wow didn't knew it.
If Close[1]>MA 100 == LONG + SL=100 TP=100
So we got 3 parameters that we want to change (lets exclude the Bar Number for this example.)
We can have few ways to check it while it is still DID NOT entered our databank, the builder just came up with those parameters right now,
Method #1:
Check 25% with Steps of 5% of each parameter separately, while keeping the others as original values, so for example first the MA 100 would be changed to 75,80,85,90,95,100,105,110,115,120,125, While the other 2 parameters of SL and TP would stay as they are, which is 100 and 100, if those neighbors passed our criteria and the strategy seems to be the same (in terms of R-Squared for example) then move to the next parameter: SL now the MA would stay as the original value which is 100, and we will now focus on the SL parameter: 75,80,85,90,95,100,105,110,115,120,125, Passed?, great we set it back to the original value and move on to the next parameter which is TP: 75,80,85,90,95,100,105,110,115,120,125, Passed?, this means we have all the neighbors checked and they all passed our criteria, now the engine will add the OUT OF SAMPLE data, Passed?, Save to the Databank.
_ So what we achieved here is we checked all the parameters step by step: MA=CHECKING (75,80,85,90,95,100,105,110,115,120,125) SL=100 TP=100 MA=100 SL=CHECKING (75,80,85,90,95,100,105,110,115,120,125) TP=100 MA=100 SL=100 TP=CHECKING (75,80,85,90,95,100,105,110,115,120,125) Out of sample validation, Save to databank if passed.
Method #2:
We do everything like explained above with ALL parameters optimized at once. MA+SL+TP (75) MA+SL+TP (80) MA+SL+TP (85) MA+SL+TP (90) MA+SL+TP (95) MA+SL+TP (100) MA+SL+TP (105) MA+SL+TP (110) MA+SL+TP (115) MA+SL+TP (120) MA+SL+TP (125) Out of sample validation, save to databank if passed.
Thats it,
We can use it as an MC also, but it would be preferred to have some more in-depth statistics on it,
And maybe some fitness that would stimulate the genetic algorithm to pass this test.
Thank you for your time reading this, this is very impotent and i noticed many known individuals use those methods manually,
if we can do it automatically thats powerful.
Kevin Davey, Andrea Unger, Larry Williams, etc.
here are some examples:
https://youtu.be/q_02_zeFq4w
While there are many more example of those, we can even do this with any trading platform that offers backtesting and optimization already... but SQ is just better and missing this feature currently..
Thanks for your attention, hope it gets implemented asap..
So you could already during building do a montecarlo with strategy parameter changes. Somebody could just do the montecarlo script to check each variable one at a time I think if that's what you want.
Ill try to play with it a little thanks..., lets see how it goes
Attachment e3vty7Vk1p.png added
I attached an example of what would be a cool visual feature of this method i wrote about,
and if it can be built visually it can be built automatically with no GUI and make the decision automatically for us if it passed the score for all parameters overall or individually,
this can be done in no time i believe because some of those functions are already built into SQ and can be combined to create it.
We can make something like "Optimization Profile and System Parameter Permutation" which is already implemented but rather checking all available parameters,
we will do it as explained in detail, above..
this would be more efficient, faster, logical etc..
Im sure you got the idea..
We can make something like "Optimization Profile and System Parameter Permutation" which is already implemented but rather checking all available parameters,
we will do it as explained in detail, above..
MA+SL+TP (75) MA+SL+TP (80) MA+SL+TP (85) MA+SL+TP (90) MA+SL+TP (95) MA+SL+TP (100) MA+SL+TP (105) MA+SL+TP (110) MA+SL+TP (115) MA+SL+TP (120) MA+SL+TP (125)
I'd stay away from that one (method #2) it will be severely biased for some strats. The 75 MA if it's a cross for instance will produce more trades than the 125 MA. So way more trades will have smaller TP and SL skewing the results. There's nothing wrong with one at a time (method #1) or monte-carlo is standard.
Yes, Method #1 would be what i aim for,
But this should be flexible,
Users can choose MIN/MAX/STEPS in %,
What parameters to choose from, like Integers/Doubles/Bools etc..,
Method #1 or Method #2,
Show Visual Statistics or not,
etc..
But it should be something similar as SPP "Optimization Profile and System Parameter Permutation",
The interface is ready, and have most of the stuff there, but we cannot manipulate it,
it will just search for all combinations of parameters rather than staying with-in the range of the closest neighboring parameters in % and in steps of %..
So i would say this method is like more than 50% built.
Subject changed from More Accurate & Robust Genetic Engine to DELETE - IT'S IMPLEMENTED ALREADY
Description changed: