Mastering the Search: Unlocking 40,000+ Ensemble Possibilities 🚀
I’ve reached an important milestone after extensive testing and experimentation. It’s no longer just about running many training passes or trying different combinations 🔁.
It’s also not enough to mix ensembles across models, architectures, timeframes, thresholds, and learning rules 🤖📉📈. The real breakthrough is understanding that we need a single, structured “master sweep” of all possible variants.
Now I have a standard ensemble structure made from a defined set of models 🧩. For each of these ensembles, I generate multiple optimized versions, for example the top 10 from all training runs. This leads to approximately 4,000+ variations of standard ensembles, or even more than 40,000 total configurations 🔥.
This is the full search space that needs to be explored to properly evaluate performance across all combinations. On top of that, I evaluate results across two separate test periods to ensure robustness and consistency. This approach removes guesswork and replaces it with systematic, exhaustive coverage of possibilities ⚙️.
If there is a truly stable, high-performing configuration hidden in this space, this method will surface it 🔍. The idea is simple: leave nothing untested that could potentially matter. With this level of structure and scale, I am maximally positioned to identify the strongest signal combinations 🚀.
If a valid edge exists across these conditions, it will be found through this process 💡🔥 within several days... or maybe even hours... alright, maybe 2-3 days...
And below is one variation amonth the 4,000+ variations:
=== live_h6_tcn + nice_h6_trans + nice_h2_trans ===
ens0: hist=+12577.0 / 314 test=+835.9 / 19 sig=0.44/0.48 qlt=0.35/0.3
ens0: hist=+10858.8 / 167 test=+900.2 / 11 sig=0.45/0.55 qlt=0.52/0.35
ens1: hist=+13781.2 / 242 test=+769.0 / 13 sig=0.42/0.46 qlt=0.36/0.47
ens1: hist=+13160.3 / 177 test=+873.2 / 13 sig=0.41/0.49 qlt=0.43/0.46
ens2: hist=+10283.9 / 230 test=+1364.6 / 8 sig=0.4/0.48 qlt=0.37/0.5
ens2: hist=+11571.6 / 265 test=+834.8 / 7 sig=0.43/0.51 qlt=0.41/0.47
ens3: hist=+13921.5 / 245 test=+771.8 / 13 sig=0.47/0.54 qlt=0.47/0.41
ens3: hist=+13956.5 / 291 test=+695.9 / 13 sig=0.48/0.53 qlt=0.46/0.38
ens4: hist=+10400.5 / 114 test=+589.5 / 11 sig=0.41/0.59 qlt=0.55/0.44
ens4: hist=+12737.8 / 507 test=+297.7 / 46 sig=0.45/0.48 qlt=0.44/0.38
ens5: hist=+12852.0 / 231 test=+840.8 / 13 sig=0.42/0.48 qlt=0.43/0.44
ens5: hist=+12264.1 / 194 test=+1023.1 / 11 sig=0.41/0.5 qlt=0.45/0.41
ens6: hist=+8095.7 / 129 test=+1132.0 / 4 sig=0.53/0.59 qlt=0.58/0.43
ens6: hist=+13593.5 / 213 test=+1164.1 / 8 sig=0.4/0.52 qlt=0.46/0.38
ens7: hist=+11901.9 / 264 test=+779.0 / 8 sig=0.44/0.51 qlt=0.49/0.3
ens7: hist=+11726.7 / 214 test=+865.1 / 6 sig=0.43/0.52 qlt=0.51/0.45
ens8: hist=+12077.7 / 262 test=+809.8 / 32 sig=0.49/0.51 qlt=0.41/0.38
ens8: hist=+6980.5 / 71 test=+1159.2 / 4 sig=0.46/0.6 qlt=0.6/0.53
ens9: hist=+10830.7 / 166 test=+1030.9 / 11 sig=0.47/0.57 qlt=0.54/0.41
ens9: hist=+11642.1 / 212 test=+890.1 / 8 sig=0.45/0.53 qlt=0.45/0.39
hist: avg=+11760.7 min=+6980.5 max=+13956.5 pos=20/20
test: avg=+881.3 min=+297.7 max=+1364.6 pos=20/20
