What Are the Core Data Requirements for Proving Best Execution under MiFID II?

Concept

The regulatory obligation known as Markets in Financial Instruments Directive II (MiFID II) has fundamentally reshaped the landscape of European financial markets. At its core, the directive mandates a rigorous, evidence-based approach to best execution. The recent suspension and planned removal of the prescriptive RTS 27 and RTS 28 reporting formats does not, in any way, diminish this core duty.

Instead, this regulatory evolution elevates the conversation. The focus shifts from a compliance exercise of report generation to a far more profound operational challenge ▴ constructing an internal, systemic, and auditable data framework that can demonstrably prove that all sufficient steps have been taken to achieve the optimal outcome for a client.

This transition presents a critical inflection point for investment firms. The removal of a standardized reporting template means that the responsibility for defining, capturing, and analyzing the relevant data falls squarely on the firm itself. It demands a move away from siloed data pools and post-trade report cards toward an integrated data architecture.

This system must be capable of weaving together a coherent narrative for every single client order, from the pre-trade analysis of potential venues to the final settlement and cost reconciliation. The objective is to build a definitive, internally consistent proof of process.

The core of MiFID II compliance lies not in periodic reporting, but in the continuous, systemic ability to demonstrate execution quality through a robust data infrastructure.

Proving best execution is now an engineering problem. It requires the design of a data-centric operating system for trade execution, one that captures high-fidelity data at every stage of the order lifecycle. The quality of this data, its granularity, and the sophistication of the analytical models applied to it are the new grounds upon which a firm’s adherence to its fiduciary duty will be judged.

The core data requirements, therefore, are not a simple checklist of fields to be populated. They are the foundational elements of a system designed to provide empirical validation of a firm’s execution policy in action, transforming a regulatory mandate into a source of operational intelligence and a distinct competitive advantage.

Strategy

A strategic approach to MiFID II best execution data transcends mere compliance. It involves architecting a data ecosystem that serves two primary functions ▴ providing irrefutable proof of the execution process and generating actionable intelligence to refine that process over time. The foundational strategy rests on systematically capturing and analyzing data related to the core execution factors defined within the directive ▴ price, costs, speed, likelihood of execution, size, and nature of the order.

The Execution Factor Data Framework

The first step is to establish a comprehensive data framework organized around the MiFID II execution factors. This is not a static repository but a dynamic system that links pre-trade market conditions with post-trade results. The strategy involves creating a logical data model that can answer not only “What happened?” but also “Why did it happen?” and “Could a better outcome have been achieved?”.

A successful strategy requires treating every client order as a discrete event to be analyzed within a rich data context. This involves a three-pronged data collection approach:

• Static and Semi-Static Data This includes client-specific information and instrument characteristics. For example, a client’s classification as retail or professional directly influences the weighting of execution factors, with total consideration (price and costs) being paramount for retail clients. The data strategy must ensure this classification is appended to every order record.
• Pre-Trade Dynamic Data This is the most critical and challenging dataset to capture. For every client order, the system must record a snapshot of the available liquidity and pricing across all relevant and available execution venues at the moment the routing decision is made. This forms the basis of any credible ex-ante analysis.
• Post-Trade Execution Data This involves capturing the complete lifecycle of the executed order with microsecond-level timestamping. It includes the execution price, venue, explicit costs, and settlement details. This data provides the “ground truth” against which the pre-trade analysis and execution policy are validated.

A Comparative Analytical Engine

With a robust data collection framework in place, the next strategic pillar is the development of a powerful comparative analytical engine. This engine’s purpose is to continuously benchmark execution quality against internal policies and external data sources. The strategy here is to move beyond simple post-trade reports to a system of active monitoring.

The table below outlines a strategic framework for mapping data inputs to analytical outputs, forming the core of a firm’s best execution monitoring capability.

An effective data strategy for MiFID II transforms the best execution obligation from a static compliance check into a dynamic feedback loop for continuous improvement.

This strategic framework ensures that the firm is not only collecting data but is actively using it to test the effectiveness of its execution arrangements. For instance, if the data consistently shows that a particular venue offers superior prices but suffers from high latency and low fill rates for certain order types, the firm can make a data-driven adjustment to its routing logic. This is the essence of taking “all sufficient steps” ▴ a continuous, evidence-based process of optimization. The strategy culminates in the ability to produce, on demand, a comprehensive audit file for any given trade that justifies the execution outcome through a complete and coherent data narrative.

Execution

The execution of a MiFID II-compliant data framework is a complex undertaking that requires a meticulous, engineering-led approach. It moves beyond high-level strategy to the granular detail of data schemas, quantitative models, and technological integration. This is the operational core where regulatory theory is translated into auditable reality.

The Operational Playbook

Implementing a robust data infrastructure for best execution requires a step-by-step operational playbook. This process ensures that all necessary data points are captured with the required fidelity and are structured for effective analysis.

1. Data Source Identification and Integration The first step is to map and connect all data sources. This involves integrating the Order Management System (OMS), Execution Management System (EMS), market data feeds from direct and indirect sources, and post-trade settlement systems into a unified data lake or warehouse.
2. Order Lifecycle Timestamping A critical requirement is the implementation of high-precision timestamping (ideally to the microsecond) at every key stage of the order’s life. This must be synchronized across all systems using a common time source like Network Time Protocol (NTP).
3. Development of a Unified Data Schema A canonical data model must be established to store all order-related information. This schema must be flexible enough to handle various asset classes but rigid enough to ensure data consistency. It should contain hundreds of fields, linking client information, pre-trade conditions, execution details, and post-trade costs into a single, comprehensive record.
4. Implementation of Ex-Ante Data Capture At the point of making a routing decision, the system must automatically query all potential execution venues and record their state. This includes capturing top-of-book prices, depths, and any explicit cost information available at that instant. This “best-efforts” snapshot is the evidentiary foundation for justifying the routing choice.
5. Automated Post-Trade Reconciliation The system must automatically reconcile executed trade data with the original order and the associated pre-trade market snapshot. It should also ingest and allocate all explicit costs, from exchange fees to settlement charges, back to the parent order.
6. Exception Reporting and Alerting An automated monitoring system must be established to flag any execution that deviates significantly from the firm’s execution policy or from expected outcomes based on the pre-trade analysis. These alerts trigger a manual review process, which itself is logged and documented.

Quantitative Modeling and Data Analysis

With the data infrastructure in place, the focus shifts to quantitative analysis. The goal is to apply a suite of models that can measure execution quality from multiple perspectives, satisfying the multi-faceted nature of the MiFID II requirements.

Transaction Cost Analysis (TCA)

TCA is the cornerstone of post-trade analysis. A comprehensive TCA framework must be implemented, calculating a range of metrics to provide a holistic view of performance. The primary metric is Implementation Shortfall, which captures the total cost of execution relative to the decision to trade.

Implementation Shortfall Formula ▴

IS = (Execution Price – Arrival Price) + Explicit Costs

Where the Arrival Price is the mid-price of the security at the time the order is received by the trading desk. This core metric is then decomposed to isolate different sources of cost and performance.

The following table provides a sample of a detailed TCA output for a single institutional order, illustrating the granularity of data required.

Predictive Scenario Analysis

To satisfy the ex-ante component of best execution, firms must demonstrate that their routing logic is intelligent and data-driven. This involves conducting predictive scenario analysis to model and compare potential execution outcomes.

Case Study a Hypothetical Large-Cap Equity Order

Consider a portfolio manager placing an order to buy 500,000 shares of a liquid DAX-listed stock. The time is 10:30 AM CET. The firm’s Best Execution System (BES) immediately captures the arrival price (€150.50) and analyzes the available liquidity across three primary venues ▴ the lit primary exchange (Venue A), a large MTF (Venue B), and a consortium of dark pools (Venue C).

The BES’s ex-ante model runs a simulation based on historical data for similar orders. It predicts the following outcomes:

• Scenario 1 Pure Lit Market Execution (Venue A) The model predicts high market impact. Executing the full size on the lit book would likely drive the price up by 15 basis points. However, the likelihood of full execution is very high (99.9%) and the explicit costs are low (2.0 bps). The predicted latency is minimal.
• Scenario 2 Mixed MTF and Dark Pool Execution (Venues B & C) The model suggests routing 60% of the order to the MTF and 40% to the dark pools. It predicts a lower market impact, with an expected price slippage of only 5 basis points. The explicit costs are slightly higher (3.0 bps), and the likelihood of achieving a full fill within the desired timeframe is lower (85%), potentially leaving a residual amount to be executed later at an unknown price.
• Scenario 3 Algorithmic Execution (VWAP Strategy) The model simulates a VWAP algorithm executing passively over the course of four hours. This scenario predicts the lowest market impact (near zero slippage against the interval VWAP) but carries significant timing risk; if the market trends upwards throughout the day, the final execution price could be substantially higher than the arrival price.

In this case, the client is a professional investor whose policy prioritizes minimizing market impact over speed. The BES, having logged the client’s profile and the ex-ante analysis, recommends and executes Scenario 2. The system logs the rationale for this decision, including the predicted cost savings in terms of market impact versus the slightly higher explicit costs and fill uncertainty.

After the execution is complete, the post-trade TCA report confirms that the actual market impact was 6 basis points, closely aligning with the model’s prediction. This entire workflow, from pre-trade modeling to post-trade analysis, forms a complete and defensible audit trail, proving that a sufficient, data-driven process was followed to achieve the best possible result for the client based on their specific priorities.

System Integration and Technological Architecture

The technological foundation for this capability requires a sophisticated and highly integrated architecture. It is not a single piece of software but a network of communicating systems.

Core Components

• Central Data Warehouse A high-performance database, often a columnar or time-series database, serves as the central repository for all trade and market data. This is the single source of truth for all analysis.
• Market Data Capture Engine This component subscribes to low-latency data feeds from all relevant venues. It must be capable of processing millions of messages per second and writing them to the data warehouse with synchronized timestamps.
• Order and Execution Data Bus An enterprise messaging bus (like Kafka or a proprietary equivalent) is used to stream order and execution data from the OMS and EMS in real-time. This ensures that the data warehouse is continuously updated as orders progress.
• TCA and Analytics Engine This is the computational heart of the system. It runs both real-time (for ex-ante analysis) and batch (for post-trade TCA) calculations on the data stored in the warehouse. This engine can be built using languages like Python or Java, leveraging data science libraries for statistical analysis.
• Reporting and Visualization Layer A business intelligence tool (like Tableau, Power BI, or a custom web application) provides the interface for compliance officers, traders, and management to review execution quality, drill down into individual orders, and monitor system performance through dashboards and reports.

The integration between these components is paramount. For example, when a trader enters an order into the EMS, a message is published to the data bus. The analytics engine consumes this message, immediately pulls the latest market data from the warehouse, runs its ex-ante models, and can even provide a real-time “best venue” suggestion back to the EMS via an API call. This seamless flow of data and analysis is the ultimate expression of a system designed for the rigorous demands of MiFID II best execution.

Reflection

The transition away from prescriptive reporting towards a principles-based demonstration of best execution compels a fundamental re-evaluation of a firm’s internal data architecture. The systems and processes constructed to meet this enduring obligation should not be viewed as a mere compliance utility. Instead, they represent the central nervous system of the trading function itself. The quality of this system ▴ its speed, its analytical depth, its completeness ▴ directly reflects the firm’s commitment to its clients.

The data points and analytical models discussed are the building blocks of a much larger construct an operational framework for institutional intelligence. By mastering the flow of execution data, a firm gains a high-fidelity lens through which it can view its own performance and the broader market structure. This capability creates a powerful feedback loop, where the evidence gathered to prove compliance simultaneously illuminates pathways to superior execution. The ultimate objective is to create a system so robust and transparent that the quality of execution becomes an inherent property of the process, not a conclusion reached after the fact.