# How Can Feature Engineering in RFQ Data Improve Model Accuracy? ▴ Question

**Published:** 2025-08-05
**Categories:** Question

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![An abstract metallic circular interface with intricate patterns visualizes an institutional grade RFQ protocol for block trade execution. A central pivot holds a golden pointer with a transparent liquidity pool sphere and a blue pointer, depicting market microstructure optimization and high-fidelity execution for multi-leg spread price discovery](https://options.greeks.live/wp-content/uploads/2025/07/digital-asset-derivatives-rfq-market-microstructure-optimization.jpg)

![The abstract composition visualizes interconnected liquidity pools and price discovery mechanisms within institutional digital asset derivatives trading. Transparent layers and sharp elements symbolize high-fidelity execution of multi-leg spreads via RFQ protocols, emphasizing capital efficiency and optimized market microstructure](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-protocols-market-microstructure-1.jpg)

## Concept

An institution’s ability to generate alpha is a direct function of its ability to process information more effectively than its competitors. In the world of off-book liquidity, particularly within the Request for Quote (RFQ) protocol, the information advantage is buried in the data exhaust of every interaction. The raw data from an RFQ system ▴ a stream of requests, quotes, rejections, and fills ▴ is a high-dimensional record of behavior. It documents not just prices, but the intent, urgency, and strategic positioning of every counterparty.

[Feature engineering](https://prime.greeks.live/area/feature-engineering/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) is the systematic process of transforming this raw behavioral data into a structured, predictive language that a [machine learning](https://prime.greeks.live/area/machine-learning/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) model can understand. It is the architectural work of converting noise into signal.

At its core, the RFQ process is a series of structured conversations. An initiator solicits prices for a specific instrument and size from a select group of liquidity providers. The providers respond with their bids and offers, and the initiator chooses whether to transact. This entire sequence, from the initial request to the final fill or rejection, generates a rich dataset.

Without feature engineering, this data remains a simple historical log. With feature engineering, it becomes a predictive tool. The objective is to deconstruct these conversations into their fundamental components and then reconstruct them as quantitative signals, or ‘features’, that a model can use to forecast outcomes. These outcomes include the likelihood of a dealer to provide a competitive quote, the probable slippage on a trade, or the risk of [information leakage](https://prime.greeks.live/area/information-leakage/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) to the broader market.

The practice moves beyond simple data logging. It involves creating new variables from the existing ones to reveal latent patterns. For instance, the raw data contains timestamps for when a quote was received. A more powerful engineered feature is the ‘[time-to-respond](https://prime.greeks.live/area/time-to-respond/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite)’ for each dealer, calculated as the delta between the request time and the [response time](https://prime.greeks.live/area/response-time/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite).

This single feature can be a powerful predictor of a dealer’s interest and capacity at that specific moment. A consistently fast response time might signal an automated market maker, while a slower, more variable time could indicate a human trader. By creating dozens or even hundreds of such features, an institution builds a multi-dimensional profile of each counterparty and the market’s micro-dynamics at any given time. This constructed information architecture is what allows a model to achieve high accuracy, turning the art of trading into a quantitative science.

![The image depicts an advanced intelligent agent, representing a principal's algorithmic trading system, navigating a structured RFQ protocol channel. This signifies high-fidelity execution within complex market microstructure, optimizing price discovery for institutional digital asset derivatives while minimizing latency and slippage across order book dynamics](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-protocol-execution-optimizing-market-microstructure-for-principal-trading-intelligence-layer.jpg)

![A metallic precision tool rests on a circuit board, its glowing traces depicting market microstructure and algorithmic trading. A reflective disc, symbolizing a liquidity pool, mirrors the tool, highlighting high-fidelity execution and price discovery for institutional digital asset derivatives via RFQ protocols and Principal's Prime RFQ](https://options.greeks.live/wp-content/uploads/2025/08/institutional-digital-asset-derivatives-rfq-protocols-driving-high-fidelity-execution-and-market-microstructure-optimization-via-principal-prime-os.jpg)

## Strategy

A successful strategy for feature engineering in [RFQ data](https://prime.greeks.live/area/rfq-data/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) hinges on a multi-layered approach that mirrors the complexity of the trading environment itself. The goal is to create a rich, orthogonal set of features that capture different facets of the negotiation process. This involves looking at the data through three primary lenses ▴ the temporal dimension, the counterparty dimension, and the market context dimension. By systematically architecting features within each of these categories, a firm can build a comprehensive analytical framework that powers predictive models for [execution quality](https://prime.greeks.live/area/execution-quality/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite).

![A polished, dark teal institutional-grade mechanism reveals an internal beige interface, precisely deploying a metallic, arrow-etched component. This signifies high-fidelity execution within an RFQ protocol, enabling atomic settlement and optimized price discovery for institutional digital asset derivatives and multi-leg spreads, ensuring minimal slippage and robust capital efficiency](https://options.greeks.live/wp-content/uploads/2025/08/quantitative-finance-driven-rfq-workflow-for-best-execution-in-digital-assets.jpg)

## Feature Families for RFQ Data

The strategic creation of features can be organized into distinct families, each providing a unique perspective on the RFQ event. A disciplined approach ensures that the resulting feature set is comprehensive and that the signals are clearly understood.

- **Temporal Features** These features dissect the timing and sequencing of events within the RFQ lifecycle. Time is a critical variable in trading, often revealing information about urgency, automation, and market stress. Engineered features in this family include calculations of response latencies, the time between competing quotes, and the duration of the entire RFQ auction.

- **Counterparty Behavioral Features** This family focuses on building a quantitative profile of each liquidity provider. RFQ is a bilateral or multilateral negotiation, and understanding the historical behavior of each counterparty is paramount. These features act as a ‘scorecard’ for each dealer, quantifying their past performance and tendencies.

- **Market Context Features** No RFQ occurs in a vacuum. These features place the RFQ interaction within the broader state of the public market. They provide the backdrop against which the private negotiation is taking place, allowing a model to understand if a dealer’s quote is aggressive or defensive relative to prevailing conditions.

- **Interaction and Relational Features** This advanced family of features examines the relationships between other variables. They are created by combining features from the other families to uncover more complex, second-order effects. For example, combining a counterparty’s response time with the market’s volatility can reveal how that specific dealer behaves under stress.

> A structured feature set allows a model to differentiate between a dealer who is always aggressive and one who is only aggressive in specific market conditions.

![Intricate metallic components signify system precision engineering. These structured elements symbolize institutional-grade infrastructure for high-fidelity execution of digital asset derivatives](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-protocol-precision-engineering-for-high-fidelity-execution-and-latency-optimization.jpg)

## What Are the Key Counterparty Performance Metrics?

Developing a robust scorecard for each liquidity provider is a central strategic objective. The model’s accuracy is heavily dependent on its ability to predict the behavior of individual dealers. The following table outlines a set of foundational features engineered to quantify dealer performance. These metrics transform subjective assessments of a dealer into objective, model-ready inputs.

### Table 1 ▴ Counterparty Behavioral Feature Engineering

| Feature Name | Calculation | Strategic Value |
| --- | --- | --- |

This strategic approach to feature creation ensures that the model is fed a rich, multi-dimensional view of the trading environment. The transformation of raw log files into a structured feature set is the critical step that allows machine learning algorithms to move from simple data processing to genuine predictive intelligence. It provides the system with the language to understand the intricate dynamics of RFQ-based liquidity sourcing.

![A precision mechanism with a central circular core and a linear element extending to a sharp tip, encased in translucent material. This symbolizes an institutional RFQ protocol's market microstructure, enabling high-fidelity execution and price discovery for digital asset derivatives](https://options.greeks.live/wp-content/uploads/2025/07/precision-engineered-institutional-digital-asset-derivatives-rfq-execution-engine-microstructure-facilitating-optimal-price-discovery-and-best-execution.jpg)

![A sleek, symmetrical digital asset derivatives component. It represents an RFQ engine for high-fidelity execution of multi-leg spreads](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-engine-for-optimized-market-microstructure.jpg)

## Execution

The execution of a feature engineering pipeline for RFQ data is a rigorous, multi-stage process that demands both domain expertise in [market microstructure](https://prime.greeks.live/area/market-microstructure/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) and technical proficiency in data science. It is the operationalization of the strategy, translating theoretical features into a robust, automated system that feeds a predictive model. This process begins with raw, often messy, RFQ log data and culminates in a clean, high-dimensional feature matrix ready for model training and inference.

![A sophisticated, layered circular interface with intersecting pointers symbolizes institutional digital asset derivatives trading. It represents the intricate market microstructure, real-time price discovery via RFQ protocols, and high-fidelity execution](https://options.greeks.live/wp-content/uploads/2025/07/prime-os-institutional-digital-asset-derivatives-rfq-market-microstructure.jpg)

## The Data Transformation Workflow

The journey from raw data to a predictive feature set follows a disciplined workflow. Each step is critical for ensuring the integrity and predictive power of the final data product. A failure at any stage can introduce noise or bias that will degrade model accuracy.

- **Data Ingestion and Parsing** The process starts with the collection of raw RFQ logs from the trading system. This data often arrives in a semi-structured format (e.g. FIX messages, JSON logs). The first step is to parse this data into a structured tabular format, with each row representing a unique event in the RFQ lifecycle (e.g. request, quote, trade).

- **Data Cleaning and Imputation** Raw financial data is rarely perfect. This stage involves handling missing values (e.g. a dealer who failed to quote), correcting erroneous data points (e.g. a timestamp error), and standardizing instrument identifiers. Decisions made here, such as how to impute a missing quote, can have a material impact on the model.

- **Feature Construction** This is the core of the execution phase. Using the cleaned data, the engineering process begins. Simple features are calculated first (e.g. quote spread), followed by more complex, composite features (e.g. dealer’s spread relative to the average spread in the auction). This is often an iterative process, where new features are designed and tested for their predictive value.

- **Feature Scaling and Normalization** Machine learning models often perform better when input features are on a similar scale. Techniques like Z-score standardization (subtracting the mean and dividing by the standard deviation) are applied to continuous features like spreads or response times to prevent variables with large scales from dominating the model’s learning process.

- **Feature Validation and Selection** Before training a model, the engineered features must be validated. This involves checking for multicollinearity between features and assessing their individual predictive power. Techniques like calculating the Variance Inflation Factor (VIF) or using [feature importance](https://prime.greeks.live/area/feature-importance/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) scores from a preliminary model can help in pruning redundant or uninformative features.

![A reflective, metallic platter with a central spindle and an integrated circuit board edge against a dark backdrop. This imagery evokes the core low-latency infrastructure for institutional digital asset derivatives, illustrating high-fidelity execution and market microstructure dynamics](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-rfq-execution-infrastructure-precision.jpg)

## How Does One Quantify Feature Impact on Model Accuracy?

After engineering a comprehensive set of features, the next critical step is to quantify their impact. This is accomplished by training a predictive model and analyzing which features the model found most useful for making its predictions. Gradient Boosting Machines (like XGBoost or LightGBM) are exceptionally well-suited for this task, as they can handle tabular data effectively and provide clear metrics on feature importance.

The goal is to predict an outcome, such as Execution_Quality (e.g. ‘Good’ or ‘Poor’, based on slippage relative to the mid-price at the time of execution).

> The analysis of feature importance moves the process from art to science, providing empirical evidence of what truly drives predictive accuracy.

The following table illustrates a typical output from a feature importance analysis on a model trained to predict RFQ execution quality. The importance scores (often measured by metrics like ‘Gini Importance’ or ‘SHAP Values’) represent the contribution of each feature to the model’s accuracy. This analysis provides a clear, data-driven hierarchy of which engineered signals are most valuable.

### Table 2 ▴ Feature Importance Analysis for RFQ Execution Quality Model

| Rank | Engineered Feature Name | Importance Score | Interpretation and Operational Use |
| --- | --- | --- | --- |

This quantitative execution framework transforms RFQ data from a simple record of past trades into a forward-looking analytical asset. By systematically engineering, validating, and ranking features, an institution builds a robust, data-driven intelligence layer atop its trading protocol. This layer provides the predictive power necessary to optimize counterparty selection, anticipate execution costs, and ultimately, achieve a superior operational edge in sourcing liquidity.

![Polished metallic disc on an angled spindle represents a Principal's operational framework. This engineered system ensures high-fidelity execution and optimal price discovery for institutional digital asset derivatives](https://options.greeks.live/wp-content/uploads/2025/08/high-fidelity-digital-asset-derivatives-rfq-execution-platform.jpg)

## References

- Madhavan, Ananth. “Market microstructure ▴ A practitioner’s guide.” Financial Analysts Journal, vol. 59, no. 5, 2003, pp. 28-42.

- Harris, Larry. Trading and exchanges ▴ Market microstructure for practitioners. Oxford University Press, 2003.

- De Jong, Frank, and Barbara Rindi. The microstructure of financial markets. Cambridge University Press, 2009.

- O’Hara, Maureen. Market microstructure theory. Blackwell Publishers, 1995.

- Kyle, Albert S. “Continuous auctions and insider trading.” Econometrica, vol. 53, no. 6, 1985, pp. 1315-1335.

- Cont, Rama, et al. “Machine learning for optimal trade execution.” SSRN Electronic Journal, 2020.

- Ntakaris, A. et al. “Feature engineering for stock price direction prediction.” Expert Systems with Applications, vol. 159, 2020, p. 113620.

- Gu, S. Kelly, B. and Xiu, D. “Empirical asset pricing via machine learning.” The Review of Financial Studies, vol. 33, no. 5, 2020, pp. 2223-2273.

- Easley, David, et al. “Microstructure and learning.” Journal of Financial Economics, vol. 140, no. 1, 2021, pp. 34-51.

![Polished metallic disks, resembling data platters, with a precise mechanical arm poised for high-fidelity execution. This embodies an institutional digital asset derivatives platform, optimizing RFQ protocol for efficient price discovery, managing market microstructure, and leveraging a Prime RFQ intelligence layer to minimize execution latency](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-precision-execution-via-advanced-rfq-protocols-and-prime-os-intelligence-layer-for-optimal-market-microstructure.jpg)

## Reflection

The architecture of a predictive system built upon RFQ data is a mirror to the institution’s own operational philosophy. The features that are engineered, tracked, and valued reflect what the organization deems important in its interactions with the market. A framework that heavily weights response time reveals an obsession with speed and automation.

One that prioritizes historical dealer performance shows a focus on relationship management and trust. The process of building such a system, therefore, becomes an exercise in institutional self-awareness.

Consider the data your own systems currently capture. Does it merely log transactions, or does it record the full context of the negotiation? Does it track the quotes you received but did not act upon? Within that rejected data lies a universe of information about counterparty appetite and market depth.

The framework detailed here is a blueprint for transforming that latent information into an active, predictive asset. It is a method for building an intelligence layer that allows your execution strategy to learn, adapt, and evolve with every single query and response.

The ultimate objective is to construct a system that not only predicts outcomes but also understands the ‘why’ behind them. When a model indicates a high probability of slippage, the underlying features should provide a clear, quantitative reason. This elevates the system from a black box to a transparent decision-support tool, empowering traders with insights that are both data-driven and interpretable. The true potential is unlocked when this intelligence is integrated into every part of the trading lifecycle, creating a feedback loop where each RFQ enriches the system’s understanding of the market, leading to progressively more intelligent execution decisions.

![Interconnected, sharp-edged geometric prisms on a dark surface reflect complex light. This embodies the intricate market microstructure of institutional digital asset derivatives, illustrating RFQ protocol aggregation for block trade execution, price discovery, and high-fidelity execution within a Principal's operational framework enabling optimal liquidity](https://options.greeks.live/wp-content/uploads/2025/07/precision-geometric-prisms-reflecting-institutional-digital-asset-derivatives-rfq-price-discovery-and-optimized-execution-market-microstructure.jpg)

## Glossary

<a href="https://prime.greeks.live/area/feature-engineering/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![Abstract metallic and dark components symbolize complex market microstructure and fragmented liquidity pools for digital asset derivatives. A smooth disc represents high-fidelity execution and price discovery facilitated by advanced RFQ protocols on a robust Prime RFQ, enabling precise atomic settlement for institutional multi-leg spreads](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-market-microstructure-optimizing-rfq-protocols-for-high-fidelity-execution-on-prime-os.jpg)

## Feature Engineering

Meaning ▴ Feature Engineering is the systematic process of transforming raw data into a set of derived variables, known as features, that better represent the underlying problem to predictive models. </a><a href="https://prime.greeks.live/area/machine-learning/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![Abstract geometry illustrates interconnected institutional trading pathways. Intersecting metallic elements converge at a central hub, symbolizing a liquidity pool or RFQ aggregation point for high-fidelity execution of digital asset derivatives](https://options.greeks.live/wp-content/uploads/2025/07/prime-os-cross-market-microstructure-for-digital-asset-derivatives.jpg)

## Machine Learning

Meaning ▴ Machine Learning refers to computational algorithms enabling systems to learn patterns from data, thereby improving performance on a specific task without explicit programming. </a><a href="https://prime.greeks.live/area/information-leakage/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![A precision metallic instrument with a black sphere rests on a multi-layered platform. This symbolizes institutional digital asset derivatives market microstructure, enabling high-fidelity execution and optimal price discovery across diverse liquidity pools](https://options.greeks.live/wp-content/uploads/2025/07/prime-os-rfq-protocol-engine-component-digital-asset-derivatives-market-microstructure.jpg)

## Information Leakage

Meaning ▴ Information leakage denotes the unintended or unauthorized disclosure of sensitive trading data, often concerning an institution's pending orders, strategic positions, or execution intentions, to external market participants. </a><a href="https://prime.greeks.live/area/time-to-respond/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![Polished metallic surface with a central intricate mechanism, representing a high-fidelity market microstructure engine. Two sleek probes symbolize bilateral RFQ protocols for precise price discovery and atomic settlement of institutional digital asset derivatives on a Prime RFQ, ensuring best execution for Bitcoin Options](https://options.greeks.live/wp-content/uploads/2025/07/prime-os-institutional-digital-asset-derivatives-rfq-engine-core-market-microstructure-intelligence-layer.jpg)

## Time-To-Respond

Meaning ▴ Time-to-Respond, in the context of institutional digital asset derivatives, quantifies the temporal interval between a system's reception of a market event or a specific request and its subsequent initiation of a defined, counter-action or response. </a><a href="https://prime.greeks.live/area/response-time/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![A complex, layered mechanical system featuring interconnected discs and a central glowing core. This visualizes an institutional Digital Asset Derivatives Prime RFQ, facilitating RFQ protocols for price discovery](https://options.greeks.live/wp-content/uploads/2025/07/institutional-crypto-derivatives-prime-os-market-microstructure.jpg)

## Response Time

Meaning ▴ Response Time quantifies the elapsed duration between a specific triggering event and a system's subsequent, measurable reaction. </a><a href="https://prime.greeks.live/area/execution-quality/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![Central axis with angular, teal forms, radiating transparent lines. Abstractly represents an institutional grade Prime RFQ execution engine for digital asset derivatives, processing aggregated inquiries via RFQ protocols, ensuring high-fidelity execution and price discovery](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-protocol-high-fidelity-execution-market-microstructure-price-discovery-volatility-surface-analytics.jpg)

## Execution Quality

Meaning ▴ Execution Quality quantifies the efficacy of an order's fill, assessing how closely the achieved trade price aligns with the prevailing market price at submission, alongside consideration for speed, cost, and market impact. </a><a href="https://prime.greeks.live/area/rfq-data/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![Geometric planes and transparent spheres represent complex market microstructure. A central luminous core signifies efficient price discovery and atomic settlement via RFQ protocol](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-execution-microstructure-4.jpg)

## Rfq Data

Meaning ▴ RFQ Data constitutes the comprehensive record of information generated during a Request for Quote process, encompassing all details exchanged between an initiating Principal and responding liquidity providers. </a><a href="https://prime.greeks.live/area/market-microstructure/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![A precision metallic mechanism, with a central shaft, multi-pronged component, and blue-tipped element, embodies the market microstructure of an institutional-grade RFQ protocol. It represents high-fidelity execution, liquidity aggregation, and atomic settlement within a Prime RFQ for digital asset derivatives](https://options.greeks.live/wp-content/uploads/2025/08/prime-os-core-rfq-engine-microstructure-for-digital-asset-derivatives.jpg)

## Market Microstructure

Meaning ▴ Market Microstructure refers to the study of the processes and rules by which securities are traded, focusing on the specific mechanisms of price discovery, order flow dynamics, and transaction costs within a trading venue. </a><a href="https://prime.greeks.live/area/feature-importance/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![Abstract composition featuring transparent liquidity pools and a structured Prime RFQ platform. Crossing elements symbolize algorithmic trading and multi-leg spread execution, visualizing high-fidelity execution within market microstructure for institutional digital asset derivatives via RFQ protocols](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-trading-system-precision-engineering-facilitating-high-fidelity-execution-via-rfq-protocols-and-optimal-liquidity-aggregation.jpg)

## Feature Importance

Meaning ▴ Feature Importance quantifies the relative contribution of input variables to the predictive power or output of a machine learning model. </a>

**Tags:** [Market Impact Model](https://options.greeks.live/area/market-impact-model/), [Response Time](https://options.greeks.live/area/response-time/), [Adverse Selection](https://options.greeks.live/area/adverse-selection/), [Feature Engineering](https://options.greeks.live/area/feature-engineering/), [RFQ Auction Dynamics](https://options.greeks.live/area/rfq-auction-dynamics/), [Execution Cost Analysis](https://options.greeks.live/area/execution-cost-analysis/)	

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ML models build predictive systems for information leakage by classifying market microstructure responses to an institution's trading actions.

- 
### [How Can a Quantitative Scorecard Mitigate Adverse Selection in RFQ’s?](https://options.greeks.live/question/how-can-a-quantitative-scorecard-mitigate-adverse-selection-in-rfqs/)
![A transparent, multi-faceted component, indicative of an RFQ engine's intricate market microstructure logic, emerges from complex FIX Protocol connectivity. Its sharp edges signify high-fidelity execution and price discovery precision for institutional digital asset derivatives. This Prime RFQ module optimizes algorithmic trading and capital efficiency.](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-engine-optimized-for-best-execution.jpg)

A quantitative scorecard mitigates adverse selection by transforming counterparty interaction into a data-driven, performance-based system.

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---

**Original URL:** https://options.greeks.live/question/how-can-feature-engineering-in-rfq-data-improve-model-accuracy/
