How to Prepare Your Trading Strategy for Live Markets 📈 Learn Robustness Testing🔍

Launching a trading strategy into the live market is one of the most significant moments for any trader. Whether you're a novice or a seasoned expert, the transition from back-testing to live trading can bring feelings of doubt and risk. However, it doesn't have to be that way. Proper robustness testing ensures that your strategy is ready to face the live market with high confidence.

What is Robustness Testing in Trading?

Robustness testing refers to the process of verifying whether a trading strategy will continue to perform as expected on unseen data in the future based on historical backtests. While nothing can guarantee future success, the primary goal of robustness testing is to determine whether the strategy is likely to perform within acceptable ranges and withstand the market’s fluctuations.

We achieve this by varying the strategy's variables and measuring the effect on strategy metrics. If the effects are minimal and within an acceptable range, then we can assume that the strategy is robust. As algo traders, quantifying acceptable metrics helps us to build a repeatable workflow that can be automated.

Understand the Limitations of Backtesting

Back-testing is the first step in developing a trading strategy, but it often paints an overly optimistic picture. When testing historical data, there's no certainty that the conditions that were present in the past will repeat in the same manner.

It's essential to realize that even with a perfect backtest, the strategy might underperform in the future due to changing market conditions, unexpected events, or simply because the statistical edge doesn't apply to future data.

Jim Simons' Cautionary Example

Jim Simons, the legendary founder of the Medallion Fund with +65% annual returns over 30 years, once considered turning off his systems during the financial crisis, despite consistently outperforming everyone on Wall Street by a mile. Even he recognized the uncertainty that comes with relying on past performance as a guarantee for future results.

This cautionary tale serves as a reminder that robustness testing isn't about finding certainty but about stacking the probabilities in your favor.

"There is nothing on Earth that can guarantee the strategy will perform in the future the same as it did in the backtest."

Single Variable Strategy

Let’s begin with a basic moving average strategy to explain how single variable robustness testing works.

• Market: S&P500 ETF

• Data: 1995-2024

• Entry: When the price closes above the 200-day simple moving average

• Exit: When the price closes below the 200-day simple moving average

To test the strategy's robustness, we modify the variable values (the 200-day moving average) up and down by 25% in 5% intervals and observe the effect on the strategy metrics. For this test, we’ll focus on Net Profit as our key metric (Fitness).

What to Look For?

When testing the sensitivity of a strategy to changing environments (i.e., unseen data), you want to identify an area where the change from the highest high to the lowest low in your metric (Net Profit) is less than 25%, within 25% change in variable value. Once you find that area, pick the middle value as your robust strategy parameter.

Two Variables Strategy

Sticking with the simple moving average (SMA) strategy, let's now add another moving average to create a strategy with two variables.

Entry: When the 50-day SMA crosses above the 200-day SMA
Exit: When the 50-day SMA crosses below the 200-day SMA

We now adjust both variables up and down by 25%, again in 5% intervals. Instead of a bar chart, the result will be represented as a 3D surface where the X and Y axes are the variables (Short and Long SMA) and the Z axis is the fitness function (Net Profit).

As before, the goal is to find an area where the fitness function doesn't change more than 25% within 25% of each variable. Once this stable area is identified, choose the middle point as your robust strategy parameter.

Strategies With More Than Two Variables

Strategies with more than two variables are harder to visualize, so we need to use more advanced methods such as the Walk Forward Matrix (WFM), System Parameter Permutation (SPP), Sequential Optimization or other statistical workflows.

Each one of the methods mentioned has their pros and cons but follows the same principle: vary the strategy’s variables by a certain percentage and measure the strategy’s metrics to ensure they stay within defined limits.

Here’s an example of a three-variable strategy:

• Entry: When the 10-day SMA > 50-day SMA > 200-day SMA

• Exit: When the 10-day SMA < 50-day SMA < 200-day SMA

The image below shows StrategyQuant X's implementation of System Parameter Permutation (SPP) for this strategy.

"You need a portfolio of strategies across various instruments for higher long-term confidence."

The Role of Portfolio Diversification in Robustness

Another critical aspect of strategy robustness is portfolio diversification. Even if a strategy passes all robustness tests, relying solely on one strategy is risky. Diversifying across multiple strategies and asset classes reduces the risk of a single strategy’s failure.

By diversifying your trading portfolio, you also reduce the likelihood that one or two strategies’ underperformance will severely impact your overall returns. This layered approach to robustness adds another level of confidence when deploying strategies in the live market.

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