ответвлён от animatedread/Warrior_EA
| Автор | SHA1 | Сообщение | Дата | |
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AnimateDread
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a970405042 |
feat(pool,mi): one feature layout fleet-wide, and the keep-screen stops self-disabling on a cold start
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
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AnimateDread
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d9092a2408 |
fix(vote): persist the member's skill verdict - a converged model was ruled no-skill on every restart
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> |
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AnimateDread
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32eb5c5f58 |
feat(vote): edge-over-chance currency, no-skill exclusion, checkpoint burn-in
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> |
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AnimateDread
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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. |
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AnimateDread
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781ae3a702 |
perf(deinit): I/O-free chart cleanup, dead-panel purge, skip clean weight saves
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> |
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AnimateDread
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4e4bff51d4 |
feat(vote): backfill the ensemble win-rate record from the overlay sweep
"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> |
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AnimateDread
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26fc9a1217 |
fix(replay): resolve labels inline - the prebuilt cache's window never overlapped the rescan
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> |
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AnimateDread
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5aec69fe4c |
feat(vote): replay pass rebuilds a deployed model's ladder without retraining
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> |
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AnimateDread
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b92e233b88 |
fix(chart): a deployed model rescans history to rebuild its vote arrows
The sidecar added in
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AnimateDread
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484a9d8b0f |
fix(panel,arrows): one deploy predicate, a deployed-only readout, and persist the vote arrows
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> |
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AnimateDread
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15827a6b77 |
refactor(trade-mgmt): remove all confidence-scaled trade management
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> |
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AnimateDread
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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>
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AnimateDread
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ad5c2542ec |
perf(tester): skip the signal DB in tester/optimizer, drop ExportFeaturesOnly
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> |
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AnimateDread
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ec1692f348 |
feat(mi): the screen is an alarm, not a gate
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> |
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AnimateDread
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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> |
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AnimateDread
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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>
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AnimateDread
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1882f87451 |
feat(geometry): price every stop/target pair on the trades the model actually called
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> |
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cbd077c679 |
diag(training): report the window an era ACTUALLY trains on
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> |
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AnimateDread
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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> |
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AnimateDread
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e366bb74ad |
refactor(config-lock): CConfigLock is a real collaborator, not a raw-include partial
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> |
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AnimateDread
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b95ee72f4d |
refactor(labels): split the close-all budget / horizon ladder into CBarrierHorizon
Expert/AIBase/Labels.mqh (1807 lines) exclusivity-grepped almost entirely SHARED: the label/win/excursion/ladder caches and the geometry-derivation/ prebuild state are touched with real per-bar array logic by Training.mqh's hot era loop (m_labelCacheBuy/Sell/HasValue at 22+ sites), by FeatureScreen.mqh's geometry scan (direct writes to m_barrierScanSlMult/TpMult, m_geometryAdopted, m_geometryCfgSaved), by AutoTune.mqh and by SignalMETA.mqh - moving that state into a collaborator would mean wrapping dense hot-loop array indexing behind method calls across 5 files for no coupling reduction (same judgment already recorded for AutoTune.mqh's remainder / Inference.mqh). One genuinely closed sub-cluster survived the grep: the scheduled close-all budget and the horizon ladder snap (NextScheduledCloseAll, MeasureCloseAllBudget, EffectiveHorizonMax, RequiredHorizonBars, SnapHorizonToLadder, GrantedHorizonBars). Only 2 fields are exclusive (m_closeAllCycleBars/m_closeAllMeanBudget - grep- verified, Lifecycle.mqh's touch was constructor-init-list only) and NONE of the 6 methods has any external caller outside Labels.mqh (grep-verified whole-repo), so nothing needed rewiring. New Expert/BarrierHorizon/: IBarrierHorizonView.mqh (abstract, 4 accessors, 3 reused from the signal's existing Chart* getters, 1 new HorizonSwingMedianBars() wrapper) + AIBaseBarrierHorizonView.mqh/ AIBaseBarrierHorizonViewImpl.mqh (the adapter) + BarrierHorizon.mqh (CBarrierHorizon, STATEFUL - owns the 2 exclusive fields as real members). Every method body is a verbatim relocation (diffed programmatically against git HEAD modulo the field-> view substitutions - identical except one comment-wording update). The original 6 declarations on CExpertSignalAIBase became one-line forwards at their existing position; Labels.mqh's own callers of these six needed zero changes since they call them unqualified, which now resolves through the forwards. Labels.mqh: 1807 -> 1643 lines. The rest of the file (label-cache population, TripleBarrierLabel, DeriveBarrierGeometry, StartLabelCachePrebuild/ AdvanceLabelCachePrebuild, exit-policy simulation) is deliberately left as a raw-include partial - not separable without relocating Training.mqh's era-loop coupling, not reducing it. Self-compiled 0 errors, 0 warnings (_claude_stage, ~94s). |
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AnimateDread
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4dede6f6db |
refactor(features): FeatureBuilder is a real collaborator, not a raw-include partial
Expert/AIBase/Features.mqh (2017 lines, 38 methods) split by exclusivity grep (whole-repo, not just Expert/): 30 methods -> Expert/Features/FeatureBuilder.mqh (CFeatureBuilder + CFeaturesView/CAIBaseFeaturesView), 8 stay behind as a much smaller raw partial. CFeatureBuilder is STATEFUL, same shape as Excursion/OnlineLearning: owns the 10 feature-only indicator handles (m_Volumes/m_MA/m_RSI/m_MACDFeature/ m_Ichimoku/5 AD* CiCustom indicators - grep-verified touched nowhere else in the repo, only their bare declarations) plus the depth-probe/handle-repair/ spread-series/detectability-latch scalars (exclusive, Lifecycle.mqh ctor-init only elsewhere). m_Open/m_Close/m_High/m_Low/m_Time/m_ATR/m_ADZigZag stay signal-owned - Labels.mqh/AutoTune.mqh/Training.mqh read them directly - and are reached read-only through the view (FeatureOpenAt/FeatureHighAt/ FeatureLowAt/ChartBarClose/ChartBarTime/OnlineAtrMain, all reused where a forward already existed). Deliberately did NOT move InitOpen/InitClose/InitHigh/InitLow/InitTime/ InitADZigZag/ResizeBuffers/RefreshData: they manage the 7 shared indicators' Create/BufferResize/Refresh lifecycle, which would need a pure-relay wrapper per operation per indicator for zero coupling benefit - same judgment as Topology's boot sequence. They stay in Expert/AIBase/Features.mqh and reach CFeatureBuilder's 10 owned indicators through 20 new Feature*BufferResize()/ Feature*Refresh() forwards (signal calling into its own owned collaborator directly, no view needed in that direction). Whole-repo grep (not just Expert/) caught a real external miss the campaign's own doctrine warns about: Signals/SignalMETA.mqh read m_spreadSeries/ m_spreadSeriesBars directly as an inherited protected field (a subclass, not an AIBase/*.mqh partial) - fixed with two new FeatureSpreadSeriesBars()/ FeatureSpreadSeriesAt() forwards. Verified: if(/for(/while( counts identical between the original file and the new split (269/20/1); return-count delta (+12) fully accounted for by the 12 new trivial one-line forwards added (10 indicator BufferResize + 2 spread- series getters); quoted-string-literal diff empty except two doc-comment paraphrases. Self-compiled 0 errors, 0 warnings. |
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AnimateDread
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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> |
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AnimateDread
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78a070eb5c |
refactor(online-learning): OnlineLearning is a real collaborator, not a raw-include partial (S5)
Expert\AIBase\OnlineLearning.mqh (595 lines) -> Expert\OnlineLearning\: IOnlineLearningView.mqh (abstract, ~50 accessors) + AIBaseOnlineLearningView.mqh/ AIBaseOnlineLearningViewImpl.mqh (the adapter) + OnlineLearning.mqh (COnlineLearning). STATEFUL, unlike CModelPersistence: grep-verified the shadow net, the OOS continual-learning simulation state and the pattern-database backfill state are genuinely exclusive to this file's own methods - Training.mqh/Topology.mqh/ Lifecycle.mqh/the signal's own header only ever CHECKED or RESET this state at era/lifecycle boundaries, never owned it, so it moved onto the collaborator as real members (same doctrine as Excursion). Those external touch points became consolidated view/forward calls instead of raw field pokes - AbortSimIfActive() replaces THREE separate copies of the same delete/null/false triple (Training.mqh's stop path, FlushTrainRun, ResetWeights), matching the geometry-scan duplicate-reset precedent in project memory. ResetForFreshTopology() replaces Topology.mqh's five- field reset block, DeployNet() replaces the shadow-preferred net selection duplicated in Inference.mqh and ChartScoreBarForRescan, and BlendTowardNet() replaces the era-end blend Training.mqh used to poke m_shadowNet for directly. Reused the signal's existing Data*()/Chart*()/Persist*() getters wherever one already answered the question (labels/outcome/history/horizon/priors/servable-bars/etc.); added ~30 new Online*() wrappers only for what nothing else exposed yet. The three PersistOnline*() get/set pairs (WST3 .stats fields) now forward through the owning member instead of touching the field directly - CModelPersistence is unaffected. Every method body is a pure relocation of the original's statements in original order; verified against `git show HEAD~1:Expert/AIBase/Excursion.mqh`-style diff against the pre-extraction file kept in the working tree until this commit. Compiled 0 errors, 0 warnings (stage mirror + MetaEditor64 /compile, ~91s). Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com> |
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AnimateDread
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3278ea4ae2 |
refactor(excursion): ExcursionHead is a real collaborator, not a raw-include partial (S4)
Expert/AIBase/Excursion.mqh was 13 method bodies of CExpertSignalAIBase, #include'd after its declaration - same "not a module" problem already fixed for ChartUI (S2) and Persistence (S3). Extracted to Expert/Excursion/CExcursionHead behind CExcursionHeadView/ CAIBaseExcursionHeadView, same view+adapter shape. STATEFUL, unlike Persistence (0 exclusive fields): grep-verified 26 fields (m_excNet and every accumulator/trailing-ring field) touched nowhere else in Expert\ except Lifecycle.mqh's old ctor-init-list defaults and destructor deletes (now moved onto CExcursionHead's own ctor/dtor). m_geo (SGeometryScan) and m_ladder (CFirstPassageLadder) stay on the signal - both are genuinely shared with Labels.mqh/ Training.mqh at era boundaries - and are reached only through 15 new Exc*() view wrappers, including one consolidated ExcGeometryScanAccumulate() call (same doctrine as Persistence's RunCpuInferenceSelfCheck) rather than field-by-field pokes. All 13 original public methods stay at their same declaration point as one-line forwards to m_excursionHead. Training.mqh's 2 raw m_excUs reads now go through the new ExcursionMicroseconds() forward. Every method body is a pure relocation, verified statement-by-statement against the original (git show HEAD~1:Expert/AIBase/Excursion.mqh). Compile-verified: 0 errors, 0 warnings. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com> |
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AnimateDread
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87382748d3 |
refactor(persistence): ModelPersistence is a real collaborator, not a raw-include partial (S3)
Expert/AIBase/Persistence.mqh (598 lines, 8 methods) -> Expert/Persistence/: IPersistenceView.mqh (abstract, 68 read+write accessors) + AIBasePersistenceView.mqh/ AIBasePersistenceViewImpl.mqh (the adapter) + ModelPersistence.mqh (CModelPersistence, the real collaborator - signal owns m_modelPersistence and binds it to m_persistenceView, same shape as ChartUI's S2). Grep-verified before starting: every field these 8 methods touch is ALSO touched elsewhere in the class (Training/Lifecycle/OnlineLearning/Topology/FeatureScreen/ Labels.mqh) or already exposed via ChartView. Zero exclusive state, unlike ChartUI's arrow-restore/rescan queues - CModelPersistence is stateless, holding only the borrowed view pointer, operating entirely through 68 Persist*/PersistSet*() accessors on the signal. ValidateCpuInference's Net-pointer/throwaway-clone core is ONE consolidated view call (PersistRunCpuInferenceSelfCheck) rather than field-by-field - irreducible pointer/ object work, not signal state, same doctrine as ChartScoreBarForRescan. LoadNetWithRetry keeps its original CheckPointer(Net)-free Net.Load() call unchanged (no guard added - would change failure behaviour on what must be a pure relocation). This code writes the actual on-disk .cfg/.stats binary layouts every deployed model depends on (explicit "DO NOT REORDER" comment in the original), so beyond compiling clean (0 errors, 0 warnings) this was verified with a positional field-order diff: every FileWrite*/FileRead* call's target field, extracted and normalized from both the original and the new file, matches 1:1 in the same order (43/43 on the write side covering SaveModelStats+SaveTopologyConfiguration, 17/17 on LoadModelStats' read side; LoadAndCompareTopologyConfiguration's local-variable read block was copied verbatim, untouched, so nothing to diff there). The magic-version conditionals (WST2-6, haveDerivedStages/haveBarrierGeometry/etc.) moved unchanged. All 8 methods keep their exact original signatures as one-line forwards - zero external call sites changed. |
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AnimateDread
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053d704a84 |
refactor(chart): ChartUI is a real collaborator, not a raw-include partial (S2)
Expert/AIBase/ChartUI.mqh was 869 lines of method bodies of CExpertSignalAIBase, #included after the class declaration - free to touch any of its ~500 members. First of the eleven AIBase/*.mqh partials to come out (fewest inbound edges - see the SOLID campaign session order), using the same view+adapter shape already proven for CTrainingDataView. CChartView (Expert/Chart/IChartView.mqh) is the abstract read/behaviour surface a chart-rendering collaborator needs - identity, bar/model access, the prediction cache, and the training/vote/meta scalars the panel and HUD line summarise. CAIBaseChartView is the adapter the signal owns and binds to itself (MQL5 gives a class exactly one base, so CExpertSignalAIBase cannot implement the view directly). CChartUI is the real collaborator: it owns the arrow-restore queue, the rescan queue/tally, the last-arrows-saved count and the purge-mismatch latch as its own fields (verified via grep to be touched nowhere else in Expert/), and reaches everything else - including StartChartSignalRescan, moved in from its old inline home in the header since it drives the exact same rescan state machine AdvanceChartSignalRescan drains - through the view. m_arrowSignalCache and m_signalClusterWindow stay on the signal: Training.mqh writes the cache directly every era and the training-data view already reads it, so moving it would mean rewriting Training.mqh's write sites too - out of scope here. CChartUI reaches it through four bounds-checked accessors instead of a raw member poke. All 10 public methods keep their exact signatures and become one-line forwards on the signal, so no other file's call sites change except Training.mqh's one era-end status refresh, which now reads RefreshStatusLabel() rather than reaching into CChartUI's now-private last-displayed-neuron cache directly. Verified structurally, not compiled (never compile - the operator does, in MetaEditor): brace balance checked on every touched/new file against HEAD, and the view/adapter/impl method lists cross-diffed to confirm all 59 accessors match 1:1 across the interface, the adapter declaration and the adapter body. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com> |
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AnimateDread
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909f2385bc |
refactor(build): retire the WARRIOR_EXPORT_FEATURES compile flag
Last surviving compile-time feature switch in the codebase - the same pattern
already killed for the MARKET build and DirectML tier (
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AnimateDread
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fec4d47eb2 |
refactor(labeling): CTripleBarrier - one copy of the fill/barrier arithmetic
Session B of the feature-selection/labeling refactor track. Extracts the two
pieces of triple-barrier arithmetic that were genuinely duplicated or
scattered, taking price/ATR/geometry as plain arguments - no chart, no
indicator handle - so it is testable with synthetic numbers.
CTripleBarrier::ComputeLevels() replaces the fill/barrier level arithmetic
that TripleBarrierLabel() and SimulateTradeOutcome() each spelled out by
hand; their own comments already called it "IDENTICAL... deliberately and by
copy." One caller resolves both sides at once (the both-won tie-break needs
both); the other selects the side its isLong argument names. Same for
ApplyMinStopWidening(), the broker-minimum-stop floor both walks applied.
Fuzzed 200k random (entry, spread, risk, reward, minStop, isLong) tuples
against both original hand-written forms: 0 mismatches.
CLabelOverlap replaces m_labelLifespanSum/m_labelLifespanCount - two members
reset from three separate call sites (constructor, label-cache rebuild), the
exact "N loose members cleared in more than one place" shape a candidate-
geometry incident (
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AnimateDread
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303c9bf412 |
refactor(meta): the era's candidates are an object, not eight base members
Five parallel arrays, a count and a two-array intrusive chain sat on
CExpertSignalAIBase - inherited by every direction model, filled and read
by exactly one subclass. CMetaCandidateStore takes all eight.
What that fixes beyond the clutter:
- THE CHAIN WAS LINKED BY HAND. MetaPrepareEra wrote next[id] = head[bar]
then head[bar] = id itself, after six ArrayResize calls it also wrote out
itself. Add() does the linking, Reset() does the sizing, and a bar off
the grid now cannot be stored at all rather than stored unreachable.
- THE BOUNDS TEST HAD FOUR SITES AND THREE IMPLEMENTATIONS.
MetaCandidateWon indexed side[] with no test at all and answered
"short" for any id out of range - the same shape as the ladder's
negative-index read (
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AnimateDread
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3d2ee517ca |
refactor(meta): the veto is a gate, not a virtual every signal carries
Since S3 (
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AnimateDread
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93d7bbe677 |
refactor(oos): twenty-one counters with one lifetime become one object
SOosTally holds this era's OOS confusion counts and the rates they imply.
The signal keeps one member where it kept twenty-one, and the era-reset
block loses twenty of its twenty-one clearing lines.
THE SHAPE THIS ENDS is the one that produced
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AnimateDread
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2c351a02ca |
refactor(barriers): the ladder is an object, and its snap rule is one rule
CFirstPassageLadder owns the three caches (per-rung up/down first-touch ages
plus the terminal travel) and every question asked of them. The signal keeps
one member where it kept three arrays and a lifespan scalar.
WHAT THIS ENDS. The log-space rung snap existed THREE times: once as
LadderRungFor, twice written out inline inside LadderWinShare - and
LadderRungFor's own header said "Same rule LadderWinShare snaps with, so a
rung chosen here and a rung chosen there are the same rung". A comment asking
a reader to keep three copies equal by hand is the arrangement CMetaFamilies
was built to end. It is now one static RungFor(), so the two rungs agree by
construction.
The bounds test was spelled out at four sites and the "0 means never, tie
goes to the stop" comparison at three. Now Has() and FirstTouch(), once.
The four-site bounds test was also subtly weak: it computed
`idx * COUNT` and tested only the upper end, so a negative index slipped
through into a negative array read. Row() rejects it.
Spread and horizon are ARGUMENTS, not state. The ladder is pure travel in ATR
multiples; what a spread costs and how long the walk ran are facts the caller
supplies. Every answer is now a function of its inputs alone - which is the
point, because this is the barrier arithmetic that failed its own acceptance
test in
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AnimateDread
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7452bd1ba9 |
fix(geometry): a shutdown abort cleared one tally of ten
Found by grouping, not by looking for it.
The candidate-geometry scan kept ten accumulators as ten separate
members. Era start cleared all ten in a ten-line block. The
shutdown-abort path inside the exit-policy simulation cleared
m_geoTrades and nothing else, so nine partial sums - diffSum,
diffSumSq, incSum, candSum, candSl, candTp, incOpen, candOpen,
startTick - survived the abort with the aborted era's values.
The next era then accumulated onto those sums while counting from
zero, so the paired mean is sum/trades with a numerator carrying an
extra era's worth of difference. The paired sigma is worse: diffSumSq
inherits the same contamination, so the scan reports a tighter or wider
spread than it measured depending on what the abort happened to be
holding.
That is the SAME arithmetic that failed its own acceptance test in
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AnimateDread
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37b3e0e36a |
refactor(pool): the cross-instrument gate owns a directory, not a model
PooledGate was three CExpertSignalAIBase method bodies in an #included partial. It is now CPooledGate, a class the signal owns. It needed NO data view. Diagnosing that first is the point: the module reads a directory of CSV files and knows nothing about a model. The only things it needs from its owner - the symbol's own numbers and the ratio they were measured at - are arguments. Handing it a CTrainingDataView would have been machinery for a dependency that does not exist. The owner fills SPoolRecord (the on-disk shape, which already existed) because only it knows its symbol, its actual TargetRR and its label lifespan. `id` is passed per call rather than bound, so there is no init-order question about when the identity became available - m_symbol is set by CExpertSignal::Init and ID by SetIdentity, at different times. m_poolWriteWarned was a one-shot latch living on the signal for a warning only this module emits. It is m_writeWarned, private. targetRR is now threaded into ReadPooledEvidence rather than read from the owner. That is not plumbing for its own sake: a peer measured at a different ratio has a different structural break-even, and only the caller knows which ratio it is asking about. Caught before compiling: I declared ReadPooledEvidence from memory as (..., double &pooledEffN, const double targetRR). The real signature ends in `string &detail`. Read the definition, aligned both ends. Call sites in Training.mqh are untouched - PublishPoolRecord and PooledGatePasses remain on the signal as the thin fillers that know its geometry. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> |
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AnimateDread
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a0f9cfa3e0 |
refactor(baselines): the first real module - a class, not an #included partial
Baselines was 951 lines of CExpertSignalAIBase method bodies in a file that only looked like a module. It is now CBaselineComparator: a class the signal OWNS, which reads a CTrainingDataView and prints. It does not name the signal anywhere in its code. What the seam forced out into the open: - Thirty-odd ArraySize() bounds tests, each carried by its caller, are now one test per accessor next to the data. The two `hasValueN` and one `arrowN` locals are gone with them. - The -2.0 "never scored" sentinel on the arrow cache was tested at the call site. It is now inside DataDirectionalCall, where it cannot be read as a small confidence. - DoubleToSignal needs m_outputNeuronsCount, so a raw double could not be turned into a side by any reader. The view answers DirectionalCall(bar, isBuy, magnitude) instead - the conversion happens where the head width lives, and the module no longer needs ENUM_SIGNAL at all. - m_baselineDone was a latch on the signal for a decision only this module makes. It is m_done, private, where it belongs. Correction to my own earlier claim: I said Baselines had nine exclusive members "polluting the signal class". It had none. m_x, m_f, m_ngrad, m_AvgCE and the rest are FIELDS OF ALGLIB REPORT OBJECTS (state.m_x, mrep.m_AvgCE) that my `\bm_\w+` scan matched after the dot. The module needs no private state but its view pointer and that latch - which is why it came out this cleanly. The include sits below the g_ens* vote globals and the Alglib headers it reads, because unlike the AIBase\*.mqh partials this is a real class declaration compiled where it stands. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> |
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AnimateDread
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2d8f231f12 |
refactor(arch): a read-only training-data view, so modules stop being #included code
The AIBase\*.mqh files are not modules. They are method bodies of one
3,400-line class, textually #included after its declaration. Every one
of them can touch every member of every other, which is why "move this
out" has so far meant "move the whole class".
Introduce the seam that ends that:
CTrainingDataView abstract - the ONLY thing a training-side
collaborator may see: a feature row, a label, an
outcome, an excursion, the shape they share, and
the identity to log under.
CAIBaseTrainingData the adapter. MQL5 gives a class exactly one base
and CExpertSignalAIBase is already a
CExpertSignalCustom, so it cannot implement the
view itself. It owns one of these instead.
Data*() on the the published read API the adapter forwards to.
signal MQL5 has no `friend`, so reaching in from outside
was never an option - and making it explicit is
the point rather than a workaround.
Every row accessor OWNS ITS BOUNDS TEST and answers false for a bar it
has nothing for. Thirty-odd call sites currently carry their own
ArraySize() guard; one that forgets reads past a cache that is shorter
than the bar count for the whole warm-up. The -2.0 "never scored"
sentinel on the arrow cache is folded in the same way, so it can no
longer be mistaken for a small confidence.
Nothing uses it yet - this is the seam only, kept as its own commit so
the pattern compiles before 951 lines of Baselines move onto it. The
pattern is the stdlib's own: abstract base with =0 (Canvas\DX\DXObject),
concrete override, forward-declared owner pointer.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
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AnimateDread
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788115970c |
fix(vote): unranked members voted with the stock 25/50/75/100 ladder
Two defects behind "arrows drawn while members are still mid-era". 1. THE DRAW. The filtered overlay armed on the FIRST member to finish pass 3 and leaned on a 60 s rate limit to "collapse the burst", assuming members finish seconds apart. They do not - on USDJPY one member was at sample 10496 of pass 2 while another was at 2304, minutes apart. A member with no era-end snapshot returns false from SnapshotVoteAt, and the sweep's `if(!hasData) continue;` skips it BEFORE `den += ModuleWeight()`, so the one finished model's tier weight became the entire vote and was drawn as a consensus arrow. An abstention is a member that looked at the bar and said nothing; a missing snapshot is a member that has not looked. The first must dilute the vote, the second must suppress the draw. The arm is now a readiness MASK - one bit per m_ensembleIndex, set at that member's pass-3 completion, cleared when a sweep arms - and a sweep waits for every enrolled member. Bounded at 10 minutes so a member that stops cannot freeze the chart, and the partial draw PRINTS which members were missing: the |
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AnimateDread
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042f20bdb9 |
fix(barriers): cap the horizon at what the close-all actually grants
The diagnostic shipped in
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AnimateDread
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b5e22a1e34 |
fix(geometry): a free zero made "never resolve" the winning geometry
The CANDIDATE GEOMETRY line shipped in |
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AnimateDread
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05f1a539c4 |
feat(geometry): measure per-candidate barriers against the one global pair
Stage 2a of the candidate-conditional geometry the record has named as next and never built. MEASUREMENT ONLY - no order uses it yet. WHY THIS AND NOT META-LABELING. Meta-labeling asks take-it-or-skip-it at fixed geometry, and its verdict stands: real skill, 0 operating points clearing break-even, and the |
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AnimateDread
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b91c7b1f7a |
refactor(comments): box headers to stdlib length
The //| box blocks were excluded from |
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AnimateDread
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5efdb48de4 |
refactor(comments): stdlib comment style across the remaining in-scope files
Same pass as
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AnimateDread
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ee4d459c6a |
fix(excursion): the sample gate asked for more windows than the configuration can contain
EXCURSION_MIN_DISJOINT was 200, sized as "~16k scored bars over a 64-bar horizon leaves ~250 independent ones". The head scores the OOS SLICE, not the history. At a 30% split and a 32-bar horizon the ceiling is 4691/32 = 147, so 200 was unreachable and every era printed "[disjoint sample too small]", which an operator reads as "wait longer". Unpassable by construction - the identical failure this file already documents one gate down, one layer up. The trail gate inherited it: m_excTrailScored is a subset of the disjoint bars, so it failed the same 200 for the same reason, at 130. Raising OOSSplit or shortening the horizon would clear it and would be fitting the experiment to the answer. Instead, ask the question the count was standing in for - is the skill bigger than its own noise: - The scorer banks one paired Brier difference per DISJOINT window over the decision rungs (base-head, and trail-head). Disjoint by construction, so no EffectiveSampleSize deflation applies - striding by the horizon is what buys that - and paired on identical bars, so the correlation between the two predictors cancels instead of needing to be estimated. - passDj and passTrail now require skill >= EXCURSION_SKILL_USEFUL_PCT AND >= 2 sigma, with the count reduced to a sanity floor of 30. - Both sigmas print on the verdict line. This is not a lowered bar. The 2% skill requirement, the oracle control and the monotone test are untouched, and the sigma test can fail where the count test never spoke: if +8.5% is noise across 147 windows, it will now say so. DecisionRungMask() is the single definition of "rung the decision depends on", called by both the scorer and the report, so the standard error is computed over exactly the rungs the skill score is. The report's inline copy of the bracketing test is gone. Also prints whether the disjoint count is BELOW ITS CEILING or at it, so "not enough yet" and "not in this configuration" stop reading the same. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> |
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AnimateDread
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f8ac10c808 |
fix(replay): the exit replay held trades through the Friday flat that the label and the live EA both close
The EXIT-POLICY REPLAY line reported an expectancy from SimulateTradeOutcome beside a win rate read out of the label cache, and called them "the SAME calls". Same calls, two different walks - and the walks did not agree. TripleBarrierLabel stops at NextScheduledCloseAll ( |
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AnimateDread
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4070c5c9bd |
feat(breakeven): the break-even every layer scores against prices a trade that always resolves
CostAdjustedBreakEvenPct is risk/(risk+reward) and has no horizon term. It is the win rate a trade
needs when it is CERTAIN to end at one barrier or the other. SimulateTradeOutcome has an explicit
branch for the case where it does not - runs out of horizon, closes at the last bar seen for
whatever P&L that is - so on this label geometry the figure describes a different trade than the
one being replayed.
The gap is measurable and large. Across 21 exit replays today on SP500 H4 the geometric figure read
34.5% while the EA's own R simulation crossed zero between 27.4% (lowest positive) and 28.9%
(highest non-positive). Independent corroboration: the zero-skill reference, computed empirically
over every scored bar as max(winLong,winShort)/bars, reads 25.4% - add cost and it lands on the
same ~28%. The geometric number is the outlier, and every edge printed against it was ~6.5pp too
pessimistic: LSTM's 30.6%-win era reported -4.0pp while its replay returned +0.075 R on the same
trades.
With a timeout share t paying a mean m R apiece, expectancy is w(1+RR) + t(1+m) - 1, so
w* = (1 - t(1+m)) / (1 + RR) = CostAdjustedBreakEvenPct x (1 - t(1+m))
which needs no new geometry - the existing figure already carries 1/(1+RR).
This commit MEASURES ONLY. The replay now separates timeout exits from barrier exits and latches
t and m for the next era to read (the accumulators are zeroed at era start and filled at era end,
so a mid-era reader sees zero trades and would fall back forever). Both break-evens print side by
side on the replay line with t and m beside them, and the threshold line's REPORTED edge - which
selects nothing - switches to the horizon-aware figure so the operator stops reading a wrong sign.
DELIBERATELY NOT CHANGED: LiveMetaGate's veto and the rung selector's BarrierMinReachPct still read
the geometric value. Both are decisions - the second re-derives geometry and therefore relabels -
and t and m have so far only been inferred from a zero-crossing, never seen on a log. One era of
this instrumentation settles that.
The file already contained the argument, one branch away, in the vote-exit comment: a vote exit
produces a CONTINUOUS payoff, not a win or a loss, and that is why an exit-aware gate cannot go on
scoring win-rate against a fixed break-even. A horizon timeout is the same thing, and unlike vote
exits it is on by default.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
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AnimateDread
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2de62241a5 |
fix(init): the OnInit failure the operator reads did not name the reason it failed
Two same-config charts collide on the config lock. AcquireConfigLock prints a precise REFUSED line, but CExpert::InitIndicators then returns a bare false, RetryInitStep retries it five times - a lock held by a live chart gives the same answer every time - and the last line on screen is "Failed to initialize Indicators after retries", which names neither the lock nor the owner. The explanation is six lines further up, under identical-looking retry noise. A refusal that cannot change on a retry now says so and stops: AcquireConfigLock records the reason, RetryInitStep repeats it and returns immediately. Found because a second SP500 H4 chart was started to run Run_Alglib_Baselines. That input is a diagnostic and is deliberately not in the fingerprint, so both charts resolved to the same filename - the guard was correct and the message was not. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> |
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AnimateDread
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667f2bcb6b |
revert(labels): drop the one-sided exit target; measure the calibration drift instead
Reverts |
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AnimateDread
|
68ef19797b |
fix(telemetry): one ensemble member had never printed a single era line, in any run on record
The era-progress rate limit was a function-scope `static`:
static uint lastProgressLogTick = 0;
shouldLogProgress = (nowTick - lastProgressLogTick >= 5000);
In MQL5 that is ONE variable for the whole build, not one per object. A 5s
limit meant to keep a single model's console readable was therefore a limit
across the WHOLE ENSEMBLE, and it did not distribute fairly - it starved
whichever member finishes last, every era, deterministically.
Measured on today's run: the era barrier releases the members together and
LSTM landed 2.06s, 2.43s and 2.28s behind ConvLSTM on eras 1-3, against a 5s
window it could never reach. LSTM printed zero era lines. PAI, CONV and HYB
printed all of theirs - 3 each this run, 17 each in the 10:00 run, LSTM 0 in
both, and 0 again in the 08:32 run.
So one model in four has been training with NO per-era telemetry: no
per-class recall, no dW/W ratios, no zero-skill comparison, no deploy-bar
line. It was still doing the work - tier re-ranks, threshold fits and
exit-policy replays all appear on cadence - which is what made the hole look
like a grep that kept missing the line rather than a line that was never
written. It cost me the LSTM half of a gradient check earlier today.
Now a member, so each model rate-limits its own console output.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
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AnimateDread
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a86379621c |
feat(labels): on a one-sided book the blocked side's class is retargeted from an entry it can never take to the EXIT of the one it holds
User request: "when an asymmetry is noticed in a market (like sp500 upward drift) ... it does not need to predict shorts, but exit points. a sell signal needs to be preceded by a buy so that it can say I predict we must close that long." Until now a LONG_ONLY verdict only BLOCKED short entries. The network went on being trained to predict them - a third of its output capacity spent learning an answer the direction policy guarantees it can never act on, while the question the book actually faces (when to get out of the long) was never asked. The two are not the same event: "a short pays" needs price to travel the SHORT's target before the SHORT's stop, and at any geometry where reward != risk that is a different bar from "this long hits its stop first". The exit is the second one. So on a one-sided book TripleBarrierLabel re-cuts all three classes around the only position the book can hold: Buy = it reaches its target, Sell = it reaches its STOP first, Neutral = the horizon expired with it still open. Both come off the allowed side's own barriers, which the walk already computed - this reads longLost where it used to read shortWon, so it costs nothing. Label lifespan and the timeout flag follow the allowed side too, so the overlap correction is sized on the window this label actually spans. DECIDED ONCE, AT ERA 0, AND PINNED. m_exitTargetSide goes in the .cfg beside the derived geometry under the same doctrine and for the same reason: it decides what Buy and Sell MEAN, and a target that moved mid-run would retrain a fitted model against something it never saw. A .cfg from before this ends early and reads 0/0 - "not decided, symmetric" - which is exactly what every existing model was trained as, so nothing needs migrating. The weights fingerprint keys on the INPUT only (explicit Long only / Short only); under Intelligent the measured verdict must never reach a filename, or the model is orphaned the moment more history downloads. THE DRIFT VERDICT HAD TO MOVE OFF THE LABELS FIRST, and it turns out it was measuring the wrong thing anyway. It counted m_labelCacheBuy/Sell and called them "always-long vs always-short win rate", but the label pair is the COLLAPSED first-touch verdict: a bar where both sides reached their target carries only the side touched first, so long wins were undercounted by the both-won-goes-to-short share. m_winLongCache/m_winShortCache are the actual per-side win rates, published before the collapse, and that is what it reads now. Necessary as well as more correct - deriving the verdict from labels the verdict shapes is a feedback loop, since Sell-as-exit is near complementary to Buy and would close the very gap that produced it. The gap's SE now leans conservative rather than anti-conservative for the same reason. LIVE. The retargeted class is wired to close the position, or training it would be pointless: CheckClosePosition's "never vote-exit a certified position" rule keeps governing symmetric books and gains a one-sided exception, and the replay reads the identical rule through one LiveVoteExitThreshold() so certified and traded cannot describe different policies. Armed only when the operator picks a close threshold (Signal_ThresholdClose ships Disabled) AND the model's own pin says its blocked-side class means "close" - a model trained symmetric never fires it, whatever the verdict has since become. This does trade a different game from the one the win-rate certificate grades; the era's EXIT-POLICY REPLAY line already reports expectancy in R for exactly this case and says so in words. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> |