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I have tried excluding certain time periods, like the open, or news, etc and found results were not improved. It takes fewer trades and makes less money, that is expected. But what I mean is the win percentage or draw down were not any lower. There was not any perceived reduced risk which was surprising but nonetheless I run them from early morning (5am or so) to middle of afternoon. I have some that stop at lunch but there was no major reason backtesting wise, it was a personal preference of not wanting to trade the whole day.
Thank you for your question. It is not complicated anymore after I wrote the initial function. The purpose was to produce something similar to a trailing stop, but SetTrailStop() has many problems in Ninja like many things do and I wanted greater control of when to adjust the stops a fixed stop increment is too restrictive.
I'll give chasing targets some more thought but I prefer setting targets based on factual historical data. There is a good thread about something similar here:
I am still fairly new to the CL, but like what I see as compared with the ES and ZN, and thus have been focusing much of my attention on this instrument for the last couple of weeks.
Something I started doing (which was long overdue) …
"Let us be thankful for the fools. But for them the rest of us could not succeed." - Mark Twain
You might want to check out the sample on NT forums of how to manually do a trailing stop rather than use SetTrailStop(). You can modify it slightly to use TrailThreshold, TrailStep and TrailFrequency like the NT ATM system.
The benefit is that you only have 3 parameters to optimize instead of 9, and you can also implement techniques like chasing target which is where price can move through the target and the target chases it upwards until there is a downtick (or vice versa).
I make quite a lot of pips chasing volatile thrusts, compensates for the slippage incurred when you are on the wrong side of the thrust
I don't visit that forum I can't stand it. You can post the content here if you wish or nevermind about it as my method works fine and is very simple and I would imagine much more powerful.
No real savings in optimization job using a GO. The one piersh posted I can optimize billions of combinations in about 10 minutes to the first 5 or 6 generations which is usually plenty.
"Let us be thankful for the fools. But for them the rest of us could not succeed." - Mark Twain
Actually reducing parameter count is hugely beneficial for GO because of their tendency to get stuck in a local minimum. In other words, GO algorithms often trade accuracy for speed and in general more parameters means less accuracy, less chance of finding the optimal solution.
That said, it sounds like your current solution is working well for you so no sense in fixing what isn't broken
something to consider. I have been playing with some of my own weighted optimizer types to encourage certain behavior out of the GO and it is working well for me.
"Let us be thankful for the fools. But for them the rest of us could not succeed." - Mark Twain
Intrigued by the weighted stops idea - never thought of that before. Will have to investigate further. My "problem" with scaling out is the huge drop off in expectancy at the 'cost' of an improved win percentage. I have never quite come to terms with the mix on this - would be interested in your views.