Author	SHA1	Message	Date
AnimateDread	8ab8cf6b92	feat(chart): on-chart vote readout, and Min_Vote_Open 50 -> 40 ... THRESHOLD. 40 is a measured correction, not a preference. Once RankTiersFromOos() replaced the designed tier priors with each model's real held-out win rate, the vote converges on that win rate - logged 2026-08-18 as pooled 23-36% across four members on three symbols - so a 50% bar could not be reached by anything on offer and the ensemble gate fired on 0 of 4,865 OOS bars. 40 clears the ~34% break-even those same lines report without being unreachable. The comment says plainly not to copy the number: break-even is a function of the barrier geometry, so read the gate's own "needs >N%" for the config in front of you. READOUT. One line, top-right: VOTE SELL 37.2% peak 44.1% need 40% 3 voter(s) -> no trade Every other number on the chart is downstream of the weighted mean the open threshold is compared against, and that was the one quantity never displayed. A chart with no arrows could mean the models abstained, the vote was diluted, or the threshold is unreachable - and telling those apart meant waiting for an era to end and reading the gate line, which is how the last two sessions went. PEAK is the part that earns its space. A threshold above what the vote ever attains can never fire, and that is not knowable from a single bar - it is precisely the "unreachable gate vs merely unmet gate" confusion this project has paid for twice. Colour carries the verdict rather than the direction: green/red ONLY when the vote would actually place an order, grey otherwise. Green-for-buy would make a below-threshold buy look like a trade, which is the specific misreading the display exists to prevent. Guarded on `total > 0` for the same reason the normalization is: Direction() is inherited as-is by every leaf filter, so without it each filter would write its own opinion into the one shared label and the last to run would win - the reader would be looking at an arbitrary member's number believing it was the vote. Drawn after the +-100 range check, so it shows what the threshold is actually tested against. CORNER_RIGHT_UPPER: the status lines, control panel and ensemble panel all live on the left. Registered in WarriorChartPrefixes() explicitly even though the "Warrior" catch-all already reaches it - that catch-all exists because the list has drifted twice, not to make entries optional. NOT COMPILED - user compiles in MetaEditor. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>	2026-08-18 18:22:45 -04:00
AnimateDread	4858507146	feat(vote): thresholds become confidence percentages, on ONE scale everywhere ... User request: "the entry/exit thresholds are manual numbers, I would like them to be confidence percentages, so the current 20 would be only 20% confidence in a profitable trade." WHY 20 WAS EVER SENSIBLE. Under UseDatabaseRanking both factors of a filter's contribution are win rates: the pattern weight is that pattern's measured win rate (UpdateSignalsWeights -> ApplyPatternWeight) and m_weight is the filter's average win rate over its patterns, /100. Dividing the sum by the VOTER COUNT therefore produced a mean of PRODUCTS of two win rates - a genuinely 60%-accurate filter firing a 60% pattern scored 0.60 x 60 = 36. The number was never on a probability scale, so its magnitude meant nothing on its own. Dividing by Sum(m_weight) instead makes it a weighted MEAN of win rates, which is a win rate: result = Sum(w_i*p_i)/Sum(w_i). Every voter at 60% now reads 60; MACD's double-divergence pattern (weight 100) voting alone reads 100. m_weight stops being a discount on the probability and becomes how much a filter's opinion COUNTS - which is what a module weight should always have been. Default Min_Vote_Open 20 -> 50: not a tightening, the same bar re-expressed. ONE SCALE, EVERYWHERE - the part that made this bigger than a rescale. Three other places compared against a 0..1 softmax confidence and would each have become a fresh currency mismatch the moment the input changed meaning: * the AI early-exit route (LiveSignedConfidence vs m_ai_exit_threshold) now reads m_lastAiVote - the AI filters' own weighted mean, undiluted by the classic side, which is the only reason that route exists - against the same m_threshold_close the averaged vote uses. m_ai_exit_threshold is retired rather than left dangling. * m_oosDecisionSeries now carries the vote, not the confidence, so the exit SIMULATION stops modelling a close rule the EA does not run. * ExitPolicy() clamped anything > 1.0 to zero. Passing the unscaled input through that would have silently switched vote exits off in the simulation while live went on running them - found before it shipped; the bound now tracks the scale. LiveSignedConfidence() is deliberately untouched and still 0..1: MM sizing, SL/TP scaling and the intelligent trailing want a model confidence, not a win rate. CALIBRATION CAVEAT, stated in the code where the claim is made: this is only a real probability to the extent the pattern weights are. A pattern with fewer than MIN_TRADES_FOR_WIN_RATE journaled trades keeps its DEFAULT weight - a designed prior (25/50/75/100 for the AI tiers), not a measurement. Until the signal DB fills, "60" means "the designed conviction of the patterns that fired". Closing that gap is the next commit. Also corrects VOTE_CLOSE_PRESETS' comment, which documented the two scales this removes. NOT COMPILED - user compiles in MetaEditor. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>	2026-08-18 15:52:08 -04:00
AnimateDread	282b535037	feat(chart): filtered view - one arrow per trade the bot would actually take ... Adds DrawUnfilteredSignals (default OFF) and, with it off, replaces the per-model arrow layer with the decision the EA would really have made. THE FILTERED ARROW IS DRAWN AT THE ORDER, NOT AT THE THRESHOLD. Clearing Min_Vote_Open is not the same as trading: a setup can pass the vote and still never reach the broker (invalid SL/TP, stops-level, ATR warm-up, unsynced swing history), and every one of those lands in OpenParams' failure branch. So DrawVoteArrow() fires only after the order parameters validate, and the failure branch withdraws any arrow already standing on that bar. One arrow is one entry the EA would have placed - carrying the vote, the threshold it cleared, and the SL/TP the order would have had. Classic signals now draw too, under their own name and weight, so a chart running MA/RSI/MACD/Ichimoku alongside the nets reads the same way an ensemble chart does. They can only be drawn from the aggregate's once-per-bar pass, because unlike the AI members they have no cached per-bar scan. Two subtleties that would each have produced a quietly wrong chart: - The raw classic draw sits AFTER filter.Direction(), not beside the journaling block. GetActivePattern*() are CONSUMING reads holding the PREVIOUS evaluation - "one tick later", which at Expert_EveryTick=false is one BAR later. Keyed off those and placed at StartIndex(), every classic arrow would have been drawn one bar early, which on a chart is indistinguishable from a model that genuinely leads. Peek*() accessors (non-consuming) let pattern, weight and bar come from one evaluation. - CExpertSignalAIBase::DrawObject() early-returns instead of gating its five call sites, so the switch cannot be honoured in three passes and missed in the fourth. Its delete counterparts stay ungated so flipping the input off and rescanning clears the raw layer rather than stranding it. SIG_ARROW_PREFIX and g_signalsVisible move from ExpertSignalAIBase.mqh down to ExpertSignalCustom.mqh - the nearest common ancestor - because the classic signals cannot see the AI header (it is included later in Warrior_EA.mq5). The vote layer gets SIG_VOTE_PREFIX under the same bare prefix, so WarriorChartPrefixes()' purge still reaches every arrow without knowing they exist. NOT YET BUILT: the reconstructed history behind attach. Filtered arrows currently start where the EA starts. See the next commit. NOT COMPILED - user compiles in MetaEditor. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>	2026-08-18 15:41:28 -04:00
AnimateDread	94019f363e	feat(gate): grade OOS calls on the exit policy actually in force, and move vote combining out of the members and into the orchestrator ... Option (a) from the exit-policy question: the certified number must be the traded number. Plus the modularity correction the user called for on 778b6c0. 1. VOTE COMBINING BELONGS TO THE ORCHESTRATOR, NOT TO A MEMBER. 778b6c0 fixed the last-writer-wins bug on g_LiveAISignedConfidence by having a member average its siblings through g_warriorEnsemble. That trades a scheduling bug for a coupling bug, and it is the wrong shape for this EA: every signal runs in its own instance, minds its own state, and VOTES to the orchestrator, which is the only thing allowed to combine opinions. Replaced with a publish/aggregate pair in Variables\ConfidenceBridge.mqh whose split is enforced by shape rather than by convention: - PublishAIVote(slot, conf) - a member writes ONLY its own slot, reads nobody's; - AggregateAIVotes() - called by CExpertSignalCustom::LiveSignedConfidence. CExpertSignalAIBase::EnsembleLiveSignedConfidence is gone. The orchestrator also republishes the aggregate into g_LiveAISignedConfidence, because the intelligent trailing reads that global directly and must act on the same number the exit route does rather than on a leftover from whichever member ticked last. A solo AI signal owns slot 0, so the non-ensemble path is unchanged. 2. THE GATE NOW REPLAYS THE REAL EXIT RULE. SimulateTradeOutcome() walks the same price series with the same fill/barrier/spread convention as ComputeLabelForBar - deliberately by copy, so a disagreement between the two can only be a policy effect and never a discrepancy between two pieces of our own arithmetic - and terminates at the FIRST of stop / target / vote reversal / horizon. Barriers are tested before the vote on the same bar: intrabar we cannot know which came first, and the barrier is what the broker executes automatically, so checking the vote first would credit the exit policy with escapes a real stop would have taken out of its hands. It runs AFTER pass 3, not inside it. A vote-flip exit for a trade entered at bar r is decided by the model's output at bars r-1, r-2, ... - NEWER bars - and pass 3 walks oldest-to-newest, so at the moment r is graded its own exit does not exist yet. Only once m_oosDecisionSeries is complete over the whole OOS window can the replay run. In ensemble mode that series carries the member's adjusted decision and the live exit reads the ensemble aggregate, which is the coupling the user identified: an LSTM entry really can be closed by the ensemble turning against it. 3. THE STATISTIC HAS TO CHANGE WITH THE POLICY, AND THAT IS THE REAL FINDING. A barrier exit pays a fixed R. A vote exit pays whatever the close happens to be. So the moment vote exits are enabled the payoff is CONTINUOUS, and "win rate vs break-even" stops being a meaningful test - there is no fixed break-even for a variable payoff. SimulateTradeOutcome therefore returns R rather than a bool, and the replay reports expectancy in R with its SE taken from the R distribution (overlap- deflated on the same EffectiveSampleSize doctrine as every other SE here), not from a binomial. This is the same class of error as win-based scoring in 2026-08-09: measuring a variable-payoff process with a fixed-payoff statistic. Naming it now, while vote exits are still off, is much cheaper than discovering it after they go on. 4. WHY THIS IS SAFE TO SHIP TODAY. Min_Vote_Close ships Disabled (101, unreachable on both scales it drives) and reaches the AI signal through the new ExitPolicy() setter as 1.01, which the setter turns into 0.0 = no vote exit. Under that policy every replayed trade resolves at a barrier and the simulation is arithmetically the same trade the deploy gate already certifies - they cannot drift. The report says so explicitly, and prints ONCE per run in that state; when vote exits are on it prints every era, because then the divergence is the thing to watch. Nothing about today's numbers moves. The gate switchover is wired but dormant by construction: it becomes exit-aware the moment the input is enabled, which is exactly what "the certified number is the traded number" has to mean. KNOWN LIMIT, stated rather than hidden: only the AI early-exit route is replayed. The rule-based averaged-vote close (m_threshold_close) depends on every other filter's live vote, which pass 3 does not reproduce, so a position the classic filters would have closed is held to its barrier here. The replay therefore holds LONGER than live and overstates barrier-reached outcomes. Faithful only while the AI is the dominant vote - which is the configuration this is being built for. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>	2026-08-17 16:25:57 -04:00
AnimateDread	34fa0045e3	feat: extend MAX_ERAS_PRESET enum and update MaxErasPerRun to ME_10000	2026-08-16 23:52:22 -04:00
AnimateDread	c0c9f4a285	feat(target): withdraw the TrainingTarget option - barrier is the only live one ... NOT COMPILED - user compiles. The private build still DEFAULTED to TARGET_FRACTAL, so every fresh attach was training the target adjudicated dead that morning (5,700 model-eras flat at -2pp, best-of-243 p=0.17). The campaign closed; the default was never flipped back. Rather than re-default it, the input is withdrawn entirely (user: "remove the option if there is only one choice for now"). An input offering a single live choice is worse than no input - it presents a dead option as supported, and an operator picking it silently trains a model already known to carry nothing. Direction models are now unconditionally triple-barrier. Removed: the input, the TrainTargetFractal() call in the signal setup, and the HoldToBarrier() exit-policy block (which existed only because the fractal vote flips at swing-marker cadence, ~3-5 bars, far inside the barrier's travel time - barrier-target models keep vote exits and always did, their label IS the vote's horizon). Verified no code reference to TrainingTarget survives; the four remaining mentions are comments. Kept deliberately, so a rerun is a re-enable and not a rebuild: the TRAINING_TARGET enum, the fractal label itself, its |TGT:FRA1 fingerprint token, its conditional barrier-geometry derivation, HoldToBarrier()/m_holdToBarrier, and the campaign's trained models on disk. Three lines bring it back; Inputs.mqh names them. ALSO CORRECTS THE RECORD from 1b5a412. I claimed the live run was on the barrier target, "confirmed" by break-even 34.3% matching the 0.62/1.18 geometry. That proved nothing - break-even comes from BarrierMultiples, which grades wins identically under either target. Neutral's share is the real tell: ~31% would say barrier, the measured 10.6% says fractal. So the imbalance finding stands and its mechanism is unchanged, but the cause of Neutral being rare was the FRACTAL target, not the triple-barrier relabel. Neutral fell twice - 94% under the old exact-pivot ZigZag label, ~31% under triple-barrier, 10.6% under fractal - and the correction was re-checked at neither step. The fractal campaign was chosen FOR its balanced classes and did balance Buy vs Sell (48.3/41.1) while quietly making Neutral the thin residual the correction then subsidised. Under the barrier target the same geometry gives roughly 34/34/31, where Neutral is neither rare nor dominant, so 1b5a412's fix should be close to a no-op there - which is the right answer when there is nothing to correct. It stays: never subsidising the abstain class is correct under both targets. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>	2026-08-16 22:53:04 -04:00
AnimateDread	3e9b46452b	fix(altdata): robustness pass for arbitrary symbol/timeframe - validation + error handling ... Systematic audit of the alt-data stack against "any symbol, any timeframe", prompted by the H4 surprise. Findings, each fixed: CAPACITY: ALTDATA_MAX_FEATURES was 16 with FX symbols already at 15 - the next added column would have been silently truncated by a MathMin. Raised to 32, pin-chars 512 -> 1024. SYMBOL NAMES: the panel reload path re-derived symbol/timeframe by splitting the file path on its FIRST underscore - mis-parsing every symbol containing one (OANDA-style EUR_USD and US_500 are in our own alias lists) and knowing only three timeframes. It now stores the (symbol, period) Load() was called with and reuses them verbatim. Path-hostile characters in broker symbols ("EUR/USD") are sanitized by a shared AltDataFileSymbol() used by the panel, the fetcher and TunedPeriods, so a slash cannot route a write into an unintended subfolder. DOWNLOAD VALIDATION: every FRED-family fetch now enforces a per-series plausibility band (VIX 1-200, yields -5..30, CPI index 20-1000, ...) - StringToDouble on transport garbage returns 0.0, and one absurd value poisons every change/percentile feature computed across it (the BatchNorm NaN-latch incident came from exactly one huge-but-finite input). Rejected rows are counted and reported, never dropped silently. LOUD EMPTINESS: a successful response with zero observations on an empty cache now says so - naming the series (wrong id / format drift) or the COT predicate (the unverified like-clauses) instead of leaving 0-filled features unexplained. GEX gains a truncation guard: a day-over-day contract-count collapse >50% is the fingerprint of a partial 13 MB download, not of markets, and is skipped rather than recorded as a plausible-but-wrong number. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>	2026-08-16 17:59:10 -04:00
AnimateDread	0a198fb95f	feat(altdata): EIA wired, 24-instrument symbol catalog, mapping dialog for unknown symbols ... EIA (user directive: "the NN might find patterns in it for both oil and regular symbols"). Weekly Petroleum Status Report via the v2 API - crude stocks ex-SPR, field production, refinery utilization - three features (1y percentile, 4w change, utilization) on EVERY catalog symbol, not just oil. EIA screened NULL on WTI's short 7y sample, so these ship as EXPLORATORY inputs: the deploy gate, not the screen, decides whether a model trained on them trades. Publication stamp observed+6d mirrors research/altdata/eia.py. Symbol handling was hardcoded to three if-blocks; it is now a catalog of 24 instruments x alias lists covering The5ers/FTMO/AvaTrade/Dukascopy/OANDA/IC Markets naming, with prefix matching for the broker suffix zoo (US500.cash, XAUUSDm, EURUSD.r). Adding an instrument is one AddSpec row. COT caches are named by CANONICAL so two brokers' names for one contract share a download. Unrecognised symbol -> a chart dialog (Panel\AltDataMapDialog.mqh, CAppDialog + dropdown) asks which instrument it is; the answer persists in symbol_map.cfg and "No alternative data" is a recorded choice, not a nag. Non-blocking by design: an unmapped symbol contributes 0 features and must never hold up a chart. Also: UrlEncodePart now escapes '%' - SoQL like-predicates use it as the wildcard and an unescaped one corrupts the query; docs/ gains the whitelist URLs, an API-key backup, and the catalog reference. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>	2026-08-16 16:18:29 -04:00
AnimateDread	e2e6d61855	feat(altdata): API keys are now EA inputs - defaults survive the folder wipe ... The AltData folder in Common\Files gets wiped before every fresh test, and keys.txt died with it (2026-08-16 silent-FRED incident). The credential now travels with the EA: FredApiKey input, owner key as default; keys.txt demoted to a fallback consulted only when the input is blanked. EiaApiKey stored the same way - reserved, nothing consumes it since the WTI screen came back null. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>	2026-08-16 15:48:05 -04:00
AnimateDread	0788238c00	feat(inputs): unify ALL indicator periods under the tuner; EnableAltData input; AI-first defaults ... - PeriodMA/MA_Type/PeriodRSI: input -> const seeds (closing the set: every indicator parameter is now tuner-owned) - Variables\TunedPeriods.mqh: chart-level tuned-period state. A gated install writes TunedPeriods_{SYM}_{TF}.cfg; next attach reads it BEFORE the DB fingerprint and classic-signal config, so classic votes, DB key, and tuner seeds always describe the same indicators regardless of classic/AI/hybrid use. Restart-grained adoption by design (no mid-run handle churn); new periods re-key the signal DB (semantics rule). - EnableAltData input in AI Input Features (consumption gate only; collection keeps running); |ALT DB-fingerprint token; opt-out on an alt-trained model correctly starts fresh via the width compare. - Defaults: all four classic votes OFF (AI-first; WARRIOR_MARKET_BUILD branches collapsed with the marketplace pivot), order-flow/Wyckoff NN features OFF (alt data is the default information diet; toggles stay). Compiles 0 errors / 0 warnings. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>	2026-08-16 15:12:54 -04:00
AnimateDread	ffeb136537	refactor(inputs): prune 18 AD/Wyckoff menu inputs; auto-tuner defaults ON ... The 18 inputs added 2026-08-08 (when the tuner defaulted off and the values needed an operator path) become compile-time aliases of their own defaults - same names, zero consumer churn, byte-identical values. The tuner is now the only path by which these values move: it defaults ON (the 08-08 off-flip was measured against the direction target's flat landscape; the objective is now RANGE, which has signal), searches from the seeds under the Sidak family-wise gate, and persists winners in the .nnw beside the weights. ADP fingerprint token retired (deviation now impossible by construction; tuned values were never its job). Menu shrinks 102 -> 84 inputs. Compiles 0 errors / 0 warnings. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>	2026-08-16 14:51:24 -04:00
AnimateDread	609be10391	feat(ai): AI_HYBRID = ensemble preset (all NNs, one chart); conv+recurrent renamed AI_CONVLSTM ... The user is right that no special combination logic is needed: the AI signals are ordinary voting filters, and the aggregate already has union semantics - abstaining filters do not dilute the average, so an ensemble chart trades whenever ANY deployed member clears the vote threshold and disagreeing members net out. What the ensemble preset actually adds: - AI_CHOICE value 4 renamed AI_CONVLSTM (the name says the front-end); enum VALUES stable, CSignalHYBRID class and State\HYBRID\ folder kept, so saved configs and trained models keep their identity. - New AI_HYBRID = 6: enables PAI+CONV+LSTM+CONVLSTM together on one chart - replaces four separate charts of the same symbol. Each member trains and self-gates independently; only certified members ever vote. - |ENS1 fingerprint token on every member, so an ensemble member's weight files can never collide with a solo model of identical settings on another chart of the same symbol (the duplicate-chart guard would otherwise correctly fight over one .nnw). - Private default AIType = AI_HYBRID: one D1 drop now yields every topology's gate verdict for that symbol. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>	2026-08-15 16:50:36 -04:00
AnimateDread	61a8c42a9c	feat(ai): TrainingTarget input - fractal-direction label for the direction models ... User direction (2026-08-15): back to predicting swing turns, D1 charts, fractals over ZigZag pivots (their call - balances classes, matches the reference library target, and a 5-bar fractal confirms 2 bars after its extreme so labels resolve nearly to the present with no repaint embargo). - TRAINING_TARGET enum + TrainingTarget input: TARGET_BARRIER (Market default - existing models keep their meaning and fingerprints) or TARGET_FRACTAL (private default). - FractalDirectionLabel (Labels.mqh): per-bar 3-class label = direction from the bar close to the next confirmed strict 5-bar fractal extreme, costs charged in the same bid-series convention as the barrier label, Neutral when the move cannot clear max(2 spreads, 0.10 ATR) or on an outside bar (both-extreme bars are unorderable within OHLC). - The barrier walk still runs in full: measured SL/TP geometry, the expectancy scan, excursion caches and the era gate all keep scoring what a trade at the EA's own stop/target actually collected - only the TRAINING label changes. NOT the pre-b4a704d "is this bar the pivot" form; that target's 31:1 imbalance stays retired. - Fingerprint token |TGT:FRA1 so switching targets trains a separate model; AI_META unaffected (guarded setter). - Private defaults: AIType back to AI_HYBRID (direction topology needed) + TrainingTarget=TARGET_FRACTAL = drop-on-D1-chart workflow. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>	2026-08-15 04:44:10 -04:00
AnimateDread	a7c31e2352	feat(meta): increase DB_MaxRowsPerTable to accommodate larger pattern datasets	2026-08-13 19:25:02 -04:00
AnimateDread	10253b581b	feat(inputs): private-build defaults = the drop-on-chart meta-pooling workflow ... User request: attaching a chart must need zero Inputs-tab edits. Private (non-Market) build now defaults to AIType=META, all four classic families ON (they are the sweep's candidate sources), Meta_ExportDataset=true. Market-build defaults unchanged (AI_NONE, MA/RSI only, no export); UseDatabaseRanking=true applies to both per the earlier request. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>	2026-08-13 16:31:15 -04:00
AnimateDread	1bf3eba68a	feat(meta): self-contained corpus - the META chart sweeps the real classic ladders over its own history ... The user should not need a tester corpus run per symbol. Every pattern condition in Signals\Signal{MA,RSI,MACD,Ichimoku}.mqh anchors its reads on `int idx = StartIndex()` with zero hardcoded indices (verified), so a name-hiding StartIndex override + EvalShift(i) on CExpertSignalCustom makes the EXACT live ladder code answer "what would you have fired at bar i" - the silent-divergence trap that justified the DB corpus does not exist on this path, and neither do the GMT-offset ambiguity, the DB row caps, or the wipe procedure. - CExpertSignalCustom: m_evalShift + StartIndex()/EvalShift() + SweepPrepare(bars) (deep-resizes the shared price series); the four classic signal classes override SweepPrepare to deep-resize their own indicator buffers. - CSignalMETA::BuildCorpusBySweep: per bar x per source filter, run Direction() shifted, harvest the per-side pattern slots + netVote into the same corpus arrays the DB loader fills; entry=bar open so MetaPrepareEra's resolution matches at offset +0 with zero price error. DB corpus remains the fallback when classic filters are disabled. - Warrior_EA.mq5: META gets the enabled classic filters as candidate sources (family ids match the descriptor one-hot). - UseDatabaseRanking default false -> true (user request): a META chart journals + ranks out of the box. Workflow per symbol is now: attach ONE chart with AIType=META (optionally Meta_ExportDataset=true for the offline pool) - candidates, labels, training and export all happen in place, ~10 seconds of sweep instead of a tester run. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>	2026-08-13 16:19:43 -04:00
AnimateDread	d20058fc1b	feat(meta): dataset export for offline cross-sectional pooled training ... Meta_ExportDataset input: with AIType=META the chart writes its complete training set once per attach - every resolved+labeled candidate as [barTime|family|pattern|side|won|NetInputWidth floats] using the SAME window builder, descriptor and label caches pass 2 trains on, so offline examples are byte-equivalent to the EA's own. Sidecar .meta.csv carries layout + the geometry/BE the labels were computed at. Files land in Common\Files\Warrior_EA\MetaExport\<sym>_<period>.f32. This is the pooling architecture decision: multi-symbol training INSIDE the per-chart God-class would be the riskiest surgery this codebase has seen; instead each chart exports, the pooled head trains offline (small dense+BN net, minutes on this box), is validated per-symbol under the same chronological splits and coverage x (p - BE) gate, and only a WINNER gets written back into a .nnw for the EA to load natively (format fully mapped). Also turns every future meta experiment from a 20-minute tester cycle into minutes of offline iteration. Cost-model note for the record (user challenge, verified): spread is 0.099 ATR = ~2% of the 4.74 ATR trade width - tiny per bar, but expressed in win-rate points it is 0.099/4.74 = 2.1pp, which is the measured base-vs-BE gap and the size of the entire observed skill lift. Zero-spread relabeling would put base == BE by construction. Multi-day holds additionally pay swap, which the label does NOT charge - the true bar is higher, not lower. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>	2026-08-13 13:14:12 -04:00
AnimateDread	444909d0a3	feat: S2 meta-labeling head - binary trade-quality model over the classic-candidate corpus ... The NN now has a target that is not per-bar direction (closed, best-of-999 p=1.0000): P(win | this journaled candidate, at the EA's own SL/TP, net of cost). One net for all 52 pattern-sides, AIType=AI_META. - NetForward.mqh: the host-side softmax+CE gradient generalized total==3 -> 2||3 on both backprop paths; a 2-class softmax IS a logistic head, and no compute backend changes. - SignalMETA.mqh (new): corpus loaded read-only from the LARGEST signal DB on disk (decoupled from the config fingerprint that burned four S1 runs); the GMT->server offset is measured PER ROW against entryPrice vs bar open (DST-immune, histogram logged); a window-span regime filter drops the pre-2017 daily-backfill rows; 31-feature setup descriptor appended at the input (26 one-hot + side + tanh netVote + SL/TP ATR + spread/ATR). - Training.mqh: candidate-queued pass 1, binary-target pass 2, per-candidate calibration (2.5) and OOS (3) walks. Counter mapping win->Buy / loss->Sell lets checkpoint selection, the edge floor, the plateau ladder and the family-wise deploy gate run UNCHANGED: precision reads as win rate among traded candidates, chance as the base win rate, recalls as sensitivity/ specificity. Era-end META line: coverage x (p - break-even) vs the null. - Labels are the side-conditional triple-barrier win caches - never the DB's stop-and-reverse outcome. Logit adjustment deliberately skipped (~40% base rate). Live inference + online learning guarded off until S3. - Fingerprint: conditional |TGT:META1; State\META\ folder + 2-output filename slot keep meta models fully separate from direction models. Compiles clean (0 errors, 0 warnings). S2 run = attach a chart with AIType=AI_META; S3 wires the votes via the per-side hooks. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>	2026-08-13 06:52:31 -04:00
AnimateDread	4507ea69a9	feat(meta): S1 - the signal DB becomes the meta-label training corpus ... Implements stage S1 of Meta_Labeling_Design.md, superseding the original "training-time ladder sweep": the per-side journaling from 652bf81/195be20 already produces the exact candidate stream a sweep would compute - every pattern instance the live ladders fire, both sides, uncensored, with netVote and touchable entry price - so the corpus is READ from the DB instead of re-implementing 26 ladder conditions in training code. That eliminates the silent-divergence trap outright: the corpus is by construction identical to live behaviour. Accepted costs are documented in the module and the doc: coverage equals the populating backtest, and sampling is one candidate per fire-stretch (the right dedup for training anyway). - Expert\AIBase\MetaCorpus.mqh: CMetaCorpus reader (52 tables -> SMetaCandidate rows) + VerboseMode OnInit report: volume/closed/ S&R-win-rate per family, span, and the GMT->server bar-offset match table (offsets +0..+3h) that S2''s label plumbing pins to - measured, not assumed. - DB_MaxRowsPerTable input (default 1000 = old MAX_TABLE_ROWS): a corpus build raises it (e.g. 20000) so a 15-20 year backtest isn''t pruned; wired through CExpertSignalCustom::MaxTableRows(). - Report-only stage: nothing downstream consumes the corpus yet. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>	2026-08-12 15:20:33 -04:00
AnimateDread	195be2025b	fix(db): the log no longer asks the decision layer for permission ... The DB system logs objectively; the decision layer reads it to compute win rates and adjust weights. The journaling path still had one decision-layer tendril: rows were only written when the root''s OpenLongParams()/OpenShortParams() succeeded. Those calls validate ORDER PLACEMENT (broker stops-level, ATR warm-up, entry-mode rejection) and their failures cluster in volatility/spread conditions, so the gate non-randomly censored exactly those bars out of every pattern''s win-rate sample - the same censoring class 652bf81/c8ef478 removed, one layer down. The ledger never needed placement to be possible: entries are marked at the touchable side of the spread and exits are same-pattern reversals, not broker fills. Also documents netVote for what it is: a record of the decision layer''s state at log time (per-pattern weights inside it drift as ranking updates land), not an objective measure - the objective part of a row is pattern/direction/price/result. SIGNAL_DB_SEMANTICS_VERSION 4 -> 5: row populations gain the previously censored bars, so the database re-keys. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>	2026-08-12 12:02:05 -04:00
AnimateDread	c8ef478ce8	fix(db): reversing signals register their own trade (true stop-and-reverse) ... Verification of 652bf81 on a fresh 7-month backtest DB surfaced the last one-sided mechanism: ProcessSignal absorbed a reversing signal as the exit of the opposite trade and skipped registering it. For pure EVENT patterns that strictly alternate (MACD model 3, the zero-line cross), every reversal was consumed and the whole ledger landed on whichever side fired first - 60 Buy rows, 0 Sell rows - so the silent side never earned a win rate and UpdateSignalsWeights() weighted the pattern from one side only. State patterns escaped by re-firing one bar later. The reversal now closes the opposite trade AND registers its own row; the existing duplicate/outdated/open-trade checks still bound the table at one open trade per pattern+side. Row populations change meaning, so SIGNAL_DB_SEMANTICS_VERSION 3 -> 4 re-keys the database (the v3 file is orphaned, not wiped - schema is unchanged). Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>	2026-08-12 11:32:36 -04:00
AnimateDread	652bf81112	fix(db): per-side pattern journaling + versioned journaling semantics ... The labelMatchesVote gate compared a single last-writer-wins label (LongCondition then ShortCondition) against the net vote sign, which structurally censored the pattern tables: a long event co-occurring with any short-side state model lost its label to the later writer and was dropped, while the mirrored short event journaled fine. Ichimoku models 0/3 and MA model 1 could not produce a row at all by construction (MA model 1 was "revived" in 8710240 yet still could never journal - its weight-10 vote is exactly cancelled by the opposing Pattern_0 state), and every pattern's win rate was measured on a with-trend-only subset - the exact statistic UpdateSignalsWeights() feeds back into the weights, self-sealing: no rows -> no win rate -> default weight -> still censored. - Direction() now evaluates the two ladders separately and snapshots each ladder's matched pattern into its own side slot; each side that matched journals its own row. The flat-vote poisoning the old gate fixed stays fixed: a label can no longer contradict its side. - The filter's net vote (raw pattern-weight units) is stored as a new netVote column - data, never a drop filter. Snapshot is keyed on the ladder setting a label, not on its weight, so a 0%-win-rate pattern keeps journaling and can recover. - SIGNAL_DB_SEMANTICS_VERSION is folded unconditionally into the DB filename fingerprint: pattern-definition changes (b2069bc, 8710240) re-key the database instead of blending incompatible Pattern_N populations under one key, which the input-hash fingerprint cannot see. 7 months of mixed-semantics rows shared one file because of it. - dbVersion 2.0 -> 3.0: schema changed, and inserts carry the new column, so the version-mismatch folder wipe is the migration. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>	2026-08-12 10:37:57 -04:00
AnimateDread	36e8463310	refactor: derive history bars for input sequences and update related configurations	2026-08-11 21:53:37 -04:00
AnimateDread	2d28f6542b	feat: excursion-size head (Stage 1, measurement only) ... Direction is closed - normalised asymmetry fails on three instruments with a working positive control, and the classifier's own best-of-999 era-cap test agrees (+0.9pp = 1.48 sigma, family-wise p=1.0000). SIZE is a different question and RANGE clears at ~4x its null. Checked the denomination before building on that, since the source memo warns to: m_excUpCache holds (maxHigh - fill)/ATR, so "RANGE is predictable" is a claim about travel RELATIVE to current ATR, not a restatement of "ATR is autocorrelated". It is exactly the part a fixed multiple (stop 3.31*ATR, target 1.64*ATR) discards. A second small CNet, 760 -> 24 -> 32 sigmoid outputs = P(price reaches ladder rung k) upward and downward. Survival parameterisation rather than regressing the multiple, because it needs nothing new from CNet: sigmoid outputs and the per-neuron delta the `total != 3` branch already applies (a quantile head would need a linear activation and a pinball gradient in Network.mqh, Network.cl and the DirectML path, on a class four topologies share). Targets are free - m_ladderUpAt already records first-touch age per rung with 0 meaning never reached. Separate net, not extra outputs on the classifier: more outputs would change m_outputNeuronsCount, the .nnw shape and the fingerprint, and push the count off 3 - the exact condition backProp uses to select the joint softmax gradient the 3-class head depends on. The classifier is bit-for-bit unaffected and this is removable without trace. STAGE 1 PLACES NO ORDERS. It reports a Brier skill score against the constant per-rung base rate - the baseline a fixed ATR multiple already assumes - with both predictors fitted IS and evaluated OOS, so neither gets a look at the test set. Positive skill justifies Stage 2 (drive SL/TP and sizing off ExcursionQuantile, which is defined and deliberately uncalled). Zero or negative means ATR already carries everything and Stage 2 must not be built. Trains only on primary occurrences: the replay queue oversamples for CLASS balance, and a direction-balanced sample is a biased SIZE sample. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>	2026-08-11 07:40:01 -04:00
AnimateDread	d919a4aea2	feat: 10-bar decluster window + alternation on every signal consumer ... SignalClusterWindow 3 -> 10 for all topologies. On H1 a 3-bar window collapsed only the tightest runs and left visible clusters at every turn; 10 bars is closer to the spacing of genuinely distinct setups. ALTERNATION. Rule 1 only collapses a same-direction run INSIDE the window; past it a second Buy is emitted with no Sell between, giving Buy/Buy/Buy/Sell. With both directions tradeable that sequence is the model re-entering a move it is already in rather than finding a new one. The kept sequence must now alternate: the first signal passes, and after that a direction passes only if the last KEPT signal was the opposite one. Added to ALL THREE consumers, with identical logic, because they must agree: - NmsLiveAccept -> the live trade - pass 3's OOS replay -> the tally the deploy gate grades - PruneDirectionalClusters -> the drawn history A rule applied to only some of these certifies one strategy and trades another - the same defect class as the geometry the gate certified while OpenParams placed something else (9a7c37f) - and would draw the user arrows the EA would never have taken. Deliberately NOT applied to the LABEL. The barrier target has no "must flip" invariant: consecutive Buy labels are routinely correct, and an earlier alternation gate was removed with the triple-barrier relabel for exactly that reason. This filters what is ACTED ON, which is what "applies to training" can honestly mean here - pass 3's declustered tally is the training-side number that decides deployment. BothDirectionsTradeable() is the stated precondition (with one side disabled there is no opposite to wait for, so alternation would suppress everything after the first call). This build has no long-only/short-only input, so it is constant true - kept as a named predicate so a future direction restriction has one place to change rather than three call sites silently assuming both sides. Build tag -> nms-alternate-v4. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>	2026-08-10 14:26:12 -04:00
AnimateDread	9a7c37f334	fix: live trades now use the geometry the gate certifies; perf: BN kernels ... Three changes, one theme: the trade placed, the trade graded, and the trade computed are now the same trade. 1) GEOMETRY WIRE (correctness, the ranked #1 open issue). The measured barrier pair reached the LABELS only - OpenParams still placed orders at the enum geometry (2*ATR/6*ATR), so the deploy gate certified "reaches 1.62*ATR before 3.33*ATR above break-even" about trades the EA never placed. Published via g_DerivedSlAtrMult/g_DerivedTpAtrMult (ConfidenceBridge, same same-tick contract as the confidence globals, because OpenParams runs on the root signal which has no pointer to the AI filter). Two writers: DeriveBarrierGeometry at era 0, and the .cfg adoption a deployed model takes. Overrides both legs and both Intelligent modes - the certificate is exact or it is nothing. TP is ATR-anchored like the label, NOT risk-relative, so a floor-widened stop cannot reshape the certified target. 2) BATCH NORM RUNS DEVICE-SIDE ON OPENCL. Four kernels in Network.cl - forward, hidden gradient, gamma/beta accumulate, gamma/beta apply - each a line-for-line transcription of the host implementation (NormalizeHost / HiddenGradHost / StepGammaBeta) including every NaN guard, clamp, and the exact moment-write ordering. The host copies remain the runtime for the DLL and pure-MQL5 tiers and the reference the kernels must match. Because this box has no OpenCL platform, the safety story is layered: - shim validation: kernels compiled as C and driven against a fp64 host transcription over NaN-poisoned stats, NaN gamma, over-clamp inputs, the frozen path, both optimizers, 3 batches - ALL PASS, worst normalized diff 0.132 vs tolerance 1.0 - in-situ self-check: each kernel is compared against its host twin ON FIRST USE on the real device (SelfCheckBn*), covering what the shim cannot - arg indices and buffer bindings. Any disagreement resyncs from the good copy, latches all BN kernels off process-wide, and training continues host-side. A transcription bug costs a warning and some speed, never a poisoned .nnw. - sync discipline: BatchOptions is now a CBufferDouble with explicit authority tracking (m_bnDeviceAuthoritative). Checkpoints/saves pull read-only; restores/loads/resets push; a mid-batch handover drains the device gamma/beta accumulator into the host arrays so no sample is lost. 3) SMALL FIXES. Apply-kernel build failure now latches the dispatch path at init (one warning instead of warning + failed Execute). Build tag bumped to win-scoring-gpu-v1 - first tag change since expectancy-stop-v1 despite five binary-changing commits. Both build variants compile 0 errors, 0 warnings. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>	2026-08-09 17:51:40 -04:00
AnimateDread	217b9bc9bf	feat: remove Min_Risk_Reward_Ratio - a guess was overriding a measurement ... The barrier geometry is derived from the instrument's own excursion distribution (stop at q75 of adverse travel, target at q50 of favourable), and then a 1:2 floor was applied on top, raising the target to twice whatever the stop happened to be. On SP500 H1 that pushed the target to 6.66*ATR, reached on 3.3% of bars inside the horizon - so the label became "almost never a win" and every topology was trained to predict an event that essentially does not occur. A measured target has to stay measured. The ratio never bought what it was believed to buy. A reward:risk floor does not create expectancy; it trades hit rate against payoff at a break-even the geometry already fixes - which this project has separately MEASURED (payoff 0.92 -> 5.72 with expectancy flat). What it did buy was two outages: four consecutive Market validation rejections for "no trading operations" when it rejected 100% of setups, and the label corruption above. Removed: - the input and the RISK_REWARD_RATIO enum (deleted, not left dangling - a live enum with no input behind it is the shape of the stale-.set incident that trained ~250 eras on the wrong target) - the forced target raise in the label geometry - the rrOK eligibility gate in the barrier-geometry scan, so every unclamped pairing now competes on the measurement alone. Clamping stays disqualifying for its own unrelated reason. - the reward < minRR*risk veto in OpenParams Kept: g_TradeRewardRiskRatio still computed and still bridged to Kelly sizing in MoneyIntelligent - the ratio as a SIZING input was always the sound use. Risk stays bounded where it actually is - account risk % and CRiskBudget. The low-reachability warning survives but is re-aimed: with nothing inflating the target, a target the market rarely reaches can only mean the horizon is truncating the excursions the geometry is derived from. Both build variants compile 0 errors / 0 warnings. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>	2026-08-09 14:51:59 -04:00
AnimateDread	c690a73901	docs: remove outdated note from AutoTuneIndicators comment ... The comment previously included "; see note" which is no longer applicable. Removed to keep the input description concise and accurate.	2026-08-09 09:51:27 -04:00
AnimateDread	922484e8d9	feat: expose the AD/Wyckoff parameters; default the indicator tuner off ... AutoTuneIndicators now defaults to FALSE, and the 33 AD/Wyckoff parameters it used to search are now inputs. WHY THE DEFAULT FLIPPED - not because the search is broken. It is correct, and its own Sidak gate is what proves it: 324 candidates per model on SP500 H1, "no improvement" on all four topologies (0.00236 -> 0.00236 on the AD configs, 0.00370 -> 0.00370 on PAI), winner rejected at p=1.0000. It cannot do better here by construction - it ranks candidates by MARGINAL MI, and the headline MI is 0.00370 nats against a shuffled null of 0.00379 +/- 0.00061 (p=0.4975), so every candidate is a noise draw and the maximum over N of them is noise too. The cost is 45-56 min per model in one synchronous call with no yield, and it was the amplifier for the handle leak fixed in 33f106d. The EA's own report says it plainest: "no per-feature indicator retuning will help." THE INPUT STAYS. TuneIndicatorsByFilter is one function of twelve in AIBase/AutoTune.mqh; the other eleven are the MI/lag/excursion/geometry diagnostics that produced every verdict this project relies on, and they run regardless of this flag. Removing the input invites removing the file. WHY THE INPUTS WERE NEEDED. All 33 were literals in CADIndicatorTuner's constructor with no input of any kind, while MA/RSI/MACD/Ichimoku have had their periods exposed from the start. On the AD configs those indicators contribute 28 of 64 features per bar. Survivable while the tuner searched them; indefensible with it off, where they would freeze at values nobody chose. CONSOLIDATED 33 -> 18. volClimax/volHigh/rangeClimax/rangeSignificant/ stVolRatio/atr were duplicated verbatim across CumulativeDelta, Wyckoff Events, Failed Structure and Bar Inversion - the same constants restated 3-4 times. One concept, one input. They are SEEDS: each fans out to the indicator's own struct field, so with the tuner on it retains full per-indicator freedom to move them apart. Same contract as PeriodMA. NO RETRAIN. Every default is byte-identical to the literal it replaces, and the fingerprint's new ADP token is appended ONLY on deviation (MACD/Ichimoku/BN/XA convention), gated on the AD features being enabled. At defaults the token is absent, so every model on disk keeps its filename and stays loadable. Without that guard, merely EXPOSING these parameters would have re-keyed every config and forced a from-scratch retrain of all four topologies for a change that alters no number anywhere. All-or-nothing rather than per-input, so the token can never encode a partial picture of what the features were built from. Compiles clean: 0 errors, 0 warnings. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>	2026-08-08 20:37:21 -04:00
AnimateDread	da54639996	feat: expectancy stop - halt when the measured result says the strategy loses ... The daily (4%) and total (8%) rules bound how FAST an account can lose. Nothing noticed WHETHER it was losing. A negative-expectancy signal traded at 1% inside that envelope breaches no rule and still arrives at zero - it just takes longer, with every limit green the whole way down. That is the realistic way this EA destroys an account, and no existing guard could see it. THE ARITHMETIC THIS ENFORCES. Expected value per trade is p*TP - (1-p)*SL - cost. With no directional edge p equals SL/(SL+TP), which is also the break-even rate, so the payoff terms cancel exactly and EV = -cost. Expected P&L is -(trades) x cost: strictly negative, proportional to activity. Measured here: directional precision 23-24% against a 25% break-even, flat across every confidence tier, with 58 points of spread on SP500. Sizing, stop placement and trailing move variance around that mean; none of them changes its sign. So every closed position now reports its result in R (net profit over money actually at risk) and the running mean is tested against zero. Above the configured minimum sample, if mean + sigma*SE < 0, new entries stop. - SIGNIFICANTLY below, not merely below. A run of losers is ordinary variance even for a profitable system; halting on the raw mean would be the same act-on-noise error the MI gates exist to prevent. Using the standard error means a wide spread simply demands more trades before the rule can fire. - NET of swap and commission (ResolveClose already sums all three). Deliberate and load-bearing: when the edge is zero, cost IS the expectancy, so a gross version would measure a strategy nobody can trade. - Reported in R so symbols, lot sizes and balances share one scale and one mean. Trades without a stop are not scored rather than assigned a guessed R. - LATCHED across restarts, like the daily halt and for the same reason: a latch a reattach clears is not a latch. Clearing it means deleting the risk state file, deliberately, after looking at why. State is appended to the risk file length-guarded, so files written before this still load and start their sample at zero rather than misreading. Defaults 40 trades / 2 sigma; ExpectancyMinTrades = 0 disables it. This does not make the strategy profitable and is not meant to. It stops paying tuition on one the results say is losing, and does it on measurement rather than on a drawdown limit finally being reached. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>	2026-08-07 14:20:00 -04:00
AnimateDread	3482b6c238	feat: entry/SL/TP stop being inputs - the barrier geometry is measured ... Three enums left the Inputs tab. They were three things a user had to pick and, in the tester, three more axes for a genetic optimization to overfit. Entry_Multiplier is pinned to MARKET. Its pending modes place the entry at a LEVEL while the rest of the pipeline measures from the bar open - the exact mismatch that manufactured the +0.097 R "retail fade" result later retracted as a fill artifact. This codebase's fill model cannot honestly simulate a pending entry, so it is no longer offered. SL_Mode/TP_Mode become a STARTING pair. ReportBarrierGeometryScan now ADOPTS its winner instead of printing "set SL_Mode/TP_Mode to X and retrain": - only when it clears the family-wise gate from 04ee2e1 (beat the null of the MAXIMUM, not merely the incumbent). This is why that gate had to land first: without it, removing the inputs would hand a noise-picked geometry direct control over the training target with no human in the loop - strictly worse than the input it replaced. On SP500 H1 today it does NOT clear (p=0.1463), so 2:6 is what you get - now chosen by measurement rather than assumed. - only at m_eraCount == 0. Relabelling a partly-trained net moves the target out from under weights already fitted to the old one. THE GEOMETRY LEFT THE WEIGHTS-FILENAME HASH, because it is now measured. Same rule that moved the horizon and the derived topology values out: a filename keyed on a measured quantity changes the moment the measurement does - a few more bars shift which pairing wins - and the EA then looks for a file that does not exist, starts from era 0 and orphans a trained model silently. It is PINNED IN THE .cfg instead: appended at the end (the only backward-safe change), length-guarded like the 2026-07-30 derived pair, and ADOPTED on load rather than compared, so a trained model keeps the barriers it actually learned and never re-measures. Two traps closed while wiring it, neither of which announces itself: - m_barrierHorizonResolved latches the horizon ONCE PER PROCESS. Adopting 2:8 (wants ~192 bars) after it settled for 2:6 (128) would label the new target against the old ceiling - the truncation fixed in 168422f, where every model learned "target within 128 bars" while the EA holds to SL/TP. It lands in Neutral, not in the timeout counter watching for it. Unlatched on adoption, along with the label cache the old barriers filled. - the .cfg adopt runs at init, before the horizon latches and before any label is computed, so a resumed model has its pinned pair in place first. Verified, not assumed. FORCES A FULL RETRAIN: the fingerprint change orphans every existing .nnw. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>	2026-08-07 09:39:30 -04:00
AnimateDread	8c5ea639ee	feat: extend ADWyckoffEventStream with new range-lifecycle parameters and update related features	2026-08-02 17:08:48 -04:00
AnimateDread	8ccbddb051	Add new research scripts for trading strategy analysis ... - Implemented sqx_audit.py to audit StrategyQuant X trade lists, focusing on performance metrics and cost analysis. - Created sqx_portfolio.py to evaluate portfolio performance based on uncorrelated components and their impact on risk and return. - Developed swing.py to analyze cost ratios across different holding periods and assess swing trading structures. - Introduced test_management.py to investigate the effectiveness of exit rules on random entries and their impact on expectancy.	2026-08-02 12:25:20 -04:00
AnimateDread	5f647ba5db	fix: improve error messages and suppress false sharing-violation logs ... - BufferDouble: replace hardcoded "DirectML/CPU-DLL" with dynamic backend name and add buffer index/element count to all error prints for easier debugging. - NetPersistence: distinguish missing file from transient lock by probing FileIsExist before logging, eliminating false "sharing violation" warnings when no saved model exists on first run.	2026-08-02 01:09:18 -04:00
AnimateDread	ceb6342dfd	feat(ai): spread as a volatility-regime feature, and fix a stale-index cache in both new blocks ... Adds spread/ATR and the spread change ratio as network inputs (EnableSpreadFeature, default on). Spread is the one microstructure channel that is both FX-available and genuinely historical in the Strategy Tester - "during testing, the spread is not modeled but is taken from historical data" - so unlike swap, signed tick flow or depth of market it is something a backtest can honestly validate. What it encodes, stated precisely because the raw measurement overstates it. research/test_spread.py found spr/atr the strongest single feature in this codebase, on 5 of 8 instrument/geometry cells at 2-4x any volume feature. But the barrier LABEL charges the spread inside its own barriers, so a wide-spread bar is mechanically likelier to resolve as a loss and the feature would partly be predicting its own cost model. Relabelling at zero cost and re-measuring the identical feature showed 20-40% of it WAS that tautology and the majority was not (XAUUSD retained 97%). What survives is a volatility-regime reading: spread is near-fixed while ATR is not, so the ratio runs high exactly when realised volatility is below its own ATR estimate, which genuinely predicts whether ATR-scaled barriers get reached. It is UNSIGNED - Neutral-vs-directional only, never a side. Also fixes a stale-index bug I introduced with the cross-asset panel and had just repeated in the spread series. Both cached on length alone: if(m_crossAsset.Bars() >= bars) return true; MQL5 series indices are relative to NOW, so one new closed candle shifts every index by one. Keyed only on length, the panel keeps serving its index 0 as a bar that is no longer the newest, and every cross-asset value is read one bar out of step with the price features sitting beside it in the same vector - silently, with no error and no shape change. This is the same class of defect as the dtStudied watermark behind the zero-direction backtests. Both now carry a datetime anchor on m_Time.GetData(0), the same invalidation key the label/feature bar caches already use. And a performance fix that fell out of it: with correct invalidation the panel rebuilds on every new bar, and RefreshConvergedSignal runs per bar - which in the tester would mean one full multi-symbol resample per simulated bar at training depth. Inference only reads bars 0..m_historyBars-1 plus the panel's own slow window, so it now requests exactly that. The cache check is >=, so a deeper panel left from training still satisfies it. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>	2026-08-01 17:42:40 -04:00
AnimateDread	8710240cd5	fix(signals): revive a dead MA model, and demote Sanyaku from state to event ... Two defects surfaced by research/test_classic.py, both verified fixed by re-running the transcription against 178k bars of EURUSD H1. CSignalMA model 1 could never fire. For any recursive average - and MA_TYPE_EMA is the shipped default - MA(i) = a*Close(i) + (1-a)*MA(i+1), so DiffMA(i) = a * (Close(i) - MA(i+1)) DiffCloseMA(i) = (1-a) * (Close(i) - MA(i+1)) are positive multiples of one quantity and always share a sign. Model 1 asks for a close BELOW a RISING average, which is precisely the combination that identity forbids: 0.000% of bars, either direction, any symbol. The MQL5 standard library this was ported from defaults to MODE_SMA, where the two are merely correlated - the bug arrived with the EMA default, not with the port. Reading the slope one bar back (DiffMAPrev) breaks the tie for every MA type while keeping the model's stated meaning. Now fires on 7.92% of bars. CSignalIchimoku model 11 fired on 27% of bars at weight 100. Sanyaku is three standing STATES conjoined with no transition term, so it held across long stretches - and being last in the if-chain at the top weight, the module's highest-conviction reading was also its most common one, overwriting all eight event models below it on a quarter of all bars. The old comment rejected an event form because "demanding all three flip on the same bar would fire almost never" - true, but that is not the alternative. Kouten is the TURN: the ALIGNMENT transitions, and only one role need change for it to. Testing !Sanyaku(idx+1) fires once per aligned stretch. Now 2.17%, in line with Kumo breakout (2.4%) and the strong TK cross (1.1%). DataReady() extended one bar deeper to cover the lookback. Neither pattern showed edge before or after; this is about the models meaning what they say and the vote not being dominated by a constant. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>	2026-08-01 17:14:34 -04:00
AnimateDread	6db0519472	perf(autotune): replace the genetic search with a filter score - hours to seconds ... MEASURED COST OF THE GA, which is what retired it. Per generation: rung 0: 8 cand x 3 seeds x 3 eras = 72 eras rung 1: 4 cand x 3 seeds x 8 eras = 96 rung 2: 2 cand x 3 seeds x 20 eras = 120 = 288 eras/generation x 4 generations = 1152 eras BEFORE the winner's real training began. Against the observed era times on SP500 H1: PAI 29.1 s/era -> 9.3 h (matches the observed 00:37 -> 09:22) CONV 41.3 s/era -> 13.2 h LSTM 150.4 s/era -> 48.1 h HYBRID 154.6 s/era -> 49.5 h Two days to tune is not a first-run experience, and it is the phase in which the panel goes quiet, which is what made it look like a hang. It also bought nothing. The space is 90 points (10 MA periods x 9 MA types), so 1152 evaluations revisited each point ~13 times; and rungs of 3 and 8 eras cannot separate two MA periods at all. The 2026-08-01 run proves it: every finalist scored 25.0-25.9% balanced accuracy - below the 33.3% one-class floor, i.e. indistinguishable noise - and the search then "deployed the winner" of that. THE ERROR WAS THE SCORING FUNCTION, not its constants. Using a full training run to choose a feature's period is a wrapper method paying wrapper prices for a decision that does not need one. The reference book does not do this: ch. 3.3 selects inputs by measuring each candidate indicator's CORRELATION with the target and dropping the ones with none, with no network involved. So: rank candidates by the MUTUAL INFORMATION between the resulting feature vector and the triple-barrier label. MI rather than correlation because the label is 3-class categorical and the features are not monotonically related to it. Equal-FREQUENCY binning (rank-based), because these features are ATR-normalised and heavy-tailed - fixed-width bins put nearly everything in one bucket and report ~0 information for a genuinely useful feature. Scoring is arithmetic over the feature cache, so it costs seconds and its cost is independent of topology: LSTM now tunes as fast as the MLP. Coordinate sweep, not product sweep - cost is the SUM of per-parameter candidate counts, so enabling every indicator stays affordable - with a second pass that breaks early once nothing moves. Sampling is IS-ONLY. Letting the OOS window influence which indicator settings ship would mean the holdout had been used for selection and had stopped being a holdout. HONEST LIMIT, recorded because it is the price: MI is marginal, so a parameter that only pays off in combination with another can be missed (Guyon & Elisseeff 2003, filter vs wrapper). Given the wrapper it replaces was ranking pure noise at 48 h a run, this is strictly better. Deleted with it: GaRungEras/GaExtract/GaStore/GaMutate/GaRandomCandidate/ GaBlockCrossover/GaSortAliveByScoreDesc/GaBreedNextGeneration, 14 m_ga* members, the GA_*/TUNE_POP_* constants, and ComputeTuneTrialBudget. AND m_evalMode/m_evalEraBudget, because nothing set them any more - 28 read sites all permanently inert. That is not a tidy-up: the `if (!m_evalMode)` guard on UpdateClassPriors is exactly what silently disabled the imbalance correction for entire runs two commits ago. Dead machinery that still reads like live machinery is this codebase's most expensive recurring bug, and leaving 28 more instances of it would have been indefensible. The panel's tuning-progress state goes too - tuning no longer takes long enough to need one. Both builds compile 0 errors / 0 warnings. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>	2026-08-01 10:29:31 -04:00
AnimateDread	e83e30344f	fix: disable auto-tune indicators by default ... The AutoTuneIndicators input is now automatically derived via `ComputeTuneTrialBudget()`, so the default is set to false to prevent manual interference.	2026-08-01 10:03:23 -04:00
AnimateDread	8ff3f5d632	refactor(inputs): set default SL to ATRx2 and TP to ATRx6 ... Adjust default stop-loss mode from SL_ATR_x1 to SL_ATR_x2 and default take-profit mode from TP_ATR_x3 to TP_ATR_x6. This improves the risk-reward alignment in line with the recommended minimum ratio and ensures setups are not rejected under the EA's target reward parameters.	2026-08-01 00:34:58 -04:00
AnimateDread	f48bc93f9b	refactor(inputs): 96 -> 70 inputs; remove two untested/unusable filter modules ... Every removal below is FINGERPRINT-NEUTRAL by construction: each retired input is pinned to the exact value it already shipped with, so running models keep their filenames and resume rather than restarting at era 0. Verified field by field against BuildConfigFingerprint. Removed as inputs, kept as pinned constants (the value was never a preference the user had a basis to change): - OutputNeuronsCount. The regression head predicts a continuous quantity the triple-barrier label does not contain; the target is an EVENT, so the right output is its probability. The regression code paths stay implemented and dormant - they cost nothing and removing them would touch every scoring path at once. - MinRecall. A safety floor, not a preference, and the only direction a user can move it is the harmful one: raising it past what the config reaches yields NO model, not a better one (observed repeatedly at 60). - SwingConfirmationBars. Stopped gating the labels with the relabel, but is STILL load-bearing for the swing-context input features - it is the ZigZag repainting embargo, and without it those 9 features read a leg the live bar could not have had yet. Pinned, not deleted. - MaxErasPerRun (runaway backstop, never reached in a healthy run), FreezePriorCalibration (unanswerable by a user; near-balanced labels make the priors stable anyway), VerboseMode (developer view, joins DebuggingMode), MACD/Ichimoku periods x6 (both indicators ship disabled, and as optimizer dimensions they are pure overfitting surface - the AI auto-tuner is the supported way to move them). - SignalClusterWindow -> 3, no longer an input. Barrier labels make consecutive setups real, which argued for 0; it is not 0 because on D1+ a 6-bar window spans over a week and two arrows a day apart on a weekly-scale move are one event. 3 splits it correctly by timeframe. - EnableOnlineLearning -> ON. Adapting to a changing market is what keeps a months-attached model from going stale, and the rolling-accuracy freeze is what makes it safe. See the caveat noted in the handoff: it had not been forward-tested on a live feed when this became default. Removed entirely: - Intraday Time Filter (5 inputs + Signals/SignalITF.mqh). Two of its five inputs were raw BITMASKS, which is an implementation detail exposed as a control. The job is covered three times over by things that are declarative or that learn: the session filter, the time-of-day/day-of-week input features (the network discovers which hours are good rather than being told), and the journal's time buckets. - Market Depth Filter (5 inputs + Signals/SignalMarketDepth.mqh, plus its OnInit probe and OnDeinit release). It needs real level-2 data that this broker - and most retail MT5 brokers - do not provide, so the module has never once executed against real data. Shipping four tuning dropdowns for an untested path is worse than shipping nothing: the only users who could enable it would be its first-ever testers, live. If DOM returns it should be a FEATURE fed to the network, not a rule-based veto with hand-tuned thresholds - imbalance is data. - IndicatorTuneTrials, replaced by ComputeTuneTrialBudget(). The useful budget depends on how many parameters are actually being searched, which depends on which features are enabled - so one number meant wildly different things run to run. The shipped 32 was ~10 candidates per dimension against one enabled indicator (wasteful: each costs GA_SEEDS full training runs) and under one per dimension against all nine (blind). Now population ~ 4 x active dimensions, clamped [8,64], with CADIndicatorTuner::ActiveDimensions() defined immediately above PerturbRandom() so the two cannot drift apart. - Six orphaned enums (TUNE_TRIALS_PRESET, DOM_*, ENTRY_HOUR_OF_DAY, TIME_FILTER_DAY_OF_WEEK), 81 lines. Other UX: - SL_ATR_x1 / TP_ATR_x3 now carry the "(classic)" default marker every other preset enum in the file already used. Nothing in the SL/TP dropdowns previously told a user which pair was the shipped default - which matters far more since the relabel, because those two define the labels and changing either forces a retrain. - Neural Network section moved directly ABOVE AI Input Features: choose the architecture, then choose what it sees. NN Optimizer / Performance stays last - the Adam/Sgd inputs are declared in AI/Network.mqh and render immediately after that divider. - News feature + window moved to the end of the AI feature list, below Wyckoff Bar Inversion. - Dropped "(0-100)" from Min vote to open - it is an enum, not a number. Both builds compile 0 errors / 0 warnings. No retrain forced. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>	2026-07-31 21:22:02 -04:00
AnimateDread	b4a704d309	feat(ai): triple-barrier labels replace exact-pivot ZigZag targets ... The 31:1 class imbalance was self-inflicted by the TARGET, not a property of the market. Labelling only the exact bar where a ZigZag pivot confirms gave Buy 1164 / Sell 1164 / Neutral 35841, and every correction mechanism this codebase accumulated sits downstream of that one choice: the logit-adjusted loss and its range cap, the prior EMA, the +-3.0 output-bias seed, balanced-accuracy-then-precision selection with its coverage floor, the recall floor and its catch-22, the alternation gate, NMS, and the four oversampling designs that collapsed before them. The reference this engine is built on (references/neuronetworksbook.pdf ch. 3.1/3.3) also uses ZigZag, but targets the DIRECTION TO THE NEXT EXTREMUM on every bar - ~50/50 by construction, with no imbalance to correct at all. It never had this problem because it never asked "is this the pivot bar". Labels are now the triple barrier (Lopez de Prado ch. 3), using the EA's OWN SL_Mode/TP_Mode: does a trade opened at this bar's close reach its target before its stop, within a horizon. Buy = long resolves, Sell = short resolves, Neutral = neither. Consequences: - dir-precision in the era line stops being a proxy and becomes the win rate of the strategy under its own exit rules. - Expected balance ~25/25/50 at the shipped 1:3 (gambler's ruin), i.e. ~2:1 instead of 31:1. Measured and logged at the end of the prebuild. - Spread is charged on both legs, so it is a NET win rate. - Intrabar ambiguity resolves to the STOP. OHLC cannot order two touches inside one bar and the optimistic reading is how a backtested edge becomes a live loss. ZigZag stays as input features (EnableSwingContext) and now also supplies the vertical barrier: the horizon is the median confirmed leg length, snapped to a coarse ladder. Derived, not configured, and deliberately kept out of the filename fingerprint - a filename keyed on a measured quantity orphans a trained model the moment the measurement moves. Removed, because the premise died with the old target: - the alternation gate. Correct for pivot labels (a ZigZag cannot emit two same-type pivots in a row, so a repeat was provably a false fire), and wrong for barrier labels, which answer each bar independently. It also took its worst consequence with it: a one-sided model previously got ONE trade per backtest, a hard blocker on marketplace validation. - SignalClusterWindow now defaults off - it de-duplicated repeats that are now real trades. Kept as an opt-in display control. - LABEL_WINDOW_BARS, the pivot-widening pass, ConfirmedZigZagLabel. - the era-0 output-bias seed now needs a genuinely dominant class (0.70) rather than 0.40; at ~50% Neutral a +-3.0 seed is a distortion, not a correction. Also fixed, both found while wiring the above: 1. RefreshConvergedSignal sized its buffers from a date delta (Bars(sym, period, dtStudied, TimeCurrent())). dtStudied is a training watermark; in the tester it is loaded from a live-chart save AHEAD of the simulated date, so the interval inverted, Bars() returned ~0, and the buffer came out at exactly m_historyBars - deep enough for the OHLC window and far too shallow for the Donchian-50 / 20-bar-return / SMA extension behind it. Inference silently computed DIFFERENT features from the ones training learned on, live as well as in the tester. Now sized from what the feature builder actually needs. 2. The barrier horizon is resolved on the deployed path too. A deployed model never enters Train(), so it never reached the prebuild, and OnlineLearnStep reads the horizon as its confirmation delay - left at the fallback it would have backpropped bars whose barriers had not resolved. Silent lookahead in the one place that writes to a live model. SL_Mode/TP_Mode join the weights fingerprint: they define the labels now, so a model trained at 1:3 must never be silently reused at 1:1. This re-keys every pre-existing model by design - none were trained on this task. Inference census extended with the vote gate. LongCondition/ShortCondition open with a readiness check the refresh counters never see; in the tester it reduces to "the seeded _optcache.nnw must have LOADED", and if it did not, every vote is hard-zeroed while the model still answers Buy. The old three counters would have read that as "the model says Neutral" - false, and a completely different fix. This is the leading candidate for the zero-direction backtest and the census can now name it in one run. Both builds compile 0 errors / 0 warnings. Forces a full retrain. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>	2026-07-31 20:39:49 -04:00
AnimateDread	397b0eac1f	refactor(ai): nine class-imbalance inputs down to two ... The imbalance section offered nine controls for one job. Audited against the code, five of them did not do what their names said at the shipped defaults: AILogitPriorStrength DEAD - Inference.mqh's post-hoc prior early-returns whenever the adjusted loss is on, which is default. OversampleParity DEAD in training - Training.mqh gated the replay loop on !useLogitAdjustedLoss (correctly, citing Buda et al. 2018). Live only in the online-learning path. EnableMinorityReplay DEAD as replay. It survived ONLY as a focal-gamma damper - "replay minority bars through pass-2 oversampling" was a focal-loss switch. ConstrainReplay DEAD as a cap; it only chose damper 0.125 vs 0.25. UseStaticPrior An exact duplicate of FreezePriorCalibration - the two were OR'd together in the single place either is read. So they were not five mechanisms fighting; they were one mechanism plus eight knobs that mostly described machinery that no longer ran. That is worse than a real conflict, because the log agreed with the names: the label-cache line printed "reps up to 28x (90% parity) (seeding era 0's class-balance oversampling)" on every run, describing an oversampling pass that had been switched off. It is fixed here too - it cost this session a wrong diagnosis. The one genuine redundancy was focal loss, running at gamma*0.125 alongside the adjusted loss: two corrections on the same axis, the exact stacking failure this file already cited Buda et al. for in two other places, damped by a replay flag whose replay path was itself dead. Removed rather than re-tuned. The plateau ladder is unaffected - its escape is the learning-rate warm restart; the gamma anneal beside it only ever stepped toward zero. WHAT REMAINS is logit-adjusted loss (Menon et al. 2021) plus a prior freeze: LogitAdjustTau 0 = off; replaces the separate EnableLogitAdjusted- Loss boolean, since a strength dial where 0 already means off does not need an on/off switch beside it. FreezePriorCalibration unchanged. It is the only one of the six corrections with a consistency guarantee, and it is consistent for exactly the balanced-error metric checkpoint selection already ranks on - so the loss and the deploy decision optimize one thing. The online continual-learning path keeps its own alpha-balanced focal weight, now as constants pinned to the removed inputs' shipped defaults, so its behaviour is unchanged. It legitimately needs its own correction: ApplyLogitAdjustment() only runs inside a training run, so a deployed model that was reloaded carries no logit offsets and would otherwise stream 31:1 data into itself uncorrected. The weights-filename fingerprint is BYTE-IDENTICAL. The focal slot was a double fed to a %d conversion and had always emitted a literal 0; the |MR: segment is written as the constant its shipped defaults produced. Dropping either would have re-keyed every model and forced a from-scratch retrain of the one topology currently converged and trading. Also removed as orphans: FOCAL_GAMMA_PRESET, MAX_OVERSAMPLE_REPLICAS, OVERSAMPLE_PARITY_FRACTION, PLATEAU_GAMMA_STEP, and the now-unreachable "neutralized by prior correction" diagnostic. Both builds compile 0 errors, 0 warnings. No retrain forced. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>	2026-07-31 11:46:57 -04:00
AnimateDread	7eb48f5038	feat(trade): anchor SL and TP to the entry price, not the last swing ... Stops keyed to the recent swing extreme make a trade's risk a function of how far the last swing happens to sit rather than of current volatility. On a shallow pullback the swing sits close to the fill, so the stop is tight enough to be taken out by noise on setups that then run to target - which is what the Perceptron's signals were showing. SL: lowest_low/highest_high -/+ mult*ATR -> entry -/+ mult*ATR TP: TP_PREV_SWING (opposite swing) -> removed; ATR-from-entry SL_PREV_SWING, TP_PREV_SWING -> removed from the enums The SL anchors to `price` (the resolved entry), not to base_price: with a pending entry those differ by the whole entry offset, and the risk Money sizes against is entry-to-stop. MIN_SL_ATR_MULTIPLIER 2.0 -> 0.5. That floor existed because a swing- anchored stop could land arbitrarily close to the entry and needed a bound unrelated to the chosen multiple. An entry-anchored stop is exactly mult*ATR by construction and cannot collapse, so leaving it at 2.0 would have silently overridden SL_ATR_x1 to 2*ATR - making the input a lie AND forcing TP >= 4*ATR just to clear the default 1:2 rejection filter. The broker's own stop level is enforced separately and precisely by TCAdjustStops(), so this is now a pure sanity net. Default TP_Mode TP_PREV_SWING -> TP_ATR_x3, so SL_ATR_x1 + TP_ATR_x3 gives a realised 3:1 against the 1:2 filter. TP_ATR_x2 would sit EXACTLY on the 2.0 boundary where price-normalization rounding alone can reject the setup; the default leaves a deliberate gap. This is the same interaction that once rejected 100% of setups on every symbol (see TP_INTELLIGENT_BASE_RR). Swing validity guards now reject only when the configuration actually uses a swing - i.e. ENTRY_PREV_SWING. Previously an unsynced or thin history rejected EVERY trade, including configurations whose levels no longer reference a swing at all. The guards are kept, not deleted: a bad swing must still never reach an entry price, and iLow/iHigh are no longer called with a possibly-negative index. TP_INTELLIGENT stays risk-relative. Now that risk is exactly mult*ATR the risk- and ATR-relative forms coincide, but risk-relative keeps its reward:risk guarantee exact after the floor or TCAdjustStops widens a stop. Both builds compile 0 errors, 0 warnings. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>	2026-07-31 10:11:13 -04:00
AnimateDread	45b35b3d1d	feat(nn): derive dense depth, train on all history, pin the shape in .cfg ... Completes the derived-topology work. Three inputs removed. AIType loses its depth suffix - AI_MLP/AI_CONV/AI_LSTM/AI_HYBRID, five entries instead of eight. Depth is now derived from the two endpoints the taper already has to connect (derived first-layer width, output-tied final width) at a 2x per-layer compression target, clamped [2..5]. Asking a user to pick a layer count while the code derives the widths those layers taper between was asking for half a decision: at 64 units tapering to 12, four layers compress by 1.4x per step and five by 1.3x, so the extra depth bought no abstraction. On the shipping H1/10y default the derivation lands on 3 layers - the depth that actually won Run 2. StudyPeriods removed. There is no case for training on less data than the broker provides at a ~6% directional base rate; the honest generalization read comes from the OOS holdout, not from withholding history. Training now starts at the earliest available bar, floored by MinTrainYear, which answers a different question (excluding dubious pre-history) and stays. That required closing the hazard the old code documented: the capacity budget now MEASURES the symbol's real bar count, and a topology derived from a measurement would widen as history downloads. Both ends are now pinned. Every derived value left the weights-filename fingerprint - keying a filename on a measured quantity means the EA looks for a file that does not exist, starts from era 0 and orphans a trained model, silently, because a missing cache is the normal first-run state. The shape lives in the .cfg instead, where LoadAndCompare now ADOPTS the four derived fields rather than diffing them; a mismatch there would discard a fully-trained model over nothing the user did. Two fields appended to the .cfg for the conv/LSTM stages, length-guarded on read because FileReadInteger past EOF returns 0 with no error. ForceHiddenLayers, a compile-time constant like DebuggingMode, pins depth for diagnostic comparisons. It joins the fingerprint only when non-zero, so forced depths get their own files - sequential comparisons only, not simultaneous from one .ex5. Derived shape, H1/10y defaults (21 features x 20 bars): first layer 64, 3 dense, 8 conv filters, 16 LSTM units. The LSTM block halves from ~58k to ~28k weights. Both builds compile 0 errors, 0 warnings. Re-keys existing models. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>	2026-07-30 10:05:40 -04:00
AnimateDread	3bc551b6e1	feat(nn): derive conv filter count and LSTM hidden size from the data ... Same defect the first-layer width had before 2026-07-29: both were inputs whose defaults were fixed constants picked with no reference to the input they sit on, which is the only thing that decides whether either number is sane. The conv layer is a per-bar projection - AddConvStage sets window = step = one bar's features - so its filter count should be read against the per-bar feature count. Sixteen filters COMPRESSED a 50-feature configuration 3x but EXPANDED a minimal 4-feature one 4x, and the expanding case adds parameters below every learnable layer without adding information. Now derived as half the per-bar feature count, snapped down a power-of-two ladder. The LSTM stage was the bigger miss. Its weight count is exactly 4*H*(H+inputs+1) (CNeuronLSTMOCL::SetInputs) and AddLstmStage feeds it the whole flattened vector, so the shipped 32 units against a 540-wide input is ~73k weights - more than DOUBLE the entire derived dense taper it feeds. It was the one stage the capacity budget never covered, which is why deriving the dense stack alone did not stop LSTM and HYBRID from being over-parameterized. Now solved from the same one-weight-per-in-sample-bar budget the first layer spends. Factored EstimatedInSampleBars() out of ComputeFirstLayerWidth so all three decisions spend one budget rather than each guessing at the training-set size separately. Both new values are assigned alongside the first-layer width, before the fingerprint that hashes them, and are functions of inputs already in that hash - so they need no entry of their own, and the same reasoning removes them from the DB config key. Both builds compile 0 errors, 0 warnings. Re-keys existing models. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>	2026-07-30 09:22:11 -04:00
AnimateDread	ebf2e73667	fix(ui): unique chart tag, product-grade panel, responsive under load ... Three separate reports from one deploy. 1. CONV, LSTM and HYBRID all came back tagged [4109]. The weights fingerprint omits the topology type on purpose - the file path already separates it (State\CONV\ vs State\LSTM\ vs State\HYB\) and hashing a value that is constant within a folder buys nothing while re-keying every trained model into a forced retrain. So the files were never at risk, but the tag could not do its one job. Prefixing the short id makes it unique on the display side only; the hex half still greps straight to the .nnw inside the folder the prefix names. 2. The default panel read like a training console. Six lines down to three, each answering a question an owner actually has. The deploy internals (best score, eras-since-best, ladder stage) were developer diagnostics describing a recall floor that no longer decides anything, and were already in the era-end journal line. In-sample accuracy left the panel too: it grades the model on bars it trained on, so it always flatters, and showing it beside the honest number invites reading the wrong one. New compile-time DebuggingMode constant - deliberately not an input - carries the IS/OOS pair and the resolved model path into the journal instead. No extra Inputs row, no extra Market description line, no user-reachable firehose. 3. Panel drag and buttons stuttered under training load, exactly as the 2026-07-26 note raising the chunk budget to 200ms warned they might. Backed off to the documented 120ms - worst-case click latency is that budget - and the derived topology (~292k weights to ~29k) makes the throughput this costs far cheaper than when that note was written. Also halved the panel redraw rate to 2.5 Hz: ChartRedraw repaints the whole chart, so its cost scales with accumulated arrows, and 5 Hz was the larger half of the stutter. Era-end still force-refreshes. Both builds compile 0 errors, 0 warnings. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>	2026-07-30 09:05:58 -04:00
AnimateDread	16632c1c3c	feat: make batch normalization mandatory, and record the run-3 results ... EnableBatchNorm and BatchNormWindow demoted from inputs to constants. Batch norm is required, not optional: measured on identical MLP_3L topologies it was worth +11.3 points of balanced accuracy (57.0% with, 45.7% without), stable across 150+ and 200+ eras, and the no-BN control converged to ~5% IS and OOS accuracy with no chart signals at all. A user cannot make a good decision here and can easily make a ruinous one, so the choice is not offered. BatchNormWindow goes with it - a running-statistics window in samples has no meaningful setting a trader could reason about, and its only other reachable state (<=1) silently disables the layer. Kept as named constants rather than deleted: the topology builder, the weights fingerprint and the .cfg guard all read them, and a constant keeps those paths - and the ability to flip one for a diagnostic rebuild - intact. Fewer knobs also means a shorter Market description and less room for a buyer to misconfigure. EXPERIMENTS.md records runs 2 and 3, since the MT5 logs are wiped between runs and these measurements are what the design decisions rest on. Run 3 (12h, uncapped tau=1.0) is a write-off: zero eras out of 1,993 across the five batch-norm charts ever called a direction on fewer than half of all bars, at a median precision equal to the ~6.1% base rate. The damage was present at era 1 and never recovered over 292-766 eras. Both builds compile 0 errors, 0 warnings. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>	2026-07-30 08:51:10 -04:00
AnimateDread	70cdec2717	fix(ai): drop the conv pooling stage - it reduced across filters, not time ... FeedForwardConv emits POSITION-MAJOR output, matrix_o[out + window_out * i], so one bar's window_out filter responses are contiguous and consecutive bars sit window_out apart. Both pooling implementations (FeedForwardProof and CPU_FeedForwardProof) slide FLAT over that buffer - pos = i * step, reducing `window` CONSECUTIVE elements. On a position-major layout those neighbours are different FILTERS of the same bar, never one filter across time. At the shipped 3/2 the pool computed max(bar0_f0, bar0_f1, bar0_f2), then max(bar0_f2, bar0_f3, bar0_f4), with every 8th window straddling a bar boundary. So it collapsed unrelated feature detectors into whichever fired hardest, passed gradient to that winner only, and halved the feature map while doing it - all below every learnable layer, where nothing above can recover it. The removed inputs' own labels ("3 Bars") show time-axis pooling was the intent throughout. Measured cost: CONV sat pinned at ~40% balanced accuracy for 510 eras with Sell recall 0%, while plain MLPs on the same data reached 57-61%. HYBRID, which also carried this stage, came second-worst of the batch-norm group. Not fixable in the topology: pooling one filter across time needs a stride of window_out BETWEEN samples within a window, which a consecutive-window kernel cannot express at any window/step. That needs a stride-aware kernel in Network.cl + WarriorCPU.cpp + WarriorDML.cpp and a DLL rebuild, and is only worth doing if a conv front-end earns its place without downsampling first - with 20 sliding positions there is little to gain by halving them. ConvPoolWindow/ConvPoolStep and their enums are removed with it, along with the |CP: fingerprint term added earlier today. Both builds compile 0 errors, 0 warnings. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>	2026-07-29 19:28:44 -04:00
AnimateDread	f2ec1edf84	feat(ai): logit-adjusted loss, replacing oversampling and the post-hoc prior ... Menon et al. 2021 (ICLR), "Long-tail learning via logit adjustment": add tau*log(prior_c) to each class logit inside the training gradient. Softmax CE on adjusted logits is consistent for BALANCED error - the metric checkpoint selection already ranks on - so the loss and the deploy decision finally optimize the same thing. The engine already computed a true softmax + categorical-CE gradient and wrote it over the per-neuron sigmoid delta, so this is an offset added to three logits in the two places that gradient is built (backProp scalar path and backPropOCL). No backend, kernel or DLL change; the forward pass and every inference path are untouched, which is the point - the network learns to absorb the offset, so its raw argmax becomes the balanced-optimal decision with nothing applied at inference. Replaces rather than stacks. Minority replay is disabled while this is on, and the post-hoc inference prior is forced off. Stacking is not a theoretical worry: simulated on the measured 1118/1119/34298 distribution in the weak-signal regime, plain CE collapses to Neutral (33.4% balanced, Buy 0%), replay reaches 48.1%, logit adjustment 50.9% with better balance - and BOTH together score 45.4% with Neutral recall at 0%, worse than either alone. Buda et al. 2018 predicts exactly that. Motivation from the six-chart run: every topology took one direction to ~50% recall and abandoned the other, the direction chosen arbitrarily (the batch-norm control went Buy 1% / Sell 42%, the inverse of the other five). One era in 1,301 cleared the per-class recall floor. Fingerprinted conditionally, so the converged 60.7% models on disk keep their filenames and stay loadable as the fallback. Both builds compile 0 errors, 0 warnings. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>	2026-07-29 19:05:14 -04:00
AnimateDread	2695a961c4	refactor(perf): pin CPU threads per network, drop the TargetCPULoad input ... Dividing a machine budget by the live chart count was wrong twice over. The count is a snapshot taken when each net's pool is built, and charts attach one at a time: five charts measured 10/6/5/4/4% of the same budget, because the first only ever saw itself and the last saw all five. So the earliest chart got several times the threads of the latest - skewing any cross-topology comparison run on those charts, which is the exact thing the setting existed to make fair. Nothing rebalanced afterwards either, and rebalancing would mean tearing down a DLL context under a live trainer. Both problems disappear once the answer stops depending on how many charts are running. Each net now asks for a fixed 2 worker threads, converted to the percentage the DLL wants from the detected core count. Two is not a compromise: since the topology became data-derived the widest dense layer is 64 units, so each ParallelFor has almost nothing to split and per-dispatch overhead dominates. An MLP era cost ~66s at a wildly oversubscribed 12 threads and ~80s at 1 thread - a 20% spread across a 12x difference in thread count. Two per net also lands six concurrent charts exactly on a 12-core box. Removing the input costs nothing on the product side: a Market build has no DLL tier at all, so it was already compiled out to a constant there and no buyer could reach it. Both builds compile 0 errors, 0 warnings. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>	2026-07-29 15:33:37 -04:00