# How Can Machine Learning Be Applied to Optimize Dealer Selection in RFQ Protocols over Time? ▴ Question

**Published:** 2025-08-10
**Categories:** Question

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## Concept

The operational mandate for any trading desk is the achievement of high-fidelity execution. In the context of Request for Quote (RFQ) protocols, this objective finds its most complex expression. The process of selecting which dealers to include in a quote solicitation is a critical determinant of the final execution price. A suboptimal selection process introduces information leakage and fails to access the deepest pools of liquidity, directly impacting performance.

The core of the challenge resides in the dynamic nature of dealer behavior and market conditions. A dealer who provides the best price for a specific asset class in the morning may not be competitive in the afternoon. This temporal variability, compounded by factors like dealer inventory, risk appetite, and prevailing market volatility, transforms [dealer selection](https://prime.greeks.live/area/dealer-selection/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) from a simple relationship management task into a high-dimensional data problem.

Applying [machine learning](https://prime.greeks.live/question/what-are-the-trade-offs-between-a-machine-learning-model-and-a-heuristic-approach-for-leakage-prediction/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) to this domain provides a quantitative framework for navigating this complexity. The fundamental purpose is to construct a predictive system that dynamically ranks potential counterparties based on their probability of providing the winning quote for a specific inquiry at a specific moment. This system moves beyond static, relationship-based dealer lists, introducing a layer of empirical rigor to the selection process. It is a direct intervention designed to enhance the precision of liquidity sourcing.

By analyzing vast amounts of historical RFQ data, a [machine learning model](https://prime.greeks.live/question/what-are-the-key-differences-in-validating-a-machine-learning-model-versus-a-traditional-econometric-model/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) can identify subtle patterns that are invisible to human traders. These patterns connect trade characteristics ▴ such as asset type, size, time of day, and market volatility ▴ with dealer performance metrics like [win rate](https://prime.greeks.live/area/win-rate/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite), price improvement over a benchmark, and response latency.

The introduction of a machine learning layer into the RFQ workflow represents a fundamental shift in operational philosophy. It reframes dealer selection as a continuous optimization process, where each new trade provides data that refines the model’s future predictions. This feedback loop is the engine of adaptation. The system learns over time, adjusting its recommendations as dealer behavior evolves and market regimes shift.

The objective is to create a systemic advantage by consistently and systematically identifying the most competitive counterparties for every trade. This data-driven approach augments the trader’s expertise, providing a powerful tool to mitigate the adverse selection and information leakage inherent in traditional, static RFQ processes. The result is a more efficient, targeted, and ultimately more profitable execution framework.

![Intersecting translucent blue blades and a reflective sphere depict an institutional-grade algorithmic trading system. It ensures high-fidelity execution of digital asset derivatives via RFQ protocols, facilitating precise price discovery within complex market microstructure and optimal block trade routing](https://options.greeks.live/wp-content/uploads/2025/07/precision-algorithmic-execution-infrastructure-for-institutional-digital-asset-derivatives-leveraging-advanced-rfq-protocols.jpg)

![A circular mechanism with a glowing conduit and intricate internal components represents a Prime RFQ for institutional digital asset derivatives. This system facilitates high-fidelity execution via RFQ protocols, enabling price discovery and algorithmic trading within market microstructure, optimizing capital efficiency](https://options.greeks.live/wp-content/uploads/2025/07/prime-os-institutional-digital-asset-derivatives-rfq-protocol-core.jpg)

## Strategy

Developing a machine learning-driven dealer selection strategy is a multi-stage process that transforms raw trading data into actionable intelligence. The overarching goal is to build a predictive model that, for any given RFQ, provides a ranked list of dealers most likely to offer the best price. This requires a systematic approach to data collection, feature engineering, model selection, and performance evaluation.

![A precision-engineered system component, featuring a reflective disc and spherical intelligence layer, represents institutional-grade digital asset derivatives. It embodies high-fidelity execution via RFQ protocols for optimal price discovery within Prime RFQ market microstructure](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-execution-intelligence-prime-os.jpg)

## The Data Foundation

The entire system is predicated on the availability of clean, granular, and comprehensive data. The primary data source is the institution’s own history of RFQ transactions. For each RFQ sent, the system must capture a complete record of the event.

- **Request Details** ▴ This includes the instrument being traded (e.g. corporate bond CUSIP, equity option series), the size of the request, the direction (buy/sell), and the timestamp.

- **Dealer Responses** ▴ For each dealer invited to quote, the system must log whether they responded, the price they quoted, the time it took them to respond (latency), and whether their quote was the winning one.

- **Market Context** ▴ To provide the model with context about the trading environment, it’s essential to capture relevant market data points at the time of the RFQ. This could include the prevailing bid-ask spread for the instrument, a measure of recent price volatility, and data on recent trade volumes.

> The quality and depth of this historical data directly constrain the predictive power of any subsequent machine learning model.

![Abstract geometric planes in grey, gold, and teal symbolize a Prime RFQ for Digital Asset Derivatives, representing high-fidelity execution via RFQ protocol. It drives real-time price discovery within complex market microstructure, optimizing capital efficiency for multi-leg spread strategies](https://options.greeks.live/wp-content/uploads/2025/07/prime-os-rfq-protocol-digital-asset-derivatives-high-fidelity-execution.jpg)

## Feature Engineering the Language of the Model

Raw data is rarely in a format that a machine [learning model](https://prime.greeks.live/question/what-are-the-key-differences-in-validating-a-machine-learning-model-versus-a-traditional-econometric-model/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) can directly consume. The process of feature engineering involves transforming the raw data logs into a set of predictive variables, or “features,” that the model can use to learn the underlying relationships between trade characteristics and dealer performance. This is a critical step that combines domain expertise with data science.

Examples of engineered features include:

- **Dealer-Specific Performance Metrics** ▴ Calculating historical performance statistics for each dealer, such as their overall win rate, their win rate for a specific asset class, their average response latency, and the average price improvement they offer relative to a benchmark. These metrics should be calculated over various rolling time windows (e.g. last 24 hours, last week, last month) to capture both recent and long-term performance trends.

- **Trade-Specific Contextual Features** ▴ Creating features that describe the specific RFQ, such as the time of day (e.g. morning, midday, afternoon), the day of the week, and the trade size relative to the average daily volume of the instrument.

- **Interaction Features** ▴ Combining dealer-specific and trade-specific features to capture more complex relationships. For example, a feature could represent a specific dealer’s win rate for trades of a particular size in a particular asset class.

![A reflective digital asset pipeline bisects a dynamic gradient, symbolizing high-fidelity RFQ execution across fragmented market microstructure. Concentric rings denote the Prime RFQ centralizing liquidity aggregation for institutional digital asset derivatives, ensuring atomic settlement and managing counterparty risk](https://options.greeks.live/wp-content/uploads/2025/07/high-fidelity-rfq-execution-for-institutional-digital-asset-derivatives-2.jpg)

## Selecting the Right Analytical Engine

With a rich set of features, the next step is to select and train a machine learning model. The problem of predicting the winning dealer can be framed in several ways, leading to different model choices. A common approach is to frame it as a classification problem ▴ for each dealer in a potential RFQ, predict the probability that this dealer will provide the winning quote. Dealers can then be ranked by this probability.

Several types of models are well-suited for this task:

- **Logistic Regression** ▴ A foundational statistical model that is highly interpretable. It provides a clear view of how each feature contributes to the prediction, making it a good baseline model.

- **Gradient Boosting Machines (e.g. XGBoost, LightGBM)** ▴ These are powerful ensemble models that often provide state-of-the-art performance on tabular data. They can capture complex, non-linear relationships in the data. Their “black box” nature can be mitigated using explainability techniques like SHAP (SHapley Additive exPlanations) to understand feature importance.

- **[Reinforcement Learning](https://prime.greeks.live/area/reinforcement-learning/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) (RL)** ▴ A more advanced approach where an “agent” learns the optimal dealer selection policy through trial and error. The RL agent’s goal is to maximize a cumulative reward, which could be defined as the total price improvement achieved over a series of trades. This approach is computationally intensive but can adapt to changing market dynamics in a very sophisticated way.

The following table compares these strategic approaches across key operational dimensions.

| Model Strategy | Computational Cost | Interpretability | Adaptability | Primary Use Case |
| --- | --- | --- | --- | --- |
| Logistic Regression | Low | High | Low | Establishing a baseline and ensuring model transparency. |
| Gradient Boosting | Medium | Medium (with XAI) | Medium | Maximizing predictive accuracy with structured, tabular data. |
| Reinforcement Learning | High | Low | High | Developing a dynamic, self-learning policy for continuous optimization. |

![A precision metallic dial on a multi-layered interface embodies an institutional RFQ engine. The translucent panel suggests an intelligence layer for real-time price discovery and high-fidelity execution of digital asset derivatives, optimizing capital efficiency for block trades within complex market microstructure](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-engine-high-fidelity-execution-system.jpg)

## Continuous Evaluation and Refinement

A machine learning model is not a static artifact. Its performance must be continuously monitored and evaluated to ensure it remains accurate and effective. This involves a rigorous backtesting process, where the model’s predictions are compared against historical outcomes that it was not trained on. Key performance indicators (KPIs) for the model include its precision (of the top-ranked dealers, how many were winners?) and its recall (of all the winning dealers, how many did the model rank highly?).

Over time, models must be retrained on new data to incorporate the latest market information and adapt to evolving dealer behaviors. This iterative cycle of training, evaluation, and refinement is the cornerstone of a successful machine learning strategy in this domain.

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## Execution

The execution of a machine learning-based dealer selection system requires a robust technological framework and a disciplined operational workflow. This is where the strategic concepts are translated into a functioning, value-generating component of the trading infrastructure. The system must be designed for reliability, scalability, and seamless integration with existing order and execution management systems (OMS/EMS).

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## The Operational Playbook

Implementing the system follows a clear, multi-step process, moving from data aggregation to live deployment and monitoring.

- **Data Infrastructure Consolidation** ▴ The first step is to create a centralized repository for all RFQ-related data. This involves building data pipelines that automatically capture and store trade requests, dealer responses, and market data from various sources into a structured database or data lake. This “single source of truth” is the bedrock of the entire system.

- **Model Development and Backtesting Environment** ▴ A dedicated analytical environment is required for data scientists and quants to develop, train, and rigorously backtest the predictive models. This environment should contain a snapshot of the historical data and the computational resources (e.g. GPUs for deep learning) needed for model training. Backtesting protocols must be strict, simulating how the model would have performed in the past and avoiding any look-ahead bias.

- **API-Driven Model Deployment** ▴ Once a model is validated, it is deployed as a production service, typically accessible via an Application Programming Interface (API). When a trader initiates an RFQ in the EMS, the system makes an API call to the model. The API request contains the features of the current RFQ, and the model returns a ranked list of dealers with their predicted win probabilities.

- **EMS Integration and User Interface** ▴ The model’s output must be integrated directly into the trader’s workflow. The EMS interface should be augmented to display the model’s recommendations in an intuitive way. For example, the RFQ ticket could pre-populate with the top-ranked dealers, or display the dealer list color-coded by their predicted competitiveness. The trader retains ultimate control, but the system provides a powerful data-driven default.

- **Continuous Monitoring and Feedback Loop** ▴ After each trade is completed, the outcome (who won, at what price) is fed back into the central data repository. This new data is used to continuously monitor the model’s performance in real-time and serves as the training data for the next iteration of the model. Dashboards should track key model metrics and business KPIs, such as the hit rate of recommended dealers and the realized price improvement.

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## Quantitative Modeling and Data Analysis

The core of the system is the quantitative model itself. A gradient boosting model, for instance, would be trained on a dataset structured like the one below. Each row represents a single dealer’s participation in a past RFQ.

> A system that fails to capture and learn from every single quote request is a system that is discarding valuable intelligence.

The target variable for the model is Won_Quote, a binary indicator. The other columns are the features the model uses to make its prediction.

| Feature Name | Description | Data Type | Example Value |
| --- | --- | --- | --- |
| Dealer_ID | A unique identifier for the dealer. | Categorical | Dealer_XYZ |
| Asset_Class | The class of the instrument being traded. | Categorical | Corp_Bond_IG |
| Trade_Size_USD | The notional value of the trade in USD. | Numeric | 5,000,000 |
| Time_Of_Day | The hour of the trading day. | Numeric | 14 |
| Volatility_30D | The 30-day historical volatility of the instrument. | Numeric | 0.25 |
| Dealer_Win_Rate_Asset_Class_30D | The dealer’s win rate for this asset class over the last 30 days. | Numeric | 0.22 |
| Dealer_Avg_Response_Time_5D | The dealer’s average response time over the last 5 days. | Numeric | 2.5 (seconds) |
| Won_Quote | (Target Variable) 1 if the dealer won the RFQ, 0 otherwise. | Binary | 1 |

After training, the model can be queried for a new RFQ. For instance, for a new $10M trade in an investment-grade corporate bond, the model would score each potential dealer and produce a ranked list, allowing the trader to make a highly informed decision.

![A central RFQ aggregation engine radiates segments, symbolizing distinct liquidity pools and market makers. This depicts multi-dealer RFQ protocol orchestration for high-fidelity price discovery in digital asset derivatives, highlighting diverse counterparty risk profiles and algorithmic pricing grids](https://options.greeks.live/wp-content/uploads/2025/07/centralized-digital-asset-rfq-orchestration-for-multi-venue-liquidity-aggregation-and-high-fidelity-execution-across-diverse-counterparties.jpg)

## System Integration and Technological Architecture

The technology stack must be robust and performant. The system architecture typically consists of several key components:

- **Data Ingestion Layer** ▴ A set of services (e.g. using Kafka or other messaging queues) that consume real-time data from the OMS/EMS and market data feeds.

- **Feature Store** ▴ A specialized database that stores pre-computed features for both model training and real-time inference. This ensures consistency between the data used to train the model and the data used for live predictions.

- **Model Serving Infrastructure** ▴ A scalable platform (e.g. using Kubernetes) to host the trained models as API endpoints. This infrastructure must be low-latency to provide predictions to the trader without any noticeable delay.

- **Monitoring and Alerting** ▴ A comprehensive monitoring system (e.g. using Prometheus and Grafana) to track the health of the data pipelines, the performance of the model serving APIs, and the accuracy of the model’s predictions. Alerts are configured to notify the support team of any anomalies.

This systematic approach to execution ensures that the application of machine learning is not a one-off academic exercise, but a durable, evolving, and integrated part of the firm’s trading capability. It creates a system that learns, adapts, and continuously refines its ability to source liquidity efficiently, providing a persistent competitive advantage in the market.

![A precise metallic and transparent teal mechanism symbolizes the intricate market microstructure of a Prime RFQ. It facilitates high-fidelity execution for institutional digital asset derivatives, optimizing RFQ protocols for private quotation, aggregated inquiry, and block trade management, ensuring best execution](https://options.greeks.live/wp-content/uploads/2025/07/advanced-principal-trading-systems-driving-high-fidelity-institutional-digital-asset-derivatives-rfq-execution-protocols.jpg)

## References

- Rohaan, D. et al. “Using supervised machine learning for B2B sales forecasting.” Computers & Industrial Engineering, vol. 160, 2021, p. 107575.

- Marín, Paloma, et al. “Causal Interventions in Bond Multi-Dealer-to-Client Platforms.” arXiv preprint arXiv:2306.12937, 2023.

- Nevmyvaka, Yuriy, et al. “Reinforcement learning for optimized trade execution.” Proceedings of the 23rd international conference on Machine learning, 2006.

- “Deep Reinforcement Learning for Optimal Trade Execution.” MathWorks, 2023.

- Bhandari, Akash, et al. “Explainable AI in Request-for-Quote.” arXiv preprint arXiv:2402.10984, 2024.

![A sophisticated institutional-grade device featuring a luminous blue core, symbolizing advanced price discovery mechanisms and high-fidelity execution for digital asset derivatives. This intelligence layer supports private quotation via RFQ protocols, enabling aggregated inquiry and atomic settlement within a Prime RFQ framework](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-prime-os-core-intelligence.jpg)

## Reflection

The integration of a predictive intelligence layer into the RFQ protocol is more than a technological upgrade; it is an evolution in operational philosophy. The framework detailed here provides a systematic pathway to augment human expertise with machine-driven analysis, transforming the dealer selection process from a heuristic art into a data-centric science. The true potential of this system is realized not at the moment of its first deployment, but over time, as its feedback loops process more data and its predictions become increasingly aligned with the fluid dynamics of the market. The ultimate objective is the creation of a proprietary execution lens, a system that develops a unique and nuanced understanding of its liquidity environment.

This capability, built and refined internally, becomes a durable source of competitive differentiation. The knowledge gained through this process is a strategic asset, enabling the institution to navigate the complexities of modern market structure with superior precision and control.

![Stacked modular components with a sharp fin embody Market Microstructure for Digital Asset Derivatives. This represents High-Fidelity Execution via RFQ protocols, enabling Price Discovery, optimizing Capital Efficiency, and managing Gamma Exposure within an Institutional Prime RFQ for Block Trades](https://options.greeks.live/wp-content/uploads/2025/07/modular-system-for-institutional-digital-asset-derivatives-rfq-protocol-optimization.jpg)

## Glossary

<a href="https://prime.greeks.live/area/dealer-selection/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![A stylized spherical system, symbolizing an institutional digital asset derivative, rests on a robust Prime RFQ base. Its dark core represents a deep liquidity pool for algorithmic trading](https://options.greeks.live/wp-content/uploads/2025/08/institutional-grade-rfq-protocol-engine-facilitating-best-execution-and-liquidity-aggregation-for-digital-asset-derivatives.jpg)

## Dealer Selection

Meaning ▴ Dealer Selection, within the framework of crypto institutional options trading and Request for Quote (RFQ) systems, refers to the strategic process by which a liquidity seeker chooses specific market makers or dealers to solicit quotes from for a particular trade. </a><a href="https://prime.greeks.live/question/what-are-the-trade-offs-between-a-machine-learning-model-and-a-heuristic-approach-for-leakage-prediction/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![Sleek, interconnected metallic components with glowing blue accents depict a sophisticated institutional trading platform. A central element and button signify high-fidelity execution via RFQ protocols](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-execution-system-microstructure.jpg)

## Machine Learning

The trade-off is between a heuristic's transparent, static rules and a machine learning model's adaptive, opaque, data-driven intelligence. </a><a href="https://prime.greeks.live/question/what-are-the-key-differences-in-validating-a-machine-learning-model-versus-a-traditional-econometric-model/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![A centralized intelligence layer for institutional digital asset derivatives, visually connected by translucent RFQ protocols. This Prime RFQ facilitates high-fidelity execution and private quotation for block trades, optimizing liquidity aggregation and price discovery](https://options.greeks.live/wp-content/uploads/2025/07/institutional-grade-digital-asset-derivatives-rfq-protocol-engine.jpg)

## Machine Learning Model

Validating econometrics confirms theoretical soundness; validating machine learning confirms predictive power on unseen data. </a><a href="https://prime.greeks.live/area/win-rate/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![A sleek, split capsule object reveals an internal glowing teal light connecting its two halves, symbolizing a secure, high-fidelity RFQ protocol facilitating atomic settlement for institutional digital asset derivatives. This represents the precise execution of multi-leg spread strategies within a principal's operational framework, ensuring optimal liquidity aggregation](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-protocol-execution-system-2.jpg)

## Win Rate

Meaning ▴ Win Rate, in crypto trading, quantifies the percentage of successful trades or investment decisions executed by a specific trading strategy or system over a defined observation period. </a><a href="https://prime.greeks.live/question/what-are-the-key-differences-in-validating-a-machine-learning-model-versus-a-traditional-econometric-model/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![A smooth, off-white sphere rests within a meticulously engineered digital asset derivatives RFQ platform, featuring distinct teal and dark blue metallic components. This sophisticated market microstructure enables private quotation, high-fidelity execution, and optimized price discovery for institutional block trades, ensuring capital efficiency and best execution](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-protocol-system-microstructure-core.jpg)

## Learning Model

Validating econometrics confirms theoretical soundness; validating machine learning confirms predictive power on unseen data. </a><a href="https://prime.greeks.live/area/reinforcement-learning/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![A central crystalline RFQ engine processes complex algorithmic trading signals, linking to a deep liquidity pool. It projects precise, high-fidelity execution for institutional digital asset derivatives, optimizing price discovery and mitigating adverse selection](https://options.greeks.live/wp-content/uploads/2025/07/prime-os-rfq-protocols-high-fidelity-digital-asset-derivatives-execution.jpg)

## Reinforcement Learning

Meaning ▴ Reinforcement learning (RL) is a paradigm of machine learning where an autonomous agent learns to make optimal decisions by interacting with an environment, receiving feedback in the form of rewards or penalties, and iteratively refining its strategy to maximize cumulative reward. </a>

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ML-driven RFQs transform price discovery into a predictive, optimized execution framework for superior alpha generation.

- 
### [How Does Feature Engineering Impact the Accuracy of RFQ Success Models?](https://options.greeks.live/question/how-does-feature-engineering-impact-the-accuracy-of-rfq-success-models/)
![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. It signifies the robust Prime RFQ underpinning RFQ protocols for atomic settlement and best execution.](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-rfq-execution-infrastructure-precision.jpg)

Feature engineering translates market context into a predictive language, directly governing the precision of RFQ success models.

- 
### [Which Quantitative Models Best Predict Adverse Selection Probability for Dynamic Quote Skew Adjustments?](https://options.greeks.live/question/which-quantitative-models-best-predict-adverse-selection-probability-for-dynamic-quote-skew-adjustments/)
![A metallic rod, symbolizing a high-fidelity execution pipeline, traverses transparent elements representing atomic settlement nodes and real-time price discovery. It rests upon distinct institutional liquidity pools, reflecting optimized RFQ protocols for crypto derivatives trading across a complex volatility surface within Prime RFQ market microstructure.](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-trade-execution-pipeline-optimizing-rfq-protocols-for-best-execution-and-capital-efficiency.jpg)

Dynamic quote skew adjustments, informed by quantitative models, proactively mitigate adverse selection for superior capital efficiency.

- 
### [What Are the Technological Prerequisites for Implementing a Dynamic Dealer Selection System?](https://options.greeks.live/question/what-are-the-technological-prerequisites-for-implementing-a-dynamic-dealer-selection-system/)
![A central teal sphere, representing the Principal's Prime RFQ, anchors radiating grey and teal blades, signifying diverse liquidity pools and high-fidelity execution paths for digital asset derivatives. Transparent overlays suggest pre-trade analytics and volatility surface dynamics. Pinned elements indicate precise RFQ protocol engagement and atomic settlement.](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-and-market-microstructure.jpg)

A dynamic dealer selection system is the analytical core of modern trading, systematically optimizing counterparty choice to enhance execution quality.

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---

**Original URL:** https://options.greeks.live/question/how-can-machine-learning-be-applied-to-optimize-dealer-selection-in-rfq-protocols-over-time/
