# How Can the AUC-ROC Curve Be Used to Compare Different RFQ Win Rate Prediction Models? ▴ Question

**Published:** 2025-08-07
**Categories:** Question

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![Precisely engineered circular beige, grey, and blue modules stack tilted on a dark base. A central aperture signifies the core RFQ protocol engine](https://options.greeks.live/wp-content/uploads/2025/07/core-prime-os-rfq-engine-digital-asset-derivatives-trading-system-execution.jpg)

![A central glowing teal mechanism, an RFQ engine core, integrates two distinct pipelines, representing diverse liquidity pools for institutional digital asset derivatives. This visualizes high-fidelity execution within market microstructure, enabling atomic settlement and price discovery for Bitcoin options and Ethereum futures via private quotation](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-execution-engine-microstructure-3.jpg)

## Concept

In the intricate ecosystem of institutional finance, the Request for Quote (RFQ) protocol represents a foundational mechanism for [price discovery](https://prime.greeks.live/area/price-discovery/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) and [liquidity sourcing](https://prime.greeks.live/area/liquidity-sourcing/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite), particularly for large or complex financial instruments. This bilateral communication channel allows a market participant to solicit competitive prices from a select group of dealers, creating a private auction for a specific trade. The operational success of a market-maker within this framework is contingent upon a finely calibrated quoting strategy. A quote must be aggressive enough to win the trade yet prudent enough to align with the firm’s risk and inventory objectives.

This balancing act is the central challenge of RFQ participation. The ability to accurately forecast the probability of a quote being successful ▴ the [win rate](https://prime.greeks.live/area/win-rate/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) ▴ is a significant competitive differentiator. It transforms the art of quoting into a data-driven science, enabling a systematic approach to capital allocation and risk management.

At the heart of evaluating the efficacy of these predictive systems lies a powerful statistical tool ▴ the Receiver Operating Characteristic (ROC) curve and its corresponding Area Under the Curve (AUC). For any [RFQ win rate](https://prime.greeks.live/area/rfq-win-rate/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) prediction model, the output is fundamentally a probability ▴ a score indicating the likelihood that a given quote will be the winning one. The model’s task is to assign higher probabilities to the RFQs that are ultimately won and lower probabilities to those that are lost. The [AUC-ROC](https://prime.greeks.live/area/auc-roc/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) framework provides a pure measure of this discriminative power.

It assesses a model’s ability to separate the two classes (Win vs. Loss) across all possible decision thresholds. An institution’s capacity to leverage this framework moves its [quoting engine](https://prime.greeks.live/area/quoting-engine/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) from a reactive mechanism to a predictive, strategic asset. The quality of this predictive asset is not a matter of opinion; it is a quantifiable metric, and the AUC is its cardinal measure.

> The AUC-ROC curve offers a definitive, threshold-independent measure of a model’s capacity to distinguish between winning and losing RFQ bids.

![Two semi-transparent, curved elements, one blueish, one greenish, are centrally connected, symbolizing dynamic institutional RFQ protocols. This configuration suggests aggregated liquidity pools and multi-leg spread constructions](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-price-discovery-engine.jpg)

## The Anatomy of a Classification Problem

Predicting an RFQ win is a [binary classification](https://prime.greeks.live/area/binary-classification/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) problem. Each quote sent out results in one of two outcomes ▴ it is either accepted (a “win,” the positive class) or it is not (a “loss,” the negative class). A predictive model analyzes a set of features associated with each RFQ ▴ such as the instrument’s volatility, the trade size, the client’s identity, and prevailing market conditions ▴ to generate a [win probability](https://prime.greeks.live/area/win-probability/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite).

To translate this probability into a discrete prediction (e.g. “this quote will likely win”), a classification threshold must be set. For instance, a desk might decide that any RFQ with a predicted win probability above 70% will be considered a likely win.

The choice of this threshold carries a direct trade-off. Setting a high threshold makes the model more selective, reducing the number of false alarms (incorrectly predicting a win) but potentially missing more actual wins. Conversely, a low threshold increases the model’s sensitivity, capturing more wins but also raising the number of false alarms.

The ROC curve is a graphical representation of this trade-off. It plots the [True Positive Rate](https://prime.greeks.live/area/true-positive-rate/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) (TPR) against the [False Positive Rate](https://prime.greeks.live/area/false-positive-rate/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) (FPR) at every conceivable threshold.

- **[True Positive](https://prime.greeks.live/area/true-positive/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) Rate (Sensitivity)** ▴ This metric answers the question ▴ “Of all the RFQs that were actually won, what proportion did our model correctly identify?” It is the ratio of true positives to the total number of actual wins. A high TPR indicates the model is effective at recognizing winning bids.

- **[False Positive](https://prime.greeks.live/area/false-positive/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) Rate (1 – Specificity)** ▴ This metric answers ▴ “Of all the RFQs that were lost, what proportion did our model incorrectly flag as a potential win?” It is the ratio of false positives to the total number of actual losses. A low FPR is desirable, as it signifies the model does not frequently cry wolf.

The [ROC curve](https://prime.greeks.live/area/roc-curve/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) visualizes the model’s performance as the threshold is varied from 0 to 1. A model that performs no better than random chance would produce a diagonal line from the bottom-left corner (0,0) to the top-right corner (1,1). A superior model produces a curve that bows towards the top-left corner, indicating that it achieves a high True Positive Rate while maintaining a low False Positive Rate. The area under this curve, the AUC, consolidates this entire performance spectrum into a single, elegant number.

An AUC of 1.0 represents a perfect classifier, one that can flawlessly distinguish between wins and losses. An AUC of 0.5 signifies a model with no discriminative ability whatsoever. Therefore, when comparing different RFQ win rate prediction models, the one with the higher AUC is, by definition, the more powerful classification engine.

![A Prime RFQ engine's central hub integrates diverse multi-leg spread strategies and institutional liquidity streams. Distinct blades represent Bitcoin Options and Ethereum Futures, showcasing high-fidelity execution and optimal price discovery](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-prime-os-rfq-engine-optimizing-multi-leg-spread-high-fidelity-execution-with-atomic-settlement.jpg)

![Abstractly depicting an Institutional Digital Asset Derivatives ecosystem. A robust base supports intersecting conduits, symbolizing multi-leg spread execution and smart order routing](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-execution-engine-optimization.jpg)

## Strategy

Integrating predictive modeling into an RFQ workflow is a profound strategic decision. The objective extends beyond simply winning more quotes; it involves constructing a more intelligent, capital-efficient trading operation. The selection of a win rate prediction model is therefore a critical choice of strategic tooling. The [AUC-ROC curve](https://prime.greeks.live/question/what-are-the-primary-system-architecture-changes-required-to-move-from-a-single-curve-to-a-multi-curve-system/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) serves as the primary instrument for this selection process, providing a clear, quantitative basis for comparing the strategic value of different models.

A model with a higher AUC offers a more refined understanding of the competitive landscape for each RFQ, allowing a [trading desk](https://prime.greeks.live/area/trading-desk/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) to allocate its risk capital with greater precision. This translates into a direct and measurable economic advantage.

Consider the strategic implications. A desk equipped with a high-AUC model can identify RFQs with a high probability of success and offer more aggressive pricing on them, increasing its market share. Simultaneously, it can recognize RFQs where its chances are slim and offer more conservative prices, preserving its capacity for more promising opportunities. This ability to dynamically modulate pricing strategy based on a reliable win probability is the essence of a sophisticated quoting engine.

The AUC is the measure of that reliability. It quantifies the model’s ability to provide a clear signal amidst the noise of the market, enabling a firm to move from a uniform pricing strategy to a highly differentiated one tailored to the specific context of each quote solicitation.

![Robust institutional Prime RFQ core connects to a precise RFQ protocol engine. Multi-leg spread execution blades propel a digital asset derivative target, optimizing price discovery](https://options.greeks.live/wp-content/uploads/2025/07/prime-os-rfq-proto-insti-digital-asset-derivs-hi-fi-exec-capital-effi-risk-mgmt.jpg)

## Feature Engineering as a Strategic Imperative

The performance of any predictive model, and thus its AUC, is fundamentally dependent on the quality of its inputs. In the context of RFQ win rate prediction, these inputs are the features extracted from the RFQ and the surrounding market environment. The process of designing, selecting, and transforming these features ▴ known as [feature engineering](https://prime.greeks.live/area/feature-engineering/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) ▴ is a core strategic activity. It requires a deep understanding of [market microstructure](https://prime.greeks.live/area/market-microstructure/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) and the dynamics of the RFQ process.

A model is only as intelligent as the data it is fed, and superior features are the bedrock of a high-AUC model. The strategic development of features involves capturing the subtle signals that influence a client’s decision.

- **Client-Specific Features** ▴ These capture the relationship with the counterparty. A model might incorporate the client’s historical win rate with the firm, their tier or importance, and the typical hit rates for that client in the specific asset class.

- **RFQ-Specific Features** ▴ These relate to the quote itself. The notional value of the trade, the tenor of the instrument, its liquidity profile, and the complexity of the product (e.g. a single-leg option versus a multi-leg spread) are all powerful predictors.

- **Market-Context Features** ▴ These features situate the RFQ within the broader market environment. They include measures of real-time volatility, the time of day (as liquidity patterns can be diurnal), the number of other dealers invited to the auction (if known), and the firm’s current inventory in the instrument or related products.

- **Dealer-Behavior Features** ▴ These are features derived from the firm’s own actions. The quoted spread, the time taken to respond to the RFQ, and how the quote compares to an internal fair value model can all serve as inputs that the model learns from.

> A higher AUC score is the direct result of a superior feature set that captures the complex determinants of RFQ success.

![Symmetrical internal components, light green and white, converge at central blue nodes. This abstract representation embodies a Principal's operational framework, enabling high-fidelity execution of institutional digital asset derivatives via advanced RFQ protocols, optimizing market microstructure for price discovery](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-protocol-execution-engine-core.jpg)

## Comparing Models a Strategic Framework

Imagine a trading desk must choose between two competing models for its RFQ pricing engine. Model A is a traditional [logistic regression model](https://prime.greeks.live/question/what-are-the-key-differences-in-validating-a-machine-learning-model-versus-a-traditional-logistic-regression-model/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite), which is highly interpretable and computationally efficient. Model B is a more complex gradient boosting machine ([XGBoost](https://prime.greeks.live/area/xgboost/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite)), which can capture non-linear relationships in the data but is more of a “black box” and requires more computational resources. The AUC-ROC curve provides the definitive framework for making this strategic choice.

After training both models on the same historical dataset of RFQs, their performance is evaluated on a hold-out test set. The results might look as follows:

| Metric | Model A (Logistic Regression) | Model B (XGBoost) |
| --- | --- | --- |
| Area Under ROC Curve (AUC) | 0.78 | 0.85 |
| Interpretability | High (coefficients are directly interpretable) | Low (requires specialized techniques like SHAP) |
| Computational Cost (Training) | Low | High |
| Computational Cost (Inference) | Very Low | Moderate |

The data presents a clear strategic trade-off. Model B has a significantly higher AUC, indicating it is substantially better at discriminating between winning and losing bids. An AUC of 0.85 means that for a randomly chosen pair of one win and one loss, the model has an 85% chance of correctly assigning a higher win probability to the actual winning RFQ. This is a powerful predictive edge.

However, this performance comes at the cost of interpretability and computational overhead. The strategic decision then hinges on the firm’s priorities. A firm focused purely on maximizing predictive accuracy and profitability would likely choose Model B, accepting the complexity as a necessary cost of a superior signal. A firm operating under strict regulatory oversight that requires full transparency in its pricing models might lean towards Model A, unless the performance gap is deemed too large to ignore. The AUC provides the quantitative backbone for this strategic conversation, elevating the decision from one of technical preference to one of business value.

![A stacked, multi-colored modular system representing an institutional digital asset derivatives platform. The top unit facilitates RFQ protocol initiation and dynamic price discovery](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-protocol-execution-engine-15.jpg)

![The abstract visual depicts a sophisticated, transparent execution engine showcasing market microstructure for institutional digital asset derivatives. Its central matching engine facilitates RFQ protocol execution, revealing internal algorithmic trading logic and high-fidelity execution pathways](https://options.greeks.live/wp-content/uploads/2025/07/principal-rfq-engine-for-institutional-digital-asset-derivatives.jpg)

## Execution

The execution of a [model comparison](https://prime.greeks.live/area/model-comparison/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) using the AUC-ROC curve is a systematic, data-driven process. It involves a disciplined workflow that begins with data preparation and culminates in a clear, quantifiable decision. For an [institutional trading](https://prime.greeks.live/area/institutional-trading/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) desk, this process is not merely an academic exercise; it is the operational protocol for ensuring that the predictive models powering its RFQ engine are the most effective available. This section provides a granular, step-by-step playbook for executing such a comparison, using a hypothetical but realistic dataset to illustrate the mechanics of the analysis.

![A sleek, segmented cream and dark gray automated device, depicting an institutional grade Prime RFQ engine. It represents precise execution management system functionality for digital asset derivatives, optimizing price discovery and high-fidelity execution within market microstructure](https://options.greeks.live/wp-content/uploads/2025/07/prime-os-rfq-engine-optimal-institutional-digital-asset-execution-system.jpg)

## The Operational Playbook for Model Comparison

The core of the execution phase is a structured comparison of candidate models. The objective is to subject each model to the same rigorous evaluation process to produce a fair and decisive comparison of their discriminative power.

- **Data Curation and Splitting** ▴ The first step is to assemble a comprehensive historical dataset of all RFQs the desk has responded to. This dataset must contain the engineered features and the known outcome (Win or Loss). This dataset is then split into a training set, used to teach the models, and a testing set, which is held back and used for the final performance evaluation. A typical split is 80% for training and 20% for testing.

- **Model Training** ▴ The candidate models (e.g. Model A ▴ [Logistic Regression](https://prime.greeks.live/area/logistic-regression/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite), Model B ▴ XGBoost) are then trained on the training dataset. This process involves optimizing the internal parameters of each model to best learn the relationship between the features and the win/loss outcome.

- **Probability Prediction** ▴ Once trained, each model is applied to the unseen testing set. For each RFQ in the test data, the model generates a predicted probability of winning, a score between 0 and 1. This list of probabilities is the raw output that will be used to construct the ROC curve.

- **ROC Curve Generation** ▴ For each model, the list of predicted probabilities is sorted in descending order. Then, by moving a decision threshold down from 1 to 0, we can calculate the True Positive Rate (TPR) and False Positive Rate (FPR) at each unique probability value. Plotting these (FPR, TPR) pairs generates the ROC curve for that model.

- **AUC Calculation and Comparison** ▴ The final step is to calculate the area under each of the generated ROC curves. The model with the higher AUC is the superior classifier. The comparison is now complete, and the trading desk has a quantitative justification for selecting one model over the other.

![A sophisticated digital asset derivatives RFQ engine's core components are depicted, showcasing precise market microstructure for optimal price discovery. Its central hub facilitates algorithmic trading, ensuring high-fidelity execution across multi-leg spreads](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-engine-optimizing-multi-leg-spreads-for-optimal-capital-efficiency.jpg)

## Quantitative Modeling and Data Analysis

To make this process concrete, let us consider a simplified, hypothetical dataset of 10 RFQs from our testing set. The table below shows the features for each RFQ, the actual outcome, and the win probabilities predicted by our two trained models.

| RFQ ID | Client Tier | Notional (USD M) | Volatility Index | Actual Outcome | Model A Prob (Logistic) | Model B Prob (XGBoost) |
| --- | --- | --- | --- | --- | --- | --- |
| 1 | 1 | 50 | 22.5 | Win | 0.82 | 0.88 |
| 2 | 2 | 10 | 18.1 | Loss | 0.45 | 0.35 |
| 3 | 1 | 25 | 19.0 | Win | 0.75 | 0.81 |
| 4 | 3 | 5 | 25.3 | Loss | 0.33 | 0.20 |
| 5 | 1 | 100 | 21.2 | Win | 0.91 | 0.95 |
| 6 | 2 | 15 | 23.1 | Loss | 0.60 | 0.68 |
| 7 | 1 | 5 | 17.5 | Win | 0.65 | 0.72 |
| 8 | 3 | 50 | 24.8 | Loss | 0.55 | 0.45 |
| 9 | 2 | 20 | 19.9 | Win | 0.71 | 0.79 |
| 10 | 2 | 30 | 20.5 | Loss | 0.58 | 0.52 |

In this test set, there are 5 Wins (positives) and 5 Losses (negatives). We can now use the predicted probabilities from Model A and Model B to calculate the TPR and FPR at various thresholds.

> The direct comparison of AUC scores on a held-out test dataset provides the definitive verdict on model superiority.

![Abstract geometric forms in dark blue, beige, and teal converge around a metallic gear, symbolizing a Prime RFQ for institutional digital asset derivatives. A sleek bar extends, representing high-fidelity execution and precise delta hedging within a multi-leg spread framework, optimizing capital efficiency via RFQ protocols](https://options.greeks.live/wp-content/uploads/2025/07/prime-os-digital-asset-derivatives-rfq-execution-engine-architecture.jpg)

## Predictive Scenario Analysis

Let’s trace the construction of the ROC curve for Model B (XGBoost). We sort the RFQs by Model B’s predicted probability in descending order. Then, we iterate through each RFQ, considering its probability as a potential threshold. At each step, we classify all RFQs above the threshold as “Win” and all below as “Loss” and compute the resulting TPR and FPR.

For example:

- **Threshold > 0.95** ▴ We predict nothing as a win. TP=0, FP=0. TPR=0/5=0, FPR=0/5=0. Point is (0, 0).

- **Threshold = 0.95** ▴ We predict RFQ 5 as a win. It was a win. TP=1, FP=0. TPR=1/5=0.2, FPR=0/5=0. Point is (0, 0.2).

- **Threshold = 0.88** ▴ We predict RFQs 5, 1 as wins. Both were wins. TP=2, FP=0. TPR=2/5=0.4, FPR=0/5=0. Point is (0, 0.4).

- **Threshold = 0.81** ▴ We predict RFQs 5, 1, 3 as wins. All were wins. TP=3, FP=0. TPR=3/5=0.6, FPR=0/5=0. Point is (0, 0.6).

- **Threshold = 0.79** ▴ We predict RFQs 5, 1, 3, 9 as wins. All were wins. TP=4, FP=0. TPR=4/5=0.8, FPR=0/5=0. Point is (0, 0.8).

- **Threshold = 0.72** ▴ We predict RFQs 5, 1, 3, 9, 7 as wins. All were wins. TP=5, FP=0. TPR=5/5=1.0, FPR=0/5=0. Point is (0, 1.0).

- **Threshold = 0.68** ▴ We predict RFQs 5, 1, 3, 9, 7, 6 as wins. RFQ 6 was a loss. TP=5, FP=1. TPR=5/5=1.0, FPR=1/5=0.2. Point is (0.2, 1.0).

And so on. By connecting these points, we trace the ROC curve for Model B. A similar process is followed for Model A. When plotted, we would visually see that Model B’s curve is consistently above Model A’s curve, indicating its superior performance across all thresholds. The calculated AUC for Model B would be higher than that for Model A, providing the final, quantitative proof. This rigorous, data-driven execution provides the confidence needed to deploy the superior model into the production trading environment.

![A dark blue sphere, representing a deep institutional liquidity pool, integrates a central RFQ engine. This system processes aggregated inquiries for Digital Asset Derivatives, including Bitcoin Options and Ethereum Futures, enabling high-fidelity execution](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-engine-for-optimized-liquidity-aggregation.jpg)

## System Integration and Technological Architecture

Once the superior model is selected, its operationalization requires careful integration into the firm’s trading architecture. The model is typically deployed as a microservice accessible via an API. The firm’s Order Management System (OMS) or a dedicated pricing engine would be responsible for orchestrating the process. When an inbound RFQ is received electronically, the OMS gathers the required features in real-time.

This involves querying internal databases for client information and inventory levels, and subscribing to market data feeds for live volatility and price data. These features are then packaged into an API call to the prediction model service. The model service responds in milliseconds with the calculated win probability. This probability is then displayed to the human trader on their screen, or it can be used directly by an automated pricing algorithm to adjust the spread on the outbound quote. The entire process, from RFQ receipt to quote dispatch, must occur within a very low latency window, making the computational efficiency of the model (the inference time) a critical architectural consideration.

![Sleek teal and beige forms converge, embodying institutional digital asset derivatives platforms. A central RFQ protocol hub with metallic blades signifies high-fidelity execution and price discovery](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-protocol-engine-microstructure.jpg)

## References

- Biais, B. Glosten, L. & Spatt, C. (2005). Market Microstructure ▴ A Survey of Microfoundations, Empirical Results, and Policy Implications. _Journal of Financial Markets, 8_(2), 217-264.

- Cont, R. & de Larrard, A. (2013). Price dynamics in a Markovian limit order market. _SIAM Journal on Financial Mathematics, 4_(1), 1-25.

- Fawcett, T. (2006). An introduction to ROC analysis. _Pattern Recognition Letters, 27_(8), 861-874.

- Gu, S. Kelly, B. & Xiu, D. (2020). Empirical asset pricing via machine learning. _The Review of Financial Studies, 33_(5), 2223-2273.

- Hasbrouck, J. (1995). One security, many markets ▴ Determining the contributions to price discovery. _The Journal of Finance, 50_(4), 1175-1199.

- Labadie, M. & Lehalle, C. A. (2023). Liquidity Dynamics in RFQ Markets and Impact on Pricing. _arXiv preprint arXiv:2309.04216_.

- O’Hara, M. (1995). _Market Microstructure Theory_. Blackwell Publishers.

- Powers, D. M. (2011). Evaluation ▴ from precision, recall and F-measure to ROC, informedness, markedness & correlation. _Journal of Machine Learning Technologies, 2_(1), 37-63.

- Provost, F. & Fawcett, T. (2013). Data Science for Business ▴ What You Need to Know about Data Mining and Data-Analytic Thinking. O’Reilly Media, Inc.

- Tantithamthavorn, C. Hassan, A. E. & Matsumoto, K. (2018). The impact of class rebalancing techniques on the performance and interpretation of defect prediction models. _IEEE Transactions on Software Engineering, 44_(8), 734-755.

![Interconnected translucent rings with glowing internal mechanisms symbolize an RFQ protocol engine. This Principal's Operational Framework ensures High-Fidelity Execution and precise Price Discovery for Institutional Digital Asset Derivatives, optimizing Market Microstructure and Capital Efficiency via Atomic Settlement](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-engine-high-fidelity-execution.jpg)

## Reflection

The adoption of a quantitative framework like AUC-ROC for comparing RFQ win rate models marks a significant step in the maturation of a trading desk’s operational capabilities. It reflects a commitment to evidence-based decision-making and the systematic optimization of performance. The knowledge of how to execute this comparison is a valuable tool, but its true power is realized when it is integrated into a broader philosophy of continuous improvement. The selected model is not a final destination; it is a snapshot of the best available tool at a single point in time.

Markets evolve, client behaviors shift, and new modeling techniques emerge. The true strategic advantage, therefore, lies not in having a single high-performing model, but in possessing the institutional capacity and intellectual framework to perpetually challenge, evaluate, and upgrade the predictive systems that drive the business. The process detailed here is a foundational component of that larger system ▴ an operational loop of innovation and validation that ensures the firm’s quoting intelligence remains at the leading edge.

![An abstract system depicts an institutional-grade digital asset derivatives platform. Interwoven metallic conduits symbolize low-latency RFQ execution pathways, facilitating efficient block trade routing](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-prime-os-rfq-execution-engine.jpg)

## Glossary

<a href="https://prime.greeks.live/area/liquidity-sourcing/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![A sleek, light interface, a Principal's Prime RFQ, overlays a dark, intricate market microstructure. This represents institutional-grade digital asset derivatives trading, showcasing high-fidelity execution via RFQ protocols](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-prime-os-execution-system.jpg)

## Liquidity Sourcing

Meaning ▴ Liquidity Sourcing refers to the systematic process of identifying, accessing, and aggregating available trading interest across diverse market venues to facilitate optimal execution of financial transactions. </a><a href="https://prime.greeks.live/area/price-discovery/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![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](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-engine-optimized-for-best-execution.jpg)

## Price Discovery

Meaning ▴ Price discovery is the continuous, dynamic process by which the market determines the fair value of an asset through the collective interaction of supply and demand. </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 polished disc with a central green RFQ engine for institutional digital asset derivatives. Radiating lines symbolize high-fidelity execution paths, atomic settlement flows, and market microstructure dynamics, enabling price discovery and liquidity aggregation within a Prime RFQ](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-protocol-core-for-optimal-execution.jpg)

## Win Rate

Meaning ▴ Win Rate, within the domain of institutional digital asset derivatives trading, quantifies the proportion of successful trading operations relative to the total number of operations executed over a defined period. </a><a href="https://prime.greeks.live/area/rfq-win-rate/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![A precise lens-like module, symbolizing high-fidelity execution and market microstructure insight, rests on a sharp blade, representing optimal smart order routing. Curved surfaces depict distinct liquidity pools within an institutional-grade Prime RFQ, enabling efficient RFQ for digital asset derivatives](https://options.greeks.live/wp-content/uploads/2025/07/high-fidelity-digital-asset-derivatives-execution-via-rfq-protocols.jpg)

## Rfq Win Rate

Meaning ▴ The RFQ Win Rate quantifies the success rate of executed trades originating from a Request for Quote framework. </a><a href="https://prime.greeks.live/area/auc-roc/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![A teal-colored digital asset derivative contract unit, representing an atomic trade, rests precisely on a textured, angled institutional trading platform. This suggests high-fidelity execution and optimized market microstructure for private quotation block trades within a secure Prime RFQ environment, minimizing slippage](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-protocol-execution-engine-optimization.jpg)

## Auc-Roc

Meaning ▴ The Area Under the Receiver Operating Characteristic Curve, or AUC-ROC, quantifies the performance of a classification model across all possible classification thresholds. </a><a href="https://prime.greeks.live/area/quoting-engine/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![A metallic, circular mechanism, a precision control interface, rests on a dark circuit board. This symbolizes the core intelligence layer of a Prime RFQ, enabling low-latency, high-fidelity execution for institutional digital asset derivatives via optimized RFQ protocols, refining market microstructure](https://options.greeks.live/wp-content/uploads/2025/08/precision-rfq-protocol-engine-for-institutional-digital-asset-derivatives.jpg)

## Quoting Engine

Meaning ▴ A Quoting Engine is a software module designed to dynamically compute and disseminate two-sided price quotes for financial instruments, typically within a low-latency trading environment. </a><a href="https://prime.greeks.live/area/binary-classification/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![A symmetrical, multi-faceted digital structure, a liquidity aggregation engine, showcases translucent teal and grey panels. This visualizes diverse RFQ channels and market segments, enabling high-fidelity execution for institutional digital asset derivatives](https://options.greeks.live/wp-content/uploads/2025/07/precision-liquidity-aggregation-engine-for-digital-asset-derivatives-trading.jpg)

## Binary Classification

Meaning ▴ Binary classification is a computational methodology for assigning data instances to one of two mutually exclusive categories, often represented as 0 or 1, true or false, or success or failure, based on learned patterns from a labeled dataset. </a><a href="https://prime.greeks.live/area/win-probability/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![A central glowing blue mechanism with a precision reticle is encased by dark metallic panels. This symbolizes an institutional-grade Principal's operational framework for high-fidelity execution of digital asset derivatives](https://options.greeks.live/wp-content/uploads/2025/07/precision-core-enabling-institutional-digital-asset-derivatives-rfq-execution.jpg)

## Win Probability

Meaning ▴ Win Probability defines a quantitative metric representing the statistical likelihood that a specific trading operation will achieve its predetermined objective, such as a target profit or a favorable execution outcome, given a set of current market conditions and historical performance data. </a><a href="https://prime.greeks.live/area/false-positive-rate/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![Geometric forms with circuit patterns and water droplets symbolize a Principal's Prime RFQ. This visualizes institutional-grade algorithmic trading infrastructure, depicting electronic market microstructure, high-fidelity execution, and real-time price discovery](https://options.greeks.live/wp-content/uploads/2025/07/digital-asset-derivatives-rfq-engine-prime-os-microstructure-analytics.jpg)

## False Positive Rate

Meaning ▴ The False Positive Rate quantifies the proportion of instances where a system incorrectly identifies a negative outcome as positive. </a><a href="https://prime.greeks.live/area/true-positive-rate/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![An intricate mechanical assembly reveals the market microstructure of an institutional-grade RFQ protocol engine. It visualizes high-fidelity execution for digital asset derivatives block trades, managing counterparty risk and multi-leg spread strategies within a liquidity pool, embodying a Prime RFQ](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-protocol-engine-architecture.jpg)

## True Positive Rate

Meaning ▴ The True Positive Rate, also known as Recall or Sensitivity, quantifies the proportion of actual positive cases that a model or system correctly identifies as positive. </a><a href="https://prime.greeks.live/area/true-positive/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![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)

## True Positive

Meaning ▴ A True Positive represents a correctly identified positive instance within a classification or prediction system. </a><a href="https://prime.greeks.live/area/false-positive/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![A central, metallic, multi-bladed mechanism, symbolizing a core execution engine or RFQ hub, emits luminous teal data streams. These streams traverse through fragmented, transparent structures, representing dynamic market microstructure, high-fidelity price discovery, and liquidity aggregation](https://options.greeks.live/wp-content/uploads/2025/07/principal-grade-digital-asset-derivatives-rfq-protocol-engine-facilitating-high-fidelity-execution-and-optimal-price-discovery-across-fragmented-market-microstructure.jpg)

## False Positive

Meaning ▴ A false positive constitutes an erroneous classification or signal generated by an automated system, indicating the presence of a specific condition or event when, in fact, that condition or event is absent. </a><a href="https://prime.greeks.live/area/roc-curve/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![A high-precision, dark metallic circular mechanism, representing an institutional-grade RFQ engine. Illuminated segments denote dynamic price discovery and multi-leg spread execution](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-protocol-execution-engine-7.jpg)

## Roc Curve

Meaning ▴ The ROC Curve, or Receiver Operating Characteristic Curve, is a graphical plot that illustrates the diagnostic ability of a binary classifier system as its discrimination threshold is varied. </a><a href="https://prime.greeks.live/question/what-are-the-primary-system-architecture-changes-required-to-move-from-a-single-curve-to-a-multi-curve-system/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![A precision metallic mechanism with radiating blades and blue accents, representing an institutional-grade Prime RFQ for digital asset derivatives. It signifies high-fidelity execution via RFQ protocols, leveraging dark liquidity and smart order routing within market microstructure](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-protocols-engine.jpg)

## Auc-Roc Curve

Transitioning to a multi-curve system involves re-architecting valuation from a monolithic to a modular framework that separates discounting and forecasting. </a><a href="https://prime.greeks.live/area/trading-desk/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![A cutaway view reveals an advanced RFQ protocol engine for institutional digital asset derivatives. Intricate coiled components represent algorithmic liquidity provision and portfolio margin calculations](https://options.greeks.live/wp-content/uploads/2025/07/prime-os-rfq-protocol-engine-digital-asset-derivatives-core-internal-mechanism-visualized.jpg)

## Trading Desk

Meaning ▴ A Trading Desk represents a specialized operational system within an institutional financial entity, designed for the systematic execution, risk management, and strategic positioning of proprietary capital or client orders across various asset classes, with a particular focus on the complex and nascent digital asset derivatives landscape. </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">
![An Institutional Grade RFQ Engine core for Digital Asset Derivatives. This Prime RFQ Intelligence Layer ensures High-Fidelity Execution, driving Optimal Price Discovery and Atomic Settlement for Aggregated Inquiries](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-engine-core-high-fidelity.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-engineering/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![A central control knob on a metallic platform, bisected by sharp reflective lines, embodies an institutional RFQ protocol. This depicts intricate market microstructure, enabling high-fidelity execution, precise price discovery for multi-leg options, and robust Prime RFQ deployment, optimizing latent liquidity across digital asset derivatives](https://options.greeks.live/wp-content/uploads/2025/08/institutional-rfq-digital-asset-derivatives-execution-engine-core-microstructure.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/question/what-are-the-key-differences-in-validating-a-machine-learning-model-versus-a-traditional-logistic-regression-model/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![A symmetrical, star-shaped Prime RFQ engine with four translucent blades symbolizes multi-leg spread execution and diverse liquidity pools. Its central core represents price discovery for aggregated inquiry, ensuring high-fidelity execution within a secure market microstructure via smart order routing for block trades](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-execution-engine-prime-os-1.jpg)

## Logistic Regression Model

Validating a logistic regression confirms linear assumptions; validating a machine learning model discovers performance boundaries. </a><a href="https://prime.greeks.live/area/xgboost/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![A central, blue-illuminated, crystalline structure symbolizes an institutional grade Crypto Derivatives OS facilitating RFQ protocol execution. Diagonal gradients represent aggregated liquidity and market microstructure converging for high-fidelity price discovery, optimizing multi-leg spread trading for digital asset options](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-protocol-execution-engine-11.jpg)

## Xgboost

Meaning ▴ XGBoost, or Extreme Gradient Boosting, represents a highly optimized and scalable implementation of the gradient boosting framework. </a><a href="https://prime.greeks.live/area/institutional-trading/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![Precision cross-section of an institutional digital asset derivatives system, revealing intricate market microstructure. Toroidal halves represent interconnected liquidity pools, centrally driven by an RFQ protocol](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-prime-os-architecture-revealing-precision-rfq-protocol-engine-for-optimal-high-fidelity-execution-and-latent-liquidity-aggregation.jpg)

## Institutional Trading

Meaning ▴ Institutional Trading refers to the execution of large-volume financial transactions by entities such as asset managers, hedge funds, pension funds, and sovereign wealth funds, distinct from retail investor activity. </a><a href="https://prime.greeks.live/area/model-comparison/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![Intersecting metallic structures symbolize RFQ protocol pathways for institutional digital asset derivatives. They represent high-fidelity execution of multi-leg spreads across diverse liquidity pools](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-protocol-framework-optimizing-multi-leg-spreads-and-liquidity-aggregation-for-best-execution.jpg)

## Model Comparison

Meaning ▴ Model comparison defines the systematic evaluation of competing quantitative models used for pricing, risk management, or execution strategy, assessing their predictive power, explanatory fit, and operational robustness against observed market data or theoretical benchmarks. </a><a href="https://prime.greeks.live/area/logistic-regression/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![An abstract, multi-layered spherical system with a dark central disk and control button. This visualizes a Prime RFQ for institutional digital asset derivatives, embodying an RFQ engine optimizing market microstructure for high-fidelity execution and best execution, ensuring capital efficiency in block trades and atomic settlement](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-execution-engine-prime-os-4.jpg)

## Logistic Regression

Meaning ▴ Logistic Regression is a statistical classification model designed to estimate the probability of a binary outcome by mapping input features through a sigmoid function. </a>

**Tags:** [Binary Classification](https://options.greeks.live/area/binary-classification/), [Institutional Trading](https://options.greeks.live/area/institutional-trading/), [True Positive Rate](https://options.greeks.live/area/true-positive-rate/), [True Positive](https://options.greeks.live/area/true-positive/), [Algorithmic Trading](https://options.greeks.live/area/algorithmic-trading/), [Trading Desk](https://options.greeks.live/area/trading-desk/)	

## Discover More

- 
### [How Does a Hybrid RFQ and CLOB Model Impact Transaction Cost Analysis?](https://options.greeks.live/question/how-does-a-hybrid-rfq-and-clob-model-impact-transaction-cost-analysis/)
![An intricate, transparent digital asset derivatives engine visualizes market microstructure and liquidity pool dynamics. Its precise components signify high-fidelity execution via FIX Protocol, facilitating RFQ protocols for block trade and multi-leg spread strategies within an institutional-grade Prime RFQ.](https://options.greeks.live/wp-content/uploads/2025/07/prime-os-digital-asset-derivatives-rfq-liquidity-ecosystem.jpg)

A hybrid RFQ/CLOB model transforms TCA by enabling strategic liquidity sourcing to minimize the total cost of execution.

- 
### [How Can Anonymous RFQ Protocols Mitigate Information Risk?](https://options.greeks.live/question/how-can-anonymous-rfq-protocols-mitigate-information-risk/)
![Clear geometric prisms and flat planes interlock, symbolizing complex market microstructure and multi-leg spread strategies in institutional digital asset derivatives. A solid teal circle represents a discrete liquidity pool for private quotation via RFQ protocols, ensuring high-fidelity execution.](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-protocols-price-discovery-and-high-fidelity-execution.jpg)

Anonymous RFQ protocols mitigate information risk by structurally severing the link between institutional identity and trade intent, enabling discreet, competitive price discovery.

- 
### [How Does the Quote Type Tag Influence the Strategic Execution of an Rfq in Financial Markets?](https://options.greeks.live/question/how-does-the-quote-type-tag-influence-the-strategic-execution-of-an-rfq-in-financial-markets/)
![A sophisticated digital asset derivatives trading mechanism features a central processing hub with luminous blue accents, symbolizing an intelligence layer driving high fidelity execution. Transparent circular elements represent dynamic liquidity pools and a complex volatility surface, revealing market microstructure and atomic settlement via an advanced RFQ protocol.](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-protocols-high-fidelity-execution-framework-optimizing-market-microstructure-price-discovery-intelligence-layer.jpg)

The Quote Type tag governs RFQ execution by defining price firmness, thereby controlling the trade-off between information leakage and execution certainty.

- 
### [What Are the Technological Requirements for Implementing a Dynamic Quote Window System?](https://options.greeks.live/question/what-are-the-technological-requirements-for-implementing-a-dynamic-quote-window-system/)
![An abstract, angular, reflective structure intersects a dark sphere. This visualizes institutional digital asset derivatives and high-fidelity execution via RFQ protocols for block trade and private quotation. It represents liquidity pool access and price discovery within market microstructure on a Prime RFQ.](https://options.greeks.live/wp-content/uploads/2025/07/prime-os-rfq-protocols-for-high-fidelity-digital-asset-execution.jpg)

A dynamic quote window's technology enables controlled, off-book liquidity sourcing and price negotiation.

- 
### [Can Increased Dark Pool Usage under Normal Conditions Actually Lower Market Volatility?](https://options.greeks.live/question/can-increased-dark-pool-usage-under-normal-conditions-actually-lower-market-volatility/)
![A sleek, metallic instrument with a central pivot and pointed arm, featuring a reflective surface and a teal band, embodies an institutional RFQ protocol. This represents high-fidelity execution for digital asset derivatives, enabling private quotation and optimal price discovery for multi-leg spread strategies within a dark pool, powered by a Prime RFQ.](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-rfq-execution-for-optimal-liquidity-discovery.jpg)

Increased dark pool usage under normal conditions can lower market volatility by absorbing large trades, but risks degrading the public price discovery it relies upon.

- 
### [Can Multi-Maker RFQ Models Be Used for Asset Classes Other than Crypto Options?](https://options.greeks.live/question/can-multi-maker-rfq-models-be-used-for-asset-classes-other-than-crypto-options/)
![Intersecting teal cylinders and flat bars, centered by a metallic sphere, abstractly depict an institutional RFQ protocol. This engine ensures high-fidelity execution for digital asset derivatives, optimizing market microstructure, atomic settlement, and price discovery across aggregated liquidity pools for Principal Market Makers.](https://options.greeks.live/wp-content/uploads/2025/07/optimized-rfq-protocol-for-institutional-digital-asset-derivatives.jpg)

The multi-maker RFQ model is a versatile liquidity sourcing protocol applicable to any asset class where discreet, competitive price discovery is paramount.

- 
### [What Are the Primary Trade-Offs between a Broad versus a Curated Dealer List in an Rfq?](https://options.greeks.live/question/what-are-the-primary-trade-offs-between-a-broad-versus-a-curated-dealer-list-in-an-rfq/)
![A precision instrument probes a speckled surface, visualizing market microstructure and liquidity pool dynamics within a dark pool. This depicts RFQ protocol execution, emphasizing price discovery for digital asset derivatives. Elements represent volatility surface data and algorithmic trading probes ensuring capital efficiency.](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-protocol-execution-flow.jpg)

Calibrating an RFQ dealer list is the essential trade-off between maximizing price competition and minimizing costly information leakage.

- 
### [How Does Information Leakage Differ between RFQ Systems and Dark Pools?](https://options.greeks.live/question/how-does-information-leakage-differ-between-rfq-systems-and-dark-pools/)
![Central teal-lit mechanism with radiating pathways embodies a Prime RFQ for institutional digital asset derivatives. It signifies RFQ protocol processing, liquidity aggregation, and high-fidelity execution for multi-leg spread trades, enabling atomic settlement within market microstructure via quantitative analysis.](https://options.greeks.live/wp-content/uploads/2025/07/prime-os-core-for-institutional-digital-asset-rfq-protocol-execution-engine.jpg)

RFQ systems control leakage through selective disclosure, while dark pools use pre-trade anonymity, trading execution risk for impact control.

- 
### [What Are the Key Risks of Using an RFQ Protocol besides Information Leakage?](https://options.greeks.live/question/what-are-the-key-risks-of-using-an-rfq-protocol-besides-information-leakage/)
![Precisely balanced blue spheres on a beam and angular fulcrum, atop a white dome. This signifies RFQ protocol optimization for institutional digital asset derivatives, ensuring high-fidelity execution, price discovery, capital efficiency, and systemic equilibrium in multi-leg spreads.](https://options.greeks.live/wp-content/uploads/2025/07/systemic-equilibrium-in-digital-asset-derivatives-rfq-protocols.jpg)

Beyond information leakage, RFQ protocols carry systemic risks of adverse selection and winner's curse, impacting execution quality.

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---

**Original URL:** https://options.greeks.live/question/how-can-the-auc-roc-curve-be-used-to-compare-different-rfq-win-rate-prediction-models/
