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To help me with walkforward, I use StratOpt WFP. The new version of Tradestation has basically the same capability, but I've never taken the time to learn it (I like what I am using already).
I should point out that walkforward can be done by hand. It is laborious, but after doing it a few times, you get a good feel for the process.
Can you help answer these questions from other members on NexusFi?
What exactly are you looking for me to do? I guess I'm a little unclear on what I could add to it. My concerns would be 1) that it was created without walkforward, and 2) I'm not sure if there is any out of sample data there (you had mentioned you usually leave last 20%, is that what you did here?).
Assuming no walkforward and last 20% out of sample, I'd probably run a Monte Carlo just on the out of sample portion, and see how that does...
No problem Kevin, I was just looking for an opportunity to show a tear down of a strategy that looks good at first glance (well, not terrible at least)
No problem. Was it optimized at all, or is it something you just came up with, and on the first try that is what the equity curve looked like?
If it was optimized at all, the interesting thing to do then is to "incubate" it - let it run for the next month or 2, and see how it does compare to the optimized results.
No I always optimize based on a hybrid of a perfect equity curve, a measure of avg profit-to-drawdown ratio, looks for a well balanced long/short system (not lopsided), plus other things I can't remember.
I know the system is not viable. I guess I was just wanting people to know they can't just look at a performance report and take it at face value, and was thinking you could tear it apart for everyone to see how bad it really is
I'd like to talk about optimizer types (mainly for Ninja, but general math and ideas apply here as well).
I'll be back to discuss what I use and why later.
Mike
Tons and tons and tons of different fitness and can all be tweaked to exactly what is important to you, and you can create your own formulas if the metric you wan't doesn't already exist.
Without originally intending to, I think I did just tear your system apart - not by analyzing your results, but by asking you how you got those results. Based on your answers (no walkforward, no out of sample, optimized over all data set), I would have no need to look further at your results. They might be spectacular results, but optimized results almost always are. Doesn;t mean jack going forward.
Where it would get tricky is if you lied and said it wasn't optimized, or that it had x% out of sample results at end. How would I be able to tell you were lying? One way I know is the "if it looks too good to be true, it probably is" test. Another way would be watching it in real time (incubation). Maybe someone else has a better approach they would like to share.
I think this is why it is so hard for people who buy strategies or systems. How do they know if the system results are "real," or if they are just some optimized BS? Most times, a vendor who sells optimized systems would probably lie to you about how he got the results anyway. I know, because sometimes for fun I converse with vendors of these super duper systems, and 9 times out of 10 it is clear to me that
a) the results are garbage
b) the vendor doesn't know what he is talking about
c) the vendor's personality is some kind of combination of unethical, immoral, irresponsible and feeble minded.
I propose you keep tracking the system, and let's see how it does a few months from now. That should be very revealing...
I am very pleased to welcome Greg (NJAMC) this Saturday, February 23rd @ 2:00 PM ET.
Some of you are probably already familiar with Greg's work, including:
My hunt for the automated holy grail (
Artificial Bee Colony (ABC) (
Support Vector …
NJAMC has been working on his project for a while now, he and @Luger were also involved in the Artificial Bee Colony thread, the predecessor to Greg's ML thread:
I have to say, my focus has started to move more in the direction of AI for Bot development. I have played with Neural Networks and fuzzy logic. One new entry which I think has promise is the "Artificial Bee Colony (ABC) Algorithm".
I have …
I tried to help where I could using my Wave59 license, but in the end those guys were just too far ahead of me. That's what I get for being a high school drop out, it sucks to be a trader that is poor at math (if only I had known...)
I bring it up because I was thinking of maybe getting together a consortium of sorts, like maybe John Ehlers, Suri Duddella, Manesh Patel, and @NJAMC, @Luger, @Fat Tails and some other fine folks who I can't think of right now, and see if maybe some sort of collaborative project could be started to further the work that Greg (NJAMC) is doing on the ML front. I figured maybe a few 1-hour conference calls or skype sessions might be enough to get some really great brainstorming done to make some real headway in this area.
Perhaps we should take this off-line... I've cluttered your thread enough.