# What Is the Role of Pre-Trade Analytics in the Success of an Automated Request for Quote System? ▴ Question

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

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![A precision-engineered RFQ protocol engine, its central teal sphere signifies high-fidelity execution for digital asset derivatives. This module embodies a Principal's dedicated liquidity pool, facilitating robust price discovery and atomic settlement within optimized market microstructure, ensuring best execution](https://options.greeks.live/wp-content/uploads/2025/08/institutional-rfq-engine-for-digital-asset-derivatives-best-execution.jpg)

![A sleek, metallic multi-lens device with glowing blue apertures symbolizes an advanced RFQ protocol engine. Its precision optics enable real-time market microstructure analysis and high-fidelity execution, facilitating automated price discovery and aggregated inquiry within a Prime RFQ](https://options.greeks.live/wp-content/uploads/2025/08/institutional-digital-asset-derivatives-rfq-engine-intelligence-layer.jpg)

## Concept

An automated Request for Quote (RFQ) system, at its core, is a specialized communication channel designed for sourcing off-book liquidity with discretion. Its structural purpose is to facilitate bilateral [price discovery](https://prime.greeks.live/area/price-discovery/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) for large or complex orders that would otherwise cause significant market impact on a central limit order book. The introduction of [pre-trade analytics](https://prime.greeks.live/area/pre-trade-analytics/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) transforms this communication channel into a sophisticated execution apparatus.

This analytical layer serves as the system’s cognitive engine, processing vast datasets to model potential outcomes and inform trading decisions before any market-facing action is taken. It provides a quantitative framework for navigating the inherent uncertainties of trading, such as predicting the cost of execution, managing [information leakage](https://prime.greeks.live/area/information-leakage/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite), and selecting appropriate counterparties.

The fundamental challenge in any RFQ process is the information asymmetry between the initiator and the liquidity providers. The act of requesting a quote, even to a limited audience, is a form of information leakage. It signals intent, size, and direction, which can alter market dynamics to the initiator’s detriment. Pre-trade analytics directly address this challenge by providing data-driven insights to guide the RFQ process.

These analytics are not merely a collection of historical data points; they are a suite of predictive models that evaluate the trade’s context. They assess factors like the instrument’s historical volatility, the time of day, the likely capacity of different dealers, and the potential for adverse selection. By quantifying these variables, the system allows a trader to make calibrated decisions, such as determining the optimal number of dealers to include in an auction or setting a realistic price target.

> Pre-trade analytics function as the system’s first line of defense against information leakage and adverse execution outcomes.

This analytical capacity moves the RFQ protocol beyond a simple messaging tool. It becomes a system for strategic [liquidity sourcing](https://prime.greeks.live/area/liquidity-sourcing/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite). The success of an [automated RFQ](https://prime.greeks.live/area/automated-rfq/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) platform is therefore directly proportional to the quality and depth of its pre-trade analytical capabilities.

A system without this intelligence layer forces its users to rely on intuition and past experience, which can be inconsistent and difficult to scale. An analytics-driven system, conversely, provides a consistent, evidence-based foundation for every execution decision, enabling traders to demonstrate the value they add through a structured and defensible process.

![A precisely engineered system features layered grey and beige plates, representing distinct liquidity pools or market segments, connected by a central dark blue RFQ protocol hub. Transparent teal bars, symbolizing multi-leg options spreads or algorithmic trading pathways, intersect through this core, facilitating price discovery and high-fidelity execution of digital asset derivatives via an institutional-grade Prime RFQ](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-protocol-engine-facilitating-high-fidelity-multi-leg-options-spread-execution-best-price-discovery-and-latent-liquidity-access-for-prime-brokerage.jpg)

![A translucent blue cylinder, representing a liquidity pool or private quotation core, sits on a metallic execution engine. This system processes institutional digital asset derivatives via RFQ protocols, ensuring high-fidelity execution, pre-trade analytics, and smart order routing for capital efficiency on a Prime RFQ](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-execution-engine-core-3.jpg)

## Strategy

The strategic integration of pre-trade analytics within an automated RFQ system creates a powerful framework for optimizing execution. This framework rests on two pillars ▴ the intelligent curation of liquidity providers and the dynamic forecasting of transaction costs. Together, these elements allow an institution to design and implement a bespoke execution strategy for each trade, balancing the need for competitive pricing with the imperative to control [market impact](https://prime.greeks.live/area/market-impact/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite).

![A sleek, institutional-grade device featuring a reflective blue dome, representing a Crypto Derivatives OS Intelligence Layer for RFQ and Price Discovery. Its metallic arm, symbolizing Pre-Trade Analytics and Latency monitoring, ensures High-Fidelity Execution for Multi-Leg Spreads](https://options.greeks.live/wp-content/uploads/2025/08/institutional-digital-asset-derivatives-rfq-execution-intelligence-layer.jpg)

## Intelligent Liquidity Curation

A primary function of pre-trade analytics is to guide the selection of counterparties for any given RFQ. A naive approach might involve sending a request to all available dealers, assuming a larger auction yields a better price. Analytical models reveal a more complex reality. Sending a request to too many dealers, or to the wrong ones, can amplify information leakage and lead to wider spreads as dealers price in the uncertainty of a crowded auction.

Pre-trade analytics solve this by building performance profiles for each liquidity provider. These profiles are not static; they are updated in real-time with every interaction, tracking metrics such as response rates, quote competitiveness, and post-trade market behavior. The system can then recommend an optimal subset of dealers for a specific trade based on the instrument, order size, and prevailing market conditions.

- **Historical Performance Analysis** ▴ The system evaluates which dealers have historically provided the tightest spreads for similar instruments and sizes.

- **Hit Rate and Fade Analysis** ▴ It tracks how often a dealer’s quote is the winning bid and, critically, the frequency with which their quotes “fade” or are withdrawn after being shown, which can be a sign of weak liquidity.

- **Information Leakage Profiling** ▴ Advanced systems can even attempt to model the statistical footprint of sending an RFQ to a particular dealer, identifying counterparties that are more discreet.

> The strategic application of pre-trade analytics transforms the RFQ from a blunt instrument into a precision tool for accessing targeted liquidity.

![A luminous central hub with radiating arms signifies an institutional RFQ protocol engine. It embodies seamless liquidity aggregation and high-fidelity execution for multi-leg spread strategies](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-hub-for-optimal-liquidity-aggregation.jpg)

## Dynamic Transaction Cost Forecasting

The second strategic pillar is the ability to generate a reliable, data-driven estimate of transaction costs before the trade is initiated. This is a departure from relying on a trader’s feel for the market. Pre-trade cost models synthesize numerous inputs to produce a probability-weighted forecast of where an order should be filled. This forecast serves as a critical benchmark for the entire execution process.

It allows a portfolio manager and a trader to have a quantitative discussion about the trade-offs between speed of execution and potential market impact. For instance, the model might indicate that executing a large block over a 30-minute window will incur a certain cost, while a more patient, two-hour execution could significantly reduce that cost. This provides an objective basis for strategy selection.

The table below illustrates the key analytical inputs that feed into a sophisticated pre-trade cost model, demonstrating the multi-faceted nature of the calculation.

### Table 1 ▴ Key Inputs for a Pre-Trade Transaction Cost Model

| Input Category | Specific Data Points | Strategic Implication |
| --- | --- | --- |
| Market-Based Inputs | Real-time bid-ask spread, order book depth, historical and implied volatility. | Provides a baseline understanding of the current liquidity and risk environment for the specific instrument. |
| Order-Specific Inputs | Order size, instrument type (e.g. option spread, single stock), side (buy/sell). | Scales the cost analysis to the specific characteristics of the trade being contemplated. |
| Historical Execution Data | Analysis of similar past trades, including execution slippage and dealer performance. | Grounds the forecast in the firm’s own historical trading experience, making it highly relevant. |
| Factor Model Inputs | Market risk factors, sector-specific volatility, macroeconomic event schedules. | Accounts for broader market conditions that could influence the cost and risk of execution. |

![An exposed high-fidelity execution engine reveals the complex market microstructure of an institutional-grade crypto derivatives OS. Precision components facilitate smart order routing and multi-leg spread strategies](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-execution-engine-microstructure-2.jpg)

![A polished spherical form representing a Prime Brokerage platform features a precisely engineered RFQ engine. This mechanism facilitates high-fidelity execution for institutional Digital Asset Derivatives, enabling private quotation and optimal price discovery](https://options.greeks.live/wp-content/uploads/2025/07/prime-os-rfq-engine-optimal-execution-digital-asset-derivatives.jpg)

## Execution

The operationalization of pre-trade analytics within an automated RFQ workflow represents the final and most critical stage. It is here that abstract strategic concepts are converted into concrete, measurable actions that define [execution quality](https://prime.greeks.live/area/execution-quality/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite). This process involves a disciplined, multi-step approach where data-driven insights guide every decision, from the initial order staging to the final post-trade review. The objective is to create a systematic, repeatable, and auditable execution process that demonstrably adds value.

![A central Prime RFQ core powers institutional digital asset derivatives. Translucent conduits signify high-fidelity execution and smart order routing for RFQ block trades](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-liquidity-aggregation-engine.jpg)

## The Operational Workflow of an Analytics-Driven RFQ

An execution protocol powered by pre-trade analytics follows a structured sequence. This disciplined process ensures that the intelligence generated by the analytical models is fully leveraged at the point of trade. It transforms the trader’s role from one of pure price-taking to one of strategic oversight and parameter management.

The workflow is designed to be a closed loop, where the results of each trade feed back into the analytical models, continually refining their accuracy and predictive power. This iterative process of analysis, execution, and feedback is the hallmark of a mature, data-centric trading desk.

- **Order Staging and Initial Analysis** ▴ A portfolio manager’s order first enters a staging area within the execution management system. Here, the pre-trade analytical engine automatically runs a profile on the order, generating initial estimates for market impact, expected cost, and optimal execution horizon based on the order’s size and the security’s liquidity profile. 

- **Strategy Selection and Parameterization** ▴ The trader reviews the initial analysis. They use the data to engage in a dialogue with the portfolio manager, deciding on the optimal strategy. If the RFQ protocol is chosen, the trader then uses the analytics to set key parameters ▴ the number of dealers to query, the specific dealers to include based on performance scores, and the time limit for the auction.

- **Auction Monitoring and In-Flight Adjustments** ▴ As quotes are returned, the system displays them in real-time against the pre-trade benchmark price. This provides immediate context. A trader can see if the quotes are within the expected range, better, or worse. Some advanced systems offer “in-flight” analytics, suggesting whether to accept a quote immediately or wait, based on the probability of a better price emerging before the auction expires.

- **Execution and Post-Trade Data Capture** ▴ Once a quote is accepted, the trade is executed. The system captures a rich dataset surrounding the execution, including the winning and losing quotes, the response time of each dealer, and the immediate post-trade price action in the public market. This data is essential for refining the analytical models.

![Institutional-grade infrastructure supports a translucent circular interface, displaying real-time market microstructure for digital asset derivatives price discovery. Geometric forms symbolize precise RFQ protocol execution, enabling high-fidelity multi-leg spread trading, optimizing capital efficiency and mitigating systemic risk](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-request-for-quote-protocol-high-fidelity-execution-market-microstructure-intelligence-layer.jpg)

## Quantitative Modeling in Practice

To make this tangible, consider the execution of a large block of call options on a specific stock. The pre-trade analytical system would not simply look at the last traded price. It would construct a comprehensive view of the execution landscape. This deep analytical dive is what separates a basic RFQ platform from an institutional-grade execution system.

It is a process of systematic risk reduction and opportunity identification, grounded entirely in quantitative evidence. The sheer volume of data processed and synthesized in this pre-commitment phase is substantial. It is this computational heavy lifting that provides the trader with the confidence and the evidence to proceed, knowing that the chosen path has been validated against a rigorous analytical framework. The success of the trade is defined before the first request is even sent.

The following table provides a hypothetical, yet realistic, example of the pre-trade data summary a trader would review before initiating an RFQ for 5,000 contracts of a 3-month, at-the-money call option.

### Table 2 ▴ Pre-Trade Analytics Dashboard for an Options Block RFQ

| Analytical Metric | Data Point | Execution Implication |
| --- | --- | --- |
| Projected Market Impact | +2.5% on implied volatility if 100% of the order is shown. | Suggests breaking the order into smaller pieces or accepting a wider spread on the full size. |
| Optimal Dealer Count | 4-6 counterparties. | Balances the need for competitive tension against the risk of information leakage from a wider auction. |
| Recommended Counterparties | Dealer A, C, F, G, K (based on historical options performance). | Provides a specific, data-driven list of providers most likely to offer competitive and stable quotes. |
| Fair Value Benchmark | $5.20 per contract. | Creates a hard, objective reference point to evaluate the quality of incoming quotes against. |
| Liquidity Timing Analysis | Optimal liquidity window ▴ 10:00 AM – 11:30 AM EST. | Advises on the specific time of day when the underlying market is deepest, minimizing slippage. |
| Adverse Selection Probability | 15% (Moderate). | Alerts the trader to the risk that informed counterparties may be on the other side of the trade. |

![A dual-toned cylindrical component features a central transparent aperture revealing intricate metallic wiring. This signifies a core RFQ processing unit for Digital Asset Derivatives, enabling rapid Price Discovery and High-Fidelity Execution](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-engine-core-best-execution-prime-os.jpg)

## References

- Eggleston, Pete. “The role of pre-trade analysis in FX algo selection.” BestX, 2018.

- “Pre-Trade Risk Analytics.” QuestDB, Accessed August 7, 2025.

- “Pre-trade analytics ▴ quantifying the benefits and creating a roadmap for implementation. Q&A with European Trader, Capital Group.” The Hive Network, 2020.

- Harris, Larry. Trading and Exchanges ▴ Market Microstructure for Practitioners. Oxford University Press, 2003.

- O’Hara, Maureen. Market Microstructure Theory. Blackwell Publishers, 1995.

- Lehalle, Charles-Albert, and Sophie Laruelle. Market Microstructure in Practice. World Scientific Publishing, 2013.

- “Pre-Trade Analysis ▴ Why Bother?” BestX, 26 May 2017.

![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)

## Reflection

![A teal sphere with gold bands, symbolizing a discrete digital asset derivative block trade, rests on a precision electronic trading platform. This illustrates granular market microstructure and high-fidelity execution within an RFQ protocol, driven by a Prime RFQ intelligence layer](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivative-rfq-protocol-high-fidelity-execution-platform-microstructure-intelligence-layer.jpg)

## From Communication Channel to Intelligence System

The evolution of the Request for Quote protocol, driven by the infusion of pre-trade analytics, offers a compelling case study in the systematization of [institutional trading](https://prime.greeks.live/area/institutional-trading/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite). What began as a digital version of a telephone call has been fundamentally reshaped into a system of predictive intelligence. This prompts a critical evaluation of a firm’s own execution architecture.

Is the technology in place merely facilitating communication, or is it actively generating a strategic edge through data synthesis? The answer distinguishes a modern trading desk from a legacy one.

Ultimately, the quality of a firm’s execution is a direct reflection of the intelligence it brings to the market. Pre-trade analytics represent a powerful component of this intelligence, a mechanism for transforming uncertainty into quantifiable risk and tactical advantage. The continued development of these analytical tools will further blur the lines between discretionary trading and automated execution, creating a hybrid model where human oversight is amplified by machine-scale data processing. The core challenge for any institution is to ensure its operational framework is designed not just to participate in this evolution, but to lead it.

![A sleek, angular metallic system, an algorithmic trading engine, features a central intelligence layer. It embodies high-fidelity RFQ protocols, optimizing price discovery and best execution for institutional digital asset derivatives, managing counterparty risk and slippage](https://options.greeks.live/wp-content/uploads/2025/07/prime-os-digital-asset-derivatives-rfq-engine-for-best-execution.jpg)

## Glossary

<a href="https://prime.greeks.live/area/pre-trade-analytics/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![A precision-engineered teal metallic mechanism, featuring springs and rods, connects to a light U-shaped interface. This represents a core RFQ protocol component enabling automated price discovery and high-fidelity execution](https://options.greeks.live/wp-content/uploads/2025/07/rfq-execution-engine-component-for-institutional-digital-asset-derivatives.jpg)

## Pre-Trade Analytics

Meaning ▴ Pre-Trade Analytics, in the context of institutional crypto trading and systems architecture, refers to the comprehensive suite of quantitative and qualitative analyses performed before initiating a trade to assess potential market impact, liquidity availability, expected costs, and optimal execution strategies. </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 sleek, multi-component device with a prominent lens, embodying a sophisticated RFQ workflow engine. Its modular design signifies integrated liquidity pools and dynamic price discovery for institutional digital asset derivatives](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-engine-microstructure-2.jpg)

## Price Discovery

Meaning ▴ Price Discovery, within the context of crypto investing and market microstructure, describes the continuous process by which the equilibrium price of a digital asset is determined through the collective interaction of buyers and sellers across various trading venues. </a><a href="https://prime.greeks.live/area/information-leakage/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![A symmetrical, intricate digital asset derivatives execution engine. Its metallic and translucent elements visualize a robust RFQ protocol facilitating multi-leg spread execution](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-protocol-best-execution.jpg)

## Information Leakage

Meaning ▴ Information leakage, in the realm of crypto investing and institutional options trading, refers to the inadvertent or intentional disclosure of sensitive trading intent or order details to other market participants before or during trade execution. </a><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 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)

## Liquidity Sourcing

Meaning ▴ Liquidity sourcing in crypto investing refers to the strategic process of identifying, accessing, and aggregating available trading depth and volume across various fragmented venues to execute large orders efficiently. </a><a href="https://prime.greeks.live/area/automated-rfq/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![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)

## Automated Rfq

Meaning ▴ An Automated Request for Quote (RFQ) system represents a streamlined, programmatic process where a trading entity electronically solicits price quotes for a specific crypto asset or derivative from a pre-selected panel of liquidity providers, all without requiring manual intervention. </a><a href="https://prime.greeks.live/area/market-impact/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![A Principal's RFQ engine core unit, featuring distinct algorithmic matching probes for high-fidelity execution and liquidity aggregation. This price discovery mechanism leverages private quotation pathways, optimizing crypto derivatives OS operations for atomic settlement within its systemic architecture](https://options.greeks.live/wp-content/uploads/2025/08/principals-rfq-engine-driving-optimal-digital-asset-derivatives.jpg)

## Market Impact

Meaning ▴ Market impact, in the context of crypto investing and institutional options trading, quantifies the adverse price movement caused by an investor's own trade execution. </a><a href="https://prime.greeks.live/area/execution-quality/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![A sleek cream-colored device with a dark blue optical sensor embodies Price Discovery for Digital Asset Derivatives. It signifies High-Fidelity Execution via RFQ Protocols, driven by an Intelligence Layer optimizing Market Microstructure for Algorithmic Trading on a Prime RFQ](https://options.greeks.live/wp-content/uploads/2025/08/institutional-digital-asset-derivatives-high-fidelity-execution-engine-optimized-rfq-protocols-prime-os-intelligence-layer.jpg)

## Execution Quality

Meaning ▴ Execution quality, within the framework of crypto investing and institutional options trading, refers to the overall effectiveness and favorability of how a trade order is filled. </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">
![Polished metallic surface with a central intricate mechanism, representing a high-fidelity market microstructure engine. Two sleek probes symbolize bilateral RFQ protocols for precise price discovery and atomic settlement of institutional digital asset derivatives on a Prime RFQ, ensuring best execution for Bitcoin Options](https://options.greeks.live/wp-content/uploads/2025/07/prime-os-institutional-digital-asset-derivatives-rfq-engine-core-market-microstructure-intelligence-layer.jpg)

## Institutional Trading

Meaning ▴ Institutional Trading in the crypto landscape refers to the large-scale investment and trading activities undertaken by professional financial entities such as hedge funds, asset managers, pension funds, and family offices in cryptocurrencies and their derivatives. </a>

**Tags:** [Information Leakage](https://options.greeks.live/area/information-leakage/), [Liquidity Sourcing](https://options.greeks.live/area/liquidity-sourcing/), [Market Microstructure](https://options.greeks.live/area/market-microstructure/), [Automated RFQ](https://options.greeks.live/area/automated-rfq/), [Market Impact](https://options.greeks.live/area/market-impact/), [Price Discovery](https://options.greeks.live/area/price-discovery/)	

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- 
### [What Are the Primary Differences in Liquidity Sourcing between a Lit Order Book and an Anonymous RFQ System?](https://options.greeks.live/question/what-are-the-primary-differences-in-liquidity-sourcing-between-a-lit-order-book-and-an-anonymous-rfq-system/)
![A metallic Prime RFQ core, etched with algorithmic trading patterns, interfaces a precise high-fidelity execution blade. This blade engages liquidity pools and order book dynamics, symbolizing institutional grade RFQ protocol processing for digital asset derivatives price discovery.](https://options.greeks.live/wp-content/uploads/2025/07/prime-os-digital-asset-derivatives-rfq-high-fidelity-execution-system.jpg)

A lit order book sources liquidity from a public auction; an RFQ system sources it via private, targeted negotiations to minimize impact.

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---

**Original URL:** https://options.greeks.live/question/what-is-the-role-of-pre-trade-analytics-in-the-success-of-an-automated-request-for-quote-system/
