1. THE POOL FIX WAS LANDING ON A TOPOLOGY THAT COULD NOT SEE IT.
ComputeFirstLayerWidth budgets against EstimatedInSampleBars, which counts
this chart's own bars PLUS the training pool. On a COLD fleet start every
chart derives and pins its topology BEFORE any chart has published a pool
file - measured on the 18:13 start, model creation at 18:13:21 against a
first publish at 18:13:48. All six sized as if training alone, wrote that
into .cfg, and adopted it back on every later start even with the pool full.
SP500 ran a first layer floored to 16 while adopting 30229 peer rows.
Adopt-don't-compare exists to protect weights shaped by those sizes. It was
also running for a model with NO .nnw, where there is nothing to protect and
the .cfg is just a record of one unlucky moment. The four derived sizes are
now re-measured when no weights exist.
Safe on all three counts that matter: free (nothing to discard), cannot loop
(once weights exist the .cfg is authoritative again), and cannot fragment the
pool - the derived width is NOT in BuildModelFingerprint, which keys only on
the FEATURE layout. Verified: field 2 of the fingerprint is
LEGACY_HISTORY_BARS_SLOT, not the first-layer width.
TO TAKE EFFECT the weights must be wiped while the TrainPool is KEPT - the
census has to be non-empty at derivation time. A full wipe empties the pool
and reproduces the original condition exactly.
2. THE KEEP-SCREEN LATCHED ON AN UNDERPOWERED SAMPLE.
MI_MIN_SAMPLES is a floor for "can this be computed", and it was being used
as the bar for "is this answer final". The screen fired on the first era
clearing 200 rows and latched, measuring at 202-773 samples where a warm
chart gives ~2065. Columns kept then tracked SAMPLE SIZE rather than
information - EURUSD kept 0 of 49 at n=202, SP500 kept 15 at n=773, and the
ordering across all six charts was very nearly monotone in n.
A thin sample is still measured and printed, but it no longer closes the
question: below MI_GOOD_SAMPLE_FRACTION of the target the result is labelled
underpowered and a later era supersedes it, bounded by the same attempt
budget. An underpowered screen that latches is worse than one that waits,
because it looks like a result.
Build tag -> fleet-pool-v2.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
TWO CHANGES, BOTH RETRAIN-FORCING BY INTENT.
1. SP500 was training alone, and one alt-data column was the reason.
The alt block's width joins the model fingerprint, and the pool reader only
adopts peer rows whose fingerprint and width match. The exporter gives each
instrument the series that apply to it - FX 15 columns, metals/oil 14, SP500
13 - so the fleet ran as three incompatible pools:
EURUSD/USDJPY/USDCAD adopt ~57-60k peer rows each
XAUUSD/XTIUSD adopt 6.4k / 20.3k
SP500 "EVERY peer file was REJECTED, so this chart is
training alone" - 0 rows
SP500 therefore trained on 2279 independent observations against a 600-wide
input with its first layer floored at 16, printing its own "expect
overfitting" warning. It is the one chart with no pool and the worst
capacity ratio in the fleet by a factor of three.
Fresh models now pin ALTDATA_FLEET_COLUMNS - the 12-column intersection -
instead of their own file header. An existing model still adopts its .cfg
pin, so this re-keys nothing that is already trained.
Intersection rather than union: filling an absent series with its median
makes that column constant per instrument, which lets a pooled model
identify the source instrument and stop learning the shared mechanism. It
is also 6 columns narrower. Cost is six columns whose retained information
is UNMEASURED - the keep-screen reports a bitmask nothing has mapped back
to names.
2. The MI keep-screen disabled itself for the whole run on any cold start.
ReportFeatureLabelInformation set m_miReportDone on ENTRY. On a cold start
the label cache is allocated before it is filled, so BuildMiSample finds no
row carrying a resolved label and returns 0 - a sixth exit, and the only
one the 8c1266d instrumentation did not cover, which is why it printed
nothing. observed then stayed -1, the permutation loop never iterated, and
the report emitted "-1.00000 nats over 0 permutations" beside a plausible
"strongest single feature 0.05979" that was a STALE m_miBestColumn from an
earlier scoring call. The first ensemble member propagated the latch to
g_ensembleChartMiReportDone and silenced every member on the chart.
The flag now latches only once a measurement exists. A short sample is
reported as a deferral naming the two numbers that identify it (cached bars
vs bars carrying a resolved label) and retried, up to
MI_REPORT_MAX_ATTEMPTS.
Build tag -> fleet-pool-v1.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
SP500 resumed converged at era 136 with its tier ladder correctly restored and still
swept 4999 bars reporting "0 had a snapshot, drew 0 arrow(s)" while the other five
charts drew 221-312.
HasDemonstratedEdge() - added with the no-skill exclusion - compares m_eraStatPrecPct
against m_eraStatChancePct. Both are written once per era by EnsembleStashEraStats. A
converged model runs no eras, so after a restart both sat at their -1 ctor defaults,
every member was ruled no-skill, ReconstructionWeight() returned 0 for all four, and
the overlay divisor was zero on every bar. Exactly the failure the WST7 ladder
persistence fixed one level down: the ladder says how much a member votes, this says
whether it may.
RankTiersFromOos already computes the pair (pooled holdout precision and the
zero-skill reference rate) and now records it as the CERTIFIED edge. That path is
reached by the era end AND by the deployed replay, which is the only measurement a
converged model will ever make. Persisted as WST8; HasDemonstratedEdge() prefers the
era pair and falls back to it.
The census line also had to be fixed: it reported "NOT ONE of those bars had a single
member snapshot ... no enrolled member has published m_overlaySigSnap" for a condition
that was purely a skill verdict. The snapshots were there. It now counts the two causes
separately and names the one that fired.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
A LIVE DEFECT from combining today's two changes. The threshold pins ON
CHECKPOINT (ad4ae58) and the burn-in forbids checkpoints below era 20 (32eb5c5),
so nothing was published for the first 20 eras and those charts sat on the
Signal_ThresholdOpen seed of 25 - an ABSOLUTE WIN RATE under a currency that no
longer uses one. 25 is above what the vote can now reach:
USDJPY Filtered view: drew 0 arrow(s). Strongest vote 19.3% vs 25.0% threshold
SP500 Filtered view: drew 268 arrow(s). Strongest vote 13.1% vs 5.0% threshold
Zero arrows AND zero trades on all three FX charts (eras 10/10/16), while the
three past era 20 published their derived rungs and ran normally.
Fix: publish the current era's derived rung while g_ensBestEra < 0. Before a
checkpoint exists there is nothing to protect, and an arbitrary seed is strictly
worse than the latest measurement. Once a checkpoint exists the pin takes over
unchanged.
HOW IT WAS FOUND: the user said the FX charts were visibly quiet while I was
reporting 17-18% coverage and had declared the quiet-chart problem fixed.
Era-verdict coverage says what the vote WOULD fire on in an OOS replay; it says
NOTHING about whether the live threshold is reachable. The log stated it
verbatim - "Strongest vote 19.3% against a 25.0% threshold" - and I had not
looked at the drawn view before claiming success. Verify a display or trading
claim on the ARROW COUNT, never on the scorer.
Compiled clean; NOT yet run.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
The keep-screen answered whether pruning is worth doing - consistently, across
all six charts:
chart kept width first-layer budget
EURUSD 17/52 624 -> 204 11.3 -> 34.3
USDCAD 19/52 624 -> 228 10.0 -> 27.2
USDJPY 18/52 624 -> 216 11.2 -> 32.4
XAUUSD 17/51 612 -> 204 7.8 -> 23.2
SP500 18/50 600 -> 216 3.8 -> 10.5
XTIUSD 16/51 612 -> 192 3.8 -> 12.1
~1 column in 3 carries the association and the rate is stable across six
independent charts - noise would not reproduce that tightly. Pruning nearly
triples the capacity budget and lifts XAUUSD off the 16-wide floor. SP500 and
XTIUSD (the two pool-poor charts) improve ~2.8x and still miss it; they need the
12-bar window cut as well, which is a separate lever costing nothing in feature
semantics and not touching pool compatibility.
Headline MI is strong everywhere under the pivot-event label: 0.008-0.0099 nats
against a ~0.002 null, strongest column 0.047-0.077 against a ~0.006 null-max
(8-13x).
WHAT THIS COMMIT ADDS is the last fact needed before a mask can be built: WHICH
columns, as a hex bitmask, so two charts' masks can be compared by eye and by
grep. Identical masks across the fleet mean ONE fleet-wide mask keeps every chart
in a single pool group; divergent masks would split six charts into six groups of
one, and pooling is the only thing currently holding the FX charts above the
capacity floor - so a per-chart prune could cost more capacity than it buys.
Still report-only. No fingerprint change, no retrain forced.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
The MI screen collapsed to "-1.00000 nats/feature over 0 permutations" on the
first COLD start after a wipe, taking the new per-column keep-screen with it. On
the same chart seconds earlier the auto-tuner had scored the same function fine:
auto-tune complete - 12 candidates scored, mutual information 0.00843 nats
feature/label information - -1.00000 nats/feature ... over 0 permutations
So the data exists and something between the two collapses the sample window.
Cold-start only - every successful report today came from a warm start where the
models loaded from disk, and wiping is what exposed it.
I formed three explanations (label-cache invalidation by the tuner, a shift pad
scaled off an unmeasured label resolution, a zero feature width) and each failed
against the log. Three failed explanations is the point where guessing stops and
instrumenting starts.
BuildMiSample has five distinct -1 exits and the caller can only observe the
collapsed result. Each now names itself and prints the terms that would explain
it: bars, lo/hi, MI_MIN_SAMPLES, OOS split, history window, shift pad and the
measured label resolution the pad scales from. Throttled via TCLog.
Deliberately NOT also "fixing" the latch that makes this stick
(ReportFeatureLabelInformation sets m_miReportDone at ENTRY regardless of
outcome, and the first member then sets g_ensembleChartMiReportDone, so one
failed attempt disables the screen for every member on the chart for the whole
run). If the cause is a genuine cold-start ordering problem, making it retry
would paper over it - the instrumentation decides which fix is correct.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Step 1 of the prune, stopping deliberately short of pruning - two blockers make
an immediate mask the wrong move, and this is the measurement that decides
whether pruning is worth doing at all.
WHY NOT PRUNE YET:
* the screen runs with cross-asset ABSENT - its own log line says the numbers
"describe a NARROWER vector than training will use". A mask built from it
would have no evidence either way about the cross-asset block.
* a per-chart mask FRAGMENTS THE POOL. The mask must participate in the
fingerprint, and the pool only accepts peers with an identical feature
layout. Pooling is currently the only thing keeping the FX trio off the
capacity floor - the three pool-poor charts (SP500, XAUUSD, XTIUSD) are
exactly the three still floored. Six per-chart masks = six pool groups of
one, and pruning could cost more capacity than it buys.
WHAT THIS ADDS: the per-column MI was always computed inside ScoreMiSample and
thrown away except for the sum and the max. It is retained now, and the same
permutation draws that build the headline null also accumulate a PER-COLUMN null,
which is what a per-column p-value needs - distinct from the null-of-the-max,
which answers the single family-wise question "is the strongest column real".
Selection uses Benjamini-Hochberg at q=0.10, NOT the family-wise bar. FWER
controls the chance of one false positive, which is right for a verdict and far
too conservative for selection - it would discard every genuinely weak-but-useful
feature. BH bounds the expected SHARE of kept columns that are noise, which is
what a feature set cares about.
The report prints the decision in capacity units: columns kept, the resulting
input width, and the first-layer budget before and after against the 16-wide
floor. 3 of 52 is not a feature set; 45 of 52 is not worth a fingerprint re-key.
The cross-asset caveat prints itself when it applies.
Context that makes this worth doing at all: under the pivot-event label the MI
screen now reads "above the noise floor - a real association" - mean 4x the null
(p=0.005), strongest column 7.7x the null-max, excess 0.80% of label entropy,
against 1.3x / 1.15x / ~0.1% under the old label. The noise-floor verdict that
closed several earlier directions was a property of the OLD label.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
RETRAIN-FORCING and deliberately so. Two independent fixes for the same symptom -
charts that go quiet while others overtrade.
1. THE VOTE CURRENCY IS NOW EDGE OVER CHANCE, not an absolute win rate.
A tier weight is a raw win rate and a raw win rate means nothing without the
chance rate behind it: 30% is strong under a 14% base rate and catastrophic under
50%, yet both entered the mean as "30". That is why the threshold needed
re-tuning every time the label changed - 25 was permissive at ~70% win rates
under the old direction label and a near-unanimity rule at ~30% under the
pivot-event one - and why one chart's 25% was never the same statement as
another's. Subtracting the member's own chance rate makes the units percentage
points of demonstrated edge, comparable across charts, labels and regimes.
Clamped at zero: a below-chance tier is anti-informative, and contributing
negatively would act on a broken model as an inverted oracle rather than
discarding it.
2. A NO-SKILL MEMBER IS NOW ABSENT, NOT ABSTAINING.
Measured on XTIUSD: a Perceptron collapsed to B97/S6/N3, pooled win rate 11.5%
against a 14% chance rate - worse than guessing - and still voting. Three healthy
members voting Sell scored -21.06/0.77 = -27.4 and cleared; with the dead one
voting Buy it became (-21.06+1.44)/0.89 = -22.0 and was BLOCKED. It vetoed its
own ensemble on ~95% of bars, and that WAS the chart's 3.3% coverage. Neither
existing guard caught it: it IS self-ranked and its tier weights were 11-14.
The fix has to remove it from the DIVISOR, not just the sum - an abstainer
contributes weight by design, so zeroing only the contribution makes the dilution
worse. VoteCapableWeight() already means exactly "may this member's weight sit in
the denominator", so the skill test belongs there. ReconstructionWeight() and the
OOS scorer's divisor move with it or the scorer certifies a vote live does not
cast. The skill test reads the PREVIOUS era's measurement - gating this era's
vote on this era's own outcome would be circular.
3. CHECKPOINT BURN-IN (ENSEMBLE_CHECKPOINT_MIN_ERA 20).
XAUUSD deployed the checkpoint from ERA 2, XTIUSD from ERA 4, each after 69 and
65 further eras failed to beat it. Ensemble coverage measures AGREEMENT, and four
models that have barely moved off their initialisation agree almost by
construction - so coverage is inflated exactly when the models know least and
decays as they differentiate (XAUUSD 6.6% at era 8 -> 0.4% at era 75). Since
selectionScore is precision discounted by coverage, an early era outscores every
mature one and the ladder freezes on it.
INTENDED CONSEQUENCE: a chart whose MATURE coverage cannot clear the floor now
refuses to deploy rather than shipping era-2 weights. Fewer deploys, honest ones.
Burn-in eras are also kept out of g_ensCandidateEras (they could not have won, so
counting them inflates the family-wise N and raises the bar for nothing) and out
of g_ensErasSinceBest (or the run reaches "no better vote for N eras" with no
best to beat, exhausting the escalation ladder before the first era may compete).
Every pinned threshold and .stats record is in the OLD currency and is now
meaningless - this forces a fresh start on its own. Nothing needs re-tuning
because the threshold is DERIVED: the sweep re-picks the rung by itself.
Compiled clean; NOT yet run.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
THE REGRESSION, mine, from c6eb908. LoadModelStats() dropped the whole ensemble
record unless the stored threshold EQUALLED the live one. That was right while
the threshold was an operator input - a record built at 25% says nothing about a
chart now running 15%. Once the threshold became derived and pinned the
comparison inverted its own meaning: at load time g_ensembleVoteThreshold is
still the Signal_ThresholdOpen SEED, so the stored derived value never matches
and the record is ALWAYS dropped. Two things died with it, silently:
* g_ensDeployApproved - a DEPLOYED ensemble came back as a training one on
every restart, discarding the family-wise deploy it had earned.
* the pinned threshold itself - PublishVoteThreshold() only fires on a positive
g_ensDerivedThreshold, so a deployed chart would have traded the .chr seed
instead of the rung its deploy was certified at. certified != traded, the
defect 2c443ba fixed, reintroduced three commits later.
Not yet observed live only because SP500 deployed at 10:20, after the last
restart at 09:54, so no restart has crossed a deployed state.
Now ADOPTED, not compared: threshold, counts and deploy flag restore together,
the only coherent state - the counts were conditional on that threshold, which is
why it is stored beside them. Same doctrine as the .cfg topology: adopt what the
model was certified with, never re-derive it underneath a checkpoint. The
most-complete-copy guard is unchanged. It now logs what it restored.
THE PANEL LABEL. "Vote win rate: 34% (338 calls at or above the 15% threshold,
this era 31%)" -> "Accuracy: 34%". The call count, threshold and this-era figure
are diagnostics, all present in the era log line, and on a panel they buried the
one number anyone reads. The threshold no longer needs naming either: it is
derived and pinned rather than an operator's choice, so it is not a caveat on the
percentage. The era/models/deployable suffix appended at era end goes with them.
Compiled clean; NOT yet run.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
THE EXIT KNOB. Exit_On_Reversal_Vote (default false) replaces the deleted
Signal_ThresholdClose with one boolean: false pins the close threshold to an
arithmetically unreachable 101, true pins it to the SAME threshold the entry
uses - the seed at first, then the derived value, republished together whenever
it moves. A second threshold was always redundant; "the bot now says the other
way" is one question.
It also arms CExpertSignalCustom::m_holdToBarrier, which was DEAD CODE:
HoldToBarrier(bool) had no caller anywhere in the build, so the flag had been
permanently false and the disabled close threshold was carrying the whole
hold-to-barrier policy alone. Both halves now move together.
Default stays false because the reason is statistical: the gate certifies
P(label agrees | vote fired) against a label that runs to the barrier, so an
early close trades something never measured. Turning it on is a different
strategy, not a tightening of this one.
THE PIN. The live threshold now moves only when an era's weights become the
checkpoint, and freezes once g_ensDeployApproved. Every era still derives its own
rung - that is how the best one is found - but the rung that TRADES belongs to
the checkpoint, exactly as the weights do. Two reasons, one measured and one
structural: the per-era rung moves on 6-34% of steps (the live run flapped
SP500 15 -> 10 -> 15 within a minute of starting), and without the pin a later
era's rung could end up applied to an earlier era's deployed model. A ladder
restart releases the pin, since clearing the checkpoint clears what it pinned.
The era line now prints the rung its own numbers came from, so it stays honest
when that differs from the pinned one.
THE ATOMIC RENAME retried zero times. Six charts share the TrainPool and AltData
directories, so a publish regularly lands while a peer chart holds the
destination open and FileMove returns 5004 - 27 times in one day on the live
fleet. Nothing was lost (the temp keeps the new content, the old file stays
intact) but the row did not update until the next publish. Now four attempts at
25ms, on the FAILURE PATH ONLY - a successful rename never sleeps - and skipped
in the tester, where the contention cannot happen and Sleep would distort a pass.
A rescued retry is logged, so worsening contention is visible.
Retrain-neutral. Compiled clean; NOT yet run.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Signal_ThresholdOpen becomes a seed. The era verdict now picks the HIGHEST
sweep rung whose vote still clears the whole deploy gate - coverage floor,
exact-binomial precision bar and two-sidedness together - computes the era's
verdict AT that rung, and publishes it to the live signal's m_threshold_open
so the bar the gate certifies is the bar the EA trades.
Measured on 619 era verdicts across all six live charts:
* every era on every symbol had at least one rung clearing the full gate.
At the fixed 25% the fleet was actually running, four of six symbols had
none, ever. The threshold, not the models, was the blocker.
* walk-forward (rung derived on era N, scored on era N+1): 10.2% coverage /
31.8% precision, against an oracle re-picking on N+1 of 10.3% / 31.7%.
Near-zero shrinkage - a measurement, not a fit. It holds because the
binding constraint is COVERAGE, a near-deterministic step function of the
vote distribution, not precision.
* vs a fixed 15% (best global value): +0.6pp precision, 3.4pp less coverage.
vs a fixed 20%: deployable on all six rather than four of six.
Selection on the highest PASSING rung, never on the best-precision rung - that
is a best-of-6 on a noisy statistic and this project has crowned noise that way
four times. The multiplicity that remains is paid for: nTried in
EnsembleSurvivesSelection is now eras x rungs. Costs nothing - all six charts
clear it by 6.5-12 sigma even forming z on effective rather than raw calls.
Also fixes, in the same path: the direction-policy gate is hoisted above the
per-rung tally so every rung is scored on the population the gate certifies.
Retrain-neutral: not in BuildModelFingerprint(), no .nnw re-keyed.
Compiled clean; NOT yet run.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
The gate could say "coverage too low" but never "and here is what it
would be one rung down", so the single parameter most responsible for a
refusal was the one its own output said least about. Working it out by
hand needed a model of the vote's quantisation (a weighted mean of member
tier weights, so the threshold is really a quorum) and that model could
not be checked: MT5 stores the input PER CHART in profiles\Charts\*
\chart*.chr, so an already-attached EA ignores a changed source default -
confirmed by a full close/recompile/relaunch after which the log still
read "fired at vote>=25%". There was no cheap A/B available.
Each era now reports coverage and precision at every PERCENTAGE_PRESETS
rung from 5% to 30%, measured on the same rows the verdict just scored,
marking the active rung and any rung that clears the coverage floor. It
is accumulated before the live threshold test so the sweep sees every
scored row, and gated by the same direction policy so its numbers are
comparable with what the gate certifies. Nothing reads it to decide
anything.
Motivation, measured overnight across 534 eras with zero runtime errors:
every symbol clears its precision bar and every symbol fails on coverage
(0.0-3.3% against a ~6.7-7.2% floor), while the members stay healthy
throughout at 22-27% precision against a 13-14% chance rate on 25-38% of
bars. Only the aggregation fails. SP500 was DEPLOYABLE at era 5 with 7.5%
coverage and sits at 0.7% by era 536 with precision unchanged - more
training is proven not to help, because a 25% threshold against ~30 tier
weights demands unanimity and the models diverge as they specialise.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
The message I added one commit ago printed, verbatim:
"Precision was 28.4% against a 39.1% bar, so the calls it DID make
were NOT good enough: the vote is too selective, not too weak."
Those two clauses say opposite things. Only the "NOT" was conditional;
the diagnosis after the colon was hardcoded, so whenever precision missed
its bar the line asserted and denied the same thing in one sentence.
The two cases are opposite diagnoses and must not share a sentence:
- Precision CLEARED its bar -> the calls were good and there were too few
of them. The vote is too selective.
- Precision MISSED its bar -> this is still not "the model is weak",
because the exact-binomial floor is computed from the INDEPENDENT call
count, so thin coverage inflates the very bar it is judged against.
Reporting that as a second, separate failure sends a reader off to fix
the model when coverage is what moved the target.
Caught by reading the diagnostic's own first live firing rather than by
review - the same way the two regressions before it were found.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
The stage-3 refusal read "no era's combined vote ever cleared the
deployability floor" and then listed all three conditions in one
parenthesis - fires on a quarter of the base rate, both directions alive,
precision above the reference by 2 sigma - without saying which one fired.
The three have nothing in common as fixes, so the list was not a
diagnosis. It cost real time to work out by hand tonight, and the answer
was coverage every time.
Keeps the best era's coverage, its floor and its precision bar alongside
the win rate already retained, and names the failing condition. The
coverage branch also states whether the calls it DID make cleared the
precision bar, because "too selective" and "too weak" are opposite
problems that the old message could not distinguish, and points at
Signal_ThresholdOpen being a quorum rather than at the models.
Cleared at both existing reset sites so a refusal can never describe an
era that is no longer the best.
Context: SP500 reached stage 3 at era 67 and was refused on coverage
0.5% against a 6.9% floor while its precision was 62.5% against a 61.4%
bar - i.e. the vote was too selective, not too weak. Same doctrine as
CTrainPoolReader::Announce's reject list.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
The old target asked "which way is the next pivot", which every bar of a
~13-20 bar leg answers identically - so the net could not tell a fresh turn
from mid-trend and learned the prevailing direction instead. Its own
zero-skill reference showed it: chance sat at 56/44, i.e. the label WAS the
drift, and the gate's standing warning ("a model that only reproduces it has
found the drift, not an edge") applied to the target itself.
Buy now means a swing LOW commits within PIVOT_LABEL_TOLERANCE_BARS bars,
Sell a swing HIGH, Neutral no turn that close. Pivot type is read from
ZigZagBuffer[p] == Low[p], exact by construction in ZigZag.mq5. The existing
P1-final-once-P2-commits rule is kept and now also settles the NEGATIVE
verdict, so the Neutral majority is permanent rather than provisional.
Measured on a full fresh run, all 6 charts:
class balance 56/44/~0 -> 13.7/13.7/72.6 (imbalance 5.3:1)
label overlap ~31 bars -> 5 bars
independent obs 368-1086 -> 2331-7032
weights/obs 9.2-26.2 -> 1.1-4.2
coverage 100% of bars -> 17-48%
23 of 24 models fire all three classes at precision 18-32% vs 13-15%
chance; SP500's ensemble reaches DEPLOYABLE (32.3% vs a 24.0% bar).
Two bindings had to move with the label:
- The capacity deflator. m_swingLifespan fed EstimatedInSampleBars() as
raw/31, measured from the legs. Overlap is now a property of the LABEL -
one turn is callable by exactly the tolerance window - so it is the
window, not a leg measurement. Missing this would have kept every model
sized for a sixth of its real evidence.
- A dormant cold-start seed. Labels.mqh seeds the output bias toward the
dominant class above COLD_START_SEED_MIN_DOMINANCE (0.70); at 56/44 it
never armed, at 72.6% Neutral it does - writing a fixed +-3.0 against a
true prior spread of ~1.75, which would start every net predicting Neutral
~95% of the time. Now seeds the measured log-prior, zero-centred and
capped by the same guard rail the logit adjustment uses (Lin et al. 2017).
TGT:SWG1 -> TGT:PVT1:<tolerance>, with the window in the token because it is
part of the label: every .nnw is invalidated and the fleet retrains.
Depth is still gated, and now for a precise reason: the first dense layer
stays at FIRST_LAYER_MIN_WIDTH because budget = effN/(inputWidth+1) is 11.2
at input 624. Reaching the next rung needs inputWidth <= ~218, i.e. feature
pruning - not architecture.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
- Added bulk read/write methods for feature caches in IFeaturesView and its implementations to optimize performance.
- Introduced LabelCacheInvalidateAll method to manage label cache invalidation alongside feature cache.
- Implemented PooledIndependentBars method in topology interfaces to account for additional independent observations.
- Enhanced risk budget management with throttling for peak-equity updates to reduce unnecessary file operations.
- Improved error handling and logging for ATR trailing stops to ensure better visibility of issues.
- Updated alt-data handling to prevent unnecessary operations during testing and optimization phases.
"Hundreds of times slower than a regular EA" decomposed into two
multiplied factors, both measured:
1. THE OPTIMIZER STEP RAN IN INTERPRETED MQL5. The CPU tier shipped
the F4 accumulate exports with deliberately no matching apply
(WarriorCPU.h said so), so on the DLL backend - this box - every
TRAIN_BATCH_SIZE=8 batch fell to the host loop in ApplyAccumToBlock:
a per-weight MQL5 pass through CBufferDouble.At()/Update() plus four
full weight-matrix BufferRead/Write round trips. The 2026-07-26
profile had already shown the per-sample Adam step at 81% of ALL
runtime (feedForward: 8%; feature building: 0.35%) - sqrt+divide
per weight vs one multiply-add; moving it into MQL5 made it worse.
New CPU_ApplyAccumAdam / CPU_ApplyAccumMomentum: one element-wise
ParallelFor takes the batch-mean step and zeroes the accumulator
DLL-side, generic over any flat block (dense/conv/LSTM/batch-norm -
all apply paths funnel through ApplyAccumToBlock, which now tries
the DLL first, with the same one-warning failure latch as the
OpenCL fast path). Math is the shipped step to the last clamp:
sqrt-stored v, ClampDelta, AdamW decay, ClampWeight.
batch_accum_check extended (check 6) and ALL PASS: apply == host
reference at B=8/B=4, accumulator zeroed, and B=1 accumulate+apply
== the unbatched Adam kernel BIT-EXACTLY (kernel-vs-kernel, no
transcription). DLL rebuilt with the shipped /fp:fast recipe.
2. A 24% DUTY CYCLE. Train sliced 120ms per 500ms timer period
(30ms/member x4), leaving the chart thread idle 76% of the time.
Now 300ms total (75ms/member): ~60% duty, ~2.5x, click latency
bounded at ~300ms while training runs - between the fully-reactive
120 and the documented "sticky drag" 480.
DEPLOYMENT COUPLING: the new .ex5 #imports the new exports, so it will
NOT LOAD against the old WarriorCPU.dll ("cannot find function"). Copy
DirectML\WarriorCPU.dll into MQL5\Libraries (terminal closed) in the
same step as deploying the new .ex5.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
The 18:23 terminal close (20260825.log) killed two of six charts inside
OnDeinit: they printed "shutting down" then nothing for 5.9 s until
"Abnormal termination", stranding ~700 objects each - including the one
family no prefix sweep can reach, the control panel (CAppDialog names
its 15 objects <numeric instance id><control>, and a re-attach mints a
new id, so a killed panel is a permanent ghost; XTIUSD carried one
across sessions). The stall sat in the two file writes that preceded
all visible cleanup while the four sibling charts flooded the same
2013-era disk - the ~4x18MB-per-chart shutdown weight saves.
Three changes:
1. OnDeinit touches no file until the chart is clean. CVoteArrowStore
splits Save() into Snapshot() (the chart scan, in memory) and
WriteSnapshot() (the disk half, consuming). New order: status label,
vote-arrow snapshot, prefix sweep, panel destroy - all object ops -
then member sidecars, final sweep, timings, and only then the
visibility file, the vote-arrow write and the weight saves.
2. PurgeOrphanedPanelObjects() at OnInit: deletes numeric-prefix
CAppDialog ghosts by name (6 chrome + 9 buttons), qualifying a
prefix only when >=4 of OUR button names carry it, so a foreign
dialog sharing stock chrome names is never touched.
3. m_netDirty: set by every net mutation (both backProp sites, both
RestoreWeights sites, online learning conservatively, panel reset),
cleared only on a successful Net.Save. Shutdown AND the per-bar
autosave now skip the ~18MB write when the net is provably unchanged
- for converged ensembles that is every save - which removes the
very flood that starved the sibling charts. .stats still writes
every time (small; carries the vote record and calibration). A
skipped save leaves the .nnw header dtStudied stale, which is the
already-handled attach-after-offline-gap case.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
"Vote win rate: measuring..." never resolved on a deployed chart whose
.stats predate the WST7 ensemble record: g_ensCumOosTotal is fed only by
the era-end combined-vote scorer (Training.mqh), and a deployed ensemble
runs no further eras. The replay pass rebuilt every MEMBER's ladder
(64-71% each, per the 16:12 log) but nothing ever scored the COMBINED
vote, so the aggregate line sat on "measuring" while 300+ arrows drew.
The overlay sweep already reconstructs the vote per bar with the live
threshold and direction policy - so it now also tallies, BEFORE
declustering (NMS thins arrows, not calls), each threshold-clearing bar
against the inline swing-pivot label (same resolution ScoreReplayFromCache
uses, same window-mismatch reason). On sweep completion Warrior_EA.mq5
harvests the tally through a consuming one-shot read and adopts it ONLY
when the record is empty and the models are deployed - a training-time
sweep can never pre-empt the era scorer, and a restored record always
wins. The result is persisted immediately into every member's .stats.
Also verified against the same log: the sweep does NOT ignore
DrawUnfilteredSignals - 4986 voter bars -> ~300 arrows, all gated on the
25% open threshold. The arrow increase vs the restored set (41-312 saved)
is the replay-minted ladder reading stronger (partly in-sample), plus the
reconstruction deliberately not replaying order validation/session hours
(tooltip says so); the backfilled record carries the same caveat and is
labelled so in the log.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
The 15:13 session proved the replay pass ran end-to-end on all 24 models
and scored ZERO labelled bars on every one of them, while each rescan sat
on ~5000 scored predictions (~2755 Buy / ~2232 Sell). The two windows
never overlapped:
StartLabelCachePrebuild deliberately keeps a CONVERGED model's
dtStudied watermark (it gates inference recency and must not move), so
the prebuild's window was the handful of bars since the last studied
bar - all with uncommitted pivots, hence "label cache pre-built -
Buy: 0 | Sell: 0 | Neutral: 0" on every member.
The label never needed a cache. SwingPivotDirectionLabel(idx) is a pure
function of the ZigZag/Close/ATR buffers the rescan itself refreshes over
exactly the scoring window, and m_lastLabelLifespan == 0 is its own
unresolved flag - the same finality gate the cache applies, applied
directly. ScoreReplayFromCache now resolves each bar's label inline and
the label-prebuild stage is deleted from the rebuild state machine
outright; going through a cache built for a different window was
indirection that changed the answer.
Also splits the empty-result diagnostics: "no resolved labels" (a
windowing/data fault) is now distinguished from "labels present, every
call Neutral" (a calibration verdict). The first version reported the
second message for both, which mislabelled this very bug as a calibration
outcome in the same breath as reporting scored=0.
Honest limitation, stated in the code too: the replay window includes
bars the model trained on, so a replay-minted ladder is measured partly
in-sample and will read stronger than a holdout-measured one. It is
replaced by the genuine article at the next completed scoring pass; until
then it is what makes a restarted deployed model able to vote at all.
Compile-verified in _claude_stage: 0 errors, 0 warnings.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
The previous commit persisted the tier ladder, which fixes this going
forward but did nothing for models whose .stats predates WST7 - they
still had to retrain to mint one. They never did. Every number a
converged model needs in order to vote is a pure function of weights
already on disk plus labels derivable from the chart, so replay them:
stage 1 build the label cache (existing chunked prebuild)
stage 2 rescan history (existing chunked rescan, deployed net)
stage 3 score + rank + persist (one walk over two arrays)
ScoreReplayFromCache() walks m_arrowSignalCache against
m_labelCacheBuy/Sell, fills the same m_oosTierFired/Hits and per-class
totals pass 3 fills, and hands them to RankTiersFromOos() - deliberately
feeding the existing ranker rather than reimplementing it. The shrinkage,
the chance reference and the module trust weight are subtle enough that a
second copy would drift, and a ladder measured by a slightly different
rule would be silently incomparable with every ladder training produced.
AdvanceDeployedRebuild() sequences the three stages off the timer. It has
to be a sequence: stages 1 and 2 are each minutes of work draining in
time-boxed slices, and stage 2's output is meaningless until stage 1 has
labels to score against. The previous version ran the rescan with no
labels at all, which is why it could only ever rebuild arrows and never
the ladder - the thing actually blocking the vote.
The result is written to .stats immediately. The failure being repaired
is state that lived in memory and was never written down; recomputing it
and not saving it would repeat that exactly.
Also routes every rescan completion through one hook, so there is a
single place that knows what a finished rescan means - republish for a
manual one, score and rank for a rebuild.
Compile-verified in _claude_stage: 0 errors, 0 warnings.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
THIS IS NOT A DISPLAY BUG. A deployed model could not vote, or trade, at
any point after a terminal restart, and never would have.
LiveVoteContribution() returns 0 for every call until m_tiersSelfRanked
is set - deliberately, and correctly: before RankTiersFromOos() runs,
m_pattern_0..3 hold the constructor's stock 25/50/75/100, which since the
2026-08-18 currency change is the WRONG UNIT rather than a weak opinion,
and one unranked member would drag the whole ensemble over any threshold.
But that ladder is produced ONLY by a completed pass 3, and it was never
persisted - the code comment at LiveVoteContribution says so outright.
A converged model runs no further passes. So on every restart it lost its
entire vote permanently:
LiveVoteContribution -> 0 => no live vote ("0 vote/4 flat")
ReconstructionWeight -> 0 => overlay divisor 0 ("0 had a snapshot")
=> no arrows
=> no fired bars, so g_ensCumOosTotal stays 0
=> "measuring..." forever
Every symptom reported over the last three exchanges is that one cause.
The log is unambiguous: six H4 charts resumed at era 70/71, all 24
rescans completed with ~2700 Buy / ~2200 Sell per model, and the overlay
then swept 4999 bars finding "0 had a snapshot". The calls were there;
nothing was permitted to count them.
WST7 now stores the four tier weights, the module trust weight and the
self-ranked flag beside the model. Restored only when the stored flag
says the ladder was MEASURED - a .stats written before a model's first
pass 3 holds the stock ladder, and adopting that as if measured is the
exact error the flag exists to prevent.
A .stats predating WST7 has no ladder, so existing converged models stay
silent until their next scoring pass mints one. That case now prints a
warning naming all three of its symptoms, because each one independently
looks like a different bug.
Compile-verified in _claude_stage: 0 errors, 0 warnings.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
The sidecar added in 484a9d8 restores the vote arrows from the previous
session - but there was no previous session to restore from, and a
deployed ensemble could never produce one.
The overlay that draws the vote layer replays each member's
m_overlaySigSnap, published in exactly one place: RankTiersFromOos, at
pass-3 completion. A converged model runs no further eras. So after a
restart every member's snapshot was empty, would never fill, the sweep
had nothing to replay and the chart stayed blank permanently - no route
back by any path.
The chart rescan is the route: it runs the DEPLOYED net forward over
history and rebuilds the per-bar cache, which is the same quantity pass 3
produces, obtained without training. It already existed for the panel's
Show-Signals button; it just never handed its result to the overlay, so
on the default filtered view a rescan rebuilt only the RAW per-member
layer - the one that is hidden - and appeared to do nothing.
- PublishOverlaySnapshotFromCache() extracted from RankTiersFromOos, so
the era end and a completed rescan publish through one implementation.
- A completed rescan now calls it, which also arms the sweep.
- PollTraining auto-arms one rescan for a model that is converged, has no
snapshot, and is on the filtered view. One-shot: a model that
legitimately calls Neutral everywhere must not rescan forever chasing a
snapshot that is correctly empty. On the timer, not in OnInit - it is a
full feedForward per bar over up to 5000 bars and drains in the same
time-boxed slices as a manual rescan.
Together with the sidecar this closes both halves: the rescan covers the
first session and any chart whose file was lost or invalidated by a
threshold change; the sidecar covers every session after one is saved.
Compile-verified in _claude_stage: 0 errors, 0 warnings.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Four reported symptoms, three of them one root cause: the ensemble's
certified record was session-scoped and written ONLY at pass-3
completion. A deployed ensemble runs no further eras, so every restart
lost the aggregate win rate, the aggregate panel line and the overlay
snapshots - and could never regenerate them, because regeneration only
happens at an era end that will never come.
THE SELF-CONTRADICTION. Member rows read "Live - learning from new bars"
(from m_trainingComplete) while the line under them read "training, not
tradable yet" (from `prospective`, which means "this number came from
ProspectiveVote() rather than a real Direction() call" - what happens on
any bar where every member abstains, and which says nothing whatever
about training state). Both now resolve through one predicate:
WarriorChartModelsDeployed(), fed by members publishing their own state
on the same slot and cadence as their vote. Adds a third verdict word,
"armed (bar still open)", for a deployed model on a prospective
recompute - the case that used to claim it was training.
DEPLOYED PANEL. Once every published model is converged the per-member
rows are dropped: what ships is the aggregate vote win rate, the live
vote, and the verdict. While training the rows stay - they are the only
way a collapsed or lagging member is visible, since a collapsed member
abstains and so is invisible in the aggregate by construction.
ACCURACY NOW RESPECTS THE ENTRY THRESHOLD. The panel's "precision 65%"
came from m_cumOosCorrect/m_cumOosTotal, which counts every bar a model
called Buy or Sell - threshold-blind, and per-model rather than
per-vote. The correct number already existed (votePrecPct: bars where
|vote| >= threshold and the direction policy allows) and is now what the
panel shows, with the threshold named in the text because the number is
meaningless without it.
VOTE ARROWS PERSIST. With DrawUnfilteredSignals off - the default - the
chart shows SIG_VOTE_PREFIX arrows, and nothing saved them:
CChartUI's .arrows sidecar is member-scoped and never saw that layer.
New CVoteArrowStore mirrors them to a chart-keyed sidecar and restores
them progressively at init, on the same budgeted non-blocking path.
The header stores the open/close thresholds; a mismatch on load DISCARDS
the arrows rather than redrawing a picture of a strategy no longer
configured - stale arrows are worse than none, because none is visibly
empty and stale is confidently wrong.
Also: .stats bumped to WST7 carrying the ensemble record (guarded on
threshold match, most-complete-copy-wins), and the loader's version
tests collapsed from an or-chain to ">=" - the magics are ASCII 'WST1'..
'WST7' so they are already ordered, and a missed arm in that chain reads
the NEXT field's bytes into this one, which fails as plausible numbers
rather than as an error.
Compile-verified in _claude_stage: 0 errors, 0 warnings.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Five modes went, all of them staking real risk on the model's confidence:
Intelligent entry (ENTRY_INTELLIGENT), stop (SL_INTELLIGENT), target
(TP_INTELLIGENT), trailing (CTrailingIntelligent) and lot size
(CMoneyIntelligent's quarter-Kelly). With them, the Confidence_Source
input and the CONFIDENCE_SOURCE enum, whose only job was choosing which
number those five read.
The reason is calibration, not correctness: the confidence magnitude is
known to be miscalibrated against the label prior, so every one of these
modes multiplied money by a quantity whose units were never established.
The DB arm had a second, independent defect - since the tester DB guard
(SignalDatabaseActive) it reads 0 in tester and optimizer but non-zero
live, so any backtest of CONF_DB/CONF_BLENDED could not reproduce live
trading. And what the DB produces is a filter-RANKING win rate, not a
per-trade win probability.
Both confidence numbers are still recorded per trade (aiConfidence /
dbConfidence) and still bucketed against outcome in TradeJournalReport.
Recording is what keeps the question answerable; acting on it was the
part with no evidence behind it. ConfidenceBridge.mqh now carries an
explicit telemetry-only rule at the top.
ENUM ORDINALS PINNED. Removing a member vacated a value in four enums at
once and MT5 does not validate an enum input replayed from a saved .set
or a stored optimization pass. TRAILING_STRATEGY and
MONEY_MANAGEMENT_STRATEGY now carry explicit values so the survivors keep
the numbers they were saved as, and ValidateBarrierInputs is widened into
ValidateTradeManagementInputs covering SL_Mode, TP_Mode,
Entry_Multiplier, TrailingStrategy and MM_STRATEGY. Without that gate a
chart saved with the Intelligent stop would feed SL_Mode = -1 into a
multiplier now used verbatim, placing the stop on the wrong side of entry.
RETRAIN-NEUTRAL: neither SL_Mode nor TP_Mode appears in
BuildModelFingerprint() or ComputeDbConfigFingerprint() since the
swing-pivot target replaced the barrier labels. No .nnw, .cfg or .db
re-keys. Also drops the now-dead g_TradeRewardRiskRatio bridge, the
CMoneyRiskBase::AdjustRiskAmount hook and the unsigned AIConfidence().
Compile-verified in _claude_stage: 0 errors, 0 warnings.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
~2,300 lines. META had real, repeatedly measured ranking skill and ZERO
operating points that ever cleared break-even (0/350 H1 eras, 1/999 H4
pre-2-sigma, 0/8 pooled fitted points). The clinching arithmetic was edge x
width = 0.095 ATR/trade against spread 0.099 ATR/trade, and the
dose-response showed the high-conviction tail is temporally unstable -
the precision-vs-threshold slope flips sign between calib and test on 3 of
4 symbols, so no ex-ante threshold rule exists. It shipped default-off and
never gated a live entry. The self-measured tier weights are what actually
rank the vote, and all six H4 instruments converged on them alone.
RETRAIN-NEUTRAL, and that is the property that made this safe:
- The weights fingerprint emitted "|TGT:META2" or "|TGT:SWG1" from an
if/else. Every direction model already took the SWG1 arm, so
collapsing it to an unconditional append is byte-identical. No .nnw or
.cfg is orphaned or re-keyed.
- NetInputWidth() lost its "+ MetaDescWidth()" term. MetaDescWidth()
returned 0 for every direction model, so the input layer is unchanged.
- DbLegacyAiSlot()'s slot 5 was reachable only with all four Use_* NNs
off AND meta on - a config that never shipped. Every existing .db keeps
its filename.
Deleted outright: Signals/SignalMETA.mqh, Expert/Trading/MetaGate.mqh (the
directory is now empty), Expert/Training/{MetaCorpus,MetaCandidateStore,
MetaFamilies}.mqh, Tests/Test_MetaFamilies.mq5, Meta_Labeling_Design.md.
Unwound in place, the delicate part: Training.mqh carried four
IsMetaTarget() branches whose else-arm WRAPPED the direction body (pass 1
queueing, pass 2 backprop, pass 2.5 calibration, pass 3 OOS scoring). Each
wrapper is removed and the direction body promoted back to its original
nesting - the bodies were never re-indented when the wrappers were added,
so the promoted code is byte-identical to what ran before META existed.
Also gone: the ensemble verdict's meta-veto replay and its
approved/vetoed/unscored counters, the per-family/per-side OOS
decomposition arrays, the m_isTrainQueueCand parallel queue and its
lockstep shuffle, and the S2 era report.
Also removed: the CMetaGate abstraction and the live CheckOpenPosition
veto; m_gates plus AddFilter's non-voter routing and IsVotingSignal()
(META was the only non-voting child, so m_gates was always empty);
m_parentSignal/SetParentSignal (existed only to reach the root's gate);
SweepPrepare/SweepPrepareIndicator (only caller was the corpus sweep);
IsMetaTarget() from all four view interfaces and their adapters;
Use_MetaLabeling, EnableMETA, Meta_ExportDataset, m_trainTarget.
EvalShift is KEPT - HistoricalNetVote() uses it for the filtered overlay,
not just the corpus sweep; only its comment changed. The 2-output softmax
arm in NetForward.mqh is kept too: it costs nothing and is the reusable
binary-head path, now commented as unclaimed rather than as META's.
Compile-verified in _claude_stage: 0 errors, 0 warnings, matching the
pre-edit baseline.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Two removals of work that a backtest was paying for and never using.
1. SignalDatabaseActive() gates the signal DB off in tester/optimizer.
A backtest opened the fingerprinted SQLite DB under FILE_COMMON - and so
did every parallel optimization agent, against the same file, with the
per-tick journal Update() behind them. Measured 2026-08-25 on a 12-agent
SP500 H4 run: zero passes completed in 75 minutes.
It bought nothing, for a reason specific to this EA's current shape: the
DB's only effect on a trading decision is ApplyPatternWeight overriding a
filter's module weight, and that is declined for any self-ranking filter
(CExpertSignalCustom's !filter.SelfRanked() guard). The AI members
self-rank once their tiers are measured, and the classic votes that DID
consume the ranking are gone - so a tester run's DB was written and never
read. Skipping it changes no decision.
One predicate, not two inline guards: OnInit asks the question twice
(InitDatabaseAndJournal, then VerifyDatabaseTransactionCycle) and a run
where those disagreed would try to open a database it never initialised.
The tester now takes journal.InitTrackingOnly(), so close detection,
MAE/MFE and the expectancy-stop feed still run - only the SQLite half is
dropped, and Update() already skipped its INSERT when there is no DB.
Caveat recorded at the predicate: if a future filter consumes DB ranking
WITHOUT self-ranking, this needs revisiting - a backtest would then stop
reproducing live.
2. ExportFeaturesOnly and its two exporters are gone.
Research-only CSV dumps (feature matrix + a hardcoded 8-symbol x 5-TF raw
rates grid), superseded by the research/ python path that reads its own
data. Removed the input, m_exportFeaturesOnly, the setter, both method
declarations, ExportFeatureMatrix()/ExportRawRates() (111 lines in
AutoTune.mqh), the OnTick early-return, and the ctor initialiser.
The config-lock bypass it owned collapses to the plain tester test:
`if(!inTesterOrOpt && !AcquireConfigLock())`. Shared helpers it called -
ServableBars, EnsureBarCachesCapacity, ResizeBuffers, RefreshData - all
have other callers and are untouched.
Compile-verified in _claude_stage: 0 errors, 0 warnings, identical to the
baseline taken before either edit.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Two chart-display fixes reported after watching a converged 4-model
ensemble: the ensemble panel's trailing "(era 69, 4 models,
DEPLOYING)" was frozen at whatever era the ensemble happened to
deploy on, and the separate top-right HUD (one line per model, raw
B/S/N + weight + era + error) was clutter once the vote itself is
what matters.
Root cause of the freeze: g_ensembleVoteLine is written once per era,
at pass-3 completion. A deployed/converged ensemble runs no further
eras (ScheduleTrainingIfNeeded's trainingComplete branch skips
Train() entirely), so that line could never update again - the era
count and "DEPLOYING" marker were permanent set-dressing from the
deploying era, not a live reading.
- EnsembleScoreCombinedVote() drops the era/DEPLOYING tail once
g_ensDeployApproved - nothing left there worth freezing.
- UpdateVoteReadout() (the aggregate "VOTE ..." line, previously its
own top-right chart object) now writes g_liveVoteLine instead of
drawing anything. Both status-label builders - PublishEnsembleStatus
for the ensemble panel, PublishStatus's choke point for the solo
panel - append it as one line, refreshed every tick/timer exactly
as the old HUD was, so the live vote replaces the frozen era tail
in the same visual slot.
- RefreshVoteReadout()'s per-member loop (DisplayHudLine, one
ObjectLabel per model) is deleted outright rather than folded in -
the operator asked for the aggregate only, "without telling me each
individual network".
Follow-on dead-code removal, since DisplayHudLine was the only
caller: the DispProb/DispSignal/MetaGateArmedNow/MetaHasScore/
MetaLastP/MetaLastBe/MetaApproved/MetaVetoed leg of IChartView (and
its AIBaseChartView/AIBaseChartViewImpl/ExpertSignalAIBase forwards)
had no other reader. The underlying data survives untouched -
m_metaTelemetry is still populated live by SignalMETA.mqh,
m_dispSignal still feeds ProspectiveVote - only the chart-view
forwarding that existed solely to reach the deleted HUD is gone.
Compile: 0 errors, 0 warnings (stage).
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
DIRECTION_INTELLIGENT and the drift verdict it fed were removed in
the step-3 demolition (8f21646); WarriorDirectionAllows() now
resolves purely from tradingdirection (LONG_ONLY/SHORT_ONLY/BOTH).
Two comments in the OOS-verdict certification path and the filtered-
overlay reconstruction still described the deleted mechanism -
found while auditing both paths for correctness. No behavior change.
Compile: 0 errors, 0 warnings (stage).
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
The capacity budget is stated in weights per INDEPENDENT observation
and divides by the mean label lifespan to get there. It never once
did: EstimatedInSampleBars() deflates via m_labelOverlap, but it is
only ever called from InitNeuralNetwork, where the label cache does
not exist yet (that same function sets m_labelCachePrebuilt = false
a few lines below), so MeanLifespan() returned its "nothing measured"
default of 1.0 at every call. Every fresh model was sized as though
its labels did not overlap - over-budgeting the first dense layer by
a factor of L, which is several rungs of a power-of-two ladder. The
"expect overfitting, reduce the feature set or pool instruments"
warning is the branch that should fire on H1 and structurally could
not.
Fixed at the source rather than by reordering the boot sequence (the
prebuild is chunked across Train() calls and cannot complete inside
init): MeasureSwingGeometry() walks the ZigZag ONCE at init and
answers both questions from it - the median leg gives the window,
and the leg series gives the mean label lifespan analytically.
SwingPivotDirectionLabel resolves bar i when the SECOND pivot after
it commits, so a bar d bars before pivot P waits d + (the leg
leaving P); summed over every bar of every leg that is exactly the
mean the label walk accumulates.
That also closes the coherence gap the swing target opened: the
window was measured with a private +/-12-bar fractal while the label
aimed at ZigZag(12,5,3) pivots, so it was sized against a leg
distribution the label never used. One pivot source now, the
label's.
Also:
- ResetWeights() re-derives the shape. It rebuilt from the members a
history-starved init had pinned and re-saved them - so the "let
history download, then reset from the panel" advice in both
fallback warnings did nothing at all.
- The CAPACITY line prints the measured lifespan beside the one the
topology was sized for, and warns when they differ by more than a
ladder rung. That is the check that makes the estimator falsifiable.
- Topology reads the view's symbol, not _Symbol (latent for pooling).
- Unmeasured geometry defaults to HISTORY_BARS_FALLBACK, never 1.0:
under-sizing is recoverable, over-sizing silently is not.
Compile: 0 errors, 0 warnings (stage).
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
The MI suite kept its one irreplaceable job - the label-alignment
lookahead scan, whose margin is priced by the headline permutation
null and whose validity is proven by the positive control. Everything
that judged or vetoed on top of that measurement is gone:
- m_dirEvidence deploy veto deleted from all four deploy sites. The
policy is that screens are priors, not gates; the family-wise
selection test on held-out precision is the deploy protection, and
a marginal per-bar MI test cannot veto a model that reads the
window jointly (the report itself said so on every print).
- Per-column CFeatureSelector deleted; BlockPermuteLabels (the null
engine ScoreMiSample depends on, ragged-tail fix intact) moves to
AutoTune.mqh as a free function.
- Feature-lag profile deleted, with its MI_LAG_* constants and
BuildMiSample's featureBarOffset; MiShiftPad no longer pads by
m_historyBars.
Compile: 0 errors, 0 warnings (stage).
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Step 3 of the swing-pivot plan, whole-hog. The swing label is now the ONE
target and the era verdict is precision + recall per class against the
label's own base rate - no win rate, no break-even, no expectancy, no
geometry anywhere in training.
DELETED
- Expert/Excursion/ (4), Expert/BarrierHorizon/ (4), GeometrySweep,
FirstPassageLadder, Labeling/TripleBarrier.mqh (CLabelOverlap survives
in Labeling/LabelOverlap.mqh), 3 test EAs.
- TripleBarrierLabel + walk, fractal label, geometry derivation/scan/
adoption, exit-policy replay, excursion MI targets, the drift verdict
(DIRECTION_INTELLIGENT), the recall floor, balanced-accuracy telemetry,
the barrier defines, the .cfg geometry adopt (slots kept as zeros for
the positional layout), the derived-geometry live-order override.
- TRAINING_TARGET input/enum: direction models are always swing; META2
re-keys the meta head onto label agreement (descriptor loses its two
geometry slots).
REWORKED
- Labels.mqh (1795 -> ~370 lines): AdvanceSwingLabelState with
FINALITY-GATED CACHING - an unresolved bar (pivot pair uncommitted) is
never cached, so it can never freeze as a false Neutral; training,
calibration, OOS scoring and online learning all skip unresolved bars.
- SDeployVerdict: significance-only; SOosTally chance = larger
directional class share; pooled gate poolability = timeframe (record v2).
- Purge/embargo/declustering gaps: the measured mean label resolution
lag (LabelResolutionBars), not a barrier horizon.
- Pool purge key + backfill DB rows: marked at the bar the label
resolved on (m_labelResolveAge), not a fabricated barrier touch.
- Online learning frontier: finality, not a horizon delay.
- m_bestBalancedOos -> m_bestSelectionScore, m_erasSinceBestBalanced ->
m_erasSinceBest, ensemble vote outcome arrays -> label arrays.
STEP 4 folded in: Entry_Multiplier / SL_Mode / TP_Mode / tradingdirection
are inputs again - trade management is the tester GA's search space.
Fingerprints: every direction model re-keys (TGT:SWG1 now unconditional,
CUT token gone); META1 -> META2. Full retrain, as planned.
Compile-verified in _claude_stage: Warrior_EA + both surviving test EAs,
0 errors, 0 warnings each.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
- TrainingTarget defaults to TARGET_SWING.
- LogitAdjustTau input, preset enum and all plumbing deleted: tau is fixed
at 1.0 (the full log-prior, Menon et al.'s consistent value); the
delivered strength is capped to the head's usable logit range from the
priors the prebuild measures. The CAPPED journal line is the step-1
measurement. |LA💯BS becomes a frozen legacy fingerprint slot, so no
existing model re-keys.
- The swing label measures its own resolution lag (idx - P2, the earliest
bar P1 can be final on) into the overlap/SE machinery, capped at
SWING_SCAN_CAP_BARS instead of a barrier horizon it does not have.
- The prebuild line is target-aware: both-won, timeout and horizon-lifespan
fragments are barrier-walk facts and no longer decorate swing counts.
Compile-verified in _claude_stage: 0 errors, 0 warnings.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Operator's observation, verified against Examples/ZigZag.mq5's selection loop:
the only erasures it performs are ZigZagBuffer[last_high_pos] while hunting a
bottom and ZigZagBuffer[last_low_pos] while hunting a peak. A pivot therefore
leaves the erasable slot permanently the moment the OPPOSITE pivot is committed,
and can never move again - the opposite pivot does not itself need to be final.
SwingPivotDirectionLabel now waits for that event instead of for
m_swingConfirmationBars. The bar aims at P1, so it becomes trainable once P2
exists; pivots alternate by construction, so P2 is the next non-zero bar and
needs no type test. Until then the label is not knowable and the bar is Neutral.
Exact rather than a guess, and it removes the need to measure a repaint-lag
distribution at all. SwingConfirmationBars keeps its other uses; it is no longer
this target's lookahead control.
Compile-verified in the staging copy: 0 errors, 0 warnings.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Series indices are relative to now, so one new bar moves every cached bar's
index by one. EnsureBarCachesCapacity answered that by wiping the label cache,
the excursion caches, the ladder and the feature cache and rebuilding the whole
prebuild from scratch - on any timeframe where a bar closes before a run
finishes, the labels were being recomputed continuously and the training set
never held still.
The labels do not change when a candle closes. ShiftBarCaches moves every
per-bar cache up by the number of new bars, marks only those newest bars as
unfilled, and leaves the rest exactly as computed. CFirstPassageLadder gets a
matching Shift (resizing directly rather than through Allocate, which zeroes the
ages this is preserving).
Refuses, falling back to the full rebuild, when a prebuild is mid-flight: its
cursor is an index into the array being moved.
Compile-verified in the staging copy: 0 errors, 0 warnings.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
TARGET_SWING: the direction models learn which way the next CONFIRMED SWING
PIVOT lies from the current close. Geometry-free - the label owes nothing to a
stop, target or horizon - which is what lets trade management be tuned
separately instead of being baked into what the net learns.
SwingPivotDirectionLabel reuses the ZigZag pivot the horizon and leg-size
measurement already walk, so there is ONE notion of "pivot" in the codebase. It
walks forward in time and stops at m_swingConfirmationBars: a pivot nearer than
that is still repainting, so its label is not knowable yet and the bar stays
Neutral. That boundary is the whole lookahead control for this target.
TrainingTarget input is back (TARGET_BARRIER default, unchanged behaviour) with
TARGET_FRACTAL and TARGET_SWING beside it; |TGT:SWG1 joins the fingerprint so
switching trains a separate model rather than relabelling an existing one.
ADZigZag was renamed to ZigZag throughout (30 identifiers). It has loaded
MetaTrader's stock Examples\ZigZag at its stock defaults for some time - the
migration was done, only the name was left behind, and a name that says "AD"
about a stock indicator is exactly the legacy pointer this codebase should not
carry. No behaviour change: same #resource, same params.
Compile-verified in the staging copy: 0 errors, 0 warnings.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
First 35 eras across both charts, this run:
shipped 1.21/2.43 (SP500) and 1.26/2.52 (USDJPY): mean -0.0525R,
positive in 6 of 35 eras
best plateau after the neighbourhood guard: mean +0.0292R,
positive in only 17 of 35
most-recommended pair: 20.00/0.50, seven times - a ~40:1 lottery that is
simply the least negative cell in an all-negative grid
The recommendation jumps between opposite corners of the ladder between
consecutive eras, which is a grid fitting noise rather than a geometry worth
adopting. Two changes so the line cannot be misread:
- GEOSWEEP_MAX_TIMEOUT_SHARE (0.70): a cell where most trades never touch
EITHER barrier is not a geometry being tested, it is the horizon close being
measured. 20.00/20.00 timed out on 100% of trades and was still selected.
Excluded from SELECTION only; the cell stays filled and readable.
- When the winning plateau is <= 0 the line now says so in those words:
"NOTHING ON THE LADDER PAYS ... the pair below is the LEAST NEGATIVE cell,
not an edge."
Still measurement only - nothing reads the recommendation and no geometry moves.
Compile-verified in the staging copy: 0 errors, 0 warnings.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Step 1 of decoupling SL/TP from training. The geometry is currently chosen
BEFORE the model exists - excursions -> stop at a quantile -> target at the
policy minimum ratio -> labels -> the net learns those labels - so it has never
been asked which pair maximises expectancy GIVEN WHAT THE MODEL CAN PREDICT.
The scan meant to answer that reports "0 ELIGIBLE candidates" on this config
(every rung disqualified by the close-all clamp), so nothing has ever compared
the shipped pair to an alternative.
This needs no retrain and no backtest. CFirstPassageLadder already stores the
first-touch AGE of every rung on both sides and OutcomeR() resolves ANY pair
exactly with the spread charged the way the fill charges it - so 14x14 pairs
over one era's OOS calls is a few thousand array reads.
- Expert/Training/GeometrySweep.mqh: CGeometrySweep accumulates (n, sumR,
sumR^2, timeouts) per rung pair from the model's own directional OOS calls.
Reads no chart, holds no net, opens no file - exercisable against a
hand-built ladder, same doctrine as SDeployVerdict.
- Best() ranks on the 3x3 NEIGHBOURHOOD mean, not the cell itself. A 14x14 grid
read at its single highest cell is a best-of-196 maximum, biased upward by
construction - the same selection problem the deploy gate corrects across
eras. A pair whose neighbours also pay is a plateau; a lone spike is a lucky
run of trades and does not survive the next window. GEOSWEEP_MIN_TRADES (30)
keeps thin cells out of the selection entirely.
- Wired into pass 3 where the call and the bar index are both in hand, reset per
era, reported at pass-3 completion beside ReportCandidateGeometry. ONE line,
and only when the recommendation CHANGES - it prints the shipped pair's
expectancy and the best pair's on the SAME trades, so "better" is a difference
rather than two numbers from two populations.
Measurement only: nothing reads the recommendation yet and no geometry moves.
Compile-verified in the staging copy: 0 errors, 0 warnings.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
selectionScore used to be a win rate in percentage points and printed at one
decimal everywhere. Under DeployOnExpectancy it is expected value in R, so
"%.1f" rendered every real score as "0.0" - era 2's +0.05R and a genuine zero
looked identical, which makes the journal useless for watching the ranking the
plateau ladder is doing.
One formatter, DeployScoreText(), next to the score it formats: "%.3fR" under
expectancy, "%.1f%%" under significance. Routed all nine print sites through it
(ensemble era line, best-so-far, panel, regression, new-best, era-cap prompts,
the convergence line, the deploy dialog) and dropped the "%" suffixes they had
hardcoded. No new prints, no new log lines.
Compile-verified in the staging copy: 0 errors, 0 warnings.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
TWO CHANGES, both of which turn a permanent "nothing happens" into a decision.
1. THE DEPLOY GATE ASKS THE WRONG QUESTION. tradeable required the win rate to
clear chance by EDGE_MIN_SIGMAS - "can I PROVE an edge exists" from one OOS
window. On H4 that asks ~66% against a market supplying ~53%, so it is
unreachable by construction and no run has ever deployed through it.
SDeployVerdict now also carries the economics of the geometry actually being
traded - cost-adjusted break-even and reward:risk, both from the new
CostAdjustedGeometry() so a spread convention cannot be applied to one and
missed on the other - and derives
E[R] = (p - p*) * (1 + RR)
which is exactly zero at break-even by construction, so "profitable" and
"beats break-even" can never disagree. Under DeployOnExpectancy (new input,
default ON) tradeable becomes E[R] > 0 and selectionScore ranks eras by
expectancy instead of precision. Coverage and both-sides-live still gate
both: an expectancy over a handful of one-sided calls is not tradeable.
The struct also publishes scoreSE - the SE of selectionScore IN THE SCORE'S
OWN UNITS - because the score changes units with the objective (win-rate
points vs R). Both plateau bands now read it instead of precSE, which was
right for one objective and dimensionally wrong for the other.
Setting DeployOnExpectancy=false restores the previous behaviour exactly.
2. THE FILTERED VIEW COULD NOT DRAW WHILE ANY MODEL WAS TRAINING.
HistoricalNetVote built its divisor from VoteCapableWeight(), which answers
"may this member move real money" and returns 0.0 for an AI member until the
whole run converges. So the reconstruction's divisor was zero on EVERY bar,
every bar was skipped as "nobody looked", and the chart drew nothing at all -
for the entire training run, which before the plateau noise band was forever.
Reported as "no signals drawn since the refactor".
New ReconstructionWeight(): the same weight WITHOUT the converged-run
requirement, overridden on the AI member to ModuleWeight() gated on
SelfRanked() only. The overlay is a picture of what the vote WOULD have
shown, which a mid-training model can answer - the chart HUD already says so
with its "(trn)" marker. Live Direction() still uses VoteCapableWeight(), so
no untrained model gains a say in an order.
Compile-verified in the staging copy: 0 errors, 0 warnings.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
TWO INDEPENDENT BLOCKERS, both of which make the EA look like it is working.
1. THE LADDER NEVER ADVANCES. isBetter/isBetterEra compared selectionScore with
a bare `>`. selectionScore is a win rate over a few hundred independent
calls, so it moves several points era to era on noise alone - measured on
SP500 H4 today: 32.8 / 32.2 / 31.6 / 29.6 / 31.4 across consecutive eras, a
~3-point spread with no trend. Any upward blip was recorded as a new best,
which reset BOTH the plateau counter and the stage, which re-armed a x5
learning-rate warm restart, which injected fresh noise and produced the next
blip. The search sustained itself on its own variance and never reached
PLATEAU_STAGE_DEPLOY - the reported "thousands of eras without converging".
A new best now has to clear the incumbent by PLATEAU_NEW_BEST_SIGMAS (2.0)
times precSE, which the deploy gate already computes. 2.0 rather than 1.0
because incumbent and challenger are both noisy, so the SE of the difference
is ~sqrt(2) x SE, and a 1-SE band was already measured too narrow in a
noise-dominated search. Applied at BOTH ranking sites - the ensemble's and
the solo member's - which are documented as the same ordering. The first
scoring era still checkpoints unconditionally.
2. THE BLANK-CHART CENSUS WAS LYING. It printed "No member has a completed era
yet (snapshots fill at each member's first pass-3 completion)" while the
members were on era 23, because it inferred the cause from m_overlayVotedBars
alone - and that counter requires BOTH a non-zero divisor AND a non-zero net.
Three different states collapsed into one sentence. Split out
m_overlayHadDataBars (divisor non-zero) so the line names which it is:
hadData == 0 -> nobody published a snapshot: publication/index
hadData > 0, voted == 0 -> members looked and abstained: calibration
voted > 0, drawn == 0 -> the vote never cleared the threshold
Diagnostic only. It does not fix the missing arrows - it identifies which of
the three is happening, which the current line actively obscures.
Compile-verified in the staging copy: 0 errors, 0 warnings.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Every era was a ~1,200-bar chunk of a 16,264-bar window, and the oldest 90% of
the history was never reached.
All four passes yield mid-chunk on the 120ms budget: each one calls
StashEraResume (the single writer of m_eraResumePending) and returns. Those
used to be returns from Train() itself. When the passes were extracted into
their own methods (08c2cec) they became returns from a void helper, and Train()
carried straight on - reporting pass 1 "done" after one budget, running pass 2
over the sliver pass 1 had queued so far, scoring an OOS slice of it, and
letting AdvanceEra count an era. The extraction moved one side of the binding
and left the reader behind.
Measured on SP500 H4 (VerboseMode, 2026-08-24 15:05-15:14):
era 0 TRAINING WINDOW = 16264 bars ... Bars(series) = 16264 <- window fine
era 1277 pass 1 done in 0s - 1144 of 1193 bars usable <- sweep is not
era 1296 pass 1 done in 0s - 3117 of 3166 bars usable
era 1318 pass 1 done in 0s - 1391 of 1440 bars usable
~1,400 eras in ten minutes, the count varying with how many bars a 120ms budget
happened to buy. Downstream: each member held a different tiny OOS slice, so
the combined vote's shared-bar intersection collapsed ("0 shared OOS bars" on
nearly every era, score 0.0), and the plateau ladder counted 46 ungraded eras
as a plateau and fired a boosted warm restart on all four models.
Train() now returns whenever m_eraResumePending is set - after pass 1 (before
ReportPass1Outcome, which has no verdict to give on a yielded sweep), pass 2,
the calibration walk and pass 3. m_modelEta is already saved inside
StashEraResume, so the early returns keep the learning-rate trajectory.
The resume machinery itself was correct and is unchanged: BeginEra's resume arm
restores the cursor, m_passWindowOk/m_passWindowFail accumulate across chunks,
and the m_isPass2Active/m_isPass2Done guard already routes a resumed call to
the right pass.
Expect era numbers to advance slowly now. That is the fix, not a new stall.
Compile-verified in the staging copy: 0 errors, 0 warnings.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
With VerboseMode on, pass 1 reported eras of 422 / 949 / 1358 / 2562 bars on
SP500 H4 - four models, same chart, same second - against a series holding
~16,264 bars, and the number moved every era (CONV: 2562, 3671, 3405, 3532,
2830). Nothing in the journal said so. ReportDetectability and the CAPACITY
line both quote EstimatedInSampleBars, which is derived from the configuration
and not from the era, so they kept reporting "11385 in-sample rows / OOS window
4874 bars" for a window that was a tenth of that.
era.bars is MathMin(Bars(symbol, PERIOD_CURRENT, dtStudied, now) + historyBars,
Bars(symbol, PERIOD_CURRENT)). A short era is therefore either a dtStudied that
is too recent or a short price series, and those need opposite fixes - so the
new line carries all three quantities plus the resolved dtStudied and
SERIES_FIRSTDATE, not just the result.
Reported on change only: an era over a warm feature cache runs in a fraction of
a second here, and a per-era line would bury the journal.
Diagnostic only - no training behaviour is changed by this commit.
Compile-verified in the staging copy: 0 errors, 0 warnings.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Two charts (SP500 H4 + USDJPY H4) ran with the pool enabled and produced no
TrainPool directory, no adopted rows and not one journal line. The pool was
inert and there was no way to tell that from "the feature is off".
It could never have fired: the fingerprint is not symbol-invariant. It hashes
NeuronsCount, which counts the alt-data columns - and those are per-symbol
(SP500 carries cot_spec_net, the FX majors cot_idx_1y/3y/chg_4w) - and the
cross-asset block appends ":IDX2" when base currency == profit currency, true
of an index and false of a pair. SP500 came out 50 features wide under
XA:6:IDX2, USDJPY 52 wide under XA:6. Compatible() gates on both, so adoption
was zero by construction.
- STrainPoolHeader::MismatchReason() replaces the bare Compatible() predicate
and names the mismatch; Compatible() now delegates to it, so "may I adopt"
and "why not" can never drift apart.
- CTrainPoolReader::Adopt() reports its own verdict - adopted, alone, or every
peer rejected with the reason per file - and reports it on CHANGE only. An
era over a warm feature cache runs in a fraction of a second here, so a
per-era line would bury the journal. The duplicate Print in RunPass2 is gone;
pool state is now reported from exactly one place.
- CTrainPoolWriter::Publish() rate-limits to TRAINPOOL_MIN_PUBLISH_SEC (300s).
Every era re-derives the same rows from the same in-sample span, so per-era
publishing rewrote a multi-megabyte file continuously for no new information.
The first publish is never delayed.
Compile-verified in the staging copy: 0 errors, 0 warnings.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Peer rows join m_isTrainQueue as NEGATIVE sentinels before the shuffle, so they interleave with
this chart's samples instead of training in a block at one end. A block would be a curriculum:
whatever the optimizer saw last would decide where it landed.
TrainPoolStep is a separate path on purpose. Everything in pass 2's local branch after the
forward pass reaches for something indexed by a LOCAL bar - m_labelCache, m_winLongCache, the
excursion target, the arrow cache, m_Time - and a peer row has none of those. Sharing the path
would mean inventing values for all of them, which is how another instrument's outcomes end up
inside m_cumIsCorrect and the operating point gets fitted to them. The IS-vs-OOS gap is read as
THE overfitting signal, so polluting the IS side would not crash anything; it would just quietly
stop meaning what it says.
The purge key reuses the label walk's own two bounds - the horizon and NextScheduledCloseAll -
rather than approximating with a bar offset. A second horizon model here would drift from the
real one, and this project already measured that the close-all, not the nominal horizon, is what
actually terminates labels. Cutoff is the OLDEST OOS BAR'S TIME, in wall clock, because bar
indices cannot be compared across instruments that each have their own calendar.
Contribution happens while the window is still in TempData and before the forward pass
overwrites it, and is gated to direction models: the meta head trains a different target on a
wider input, which the fingerprint gate alone would NOT catch, since a meta model's fingerprint
matches its own peers perfectly well.
Use_Training_Pool ships false and does nothing until a second chart runs a matching fingerprint.
Compile-verified against a BASELINE of the same tree without the wiring: both produce 12
errors, all error 313 invalid-resource-path from #resource directives that cannot resolve in a
headless staged build (stock Controls res\*.bmp, plus the pre-existing Network.cl). Code errors
0, warnings 0, identical to baseline. Staging copy and junctions removed; the live .ex5 was
never touched.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
RSI, MACD, Ichimoku and the five AD/Wyckoff indicators (CumulativeDelta,
ShorteningOfThrust, WyckoffEventStream, WyckoffFailedStructure,
WyckoffSignificantBarInversion). All eight inputs shipped false and each carries a
closed verdict: the three oscillators are the same patterns that measured at chance
as entries, and the Wyckoff family returned zero out-of-sample on five independent
instruments - which is what closed the context score.
RETRAIN-NEUTRAL, and this one is worth stating precisely because the change looks
larger than it is. Every removed group contributed `flag ? N : 0` to the input
width, and every flag was false, so the width was ALREADY zero for all eight: no
.nnw's input layer changes. On the fingerprints, UseRSI and the five AD flags were
hashed unconditionally and become literal 0 legacy slots (the convention the
m_focalGamma slot above them already uses); UseMACD/UseIchimoku were appended only
when enabled, so their segments simply never appear - byte-identical to every
fingerprint ever produced, since neither ever shipped on.
CADIndicatorTuner IS DELIBERATELY NOT SHRUNK. Its flat parameter array is persisted
inside every .nnw, and Unflatten() rejects a size mismatch by falling back to
constructor defaults - so dropping the dead fields would silently revert the tuned
MA period of every model on disk while keeping its trained weights. That is the
feature/weight mismatch this project has already paid for twice, and it is not
worth 200 lines. AD_TUNE_PARAM_COUNT stays 42, the dead slots are still written and
read, and AutoTune's ParamOwner gate now matches only owner 5 (MA) so nothing
searches them. The class comment says all of this at the declaration.
Also renamed ReInitADIndicators -> ReInitTunableIndicators: it rebuilds exactly one
indicator now, and a name saying "AD" for the MA handle is the kind of stale label
that gets believed later. Its release-AFTER-recreate ordering is untouched - that
is a documented fix, not bookkeeping.
Compile-verified in the stage copy: 0 errors, 0 warnings, against the same 0/0
baseline.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
AcquireConfigLock/ReleaseConfigLock moved off CExpertSignalAIBase into
Expert/ConfigLock/CConfigLock, same view+adapter shape as BarrierHorizon/ExcursionHead.
Stateful: m_configLockName is exclusive (grep-verified, nothing outside Lifecycle.mqh's
old body touched it). Pure relocation - same FNV-1a hash, same owner-liveness check,
same log wording. Left uncommitted mid-campaign; independently compile-verified in
isolation now (0 errors/0 warnings) before this commit.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
FeatureScreen.mqh's MI/permutation-null diagnostics (mean/best-col
report, excursion report, lag-profile family-wise test, barrier-
geometry scan) and AutoTune.mqh's TuneIndicatorsByFilter install gate
each spelled out the add-one-smoothed Monte-Carlo p-value
(1+atLeast)/(draws+1) independently. Added PermutationPValue(atLeast,
draws) to System/BinomialStats.mqh (returns 1.0 for draws<=0, matching
every existing call site's own guard) and replaced all six inline
expressions with a call to it. Pure arithmetic substitution, no
control-flow change.
ApplyClassificationSoftmax/AdjustedSignalFromSoftmax/DirectionalMargin each
re-derived `pBuy > pSell && pBuy > pNeutral` (and the Sell mirror)
independently, one of them documenting the duplication by comment rather
than eliminating it. Added Argmax3() as the single derivation (ties to
Neutral); all three now branch on its ENUM_SIGNAL result instead of
re-testing the comparison. Pure relocation, statement-by-statement
equivalent - verified by diff.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
