| y"> | | | | It might be possible to have a winning system if, for |
| You've probably read about the importance of having | | | | example, the stock you buy:o has a trade volume |
| a trading plan. As part of the trading plan, you need | | | | between 97,368 and 143,768 shares per dayo opens |
| to have a trading system in place. Without a system, | | | | with a price between $6.42 and $11.76o rises |
| you have no "map" to trade stocks by. | | | | between .39% and .76% between 9:30 and 9:53 |
| One of the hidden dangers of system development | | | | am.and your exit strategy is to:o wait for the stock |
| is over-optimization. As part of your system | | | | pick to rise an additional .64%o close the position by |
| development process you will be testing your system | | | | 11:44 am. |
| to see how well it has performed in the past. Some | | | | And did I mention that this is only on Wednesdays |
| system development tools will let you quickly test | | | | close to a full moon and while you're holding your |
| thousands of combinations of system parameters in | | | | breath during the test run? While such a system |
| a short period of time. | | | | COULD have 30 winning entries in a row during the |
| As you go about evaluating the profit and loss, | | | | hypothetical 4 year test period, would you really |
| drawdown and smoothness of the equity curve | | | | have confidence that this winning streak would |
| increase, you're likely to focus on the one set of | | | | continue? |
| parameters that looks best. This is the first danger. | | | | As you can see from this hypothetical example, even |
| It's better to look for a cluster of superior results | | | | though you MIGHT have gotten some great results |
| than for just one outstanding set of parameters. | | | | in the past, it's unlikely they'll continue in the future. In |
| Choosing parameters that are in the middle of a | | | | many cases, you could apply some serious math and |
| good cluster of results are likely to lead to more | | | | see at which point you're in danger of |
| consistent results in the future. | | | | over-optimizing. |
| You will also want to make sure you have enough | | | | However the answer to such an exercise will still be |
| trades in the resulting test set so they're statistically | | | | a probability. With experience, you can often develop |
| significant. It's foolish to be confident in any system | | | | the judgment to know if you're in danger of having |
| for which you have a track record with less than 30 | | | | over-optimized. In general, the fewer the parameters, |
| trades during the chosen test period. You can have | | | | the less danger there is of over-optimizing. |
| some confidence after you have more than 300 | | | | Frequently, successful systems will only have 2 to 5 |
| trades in the test period. This assumes you haven't | | | | parameters. You're also looking for a cluster of |
| over-optimized those results. | | | | superior results rather than just one outstanding test |
| Even 300 trades don't guarantee a reliable system. I | | | | run. |
| have seen any number of over-optimized systems | | | | For example, you might have a system that picks |
| that do great for over 300 trades and then lose | | | | stocks based on an average volume range and a |
| money forever after. | | | | price range. Right there, you already have 4 |
| One way to check your parameters is to hold back | | | | parameters. You could add an opening price move |
| some of your historical data as a "test set." The | | | | minimum, but anything beyond that could lead to |
| danger here is that if you use the test set more | | | | over-optimization. |
| than once at the very end of your system | | | | In the world of probabilities, you can never be certain |
| development, you're compromising the optimization. | | | | that you've got a reliable system. This is because you |
| This is because your "test set" now has become part | | | | could always be hitting an unlikely (but still possible) |
| of the optimization procedure. | | | | string of good or bad results right out of the gate. |
| Still, it's possible to over-optimize if you have a lot of | | | | However, by not over-optimizing, you stand a better |
| parameters. | | | | chance of having a consistently profitable system. |
| HYPOTHETICAL EXAMPLE: | | | | |