# What Are the Key Differences between Walk Forward Optimization and a Simple Rolling Window Analysis? ▴ Question

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

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## Concept

The evaluation of a quantitative trading system is a direct reflection of the builder’s assumptions about market behavior. At the core of this evaluation are two distinct protocols for interacting with historical data ▴ the [simple rolling window analysis](https://prime.greeks.live/question/how-does-the-collection-window-mechanism-in-modern-rfq-systems-enhance-fair-competition/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) and the more structurally complex [walk-forward optimization](https://prime.greeks.live/area/walk-forward-optimization/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite). The decision to employ one over the other is a foundational architectural choice, defining the system’s capacity to adapt and the very standard of its validation.

A simple [rolling window analysis](https://prime.greeks.live/area/rolling-window-analysis/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) operates as a mechanism of perpetual data refreshment. It maintains a fixed-size window of [historical data](https://prime.greeks.live/area/historical-data/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite). As new time periods occur, the oldest data points are discarded and the new ones are added, keeping the dataset current. A model trained on this window is, therefore, always using a recent snapshot of market conditions.

This process is computationally efficient and ensures the model’s parameters are influenced by the latest market information. It functions on the principle that recent history is the most relevant predictor of the immediate future.

> A rolling window analysis continually updates its dataset, while walk-forward optimization sequentially re-optimizes and validates a model on discrete blocks of data.

Walk-forward optimization introduces a more rigorous, multi-stage validation architecture. It segments the historical data into a series of larger “in-sample” periods and smaller, subsequent “out-of-sample” periods. The system first undergoes an exhaustive [parameter optimization](https://prime.greeks.live/area/parameter-optimization/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) on the initial [in-sample data](https://prime.greeks.live/area/in-sample-data/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) block to find the highest-performing settings. These specific parameters are then locked and applied to the immediately following out-of-sample block, a segment of data the model has not seen during optimization.

The performance in this out-of-sample period is recorded. The entire process then “walks forward” in time, rolling the in-sample and out-of-sample windows forward, and repeats the optimization and validation cycle. The final performance report is a composite of these individual out-of-sample results, creating a more robust picture of how the strategy might adapt to new data over time. This method directly confronts the problem of overfitting by repeatedly testing optimized parameters on unseen data.

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## Strategy

The strategic selection between a rolling [window analysis](https://prime.greeks.live/question/how-does-the-collection-window-mechanism-in-modern-rfq-systems-enhance-fair-competition/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) and walk-forward optimization hinges on the system designer’s primary objective ▴ simple adaptation versus true robustness. Each methodology provides a different lens through which to view a strategy’s viability, with profound implications for capital allocation, risk management, and long-term performance expectations.

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## The Strategic Objective of Parameter Stability

A core challenge in quantitative trading is parameter decay, where the optimal settings for a strategy today become suboptimal tomorrow. A [simple rolling window](https://prime.greeks.live/question/how-does-the-collection-window-mechanism-in-modern-rfq-systems-enhance-fair-competition/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) analysis addresses this by allowing for frequent, incremental adjustments. The strategy’s parameters can be re-calculated with each roll of the window, creating a system that is constantly adapting to the most recent market behavior.

The strategic assumption is one of continuous, gradual market evolution. This approach is well-suited for strategies designed to operate in relatively stable market regimes where recent data provides a sufficient guide.

Walk-forward optimization, conversely, is built on a more skeptical strategic foundation. It assumes that parameters optimized on one dataset may be the product of random noise or “curve-fitting.” By systematically optimizing on one period and testing on another, it measures the stability of those parameters. The goal is to see if the logic that produced a certain set of optimal parameters in the in-sample data holds true in the out-of-sample data. A strategy that shows consistent performance across multiple, sequential out-of-sample windows is considered more robust, as its parameters have proven effective across different market segments.

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## How Does One Quantify Overfitting Risk?

Overfitting occurs when a model learns the noise in a specific dataset so well that it fails to generalize to new data. A [simple rolling](https://prime.greeks.live/question/from-a-quantitative-perspective-how-is-rfq-price-quality-measured-beyond-simple-slippage/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) window analysis possesses a structural vulnerability to this risk. While it updates its data, the performance is typically measured across the entire backtest period, blending periods where the model was being trained and tested. There is no clean separation between data used for optimization and data used for validation.

> Walk-forward optimization provides a structured framework for assessing model robustness against overfitting, a risk less explicitly managed in a simple rolling analysis.

The walk-forward protocol is an explicit anti-overfitting mechanism. The performance that matters is the aggregated result of the out-of-sample periods. Because the parameters were chosen before seeing the out-of-sample data in each step, it simulates the real-world process of deploying a strategy with a fixed parameter set.

If the out-of-sample [equity curve](https://prime.greeks.live/question/what-are-the-key-differences-in-transparency-for-equity-versus-non-equity-rfqs-under-mifid-ii/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) is strong, it provides much higher confidence that the strategy has a genuine edge. If the out-of-sample performance degrades significantly compared to the in-sample results, it is a clear signal of overfitting.

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## A Comparative Framework

The choice between these two protocols can be clarified by comparing their core attributes from a strategic viewpoint.

| Strategic Dimension | Simple Rolling Window Analysis | Walk-Forward Optimization |
| --- | --- | --- |
| Primary Goal | Adaptation to recent market data. | Validation of strategy robustness and parameter stability. |
| Overfitting Defense | Implicit; relies on the constant influx of new data. | Explicit; achieved through sequential out-of-sample testing. |
| Performance Metric | A single, continuous equity curve over the entire history. | A composite equity curve built only from out-of-sample periods. |
| Computational Cost | Lower; involves re-training on a single window at each step. | Higher; involves full optimization at each walk-forward step. |
| Market Assumption | Markets evolve gradually; recency is paramount. | Market regimes can shift; robustness across periods is key. |

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## What Is the Impact on Computational Resource Allocation?

From an operational standpoint, the resource requirements of these two methods are vastly different. A [rolling window](https://prime.greeks.live/area/rolling-window/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) analysis is computationally lean. At each step, the model is simply re-trained on the updated dataset. This can often be done quickly, even for complex models.

Walk-forward optimization is a far more demanding protocol. Each in-sample period requires a full optimization run, which might involve testing hundreds or thousands of parameter combinations. This process is repeated for every walk-forward step. For a ten-year backtest with monthly walk-forward steps, this could mean performing nearly 120 separate, full-scale optimizations.

This requires significant computational power and time, a strategic consideration for any trading desk or individual researcher. The trade-off is clear ▴ the higher computational budget is spent to gain a higher degree of confidence in the strategy’s future viability.

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## Execution

The theoretical distinctions between these two methods become tangible in their execution. The procedural protocols and the structure of the data analysis differ fundamentally, leading to different operational workflows and analytical outputs. Understanding these execution mechanics is essential for any practitioner seeking to build and validate a trading system.

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## Procedural Protocol for a Simple Rolling Window Analysis

The execution of a rolling window analysis is a linear and repetitive process. It is designed for continuous model updating with a focus on maintaining data currency.

- **Define Window Size**
Select a fixed duration for the lookback window (e.g. 252 trading days for one year). This choice is a critical parameter itself, balancing responsiveness against statistical significance.

- **Train Initial Model**
Using the first complete window of data, train the trading model and determine its parameters. For example, if the strategy is a moving average crossover, this step would identify the optimal moving average lengths for that initial period.

- **Generate Signal for Next Period**
Apply the trained model to the data at the end of the window to generate a trading signal or forecast for the next time step (e.g. the next day).

- **Roll Window Forward**
Advance the analysis by one time step. The oldest data point in the window is removed, and the newest data point is added. The window size remains constant.

- **Retrain and Repeat**
The model is retrained using the newly constituted data window. This updated model is then used to generate the next signal. This loop of rolling, retraining, and signaling continues for the entire duration of the historical data.

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## Procedural Protocol for Walk-Forward Optimization

Walk-forward optimization is a more complex, nested process. It is architected to create a series of independent validation events. This protocol was first detailed by Robert Pardo and is considered a high standard in strategy validation.

- **Define Window Sizes**
Two window sizes must be specified ▴ the in-sample (IS) training/optimization period and the out-of-sample (OOS) testing period. A common ratio is 4:1 or 5:1 for IS to OOS, for instance, 1000 bars for optimization and the subsequent 250 bars for testing.

- **Select Optimization Objective**
Define the metric the system will seek to maximize during the optimization phase, such as net profit, Sharpe ratio, or Calmar ratio.

- **Execute Walk-Forward Loop**

<li>
On the first IS window, perform a full optimization. Test all relevant parameter combinations and identify the single set that performs best according to the chosen objective function.

- 
Take the best parameter set from the IS period and apply it, without changes, to the subsequent OOS period.

- 
Record the performance metrics (e.g. profit, drawdown) for this OOS period. These are the only results that contribute to the final strategy evaluation.

- 
Roll the entire testing apparatus forward by the length of the OOS period. The previous OOS period now becomes part of the new IS period.

- 
Repeat this cycle of optimization, testing, and rolling until the end of the historical data is reached.

</li>
- **Aggregate Out-of-Sample Results**
Combine the recorded performance metrics from all OOS periods to construct a single, continuous out-of-sample equity curve. This aggregated result is the definitive measure of the strategy’s robustness.

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## How Does Data Segmentation Differ in Practice?

The most important difference in execution is how the data is used. A rolling window reuses data constantly, while walk-forward analysis maintains a strict separation between training and testing data at each step.

> A rolling window analysis offers continuous adaptation, whereas walk-forward optimization provides discrete, robust validation cycles.

The following table illustrates this fundamental structural divergence for a hypothetical 10-period dataset.

| Analysis Step | Rolling Window (Size=5) | WFO IS Window (Size=5) | WFO OOS Window (Size=2) |
| --- | --- | --- | --- |
| Step 1 | Periods 1-5 | Periods 1-5 (Optimize) | Periods 6-7 (Test) |
| Step 2 | Periods 2-6 | Periods 3-7 (Optimize) | Periods 8-9 (Test) |
| Step 3 | Periods 3-7 | Periods 5-9 (Optimize) | Period 10 (Test) |
| Step 4 | Periods 4-8 | N/A | N/A |

This visualization makes the distinction clear. In Step 2, the rolling window uses periods 2-6 for training. In the same step, the walk-forward protocol is optimizing on a completely different dataset (periods 3-7) to then test on unseen data (periods 8-9). This disciplined use of data is the essence of the walk-forward execution.

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## References

- Pardo, Robert. _The Evaluation and Optimization of Trading Strategies_. 2nd ed. John Wiley & Sons, 2008.

- Aronson, David. _Evidence-Based Technical Analysis ▴ Applying the Scientific Method and Statistical Inference to Trading Signals_. John Wiley & Sons, 2006.

- Bailey, David H. and Marcos Lopez de Prado. “The Strategy Approval Process ▴ A Test of Portfolio Manager Skill.” _Journal of Portfolio Management_, vol. 40, no. 5, 2014, pp. 109-119.

- Harris, Michael. “A Comprehensive Guide to Walk Forward Optimization.” _PyQuant News_, 2024.

- QuantInsti. “Walk-Forward Optimization ▴ How It Works, Its Limitations, and Backtesting Implementation.” _QuantInsti Blog_, 2025.

- QuantConnect. “Walk Forward Optimization.” _QuantConnect Documentation_, 2023.

- Hsu, Jason, and Brett W. Myers, and Ryan Kennedy. “The Dangers of Backtesting.” _Journal of Portfolio Management_, vol. 42, no. 5, 2016, pp. 60-72.

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## Reflection

Having examined the architecture of both rolling window analysis and walk-forward optimization, the essential question moves from “which is better” to a more personal inquiry. How does my current validation framework align with my tolerance for uncertainty and my system’s operational objectives? Does my process for strategy approval truly confront the possibility of random success, or does it implicitly favor adaptation over proven robustness?

The structure of a backtest is a choice, and that choice designs the character of the strategies that emerge from it. Viewing these protocols as components within a larger system of intelligence prompts a deeper consideration of the structural integrity of one’s entire quantitative research process.

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## Glossary

<a href="https://prime.greeks.live/question/how-does-the-collection-window-mechanism-in-modern-rfq-systems-enhance-fair-competition/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
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## Simple Rolling Window Analysis

The collection window enhances fair competition by creating a synchronized, sealed-bid auction that mitigates information leakage and forces price-based competition. </a><a href="https://prime.greeks.live/area/walk-forward-optimization/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
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## Walk-Forward Optimization

Meaning ▴ Walk-Forward Optimization defines a rigorous methodology for evaluating the stability and predictive validity of quantitative trading strategies. </a><a href="https://prime.greeks.live/area/rolling-window-analysis/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
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## Rolling Window Analysis

Meaning ▴ Rolling Window Analysis defines a computational technique that applies a specified function or calculation to a fixed-size subset of data, which then systematically advances through a larger dataset, generating a continuous series of localized insights. </a><a href="https://prime.greeks.live/area/historical-data/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![Sleek, layered surfaces represent an institutional grade Crypto Derivatives OS enabling high-fidelity execution. Circular elements symbolize price discovery via RFQ private quotation protocols, facilitating atomic settlement for multi-leg spread strategies in digital asset derivatives](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-trading-system-core.jpg)

## Historical Data

Meaning ▴ Historical Data refers to a structured collection of recorded market events and conditions from past periods, comprising time-stamped records of price movements, trading volumes, order book snapshots, and associated market microstructure details. </a><a href="https://prime.greeks.live/area/parameter-optimization/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
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## Parameter Optimization

Meaning ▴ Parameter Optimization refers to the systematic process of identifying the most effective set of configurable inputs for an algorithmic trading strategy, a risk model, or a broader financial system component. </a><a href="https://prime.greeks.live/area/in-sample-data/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
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## In-Sample Data

Meaning ▴ In-sample data refers to the specific dataset utilized for the training, calibration, and initial validation of a quantitative model or algorithmic strategy. </a><a href="https://prime.greeks.live/question/how-does-the-collection-window-mechanism-in-modern-rfq-systems-enhance-fair-competition/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
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## Window Analysis

The collection window enhances fair competition by creating a synchronized, sealed-bid auction that mitigates information leakage and forces price-based competition. </a><a href="https://prime.greeks.live/question/how-does-the-collection-window-mechanism-in-modern-rfq-systems-enhance-fair-competition/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![Angular metallic structures intersect over a curved teal surface, symbolizing market microstructure for institutional digital asset derivatives. This depicts high-fidelity execution via RFQ protocols, enabling private quotation, atomic settlement, and capital efficiency within a prime brokerage framework](https://options.greeks.live/wp-content/uploads/2025/07/precision-execution-for-institutional-digital-asset-derivatives-via-rfq-protocol-microstructure-optimization.jpg)

## Simple Rolling Window

The collection window enhances fair competition by creating a synchronized, sealed-bid auction that mitigates information leakage and forces price-based competition. </a><a href="https://prime.greeks.live/question/from-a-quantitative-perspective-how-is-rfq-price-quality-measured-beyond-simple-slippage/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![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)

## Simple Rolling

Measuring RFQ price quality beyond slippage requires quantifying the information leakage and adverse selection costs embedded in every quote. </a><a href="https://prime.greeks.live/question/what-are-the-key-differences-in-transparency-for-equity-versus-non-equity-rfqs-under-mifid-ii/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![Interlocking transparent and opaque geometric planes on a dark surface. This abstract form visually articulates the intricate Market Microstructure of Institutional Digital Asset Derivatives, embodying High-Fidelity Execution through advanced RFQ protocols](https://options.greeks.live/wp-content/uploads/2025/07/geometric-abstraction-of-institutional-digital-asset-derivatives-market-microstructure-driving-high-fidelity-execution-via-principal-rfq-protocols-and-optimal-liquidity-aggregation.jpg)

## Equity Curve

MiFID II tailors RFQ transparency by asset class, mandating high visibility for equities while shielding non-equity liquidity sourcing. </a><a href="https://prime.greeks.live/area/rolling-window/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![A sleek, layered structure with a metallic rod and reflective sphere symbolizes institutional digital asset derivatives RFQ protocols. It represents high-fidelity execution, price discovery, and atomic settlement within a Prime RFQ framework, ensuring capital efficiency and minimizing slippage](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-protocols-high-fidelity-execution-via-prime-os.jpg)

## Rolling Window

Meaning ▴ A Rolling Window defines a fixed-size subset of sequential data points, typically from a time series, which continuously advances through the dataset, enabling the calculation of metrics over a consistent, recent period. </a>

**Tags:** [Parameter Optimization](https://options.greeks.live/area/parameter-optimization/), [Walk-Forward Optimization](https://options.greeks.live/area/walk-forward-optimization/), [Data Segmentation](https://options.greeks.live/area/data-segmentation/), [Out-of-Sample Testing](https://options.greeks.live/area/out-of-sample-testing/), [Rolling Window Analysis](https://options.greeks.live/area/rolling-window-analysis/), [Historical Data](https://options.greeks.live/area/historical-data/)	

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Walk-forward analysis mitigates overfitting by systematically validating a strategy on unseen data, ensuring its robustness.

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A robust SOR model is defined by its predictive accuracy on unseen data, achieved by systematically penalizing complexity.

- 
### [What Are the Practical Challenges of Implementing the Deflated Sharpe Ratio in a Large Investment Firm?](https://options.greeks.live/question/what-are-the-practical-challenges-of-implementing-the-deflated-sharpe-ratio-in-a-large-investment-firm/)
![A sleek, dark, angled component, representing an RFQ protocol engine, rests on a beige Prime RFQ base. Flanked by a deep blue sphere representing aggregated liquidity and a light green sphere for multi-dealer platform access, it illustrates high-fidelity execution within digital asset derivatives market microstructure, optimizing price discovery.](https://options.greeks.live/wp-content/uploads/2025/07/advanced-rfq-system-for-digital-asset-derivatives-price-discovery.jpg)

Implementing the Deflated Sharpe Ratio is an architectural challenge of integrating data governance with quantitative rigor.

- 
### [How Does the Governance of AI-Based Trading Models Differ from Traditional Quantitative Models?](https://options.greeks.live/question/how-does-the-governance-of-ai-based-trading-models-differ-from-traditional-quantitative-models/)
![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)

The governance of AI trading models shifts from static, pre-deployment validation to continuous, dynamic behavioral oversight and risk containment.

- 
### [How Do CCPs Calculate Initial Margin for Complex Derivatives Portfolios?](https://options.greeks.live/question/how-do-ccps-calculate-initial-margin-for-complex-derivatives-portfolios/)
![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)

CCPs compute initial margin by modeling a portfolio's potential future loss to a high confidence level, securing the system against default.

- 
### [What Are the Primary Operational Challenges Firms Face When Migrating from Span to Var?](https://options.greeks.live/question/what-are-the-primary-operational-challenges-firms-face-when-migrating-from-span-to-var/)
![A sleek, multi-layered digital asset derivatives platform highlights a teal sphere, symbolizing a core liquidity pool or atomic settlement node. The perforated white interface represents an RFQ protocol's aggregated inquiry points for multi-leg spread execution, reflecting precise market microstructure.](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-core-execution-system.jpg)

Migrating from SPAN to VaR is an architectural shift from deterministic calculations to a data-intensive, probabilistic risk engine.

- 
### [How Can Data Snooping Bias Distort the Results of a Trading Strategy Backtest?](https://options.greeks.live/question/how-can-data-snooping-bias-distort-the-results-of-a-trading-strategy-backtest/)
![A sophisticated metallic mechanism, split into distinct operational segments, represents the core of a Prime RFQ for institutional digital asset derivatives. Its central gears symbolize high-fidelity execution within RFQ protocols, facilitating price discovery and atomic settlement. This system manages fragmented liquidity and optimizes portfolio margin for block trades.](https://options.greeks.live/wp-content/uploads/2025/07/precision-rfq-engine-for-institutional-digital-asset-derivatives-trading.jpg)

Data snooping bias manufactures a false history of success by fitting a model to noise, creating an illusion of predictive power.

- 
### [How Does Window Size Selection Impact Walk-Forward Optimization Results?](https://options.greeks.live/question/how-does-window-size-selection-impact-walk-forward-optimization-results/)
![A precision execution pathway with an intelligence layer for price discovery, processing market microstructure data. A reflective block trade sphere signifies private quotation within a dark pool. This visualizes high-fidelity execution and capital efficiency for institutional digital asset derivatives via RFQ protocols.](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-engine-precision-execution.jpg)

Window size selection dictates a trading system's adaptive capacity, directly impacting risk-adjusted returns in dynamic crypto markets.

- 
### [How Can an Institution Effectively Backtest and Validate a Machine Learning Trading Model?](https://options.greeks.live/question/how-can-an-institution-effectively-backtest-and-validate-a-machine-learning-trading-model/)
![A precision metallic instrument with a black sphere rests on a multi-layered platform. This symbolizes institutional digital asset derivatives market microstructure, enabling high-fidelity execution and optimal price discovery across diverse liquidity pools. Essential for managing counterparty risk and multi-leg spread strategies within the Prime RFQ protocol intelligence layer.](https://options.greeks.live/wp-content/uploads/2025/07/prime-os-rfq-protocol-engine-component-digital-asset-derivatives-market-microstructure.jpg)

A robust validation architecture systematically proves a model's structural edge beyond the illusion of a single, overfit backtest.

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---

**Original URL:** https://options.greeks.live/question/what-are-the-key-differences-between-walk-forward-optimization-and-a-simple-rolling-window-analysis/
