forked from MrBaro75/Warrior_EA
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
AnimateDread
|
98f485b901 |
fix(gate): the payoff horizon ended before the pivot it was measuring
The first cut measured payoff over SwingLifespanEstimate() bars. That is
PIVOT_LABEL_TOLERANCE_BARS - a constant of the TARGET describing how many bars
share one pivot event - and it is the wrong horizon for what a call is worth.
The label fires when a pivot lands WITHIN that window. So at that horizon the
pivot may only just have committed, and a perfectly correct call can still show
a negative forward move because the turn it predicted has not had one bar to
run. Measuring only there would understate the payoff of a signal working
exactly as designed, and could inflip its sign.
Measures two horizons and reports both:
SHORT = PIVOT_LABEL_TOLERANCE_BARS "has the pivot arrived" - a control
HOLD = that + the median ZigZag leg the pivot PLUS the leg it opens,
which is how long a trade on this
call would actually be held
Adds CTopology::SwingLegMedianBars(). It is deliberately NOT the same thing as
SwingLifespanEstimate() and the declaration says so: the lifespan is a constant
of the target and is what the effective-sample-size deflation divides by, while
the leg median is a measurement of the chart and is how long the move runs.
Conflating them is what produced the wrong horizon in the first place.
Non-const and lazily measured, because a model that adopted its .cfg never
walked the chart and would otherwise report HISTORY_BARS_FALLBACK as if it were
a measurement - the same lazy pattern DeriveHistoryBars() already uses.
Reporting both horizons is also the guard against picking one and calling it
the truth. A break-even conclusion here has already been overturned once purely
by getting a horizon wrong.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
|
||
|
AnimateDread
|
0e1e952b96 |
feat(topology): cap the input window at 6 bars for capacity - 588 inputs -> 294
Three charts (SP500, XAUUSD, XTIUSD) sat on the FIRST_LAYER_MIN_WIDTH floor
even after pooling took SP500 from 4.1 to 1.8 weights per independent
observation. ComputeFirstLayerWidth needs width <= ~331 to clear it; 49 columns
x 12 bars = 588.
TWO QUESTIONS, AND THE WINDOW IS NOW THE SMALLER ANSWER. The ZigZag ladder
answers "how far back is a swing worth looking" and says 12. The capacity
budget answers "how far back can this much data support" and says 6. Taking the
min stops the first writing a cheque the second cannot cover.
WHY THE LAG AXIS AND NOT THE COLUMN AXIS - the choice was between this and a
per-column mask (designed, parked on feature/column-mask):
- On the LAG axis there is a measured null. The corrected lag profile finds no
linear structure at any lag within +/-50, on all six charts, family-wise
p=1.0000, argmax scattered across different columns and lags per chart.
- On the COLUMN axis the two measures that would justify a mask - marginal MI
retention and variance share - are explicitly blind to joint and temporal
structure, and the columns they would delete include the entire price core,
which is the one place such structure would plausibly live.
Cutting where there is a measured null beats cutting where the instrument
cannot see. Corroborating: PAI/CONV/LSTM/HYBRID score within ~1pp of each
other, so the temporal machinery is not visibly earning the deeper lags.
THE CAP IS A FLEET CONSTANT, NOT A PER-CHART DERIVATION. Pool rows are keyed on
`bars x columns`, so a capacity cap computed from a chart's own observation
count would differ across the fleet by construction and hand every chart its
own layout, its own fingerprint and its own pool of one - exactly what orphaned
SP500. Set from the most starved chart; every chart shares it.
Conv survives: CONV_RECEPTIVE_FIELD_BARS is 3, so a 6-bar window still leaves 4
sliding positions. LSTM sequence length becomes 6.
RETRAIN-FORCING and POOL-INVALIDATING: width changes, so old .nnw and old
TrainPool rows are both incompatible. Wipe both - which puts the fleet back in
the cold-start condition
|
||
|
AnimateDread
|
994fe3899c |
feat(label): pivot-EVENT target replaces direction-to-next-pivot
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>
|
||
|
AnimateDread
|
b2784b5a4d |
Enhance Feature and Topology Interfaces with Bulk Operations and Cache Management
- 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. |
||
|
AnimateDread
|
1baa13c5b4 |
refactor(meta): remove meta-labeling entirely - RETRAIN-NEUTRAL
~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>
|
||
|
AnimateDread
|
4ad079aaed |
fix(topology): size the network against observations, not bars
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> |
||
|
AnimateDread
|
8f2164698b |
feat(target): delete the barrier/geometry stack - the label is the verdict
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> |
||
|
AnimateDread
|
8c945bf752 |
feat(target): swing is the default, and tau is measured, not chosen
- 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>
|
||
|
AnimateDread
|
579e8b45ef |
feat(target): swing-pivot direction label, and drop the ADZigZag name
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> |
||
|
AnimateDread
|
6974fb03af |
ditch(features): remove the eight dead feature groups from the input matrix
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> |
||
|
AnimateDread
|
c439878a82 |
refactor(topology): split shape derivation into CTopology, leave the boot sequence in place
Expert/AIBase/Topology.mqh (1191 lines) held two genuinely different jobs: the fingerprint/derived-shape/BuildFreshTopology math, and InitNeuralNetwork/ InitFeatureIndicators - the network boot sequence (config-lock, tester-cache seeding, load/save the .cfg, net-load backend fallback, chart/persistence/ online-learning orchestration). Extracted the first job to Expert/Topology/ as CTopology + CTopologyView/ CAIBaseTopologyView (20 methods: BuildModelFingerprint, the Estimated*/Compute* budget math, the Conv*/Lstm* shape helpers, Add*Stage, BuildFreshTopology). STATELESS, like ModelPersistence - grep-verified zero exclusive fields, every member these methods touch is shared elsewhere in the signal. Reused ~15 existing Data*/Chart*/Persist*/Exc* getters per the established convention; added ~20 new getter overloads next to their existing setters (UseVolumes(), MinDirectionalRecall(), etc. - same pattern as SignalClusterWindow) and ~16 new Topology*() wrappers for fields with no prior accessor. The Net-pointer swap in BuildFreshTopology is one consolidated view call (TopologyReplaceNetFromTopology), same doctrine as Persistence's RunCpuInferenceSelfCheck - irreducible pointer work, not signal state. Deliberately did NOT extract InitNeuralNetwork/InitFeatureIndicators: they orchestrate nearly every other collaborator (chart, persistence, online- learning, cross-asset, config-lock) rather than deriving a shape, so moving them would just relocate a hub, not reduce coupling - same judgment call as Inference.mqh (assessed, not extracted). They stay in the AIBase/Topology.mqh partial, byte-identical to before (diffed against git HEAD to confirm), and now call the extracted math through the same public forwards every other caller already used. Verified: string- and numeric-literal diff of the old file's 20 method bodies against the new CTopology methods (0 differences), InitNeuralNetwork/ InitFeatureIndicators byte-identical, self-compiled 0 errors/0 warnings. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com> |