V.18:4 (52-61):Are Your Profits Robust? by Steve Notis
Product Description
Are Your Profits Robust? by Steve Notis
Your trading system probably has a set of parameters you think are the most profitable. But is it the most consistent? Here’s a technique to find the most robust set that will give you regular profits.
For those who use trading systems, the most important concern should not be to find parameters that yield the greatest profit; rather, it should be to find the most robust parameters. Anyone can tweak a system until it shows startling results, but unless those parameters are robust, they will not hold up in real-world trading.
Several testing methods can be used to increase a system’s robustness. The first, and simplest, is to test over long periods. This assures that the test includes a variety of market conditions. The second method is the walk-forward, or blind, data test. This typically consists of optimizing trading parameters over a long period, but stopping short of the present. Finally, the best parameters are tested over the remaining, nonoptimized, data. This is also referred to as walk-forward, blind, virgin, out-of-sample, and it has even been referred to as real-time testing.
If the optimized parameters work well over the blind data, then it can be argued that the parameters are robust and likely to continue to work for some time into the future. Since the final test is run without the benefit of hindsight or optimization, it’s as close to real trading as you can simulate with historical data. However, don’t assume that the most profitable parameters are the best parameters - meaning those that will be the most profitable in the future.
Your trading system probably has a set of parameters you think are the most profitable. But is it the most consistent? Here’s a technique to find the most robust set that will give you regular profits.
For those who use trading systems, the most important concern should not be to find parameters that yield the greatest profit; rather, it should be to find the most robust parameters. Anyone can tweak a system until it shows startling results, but unless those parameters are robust, they will not hold up in real-world trading.
Several testing methods can be used to increase a system’s robustness. The first, and simplest, is to test over long periods. This assures that the test includes a variety of market conditions. The second method is the walk-forward, or blind, data test. This typically consists of optimizing trading parameters over a long period, but stopping short of the present. Finally, the best parameters are tested over the remaining, nonoptimized, data. This is also referred to as walk-forward, blind, virgin, out-of-sample, and it has even been referred to as real-time testing.
If the optimized parameters work well over the blind data, then it can be argued that the parameters are robust and likely to continue to work for some time into the future. Since the final test is run without the benefit of hindsight or optimization, it’s as close to real trading as you can simulate with historical data. However, don’t assume that the most profitable parameters are the best parameters - meaning those that will be the most profitable in the future.
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