# Can Game Theory Be Applied to More Accurately Model Competitive RFQ Responses in a Backtest? ▴ Question

**Published:** 2025-07-30
**Categories:** Question

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![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)

![A proprietary Prime RFQ platform featuring extending blue/teal components, representing a multi-leg options strategy or complex RFQ spread. The labeled band 'F331 46 1' denotes a specific strike price or option series within an aggregated inquiry for high-fidelity execution, showcasing granular market microstructure data points](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-execution-protocol-1.jpg)

## Concept

Applying [game theory](https://prime.greeks.live/area/game-theory/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) to model competitive Request for Quote (RFQ) responses within a backtest is a direct application of system modeling to [price discovery](https://prime.greeks.live/area/price-discovery/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite). The core objective is to move from a reactive analysis of historical quote data to a predictive simulation of counterparty behavior. An RFQ is fundamentally a strategic interaction defined by incomplete information. The initiator of the quote solicitation protocol possesses knowledge of their own trading intent but lacks certainty about the inventory, risk appetite, and competitive pressures faced by each responding dealer.

Conversely, each dealer must formulate a price based on their own position and their beliefs about the prices other dealers might submit. This environment of interdependent decision-making under uncertainty is the precise domain of game theory.

The process begins by architecting the [RFQ](https://prime.greeks.live/area/rfq/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) interaction as a formal game. This involves defining the players (the initiator and the panel of dealers), the available actions (submitting a specific bid or offer, or declining to quote), and the payoffs (the profit or loss for the winning dealer and the execution quality for the initiator). Building a robust model requires moving beyond simple assumptions. It necessitates a quantitative understanding of how dealers react not just to the instrument being quoted, but to the context of the inquiry itself.

A [backtesting](https://prime.greeks.live/area/backtesting/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) framework then uses these game-theoretic models to simulate dealer responses to historical RFQs, allowing for a rigorous assessment of the model’s predictive power. The value of this approach lies in its ability to quantify the strategic elements of liquidity provision that are invisible to traditional execution analysis.

> A successful game-theoretic model transforms a backtest from a simple performance report into a sophisticated simulator of market participant behavior.

This method provides a structured way to answer critical questions about the [price discovery process](https://prime.greeks.live/question/how-does-information-asymmetry-in-an-rfq-workflow-affect-the-price-discovery-process-for-illiquid-assets/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite). It allows an institution to analyze how the composition of the dealer panel, the size of the request, or the prevailing market volatility influences the competitiveness of the quotes received. By simulating these complex interactions, the system can identify patterns of behavior and create a more accurate expectation of execution costs, which is the foundational goal of any high-fidelity backtesting protocol. The result is a deeper, mechanistic understanding of an institution’s own trading ecosystem.

![A sophisticated modular apparatus, likely a Prime RFQ component, showcases high-fidelity execution capabilities. Its interconnected sections, featuring a central glowing intelligence layer, suggest a robust RFQ protocol engine](https://options.greeks.live/wp-content/uploads/2025/07/institutional-rfq-engine-core-for-digital-asset-derivatives-high-fidelity-execution-protocols.jpg)

![A central, multifaceted RFQ engine processes aggregated inquiries via precise execution pathways and robust capital conduits. This institutional-grade system optimizes liquidity aggregation, enabling high-fidelity execution and atomic settlement for digital asset derivatives](https://options.greeks.live/wp-content/uploads/2025/07/institutional-rfq-protocol-engine-high-fidelity-digital-asset-execution.jpg)

## Strategy

The strategic implementation of game theory in an RFQ backtesting system centers on creating realistic, data-driven models of dealer behavior. This involves selecting an appropriate game-theoretic framework and populating it with parameters derived from [historical data](https://prime.greeks.live/area/historical-data/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite). The most effective approach for this purpose is the use of [Bayesian games](https://prime.greeks.live/area/bayesian-games/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite), which are specifically designed to model situations with incomplete information.

![A macro view reveals a robust metallic component, signifying a critical interface within a Prime RFQ. This secure mechanism facilitates precise RFQ protocol execution, enabling atomic settlement for institutional-grade digital asset derivatives, embodying high-fidelity execution](https://options.greeks.live/wp-content/uploads/2025/07/core-secure-institutional-digital-asset-derivatives-rfq-trade-system-element.jpg)

## Architecting the RFQ as a Bayesian Game

In a Bayesian game framework, each player has a “type” that is unknown to the other players. In the context of an RFQ, a dealer’s type represents their private information and internal state. This could include their current inventory, their risk limits, their immediate need to hedge, or their view on the asset’s short-term direction. The strategy involves using historical data to build probabilistic models that connect observable market conditions to these unobservable dealer types, and in turn, how those types map to specific quoting behavior.

The core strategic components to model are:

- **Dealer Types** ▴ Each dealer is modeled not as a single entity, but as a distribution of potential types. For example, a dealer could be in an “Aggressive,” “Passive,” or “Inventory-Constrained” state. Historical analysis can reveal the probability of a dealer being a certain type given market volatility or the time of day.

- **Beliefs** ▴ The model must define a dealer’s beliefs about the types of other dealers in the auction. A dealer who believes others are desperate to offload inventory will quote differently than one who believes others are passive. These beliefs are updated based on the initiator’s past behavior and the overall market context.

- **Payoff Functions** ▴ The payoff for a dealer is a function of winning the auction versus the cost of that win (the “winner’s curse”). The payoff for winning is the spread captured, adjusted for the risk of holding the new position. The payoff for losing is zero. The model must calculate the optimal bid that maximizes the expected payoff, which is the probability of winning multiplied by the value of winning.

![Intersecting digital architecture with glowing conduits symbolizes Principal's operational framework. An RFQ engine ensures high-fidelity execution of Institutional Digital Asset Derivatives, facilitating block trades, multi-leg spreads](https://options.greeks.live/wp-content/uploads/2025/07/principal-digital-asset-derivatives-rfq-engine-high-fidelity-execution.jpg)

## How Can Different Dealer Strategies Be Modeled?

To construct a realistic backtest, the system must simulate a variety of strategic approaches that dealers might employ. These strategies can be parameterized and assigned to simulated dealers to reflect a diverse and competitive panel. The table below outlines a few archetypal dealer strategies and the parameters that define them within a game-theoretic model.

| Dealer Strategy Archetype | Core Objective | Primary Influencing Factors | Typical Quoting Behavior |
| --- | --- | --- | --- |
| Volume Maximizer | Win a high percentage of auctions to meet volume targets. | Market share goals; low sensitivity to short-term volatility. | Consistently tight spreads, willing to quote aggressively even on small-margin trades. |
| Risk Manager | Minimize inventory risk and adverse selection. | Current inventory levels; high market volatility; perceived initiator toxicity. | Wider spreads, particularly in volatile markets or for large sizes. May decline to quote frequently. |
| Opportunistic Hedger | Use the RFQ to offload or acquire specific inventory imbalances. | Directional inventory skew; desire to flatten the book. | Highly aggressive quotes when the RFQ helps their position; uncompetitive or no-quotes otherwise. |
| Information Seeker | Gauge market depth and flow without a strong intent to win. | Low confidence in current market price; desire for price discovery. | Quotes near the perceived market mid-price, often last to respond. |

By simulating a panel of dealers composed of these different strategic types, the backtesting engine can generate a much richer and more realistic distribution of potential quote responses than a simple historical replay. This allows an institution to stress-test its execution strategies against a variety of plausible market scenarios and counterparty behaviors.

![A central teal sphere, secured by four metallic arms on a circular base, symbolizes an RFQ protocol for institutional digital asset derivatives. It represents a controlled liquidity pool within market microstructure, enabling high-fidelity execution of block trades and managing counterparty risk through a Prime RFQ](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-protocol-execution-hub-1.jpg)

![A complex central mechanism, akin to an institutional RFQ engine, displays intricate internal components representing market microstructure and algorithmic trading. Transparent intersecting planes symbolize optimized liquidity aggregation and high-fidelity execution for digital asset derivatives, ensuring capital efficiency and atomic settlement](https://options.greeks.live/wp-content/uploads/2025/07/institutional-rfq-engine-optimizing-digital-asset-derivative-execution.jpg)

## Execution

Executing a backtest of RFQ responses using a game-theoretic framework is a quantitative and data-intensive process. It requires building a simulation engine that can replay historical RFQs, generate probabilistic quotes from a panel of modeled dealers, and compare the simulated outcomes to the factual record. This process provides a clear measure of the model’s accuracy and delivers insights into the dynamics of the institution’s price discovery process.

![Abstract bisected spheres, reflective grey and textured teal, forming an infinity, symbolize institutional digital asset derivatives. Grey represents high-fidelity execution and market microstructure teal, deep liquidity pools and volatility surface data](https://options.greeks.live/wp-content/uploads/2025/07/prime-os-rfq-protocols-for-digital-asset-derivatives-execution.jpg)

## Building the Simulation Engine a Procedural Outline

The creation of the backtesting environment follows a systematic procedure, from data aggregation to model calibration and analysis. Each step is designed to ensure the integrity of the simulation and the relevance of its outputs.

- **Data Aggregation and Preprocessing** ▴ The foundation of the model is a comprehensive dataset of historical RFQs. For each request, the following data points are essential:

<li>**Request Parameters** ▴ Timestamp, instrument, size, and direction (buy/sell).

- **Market State** ▴ The state of the order book (top-level bid/ask, depth), and recent volatility at the moment of the request.

- **Dealer Responses** ▴ The full list of dealers solicited, their individual quotes (bid/offer), and their response times.

- **[Winning Quote](https://prime.greeks.live/question/what-is-the-meaning-of-quote-latency-in-an-rfq/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite)** ▴ The quote that was ultimately selected by the initiator.

</li>
- **Dealer Profile Generation** ▴ Using the historical data, create a statistical profile for each dealer on the panel. This involves calculating key metrics that will serve as inputs for the behavioral model. These metrics include historical win rates, average spread competitiveness relative to the best quote, and response patterns in different market conditions.

- **Probabilistic Quote Modeling** ▴ This is the core of the execution. For each dealer, a Bayesian model is constructed to generate a quote. The model calculates the probability distribution of a dealer’s likely quote based on the RFQ’s parameters and the market state. The model attempts to find the optimal quote b for a dealer d that maximizes their expected utility E , where V is the value of winning and C is the set of competing quotes.

- **Simulation Loop** ▴ The backtest iterates through each historical RFQ event. For a given event, the engine simulates the response from each solicited dealer by drawing from their respective probabilistic quote models. This generates a set of simulated quotes for each historical RFQ.

- **Performance Analysis and Calibration** ▴ The simulated winning quote is compared to the actual historical winning quote. The performance of the model is measured using several key metrics. The model’s parameters can then be calibrated to minimize the error between the simulated and historical outcomes.

![A sleek, multi-layered system representing an institutional-grade digital asset derivatives platform. Its precise components symbolize high-fidelity RFQ execution, optimized market microstructure, and a secure intelligence layer for private quotation, ensuring efficient price discovery and robust liquidity pool management](https://options.greeks.live/wp-content/uploads/2025/07/high-fidelity-rfq-execution-digital-asset-derivatives-platform-prime-os.jpg)

## What Key Metrics Validate the Model’s Accuracy?

The validation of the game-theoretic backtesting model relies on a set of specific and quantifiable performance indicators. These metrics assess the model’s ability to accurately predict not only the winning price but also the overall competitive landscape of the RFQ.

> A well-calibrated model should not only predict the winning bid but also accurately forecast the distribution and competitiveness of the entire set of quotes.

The following table details the primary metrics for evaluating the backtesting engine’s performance. These metrics provide a comprehensive view of the model’s predictive power and its utility for strategic execution analysis.

| Performance Metric | Definition | Purpose and Interpretation |
| --- | --- | --- |
| Hit Rate | The percentage of backtested RFQs where the model correctly predicts which dealer will provide the winning quote. | Measures the model’s ability to identify the most competitive counterparty in a given situation. A high hit rate indicates a strong understanding of individual dealer behavior. |
| Spread Prediction Error | The average absolute difference between the simulated winning spread and the actual historical winning spread. | Quantifies the model’s pricing accuracy. A low error suggests the model is well-calibrated to the market’s pricing dynamics. |
| Quote Distribution Analysis | A comparison of the statistical properties (e.g. mean, variance) of the simulated quote distribution versus the historical distribution. | Assesses whether the model is generating a realistic level of competition. This ensures the model is not just predicting the winner, but also the broader competitive context. |
| Adverse Selection Indicator | Measures the frequency with which the model predicts a “winner’s curse” scenario, where the winning quote is significantly through the prevailing market mid-price. | Helps in identifying market conditions and RFQ characteristics that lead to higher execution risk and information leakage. |

Through the rigorous application of this execution framework, an institution can build a powerful analytical tool. This system allows for the simulation of different RFQ strategies, such as changing the number of dealers or the timing of requests, to optimize execution outcomes based on a data-driven, predictive model of counterparty behavior.

![A sleek, reflective bi-component structure, embodying an RFQ protocol for multi-leg spread strategies, rests on a Prime RFQ base. Surrounding nodes signify price discovery points, enabling high-fidelity execution of digital asset derivatives with capital efficiency](https://options.greeks.live/wp-content/uploads/2025/07/precision-orchestration-of-institutional-digital-asset-derivatives-rfq-protocols-for-optimal-market-microstructure.jpg)

## References

- Albano, Gian Luigi, and Frédéric Jouneau. “A Bayesian Approach to the Econometrics of First-Price Auctions.” CORE Discussion Papers, 1996.

- Harsanyi, John C. “Games with Incomplete Information Played by ‘Bayesian’ Players, I-III.” Management Science, vol. 14, no. 3-7, 1967-68, pp. 159-82, 320-34, 486-502.

- Kastl, Jakub. “Auctions in Financial Markets.” Princeton University, 2019.

- Lines, B.C. et al. “Does Best Value Procurement Cost More than Low-Bid? A Total Project Cost Perspective.” International Journal of Construction Education and Research, vol. 18, 2020, pp. 85-100.

- Pathirage, C.P. et al. “The role of tacit knowledge in the construction industry ▴ Towards a definition.” CIB W89 International Conference on Building Education and Research (BEAR), 2008.

- Zhang, Hanzhe. “Auction as a Bayesian Game.” ECON20710 Lecture, University of Chicago, 2012.

- Peilin, Deng, and Zhang Ruiming. “Review of Theoretical Research and Application of Game Theory in Preventing Together-Conspired or Colluded Bidding Behavior.” Atlantis Press, 2017.

![Central reflective hub with radiating metallic rods and layered translucent blades. This visualizes an RFQ protocol engine, symbolizing the Prime RFQ orchestrating multi-dealer liquidity for institutional digital asset derivatives](https://options.greeks.live/wp-content/uploads/2025/07/high-fidelity-digital-asset-rfq-execution-and-liquidity-management.jpg)

## Reflection

![A central Principal OS hub with four radiating pathways illustrates high-fidelity execution across diverse institutional digital asset derivatives liquidity pools. Glowing lines signify low latency RFQ protocol routing for optimal price discovery, navigating market microstructure for multi-leg spread strategies](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-protocol-execution-intelligence.jpg)

## From Historical Data to a System of Intelligence

Viewing RFQ responses through the lens of game theory prompts a fundamental shift in perspective. It encourages a move away from treating counterparties as sources of static, historical data points and toward understanding them as dynamic, strategic actors within a complex system. The framework detailed here provides the architecture for building a predictive model, yet its true value extends beyond the quantitative outputs of a backtest. The process of modeling dealer behavior compels a deeper inquiry into the nature of an institution’s own liquidity ecosystem.

What are the subtle behavioral patterns of your most consistent counterparties? How does information, both explicit and implicit, flow between you and your dealer panel during the price discovery process? Answering these questions transforms a collection of execution data into a genuine system of intelligence. The ultimate goal of this analytical structure is to provide a more sophisticated understanding of the market’s mechanics, empowering an institution to design more effective, data-driven protocols for sourcing liquidity and managing execution risk.

![A sleek Principal's Operational Framework connects to a glowing, intricate teal ring structure. This depicts an institutional-grade RFQ protocol engine, facilitating high-fidelity execution for digital asset derivatives, enabling private quotation and optimal price discovery within market microstructure](https://options.greeks.live/wp-content/uploads/2025/07/prime-os-execution-engine-for-digital-asset-derivatives-rfq.jpg)

## Glossary

<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 sleek, metallic, X-shaped object with a central circular core floats above mountains at dusk. It signifies an institutional-grade Prime RFQ for digital asset derivatives, enabling high-fidelity execution via RFQ protocols, optimizing price discovery and capital efficiency across dark pools for best execution](https://options.greeks.live/wp-content/uploads/2025/07/advanced-principal-rfq-protocol-architecture-for-institutional-digital-asset-derivatives-optimal-price-discovery-and-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/game-theory/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![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)

## Game Theory

Meaning ▴ Game Theory is a mathematical framework analyzing strategic interactions where outcomes depend on collective choices. </a><a href="https://prime.greeks.live/area/rfq/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![A sleek, angled object, featuring a dark blue sphere, cream disc, and multi-part base, embodies a Principal's operational framework. This represents an institutional-grade RFQ protocol for digital asset derivatives, facilitating high-fidelity execution and price discovery within market microstructure, optimizing capital efficiency](https://options.greeks.live/wp-content/uploads/2025/08/prime-os-rfq-for-institutional-digital-asset-derivatives-execution.jpg)

## Rfq

Meaning ▴ Request for Quote (RFQ) is a structured communication protocol enabling a market participant to solicit executable price quotations for a specific instrument and quantity from a selected group of liquidity providers. </a><a href="https://prime.greeks.live/area/backtesting/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![A sleek, illuminated control knob emerges from a robust, metallic base, representing a Prime RFQ interface for institutional digital asset derivatives. Its glowing bands signify real-time analytics and high-fidelity execution of RFQ protocols, enabling optimal price discovery and capital efficiency in dark pools for block trades](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-system-for-optimal-execution.jpg)

## Backtesting

Meaning ▴ Backtesting is the application of a trading strategy to historical market data to assess its hypothetical performance under past conditions. </a><a href="https://prime.greeks.live/question/how-does-information-asymmetry-in-an-rfq-workflow-affect-the-price-discovery-process-for-illiquid-assets/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![A metallic, modular trading interface with black and grey circular elements, signifying distinct market microstructure components and liquidity pools. A precise, blue-cored probe diagonally integrates, representing an advanced RFQ engine for granular price discovery and atomic settlement of multi-leg spread strategies in institutional digital asset derivatives](https://options.greeks.live/wp-content/uploads/2025/08/institutional-digital-asset-derivatives-rfq-protocol-system-facilitating-high-fidelity-execution-price-discovery.jpg)

## Price Discovery Process

Information asymmetry in an RFQ for illiquid assets degrades price discovery by introducing uncertainty and risk, which dealers price into their quotes. </a><a href="https://prime.greeks.live/area/historical-data/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![A sleek, bimodal digital asset derivatives execution interface, partially open, revealing a dark, secure internal structure. This symbolizes high-fidelity execution and strategic price discovery via institutional RFQ protocols](https://options.greeks.live/wp-content/uploads/2025/08/institutional-digital-asset-derivatives-rfq-protocols-price-discovery.jpg)

## Historical Data

Meaning ▴ Historical Data refers to a structured collection of recorded market events and conditions from past periods, comprising time-stamped records of price movements, trading volumes, order book snapshots, and associated market microstructure details. </a><a href="https://prime.greeks.live/area/bayesian-games/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![A dark, precision-engineered module with raised circular elements integrates with a smooth beige housing. It signifies high-fidelity execution for institutional RFQ protocols, ensuring robust price discovery and capital efficiency in digital asset derivatives market microstructure](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-rfq-system-core-high-speed-price-discovery-module.jpg)

## Bayesian Games

Meaning ▴ Bayesian Games represent a class of game theory models where participants possess incomplete information regarding certain parameters of the game, such as the private types or beliefs of other players. </a><a href="https://prime.greeks.live/question/what-is-the-meaning-of-quote-latency-in-an-rfq/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![Layered abstract forms depict a Principal's Prime RFQ for institutional digital asset derivatives. A textured band signifies robust RFQ protocol and market microstructure](https://options.greeks.live/wp-content/uploads/2025/07/digital-asset-derivatives-rfq-high-fidelity-execution-prime-os-market-microstructure.jpg)

## Winning Quote

Quote latency in an RFQ is the critical time interval that quantifies the information risk transferred between a liquidity requester and provider. </a>

**Tags:** [Counterparty Analysis](https://options.greeks.live/area/counterparty-analysis/), [Liquidity Sourcing](https://options.greeks.live/area/liquidity-sourcing/), [Quantitative Finance](https://options.greeks.live/area/quantitative-finance/), [Game Theory](https://options.greeks.live/area/game-theory/), [Backtesting](https://options.greeks.live/area/backtesting/), [Algorithmic Trading](https://options.greeks.live/area/algorithmic-trading/)	

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Firms backtest influential models by simulating the market's reaction to their trades, accounting for the model's own price impact.

- 
### [What Are the Key Differences in Applying TCA to RFQs versus Algorithmic Order Book Executions?](https://options.greeks.live/question/what-are-the-key-differences-in-applying-tca-to-rfqs-versus-algorithmic-order-book-executions/)
![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)

TCA for RFQs evaluates private negotiations and counterparty skill; for algorithms, it measures interaction with public, continuous markets.

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---

**Original URL:** https://options.greeks.live/question/can-game-theory-be-applied-to-more-accurately-model-competitive-rfq-responses-in-a-backtest/
