Btw just to show an example of how i work/create algos.
Following is 2 photos, 1 where i have chosen the first 50% of the data and started optimizing, then the second image is the same algo but backtested over the 100% of all the data after i have optimized on the first 50%..
In other posts i have gone thru my way of creating systems, simply put its:
1. Make ur algo on 100% of the data (dont change 1 single variable at all. If your idea is “Buy when price crosses over supertrend and sell when crosses under supertrend” then this is your entire algo with no optimization.)
2. use only 30-50% of the data for optimizing the algo, optimize everything from stops/targets/trailing stops/variables..
3. go back to 100% of the data to check for overfitting.
As u can see from the Poirot pictures included, the first half is a lot smoother and not as choppy as the second half. This is because the first half is optimized, so my variables have been tuned to fit it “as perfect as possible” while the other half just have the same variables to go after and of course is less smooth then because the “same data” will obviously not occur in the future, but SIMILAR data might come..
Edit: @GraHal The more data the better it is imo 🙂
Edit2: Ill add another strategy thats currently in demo, same thinking here, optimized on 50% of the data, then backtested on 100% of the data on picture 2, notice the difference in smoothness of the EQ curve, but all in all “similar results”. Added photos of the results also (not sure if spread is added in this one tbh.)
