forked from mnbvc188199/Warrior_EA
476 commits
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AnimateDread
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1a900a0a35 |
feat(vote): CONSENSUS arithmetic - agreement is now what the threshold dials
Era-680 report, all three observations one equation: "peak 29, no arrows at
threshold 30" / "at 20, arrows on EVERY bar" / "label at 12 while arrows
everywhere". Under the voters-only divisor, any bar with at least one
directional voter read the weighted mean of the firing tiers' weights - and
once the tiers self-ranked to each model's pooled win rate (~28-31), that
mean was NEAR-CONSTANT regardless of headcount. One member alone: ~29. Four
unanimous: ~29. Min_Vote_Open was a step function around that constant -
above it nothing ever fired, below it everything did - and the label's 12
was a 3v1 split netting through the same divisor. Not three display bugs:
one arithmetic that could not express agreement.
The divisor is now the CAPABLE weight - every filter that could vote,
whether it did or not:
* live (Direction): VoteCapableWeight() - classic pattern ladders always,
veto filters never, AI members once past the same readiness test
LongCondition gates on. A model still training must not dilute an
ensemble it cannot join: four trainees + one deployed model is a solo
chart wearing an ensemble label, and the solo vote reads full strength.
* gate (EnsembleEraVerdict): g_ensVoteWeightSum accumulates for every
member that EVALUATED the bar, Neutral included.
* overlay sweep + prospective readout: weight counts whenever the member
has data; a snapshotted Neutral dilutes.
One arithmetic, four sites, same numbers everywhere.
What the numbers become (four members, w~0.29, tiers~29): unanimous ~29 -
the CEILING, which is the pooled win rate and is what the peak displays;
3-of-4 ~22; 2-of-4 ~14.5; 3v1 ~14.5. Min_Vote_Open 20 now means "roughly
three-quarters of the ensemble's trust agrees, net". It MUST sit below the
ceiling to ever fire - the census/peak states the ceiling.
This is the ensemble the user specified in the original design discussion
("if the perceptron also votes, both together reach the threshold; if
another NN votes the other side, the threshold is not reached") - union
semantics was the pre-ensemble behaviour, kept until measurement showed its
vote magnitude was a constant.
Plus overlay DECLUSTERING, the other half of "arrows on every bar": the
same three NMS rules as the per-member arrows (same-direction runs collapse
to their first bar, cross-direction flicker keeps the stronger side), online
over the sweep's strictly oldest->newest walk. Suppression is a verdict and
deletes a standing arrow; the den==0 no-data skip still never does.
NOT COMPILED - user compiles in MetaEditor.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
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AnimateDread
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c393497fd6 |
fix(chart): display now reads era-end SNAPSHOTS - the live cache is wiped mid-era
Full-pipeline analysis after "threshold 30, attained often, nothing drawn,
still glued to buy". The log falsified the premise before any code did:
21:40:43 swept 4999, 794 voters, drew 491. Strongest 43.0% vs 30.0%
21:42:07 swept 4999, 0 voters, drew 0
21:51:30 swept 4999, 0 voters, drew 0
21:56:30 swept 4999, 922 voters, drew 382. Strongest 44.0% vs 30.0%
The arrows WERE drawn - 491 of them, then 382 - and then erased. ONE root
cause, three symptoms: every display path read m_arrowSignalCache, which is
wiped to sentinel at each era start and only complete again when pass 3
finishes. With eras at ~30s and a sweep at ~17s:
* ARROW FLICKER: a sweep landing mid-era found no voters anywhere, and its
else-branch deleted the arrow on every voteless bar - erasing the previous
sweep's entire output. The chart cycled populated -> blank -> populated;
the user kept catching the blank phase.
* READOUT GLUE: the newest-cache walk found only sentinel for ~90% of every
era and fell through to dPrevSignal - the frozen purge-band edge bar that
reads Buy.
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AnimateDread
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659638e072 |
fix(chart): the prospective vote was four models' opinion of ONE frozen bar
"Still glued to buy." Verified in the pass structure rather than guessed: dPrevSignal is written ONLY by pass 1 (Training.mqh 1439/1442 - the sole assignment sites), and pass 1 SKIPS the feedForward for any bar a later pass will forward anyway (laterPassForwards) - which is the whole OOS window and the calibration band. Pass 3 forwards the newest bars every era but never writes dPrevSignal. Net effect: after every era, dPrevSignal holds the model's opinion of the newest PURGE-BAND EDGE BAR pass 1 happened to forward - one fixed mid-history bar, re-evaluated era after era. The readout was therefore showing four models' verdict on the same frozen bar, and that bar reads Buy. Glued to Buy, with flashes of Sell only while pass 1 was actively walking (the one window where dPrevSignal moves). ProspectiveVote() now reads the newest ARROW-CACHE entry first (walking back from the decision bar, bounded at 16), falling back to dPrevSignal only when the cache holds nothing. Pass 3 writes the adjusted decision for the newest (OOS) bars each era, so the cache's first non-sentinel entry is the model's most recent verdict on near-current data - and it is the same value the filtered overlay draws from, so the label and the reconstruction stay one quantity. A cached Neutral stops the walk: that is a real decision (vote 0, abstain -> shows in the "flat" count), not a missing one. Early in an era the cache is wiped to sentinel and everything falls through to dPrevSignal exactly as before, until pass 3 refills the newest rows. Expect the label to change per era now (as each pass 3 re-scores the newest bars under that era's weights), with the vote/flat split moving as members genuinely flip between direction and Neutral on recent data. NOT COMPILED - user compiles in MetaEditor. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> |
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AnimateDread
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4f2d81a52e |
fix(chart): sweep hammered the news filter; peak was a fossil; neutrals invisible
Careful read of the 21:14 log window (user report: peak stuck at 50, label sticky, neutrals never shown). Three distinct defects, one commit because they share the two files. 1. 15,508 "CalendarValueHistory failed" lines in 68 SECONDS - ~230/second. The overlay sweep replayed Direction() on EVERY non-AI filter, including the news/session/risk-guard veto filters. The news filter calls CalendarValueHistory per evaluation and MT5's calendar cannot answer more than ~30 days back (the known calendar cliff), so every historical bar logged a failure - real wall-clock burned inside a sweep whose whole point is to stay cheap. Veto filters keep m_pattern_count at its 0 default (the same test UpdateSignalsWeights keys on): they cast no weighted vote, and a prohibition cannot be reconstructed faithfully anyway - it joins order validation in the cannot-replay family. Skipped. Compounding it: at era ~200 the four members complete a barrier round every ~20s while a full 5,000-bar sweep takes ~17s of slices - the sweep finished and instantly re-armed, forever, against arrow caches half-rebuilt mid-era. That is why the census's "had a voter" flapped 1299 -> 257 -> 1113 across three back-to-back sweeps. Re-arms now rate-limited to one per 5 minutes. 2. Peak 50 was a FOSSIL. m_votePeak never reset, so it still held a value attained under the 25/50/75/100 DEFAULT tier weights from the attach window before the first re-rank - unreachable ever since the weights became measured (pooled 27-32 in the same log). A ceiling nothing can reach reads as "the models are underperforming their own history", which is backwards: the history was priced in different money. The peak now resets at the same regime boundary as the census (StartFilteredOverlay), and the label shows max(live peak, census strongest-vote) - the census number is the actual answer to "can Min_Vote_Open ever be reached", measured over ~5,000 bars under the CURRENT weights. 3. Neutrals were invisible. The prospective count lumped Neutral-deciding models in with voters, so "4 model(s)" read identically whether all four voted or three sat flat. Now "2 vote/2 flat", and an all-neutral bar reads "VOTE flat ... 0 vote/4 flat" instead of "--" - the models answered, and the answer was Neutral. Expected values, from this log's own re-ranks (all four members' fires land in T3; tier weights 27-32; module weights 0.27-0.32): a unanimous-buy bar reads ~29-30%, mixed membership 28-34. The reported "stuck at buy 28, climbed to 30, flashes of sell, now 33.4" is those weights doing exactly what they should. The stickiness between moves is pass 2/2.5/3 - only pass 1 writes dPrevSignal, so the label holds the last pass-1 bar's decision for the remainder of each era. Display-only, and honest: it is the model's most recent output. NOT COMPILED - user compiles in MetaEditor. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> |
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AnimateDread
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b05b4f21d7 |
fix(chart): the vote readout was repainted once per bar, not once per timer tick
"Still stuck at 0" after |
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AnimateDread
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a042cb4359 |
feat(chart): show the PROSPECTIVE vote while the models are still training
The readout sat at "VOTE 0.0%, 0 voters" constantly. Correct, and useless.
LongCondition()/ShortCondition() return 0 behind the readiness gate for the
entire training run - a model that is not deployed does not vote - so the LIVE
vote is structurally zero for hours, which is exactly the period the readout
is being watched. Worse, it was the same display whether the models were
silent, undeployed, or the filter list was empty: three different situations,
one number.
When no filter casts a real vote, the readout now shows the PROSPECTIVE one -
what these models are saying right now, through the identical tier/weight
arithmetic, minus the readiness gate. That is the same quantity the historical
overlay reconstructs on cached bars, deliberately, so the live line and the
reconstructed arrows are the same measure and can be read against each other.
It can never be mistaken for a decision: labelled "-> training, not tradable
yet", drawn dimmer than "no trade", and `fires` is forced false regardless of
magnitude, because saying "-> TRADE" about a number that cannot place an order
is the precise overstatement this readout exists to prevent. m_direction is
untouched - display only, no trading path reads it.
Confirms the sweep fix from
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AnimateDread
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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> |
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AnimateDread
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155f56e0c0 |
fix(chart): the overlay sweep compared a series index against a bar count
"Filtered view: swept 0 bar(s), 0 had a voter" on charts whose models were
reporting thousands of held-out fires in the same second - the contradiction
the user spotted in the log.
m_overlayIndex is a SERIES index (0 = newest, counting backwards in time), but
its floor was computed as `barsAvail - span`, which is a count from the OLDEST
end. Two different coordinate systems. On XAUUSD's 15,049 bars that produced a
floor of 10,049 against a start of 5,000, so `m_overlayIndex >=
m_overlayStopIndex` was false on the very first test: the sweep reported
completion having touched nothing, and re-armed and "completed" again on every
era boundary. Both bounds are now series indices - start at the oldest bar to
reconstruct, stop at 2 (bar 1 is the decision bar the forward path owns, bar 0
is still forming).
The 150-bar indicator warm-up margin was being applied to the floor, where it
could only ever be wrong; it is a cap on how far BACK the START may reach, on
the same axis. m_overlayOldest is renamed m_overlayStopIndex because with
index 0 = newest that bound is the most RECENT bar, not the oldest - the name
said the opposite of what it held.
Verified across chart sizes: 15,049 bars -> 4,999 swept; 4,865 -> 4,714;
400 -> 249; 301 -> 150.
The census line added in
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AnimateDread
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129a0d448d |
fix(vote): a leaf filter was dividing its own module weight back out
"Nothing on the charts." My bug, from
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AnimateDread
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e59dc1629f |
fix(deinit): vote arrows survived the cheap sweep, and 5 long loops ignored the stop
Leftover chart objects on long-history charts. Two causes, one of them introduced by |
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AnimateDread
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07aa01777c |
feat(chart): reconstruct the filtered view behind the handover point
Completes the filtered view from
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AnimateDread
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b28c81eb78 |
feat(rank): AI models rank their own confidence tiers from held-out outcomes
Closes the caveat
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AnimateDread
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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>
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AnimateDread
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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> |
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AnimateDread
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2c443ba3ad |
fix(gate): the ensemble gate certified a vote the EA never casts
g_ensembleVoteThreshold's comment claims the combined-vote scorer "fires on
the same criterion the live trade does". It did not. Two independent
mismatches, both silent:
CURRENCY. Each member contributed its raw signed confidence x100 - a 33..100
number straight off the softmax head. Live contributes m_weight x the tier's
pattern weight, and BOTH of those are rewritten from the signal DB by
UpdateSignalsWeights(). A head output and a DB-ranked win-rate weight share
an axis and nothing relates them, so the same bar was one number to the gate
and a different one to the order path. Same shape as the 2026-08-09 geometry
incident: certified on one game, paid on another.
DENOMINATOR. The gate divided by the member count, so an abstaining member
pulled the average toward zero. CExpertSignalCustom::Direction() skips a zero
contribution in BOTH the sum and the count (`if(direction == 0) continue;`
before `number++`) - live is a mean over VOTERS. The gate was therefore
scoring a strictly more agreement-heavy set of bars than the EA trades. The
contribution hook's own comment asserted the opposite ("abstentions dilute
the average exactly as they do in the live vote"), while the AI_CHOICE enum
20 lines away correctly documented union semantics.
LiveVoteContribution() is now the single definition of "what this member
votes", called from the gate; the live path reaches the same arithmetic
through LongCondition/ShortCondition. g_ensVoteVoterMask records who actually
voted, separately from who evaluated the bar, because those are the divisor
and the shared-population test respectively.
ConfidenceTier() is split into ConfidenceTierFor(signal) plus a thin live-bar
wrapper - the OOS scan holds the scanned bar's decision in a local, and
dPrevSignal is a different bar.
NOT changed, deliberately: the gate still does not model live NMS
declustering, and the per-member solo gate still scores every directional
call rather than threshold-clearing ones. Both are selection-metric changes
and this codebase has twice been bitten by switching one blind.
Also corrects a stale paragraph in m_pattern_0's declaration block quoting
80/87/93/100 as the tier defaults. The constructor is 25/50/75/100 and has
been since the confidence floor and alternation gate were removed; the block
carried both tables at once, and the dead one was quoted back as fact.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
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AnimateDread
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525e92ecb2 |
fix(plateau): the IS-error early stop was inert for every ensemble member
15 hours of training, and the stop that exists to END a run announced itself
1,299 consecutive times without ending anything:
SP500 ConvLSTM IN-SAMPLE ERROR PLATEAU - not improved in 1297 / 1298 / 1299
eras (best 0.2689, now 0.3269) ... era 1396, 1397, 1398
SP500 LSTM 536 eras SP500 CONV 442 eras SP500 PAI 150 eras
XAUUSD HYB 478 eras XAUUSD LSTM 296 eras XAUUSD CONV 366 eras
CAUSE: it wrote its decision into m_plateauStage, and EnsembleEraVerdict mirrors
the shared ladder onto every member - `mm.m_plateauStage = g_ensPlateauStage` -
on EVERY era, purely so each member's status line shows the collective stage. A
display mirror was silently overwriting a decision, so the stop re-armed and
re-fired the next era, forever.
This is the worst possible direction for this particular bug. Every one of those
1,299 eras was scored out of sample and joined the family the deploy gate
corrects over (Sidak, g_ensCandidateEras). The stop's entire purpose is to make
that family SMALLER; instead the run spent fifteen hours raising its own bar.
- m_isErrorPlateaued: a one-way per-member latch, cleared only by a fresh run.
Nothing in the ladder may reset it. The stop condition and the two solo deploy
conditions read the latch, not the mirrored stage.
- The orchestrator combines: EnsembleEraVerdict requires UNANIMITY across
participating members (same participation test the era barrier uses, so an
excluded or finished member cannot veto). One member still learning can still
move the combined vote, and the vote is what the gate certifies.
- Fed in as `dueStage = PLATEAU_STAGE_DEPLOY`, NOT written to g_ensPlateauStage.
The block that actually ends the run sits under `dueStage > g_ensPlateauStage`,
so assigning the stage directly makes that test false and the deploy never
happens - the same inert-write shape as the bug being fixed. Caught before
committing; raising dueStage carries it through the ladder's own path (warm
restarts skipped, family-wise vote test, measurement screen, joint checkpoint)
unchanged.
- g_ensIsPlateauAnnounced: announce once per run, not once per era.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
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AnimateDread
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65a3e4e877 |
fix(chart): a purge that reports "zero leftovers" was only ever checking its own list
2026-08-17 21:58: all three charts hit "Abnormal termination" ~5.3 s into
OnDeinit with NO cleanup-timings line - the teardown was starved again. The
22:00 init purge then removed 993 / 1373 / 1557 stranded objects and reported
ZERO by-name leftovers on every chart, and the charts still came up with
duplicated panels. "Nothing matching our prefixes remains" and "the chart is
clean" are different statements and only the first was being made.
Three changes, in the order they matter:
1. WHY the teardown starved, and it is a gap in
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AnimateDread
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64b77e4bbe |
fix(diag): the cache-invalidation stall message could never name the cause
SP500 and XAUUSD LSTM wedged at era 1 from 19:43 to 21:00+ (77 min) while their three siblings passed era 200 - the 12-minute barrier exclusion correctly kept the charts alive, so the ensembles ran three-handed. Both printed: cache invalidated at era start (era sized 16236 bars, cache holds 16236) Equal numbers, which reads as "so it wasn't the size". That inference is not available: EnsureBarCachesCapacity assigns BOTH invalidation keys (m_labelCacheBars = bars, m_labelCacheAnchorTime = m_Time.GetData(0)) before it returns true, so a message built afterwards reports the values it just overwrote. The two counts are equal BY CONSTRUCTION and ReportTrainStall's anchor= field is always the live one. The line whose stated job is to name which key tripped was structurally incapable of naming it. Capture bars/anchor BEFORE the call and say which one moved: "SIZE CHANGED 16236 -> 16240" or "size unchanged", and "ANCHOR MOVED 2026.08.18 00:00 -> 04:00" or "anchor unchanged". Not guessing at the cause. An anchor moving every era with the size steady is a new candle each pass or a Time buffer that is not being refreshed; a moving size is the era/prebuild disagreement the branch was written for. The next occurrence will say which, instead of costing another session. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> |
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AnimateDread
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f102a695d5 |
fix(geometry): the ensemble was training on TWO DIFFERENT TARGETS - propagate the adopted barrier
MEASURED 2026-08-17 19:06 on USDJPY, in the fresh run: 19:06:38 LSTM adopting barrier geometry 2:10 ... geometry authority 19:06:40 LSTM triple-barrier labels - stop 2.00 target 10.00, horizon 256 19:06:44 PAI / CONV / HYB break-even 33.3%, mean label lifespan 19.2 bars 19:06:45 LSTM break-even 16.7%, mean label lifespan 81.4 bars One chart, four members, two targets. A "Buy" from LSTM meant "10 ATR before a 2 ATR stop within 256 bars"; a "Buy" from PAI meant "3.21 before 1.61 within 64". The orchestrator averages those votes and the joint gate certifies the average as though they answered one question. And g_DerivedSlAtrMult - which places the LIVE order - is a single global, so the stop actually sent was whichever member wrote last: the same last-writer-wins class of bug as the live-exit confidence. CAUSE, and it is mine. The geometry scan sits at the end of the MI chain, and that chain runs ONCE PER CHART (g_ensembleChartMiReportDone) - whichever member reaches it first measures and the rest skip. Harmless while the scan only PRINTED; |
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AnimateDread
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cb30360c18 |
fix(handles): the MA handle was SHARED, and a rejected sweep freed it for everyone else
ROOT CAUSE of the six-session "silent block failure", measured rather than
inferred. All TWELVE dead-handle recoveries in today's log report the SAME
handle number - MA=-1(h13) - across two charts and all four members. It was
never four handles. It was one.
MT5 refcounts indicator requests, so four ensemble members asking for the same
iMA on the same symbol/period share a single handle. TuneIndicatorsByFilter
creates and drops ~35 of them scoring candidates; the sweep's runner ends up
holding a live handle while its siblings still hold a number the terminal has
already freed. Timeline, twice, to the millisecond:
USDJPY 18:10:03 PAI: auto-tune complete
18:10:29.864/.910/.953 CONV/LSTM/HYB: "already ran ... REJECTED"
18:10:30.057/.065/.074 all three: MA=-1(h13), sweep bars all rejected
XAUUSD 18:10:11 -> 18:10:42.19/.23/.27 -> 18:10:42.334 identical, same ~100ms
The adopt branch re-initialised indicators only `if(g_ensembleChartTuneInstalled)`
- exactly backwards. A REJECTED sweep churns just as many handles, and every one
of the twelve recoveries followed a rejection. Parameters are still adopted only
on an install; the HANDLES are now rebuilt either way. Four creations per chart.
RepairDeadIndicatorHandles stays - it is cause-agnostic and it is what made this
diagnosable. This removes the cause it was recovering from.
Also, consistency of the warm-up status (user-reported: "only one nn will say
scoring indicators, which leaves some doubt about what is going on"):
- the sweeping member now says it is scoring "for the whole chart", so three
idle rows read as the design rather than a stall;
- the two adopt branches (tuner and MI) publish to the panel instead of only
printing, so every row accounts for itself;
- the MI suite publishes before it runs. It is the longest stretch of the whole
warm-up - MI, lag profile, excursion targets, geometry scan, each with its own
permutation null - and it published nothing at all, so during most of the
warm-up the panel's last word described a step that had already finished.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
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AnimateDread
|
9bd7bd1b7f |
fix(reset): say what the reset actually did, per member and per file
The user reports "Delete & Reset Weights only wipes the first NN". I could not find a code path that skips ensemble members, and I am not going to assert one: the handler loops g_aiSignals[0..g_aiSignalCount), all four topologies register unconditionally in OnInit, and SetIdentity gives each its own State\<id>\ folder so the six deleted paths are genuinely distinct per member. What IS true is that the whole success path was SILENT - six FileDelete calls per member printing only on failure, and one chart-wide Alert - so a four-member reset and a one-member reset produce byte-identical output. The symptom could be neither confirmed nor refuted from a log. That is the defect I can fix today. - COMPILED <timestamp> (__DATETIME__) beside the build tag. The hand-edited tag had sat at scan-nofwd-v5 across a week of commits, so it could not answer the question it exists for. The compile stamp cannot be forgotten. Tag bumped to reset-census-v6. - RegistryLine() (public): ID, active file path, common/local, era, deployed vs training, ensemble index. The reset handler prints a numbered census of the whole registry BEFORE the confirm dialog. If that says 1 on an AI_HYBRID chart the fault is registration, not the reset - and RegisterAISignal already has a loud MAX_AI_SIGNALS message for exactly that. - The confirmation dialog now names the count, so a wrong registry is visible before anything is deleted rather than after. - ResetWeights prints one line per member: N deleted / N already absent / N FAILED, plus a per-suffix breakdown. "absent" on a member that should have had a .nnw is a completely different fault from "deleted"; they were identical. - ResetWeights' return value was discarded. A member whose BuildFreshTopology fails has had its files deleted and has no network - and the Alert still said "weights reset". Counted now, with an INCOMPLETE alert when they disagree. - Same for dbm.ResetDatabase(), whose bool was also dropped. The DB is one shared file for every signal on the chart, so there is nothing per-member to loop - the log now says that explicitly, since it is the question being asked. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> |
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AnimateDread
|
ad80e0bb57 |
fix(shutdown): make ExitPolicy public, and stop every long loop the moment MT5 asks
Two things, one of which was a compile error.
1. ExitPolicy() was declared in the protected block but is pushed in from
Warrior_EA.mq5:770. Moved to public beside the other EA-facing setters.
2. Chart objects surviving OnDeinit. The 4,500 ms teardown budget is measured
from the STOP REQUEST, not from OnDeinit's first line, and OnDeinit cannot
begin until whatever is in flight returns - so a scan still running after
_StopFlag is raised does not delay the cleanup, it SPENDS it, and the purge
never gets its turn.
New CExpertSignalAIBase::ShutdownRequested() = IsStopped() || m_shutdownInProgress.
Deliberately NOT m_trainingStopRequested: that latches, and a latched flag
would permanently disable scans that must run again on the next Start.
Guarded, longest first:
- TuneIndicatorsByFilter - per candidate, restoring the OPERATOR's settings
on the way out (best[] is mutated in place; the tuner otherwise keeps the
last trial's parameters, which nothing chose).
- ReportBarrierGeometryScan - per pairing, breaking to ONE restore point so
m_barrierScanLiveLabels can never be left true (that makes ComputeLabelForBar
read the last candidate's multiples as the configured geometry).
- ReportFeatureLabelInformation / ReportExcursionInformation / lag profile -
nulls ABANDON rather than truncate: fewer draws is not a smaller null, it
is a wrong one, and p shifts toward significance. m_dirEvidence staying
false is the safe direction.
- SimulateExitPolicyOutcomes - zeroes its accumulators so the divergence line
is dropped instead of latching a partial expectancy as the run's only report.
- ReportGeometryExpectancyScan - per ladder rung.
- HttpGet - one choke point for up to a dozen blocking WebRequests per
first-pass Update(). An in-flight request cannot be cancelled; refusing to
start another is the whole remedy.
- PollTraining, OnChartEventHandler's study event, TuneIndicatorsAndTrain -
entry points, so a queued event cannot open an era during teardown.
TuneIndicatorsAndTrain's guard is the first statement, ahead of the
m_tuneFilterDone / g_ensembleChartTuneDone latches.
- OnTick / OnTimer / OnChartEvent.
Training's own bar loops already honoured this (pass 1 per bar, passes 2/2.5/3
yield on a 120 ms budget); the warm-up scans did not, and they are the longest
uninterruptible stretches the EA has.
StopTraining() is unchanged: the operator's Stop still finalises synchronously.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
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AnimateDread
|
dbb3d7fcd8 |
fix(geometry): do not adopt a pairing that cannot be certified - information and detectability are different objectives
Closes a gap |
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AnimateDread
|
62a719f04c |
fix(consistency): one geometry authority, one exit authority, and the MI screen finally gets a veto
Consistency pass before a fresh deployment. Three places where two systems were choosing the same thing and one of them silently lost. 1. THE GEOMETRY SCAN'S DECISION WAS INERT - measured, not suspected. USDJPY, 2026-08-17: 14:24:12.844 adopting barrier geometry 2:8 ... Relabelling and training on it. 14:24:12.979 triple-barrier labels - stop 1.61*ATR, target 3.21*ATR ... this is what trains It adopted 2:8 and trained on 1.61:3.21. ReportBarrierGeometryScan wrote only m_sl_mode/m_tp_mode, and BarrierMultiples ranks the DERIVED pair ABOVE those ints - so on any model carrying a derived pair (every model with a .cfg, including a fresh one whose weights are gone but whose sidecar survived) the adoption changed nothing. Worse, had it changed something it would have been undone immediately: the adoption sets m_labelCachePrebuilt = false, and that prebuild re-runs DeriveBarrierGeometry at era 0, which overwrites m_derivedSl/TpMult from the excursion quantiles. ONE AUTHORITY: the derived pair, because it is what the labels read, what the deploy gate certifies, what g_Derived*AtrMult places on the live order, and what the .cfg pins across restarts. The scan now writes THAT (floored by MIN_SL_ATR_MULTIPLIER, the same floor DeriveBarrierGeometry applies so the live stop can never be wider than the labelled one), republishes to the bridge immediately rather than at the next era end, and forces the sidecar to be rewritten. m_geometryAdopted latches it so the derive pass the adoption itself triggers cannot overwrite it. The scan outranks the derive for an evidential reason, not an architectural one: its winner cleared a permutation test against the null of the MAXIMUM over every eligible pairing, and it scores the incumbent derived pair as a peer in that same field. The derive is a descriptive quantile read with no significance test attached. BEHAVIOURAL CHANGE, and the reason to flag it before a fresh test: barrier geometry will now actually move when the scan says so. Until today it never did. 2. ONE EXIT AUTHORITY, tied to whose certificate the trade was placed under. CheckClosePosition had two routes. The AI early-exit reads the AI vote undiluted and is exactly what the new exit replay reproduces. The blended route thresholds m_direction, the average over EVERY filter including classic ones whose live votes pass 3 never computes - so it can close a position the certificate never modelled, and no replay can ever check it. When g_DerivedSlAtrMult > 0 the AI's measured geometry is on the order, which means the deploy gate's certificate is the reason the trade exists. In that state the AI now governs the exit and the blended route is suppressed. Classic-only configurations are untouched: there the blended route is the only exit opinion and stays exactly as it was. Nothing moves at the shipped defaults either way (Min_Vote_Close = Disabled). 3. EDGEFINDER, SECOND HALF: THE MEASUREMENT NOW STEERS. The MI suite has always printed its verdicts and then trained the direction target regardless of what they said. That gap IS the difference between this and the EdgeFinder discipline: measure what the market offers, THEN aim. m_dirEvidence is set when EITHER the feature/label mutual information OR the normalised excursion asymmetry clears its block-permuted null - an OR, because the two look for the same thing by different routes and requiring both would reject on the weaker of two independent measurements. Normalised asymmetry specifically, never the raw one, which is the volatility confound. Deploy - solo AND ensemble - now requires it. A run without it still trains, and keeps its checkpoint: the research value is real and the measurement can be wrong. It simply may not go live. Reported separately from the statistical gate because the remedy is different: a failed selection test says train differently, this says look somewhere else. Excursion SIZE keeps clearing where direction does not, and that is a risk-control head rather than an entry signal. For the ensemble the check is per-chart by construction - the MI suite runs once and shares its outcome across members - which is the honest treatment: four models finding nothing between them is not four chances at an edge, it is four fits to the same absent information. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> |
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AnimateDread
|
6069581323 |
feat(search): stop on the IN-SAMPLE plateau, and shrink every best-of-K effect before quoting it
Points 3 and 4 of the four-point plan. 1. IN-SAMPLE EARLY STOP - and the reason it is worth having is not compute. The plateau ladder stops on the OOS SELECTION score. That is a peek: by the time it fires, every one of those eras has been evaluated out of sample, so all of them sit in the family the deploy gate corrects over (g_ensCandidateEras, Sidak). Training longer therefore does not merely cost time - it RAISES the bar the eventual winner has to clear. The new stop reads the TRAINING error, which the gate never looks at. When the optimiser has stopped improving on data it can see, more eras will not find a better model; they will only enlarge the OOS family. Ending there shrinks the correction, and the shrinkage is legitimate precisely BECAUSE the stopping rule never consulted an out-of-sample number. That distinction is the whole point and it is the one this project has got wrong four times: stop on IS and the family really is smaller; stop on OOS and those eras were searched and still count. Both stops now exist; only this one buys a lower bar. Deliberately more patient than the OOS ladder (IS_ERROR_PATIENCE_MULT = 3x): training error is noisy per era - mini-batch order alone moves it - and ending a run that is still learning costs far more than a few wasted eras. Improvement is RELATIVE (IS_ERROR_IMPROVE_FRAC = 1%), so it does not depend on the loss's absolute scale, and it only acts when a checkpoint exists, since otherwise it would end a run with nothing to deploy. Reset per RUN alongside the ladder, so a resumed run cannot early-stop on its first era against a previous run's best. 2. WINNER'S-CURSE SHRINKAGE ON THE BARRIER-GEOMETRY WINNER. The family-wise permutation gate already establishes that the RANKING is not noise. It says nothing about the SIZE of the winner's effect - and a best-of-K maximum is biased upward by construction, being the largest of K noisy draws. The adoption message quotes that raw maximum and compares it against the incumbent, so the number a reader plans on is the inflated one. The penalty is now measured, not assumed: the same permutation draws that produce the p-value also produce, per draw, the MAXIMUM excess across all candidates under pure noise. The mean of those maxima is exactly what a best-of-K selection is expected to report when there is nothing there. This is the empirical form of the sqrt(2 ln K) x SE penalty the SQX EdgeFinder plugin applies to every maximum it reports (Stats.java:79-88), and it needs no normality assumption because the draws ARE the null distribution. Applied in James-Stein form - effect x max(0, 1 - penalty^2/effect^2) - so a large effect is nearly untouched and a marginal one collapses toward zero. Reported, not gated. The adoption decision still turns on the permutation p-value, which is the right test for "is the ranking real"; the shrunk number is there so the magnitude quoted beside it is one worth planning on. Closes the first of the two EdgeFinder ports identified on 2026-08-12. NOTE on the second EdgeFinder port, deliberately not done here: "let the measurement steer the target" is already true where it matters most - ReportGeometryExpectancyScan ADOPTS the winning barrier geometry under the family-wise gate rather than advising it, and the MI excursion suite publishes a verdict per instrument per config. What is still missing is steering the TRAINING TARGET itself (direction vs excursion) off those verdicts, and that is a design change rather than a surgical one - direction is a closed verdict while excursion SIZE keeps clearing, so the honest version of that change is a target-selection policy, not a flag. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> |
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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 |
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AnimateDread
|
778b6c09c6 |
feat(gates): derive the recall floor instead of configuring it, publish what the configuration can PROVE, and stop an arbitrary member driving live exits
Three changes, all from the same principle: measure what is there before aiming
at it, and never certify a number you do not trade.
1. THE RECALL FLOOR IS DERIVED, AND IT MOVES BELOW CHANCE.
MinRecall=40 was a constant doing a statistical job. Its reference point is the
33.3% recall a zero-skill 3-class model gets on EVERY class, and against that the
constant was accidentally calibrated for exactly one sample size: on USDJPY CONV
(n_eff 195) 40% is chance + 2.0 SE; on SP500 PAI (n_eff 42) the same 40% is chance
+ 0.9 SE. One chart was being held to a bar twice as strict as the other, for no
reason anyone chose.
CollapseRecallFloorPct() computes it per class from that class's own effective
sample - EffectiveSampleSize(), so the overlap deflation the rest of the gates use
applies here too - as chance - EDGE_MIN_SIGMAS x SE. 26.5% at n_eff 195, 18.8% at
n_eff 42.
BELOW chance, deliberately, and this is the substantive change rather than the
arithmetic. This gate's only job is refusing to call a COLLAPSED model converged.
It is not a quality bar; the deploy gate is the quality bar and it is already
rigorous (chance + 2 SE on the deflated sample, Sidak over candidate eras, then the
cross-instrument pooled certificate). A convergence gate that ALSO demands
provably-above-chance recall on all three classes double-counts that job, and it
has failed that way twice here: MinRecall=60 blocked every SP500 H1 run in 2026-07,
and the 40 that replaced it made Neutral structurally unreachable once first-touch
resolution cut Neutral to a 0.65% residue. A floor nothing can reach does not make
a funded account safer, it stops the run converging at all.
Testing significantly BELOW chance instead catches what a fixed 40 was actually
catching - a model that has stopped emitting a class - and cannot become
unreachable by construction. It also fixes the direction the old constant scaled:
it now widens on a thin OOS window, where low recall genuinely cannot be told from
noise, and tightens on a rich one. Today's SP500 PAI (Buy 51 / Sell 18 / Neutral 30)
is still correctly blocked on Sell.
This also resolves a standing contradiction the code half-admitted at the
isBetterEra comment: selection ranks on coverage-weighted PRECISION while
convergence gated on RECALL, so a sparse high-precision abstainer - precisely the
model that could clear the deploy bar - was blocked by the floor.
The era line now PRINTS the derived floor. Anyone comparing these recalls against a
remembered "40" is reading the wrong bar.
2. DETECTABILITY: WHAT THIS CONFIGURATION COULD PROVE, BEFORE IT TRAINS.
The DEPLOY BAR line states the bar. It never said what reaching it would take, and
that is the actionable direction. ReportDetectability() inverts the same identity -
the gate passes when edge >= z x sqrt(p(1-p)/n_eff), so certifying an edge d needs
n_eff >= z^2 p(1-p)/d^2 independent calls, hence L times as many raw ones - and
prints a +2 / +5 / +10pp ladder as required independent calls, raw calls, and share
of the OOS window, marking any rung that needs more than the window holds IMPOSSIBLE.
Every term is a property of the CONFIGURATION - geometry via break-even, horizon via
mean label lifespan, window via oosCutoff - so no amount of training moves any of
them. It fires once, at the first healthy sweep, beside ReportFeatureHealth, for the
same reason: that is the first moment the bar grid, the measured geometry and the
lifespan are real numbers rather than defaults. It gates nothing.
This is the EdgeFinder discipline applied to our own gate: establish what the market
and the measurement design have to offer, then point the net at it - rather than
spending a thousand eras chasing something this OOS window could never certify.
3. AN ARBITRARY MEMBER WAS DRIVING LIVE EXITS AND TRAILING (user-identified).
Every ensemble member ran
g_LiveAISignedConfidence = SignedAIConfidence();
unconditionally, every tick. Last writer wins. Its consumers are the AI early-exit
route (CExpertSignalCustom::LiveSignedConfidence) and TrailingIntelligent - so on a
four-model chart an LSTM entry could be closed, and its stop moved, on the
Perceptron's opinion alone, decided by scheduling order. Not the vote, not a
weighted blend.
Now the mean across registered members, matching how the ensemble actually trades:
the open decision is the weighted-average vote, and an abstaining member contributes
0 and dilutes exactly as it does there. Members still training read 0, so a
half-trained ensemble reads WEAKER rather than louder - the safe direction for an
exit trigger. Deployed and paused members are included, which is the opposite of the
era barrier's exemption rule and correct for the opposite reason: that one asks who
must be waited for, this asks who has an opinion.
Latent today and staying that way for now by choice - Min_Vote_Close ships Disabled
(101, unreachable on both scales it drives) and TrailingStrategy is off, so live
exits are SL/TP only and the certified hold-to-barrier win rate is what actually
gets traded. Fixed now precisely because the plan is to enable vote exits once the
models are accurate, at which point a scheduling-order exit would be both harmful
and very hard to see.
STILL OPEN, and needs a decision before vote exits go on: the member gate and the
ensemble vote gate both grade hold-to-barrier, so enabling vote exits makes the
certified number stop describing the traded one. Warrior_EA.mq5 currently argues
barrier models may keep vote exits because "their label IS the vote's own horizon" -
that does not hold, since a vote flip at bar 5 of a 64-bar horizon is not the
target-before-stop outcome the gate measured. Either grade the OOS call on the real
exit rule (first of SL / TP / vote-flip / horizon) through the fill engine, or set
HoldToBarrier for ensemble members so the policy cannot drift from the certificate.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
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AnimateDread
|
fca610fea0 |
fix(indicators+panel): the dead handle is MEASURED now - recreate it; and order the ensemble panel by member, not by who published first
THE ANSWER, off the instrumentation added in |
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AnimateDread
|
be396749fc |
fix(ensemble+depth): the barrier had no liveness escape, and the depth gate could not report the one state the evidence pointed at
Two charts (USDJPY 50,179 bars / XAUUSD 33,982) sat at era 0 for 38 minutes with
four of their eight members completely silent. Nothing in this commit guesses at
why the sweep fails - the last five guesses were all wrong. It makes the failure
say what it is, and stops one broken member taking its whole chart down with it.
WHAT THE LOG ACTUALLY SAYS, before any of this.
- The running build IS
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AnimateDread
|
d9f834d01d |
fix(buffers): revert the MA +1 - it asked for a bar that does not exist and stopped every chart
REGRESSION I INTRODUCED IN
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AnimateDread
|
45c9e211b3 |
feat(depth): prime -> settle -> sweep, and name which handle is short
"Max bars in chart" is set to Unlimited, so the static-terminal-limit reading in |
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AnimateDread
|
7e63a8be01 |
fix(depth): route EVERY ResizeBuffers call site through one indicator-depth gate
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AnimateDread
|
1dda479261 |
fix(train): clamp the sweep to indicator-servable depth - the scan wall was CopyBuffer, not a cold indicator
Symptom: on a 3-chart run with contention ruled out (SP500 sitting at era 2552),
USDJPY and XAUUSD produced 0 usable windows out of 50,163 and 33,966 - forever,
re-sweeping on every discard, which is the panel oscillating 0->100%.
Bars() is the PRICE series depth. A CUSTOM indicator's is not: MT5 calculates it
in its own context bounded by "Max bars in chart" (TERMINAL_MAXBARS), and
CopyBuffer past that limit does not short-read, it FAILS - so CDoubleBuffer keeps
nothing and EVERY index answers EMPTY_VALUE. ADMovingAverage is the only custom
indicator whose feature block REJECTS on EMPTY_VALUE (ADZigZag, also CiCustom,
neutral-fills; RSI/MACD/Ichimoku/ATR are built-ins served at any depth), so the
sweep died on feature 25 of every bar while the 24 price features under it were
fine. That is exactly the "window had 24 of 832 values" the stall report named.
Perfectly depth-correlated, measured 2026-08-17:
SP500 16,234 bars -> era 2552 XAUUSD 33,982 -> 0 windows
XTIUSD 16,611 bars -> era 71 USDJPY 50,179 -> 0 windows
This RETIRES the 2026-08-17 cold-indicator reading of the same stall.
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AnimateDread
|
0c38bfc9ab |
fix(pooledgate): _Period, not Period() - the bare call resolves to CExpertBase's setter
Three compile errors, all the same cause. Inside a CExpertBase subclass a bare Period() no longer reaches the builtin ENUM_TIMEFRAMES Period(); MQL5's method- hiding rules resolve it to the inherited bool CExpertBase::Period(ENUM_TIMEFRAMES) setter, which takes an argument - hence 'wrong parameters count, 0 passed, but 1 requires'. Switched to the _Period predefined variable, which is what the rest of this codebase already uses (100 occurrences; ::Period() appears nowhere). AutoTune.mqh line 82 builds its own per-symbol/timeframe filename exactly this way, so the pool file naming now matches the convention it should have followed from the start. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> |
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AnimateDread
|
1cf4c57d57 |
fix(altdata): median-fill instead of zero-fill, and a one-shot feature-vector autopsy
ALT-DATA AUDIT. The files themselves are healthy - all six symbols, 6,073 daily
rows, 2010-01-01 to 2026-08-17, no constant or degenerate columns, sane tails
(mac_cpi/mac_unemp flat ~47d is monthly data behaving correctly). The problem is
not the data, it is what happens where the data ISN'T.
CAltDataPanel::Features() returned an all-ZERO vector for any bar older than the
file's first row, and left blank cells at 0 too. Both were deliberate ('the block
is additive context and must degrade, never reject the bar') and that reasoning
holds for the CHANGE columns - but half these features are LEVELS: vix, ivol,
mac_y10, mac_cpi, mac_unemp, eia_util. For a level, 0 is not a missing reading,
it is an impossible one far outside the series' range. VIX does not visit zero.
And the spike lands in exactly the wrong place. Every alt file starts 2010-01-01
while the charts run far deeper - USDJPY H4 reaches ~1994, roughly HALF its
history - so 'alt block is all zeros' is precisely the predicate 'this bar is
older than 2010'. The IS/OOS split is chronological, so that predicate covers
~half of IS and none of OOS: an in-sample feature guaranteed to be useless
out-of-sample, and a bimodal input for the first BatchNorm to normalise. Not a
lookahead leak - a distribution corruption, which is quieter and was never
reported anywhere.
Now filled with the column MEDIAN over the covered range. A constant cannot leak
whatever its source - it takes the same value on every pre-coverage bar, so it
carries no information about which of those bars won - which is what makes a
median computed over later data legitimate here. Median not mean because the
series are skewed. Blank cells get the same treatment (eia_stk_idx1y alone has
181 blanks in 6,073 rows) and the count is now logged at load.
THE BACKOFF WAS ALREADY THERE AND WAS DEAD. Training.mqh arms m_coldSweepTick on
m_featureFailTransient, but only the open/ATR guards ever set that flag, so
f0cf659's cold ADMovingAverage looked PERMANENT and the sweep re-ran at full
speed forever. Setting the flag in the indicator guards revives the mechanism
that was already designed for this; no second backoff was needed and the one I
first wrote has been removed in favour of it.
SELF-HEALING, as asked. ReportFeatureHealth() runs once, the first time pass 1
produces usable windows, samples ~400 bars spread across the whole training range
and names every feature slot that is CONSTANT or mostly-zero, tagging alt-block
slots as alt[i]. Both of today's failures were the same shape - a block silently
produces nothing while every downstream number stays plausible - and neither an
accuracy figure nor a model can tell 'this feature is always 0' from 'this
feature is genuinely 0 here'. Evenly spaced sampling so a block that dies only in
deep history is caught as surely as one dead everywhere. A report, not a gate:
a rare-flag feature can be legitimately constant, and refusing to train would
turn a diagnostic into an outage.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
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AnimateDread
|
f0cf659945 |
fix(features): a cold indicator is TRANSIENT, not a permanent miss - and back off instead of re-sweeping
Six fresh instances on USDJPY and XAUUSD swept 33,965-50,162 bars and produced
ZERO usable feature windows, repeatedly, for 40 minutes and 239 stall reports,
without ever completing era 0. The four instances already warmed up before those
charts were attached trained normally throughout.
THE STALL REPORT NAMED THE SPOT EXACTLY: 'lookback slot 0 REJECTED the bar
(window had 24 of 832 values)', and 24 is the core block to the value - 4 price +
5 swing + 4 range + 4 volume + 6 time + 1 ATR. So feature 25 was the wall, and
feature 25 is the first value of the MA block. The same 24 appeared on XAUUSD
against a 816-value window (51 features/bar vs 52), which is what ruled out any
symbol-specific data gap: the wall sits at a fixed feature index, not a date.
ADMovingAverage is a CUSTOM indicator, so MT5 fills its buffer asynchronously and
returns EMPTY_VALUE for EVERY index until it has calculated - not just the
warm-up tail. That guard did not set m_featureFailTransient, so every bar of the
sweep was cached as a PERMANENT miss. This is precisely the failure the ATR guard
twenty lines above it was fixed for on 2026-08-10; the fix was never propagated
to the indicator blocks that follow. RSI, MACD and Ichimoku had the same defect
and are fixed too. (The Donchian high/low guard is a break into a
degraded-but-usable path, not a rejection, and is deliberately left alone.)
IT ALSO SELF-SUSTAINED, which is why it never recovered. The ok=0 self-heal drops
the feature cache and re-sweeps immediately, so each stuck instance spent every
millisecond re-reading 30-50k bars - six of them at once, on a six-core box,
competing for CPU with the very indicator calculation they were all waiting on.
The recovery was preventing the recovery. A transient total failure now re-arms
m_warmupPassesRemaining, yielding the CPU for a few separately-scheduled Train()
calls - the same mechanism a fresh model already uses to let history sync finish,
pointed at indicator warm-up instead.
Verified in the terminal journal first: indicators load and unload in matched
counts and there is no OOM, so this is NOT the
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AnimateDread
|
d87f7d88ff |
feat(gate): cross-instrument pooled certification
The deploy bottleneck is CERTIFICATION, not training. A 4,738-bar OOS window at L=75.6 holds ~63 independent observations; certifying a 3pp edge at 2 sigma needs ~1,036. More bars of the same symbol barely help - they overlap. Other symbols do not. WHAT POOLS. Not win rates: symbols have different derived geometries, different break-evens and different drifts, so averaging raw rates across them is meaningless. What pools is each symbol's EXCESS OVER ITS OWN CHANCE RATE, combined by inverse-variance weighting (fixed-effects meta-analysis). Each symbol keeps its own model, geometry and chance rate; only the evidence is combined. THE CORRELATION PROBLEM, bracketed rather than assumed away. SP500 and NAS100 are ~0.9 correlated and pooling them as independent inflates the evidence. Nothing here can measure that without sharing return series, so instead of guessing a correction the gate reports both ends: SE_INDEP = sqrt(1/SUM(1/var_i)) all members independent SE_CORR = SUM(w_i * sqrt(var_i)) all members perfectly correlated The truth is always between. THE GATE USES SE_CORR, so a pass cannot be an artifact of correlated instruments - that bound already assumes the worst. The ratio is logged as the diversification credit the gate declines to claim, so the cost of that conservatism is visible instead of hidden. SCOPE, deliberately limited: the pooled result is REPORTED, never folded into tradeableOK. The local gate certifies the model that actually trades this symbol; the pool answers the different question of whether the strategy has an edge at all. Letting a cross-symbol result license a local deploy would ship a model that never cleared its own bar - so it cannot. Mechanics: one file per instrument (no concurrent-write path to get wrong), every FileOpen carrying FILE_SHARE_READ|FILE_SHARE_WRITE, records skipped rather than reinterpreted on a version mismatch, 12h staleness cutoff so a stopped chart cannot vote, and pooling refused below 3 instruments. Poolability requires matching timeframe and ratio; differing SYMBOL is the entire point. Publishing is unconditional - a pool that only hears from winners is a selection effect, not a meta-analysis. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> |
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AnimateDread
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2b5d0f8355 |
feat(measurement): fix zero-skill denominator, publish the deploy bar, measure lifespan per rung, add a MEASURE scale objective
The last run could not have demonstrated an edge either way, and nothing in the
log said so. Four changes so it does.
1. THE ZERO-SKILL LINE DIVIDED BY THE WRONG DENOMINATOR. m_oosWinLongTotal resets
every era; m_oosSamples only resets on a full model reset. So 'always-long %'
decayed as ~1/era: a run whose true rate is 37% printed 1.2% at era 33 and
0.0% at era 2219. This is the SAME bug already found and fixed for
logBuyPredPct thirty lines above ('era-15 Buy:2% that was really ~30%'), left
in the one line whose whole job is to be the reference every other number is
read against. Correct at era 1, wrong everywhere after - including the '62%
zero-skill' figure in the 2026-08-16 notes. Now per-era, and always-short is
finally readable.
2. THE DEPLOY GATE STATES ITS OWN BAR. 'edge -1pp' era after era cannot separate
'short by a hair' from 'short by an amount no strategy could cover'. The era
line now prints the required win rate, the SE, the effective n and the
lifespan it was deflated by; above 100% it says UNREACHABLE. At 4,738 OOS bars
and L=75.6 there are ~63 independent observations, putting the bar near 66% at
typical coverage.
3. LIFESPAN MEASURED PER RUNG. The first-passage cache already stores touch ages
at every ladder level, so each candidate geometry's resolution time is
readable without training on it - L-vs-width becomes a measurement across the
whole ladder in ONE run rather than a second chart. Each rung reports L,
n_eff, min provable edge and min provable EV.
4. SCALE OBJECTIVE IS PHASE-AWARE, defaulting to MEASURE. Width and detectability
are opposed: labels overlap by L, L grows like m*k = width^2 at fixed ratio,
so min provable EV ~ width^2 while the cost saving from width is only linear.
Doubling width quadruples the smallest EV you can prove. DEPLOY (widest that
clears reachability) is right once an edge is known; MEASURE (narrowest that
keeps round-trip spread under BARRIER_MAX_COST_FRACTION_PCT) is right while it
still has to be shown. The direction does not depend on the exponent, and
item 3 makes the exponent checkable.
Fixed in review: m_lastRungLifespan is cleared on every LadderWinShare entry or a
rejected rung reports the previous rung's lifespan as its own; per-rung
detectability is labelled IS-sample based (the deriver may not see the holdout),
so absolute figures are optimistic while the ranking is unaffected.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
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AnimateDread
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d1ac18ebdb |
fix(labels): correct EffectiveSampleSize clamp order, share the horizon ladder, retract a false justification
Self-review of |
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AnimateDread
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1540ba8e64 |
fix(labels): overlapping-label sample correction + horizon cap on the scale ladder
Three defects, all surfaced by the 2026-08-17 SP500 H4 run that shipped
stop 4.86 / target 9.71 (width 14.57*ATR, horizon 384).
1. EVERY STANDARD ERROR ASSUMED INDEPENDENT SAMPLES. Triple-barrier labels
started one per bar overlap by the label's lifespan, so n calls are worth
~n/L independent observations (Lopez de Prado, AFML ch. 4 - sample
uniqueness). All three sqrt(p(1-p)/n) sites divided by the RAW count.
The tell: the operating point's null-of-the-maximum gate is family-wise and
should fire on ~5% of eras under the null. Measured fire rates - PAI 47/73
(64%), ConvLSTM 9/24, LSTM 8/21 (38%), CONV 4/62 (6%). CONV, the only model
whose margin distribution admits few bins, sat on the null; the rest cleared
a bar that was too low by ~sqrt(L). PAI's deployed threshold consequently
alternated between the ENDS of its own range era to era (0.10 -> 0.88 ->
0.86 -> 0.66; coverage 16% <-> 73%).
TripleBarrierLabel now records when each label became KNOWABLE - the first
winning touch, or both stops, or the timeout - and the prebuild accumulates
the mean. EffectiveSampleSize() feeds the operating point, the member deploy
gate and the ensemble vote gate. Conservative by construction (n/L is an
upper bound on the damage); gates get harder, never easier.
2. THE SCALE LADDER RAN AWAY, again. Horizon scales as swingMedian*sl*tp, and
since
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AnimateDread
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4d8cb08501 |
fix(geometry): the reachability floor measured the WRONG WINDOW - my bug from bc57aca, and it cost real width
RECONCILED: the derivation reported "target reached on 17.7% of bars" while the
label cache reported Buy on 35.9%. Nothing was broken. They measure different
windows, and both are correct:
EXCURSION window ~12 bars (the SWING MEDIAN) - what m_excUpCache accumulates
over. Deliberately short: sizing a barrier off travel
measured over a horizon that itself scales with the barrier
is circular, and it ran away to 14-31*ATR on EURUSD/USDCAD
in 2026-08-07. That guard is correct and stays.
BARRIER horizon 64 bars - what the LABEL walk and the first-passage ladder
run over, and how long the EA actually holds the trade.
So `up >= target` is a 12-bar question and `label == Buy` is a 64-bar one, and
the second can freely exceed the first. TripleBarrierLabel gates the excursion
accumulation on `idx - t <= excWindow` while the barrier walk and the ladder run
the full horizon - the split is explicit and intentional.
THE BUG IS MINE. bc57aca's scale ladder tested reachability with `up[i] >= tp`,
i.e. it asked the 12-bar question about a 64-bar trade. That understates
reachability by ~2x, which is why EVERY wide rung was rejected and the geometry
fell back to the tightest rung at 1.61/3.21. The data supported considerably
wider; the test was just asking the wrong question.
FIX: LadderWinShare() reads the answer off the first-passage ladder - target
touched strictly before the stop, over the full horizon, tie to the stop. That
is the identical question the label walk asks, so the ladder share and the Buy
rate should now agree to within rung discretisation. Both legs snap to the
SMALLEST rung at or above the requested multiple (harder target, harder stop) so
the floor stays conservative.
Expect the scale ladder to select a WIDER rung on the next relabel. On this
data the excursion test read 17.7% at q50 where the true full-horizon share is
35.9%, so rungs that scored 8.1% and 2.8% were likely well above the floor.
ALSO:
- Window reconciliation now PRINTED every derivation: excursion travel share,
ladder win share, and the label cache's Buy share side by side, with the
ladder-vs-label gap flagged if it exceeds rung discretisation. Those two must
agree; if they ever stop agreeing, one of them is wrong and the line says so.
- Renamed tpReach/slReach -> tpTravel/slTravel and relabelled the log line. They
describe the EXCURSION window and are near-tautological there (a q50 stop is
exceeded by ~50% of bars); calling them "reached within the horizon" is what
made the two quantities look like one.
- BARRIER_MIN_TP_REACH_PCT is now BARRIER_MIN_REACH_FRACTION_OF_BE (0.60) x
break-even instead of a hardcoded 20.0. Break-even for 1:RR is 100/(1+RR), so
the absolute floor silently tightened as RR rose - 0.60x at RR=2 but 0.80x at
RR=3, penalising the user for asking for a bigger target. Evaluates to exactly
20.0% at the shipped RR=2, so this is a no-op today and correct if the knob
moves.
NOT COMPILED - user compiles.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
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AnimateDread
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bc57aca15d |
fix(geometry): the target was small BY CONSTRUCTION - ratio is now policy, scale is measured, ladder ceiling removed
The derivation read the stop from q75 of ADVERSE travel and the target from q50
of FAVOURABLE travel. Over one horizon those distributions are broadly the same
shape, so q75 > q50 MECHANICALLY - the target came out smaller than the stop no
matter what the market did. SP500 H4 shipped stop 3.07 / target 1.70: a 0.55:1
payoff needing 64.3%. That was never a measurement, it was two mismatched
constants.
The reachability line printed beside it - "target on 50.0% of bars, stop on
25.0%" - is exactly 1-q50 and 1-q75. Tautological. It cannot disconfirm
anything, and it read as validation.
WIDTH AND RATIO ARE INDEPENDENT AND ONLY ONE PAYS. EV = edge x width;
ratio is EV-neutral (a driftless walk reaches +m before -k with probability
k/(k+m), which IS break-even). Width is what buys cost efficiency: the spread
is a fixed 0.047*ATR here, so the shipped 4.77*ATR width paid it 21 times per
unit of travel. So:
RATIO = policy. BARRIER_TARGET_RR = 2.0 (user's 1:2). Break-even 33.3%.
SCALE = measured. The stop quantile is chosen from a ladder, WIDEST FIRST,
taking the first rung whose implied 2x target is still reached often
enough to be a trainable class.
That last clause is the difference from the min-reward:risk raise removed in
2026-08-09, which forced target = 2 x stop with NO reachability test, landed on
6.66*ATR reachable on 3.3% of bars, and trained the model to predict something
that essentially never happened. Same ratio; the scale now retreats until the
data says the target is attainable. Every rung is logged.
LADDER CEILING REMOVED. BARRIER_LADDER stopped at 5.00 and the expectancy scan's
"best resolvable pair on width alone" came back as stop 5.05 / target 4.95 - it
pinned to the top rung. A recommendation landing exactly on the edge of its own
search space is a boundary, not a finding: it cannot tell "5 ATR is optimal"
from "5 ATR is all we allowed". Extended to 20*ATR (8 -> 14 rungs). Nothing else
needs editing - every consumer is parameterised by BARRIER_LADDER_COUNT - and
the horizon constraints (decided >= 60%, reachability floor) now bind instead of
a constant.
THE SCAN COULD NOT SEE THE SHIPPED GEOMETRY. ReportBarrierGeometryScan looked
the configured pair up in its integer grid, and DeriveBarrierGeometry produces
CONTINUOUS multiples (3.07/1.70) that can never equal a grid point - so
cfgExcess stayed at its -1.0 sentinel and the report printed "configured 3:2
scores -1.00000", which reads as a catastrophic score and actually means "never
evaluated". Worse, the grid skipped target<stop entirely because it "inverts the
trade's whole premise" - while the derivation was shipping exactly that. The
incumbent is now always scored as a peer (never crowned; it is already in force
and is not an enum pairing the scan could adopt).
BREAK-EVEN NOW INCLUDES THE SPREAD. Every report quoted the frictionless
SL/(SL+TP). On SP500 H4 that read 64.3% while the MEASURED zero-skill rate was
62.1% - a 2.2pp gap that IS the cost, and that made every model look 2.2pp
better than it was. CostAdjustedBreakEvenPct() prices a win at (TP - spread) and
a loss at (SL + spread), matching the expectancy scan's convention exactly so
the two reports cannot disagree.
It also feeds FitDirConfThreshold, which is the correctness half: the operating
point subtracts break-even from precision, so the frictionless figure made every
candidate threshold look better by the width of the spread - 2.2pp against a
measured edge of 2.3pp, i.e. very nearly all of it.
Era line now carries both: "break-even 64.3% frictionless, 66.6% AFTER SPREAD".
Forces a full relabel and retrain. Requested.
NOT COMPILED - user compiles.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
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AnimateDread
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d30420e3f2 |
fix(batchnorm): bound the normalized value - a constant input feature was amplified 1e4x and pinned PAI's head to its rails
BN_MIN_STD = 1e-4 caps the per-unit gain at 1/1e-4 = 1e4, and the comment above
it states that as though it were a safety property. It is not. A unit whose
running variance is ~0 is a CONSTANT feature carrying no information, and
dividing its rounding noise by 1e-4 hands the next layer an activation of
several hundred. BN's contract is "output has ~unit variance"; a unit that
cannot supply that must contribute nothing, not the largest signal in the layer.
MEASURED, 2026-08-17 SP500 H4, four topologies on identical separate charts:
model spread Neutral CHOSE Neutral TIED rail
CONV 0.386 0.68% 0.10% 0.48%
LSTM 0.392 0.63% 0.00% 0.00%
HYB 0.376 1.79% 0.00% 0.01%
PAI 0.192 0.09% 80.63% 99.99%
bn1's cached nx normed 1.38e4 over 800 units. PAI's SIGMOID head was on its
rails on 99.99% of bars, with Buy and Sell landing on the SAME rail so they
compared exactly equal, and ApplyClassificationSoftmax()'s strict-majority rule
reported that tie as Neutral on ~80% of bars.
So the long-running "PAI is heavily biased toward Neutral" was never a
class-prior problem: the net CHOSE Neutral on 0.09% of bars. It was float
equality on a saturated head. The
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AnimateDread
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331ab29c56 |
feat(diagnostics): split a reported "Neutral" into CHOSE vs TIED - they need opposite fixes
ApplyClassificationSoftmax() requires a STRICT majority over both rivals and
sends every tie, 2-way or 3-way, to Neutral. So "OOS recall Neutral:100%" is
two completely different events sharing one label:
CHOSE - the net genuinely ranks Neutral highest. A class-prior/label problem.
TIED - the top two are EXACTLY equal, so the net expressed no preference and
the tie-break reported Neutral. A SATURATION problem: the head is
SIGMOID, and a saturated sigmoid returns exactly 0.0f or 1.0f in the
DLL's float32, so two classes pinned to the same rail compare equal
and the bar is silently discarded.
Nothing in the logs could tell them apart, and the fixes point opposite ways.
Eras 1-25 of the 2026-08-17 solo PAI run read "Neutral 100%" at spread avg 0.99
- fully saturated - and broke out at era 27 as the spread fell to 0.75. That is
consistent with EITHER story. The user reports the Neutral phase on most runs,
so it is worth four longs to stop guessing.
Four per-era counters on the pass 3 OOS walk, reported as:
| Neutral CHOSE 12.4% / TIED 38.1% (of which B=S 1204) | rail 61.2%
m_oosNeutralStrict - Neutral strictly highest
m_oosNeutralTie - no strict winner; the tie-break produced Neutral
m_oosTieBuySell - the costly subset: Buy and Sell tied AT the top, i.e. a
DIRECTIONAL reading thrown away by float equality
m_oosRailBars - any raw output sitting on a sigmoid asymptote, the
saturation that makes exact ties possible at all
Read on the RAW logits, before ApplyClassificationSoftmax() overwrites TempData
in place. Legitimate because softmax is strictly monotone: it cannot change the
ordering and cannot break a tie either, so the raw reading and the decision
always agree. Placed alongside the existing min/max/spread capture so all the
output diagnostics describe the same values.
Measurement only - no decision path reads these.
NOT COMPILED - user compiles.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
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AnimateDread
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17f808e90d |
fix(ensemble): MAX_AI_SIGNALS was 3 - the ensemble creates 4, so CONVLSTM was silently dropped
AI_HYBRID enables PAI + CONV + LSTM + CONVLSTM and RegisterAISignal registers
them in exactly that order. MAX_AI_SIGNALS was 3, and the guard returned
silently, so the FOURTH - CONVLSTM - never entered g_aiSignals[].
Reported as "convlstm is not listening to the control panel buttons", which is
the visible tip. Everything in Warrior_EA.mq5 that reaches a model does so by
looping g_aiSignals[], so the dropped member also lost:
- every control panel button (pause/resume, stop/start, retrain, deploy,
save, load, reset weights)
- PollTraining() in OnTimer - no wall-clock training progress, so it only
advanced on ticks
- AutosaveWeightsIfDue() -> SaveWeightsNow()
- AltDataReload() on both the mapping-dialog and hourly-upkeep paths
- StartChartSignalRescan()/RescanPending() - the Show Signals sequence
- the All*/Any* aggregates (deployed/paused/stopped/complete), which were
therefore computed over 3 of 4 members and could report the ensemble
finished while CONVLSTM was still training
- OnDeinit's MarkShutdown(), ShutdownChartCleanup() and FlushTrainRun() -
so its arrows were stranded on the chart and its training run was never
flushed on shutdown
It stayed hidden because the model still trains and still votes: it lives in
the signal's own filter array, and it registers itself with the status panel
(ENSEMBLE_PANEL_MAX_MEMBERS is 6) rather than through g_aiSignals[]. So it
appeared on the panel, drew arrows and moved the vote while being unreachable
from every action and unsaveable on exit.
MAX_AI_SIGNALS 3 -> 5 (4 is today's true maximum; the spare slot means adding
META to a preset cannot reintroduce this - the array holds borrowed pointers,
so unused slots cost nothing).
RegisterAISignal now PRINTS on overflow instead of returning silently. A cap
that discards a model without saying so is a trapdoor, not a guard.
NOT COMPILED - user compiles.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
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AnimateDread
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34fa0045e3 | feat: extend MAX_ERAS_PRESET enum and update MaxErasPerRun to ME_10000 | ||
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AnimateDread
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7414570d9d |
fix(diagnostics+calibration): the frozen-layer reading was a broken ruler; gate the operating point on a null of the maximum
Two defects behind the "training is highly unstable" report, from 101 eras of
SP500 H4 PAI logs. Neither was the optimizer.
1) LayerLearningReport's dW/W for BN layers divided by the WHOLE packed block.
getWeightsBN concatenates the outgoing dense matrix, gamma/beta, the running
mean/variance, the Adam moments AND BN_OPT_NX - the forward-pass scratch copy
of the normalized input. At era 101 bn1's dense matrix normed 15.1 against a
block norm of 15430.3, of which NX alone was 15429.3: the weights were 0.098%
of their own denominator, a 1022x inflation. NX is also near-constant between
era-end reports (same last forward pass), which pins the numerator down too,
so the layer read "bn1:0.000%" for 101 consecutive eras and was diagnosed as a
frozen first layer. It was the ruler that was broken. The ratio now covers
trainable parameters only (dense matrix + gamma + beta); mean/var/NX/Adam are
excluded. NX is reported separately because it is a health signal in its own
right - bn5 read nx 6.8e6 over 16 neurons, ~1.7e6 per unit against a healthy
~1.0, which is what a near-zero running variance in the denominator looks like.
NO historical dW/W reading on a bn* layer is admissible evidence that a layer
did or did not train. That includes every such claim in this repo's notes.
2) FitDirConfThreshold took a bare argmax of coverage x (precision - breakEven)
over 50 bins. Measured across 98 consecutive fits:
correlation(chosen threshold, win rate at it) = -0.056 over 0.00..0.74
win rate stdev across fits = 1.32pp
binomial SE of that win rate at ~1430 calls = 1.25pp
The correlation is zero - the margin does not rank trades - and the era-to-era
spread IS its own sampling error to within 0.07pp. So the objective was
coverage x (3.4 +/- 1.3) and the argmax over ~37 eligible bins returned
whichever bin drew the luckiest sample. The threshold teleported
0.42 -> 0.04 -> 0.74 in three eras, swinging OOS coverage 0% -> 39%, leaving
the era win rate measured on 1-5 calls and swinging 0% <-> 100%. That is the
entire reported instability.
The argmax is now adopted only if it beats a DETERMINISTIC fallback - the most
selective bin still clearing the coverage floor, chosen from the margin
distribution alone and never from a win rate - by more than a best-of-N
maximum could manage on noise, sqrt(2 ln N) standard errors. Same null-of-the-
maximum correction the deploy gate already applies to model selection.
A plain one-standard-error band was tried first and is NOT sufficient: its
edge is bestScore - bestSE, and with a 2.3pp edge against a 1.25pp SE that
edge is itself +/-50%, so the admitted set would still wander by half its own
width every era. The fallback has to be independent of the noisy quantity.
Simulated on the observed numbers: falls back every era at the current 2.3pp
edge (stable), adopts the argmax once a real edge reaches ~5pp.
NOT COMPILED - user compiles.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
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AnimateDread
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208da4cbaa |
fix: drop the ranking slice for the calibration band; un-collapse the tiers
NOT COMPILED - user compiles. (1) THE RANKING SLICE IS GONE. It reserved 20% of the OOS window so the pattern-DB backfill would read bars the deployed checkpoint was not SELECTED on. That objection stands; carving a new region to answer it did not. The calibration band already has every property the slice was buying: never trained on | never graded by pass 3 (which walks [0, oosCutoff) and so never reaches it) | never seen by the deploy gate | purged by a full label horizon on BOTH sides | and larger besides - 1,684 bars vs the ~970 carved So the backfill now walks [calibLo, calibHi) and pass 3 goes back to grading the entire OOS window, exactly as before any of this. The gate gets its full sample back (~10% of a sigma), the split loses a region, and the failure mode found an hour ago - a reserved region silently blanking ~10 months of chart arrows, because arrows are only drawn on bars pass 3 grades - becomes impossible. One impurity, stated in the completion log rather than hidden: m_dirConfThreshold is FITTED on that band and the walk applies it to decide which bars fired, so coverage there is mildly optimistic. One scalar under a coverage floor, against checkpoint selection over hundreds of eras. This backfill IS the deploy-time warm-up: it runs right after FinalizeTrainRun() restores the deployed weights, so it scores with exactly what is about to trade. (2) EVERY CALL WAS TIER 0, AND IT WAS ARITHMETIC. ConfidenceTier() quartiles [floorConf, 1] where floorConf = 1/3 - the lowest magnitude a 3-way softmax winner can hold. But it was fed CalibratedConfidenceMagnitude(), which multiplies by m_confidenceCalScale, clamped to [0.3, 1.5]. That lower clamp is BELOW 1/3. Whenever calibration bottoms out, t goes negative and MathMax(0, ...) pins every call to tier 0. Which is what the live run does. m_confidenceCalScale is EMA'd toward empiricalAccuracy / avgClaimedConfidence; with the model over-calling Neutral, 3-class agreement sits near 10% against a claimed confidence near 0.9, so the ratio is ~0.11 and clamps to 0.3 every era. Logged: tier prec T0:72%(828) T1:n/a(0) T2:n/a(0) T3:n/a(0) 828 calls, one bucket - the four tier weights and the entire per-tier pattern-DB ranking reduced to a single number. The backfill was feeding a mechanism that structurally could not rank. Tiering now reads the RAW head magnitude, which genuinely lives on the [1/3, 1] range these bounds were written for. Calibration keeps its real jobs - AIConfidence() for MM sizing and SignedAIConfidence() for the vote are unchanged. STILL OPEN, deliberately not touched here: the calibration TARGET itself. empiricalAccuracy is 3-class agreement, which is the wrong quantity to scale a DIRECTIONAL confidence against - it counts a Neutral class that is 0.19% of labels. The honest target is the win rate on the calls the confidence describes (directional precision), with the claimed-confidence average taken over those same called bars. That needs a new accumulator and it interacts with the Neutral over-calling being fixed elsewhere, so it wants one clean run first. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> |
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AnimateDread
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75d23e9b82 |
fix(gate): move the ranking slice to the OLD end - it walled off the recent chart
NOT COMPILED - user compiles. User: "there is quite some trading going on, but absolutely nothing on the recent area of the chart, like there is a hard wall starting around november 2025." That wall is 7caf2f6's ranking slice, and it was placed at the wrong end. Chart arrows are only ever drawn on bars pass 3 GRADES, and the slice reserved the NEWEST 20% of the OOS window plus a label-horizon purge. At the live sizing - ~4,860 OOS bars, 128-bar horizon - that is ~1,100 H4 bars withheld from grading, about ten months back from today, exactly where the wall appears. The invisible cost was worse than the visible one: it handed the deploy gate the OLDEST 80% of the OOS window and withheld the most recent regime from the single decision that has to generalise forward. Both fixed by putting the reserve at the oldest end instead: [0, oosScoreHi) OOS - graded by pass 3 (NEWEST, arrows restored) [oosScoreHi, rankLo) purge - one label horizon [rankLo, oosCutoff) RANKING - backfill only, graded by nobody [oosCutoff, calibLo) purge [calibLo, calibHi) CALIBRATION ... IS Of the three consumers competing for those bars, recency is worth least to the ranking: it is an ORDERING of confidence tiers, far less regime-sensitive than an absolute win rate, while the gate's power and the operator's read of the chart both want the newest data. The slice keeps every property that made it worth carving - never graded, never selected on, never seen by the gate, purged on both sides - so the backfilled rows are still honestly out-of-sample. RankSliceHiIndex is replaced by RankSliceLoIndex + OosScoreHiIndex; pass 3 now excludes the slice at the TOP of its walk and descends to 2 as it always did. The backfill walks [RankSliceLoIndex, oosCutoff) via a new m_dbBackfillStopIndex, clamped at both ends so a degenerate slice yields an empty walk rather than one that wanders into graded bars. Verified no reference to the old helper survives. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> |
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AnimateDread
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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
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