forked from mnbvc188199/Warrior_EA
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7414570d9d
Two defects behind the "training is highly unstable" report, from 101 eras of
SP500 H4 PAI logs. Neither was the optimizer.
1) LayerLearningReport's dW/W for BN layers divided by the WHOLE packed block.
getWeightsBN concatenates the outgoing dense matrix, gamma/beta, the running
mean/variance, the Adam moments AND BN_OPT_NX - the forward-pass scratch copy
of the normalized input. At era 101 bn1's dense matrix normed 15.1 against a
block norm of 15430.3, of which NX alone was 15429.3: the weights were 0.098%
of their own denominator, a 1022x inflation. NX is also near-constant between
era-end reports (same last forward pass), which pins the numerator down too,
so the layer read "bn1:0.000%" for 101 consecutive eras and was diagnosed as a
frozen first layer. It was the ruler that was broken. The ratio now covers
trainable parameters only (dense matrix + gamma + beta); mean/var/NX/Adam are
excluded. NX is reported separately because it is a health signal in its own
right - bn5 read nx 6.8e6 over 16 neurons, ~1.7e6 per unit against a healthy
~1.0, which is what a near-zero running variance in the denominator looks like.
NO historical dW/W reading on a bn* layer is admissible evidence that a layer
did or did not train. That includes every such claim in this repo's notes.
2) FitDirConfThreshold took a bare argmax of coverage x (precision - breakEven)
over 50 bins. Measured across 98 consecutive fits:
correlation(chosen threshold, win rate at it) = -0.056 over 0.00..0.74
win rate stdev across fits = 1.32pp
binomial SE of that win rate at ~1430 calls = 1.25pp
The correlation is zero - the margin does not rank trades - and the era-to-era
spread IS its own sampling error to within 0.07pp. So the objective was
coverage x (3.4 +/- 1.3) and the argmax over ~37 eligible bins returned
whichever bin drew the luckiest sample. The threshold teleported
0.42 -> 0.04 -> 0.74 in three eras, swinging OOS coverage 0% -> 39%, leaving
the era win rate measured on 1-5 calls and swinging 0% <-> 100%. That is the
entire reported instability.
The argmax is now adopted only if it beats a DETERMINISTIC fallback - the most
selective bin still clearing the coverage floor, chosen from the margin
distribution alone and never from a win rate - by more than a best-of-N
maximum could manage on noise, sqrt(2 ln N) standard errors. Same null-of-the-
maximum correction the deploy gate already applies to model selection.
A plain one-standard-error band was tried first and is NOT sufficient: its
edge is bestScore - bestSE, and with a 2.3pp edge against a 1.25pp SE that
edge is itself +/-50%, so the admitted set would still wander by half its own
width every era. The fallback has to be independent of the noisy quantity.
Simulated on the observed numbers: falls back every era at the current 2.3pp
edge (stable), adopts the argmax once a real edge reaches ~5pp.
NOT COMPILED - user compiles.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
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| .. | ||
| Layer.mqh | ||
| NetBuild.mqh | ||
| NetForward.mqh | ||
| NetPersistence.mqh | ||
| NetWeights.mqh | ||
| NeuronBase.mqh | ||
| NeuronConvPool.mqh | ||
| NeuronLSTM.mqh | ||
| NeuronOCLBase.mqh | ||
| NeuronOCLLSTM.mqh | ||