I have been working on a system for FDAX and I would like to ask for some feedback. Totally inspired by GFIs1s successful journal, so it is based on the 9:00 9:30 bar (=initial balance=IB).
I created Excel files with 11 years of FDAX RTH data (from Kinetic). One file for each day of the week and:
a> IB long-short trade
b> IB long-long trade
c> IB short-short trade
d> IB short-long trade
So, 20 files in total.
Each of these files contain data for:
Trade entry time every 30 minutes from 9:30 to 12:00 (6 options)
Trade exit time every 30 minutes from 10:00 to 15:30 (12 options, depending on the entry time)
Stoploss 5 to 60pts with 5pts interval (12 options)
2 days ago Open/Close red or green (2 options)
Prior day Hi/Lo pts (12 categories, like 9:25) I set some filters (matching the current situation) in the Excel files to see which direction/entry time/exit time/SL statistically is giving the best results in the pivot table. Just a few filters, so that the number of trades is still a good sample size. First analyses over the last 11 years, then over the last 2 or 3 years making sure these results are not just coming from a far past.
Questions:
1. What do you think of this approach?
2. With this approach you can not learn from the setups, because almost every day is different. Is having all this historical data sufficient to learn from?
3. In 11 years, there are roughly 2800 trading days (after excluding holidays/IFO days), if you assume 50-50 on short and long IBs, there are about 280 short trades per weekday and 280 long trades. Is the sample size big enough to count on these statistics? And if yes, to rely on the results, how many trades should be the minimum to take the trade?
4. I could work with larger ranges for the categories and less filtering, to make it more broad. Would that make it more stable?
The data seem to give some nice statistical results.
Thank you for taking the time to read this and to share your view.
Dennis