What Are the Core Data Inputs for a Machine Learning Execution Routing Model?

Concept

When you contemplate the challenge of institutional order execution, you are confronting a problem of immense dimensionality. The task is to navigate a fragmented, high-velocity, and often opaque landscape of liquidity to achieve a specific client objective. Your firm’s ability to translate that objective into optimal execution is the absolute measure of its operational competence.

An execution routing model, particularly one powered by machine learning, is the central nervous system of this capability. It is the cognitive engine at the core of your firm’s trading apparatus, designed to make a series of high-stakes decisions under conditions of extreme uncertainty and information asymmetry.

The question of its core data inputs moves directly to the heart of its intelligence. The model’s predictive power, its ability to learn and adapt, is entirely a function of the data it consumes. A poorly instrumented model, fed with low-resolution or incomplete data, is analogous to a pilot flying in dense fog with a faulty altimeter. It operates with a flawed perception of its environment, making suboptimal decisions that manifest as slippage, information leakage, and ultimately, a degradation of client trust.

A superior model, conversely, is fed a rich, multi-layered torrent of information that gives it a high-fidelity, multi-dimensional view of the market’s state and its own position within it. The quality of these inputs defines the ceiling of its performance.

The intelligence of a machine learning routing model is a direct reflection of the dimensionality and fidelity of its data inputs.

We can architecturally define these inputs not as a flat list, but as four distinct, yet interconnected, data strata. Each layer provides a unique dimension of context to the decision-making process. The synthesis of these layers allows the model to move beyond simple, rule-based logic and into the domain of predictive optimization.

It learns the subtle, non-linear relationships between an order’s characteristics, the market’s transient state, the specific attributes of available trading venues, and the accumulated memory of past execution outcomes. Understanding these data inputs is the first principle in designing, evaluating, or commissioning a truly effective execution routing system.

The Four Foundational Data Strata

The operational effectiveness of a routing model is built upon a foundation of four critical data categories. Each serves a distinct purpose, and their integration provides the model with a holistic view required for sophisticated decision-making. These are the sensory inputs for the trading system’s brain.

1. Order Specification Data This is the foundational layer, representing the specific task the model must accomplish. It contains all the static and semi-static attributes of the order itself, defining its intent and constraints.
2. Real-Time Market Data This layer provides the dynamic context of the external environment. It is a high-frequency snapshot of market activity across all relevant instruments and venues, representing the immediate conditions in which the order must be executed.
3. Historical Execution Data This is the model’s memory. It comprises a detailed ledger of all past orders, the routing decisions made for them, and the resulting execution quality. This data is the basis for learning and adaptation.
4. Venue Characteristic Data This layer is a semi-static map of the execution landscape. It contains detailed profiles of every available trading venue, including their explicit costs, implicit behaviors, and technological capabilities.

The power of the machine learning approach lies in its ability to process these four strata simultaneously. It can identify patterns that a human trader or a static rules-based system would miss. For example, it might learn that for a particular stock (Order Specification), during a specific type of market volatility (Market Data), a certain dark pool (Venue Characteristics) consistently provides better fills for orders of a certain size, a pattern only visible through the analysis of thousands of prior trades (Historical Data). This is the essence of a data-driven execution strategy.

Strategy

Developing a strategic framework for data inputs is about architecting a system that captures not just data, but meaningful signals. The objective is to construct a comprehensive, multi-dimensional feature set that allows the machine learning model to accurately predict the cost and probability of success for any potential routing decision. The strategy involves moving beyond raw data collection to a sophisticated process of data enrichment, normalization, and feature engineering. Each of the four foundational data strata must be treated as a source of predictive power, with a clear strategy for its capture and transformation.

Architecting the Input Layers

The strategic value of each data input is realized through its integration into the model’s decision matrix. The model assesses potential execution paths by weighing features derived from these inputs against its trained understanding of their impact on outcomes like slippage and fill probability.

Order Specification Data the Intent Layer

This layer defines the problem. The model must understand the order’s characteristics to select an appropriate strategy. An order to buy 100 shares of a highly liquid ETF has fundamentally different execution requirements than an order to sell a 500,000-share block of an illiquid small-cap stock. The data must capture this intent with precision.

• Instrument Identifiers These include standard codes like ISIN, CUSIP, or FIGI, which allow the model to link the order to all other relevant data, such as market data and historical performance for that specific security.
• Order Parameters This includes the side (buy/sell), quantity, desired currency, and order type (e.g. Market, Limit, Pegged). The limit price, if applicable, is a critical constraint.
• Benchmark and Constraints This defines the measure of success. Is the order benchmarked to Volume-Weighted Average Price (VWAP), Time-Weighted Average Price (TWAP), or Arrival Price? Are there specific client instructions, such as “avoid exchange X” or “maximize dark pool execution”? These constraints shape the universe of valid routing choices.
• Parent/Child Order Dynamics For large institutional orders (parent orders), the model must manage the execution of smaller child orders over time. Data inputs must include the parent order’s total size, the size of the current child slice, and the overall progress toward completion.

Real-Time Market Data the Context Layer

This is the most dynamic layer, providing a live feed of the market’s state. The strategy here is to capture data that signals liquidity, volatility, and momentum. This requires access to high-speed, low-latency data feeds from all relevant market centers.

Real-time market data provides the environmental context, enabling the model to adapt its strategy to transient liquidity and volatility conditions.

The model uses this data to assess the immediate cost and risk of placing an order on a specific venue. A deep, stable order book suggests that a lit market can absorb the order with minimal impact, while a wide spread and thin book might lead the model to prefer a dark pool or an RFQ protocol to avoid signaling risk.

The following table illustrates key inputs from this layer and their strategic relevance.

How Does Venue Analysis Influence Routing Strategy?

The selection of an execution venue is the primary output of the routing model. Therefore, a granular understanding of the characteristics of each available venue is a critical input. The strategy is to build a quantitative profile of each destination, treating it as a unique entity with its own behaviors and costs. This goes far beyond a simple fee schedule.

The model must learn, for instance, which dark pools offer meaningful size improvement but carry a higher risk of adverse selection from informed traders. It must understand the latency profiles of different exchanges to predict the probability of a limit order being “picked off” during a fast market. This venue analysis is a continuous process, as venue behaviors can change over time.

Historical Execution Data the Memory Layer

This is where the “learning” in machine learning happens. The model is trained on a vast dataset of its own past decisions and their consequences. The strategy is to capture every detail of an order’s lifecycle, from placement to final fill, and link it to the market conditions that existed at the time.

• Execution Records For every child order sent, the system must log the destination venue, the time sent, the time filled, the execution price, the filled quantity, and any associated fees or rebates.
• Slippage Metrics The core performance metric. For each fill, the system calculates slippage against relevant benchmarks (e.g. arrival price, midpoint at time of order). This becomes the “label” or “target variable” that the model learns to predict and minimize.
• Information Leakage Proxies The system can create proxies to measure information leakage. For example, it can analyze adverse price movement in the seconds and minutes after a child order is routed to a specific venue. The model learns to avoid venues that exhibit high post-trade impact.
• Fill Ratios For each venue, the model tracks the probability of a non-marketable limit order being filled. This helps it decide between passive placement on a venue with a high fill probability versus more aggressive routing.

By analyzing this historical data, the model builds its internal “intuition.” It learns complex, non-linear relationships, such as “when the order book for stock ABC is imbalanced by more than 3:1 to the offer side, and volatility is rising, routing to dark pool XYZ results in 5 basis points less slippage on average compared to routing to lit exchange PQR.” This level of granular, data-driven insight is the hallmark of a sophisticated routing system.

Execution

The execution phase of a machine learning routing system is where strategy is translated into operational reality. This involves the high-performance technical architecture required to ingest, process, and act upon the core data inputs in real time. The process is a continuous, cyclical flow ▴ data is captured from the market and the client, transformed into features, fed into the predictive model, and used to generate a routing decision. The outcome of that decision is then captured and fed back into the system as historical data, completing the learning loop.

Data Ingestion and Normalization Architecture

The foundation of the execution system is its data ingestion pipeline. This architecture must be capable of handling immense volumes of data from disparate sources with extremely low latency. The primary challenge is to normalize this data into a consistent format that the feature engineering process can consume.

The process typically involves:

1. Direct Market Data Feeds Connecting directly to exchange data feeds (e.g. ITCH/OUCH for NASDAQ, BATS PITCH) provides the lowest-latency access to raw order book data. This is a significant engineering undertaking, requiring specialized hardware and network infrastructure.
2. Consolidated Feeds Using a third-party data vendor to receive a consolidated feed simplifies the process but introduces a layer of latency. The choice depends on the trading strategy’s sensitivity to speed.
3. FIX Protocol for Order Data Client orders and their constraints are typically received via the Financial Information eXchange (FIX) protocol, the industry standard for electronic trading communication. The system must parse these FIX messages to extract the Order Specification data.
4. Time-Stamping and Synchronization All incoming data from every source must be time-stamped with high precision (nanoseconds or microseconds) using a synchronized clock source (e.g. GPS or PTP). Accurate time-stamping is absolutely essential for correctly sequencing events and calculating valid performance metrics like slippage.

From Raw Data to Predictive Power Feature Engineering

Raw data itself is rarely fed directly into a machine learning model. It must be transformed into a set of numerical “features” that provide predictive signals. Feature engineering is a critical, often artisanal, process that combines financial domain expertise with data science. The goal is to create variables that explicitly represent concepts like liquidity, volatility, and momentum.

Feature engineering is the process of converting raw data streams into a structured, predictive language that the machine learning model can understand and act upon.

The following table provides examples of how raw data inputs are transformed into engineered features.

What Is the Structure of a Model’s Input Vector?

At the moment of decision, all these engineered features are assembled into a single numerical array, often called a “feature vector.” This vector is the complete, multi-dimensional snapshot of the order, the market, and the historical context. It is the sole input that the trained machine learning model receives to make its prediction.

For a single routing decision for one child order, the feature vector might contain hundreds of values. A simplified example for a hypothetical decision to route 100 shares of stock ‘XYZ’ might look like this:

• ▴
• ▴
• ▴

The model takes this vector as input. Its output is a prediction of the expected outcome (e.g. expected slippage) for each possible routing destination. The system then executes the action associated with the best predicted outcome, for example, routing the order to the venue with the lowest predicted slippage, or perhaps splitting it across multiple venues to optimize a more complex objective function that balances cost, speed, and market impact.

Reflection

The architecture of a routing model’s data inputs is a mirror. It reflects a firm’s philosophy on market interaction, its commitment to technological excellence, and its fundamental understanding of liquidity. The framework presented here, built on the strata of order, market, historical, and venue data, is a blueprint for constructing a high-fidelity sensory apparatus for your trading operation.

The true strategic question moves beyond simply having access to these inputs. The question is, what is the resolution of your firm’s vision?

Consider your own operational framework. Is your data architecture designed for learning, or is it a static repository? Do you capture the ephemeral signals of market microstructure, or do you rely on top-of-book data alone? The difference between the two is the difference between navigating the market with a satellite image versus a hand-drawn map.

The data inputs are the foundation upon which every predictive insight and every execution advantage is built. A superior edge requires a superior operational nervous system, and that system begins with the data it is fed.