I had an inspiration for an (overly) aggressive expert advisor today. Basically, it's a simple buy low and sell high machine using oscillators.
Scope out the chart. It does fairly well for a period of time and then when the market finds it's achilles heel it is undone.
All is not lost. I have some ideas for improving the aggression factor as well as tinkering with it's currently abysmal "take a loss" strategy.
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Theoretically, you could take out $1,000 per day and end up with more money than you started with. It ran for 20 days (01-Apr to 20-Apr) before it's massive death spiral.
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Check this out. By adding a pending purchase system to delay early purchases until the price is a bit better as well as turning off loss sales (a bad strategy to be fixed up soon) we get the following.
The balance, actually I mean equity, is over $77,800 by the 16th in this run. Certainly more profitable than last time around. If we can get an intelligent way to cut losses we might be able to reduce the risk of catastrophe.
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I'm also running on various different time periods to make sure I'm not just optimizing for a certain month. That would not be good.
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Okay, after some tinkering, the system is living through a three month period, but obviously there is a tradeoff involved:
This is 01-Feb-2009 through 25-Apr-2009. There are a couple of scary draw down events and a final equity of around $56,000. So, again, in theory, at about $15,000 of profit per month you could withdraw about $500 every day and calculate the likelihood of scarfing away more than $10,000 before the system has a fatal event.
Unfortunately, removing equity will make it more likely to crash during a draw down situation. Perhaps withdraw at a slower pace? More realistically, I'd like to find a predictor for the draw down events and reduce them. This would reduce overall profit but perhaps the system could trade more aggressively if the draw down situations could be avoided.
Time to look at the reports to find the important dates and then look at the historic data in various time frames.
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