Although most quantitative traders spend most of their time on signal research and backtesting, the most important step in trading strategy development is robustness testing. A fundamental risk of backtesting is overfitting—where a strategy is too finely tuned to past data and fails to generalize in live conditions. Utilizing different types of robustness testing helps reduce the risk of overfitting. However, robustness testing has a multitude of different methods and options, so how do you know what methods are most effective?

Monte Carlo Simulation:

Monte Carlo simulation is a statistical method used to evaluate the potential performance of a trading strategy by simulating a wide range of possible market scenarios. It does this by randomly shuffling trade sequences, altering market conditions, or adjusting parameter values to introduce variability. The primary benefits include:

• Assessing drawdown risk: Traders can analyze worst-case scenarios by running thousands of simulations.

• Understanding variability: By observing how performance metrics shift across different simulations, traders can gauge how sensitive a strategy is to market fluctuations.

• Reducing overfitting risk: If a strategy performs consistently well across multiple randomized scenarios, it is more likely to be robust in live trading.

I find that optimizing a backtest results in an artifically low maximum drawdown relative to the drawdown realized in live trading. This occurs because optimizing to maximize a fitness functions typically occurs when maximum drawdowns are lowest. I use monte carlo analysis to understand what type of drawdown can be expected in live trading. These drawdowns can be 1.5x-2x as much as the backteset typically shows. This is important to set expectations of what type of drawdown will occur in live trading to endure and continue to trade the strategy.

Walk Forward Analysis:

Walk Forward Analysis is another important robustness testing method that involves dividing historical data into multiple segments. The process includes:

1. Training phase: Optimizing strategy parameters on a past segment of data.

2. Validation phase: Testing the optimized strategy on unseen future data.

3. Rolling forward: Repeating the process by shifting the time window forward.

This method simulates real trading conditions by continuously updating the strategy, ensuring it adapts to changing market environments. A robust strategy should perform well across multiple Walk Forward segments rather than excelling in only one specific period.

Different Methodologies of Walk Forward Analysis

There are different approaches to Walk Forward Analysis, each with its own benefits:

• Anchored Walk Forward: The training data remains fixed, and only the validation window moves forward. This method helps strategies retain a long-term perspective while testing their adaptability to newer market conditions.

• Rolling Walk Forward: Both the training and validation windows move forward together, maintaining a fixed sample size. This ensures the strategy remains updated with the most recent market conditions without becoming overly reliant on outdated data.

Although there are many preferences regarding the window selection, I find that testing a variety of window lengths in both anchord and rolling walk fowards provides the most comprehensive analysis. Some traders will reoptimize strategies from the walk-forward test as shown in the window, while others will use walk-forward analysis to assess robustness of parameters across different time periods.

I use walk-forward analysis differently for equity index futures strategies and for commodity strategies. For equity index futures strategies, I will typically utilize a rolling walk-forward analysis and reoptimize parameters based on a 2-3 year window. For commodity strategies, I will utilize a variety of walk-forward anaysis in order to find parameter stability and robustness over a large number of tests and I typically do not reoptimize parameters. I feel that the equity markets are much more dynamic in changing to the market sentiment and need to be reoptimized over shorter periods of time, while the commodity markets are less affected by market sentiment and retail participants.

Other Ways to Test Robustness:

There are a lot of other small tests for robustness which are easier to interpret and simpler to implement:

1. Test the strategy in other markets to see if performance holds up.

2. Test the strategy on different time frames to determine relative performance.

3. Look for ‘plateaus’ in parameters to assess robustness. You should be looking for parameters where the neighboring parameters provide similar performance.

Ironically enough, robustness testing seems to be just as much of an art as a science. There is no one silver bullet metric that determines the robustness of your strategy, but utilizing a variety of robustness testing can provide a more wholistic view of robustiness of trading strategies.