passTrail demanded m_excTrailScored >= EXCURSION_MIN_SCORED (500), but since
e2c9593 the trail race only scores DISJOINT bars: m_excTrailScored is bounded
by m_excScoredD (~OOS/horizon ~= 256 on SP500 H1) minus the post-ring-clear
warm-up (~8), so every chart failed "[trailing incumbent not warm enough to
race]" at 247-248 of a possible ~256 forever - observed live 2026-08-11 on
all four charts. The counter's statistical population is the same disjoint
sample passDj gates on, so it now takes the same minimum
(EXCURSION_MIN_DISJOINT, 200), reachable with margin after warm-up.
Compile: 0 errors, 0 warnings.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Three findings from the 2026-08-11 audit:
1. The excursion head's trailing-quantile ring was deliberately never cleared
between eras ("a rolling estimate of the market, not of the era") - but
pass 3 re-walks the SAME OOS window every era, so at each walk's restart
the ring still held the outcome masks of the newest OOS bars from the
previous walk: the chronological FUTURE of the bars about to be scored.
For the first ~window+horizon pushes of every era the "trailing" incumbent
was partly a leading one - conservative for the gate (an informed incumbent
is a harder hurdle) but exactly the self-made-artifact class 06d4785 hunts.
The ring now clears at era-score reset; the warm-up bars simply don't score
the trail race, which the m_excTrailN gating already accounts for.
2. skillTrail compared the head's FULL-block Brier (pro-rated by coverage)
against the incumbent's subset sum - valid only if head skill is uniform
across the OOS walk, while the trail-scored subset systematically excludes
each era's warm-up bars. The audit also found m_excBrierHeadD/BaseD/
m_excOosHitsD declared, zeroed and never accumulated (dead since e2c9593
made every scored bar disjoint). The dead trio is replaced by
m_excBrierHeadT: the head's Brier accumulated only on the bars the warm
incumbent also scored, so the race now compares both predictors on an
identical bar set.
3. The AD/Wyckoff feature blocks read GetData with no EMPTY_VALUE guard; a
cold (still-calculating) indicator returns EMPTY_VALUE everywhere, the
sanitize loop rewrote that to 0.0, and the bar SUCCEEDED - so
BufferTempData cached an all-zero Wyckoff block as a success for the whole
bar frame: the one path the f6150ee only-cache-successes rule cannot see,
because it never fails (the ba13eef class, arriving through values that
never fail; a resumed model's era-0 prebuild starts milliseconds after
OnInit). ADIndicatorCold() probes the NEWEST bar - EMPTY_VALUE there means
async warm-up (transient reject, retried), while deep bars beyond the
buffered depth keep the sanitize loop's neutral-fill so degraded history
still trains. Also fixed m_featureCacheValid's declaration comment, which
still described the pre-f6150ee cached-miss semantics.
Compile: 0 errors, 0 warnings.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Measured on exc-race-v3: LSTM era 300s -> 1087s (net 272->748s, "other"
30->337s). My estimate had been "single-digit percent". The cost is
per-DISPATCH, not per-FLOP, and therefore hits EVERY backend: the head is
19k weights and ~2.4 GFLOP an era - seconds of arithmetic - but ~48k
forward/backward calls x several layer submits each, and its 760-wide
layer exceeds the CPU DLL's inline threshold so each one pays a real
handoff. The classifier's own net time tripled too, from contention with
a second pool on an already-full box.
Three changes, all backend-neutral because they remove submits rather
than tune threads:
SCORE ONLY DISJOINT WINDOWS (~64x). Adjacent bars share all but one bar
of their horizon, so 16k consecutive bars were always ~250 independent
observations - the full-sample tally was never worth more than the
disjoint one, it just quoted an n that was ~64x too large. Dropping it
costs nothing statistically and removes 63 of every 64 forward passes.
The two parallel tallies collapse into one, which is also less code.
The trailing ring still advances on every bar: it needs the outcome
SEQUENCE, and that is array lookups, not a forward pass.
TRAIN ON EVERY 4th PRIMARY BAR (4x). The target is low-dimensional and
strongly autocorrelated - neighbouring bars carry near-identical
excursion information - so per-bar training buys resolution the target
does not have. Strided on ATTEMPTS, not acceptances, so a stretch of
unlabelled bars cannot silently change the spacing.
OWN TIMING COLUMN. The head's passes were landing in the era line's
"other" bucket, which is how a 3.6x regression read as an unexplained
jump in the one column nobody attributes. A cost that cannot be seen in
the timing line cannot be traded off against anything.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Beating a frozen global constant is the weakest admissible bar for
replacing a global constant. The honest incumbent is a rolling rung
frequency: it adapts to the volatility regime - exactly what the head
claims to predict - and needs no model, no 760 inputs and no training.
Implemented as a ring of per-bar outcome bitmasks (32 rungs fit one
ulong), sized horizon + EXCURSION_TRAIL_WINDOW. The newest `horizon`
entries are held back UNRESOLVED: a bar's rung outcomes are only known
one horizon later, so using them would be lookahead and would flatter the
incumbent into an opponent the head could never fairly beat. Pass 3 walks
oldest-to-newest, so "pushed more than horizon bars ago" is exactly
"resolved by now". Each push is O(rungs), not O(window).
The head's decision-rung Brier is pro-rated to the trailing estimate's
coverage before the ratio, since the incumbent only scores bars where its
window is warm.
This line is worth reading on its own, independently of the head: if the
trailing quantile beats the global constant, that is a cheap risk-control
win available with no machine learning at all - and it is the same number
either way, so the run answers both questions in one pass.
The ring is deliberately NOT reset per era - it estimates the market, not
the era, and re-warming 500 bars every era would leave the incumbent
unusable over the first chunk of every scoring pass, handing the head a
free win on exactly those bars.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Second-opinion review killed the +4.2% far-rung result, correctly, and
the mechanism is my own bug. A head trained toward {0.05,0.9} converges
to 0.05+0.85p, so its bias is 0.05-0.15p: negative where p is near 1,
POSITIVE where p < 1/3, growing monotonically as the rung gets farther.
Against a baseline frozen at the IS rate, an upward-biased head scores
positive Brier skill whenever the OOS rate merely sits above the IS rate.
Predicted signature: huge negatives near, ~zero at p=1/3, growing
positives far. Observed: -82% ... -0.6% ... +1.2/+2.7/+4.2. The far rungs
were not the clean end of a distorted measurement, they were the other
face of the same artifact. Everything before 25aca83 is void.
The gate was a bare `skill >= 2%` point estimate over 8 rungs x 4
topologies x N eras, reported per era - a best-of-~300 with no interval
and no multiplicity control, which is the shape of the four traps already
documented here. It now needs FOUR things at once:
DECISION RUNGS only the rungs ExcursionQuantile actually reads at the
live geometry (target 1.62, stop 3.31 ATR), fixed
before looking. Skill at 5 ATR is skill about a
distance no order is placed at - and the TARGET side
currently interpolates 1.5/2.0, which measured -2.2%
and -1.3%.
DISJOINT SAMPLE one bar per horizon. Adjacent bars share 63 of 64
horizon bars, so ~16k scored bars is ~250 independent
ones and every SE over the full set is ~8x understated.
VS ORACLE the best constant achievable ON THE SCORED BLOCK,
closed form from H and n (Brier = H*(1-H/n)). A head
that learned only a LEVEL nearer the OOS rate than the
frozen IS constant scores positive against the old
baseline and <= 0 here. This is the control that
separates per-bar skill from base-rate drift.
MONOTONE CURVE P(reach k) must be non-increasing in k. Nothing
constrained 8 independent sigmoids to obey that, and
ExcursionQuantile returns the FIRST crossing - so a
tangled curve is misread exactly where the head is
least sure. Counted and reported, not silently used.
The pass message now also states what a pass would and would not buy:
expectancy is -costs at zero directional edge whatever the stop distance,
and under prop DD limits LOWER variance also lowers P(reach target before
limit), so "better drawdown" is a choice of failure mode, not a win.
Still owed before any Stage 2: a race against a trailing-quantile
incumbent and a vol-feature logistic. Beating a frozen global constant is
the weakest admissible bar for replacing a global constant.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
ExcursionTargets built its 32 binary targets from the classifier's
LABEL_SMOOTH_HIGH/LOW (0.9/0.05). That caps what the head can ever output
at 0.9, and the near ladder rungs have base rates close to 1.0 - almost
every bar travels 0.5 ATR inside a 64-bar horizon. The Brier comparison
is then decided before the net learns anything:
constant at 0.99 -> 0.99*(0.01)^2 + 0.01*(0.99)^2 = 0.0099
head at 0.90 -> 0.99*(0.10)^2 + 0.01*(0.90)^2 = 0.0180 skill -82%
Which is what the first run reported at rung 0.50: PAI -61.8%,
CONV -146%. A property of the target encoding, not of predictability.
Smoothing earns its place on the 3-class head, where it stops one logit
running away inside a softmax competition. There is no competition here
and this head is scored on calibration, so it has to be free to say 0.99
when the answer is 0.99. Hard 1/0 is safe against the runaway smoothing
guards: this is an MSE-on-sigmoid gradient (calcOutputGradients) whose
(target - output) term vanishes as the output approaches the target, not
the unbounded-logit cross-entropy the classifier uses.
The far rungs, where the artifact is smallest, already showed positive
skill on the two topologies with a sequence stage (LSTM 3.00:+1.2%
4.00:+2.7% 5.00:+4.2%, HYBRID similar), so the verdict was being decided
by the most distorted end of the ladder.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Direction is closed - normalised asymmetry fails on three instruments
with a working positive control, and the classifier's own best-of-999
era-cap test agrees (+0.9pp = 1.48 sigma, family-wise p=1.0000). SIZE is
a different question and RANGE clears at ~4x its null.
Checked the denomination before building on that, since the source memo
warns to: m_excUpCache holds (maxHigh - fill)/ATR, so "RANGE is
predictable" is a claim about travel RELATIVE to current ATR, not a
restatement of "ATR is autocorrelated". It is exactly the part a fixed
multiple (stop 3.31*ATR, target 1.64*ATR) discards.
A second small CNet, 760 -> 24 -> 32 sigmoid outputs = P(price reaches
ladder rung k) upward and downward. Survival parameterisation rather than
regressing the multiple, because it needs nothing new from CNet: sigmoid
outputs and the per-neuron delta the `total != 3` branch already applies
(a quantile head would need a linear activation and a pinball gradient in
Network.mqh, Network.cl and the DirectML path, on a class four topologies
share). Targets are free - m_ladderUpAt already records first-touch age
per rung with 0 meaning never reached.
Separate net, not extra outputs on the classifier: more outputs would
change m_outputNeuronsCount, the .nnw shape and the fingerprint, and push
the count off 3 - the exact condition backProp uses to select the joint
softmax gradient the 3-class head depends on. The classifier is
bit-for-bit unaffected and this is removable without trace.
STAGE 1 PLACES NO ORDERS. It reports a Brier skill score against the
constant per-rung base rate - the baseline a fixed ATR multiple already
assumes - with both predictors fitted IS and evaluated OOS, so neither
gets a look at the test set. Positive skill justifies Stage 2 (drive
SL/TP and sizing off ExcursionQuantile, which is defined and deliberately
uncalled). Zero or negative means ATR already carries everything and
Stage 2 must not be built.
Trains only on primary occurrences: the replay queue oversamples for
CLASS balance, and a direction-balanced sample is a biased SIZE sample.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
