~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>
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>
CFirstPassageLadder (RungFor/StoreBar/FirstTouch/OutcomeR/WinShare),
CTripleBarrier+CLabelOverlap (ApplyMinStopWidening/ComputeLevels/SnapToLadder,
the label-overlap effective-sample-size correction), CMetaFamilies (the
classic-pattern taxonomy + table-naming rule), SGeometryScan::Reset() (guards
against 7452bd1's partial-reset shape recurring) and System/BinomialStats.mqh
(every deploy-gate/edge-floor formula this codebase shares). Each is a small
.mq5 Expert Advisor under Tests\ printing PASS/FAIL per assertion via
Print(), sharing Tests\TestHarness.mqh. All 5 self-compile-verified 0
errors/0 warnings. FirstPassageLadder.mqh/TripleBarrier.mqh expect
BARRIER_LADDER_COUNT/BARRIER_LADDER/BARRIER_HORIZON_LADDER_COUNT predefined
by their includer (normally ExpertSignalAIBase.mqh); the test EAs define
copies matching production values rather than including the whole AIBase
chain. SGeometryScan is reproduced verbatim from ExpertSignalAIBase.mqh for
the same reason, flagged in-file as needing to stay byte-identical.
Also adds Tests\convert_sample_data.py, which runs research/sqxbars.py's
decoder against a COPY of SP500_the5ers_H1.dat (never the SQX install
itself) so the operator has real sample data to point a manual tester run
at. Decodes structurally (52,542 H1 bars, monotonic, 0 high<low violations)
without calibrating a price scale - sqxbars.load()/sqx.calibrate_decimals()
both require a validated reference series to do that safely, which this
self-contained script does not have. Tests\sample_data\ (the raw copy +
decoded .npz) is gitignored, same policy as the existing Market Data/ rule.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
