# How Can Walk-Forward Optimization Prevent the Overfitting of a Slippage Model to Historical Data? ▴ Question

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

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## Concept

The central challenge in constructing a slippage model is not its ability to describe the past, but its capacity to accurately predict the immediate future. Any model can be forced to perfectly fit a historical dataset; this is a trivial exercise in curve-fitting. The institutional imperative, however, is to build a system that remains robust and predictive when deployed into live, evolving [market conditions](https://prime.greeks.live/question/in-what-market-conditions-should-a-waterfall-rfq-protocol-be-deployed-over-a-simultaneous-one/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite). The failure to do so results in a model that has memorized the noise of [historical data](https://prime.greeks.live/area/historical-data/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite), mistaking random fluctuations for durable, underlying market principles.

This phenomenon is known as [overfitting](https://prime.greeks.live/area/overfitting/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite). An overfit [slippage model](https://prime.greeks.live/area/slippage-model/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) is a latent liability, appearing functional in backtests yet failing at the moment of execution by providing inaccurate cost estimates that compromise entire trading strategies.

A slippage model’s primary function is to forecast the cost of a transaction, which is the deviation between the intended execution price and the realized execution price. This cost is a function of [market microstructure](https://prime.greeks.live/area/market-microstructure/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) variables like liquidity, volatility, and order size. When a model becomes overfit, it has created an overly complex and specific relationship between these variables based on the unique characteristics of a finite historical data window.

It learns the idiosyncratic behavior of a specific market period, such as the liquidity patterns of a particular quarter, and treats it as a universal law. The model becomes brittle, its [predictive power](https://prime.greeks.live/question/how-can-backtesting-be-used-to-validate-a-slippage-models-predictive-accuracy/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) collapsing when confronted with new data that does not conform to the precise patterns it has memorized.

> Walk-forward optimization systematically combats this model fragility.

[Walk-forward optimization](https://prime.greeks.live/area/walk-forward-optimization/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) (WFO) provides a robust framework to prevent this failure mode. It operates on a foundational principle of sequential validation. The system treats historical data not as a single, monolithic block to be mastered, but as a series of evolving market regimes. WFO works by optimizing the slippage model’s parameters on a designated segment of past data, known as the “in-sample” period, and then immediately testing its predictive accuracy on a subsequent, unseen segment of data ▴ the “out-of-sample” period.

This cycle of optimization and validation is then rolled forward through time, continuously re-calibrating and re-testing the model against new market conditions. This process builds a model that is compelled to demonstrate its validity on data it has not seen before, ensuring it learns the durable, repeatable patterns of market impact rather than the ephemeral noise of a single historical period. The result is a more resilient and reliable predictive engine for transaction costs.

![Abstract layers visualize institutional digital asset derivatives market microstructure. Teal dome signifies optimal price discovery, high-fidelity execution](https://options.greeks.live/wp-content/uploads/2025/08/high-fidelity-digital-asset-derivatives-rfq-execution-protocol-layers-and-market-microstructure-optimization.jpg)

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## Strategy

Adopting walk-forward optimization is a strategic commitment to building dynamic, adaptive models over static, decaying ones. It moves the objective from creating a single, “perfectly” calibrated model to engineering a resilient process for continuous [model validation](https://prime.greeks.live/area/model-validation/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) and re-calibration. This strategic shift acknowledges the non-stationary nature of financial markets; the rules governing liquidity and price impact are themselves in a constant state of flux.

A static model, no matter how well it fits past data, is an architecture destined for obsolescence. The WFO framework, in contrast, is an architecture designed for adaptation.

![Interconnected teal and beige geometric facets form an abstract construct, embodying a sophisticated RFQ protocol for institutional digital asset derivatives. This visualizes multi-leg spread structuring, liquidity aggregation, high-fidelity execution, principal risk management, capital efficiency, and atomic settlement](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-protocol-for-multi-leg-spread-execution-and-optimal-liquidity-aggregation.jpg)

## Framework Comparison Static Backtesting versus Walk Forward Validation

The strategic value of WFO is best understood when contrasted with traditional, [static backtesting](https://prime.greeks.live/question/what-are-the-primary-differences-between-backtesting-on-a-clob-versus-an-rfq-protocol/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) methodologies. Static backtesting optimizes model parameters over an entire historical dataset, a method that is highly susceptible to overfitting and provides a misleading sense of future performance. WFO offers a more rigorous and realistic assessment protocol.

| Evaluation Criterion | Static Backtesting Methodology | Walk-Forward Optimization Protocol |
| --- | --- | --- |
| Data Utilization | Uses the entire historical dataset for both optimization and validation, leading to data leakage. | Strictly separates data into sequential in-sample (optimization) and out-of-sample (validation) windows. |
| Parameter Stability | Generates a single set of “optimal” parameters assumed to be perpetually valid. | Produces a series of parameter sets, revealing how model inputs must adapt to changing markets. |
| Performance Evaluation | Measures performance on the same data used for training, resulting in inflated and unreliable metrics. | Measures performance exclusively on unseen out-of-sample data, providing a realistic expectation of future results. |
| Robustness to Regime Shifts | Fails to account for changes in market dynamics, leading to catastrophic model failure during regime shifts. | Systematically tests and re-calibrates the model across different time periods, inherently building robustness. |
| Predictive Power Assessment | Provides a measure of historical fit, which has low correlation with future predictive accuracy. | Provides a direct measure of the model’s forward-looking predictive power by aggregating out-of-sample performance. |

![Abstract forms representing a Principal-to-Principal negotiation within an RFQ protocol. The precision of high-fidelity execution is evident in the seamless interaction of components, symbolizing liquidity aggregation and market microstructure optimization for digital asset derivatives](https://options.greeks.live/wp-content/uploads/2025/08/institutional-digital-asset-derivatives-rfq-protocol-execution-engine.jpg)

## What Is the Optimal Ratio for in Sample to out of Sample Windows?

The configuration of the in-sample and out-of-sample window lengths is a critical strategic decision in the WFO architecture. This ratio governs the trade-off between model stability and adaptability. A longer in-sample period provides a larger dataset for optimization, potentially leading to a more statistically stable model. However, it also means the model adapts more slowly to recent changes in market structure.

Conversely, a shorter in-sample period makes the model more responsive to new data, but it may overreact to short-term noise. The length of the out-of-sample window determines how frequently the model’s performance is re-evaluated.

> The selection of window sizes directly impacts the conclusions drawn from the walk-forward analysis.

The ideal ratio is not universal; it depends on the specific characteristics of the asset and market being modeled. For a highly liquid, stable market, a longer in-sample period (e.g. 24 months) with a shorter [out-of-sample period](https://prime.greeks.live/question/how-does-market-illiquidity-affect-the-validity-of-a-close-out-amount-calculation/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) (e.g. 3 months) might be effective.

For a volatile asset class experiencing rapid changes in its microstructure, a shorter in-sample period (e.g. 6 months) and a more frequent re-evaluation (e.g. a 1-month out-of-sample period) may be necessary to maintain the model’s relevance.

- **Stability-Focused Configuration** ▴ A large in-sample to out-of-sample ratio (e.g. 8:1) prioritizes learning long-term, durable market patterns. This is suitable for models where the underlying mechanics of slippage are believed to be relatively constant.

- **Adaptability-Focused Configuration** ▴ A small in-sample to out-of-sample ratio (e.g. 3:1) prioritizes responsiveness to recent market conditions. This is critical in markets with high innovation, regulatory changes, or shifting liquidity profiles.

- **Balanced Configuration** ▴ A moderate ratio (e.g. 4:1 or 5:1) attempts to balance the need for sufficient historical data with the ability to adapt to new information. This is often a practical starting point for analysis.

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## Execution

The execution of a walk-forward optimization for a slippage model is a systematic, multi-stage process. It transforms the abstract concept of sequential validation into a concrete, quantifiable, and repeatable engineering protocol. This protocol is designed to produce not only an optimized model but also a clear diagnostic of its robustness over time.

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## Procedural Framework for Walk Forward Optimization

The implementation follows a disciplined, iterative cycle. The objective is to simulate, as closely as possible, how a model would be deployed and maintained in a live trading environment ▴ periodically re-calibrating it on recent data and assessing its performance on new, incoming data.

- **Data Aggregation and Segmentation** ▴ The initial step is the collection and preparation of high-fidelity historical data. For a slippage model, this includes trade execution records, order book snapshots, and market data (e.g. volume, volatility). The total dataset is then divided into a number of sequential segments. For instance, a 5-year dataset might be divided into 10 segments of 6 months each.

- **Architecture Definition** ▴ Define the structure of the rolling window. This involves specifying the length of the in-sample (IS) and out-of-sample (OOS) periods. A common configuration is to use 4 segments (24 months) for the IS period and 1 segment (6 months) for the OOS period.

- **Initial Optimization Cycle (Window 1)** ▴ The slippage model’s parameters are optimized using the data from the first IS period (e.g. months 1-24). The goal of this optimization is to find the parameter values that minimize a specific objective function, such as the Mean Squared Error (MSE) between the model’s predicted slippage and the actual slippage observed in the IS data.

- **First Validation (Window 1)** ▴ The parameter set derived from the initial optimization is then applied to the first OOS period (e.g. months 25-30). The model’s predictions are compared against the actual outcomes in this unseen data, and its performance is recorded. This is the first true test of the model’s predictive power.

- **The Rolling Mechanism** ▴ The entire window is shifted forward in time by the length of the OOS period. The new IS period now covers months 7-30, and the new OOS period covers months 31-36.

- **Iterative Re-Optimization and Validation** ▴ Steps 3 and 4 are repeated for this new window. The model is re-optimized on the updated IS data, and the resulting parameters are validated on the new OOS data. This process continues until the entire dataset has been traversed.

- **Performance Aggregation** ▴ The final step involves collating the performance metrics from all the individual OOS periods. This aggregated result provides a comprehensive assessment of the model’s strategy, showing how it would have performed in real-time as it was periodically re-calibrated.

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## How Are the out of Sample Results Aggregated to Judge Model Performance?

The individual performance reports from each OOS window are stitched together to create a single, continuous performance record. This aggregated report is the ultimate output of the WFO process. It represents a realistic simulation of the model’s live performance, stripped of any optimistic bias from in-sample fitting.

> A walk-forward analysis forces a strategy to prove itself repeatedly across different market conditions.

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## Walk Forward Performance Report Example

| Window ID | In-Sample Period | Out-of-Sample Period | Optimized Parameter A | Optimized Parameter B | OOS Prediction Error (MSE) |
| --- | --- | --- | --- | --- | --- |
| 1 | 2020-01 to 2021-12 | 2022-01 to 2022-06 | 0.45 | 1.52 | 0.0012 |
| 2 | 2020-07 to 2022-06 | 2022-07 to 2022-12 | 0.48 | 1.49 | 0.0015 |
| 3 | 2021-01 to 2022-12 | 2023-01 to 2023-06 | 0.51 | 1.55 | 0.0013 |
| 4 | 2021-07 to 2023-06 | 2023-07 to 2023-12 | 0.49 | 1.60 | 0.0018 |
| 5 | 2022-01 to 2023-12 | 2024-01 to 2024-06 | 0.53 | 1.58 | 0.0014 |

From this aggregated data, a composite “equity curve” or a cumulative error metric can be plotted. This provides a powerful visualization of the model’s robustness. A model that consistently performs well across all OOS periods is considered robust.

A model whose performance degrades significantly in certain periods may have hidden vulnerabilities to specific market regimes, a critical insight that static backtesting would completely miss. This granular, out-of-sample performance data is the core deliverable of the WFO execution protocol.

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## References

- Bailey, David H. and Marcos López de Prado. “The Strategy Approval Process ▴ A Test of Investment Skill.” Journal of Portfolio Management, vol. 40, no. 5, 2014, pp. 109-119.

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

- Hansen, Peter R. and Allan Timmermann. “Choice of Sample Split in Out-of-Sample Forecast Evaluation.” SSRN Electronic Journal, 2012.

- White, Halbert. “A Reality Check for Data Snooping.” Econometrica, vol. 68, no. 5, 2000, pp. 1097-1126.

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

- Chan, Ernest P. Quantitative Trading ▴ How to Build Your Own Algorithmic Trading Business. John Wiley & Sons, 2008.

- López de Prado, Marcos. Advances in Financial Machine Learning. John Wiley & Sons, 2018.

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## Reflection

The implementation of a walk-forward optimization protocol is more than a technical procedure; it is a statement of operational philosophy. It reflects a core understanding that financial markets are dynamic systems and that any model intended to navigate them must possess an architecture of adaptation. The process forces a continuous confrontation with reality, testing the model not against the comfortable familiarity of the past, but against the unforgiving uncertainty of the future.

Ultimately, the objective extends beyond building a single, robust slippage model. The true strategic asset is the creation of an institutional framework for model validation. This framework becomes a permanent capability, a system for quantifying the lifecycle of any quantitative model and managing its inevitable decay.

Viewing model development through this lens transforms the challenge from a one-time search for perfect parameters into an ongoing process of disciplined, evidence-based adaptation. This is the foundation upon which durable, institutional-grade quantitative operations are built.

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## Glossary

<a href="https://prime.greeks.live/question/in-what-market-conditions-should-a-waterfall-rfq-protocol-be-deployed-over-a-simultaneous-one/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
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## Market Conditions

A waterfall RFQ should be deployed in illiquid markets to control information leakage and minimize the market impact of large trades. </a><a href="https://prime.greeks.live/area/historical-data/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![A translucent blue sphere is precisely centered within beige, dark, and teal channels. This depicts RFQ protocol for digital asset derivatives, enabling high-fidelity execution of a block trade within a controlled market microstructure, ensuring atomic settlement and price discovery on a Prime RFQ](https://options.greeks.live/wp-content/uploads/2025/07/precision-execution-pathway-for-institutional-digital-asset-derivatives-leveraging-rfq-protocols-and-high-fidelity-market-microstructure.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/slippage-model/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![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)

## 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/overfitting/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![A futuristic apparatus visualizes high-fidelity execution for digital asset derivatives. A transparent sphere represents a private quotation or block trade, balanced on a teal Principal's operational framework, signifying capital efficiency within an RFQ protocol](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-execution-via-prime-os.jpg)

## Overfitting

Meaning ▴ Overfitting denotes a condition in quantitative modeling where a statistical or machine learning model exhibits strong performance on its training dataset but demonstrates significantly degraded performance when exposed to new, unseen data. </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><a href="https://prime.greeks.live/question/how-can-backtesting-be-used-to-validate-a-slippage-models-predictive-accuracy/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
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## Predictive Power

Backtesting validates a slippage model by empirically stress-testing its predictive accuracy against historical market and liquidity data. </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">
![A precision-engineered system with a central gnomon-like structure and suspended sphere. This signifies high-fidelity execution for digital asset derivatives](https://options.greeks.live/wp-content/uploads/2025/07/precision-engine-for-institutional-digital-asset-derivatives-rfq.jpg)

## 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/model-validation/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![Stacked modular components with a sharp fin embody Market Microstructure for Digital Asset Derivatives. This represents High-Fidelity Execution via RFQ protocols, enabling Price Discovery, optimizing Capital Efficiency, and managing Gamma Exposure within an Institutional Prime RFQ for Block Trades](https://options.greeks.live/wp-content/uploads/2025/07/modular-system-for-institutional-digital-asset-derivatives-rfq-protocol-optimization.jpg)

## Model Validation

Meaning ▴ Model Validation is the systematic process of assessing a computational model's accuracy, reliability, and robustness against its intended purpose. </a><a href="https://prime.greeks.live/question/what-are-the-primary-differences-between-backtesting-on-a-clob-versus-an-rfq-protocol/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
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## Static Backtesting

A CLOB backtest models order physics in a public system; an RFQ backtest models dealer behavior in a private, fragmented one. </a><a href="https://prime.greeks.live/question/how-does-market-illiquidity-affect-the-validity-of-a-close-out-amount-calculation/?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)

## Out-Of-Sample Period

Market illiquidity degrades a close-out amount's validity by replacing executable prices with ambiguous, model-dependent valuations. </a>

**Tags:** [Parameter Tuning](https://options.greeks.live/area/parameter-tuning/), [Walk-Forward Optimization](https://options.greeks.live/area/walk-forward-optimization/), [Algorithmic Trading](https://options.greeks.live/area/algorithmic-trading/), [Quantitative Finance](https://options.greeks.live/area/quantitative-finance/), [Slippage Model](https://options.greeks.live/area/slippage-model/), [Market Microstructure](https://options.greeks.live/area/market-microstructure/)	

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Predictive analytics fortifies block trade data integrity by proactively identifying and mitigating informational risks in volatile markets.

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### [How Does Smart Trading Use Predictive Analytics, If Any?](https://options.greeks.live/question/how-does-smart-trading-use-predictive-analytics-if-any/)
![A precision-engineered teal metallic mechanism, featuring springs and rods, connects to a light U-shaped interface. This represents a core RFQ protocol component enabling automated price discovery and high-fidelity execution. It signifies a Principal's operational framework for institutional block trades, multi-leg spreads, and optimizing latency within a Crypto Derivatives OS.](https://options.greeks.live/wp-content/uploads/2025/07/rfq-execution-engine-component-for-institutional-digital-asset-derivatives.jpg)

Smart trading uses predictive analytics to forecast market behavior, enabling automated, data-driven execution for a superior operational edge.

- 
### [What Are the Best Practices for the Software Development Lifecycle of a Trading Algorithm?](https://options.greeks.live/question/what-are-the-best-practices-for-the-software-development-lifecycle-of-a-trading-algorithm/)
![Polished metallic rods, spherical joints, and reflective blue components within beige casings, depict a Crypto Derivatives OS. This engine drives institutional digital asset derivatives, optimizing RFQ protocols for high-fidelity execution, robust price discovery, and capital efficiency within complex market microstructure via algorithmic trading.](https://options.greeks.live/wp-content/uploads/2025/07/optimized-rfq-protocol-engine-driving-institutional-digital-asset-derivatives-market-microstructure-execution.jpg)

A disciplined SDLC transforms a trading idea into a resilient, risk-managed system, which is the core of institutional success.

- 
### [How Do You Validate the Performance of a Market Impact Model to Avoid Overfitting in Production?](https://options.greeks.live/question/how-do-you-validate-the-performance-of-a-market-impact-model-to-avoid-overfitting-in-production/)
![A precision-engineered institutional digital asset derivatives execution system cutaway. The teal Prime RFQ casing reveals intricate market microstructure. Bright blue internal elements represent dynamic liquidity pools, enabling high-fidelity execution and efficient RFQ protocols for multi-leg spread strategies.](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-prime-os-rfq-protocol-core-engine.jpg)

Validating a market impact model requires a forward-looking, multi-layered defense to ensure it generalizes beyond historical noise.

- 
### [Can Machine Learning Models Reliably Predict Order Flow Toxicity to Adjust Quote Durations?](https://options.greeks.live/question/can-machine-learning-models-reliably-predict-order-flow-toxicity-to-adjust-quote-durations/)
![A sleek, futuristic apparatus featuring a central spherical processing unit flanked by dual reflective surfaces and illuminated data conduits. This system visually represents an advanced RFQ protocol engine facilitating high-fidelity execution and liquidity aggregation for institutional digital asset derivatives. It embodies precise price discovery, multi-leg spread processing, and atomic settlement within a Prime RFQ framework.](https://options.greeks.live/wp-content/uploads/2025/07/prime-os-digital-asset-options-rfq-liquidity-aggregation-interface.jpg)

ML models can reliably predict order flow toxicity, enabling the dynamic adjustment of quote durations to mitigate adverse selection risk.

- 
### [What Are the Quantitative Models for Predicting Block Trade Price Impact?](https://options.greeks.live/question/what-are-the-quantitative-models-for-predicting-block-trade-price-impact/)
![A sleek Prime RFQ interface features a luminous teal display, signifying real-time RFQ Protocol data and dynamic Price Discovery within Market Microstructure. A detached sphere represents an optimized Block Trade, illustrating High-Fidelity Execution and Liquidity Aggregation for Institutional Digital Asset Derivatives.](https://options.greeks.live/wp-content/uploads/2025/08/advanced-rfq-protocol-engine-for-institutional-digital-asset-derivatives.jpg)

Quantifying block trade price impact with models empowers institutional principals to optimize execution, preserving capital and enhancing strategic advantage.

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---

**Original URL:** https://options.greeks.live/question/how-can-walk-forward-optimization-prevent-the-overfitting-of-a-slippage-model-to-historical-data/
