# How Can Stratified Sampling within an Algo Wheel Prevent Biased Conclusions in Transaction Cost Analysis? ▴ Question

**Published:** 2025-08-13
**Categories:** Question

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## Concept

![The abstract composition features a central, multi-layered blue structure representing a sophisticated institutional digital asset derivatives platform, flanked by two distinct liquidity pools. Intersecting blades symbolize high-fidelity execution pathways and algorithmic trading strategies, facilitating private quotation and block trade settlement within a market microstructure optimized for price discovery and capital efficiency](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-protocol-high-fidelity-execution-1.jpg)

## The Illusion of a Level Playing Field in Algorithmic Performance

In the world of institutional trading, an algo wheel is a powerful tool for optimizing execution. It allows a [trading desk](https://prime.greeks.live/area/trading-desk/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) to systematically allocate orders among a variety of algorithms, with the goal of identifying the most effective strategies for different market conditions. The ultimate arbiter of performance is [Transaction Cost Analysis](https://prime.greeks.live/area/transaction-cost-analysis/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) (TCA), which measures the “slippage” or difference between the expected price of a trade and the actual execution price. A lower slippage generally indicates a better execution.

However, the data from an algo wheel can be misleading if the “race” between algorithms is not fair. An algo wheel without a proper allocation methodology can lead to what is known as “[selection bias](https://prime.greeks.live/area/selection-bias/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite).” This occurs when certain algorithms are consistently assigned easier or harder trades, skewing the TCA results and leading to flawed conclusions about their performance. For instance, an algorithm that is primarily used for small, liquid orders will naturally appear to have lower [transaction costs](https://prime.greeks.live/question/how-do-implicit-costs-differ-from-explicit-transaction-costs/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) than an algorithm that is used for large, illiquid blocks. The two are not comparable, and any direct comparison of their performance is meaningless.

![Translucent teal glass pyramid and flat pane, geometrically aligned on a dark base, symbolize market microstructure and price discovery within RFQ protocols for institutional digital asset derivatives. This visualizes multi-leg spread construction, high-fidelity execution via a Principal's operational framework, ensuring atomic settlement for latent liquidity](https://options.greeks.live/wp-content/uploads/2025/07/transparent-geometric-structures-visualizing-multi-leg-spread-rfq-protocols-for-high-fidelity-institutional-digital-asset-derivatives-execution.jpg)

## The Unseen Biases in Order Allocation

The core of the problem lies in the heterogeneity of orders. A simple round-robin or discretionary allocation of orders to algorithms in a wheel fails to account for the inherent differences in order difficulty. Some of the key factors that influence the difficulty of an order, and therefore its transaction costs, include:

- **Order Size** ▴ Larger orders have a greater [market impact](https://prime.greeks.live/area/market-impact/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) and are more likely to experience [slippage](https://prime.greeks.live/area/slippage/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite).

- **Liquidity of the Asset** ▴ Trading an illiquid asset is more challenging and costly than trading a highly liquid one.

- **Market Volatility** ▴ High volatility can lead to wider bid-ask spreads and increased slippage.

- **Time of Day** ▴ Liquidity and volatility can vary significantly throughout the trading day.

Without a systematic way to control for these variables, the TCA results will be contaminated by the “luck of the draw.” An algorithm may appear to be a top performer simply because it was lucky enough to receive a string of easy orders. Conversely, a potentially superior algorithm may be unfairly penalized because it was assigned a series of difficult trades. This is not just a theoretical problem; it has real-world consequences. A trading desk that relies on biased TCA data may end up allocating more of its flow to inferior algorithms, leading to higher transaction costs and reduced returns for its clients.

> A fair comparison of algorithm performance requires that each algorithm is tested on a representative sample of trades, which is where stratified sampling comes in.

The challenge, therefore, is to design an [algo wheel](https://prime.greeks.live/area/algo-wheel/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) that can isolate the true performance of each algorithm from the noise of random [order allocation](https://prime.greeks.live/area/order-allocation/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite). This requires a more sophisticated approach to order allocation, one that ensures that each algorithm is tested on a comparable mix of trades. This is the fundamental problem that stratified sampling is designed to solve.

![Sleek, two-tone devices precisely stacked on a stable base represent an institutional digital asset derivatives trading ecosystem. This embodies layered RFQ protocols, enabling multi-leg spread execution and liquidity aggregation within a Prime RFQ for high-fidelity execution, optimizing counterparty risk and market microstructure](https://options.greeks.live/wp-content/uploads/2025/08/institutional-digital-asset-derivatives-rfq-prime-os-architecture.jpg)

![A central teal sphere, representing the Principal's Prime RFQ, anchors radiating grey and teal blades, signifying diverse liquidity pools and high-fidelity execution paths for digital asset derivatives. Transparent overlays suggest pre-trade analytics and volatility surface dynamics](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-and-market-microstructure.jpg)

## Strategy

![Central metallic hub connects beige conduits, representing an institutional RFQ engine for digital asset derivatives. It facilitates multi-leg spread execution, ensuring atomic settlement, optimal price discovery, and high-fidelity execution within a Prime RFQ for capital efficiency](https://options.greeks.live/wp-content/uploads/2025/07/prime-os-digital-asset-derivatives-rfq-protocol-execution-hub.jpg)

## Engineering Fairness with Stratified Sampling

Stratified sampling provides a robust framework for eliminating selection bias in an algo wheel. The core idea is to divide the entire population of potential orders into distinct “strata” or categories based on their key characteristics. Then, within each stratum, orders are randomly allocated to the different algorithms in the wheel.

This ensures that each algorithm receives a balanced and representative sample of trades from each category of order difficulty. In essence, it creates a series of “mini-competitions” for each type of trade, allowing for a fair and accurate comparison of [algorithm performance](https://prime.greeks.live/question/under-what-market-conditions-does-a-vwap-algorithm-underperform-an-is-algorithm/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite).

![Abstract planes illustrate RFQ protocol execution for multi-leg spreads. A dynamic teal element signifies high-fidelity execution and smart order routing, optimizing price discovery](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-protocol-optimization.jpg)

## Defining the Strata

The first step in implementing a stratified sampling approach is to identify the key factors that drive transaction costs and use them to define the strata. The choice of stratification factors will depend on the specific asset class and trading objectives, but some common examples include:

- **Order Size (as a percentage of average daily volume)** ▴ This is one of the most important factors, as it is directly related to market impact. Orders can be stratified into categories such as “small” (5% of ADV).

- **Liquidity** ▴ This can be measured by factors such as bid-ask spread or the depth of the order book. Assets can be stratified into “high,” “medium,” and “low” liquidity buckets.

- **Volatility** ▴ Historical or implied volatility can be used to stratify trades into “low,” “medium,” and “high” volatility regimes.

- **Time of Day** ▴ The trading day can be divided into different sessions, such as the “opening,” “mid-day,” and “closing” periods, to account for variations in liquidity and volatility.

The goal is to create strata that are as homogeneous as possible in terms of their expected transaction costs. This will ensure that the comparisons between algorithms within each stratum are as meaningful as possible.

![A beige Prime RFQ chassis features a glowing teal transparent panel, symbolizing an Intelligence Layer for high-fidelity execution. A clear tube, representing a private quotation channel, holds a precise instrument for algorithmic trading of digital asset derivatives, ensuring atomic settlement](https://options.greeks.live/wp-content/uploads/2025/07/prime-os-rfq-digital-asset-derivatives-high-fidelity-execution-system-interface.jpg)

## An Illustration of Stratified Allocation

The following table provides a simplified example of how stratified sampling can be used to allocate orders in an algo wheel with three different algorithms (Algo A, Algo B, and Algo C).

### A simplified model of stratified order allocation in an algo wheel.

| Stratum | Order Characteristics | Allocation to Algo A | Allocation to Algo B | Allocation to Algo C |
| --- | --- | --- | --- | --- |
| 1 | Small Order, High Liquidity, Low Volatility | 33% | 33% | 34% |
| 2 | Medium Order, High Liquidity, Low Volatility | 33% | 33% | 34% |
| 3 | Large Order, Medium Liquidity, Medium Volatility | 33% | 33% | 34% |
| 4 | Large Order, Low Liquidity, High Volatility | 33% | 33% | 34% |

By allocating orders in this way, the trading desk can be confident that each algorithm is being tested on a comparable mix of trades. This allows for a much more accurate and reliable assessment of their relative performance.

![A gleaming, translucent sphere with intricate internal mechanisms, flanked by precision metallic probes, symbolizes a sophisticated Principal's RFQ engine. This represents the atomic settlement of multi-leg spread strategies, enabling high-fidelity execution and robust price discovery within institutional digital asset derivatives markets, minimizing latency and slippage for optimal alpha generation and capital efficiency](https://options.greeks.live/wp-content/uploads/2025/07/inst-digital-asset-deriv-rfq-os-for-optimal-high-fidelity-trade-exec.jpg)

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## Execution

![Two sleek, distinct colored planes, teal and blue, intersect. Dark, reflective spheres at their cross-points symbolize critical price discovery nodes](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-protocols-multi-leg-execution-liquidity.jpg)

## From Theory to Practice Implementing a Stratified Algo Wheel

The implementation of a stratified algo wheel is a data-driven process that requires careful planning and execution. It is not a one-time setup but rather an ongoing process of refinement and optimization. Here are the key steps involved:

- **Data Collection and Analysis** ▴ The first step is to gather historical data on all the relevant order characteristics, such as order size, liquidity, and volatility. This data will be used to define the strata and to calibrate the allocation methodology.

- **Strata Definition** ▴ Based on the data analysis, the trading desk needs to define a set of mutually exclusive and collectively exhaustive strata. The number of strata will depend on the complexity of the trading environment and the desired level of granularity.

- **Allocation Logic** ▴ The next step is to define the allocation logic for each stratum. In most cases, a simple random allocation will be sufficient to ensure fairness. However, in some cases, a more sophisticated allocation methodology may be required, such as one that takes into account the capacity of each algorithm.

- **Implementation and Monitoring** ▴ Once the strata and allocation logic have been defined, the stratified algo wheel can be implemented. It is important to monitor the performance of the wheel on an ongoing basis to ensure that it is working as intended. This includes tracking the number of orders allocated to each algorithm in each stratum, as well as the TCA results for each algorithm.

- **Analysis and Optimization** ▴ The final step is to analyze the TCA results to identify the top-performing algorithms for each stratum. This information can then be used to optimize the allocation of order flow and to improve the overall execution performance of the trading desk.

![A pristine white sphere, symbolizing an Intelligence Layer for Price Discovery and Volatility Surface analytics, sits on a grey Prime RFQ chassis. A dark FIX Protocol conduit facilitates High-Fidelity Execution and Smart Order Routing for Institutional Digital Asset Derivatives RFQ protocols, ensuring Best Execution](https://options.greeks.live/wp-content/uploads/2025/08/institutional-grade-digital-asset-derivatives-rfq-protocols-optimizing-high-fidelity-execution-via-prime-os-intelligence.jpg)

## Analyzing the Results a Stratified View of Performance

The true power of a stratified algo wheel lies in its ability to provide a granular and unbiased view of algorithm performance. Instead of a single, aggregated TCA number for each algorithm, the trading desk can now see how each algorithm performs in different market conditions and for different types of trades. This allows for a much more nuanced and insightful analysis of the results.

> By breaking down performance by stratum, a trading desk can identify the specific strengths and weaknesses of each algorithm and make more informed decisions about which algorithm to use for a particular trade.

The following table provides a hypothetical example of the TCA results from a stratified algo wheel. The results are shown in basis points (bps) of slippage, with a lower number indicating better performance.

### A hypothetical example of TCA results from a stratified algo wheel.

| Stratum | Algo A (bps) | Algo B (bps) | Algo C (bps) | Best Performer |
| --- | --- | --- | --- | --- |
| 1 (Small, Liquid, Low Vol) | 5.2 | 4.8 | 5.5 | Algo B |
| 2 (Medium, Liquid, Low Vol) | 10.1 | 12.5 | 9.8 | Algo C |
| 3 (Large, Medium Liq, Med Vol) | 25.6 | 22.1 | 28.9 | Algo B |
| 4 (Large, Illiquid, High Vol) | 55.2 | 60.1 | 52.5 | Algo C |

In this example, a simple, unstratified analysis might have concluded that Algo B was the best overall performer. However, the stratified analysis reveals a more complex picture. Algo B is indeed the best performer for small and large orders in less volatile conditions, but Algo C is the clear winner for medium-sized orders and for large orders in the most challenging market conditions. This level of insight is simply not possible with a traditional, unstratified algo wheel.

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

## References

- Kissell, Robert. _The Science of Algorithmic Trading and Portfolio Management_. Academic Press, 2013.

- Johnson, Barry. _Algorithmic Trading and DMA ▴ An introduction to direct access trading strategies_. 4Myeloma Press, 2010.

- 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, 2008.

- Kakushadze, Zura, and Juan Andres Serur. “40 Years of Algorithmic Trading ▴ A Review.” _SSRN Electronic Journal_, 2018.

![Precision metallic component, possibly a lens, integral to an institutional grade Prime RFQ. Its layered structure signifies market microstructure and order book dynamics](https://options.greeks.live/wp-content/uploads/2025/08/high-fidelity-institutional-digital-asset-rfq-execution-engine.jpg)

## Reflection

![Intricate internal machinery reveals a high-fidelity execution engine for institutional digital asset derivatives. Precision components, including a multi-leg spread mechanism and data flow conduits, symbolize a sophisticated RFQ protocol facilitating atomic settlement and robust price discovery within a principal's Prime RFQ](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-engine-microstructure.jpg)

## Beyond the Wheel a System of Intelligence

The implementation of a stratified algo wheel is a significant step towards a more data-driven and scientific approach to execution. It provides a framework for making fair and accurate comparisons of algorithm performance, which is essential for optimizing trading strategies and reducing transaction costs. However, the true value of this approach extends beyond the algo wheel itself. It represents a shift in mindset, from a focus on individual trades to a focus on the entire trading process as a system.

By understanding the factors that drive transaction costs and by systematically controlling for them, a trading desk can gain a deeper understanding of its own execution process and identify opportunities for improvement that would otherwise be hidden in the noise of the market. The ultimate goal is not just to build a better algo wheel, but to build a more intelligent trading system, one that is constantly learning and adapting to the ever-changing market environment.

![A precisely engineered multi-component structure, split to reveal its granular core, symbolizes the complex market microstructure of institutional digital asset derivatives. This visual metaphor represents the unbundling of multi-leg spreads, facilitating transparent price discovery and high-fidelity execution via RFQ protocols within a Principal's operational framework](https://options.greeks.live/wp-content/uploads/2025/07/institutional-grade-digital-asset-derivatives-rfq-protocols-revealing-market-microstructure-for-high-fidelity-execution-and-atomic-settlement-within-principals-operational-framework.jpg)

## Glossary

<a href="https://prime.greeks.live/area/transaction-cost-analysis/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
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## Transaction Cost Analysis

Meaning ▴ Transaction Cost Analysis (TCA) is the quantitative methodology for assessing the explicit and implicit costs incurred during the execution of financial trades. </a><a href="https://prime.greeks.live/area/trading-desk/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![Two dark, circular, precision-engineered components, stacked and reflecting, symbolize a Principal's Operational Framework. This layered architecture facilitates High-Fidelity Execution for Block Trades via RFQ Protocols, ensuring Atomic Settlement and Capital Efficiency within Market Microstructure for Digital Asset Derivatives](https://options.greeks.live/wp-content/uploads/2025/08/institutional-digital-asset-derivatives-prime-os-execution-protocol.jpg)

## Trading Desk

Meaning ▴ A Trading Desk represents a specialized operational system within an institutional financial entity, designed for the systematic execution, risk management, and strategic positioning of proprietary capital or client orders across various asset classes, with a particular focus on the complex and nascent digital asset derivatives landscape. </a><a href="https://prime.greeks.live/question/how-do-implicit-costs-differ-from-explicit-transaction-costs/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![A sleek, multi-segmented sphere embodies a Principal's operational framework for institutional digital asset derivatives. Its transparent 'intelligence layer' signifies high-fidelity execution and price discovery via RFQ protocols](https://options.greeks.live/wp-content/uploads/2025/07/prime-os-rfq-protocol-digital-asset-derivatives-execution-engine.jpg)

## Transaction Costs

Implicit costs are the market-driven price concessions of a trade; explicit costs are the direct fees for its execution. </a><a href="https://prime.greeks.live/area/selection-bias/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![A transparent blue sphere, symbolizing precise Price Discovery and Implied Volatility, is central to a layered Principal's Operational Framework. This structure facilitates High-Fidelity Execution and RFQ Protocol processing across diverse Aggregated Liquidity Pools, revealing the intricate Market Microstructure of Institutional Digital Asset Derivatives](https://options.greeks.live/wp-content/uploads/2025/07/digital-asset-derivatives-rfq-execution-system-microstructure-principal-framework.jpg)

## Selection Bias

Meaning ▴ Selection bias represents a systemic distortion in data acquisition or observation processes, resulting in a dataset that does not accurately reflect the underlying population or phenomenon it purports to measure. </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">
![Sleek dark metallic platform, glossy spherical intelligence layer, precise perforations, above curved illuminated element. This symbolizes an institutional RFQ protocol for digital asset derivatives, enabling high-fidelity execution, advanced market microstructure, Prime RFQ powered price discovery, and deep liquidity pool access](https://options.greeks.live/wp-content/uploads/2025/07/prime-os-digital-asset-derivatives-rfq-protocol-high-fidelity-execution-1.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/slippage/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![A polished blue sphere representing a digital asset derivative rests on a metallic ring, symbolizing market microstructure and RFQ protocols, supported by a foundational beige sphere, an institutional liquidity pool. A smaller blue sphere floats above, denoting atomic settlement or a private quotation within a Principal's Prime RFQ for high-fidelity execution](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-market-microstructure-and-rfq-protocols.jpg)

## Slippage

Meaning ▴ Slippage denotes the variance between an order's expected execution price and its actual execution price. </a><a href="https://prime.greeks.live/area/order-allocation/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![A stylized rendering illustrates a robust RFQ protocol within an institutional market microstructure, depicting high-fidelity execution of digital asset derivatives. A transparent mechanism channels a precise order, symbolizing efficient price discovery and atomic settlement for block trades via a prime brokerage system](https://options.greeks.live/wp-content/uploads/2025/07/prime-os-rfq-execution-digital-asset-derivatives-options-high-fidelity-trading.jpg)

## Order Allocation

Meaning ▴ Order Allocation refers to the systematic distribution of executed trade quantities, often from a single aggregated order, across multiple client accounts or sub-accounts. </a><a href="https://prime.greeks.live/area/algo-wheel/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![A sophisticated, illuminated device representing an Institutional Grade Prime RFQ for Digital Asset Derivatives. Its glowing interface indicates active RFQ protocol execution, displaying high-fidelity execution status and price discovery for block trades](https://options.greeks.live/wp-content/uploads/2025/07/principal-rfq-engine-for-digital-asset-derivatives-best-execution-1.jpg)

## Algo Wheel

Meaning ▴ An Algo Wheel is a systematic framework for routing order flow to various execution algorithms based on predefined criteria and real-time market conditions. </a><a href="https://prime.greeks.live/question/under-what-market-conditions-does-a-vwap-algorithm-underperform-an-is-algorithm/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![A beige and dark grey precision instrument with a luminous dome. This signifies an Institutional Grade platform for Digital Asset Derivatives and RFQ execution](https://options.greeks.live/wp-content/uploads/2025/08/institutional-rfq-digital-asset-derivatives-trading-system-intelligence.jpg)

## Algorithm Performance

VWAP underperforms IS in volatile, trending markets where its rigid schedule creates systemic slippage against the arrival price. </a>

**Tags:** [Market Impact](https://options.greeks.live/area/market-impact/), [Stratified Sampling](https://options.greeks.live/area/stratified-sampling/), [Transaction Cost Analysis](https://options.greeks.live/area/transaction-cost-analysis/), [Algo Wheel](https://options.greeks.live/area/algo-wheel/), [Slippage](https://options.greeks.live/area/slippage/), [Execution Algorithms](https://options.greeks.live/area/execution-algorithms/)	

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- 
### [How Does a Best Execution Committee Measure and Control for Information Leakage in Its Trades?](https://options.greeks.live/question/how-does-a-best-execution-committee-measure-and-control-for-information-leakage-in-its-trades/)
![A precise geometric prism reflects on a dark, structured surface, symbolizing institutional digital asset derivatives market microstructure. This visualizes block trade execution and price discovery for multi-leg spreads via RFQ protocols, ensuring high-fidelity execution and capital efficiency within Prime RFQ.](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-protocols-precision-execution.jpg)

A Best Execution Committee controls information leakage by architecting a data-driven system of quantitative measurement and disciplined protocols.

- 
### [What Is the Relationship between Information Asymmetry and Post-Trade Reversion?](https://options.greeks.live/question/what-is-the-relationship-between-information-asymmetry-and-post-trade-reversion/)
![A sleek pen hovers over a luminous circular structure with teal internal components, symbolizing precise RFQ initiation. This represents high-fidelity execution for institutional digital asset derivatives, optimizing market microstructure and achieving atomic settlement within a Prime RFQ liquidity pool.](https://options.greeks.live/wp-content/uploads/2025/07/optimizing-digital-asset-derivatives-rfq-through-prime-os-precision.jpg)

Information asymmetry causes temporary price dislocations, with post-trade reversion being the market's corrective process.

- 
### [How Can Post Trade Transaction Cost Analysis Be Used to Refine Future Block Trading Strategies?](https://options.greeks.live/question/how-can-post-trade-transaction-cost-analysis-be-used-to-refine-future-block-trading-strategies-2/)
![An advanced digital asset derivatives system features a central liquidity pool aperture, integrated with a high-fidelity execution engine. This Prime RFQ architecture supports RFQ protocols, enabling block trade processing and price discovery. It manages multi-leg spreads and optimizes portfolio margin through quantitative analysis for alpha generation.](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-rfq-protocol-execution-system-architecture.jpg)

TCA refines block trading by transforming execution data into a feedback loop for optimizing future strategy, venue, and algorithm selection.

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---

**Original URL:** https://options.greeks.live/question/how-can-stratified-sampling-within-an-algo-wheel-prevent-biased-conclusions-in-transaction-cost-analysis/
