See below..... EIGHT SIMPLE RULES to avoid over-optimizing a trading strategy KeyPoint Market Analyticsâ trading strategy seeks to uncover general market character. Experience, along with a few simple guidelines helps us to accomplish this. Optimization is the process of determining âoptimal valuesâ for the variable inputs that go into a trading strategy. These inputs include indicator parameters, time values, or price functions. Optimization of a trading strategy is necessary to determine precisely what is valid and what is not. Every trading strategy must therefore be optimized to some extent. The most significant risk of any trading strategy development is âover-optimizationâ, also referred to as âdata miningâ or âcurve fittingâ. The process essentially mines historical data and attempts to identify rules and parameters that fit past data. Over-optimized trading strategies are typically characterized by at least one of (i) too many rules or conditions (ii) too few trade occurrences per rule (iii) erratic results with small adjustments in input parameters. Over-optimization will result in trading strategies where the historical performance exceeds future performance. To avoid the inherent risk of over-optimization, KeyPoint Market Analytics strictly adheres to these 8 simple rules: 1. We cut the number of âandâ statements. Experience has taught us what to avoid. We generally use only one âandâ statement per entry or exit rule. For example, if âpre condition 1â exists and Momentum âcrosses the signal lineâ then buy. There are of course reasonable exceptions to using a single âandâ statement rule. It may be acceptable to use a second âandâ statement depending on the situation. For example, if âpre condition 1â exists and momentum âcrosses the signal lineâ and âclose greater then the moving averageâ then buy. In this case we have added a long-term trend filter in the form of a moving average with the additional âandâ statement. Over-optimization will occur if additional âandâ statements are added. For example, by adding âand close greater than 6 bars ago, and fourteen period RSI less than forty five. These conditions will result in fewer trade occurrences which increasingly move away from a general discovery of market character. 2. Most of our strategies use only one basic entry principle. We might have several entry rules but they all use the same entry principle. For example rule #1 might be âif pre-condition 1â exists and momentum âcrosses the signal lineâ and âclose greater then the moving averageâ then buy. Rule #2 might be if Bollinger Band Difference was âless than 5 in the previous 3 periodsâ and momentum âcrosses the signal lineâ then buy. Both rules use precisely the same âmomentum crosses signal lineâ entry, but each supplies different circumstances by which it may be followed. This ensures the strategy generates a significant number of total trades and a significant number of trades from each individual signal. In other words, itâs desirable to have a trading strategy that is generating several hundred total trades, but we also want to make sure that no individual trade signal has just a few trades in order to avoid the risk of a curve fit signal. 3. We apply robust input parameters. Any input parameter used in a trading strategy is an optimizable parameter. Weâre looking for a wide range of input parameters that are satisfactory. We also look for a gradual falling away on each side of the optimum value. Strategies that display this characteristic are referred to as ârobustâ. If our strategy does not display this characteristic it is discarded. 4. Many of our rules are portable with little variation between markets. The portability of entry/exit rules between markets reinforces that the rules at work are not curve fit to a particular data set. Therefore, portability between markets is definitely a strong positive. However, experience has taught us that portability is not a necessity as it is also a characteristic of markets that they have their own personalities. 5. Exit rules follow the same principles as entry rules. All evaluations that we apply to entries apply equally to exits. We always test any new trading approach with very simple exits such as dollar stop loss, swing point stops, profit objectives, or a profitable daily open. If any particular approach does not show promise, subjecting it to different exits may lead to over-optimizing. All exits used in a trading strategy are the same no matter what the entry signal. If entry rules all follow the same general principle, then the same exits should work with all of them. Mixing and matching exits and entries is very often just another form of over-optimizing. 6. We review buy and sell trade results separately. This way we can see how each performs within different types of market trends. During strong trends in either direction, we want to see our trading strategy produce positive results or minimize loss when taking trades against the prevailing trend. Weâre looking for a trading strategy that rarely falls out of sync regardless of the trend or lack of trend. 7. Entries and exits are not optimized based on whether it is a long trade or a short trade. While we concede that markets often behave differently on the way up than they do on the way down, and we definitely could optimize to get better results on this basis, we also believe this approach may be a small step closer to over-optimization. Since our goal is near zero trading strategy degradation after release, we use mirror image signals for either side of the market. 8. We review the length of test track record several ways. It is clear that the longer the historical test period the more reliable the results are likely to be. Equally important though, is the total number of trades. Generally, more then 500 trades provides a comfort level with the degree if reliability. We also consider how often long or short a trading strategy has historically held a position in the market. For example, a strategy that holds a position 40% of the time, it is less likely to fall victim to over-optimization than one that holds a position 10% of the time during the same test period. Any strategy that adheres to our guidelines governing entry and exits, and is âin the marketâ a significant percentage of the time is extremely difficult to over-optimize.