Discussing the strategy VECTORIAL DAX (M5)
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03/19/2020 at 12:17 PM #12257003/19/2020 at 3:05 PM #122585
put the mouse on it and you have the operation number
The values for AC vary between 0 and 16. Should AC be >0 ?
03/19/2020 at 4:35 PM #12259603/19/2020 at 4:58 PM #122602I couldn’t see anything unusual on the graph. Maybe it was just a blip? It has been working fine since that one rejection, taking trades.
Actually, for now it’s outperforming both the DAX v8, and the NAS v6 but much too early to judge. Thanks for your help!
03/24/2020 at 4:43 PM #12316804/14/2020 at 10:15 PM #126170The DAX 5m version I was running went a bit flat from Nov so I refreshed the numbers and it’s looking better than ever. Image on the left is (I think) the last version posted with mods by Paul and Fifi. I didn’t touch any of their work, just re-optimized the vectorial settings @ 60k. So the image on the right is effectively a 30-70 walk back. Mostly improved, especially over the last 6 months.
04/14/2020 at 10:20 PM #126174So once I did that, I figured I’d better do the NAS 5M as well. Made a few small changes and re-optimized over the full 200k. Robustness looks acceptable, but could probably be further improved.
Thanks again to Balmora for this exceptional algo, and to everyone else who contributed.
04/15/2020 at 12:26 AM #126190The DAX 5m version I was running went a bit flat from Nov so I refreshed the numbers and it’s looking better than ever. Image on the left is (I think) the last version posted with mods by Paul and Fifi. I didn’t touch any of their work, just re-optimized the vectorial settings @ 60k. So the image on the right is effectively a 30-70 walk back. Mostly improved, especially over the last 6 months.
Robustness went worse with this version
04/15/2020 at 6:44 AM #126196another version that i have tested about DAx vectorial. an MTF version of it.
lower gain but less ddmax and more consecutive gains
i had noticed during the big period of volatility that parameters had a positive impact between february and april.
it might be interesting to store the value of these parameters in several variables, a variable per digit, to count the parameter used during the profit or not, and as soon as there is a loss or a gain depending of strategyprofit, to apply a weight of the parameter, then, 2 possibilities, average of the number stored following the best weights or average to take the parameter with the best weight.. a possible evolution of machine learning
04/15/2020 at 8:30 AM #126203@Francesco here’s the results i got. I didn’t bother posting it because I’m not at all confident that I got all the tradeons in the right places — there are an unholy number of entry points.
The way I see it though, you’re looking at a 140k OOS back test with a better curve, better profit, more trades, much lower DD and a correction to the recent months when it has frankly been quite dead … where would you rather put your money? The robustness score would have to be utterly disastrous to change my mind.
04/15/2020 at 8:44 AM #126205@MAKSIDE what TF did you add and what did you put there?
I also tried experimenting with MTF, here’s one that might be interesting — doubling the vectorial component at the 15m TF. Very few trades, as you would expect, but 100% wins. I’m sure it can be improved if someone wants to take it on…
04/15/2020 at 10:20 AM #126222thx for sharing ! nice. if 100% wins, we can play with the positionsize.. 😉
about my version, pls see below.. cbuy and sell added to the current conditions
MTF 4H1234timeframe(4hours,updateonclose)indicator1 = SuperTrend[6,2]cbuy= (close > indicator1)csell = (close < indicator1)04/15/2020 at 10:52 AM #12623304/15/2020 at 11:01 AM #126239I got those results..i’m pretty sure i put tradeon on every single entry condition
is it the nonetheless’s version ?
04/15/2020 at 11:03 AM #126240 -
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