

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Concept

The mandate to demonstrate best execution under RTS 28 represents a fundamental architectural challenge for an investment firm. It shifts the objective from simply achieving a favorable price to engineering and evidencing a consistently optimal execution process across a matrix of competing factors. This is a system-level problem requiring a system-level solution.

The core task is to construct a data-centric framework that captures, analyzes, and justifies execution choices in a way that is both transparent to regulators and intelligible to clients. The regulation compels firms to move beyond subjective assessments and build a quantifiable, evidence-based defense of their execution quality.

At its heart, RTS 28 is about systematic proof. It demands that a firm not only establish a robust order execution policy but also rigorously monitor its own performance against that policy. This creates a continuous feedback loop where execution data informs strategy, and strategic adjustments are then validated by subsequent data. The process is one of perpetual optimization, driven by the granular analysis of every transaction.

The regulation effectively transforms the compliance function into a quantitative analysis unit, tasked with dissecting execution performance to prove that the firm is consistently delivering the best possible outcome for its clients. This requires a deep integration of trading, compliance, and data analysis functions, turning a regulatory requirement into a driver of operational and strategic improvement.

Demonstrating best execution after RTS 28 requires firms to systematically prove that their execution processes consistently deliver the best possible results for clients through rigorous data analysis and transparent reporting.

The complexity arises from the multidimensional nature of “best execution” itself. The regulation explicitly states that it is not a singular focus on price. It is a composite of price, costs, speed, likelihood of execution, settlement size, and any other relevant consideration. This multifactor model precludes simple, one-dimensional comparisons.

A firm cannot simply point to the best price at the moment of execution; it must construct a narrative, supported by data, that explains why its chosen execution strategy was optimal when all relevant factors were considered. This requires a sophisticated data capture and analysis architecture capable of weighing these often-competing variables to produce a coherent and defensible justification for each trading decision.

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Strategy

A successful strategy for demonstrating best execution under RTS 28 is built upon a foundation of three pillars ▴ comprehensive data capture, sophisticated analytical modeling, and transparent reporting. The objective is to create a seamless architecture that translates raw trade data into a clear and compelling narrative of execution quality. This process begins long before a report is generated; it starts with the systematic collection of high-quality data at every stage of the order lifecycle.

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Architecting the Data Framework

The initial strategic imperative is to ensure that the firm’s trading systems are configured to capture the necessary data with sufficient granularity. This includes not only the explicit costs of execution, such as commissions and fees, but also the implicit costs, such as market impact and timing risk. The data architecture must be able to link parent orders to their corresponding child fills, capture timestamps to the microsecond, and record the state of the market at the time of execution. This requires a close integration between the firm’s Order Management System (OMS), Execution Management System (EMS), and any proprietary trading algorithms.

Firms must also develop a systematic process for evaluating and selecting execution venues. The RTS 28 report requires firms to list their top five execution venues for each class of financial instrument. This necessitates a data-driven approach to venue analysis, where firms continuously monitor the execution quality offered by different brokers, multilateral trading facilities (MTFs), and systematic internalisers (SIs). This analysis should consider not just the headline execution price but also factors like fill rates, rejection rates, and the speed of execution.

A robust RTS 28 strategy transforms compliance from a reporting exercise into a continuous cycle of data-driven performance optimization and venue analysis.

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What Is the Role of Quantitative Analysis?

The second pillar of the strategy is the application of sophisticated quantitative analysis, commonly known as Transaction Cost Analysis (TCA). TCA provides the analytical engine for demonstrating best execution. It moves beyond simple comparisons to provide a risk-adjusted assessment of execution performance. A comprehensive TCA framework will typically incorporate a range of benchmarks to evaluate different aspects of the trading process.

For instance, a pre-trade benchmark, such as the arrival price, measures the cost of slippage from the time the decision to trade is made to the time the order is executed. An intra-trade benchmark, such as the Volume-Weighted Average Price (VWAP), assesses the performance of the execution algorithm itself. Post-trade benchmarks can be used to evaluate the opportunity cost of not trading or to assess the long-term impact of a trade on the market.

The following table illustrates a simplified comparison of execution venues based on key performance indicators that would inform a firm’s strategic routing decisions.

Execution Venue	Asset Class	Average Price Improvement (bps)	Average Fill Rate (%)	Average Execution Speed (ms)	Primary Strategic Use Case
MTF Alpha	Equities	0.50	92%	150	High-touch orders requiring complex execution logic
SI Broker Beta	Equities	0.25	99%	50	High-frequency, low-latency execution of liquid stocks
Broker Gamma	Fixed Income	1.20	85%	500	Sourcing liquidity for large, illiquid bond orders
MTF Delta	Derivatives	0.75	95%	100	Standardized futures and options execution

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Constructing the Narrative

The final pillar of the strategy is the construction of a clear and transparent narrative. The RTS 28 report is not just a data dump; it is a communication tool. The firm must be able to explain its execution policies, its venue selection process, and the results of its TCA analysis in a way that is understandable to both clients and regulators.

This requires a combination of quantitative data and qualitative commentary. The qualitative commentary should explain the firm’s trading philosophy, its approach to managing conflicts of interest, and any specific factors that influenced its execution decisions during the reporting period.

This strategic framework transforms the RTS 28 reporting requirement from a compliance burden into a source of competitive advantage. By systematically measuring and analyzing execution quality, firms can identify opportunities to improve their trading performance, reduce costs, and ultimately deliver better outcomes for their clients.

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Execution

The execution of a best execution framework under RTS 28 is a detailed, multi-stage process that integrates technology, data science, and governance. It requires the creation of a repeatable, auditable workflow for monitoring execution quality and producing the required regulatory reports. This section provides a granular, operational playbook for implementing such a framework.

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The Operational Playbook for RTS 28 Compliance

Implementing a robust RTS 28 process involves a clear, sequential flow of data and analysis. This operational playbook outlines the critical steps from data acquisition to final report dissemination.

Data Acquisition and Normalization ▴ The foundational step is the automated capture of all relevant order and execution data. This requires configuring the firm’s EMS and OMS to log specific data points using standardized protocols like the Financial Information eXchange (FIX) protocol. Key FIX tags to capture include Tag 11 (ClOrdID), Tag 38 (OrderQty), Tag 44 (Price), Tag 54 (Side), Tag 60 (TransactTime), and Tag 30 (LastMkt). This data must then be normalized into a consistent format across all execution venues and asset classes to enable meaningful comparison.
TCA Calculation Engine ▴ Once the data is normalized, it is fed into a TCA engine. This engine calculates a range of metrics against various benchmarks. The choice of benchmarks should be tailored to the specific asset class and trading strategy. For example, an algorithmic trade in a liquid equity might be measured against VWAP, while a large block trade in an illiquid corporate bond would be better assessed against the arrival price.
Exception Reporting and Investigation ▴ The TCA engine should be configured to automatically flag trades that fall outside of predefined performance thresholds. These “exception reports” trigger a manual investigation by the trading or compliance team to determine the root cause of the poor execution. This process creates a documented audit trail demonstrating active monitoring.
Qualitative Factor Overlay ▴ The quantitative results from the TCA engine must be supplemented with a qualitative analysis. This involves assessing factors that are not easily quantifiable, such as the quality of post-trade settlement, the likelihood of information leakage, and the counterparty risk associated with a particular venue.
Report Generation and Review ▴ The final step is to synthesize the quantitative and qualitative analysis into the RTS 28 report. The report must be reviewed and signed off by senior management before being made public. This governance process ensures accountability and demonstrates a firm-wide commitment to best execution.

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How Should Quantitative Modeling Be Applied?

Quantitative modeling is the engine of the RTS 28 process. The following table provides a granular example of a TCA output for a single asset class, demonstrating the level of detail required for effective monitoring. This data would be aggregated across all trades in the reporting period to inform the venue rankings in the final RTS 28 report.

Execution Venue	Trade ID	Instrument	Notional Value (EUR)	Arrival Price	Execution Price	Slippage vs. Arrival (bps)	VWAP Benchmark	Performance vs. VWAP (bps)
MTF Alpha	789012	VOD.L	500,000	135.50	135.52	-1.48	135.53	0.74
SI Broker Beta	789013	VOD.L	250,000	135.51	135.51	0.00	135.53	1.48
MTF Alpha	789014	AZN.L	750,000	8450.00	8448.00	2.37	8447.50	-0.59
Broker Gamma	789015	DBK.DE	1,000,000	9.80	9.82	-2.04	9.81	-1.02

This level of detailed analysis allows a firm to move beyond simple comparisons and have a nuanced, data-driven conversation about execution quality. It allows the firm to identify which venues are performing well for specific types of orders and to adjust its routing logic accordingly.

Effective execution of an RTS 28 framework hinges on translating granular, quantitative TCA data into a coherent, qualitative narrative that justifies strategic routing decisions.

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System Integration and Technological Architecture

The technological architecture required to support this process must be robust and scalable. It typically involves several interconnected systems:

Order and Execution Management Systems (OMS/EMS) ▴ These systems are the primary source of trade data. They must be configured to capture a rich set of data points for each order, including all relevant timestamps and order routing decisions.
Market Data Infrastructure ▴ A reliable source of high-quality market data is essential for calculating TCA benchmarks. This includes both real-time and historical data for all relevant asset classes.
TCA and Analytics Platform ▴ This can be a proprietary system or a third-party solution. The key is that it must be able to ingest data from the OMS/EMS and market data feeds, perform the necessary calculations, and generate customizable reports.
Data Warehouse ▴ A centralized data warehouse is needed to store the vast amounts of trade and market data required for historical analysis and regulatory reporting.

The integration of these systems is critical. Data must flow seamlessly from the trading desk to the analytics platform to the final report, with minimal manual intervention. This level of automation reduces the risk of errors, improves efficiency, and ensures that the firm can produce timely and accurate reports.

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References

Healey, Rebecca, and Alex Wolcough. “FIX Trading Community releases Recommended Practices for Best Execution Reporting as required by MiFID II RTS 27 & 28.” FIXimate, 18 Oct. 2017.
TRAction Fintech. “Best Execution Best Practices.” TRAction Fintech, 1 Feb. 2023.
SALVUS Funds. “Best Execution in Practice and the new RTS 27/28 requirements.” SALVUS Funds, 24 Oct. 2024.
Optiver. “A better way to measure best execution.” Optiver, 8 Nov. 2021.
European Securities and Markets Authority. “Questions and Answers on MiFID II and MiFIR investor protection and intermediaries topics.” ESMA35-43-349, 2023.

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Reflection

The architectural framework required by RTS 28 prompts a deeper inquiry into a firm’s operational philosophy. Viewing this regulation as a data and systems challenge transforms it from a retrospective reporting duty into a prospective tool for strategic refinement. The process of gathering, analyzing, and reporting on execution quality forces a systematic evaluation of every component in the trading lifecycle, from algorithmic logic to venue selection. How does your current technological architecture support this level of granular analysis?

The true value of this exercise lies in the continuous feedback loop it creates, providing a constant stream of intelligence that can be used to hone the firm’s execution strategy. The ultimate question is whether your firm’s systems are designed merely to meet a compliance threshold, or if they are engineered to produce a persistent, data-driven competitive advantage in the market.