# How Does Data Granularity Affect the Reliability of a Slippage Model Backtest? ▴ Question

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

---

![Two reflective, disc-like structures, one tilted, one flat, symbolize the Market Microstructure of Digital Asset Derivatives. This metaphor encapsulates RFQ Protocols and High-Fidelity Execution within a Liquidity Pool for Price Discovery, vital for a Principal's Operational Framework ensuring Atomic Settlement](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-protocol-high-fidelity-execution-liquidity-aggregation-optimizing-market-microstructure-principal-trading-best-execution-atomic-settlement.jpg)

![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)

## Concept

The reliability of a [slippage model backtest](https://prime.greeks.live/question/how-can-an-agent-based-model-improve-the-accuracy-of-simulating-dealer-behavior-in-an-rfq-backtest/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) is a direct function of the [data granularity](https://prime.greeks.live/area/data-granularity/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) it is built upon. An institution’s trading system architecture must account for this foundational relationship. The precision of your market data, from tick-by-tick streams to daily closes, dictates the boundary between a predictive backtest and a dangerously misleading simulation.

Viewing this challenge through the lens of a systems architect, the choice of data granularity is the initial design parameter that propagates through the entire strategy evaluation process. It defines the very texture of the market you are modeling and, consequently, the relevance of any insights derived from it.

At its core, data granularity refers to the time resolution of the market data used to feed the backtest. Slippage, the differential between the expected execution price of an order and the price at which it is actually filled, is a phenomenon born from the microscopic frictions of the market. To model slippage accurately, one must first be able to “see” the market at a resolution fine enough to capture the events that cause it.

These events include the bounce between bid and ask prices, the depletion of liquidity at one price level, and the latency in order transmission and confirmation. A model built on daily closing prices is blind to this entire world of intraday activity; it operates on a map so zoomed out that the terrain of [execution risk](https://prime.greeks.live/area/execution-risk/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) is entirely flattened.

> Data granularity determines the level of detail in time series data, which significantly impacts the performance and complexity of time series models.

The central problem is that coarse data, such as one-minute or five-minute bars, presents an averaged and sanitized version of market activity. Each bar, by its construction, discards the intricate path price took within that interval. It compresses a series of chaotic, high-frequency events into four data points ▴ open, high, low, and close.

A backtest running on this summarized data might assume a trade could be executed at the closing price, yet within that minute, the available liquidity at that price may have been consumed multiple times over, making the assumed execution entirely theoretical. This discrepancy is where [backtest reliability](https://prime.greeks.live/area/backtest-reliability/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) collapses.

![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](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-protocol-execution-flow.jpg)

## The Illusion of a Perfect Fill

Low-granularity data fosters the illusion of a perfect fill. When a strategy signal appears at the close of a 5-minute bar, the backtesting engine registers an execution at that price. The underlying assumption is that sufficient volume was available at that exact price to absorb the theoretical order without impact. High-granularity data, especially [tick-level data](https://prime.greeks.live/area/tick-level-data/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) that includes bid-ask spread information, shatters this illusion.

It reveals the true state of the [order book](https://prime.greeks.live/area/order-book/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) at the moment of the trade decision. The model can now see the spread that must be crossed, the visible depth on the bid and ask sides, and can make a far more realistic assessment of the likely execution price.

![Precision-engineered institutional-grade Prime RFQ component, showcasing a reflective sphere and teal control. This symbolizes RFQ protocol mechanics, emphasizing high-fidelity execution, atomic settlement, and capital efficiency in digital asset derivatives market microstructure](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-precision-execution-system-design.jpg)

## What Is the True Cost of a Mismatched Granularity?

Choosing a data granularity that is misaligned with the strategy’s trading frequency introduces systemic error into the backtest. For a high-frequency strategy that aims to capture fleeting arbitrage opportunities, using anything less than tick-level data is an exercise in fiction. The alpha the strategy seeks to exploit exists only in the spaces between the bars of low-resolution data. Conversely, a long-term trend-following strategy might find tick-data to be computationally burdensome and full of noise that obscures the larger market signal.

The key is aligning the resolution of the observation with the scale of the phenomenon being targeted. A failure to do so results in a model that is calibrated to a market that does not exist in reality, leading to systematically underestimated costs and overestimated performance. This is the primary reason for the common and costly divergence between backtested results and live trading performance.

![A luminous teal bar traverses a dark, textured metallic surface with scattered water droplets. This represents the precise, high-fidelity execution of an institutional block trade via a Prime RFQ, illustrating real-time price discovery](https://options.greeks.live/wp-content/uploads/2025/07/high-fidelity-real-time-institutional-digital-asset-rfq-execution-stream-visualizing-prime-os-liquidity-flow.jpg)

![A conceptual image illustrates a sophisticated RFQ protocol engine, depicting the market microstructure of institutional digital asset derivatives. Two semi-spheres, one light grey and one teal, represent distinct liquidity pools or counterparties within a Prime RFQ, connected by a complex execution management system for high-fidelity execution and atomic settlement of Bitcoin options or Ethereum futures](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-market-microstructure-2.jpg)

## Strategy

Strategically, the selection of data granularity is an exercise in balancing precision, cost, and analytical tractability. It is a foundational decision that dictates the types of trading strategies that can be reliably tested and the very philosophy of the [slippage model](https://prime.greeks.live/area/slippage-model/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) itself. An institution’s strategy for data handling will determine its capacity to innovate and compete on execution quality. From a systems perspective, we can classify these strategic choices into distinct tiers of market data resolution, each with its own profile of advantages and structural limitations.

The spectrum of data granularity forces a trade-off between detail and noise. Higher granularity provides a more detailed picture but can introduce spurious signals, while lower granularity smooths out fluctuations but may miss vital short-term patterns. This choice directly impacts model design.

For example, high-frequency data might require complex models like Long Short-Term Memory (LSTM) networks to process long sequences of events, whereas coarser data might be adequately handled by simpler models like ARIMA. The strategic objective is to match the data’s resolution to the half-life of the alpha being pursued.

![A stylized RFQ protocol engine, featuring a central price discovery mechanism and a high-fidelity execution blade. Translucent blue conduits symbolize atomic settlement pathways for institutional block trades within a Crypto Derivatives OS, ensuring capital efficiency and best execution](https://options.greeks.live/wp-content/uploads/2025/07/high-fidelity-rfq-execution-for-digital-asset-derivatives.jpg)

## A Framework for Granularity Selection

A disciplined approach to selecting data granularity involves evaluating the requirements of the trading strategy against the architectural realities of data storage and processing. High-frequency strategies depend on capturing intraday volatility and microstructure effects, making granular data a necessity. Long-term strategies, which trade less frequently, can rely on daily data to reduce transaction costs and simplify analysis. The following table outlines a strategic framework for this decision-making process.

### Table 1 ▴ Strategic Analysis of Data Granularity Levels

| Granularity Level | Primary Use Case | Slippage Model Approach | Computational & Storage Cost | Key Limitation |
| --- | --- | --- | --- | --- |
| Tick-Level Data (with BBO) | High-Frequency Trading (HFT), Market Making, Arbitrage | Order book simulation; spread-crossing cost; queue position modeling. | Very High | Susceptible to noise; requires significant data cleaning and robust infrastructure. |
| One-Second Bars | Short-Term Momentum, Intraday Reversal Strategies | Volatility-adjusted models; percentile-based price movement within the bar. | High | Averages out intra-second price action; can still mask micro-bursts of volatility. |
| One-Minute Bars | Intraday Trend & Swing Trading | Fixed percentage slippage; or a model based on bar size and volume. | Moderate | Completely hides the path of price within the minute; assumes uniform liquidity. |
| Daily Bars | Position Trading, Long-Term Trend Following | Historical average slippage (often a fixed basis point cost). | Low | Ignores all intraday execution realities; provides no insight into implementation shortfall. |

![Abstract interconnected modules with glowing turquoise cores represent an Institutional Grade RFQ system for Digital Asset Derivatives. Each module signifies a Liquidity Pool or Price Discovery node, facilitating High-Fidelity Execution and Atomic Settlement within a Prime RFQ Intelligence Layer, optimizing Capital Efficiency](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-execution-market-microstructure.jpg)

## The Strategic Impact on Model Overfitting

A primary strategic threat in using high-granularity data is overfitting. With more data points, a model can be tuned to fit historical noise with exquisite precision, leading to a strategy that appears highly profitable in backtests but fails spectacularly in live markets. The noise is mistaken for a repeatable signal. The strategic mitigation for this involves a robust validation framework.

- **Walk-Forward Analysis** This technique involves optimizing a strategy on one period of historical data and then testing it on a subsequent, out-of-sample period. This process is repeated, “walking forward” through the data to simulate how the strategy would have been adapted and traded over time. 

- **Cross-Validation** By partitioning the data into multiple “folds” and running tests where each fold gets a turn as the out-of-sample set, one can gain a more robust estimate of performance and reduce the chances of a “lucky” backtest on a single holdout period. 

- **Noise Injection** A sophisticated technique involves adding a small amount of random noise to the historical price data. A robust strategy should see its performance degrade gracefully, while an overfitted strategy may collapse entirely, revealing its fragility. 

![Precision mechanics illustrating institutional RFQ protocol dynamics. Metallic and blue blades symbolize principal's bids and counterparty responses, pivoting on a central matching engine](https://options.greeks.live/wp-content/uploads/2025/07/precision-institutional-digital-asset-derivatives-rfq-execution-engine-microstructure.jpg)

## How Does Market Structure Influence Data Choice?

The structure of the market being traded is a critical factor. In a highly liquid, electronic market like major FX pairs, tick data is abundant and reflects a continuous, two-sided market. A slippage model can be built with a high degree of confidence using this data. In a less liquid market, such as for a small-cap stock or an exotic cryptocurrency, tick data may be sporadic.

The gaps between trades are meaningful. In these cases, a “trade-by-trade” data structure is superior to time-based bars, as it reflects the true, discontinuous nature of liquidity. The strategy must adapt to the data landscape of the specific asset, as a one-size-fits-all approach to granularity will fail to capture these structural differences.

![A metallic ring, symbolizing a tokenized asset or cryptographic key, rests on a dark, reflective surface with water droplets. This visualizes a Principal's operational framework for High-Fidelity Execution of Institutional Digital Asset Derivatives](https://options.greeks.live/wp-content/uploads/2025/07/high-fidelity-institutional-digital-asset-derivatives-rfq-protocol-execution.jpg)

![A sophisticated system's core component, representing an Execution Management System, drives a precise, luminous RFQ protocol beam. This beam navigates between balanced spheres symbolizing counterparties and intricate market microstructure, facilitating institutional digital asset derivatives trading, optimizing price discovery, and ensuring high-fidelity execution within a prime brokerage framework](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-protocol-execution-engine-10.jpg)

## Execution

Executing a reliable slippage model backtest requires a disciplined, multi-stage process that moves from raw data acquisition to sophisticated simulation. This is the operationalization of the concepts and strategies, transforming theoretical understanding into a robust testing architecture. The objective is to construct a simulated environment that mirrors the frictions and realities of live execution as closely as possible. Success is measured by the convergence of backtested performance with live results.

> Accurate backtesting depends on realistic modeling of slippage and liquidity constraints, as these factors can significantly impact strategy performance.

The fidelity of the execution model is paramount. It must account not only for the granularity of the price data but also for the mechanics of order matching and the impact of the strategy’s own orders on the market. Modern backtesting platforms are moving towards this level of realism by integrating historical [order book data](https://prime.greeks.live/area/order-book-data/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) and simulating the interaction of a theoretical order with available liquidity.

![A symmetrical, high-tech digital infrastructure depicts an institutional-grade RFQ execution hub. Luminous conduits represent aggregated liquidity for digital asset derivatives, enabling high-fidelity execution and atomic settlement](https://options.greeks.live/wp-content/uploads/2025/07/prime-os-institutional-digital-asset-derivatives-rfq-system.jpg)

## A Protocol for High-Fidelity Backtesting

A robust [backtesting protocol](https://prime.greeks.live/area/backtesting-protocol/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) can be broken down into a series of distinct operational steps. Each step builds upon the last, progressively refining the simulation’s accuracy. A failure at any stage undermines the entire process.

- **Data Sourcing and Cleansing** The process begins with acquiring the highest granularity of data feasible. For institutional-grade backtests, this means Level 2 (L2) order book data, which provides depth at multiple bid and ask levels. This raw data must be meticulously cleaned to handle exchange-specific anomalies, erroneous ticks, and data gaps. Consistency is vital. 

- **Order Book Reconstruction** Using the cleaned L2 data, the historical state of the order book must be reconstructed for any given moment in time. This provides a snapshot of the available liquidity and the bid-ask spread that an order would have faced. This technique is computationally intensive but essential for accurate slippage modeling. 

- **Slippage Model Calibration** The slippage model itself must be calibrated. Instead of a simple fixed percentage, a dynamic model is required. This model should incorporate factors like the size of the order relative to the available depth, the current bid-ask spread, and recent price volatility. For example, slippage estimates must be adjusted upwards during predictable volatility spikes, like market opens and closes. 

- **Latency Simulation** An execution layer must simulate the unavoidable delays between signal generation, order placement, and exchange confirmation. This latency, even if only milliseconds, can be the difference between capturing a price and chasing it. The model should include a randomized latency component based on historical network performance. 

- **[Market Impact](https://prime.greeks.live/area/market-impact/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) Analysis** For larger orders, the model must also account for market impact ▴ the effect of the order itself on the market price. A large market order will consume liquidity, potentially causing the price to move adversely before the entire order is filled. This can be modeled by “walking the book,” simulating the fill at progressively worse prices as each level of liquidity is exhausted. 

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

## What Is the Best Way to Model Slippage in Practice?

The practical implementation of a slippage model requires choosing a method that balances realism with computational feasibility. Different asset classes and market conditions warrant different approaches. The following table details several execution modeling methods, connecting them to data requirements and their impact on backtest reliability.

### Table 2 ▴ Execution & Slippage Modeling Techniques

| Modeling Method | Required Data Granularity | Description | Impact on Reliability |
| --- | --- | --- | --- |
| Fixed Percentage | Any (commonly used with bar data) | A constant cost (e.g. 0.05%) is subtracted from each simulated trade. | Low. Fails to account for changing market conditions like volatility or liquidity. |
| Volatility-Adjusted Model | Bar Data (Minute/Hour) | Slippage is modeled as a function of recent price volatility (e.g. using ATR). | Moderate. Better than a fixed model, but still blind to order book depth. |
| Tick-Based Spread Crossing | Tick Data (Best Bid & Offer) | Assumes a market order is filled at the opposite side of the spread at the time of the signal. | High. Accurately models the cost of immediate execution for small orders. |
| Order Book Simulation | Level 2 Order Book Data | The backtest simulates the placement of a limit or market order into the reconstructed order book, modeling the fill based on available depth and queue priority. | Very High. Provides the most realistic estimate of execution cost and market impact. |
| VWAP Simulation | Trade and Volume Data | The execution price is estimated based on the Volume-Weighted Average Price over a short interval following the signal. | High. A realistic approach for strategies that execute gradually over a time window. |

Ultimately, the execution of a slippage model backtest is a testament to an institution’s commitment to intellectual honesty. It is easy to generate impressive-looking backtests using low-granularity data and simplistic slippage assumptions. It is operationally demanding to build a system that embraces the complexities of [market microstructure](https://prime.greeks.live/area/market-microstructure/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite). The latter path, however, is the only one that leads to strategies that are robust, reliable, and ultimately profitable in the unforgiving environment of live markets.

![A large, smooth sphere, a textured metallic sphere, and a smaller, swirling sphere rest on an angular, dark, reflective surface. This visualizes a principal liquidity pool, complex structured product, and dynamic volatility surface, representing high-fidelity execution within an institutional digital asset derivatives market microstructure](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-optimized-market-microstructure-precision.jpg)

## References

- “Data Granularity Impact – Trading – Alice Blue Community.” _Alice Blue_, 2023.

- “What is the impact of data granularity on time series models?.” _Milvus_, 2024.

- “Backtesting Limitations ▴ Slippage and Liquidity Explained.” _LuxAlgo_, 2025.

- Abul-Haggag, Y. and T. J. L. Olewnik. “The Impact of Data Granularity on Manufacturing and Building Asset Simulation Results Quality.” _Proceedings of the 28th Annual Conference of the International Group for Lean Construction (IGLC)_, 2020.

- Harris, Larry. _Trading and Exchanges ▴ Market Microstructure for Practitioners_. Oxford University Press, 2003.

- Chan, Ernest P. _Quantitative Trading ▴ How to Build Your Own Algorithmic Trading Business_. John Wiley & Sons, 2009.

- Lehalle, Charles-Albert, and Sophie Laruelle. _Market Microstructure in Practice_. World Scientific Publishing Company, 2013.

![Precision-engineered modular components display a central control, data input panel, and numerical values on cylindrical elements. This signifies an institutional Prime RFQ for digital asset derivatives, enabling RFQ protocol aggregation, high-fidelity execution, algorithmic price discovery, and volatility surface calibration for portfolio margin](https://options.greeks.live/wp-content/uploads/2025/07/firm-fx-options-rfq-trade-system-key-microstructure-data-flow-core-engine.jpg)

## Reflection

![A central, precision-engineered component with teal accents rises from a reflective surface. This embodies a high-fidelity RFQ engine, driving optimal price discovery for institutional digital asset derivatives](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-rfq-execution-engine-for-price-discovery.jpg)

## From Data to Decision

The technical architecture of a backtest, particularly its data granularity, is a direct reflection of an institution’s trading philosophy. The choices made in the design of these simulation environments reveal a deeper disposition towards risk, precision, and the fundamental interpretation of market behavior. A system built on high-fidelity, tick-level data acknowledges the market as a complex, fast-moving ecosystem where execution is a competitive discipline. A framework that settles for coarse, aggregated data may be implicitly prioritizing model simplicity over market reality.

![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)

## Architecting for Resilience

Considering your own operational framework, where does the emphasis lie? Is your testing architecture designed to validate the resilience of a strategy against the microscopic frictions of the real world, or is it designed to produce the most optimistic possible outcome? The insights gained from this analysis are components in a larger system of intelligence.

Integrating a disciplined, granular approach to slippage modeling is a critical step in building a trading operation that is not just notionally profitable in a spreadsheet, but robust and effective in the live market. The ultimate strategic potential lies in this alignment of simulation with reality.

![Abstract geometric planes delineate distinct institutional digital asset derivatives liquidity pools. Stark contrast signifies market microstructure shift via advanced RFQ protocols, ensuring high-fidelity execution](https://options.greeks.live/wp-content/uploads/2025/07/digital-asset-derivatives-execution-protocol-integrating-rfq-liquidity-and-advanced-market-microstructure.jpg)

## Glossary

<a href="https://prime.greeks.live/question/how-can-an-agent-based-model-improve-the-accuracy-of-simulating-dealer-behavior-in-an-rfq-backtest/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![A multi-faceted crystalline structure, featuring sharp angles and translucent blue and clear elements, rests on a metallic base. This embodies Institutional Digital Asset Derivatives and precise RFQ protocols, enabling High-Fidelity Execution](https://options.greeks.live/wp-content/uploads/2025/07/high-fidelity-digital-asset-derivatives-rfq-market-microstructure.jpg)

## Slippage Model Backtest

An agent-based model enhances RFQ backtest accuracy by simulating dynamic dealer reactions and the resulting market impact of a trade. </a><a href="https://prime.greeks.live/area/data-granularity/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![A metallic structural component interlocks with two black, dome-shaped modules, each displaying a green data indicator. This signifies a dynamic RFQ protocol within an institutional Prime RFQ, enabling high-fidelity execution for digital asset derivatives](https://options.greeks.live/wp-content/uploads/2025/07/modular-rfq-protocol-engine-for-digital-asset-derivatives-prime-os.jpg)

## Data Granularity

Meaning ▴ Data granularity refers to the precision or fineness of data resolution, specifying the degree of detail at which information is collected, processed, and analyzed within a dataset or system. </a><a href="https://prime.greeks.live/area/execution-risk/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![A multi-faceted crystalline star, symbolizing the intricate Prime RFQ architecture, rests on a reflective dark surface. Its sharp angles represent precise algorithmic trading for institutional digital asset derivatives, enabling high-fidelity execution and price discovery](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-protocols-high-fidelity-execution-market-microstructure-optimization-for-prime-os-alpha-generation.jpg)

## Execution Risk

Meaning ▴ Execution Risk quantifies the potential for an order to not be filled at the desired price or quantity, or within the anticipated timeframe, thereby incurring adverse price slippage or missed trading opportunities. </a><a href="https://prime.greeks.live/area/backtest-reliability/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![The image depicts an advanced intelligent agent, representing a principal's algorithmic trading system, navigating a structured RFQ protocol channel. This signifies high-fidelity execution within complex market microstructure, optimizing price discovery for institutional digital asset derivatives while minimizing latency and slippage across order book dynamics](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-protocol-execution-optimizing-market-microstructure-for-principal-trading-intelligence-layer.jpg)

## Backtest Reliability

Meaning ▴ Backtest Reliability quantifies the degree to which a trading strategy's simulated historical performance accurately predicts its future live performance, accounting for real-world market microstructure effects and execution costs. </a><a href="https://prime.greeks.live/area/tick-level-data/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![Detailed metallic disc, a Prime RFQ core, displays etched market microstructure. Its central teal dome, an intelligence layer, facilitates price discovery](https://options.greeks.live/wp-content/uploads/2025/07/principal-operating-system-intelligence-layer-powering-digital-asset-derivatives-rfq-protocols-for-optimal-execution.jpg)

## Tick-Level Data

Meaning ▴ Tick-level data represents the most granular temporal resolution of market activity, capturing every individual transaction, order book update, or quote change as it occurs on an exchange or trading venue, providing an unaggregated stream of raw market events precisely timestamped to nanosecond precision. </a><a href="https://prime.greeks.live/area/order-book/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![A robust circular Prime RFQ component with horizontal data channels, radiating a turquoise glow signifying price discovery. This institutional-grade RFQ system facilitates high-fidelity execution for digital asset derivatives, optimizing market microstructure and capital efficiency](https://options.greeks.live/wp-content/uploads/2025/07/institutional-rfq-engine-for-digital-asset-derivatives-execution.jpg)

## Order Book

Meaning ▴ An Order Book is a real-time electronic ledger detailing all outstanding buy and sell orders for a specific financial instrument, organized by price level and sorted by time priority within each level. </a><a href="https://prime.greeks.live/area/slippage-model/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![Abstract geometry illustrates interconnected institutional trading pathways. Intersecting metallic elements converge at a central hub, symbolizing a liquidity pool or RFQ aggregation point for high-fidelity execution of digital asset derivatives](https://options.greeks.live/wp-content/uploads/2025/07/prime-os-cross-market-microstructure-for-digital-asset-derivatives.jpg)

## Slippage Model

Meaning ▴ The Slippage Model is a quantitative framework designed to predict or quantify the price deviation between an order's intended execution price and its actual fill price, a phenomenon frequently observed in illiquid or volatile market conditions. </a><a href="https://prime.greeks.live/area/order-book-data/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![Intricate metallic mechanisms portray a proprietary matching engine or execution management system. Its robust structure enables algorithmic trading and high-fidelity execution for institutional digital asset derivatives](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-engine-market-microstructure.jpg)

## Order Book Data

Meaning ▴ Order Book Data represents the real-time, aggregated ledger of all outstanding buy and sell orders for a specific digital asset derivative instrument on an exchange, providing a dynamic snapshot of market depth and immediate liquidity. </a><a href="https://prime.greeks.live/area/backtesting-protocol/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![Polished, intersecting geometric blades converge around a central metallic hub. This abstract visual represents an institutional RFQ protocol engine, enabling high-fidelity execution of digital asset derivatives](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-protocols-high-fidelity-execution-market-microstructure.jpg)

## Backtesting Protocol

Meaning ▴ The Backtesting Protocol defines a rigorous computational methodology for evaluating the performance and robustness of a trading strategy or algorithmic model by simulating its application against historical market data. </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 complex interplay of translucent teal and beige planes, signifying multi-asset RFQ protocol pathways and structured digital asset derivatives. Two spherical nodes represent atomic settlement points or critical price discovery mechanisms within a Prime RFQ](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-protocol-for-optimal-liquidity-aggregation-and-high-fidelity-execution.jpg)

## Market Impact

Meaning ▴ Market Impact refers to the observed change in an asset's price resulting from the execution of a trading order, primarily influenced by the order's size relative to available liquidity and prevailing market conditions. </a><a href="https://prime.greeks.live/area/market-microstructure/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![A sophisticated, layered circular interface with intersecting pointers symbolizes institutional digital asset derivatives trading. It represents the intricate market microstructure, real-time price discovery via RFQ protocols, and high-fidelity execution](https://options.greeks.live/wp-content/uploads/2025/07/prime-os-institutional-digital-asset-derivatives-rfq-market-microstructure.jpg)

## 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>

**Tags:** [Market Impact](https://options.greeks.live/area/market-impact/), [Data Granularity](https://options.greeks.live/area/data-granularity/), [Backtesting Protocol](https://options.greeks.live/area/backtesting-protocol/), [High-Fidelity Execution](https://options.greeks.live/area/high-fidelity-execution/), [Slippage Model](https://options.greeks.live/area/slippage-model/), [Volatility Modeling](https://options.greeks.live/area/volatility-modeling/)	

## Discover More

- 
### [What Are the Practical Challenges in Deploying an RL Hedging Agent in Live Markets?](https://options.greeks.live/question/what-are-the-practical-challenges-in-deploying-an-rl-hedging-agent-in-live-markets/)
![Sleek, dark components with a bright turquoise data stream symbolize a Principal OS enabling high-fidelity execution for institutional digital asset derivatives. This infrastructure leverages secure RFQ protocols, ensuring precise price discovery and minimal slippage across aggregated liquidity pools, vital for multi-leg spreads.](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-rfq-protocol-systemic-execution-interface.jpg)

Deploying an RL hedging agent requires bridging the systemic gap between idealized simulation and the complex frictions of live market execution.

- 
### [What Are the Primary Data Requirements for Training Robust RL Models for Quote Generation?](https://options.greeks.live/question/what-are-the-primary-data-requirements-for-training-robust-rl-models-for-quote-generation/)
![A transparent sphere, representing a granular digital asset derivative or RFQ quote, precisely balances on a proprietary execution rail. This symbolizes high-fidelity execution within complex market microstructure, driven by rapid price discovery from an institutional-grade trading engine, optimizing capital efficiency.](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-execution-precision-1.jpg)

High-fidelity market microstructure data is paramount for RL agents to generate optimal, risk-aware quotes in dynamic institutional markets.

- 
### [How Does Data Frequency Impact the Accuracy of Slippage Models in Backtesting?](https://options.greeks.live/question/how-does-data-frequency-impact-the-accuracy-of-slippage-models-in-backtesting/)
![A stylized RFQ protocol engine, featuring a central price discovery mechanism and a high-fidelity execution blade. Translucent blue conduits symbolize atomic settlement pathways for institutional block trades within a Crypto Derivatives OS, ensuring capital efficiency and best execution.](https://options.greeks.live/wp-content/uploads/2025/07/high-fidelity-rfq-execution-for-digital-asset-derivatives.jpg)

Data frequency dictates the fidelity of a slippage model, directly impacting the predictive accuracy of a backtested trading strategy.

- 
### [How Do Machine Learning Algorithms Enhance Block Trade Liquidation?](https://options.greeks.live/question/how-do-machine-learning-algorithms-enhance-block-trade-liquidation/)
![A solid object, symbolizing Principal execution via RFQ protocol, intersects a translucent counterpart representing algorithmic price discovery and institutional liquidity. This dynamic within a digital asset derivatives sphere depicts optimized market microstructure, ensuring high-fidelity execution and atomic settlement.](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-execution-microstructure-orchestration.jpg)

Machine learning algorithms dynamically optimize block trade liquidation by predicting market impact and adapting execution strategies in real-time.

- 
### [What Are the Specific Data Requirements for Training Adaptive Block Trade Execution Models?](https://options.greeks.live/question/what-are-the-specific-data-requirements-for-training-adaptive-block-trade-execution-models/)
![Metallic platter signifies core market infrastructure. A precise blue instrument, representing RFQ protocol for institutional digital asset derivatives, targets a green block, signifying a large block trade. This illustrates high-fidelity execution, market microstructure, and capital efficiency within Prime RFQ.](https://options.greeks.live/wp-content/uploads/2025/07/precision-institutional-digital-asset-derivatives-rfq-protocol-execution-engine-market-microstructure-optimization-for-alpha-generation.jpg)

Adaptive block trade execution models demand granular market microstructure, historical transaction, and alternative data for optimal performance.

- 
### [Can a Highly Profitable Strategy in a Backtest Fail in Live Trading Solely Due to Unmodeled Slippage?](https://options.greeks.live/question/can-a-highly-profitable-strategy-in-a-backtest-fail-in-live-trading-solely-due-to-unmodeled-slippage/)
![A metallic, cross-shaped mechanism centrally positioned on a highly reflective, circular silicon wafer. The surrounding border reveals intricate circuit board patterns, signifying the underlying Prime RFQ and intelligence layer. This setup symbolizes high-fidelity execution within market microstructure, enabling algorithmic trading for institutional-grade digital asset derivatives and RFQ protocols with atomic settlement.](https://options.greeks.live/wp-content/uploads/2025/08/the-core-institutional-digital-asset-derivatives-rfq-os-engine-for-high-fidelity-market-microstructure.jpg)

A profitable backtest fails in live trading from unmodeled slippage because a simulation ignores the real cost of liquidity consumption.

- 
### [How Does Real-Time Intelligence Enhance Quote Skewing Mitigation?](https://options.greeks.live/question/how-does-real-time-intelligence-enhance-quote-skewing-mitigation/)
![A central core represents a Prime RFQ engine, facilitating high-fidelity execution. Transparent, layered structures denote aggregated liquidity pools and multi-leg spread strategies. Internal patterns symbolize market microstructure data streams, enabling algorithmic pricing and optimal price discovery for institutional digital asset derivatives.](https://options.greeks.live/wp-content/uploads/2025/07/prime-os-rfq-engine-for-institutional-digital-asset-derivatives-execution.jpg)

Real-time intelligence dynamically adjusts quotes and hedging, shielding institutional liquidity providers from adverse selection in volatile markets.

- 
### [What Are the Primary Data Sources for Building a Quote Staleness Model?](https://options.greeks.live/question/what-are-the-primary-data-sources-for-building-a-quote-staleness-model/)
![A multi-layered device with translucent aqua dome and blue ring, on black. This represents an Institutional-Grade Prime RFQ Intelligence Layer for Digital Asset Derivatives. It enables high-fidelity execution through optimized RFQ protocols, facilitating precise price discovery and leveraging deep market microstructure analysis for latent liquidity access.](https://options.greeks.live/wp-content/uploads/2025/08/institutional-digital-asset-derivatives-rfq-protocol-intelligence-layer.jpg)

A quote staleness model leverages granular market data to ensure real-time price integrity, enhancing execution quality.

- 
### [What Are the Most Effective Ways to Measure Algorithmic Information Leakage?](https://options.greeks.live/question/what-are-the-most-effective-ways-to-measure-algorithmic-information-leakage/)
![Sleek, abstract system interface with glowing green lines symbolizing RFQ pathways and high-fidelity execution. This visualizes market microstructure for institutional digital asset derivatives, emphasizing private quotation and dark liquidity within a Prime RFQ framework, enabling best execution and capital efficiency.](https://options.greeks.live/wp-content/uploads/2025/07/prime-os-rfq-protocol-for-institutional-digital-asset-derivatives.jpg)

Measuring information leakage is the process of quantifying the adverse price impact caused by an algorithm's own trading signature.

---

## Raw Schema Data

```json
{
    "@context": "https://schema.org",
    "@type": "BreadcrumbList",
    "itemListElement": [
        {
            "@type": "ListItem",
            "position": 1,
            "name": "Home",
            "item": "https://options.greeks.live"
        },
        {
            "@type": "ListItem",
            "position": 2,
            "name": "Question",
            "item": "https://options.greeks.live/question/"
        },
        {
            "@type": "ListItem",
            "position": 3,
            "name": "How Does Data Granularity Affect the Reliability of a Slippage Model Backtest?",
            "item": "https://options.greeks.live/question/how-does-data-granularity-affect-the-reliability-of-a-slippage-model-backtest/"
        }
    ]
}
```

```json
{
    "@context": "https://schema.org",
    "@type": "Organization",
    "@id": "https://greeks.live/#organization",
    "name": "Greeks.Live",
    "url": "https://greeks.live/",
    "description": "Greeks.live is the definitive toolkit for the professional options trader, dedicated to empowering our users with institutional-grade data, advanced analytics, and a comprehensive suite of trading tools designed to provide a decisive edge in the market. Every article and guide published under the Greeks.live name is a product of our deep commitment to data-driven analysis and our mission to support the global trading community.",
    "email": "support@greeks.live",
    "logo": {
        "@type": "ImageObject",
        "url": "https://options.greeks.live/wp-content/uploads/2025/07/Greeks.Live-Logo-Icon.svg"
    },
    "hasPart": [
        {
            "@type": "WebSite",
            "name": "About Us",
            "url": "https://about.greeks.live"
        },
        {
            "@type": "WebSite",
            "name": "App Download Page",
            "url": "https://about.greeks.live/#/download/basic_trade"
        },
        {
            "@type": "WebSite",
            "name": "Newcomers' Guide",
            "url": "https://twisty-sign-c8b.notion.site/Guide-for-Greeks-live-X-Deribit-Newbies-5a08a3052822427d800af6eaf3704c8d"
        },
        {
            "@type": "WebSite",
            "name": "Knowledge Base",
            "url": "https://linktr.ee/greeks.live"
        },
        {
            "@type": "WebSite",
            "name": "Option Terminology",
            "url": "https://greekslive.notion.site/88dfc00e7c584d4d8969b6302a79a12f?pvs=4"
        },
        {
            "@type": "WebSite",
            "name": "Terms of Service",
            "url": "https://greeks.live/#/tos"
        },
        {
            "@type": "WebSite",
            "name": "Disclaimer",
            "url": "https://greeks.live/#/disclaimer"
        }
    ],
    "sameAs": [
        "https://x.com/greekslive",
        "https://youtube.com/@greeks_live?si=-KjT0Lkw4WUCI0tD",
        "https://t.me/greekslive_notifications",
        "https://t.me/greekslive",
        "https://pinterest.com/greekslive/"
    ],
    "makesOffer": [
        {
            "@type": "Offer",
            "itemOffered": {
                "@type": "Service",
                "name": "RFQ Platform",
                "url": "https://rfq.greeks.live",
                "description": "Execute institutional-grade block trades for crypto options and futures. This platform provides deep, over-the-counter (OTC) liquidity via a professional Request for Quote system.",
                "provider": {
                    "@id": "https://greeks.live/#organization"
                }
            }
        },
        {
            "@type": "Offer",
            "itemOffered": {
                "@type": "Service",
                "name": "Lending Tools",
                "url": "https://lend.greeks.live",
                "description": "Manage crypto lending and borrowing activities to earn yield or secure loans. This suite of tools helps users analyze interest rates and manage collateralized debt positions.",
                "provider": {
                    "@id": "https://greeks.live/#organization"
                }
            }
        },
        {
            "@type": "Offer",
            "itemOffered": {
                "@type": "Service",
                "name": "Data Lab",
                "url": "https://greeks.live/#/deribit/tools/datalab/BTC",
                "description": "Analyze historical and real-time options data with advanced metrics. This tool is designed for backtesting strategies and identifying market trends through comprehensive data visualization.",
                "provider": {
                    "@id": "https://greeks.live/#organization"
                }
            }
        },
        {
            "@type": "Offer",
            "itemOffered": {
                "@type": "Service",
                "name": "Profit & Loss Analysis (PnL)",
                "url": "https://greeks.live/#/deribit/tools/pnl_analysis",
                "description": "Track and analyze the Profit and Loss (PnL) of your options portfolio. This tool provides detailed performance metrics to help traders evaluate and refine their strategies.",
                "provider": {
                    "@id": "https://greeks.live/#organization"
                }
            }
        },
        {
            "@type": "Offer",
            "itemOffered": {
                "@type": "Service",
                "name": "Position Visualization",
                "url": "https://greeks.live/#/deribit/tools/pv/BTC",
                "description": "Visualize complex options positions and their corresponding risk profiles. This tool generates payoff diagrams to help traders intuitively understand potential profit, loss, and exposure.",
                "provider": {
                    "@id": "https://greeks.live/#organization"
                }
            }
        },
        {
            "@type": "Offer",
            "itemOffered": {
                "@type": "Service",
                "name": "Options Calculator",
                "url": "https://greeks.live/#/deribit/tools/calculator",
                "description": "Calculate the theoretical price and risk parameters ('the Greeks') of crypto options. An essential tool for pricing new trades and analyzing the sensitivity of existing positions.",
                "provider": {
                    "@id": "https://greeks.live/#organization"
                }
            }
        },
        {
            "@type": "Offer",
            "itemOffered": {
                "@type": "Service",
                "name": "Greeks.live Main Platform",
                "url": "https://greeks.live/",
                "description": "The central hub for the Greeks.live suite of professional cryptocurrency options and derivatives trading tools. Access analytics, manage positions, and explore the ecosystem.",
                "provider": {
                    "@id": "https://greeks.live/#organization"
                }
            }
        },
        {
            "@type": "Offer",
            "itemOffered": {
                "@type": "Service",
                "name": "Community Forum",
                "url": "https://t.greeks.live",
                "description": "Join the official Greeks.live community. Connect with other traders, share insights, and receive the latest updates directly from the team.",
                "provider": {
                    "@id": "https://greeks.live/#organization"
                }
            }
        },
        {
            "@type": "Offer",
            "itemOffered": {
                "@type": "Service",
                "name": "Greeks.live for Deribit",
                "url": "http://deribit.greeks.live",
                "description": "A dedicated interface for trading on the Deribit exchange. This platform leverages Greeks.live's advanced analytics and tools specifically for users of Deribit's options and futures markets.",
                "provider": {
                    "@id": "https://greeks.live/#organization"
                }
            }
        },
        {
            "@type": "Offer",
            "itemOffered": {
                "@type": "Service",
                "name": "Greeks.live for OKX",
                "url": "http://okx.greeks.live",
                "description": "A dedicated interface for trading on the OKX exchange. This platform integrates Greeks.live's powerful analytics with the OKX marketplace, focusing on its options and derivatives products.",
                "provider": {
                    "@id": "https://greeks.live/#organization"
                }
            }
        },
        {
            "@type": "Offer",
            "itemOffered": {
                "@type": "Service",
                "name": "GLVS.AI Trading Platform",
                "url": "https://www.glvs.ai/",
                "description": "An AI-powered platform for automated crypto options trading. Leverage advanced algorithms and data-driven signals to develop and deploy sophisticated, hands-free trading strategies.",
                "provider": {
                    "@id": "https://greeks.live/#organization"
                }
            }
        },
        {
            "@type": "Offer",
            "itemOffered": {
                "@type": "Service",
                "name": "API Testnet & Sandbox",
                "url": "https://test.greeks.live",
                "description": "A dedicated testnet environment for developers to test their API integrations and automated trading strategies. This sandbox allows for full functionality testing without risking real funds.",
                "provider": {
                    "@id": "https://greeks.live/#organization"
                }
            }
        }
    ],
    "subOrganization": [
        {
            "@type": "Organization",
            "name": "GreeksLive HK Co., Limited",
            "email": "support@greeks.live",
            "telephone": "+852-56418949",
            "address": {
                "@type": "PostalAddress",
                "streetAddress": "Unit 11, 27/F, K. Wah Centre, 191 Java Road",
                "addressLocality": "Hong Kong",
                "addressRegion": "North Point",
                "addressCountry": "CN",
                "postalCode": "0000"
            },
            "geo": {
                "@type": "GeoCoordinates",
                "latitude": "22.2932357",
                "longitude": "114.2022026"
            },
            "image": "https://options.greeks.live/wp-content/uploads/2025/08/Wah-Centre-Hong-Kong.jpg"
        }
    ]
}
```

```json
{
    "@context": "https://schema.org",
    "@type": "Article",
    "mainEntityOfPage": {
        "@type": "WebPage",
        "@id": "https://options.greeks.live/question/how-does-data-granularity-affect-the-reliability-of-a-slippage-model-backtest/"
    },
    "headline": "How Does Data Granularity Affect the Reliability of a Slippage Model Backtest? \u25b4 Question",
    "description": "Data granularity sets the resolution of your market view; it dictates whether your slippage model is a reliable map or a dangerous fiction. \u25b4 Question",
    "url": "https://options.greeks.live/question/how-does-data-granularity-affect-the-reliability-of-a-slippage-model-backtest/",
    "datePublished": "2025-07-30T08:20:40+00:00",
    "dateModified": "2025-07-31T06:20:05+00:00",
    "articleSection": [
        "Question"
    ],
    "author": {
        "@id": "https://greeks.live/#organization"
    },
    "publisher": {
        "@id": "https://greeks.live/#organization"
    },
    "image": {
        "@type": "ImageObject",
        "url": "https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-rfq-execution-engine-for-price-discovery.jpg",
        "caption": "A macro view captures a central, precision-engineered control mechanism, featuring teal O-rings, emerging from a highly reflective, dark surface. This abstract representation symbolizes an institutional-grade RFQ engine facilitating multi-leg spread execution and atomic settlement for digital asset derivatives. It underscores robust market microstructure analysis, mitigating counterparty risk and information leakage, crucial for best execution and capital efficiency within a Prime RFQ."
    },
    "keywords": [
        "Algorithmic Trading",
        "Backtest Reliability",
        "Backtesting Protocol",
        "Data Granularity",
        "Execution Risk",
        "High-Fidelity Execution",
        "Market Impact",
        "Market Microstructure",
        "Order Book",
        "Order Book Data",
        "Order Book Simulation",
        "Quantitative Finance",
        "Slippage Model",
        "Tick-Level Data",
        "Volatility Modeling"
    ]
}
```

```json
{
    "@context": "https://schema.org",
    "@type": "WebSite",
    "url": "https://options.greeks.live/",
    "potentialAction": {
        "@type": "SearchAction",
        "target": "https://options.greeks.live/?s=search_term_string",
        "query-input": "required name=search_term_string"
    }
}
```

```json
{
    "@context": "https://schema.org",
    "@type": "FAQPage",
    "mainEntity": [
        {
            "@type": "Question",
            "name": "What Is The True Cost Of A Mismatched Granularity?",
            "acceptedAnswer": {
                "@type": "Answer",
                "text": "Choosing a data granularity that is misaligned with the strategy's trading frequency introduces systemic error into the backtest. For a high-frequency strategy that aims to capture fleeting arbitrage opportunities, using anything less than tick-level data is an exercise in fiction. The alpha the strategy seeks to exploit exists only in the spaces between the bars of low-resolution data. Conversely, a long-term trend-following strategy might find tick-data to be computationally burdensome and full of noise that obscures the larger market signal. The key is aligning the resolution of the observation with the scale of the phenomenon being targeted. A failure to do so results in a model that is calibrated to a market that does not exist in reality, leading to systematically underestimated costs and overestimated performance. This is the primary reason for the common and costly divergence between backtested results and live trading performance."
            }
        },
        {
            "@type": "Question",
            "name": "How Does Market Structure Influence Data Choice?",
            "acceptedAnswer": {
                "@type": "Answer",
                "text": "The structure of the market being traded is a critical factor. In a highly liquid, electronic market like major FX pairs, tick data is abundant and reflects a continuous, two-sided market. A slippage model can be built with a high degree of confidence using this data. In a less liquid market, such as for a small-cap stock or an exotic cryptocurrency, tick data may be sporadic. The gaps between trades are meaningful. In these cases, a \"trade-by-trade\" data structure is superior to time-based bars, as it reflects the true, discontinuous nature of liquidity. The strategy must adapt to the data landscape of the specific asset, as a one-size-fits-all approach to granularity will fail to capture these structural differences."
            }
        },
        {
            "@type": "Question",
            "name": "What Is The Best Way To Model Slippage In Practice?",
            "acceptedAnswer": {
                "@type": "Answer",
                "text": "The practical implementation of a slippage model requires choosing a method that balances realism with computational feasibility. Different asset classes and market conditions warrant different approaches. The following table details several execution modeling methods, connecting them to data requirements and their impact on backtest reliability."
            }
        }
    ]
}
```


---

**Original URL:** https://options.greeks.live/question/how-does-data-granularity-affect-the-reliability-of-a-slippage-model-backtest/
