forked from mnbvc188199/Warrior_EA
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
AnimateDread
|
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.
|
||
|
AnimateDread
|
b28c81eb78 |
feat(rank): AI models rank their own confidence tiers from held-out outcomes
Closes the caveat
|
||
|
AnimateDread
|
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>
|
||
|
AnimateDread
|
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>
|
||
|
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>
|
||
|
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> |
||
|
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> |
||
|
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 |
||
|
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>
|
||
|
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 |
||
|
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
|
||
|
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
|
||
|
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 |
||
|
AnimateDread
|
7e63a8be01 |
fix(depth): route EVERY ResizeBuffers call site through one indicator-depth gate
|
||
|
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.
|
||
|
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>
|
||
|
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> |
||
|
AnimateDread
|
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>
|
||
|
AnimateDread
|
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
|
||
|
AnimateDread
|
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>
|
||
|
AnimateDread
|
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>
|
||
|
AnimateDread
|
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> |
||
|
AnimateDread
|
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> |
||
|
AnimateDread
|
1eeed3ac06 |
revert(ui): restore the unconditional era-end arrow repaint
|
||
|
AnimateDread
|
b19799910d |
fix(ui): chart arrows follow the BEST checkpoint, not the latest era
User report: "as soon as the next era training begins the chart signals are
erased, they should persist for as long as they are accurate."
Cause: PruneDirectionalClusters ran unconditionally at the end of pass 3, and it
DELETEs the arrow on any bar the CURRENT era scored Neutral. A model exploring
away from its best therefore wipes the chart every era even though the best
checkpoint still calls those turns. On the run that prompted this the model sat
at OOS recall Neutral:100% for 40+ consecutive eras, so essentially every arrow
was deleted at every era boundary.
The render is now deferred to the era-end block - the first point that knows
whether the era beat the best checkpoint - and only a new best repaints. On eras
that did not improve, the previous best's arrows stay untouched. Two exceptions
keep the chart from ever showing nothing: before the first checkpoint exists
there is no best to preserve, so early eras still paint; and a finishing run
repaints unconditionally, because FinalizeTrainRun is about to restore the
deployed weights and the chart must describe THOSE.
Recorded for ensemble members too. A member's own best era is not the deployable
one (the joint checkpoint decides that), but it is still the most accurate thing
that member has drawn, and the alternative is a chart that empties itself.
Also verified against the log, since two other symptoms were reported alongside:
era cadence PAI-b6b5 (before these changes) 3.76 s/era
PAI-17ae (after) 3.53 s/era
topology both "2 dense from 16 units | input 800 (16 bars x 50)"
calibration both fitted on 1699 held-out bars
So training speed is unchanged - the ~6% is the ranking slice removing 20% of
pass 3's bars. It only FEELS fast because this is a single PAI chart taking the
whole 120 ms budget, not four ensemble members sharing it behind an era barrier.
The Neutral collapse is also pre-existing, not new: b6b5 ran at Neutral 94-100%
with 1-5% directional calls for all 723 of its eras, before any of this work.
That is the known neutral-collapse/recall-gate failure mode, and it is what
"barely drawing signals" actually is. Worth watching, separately: b6b5 reached
best-bal 34.2% by era 723 while 17ae is at 13.6% after 77 - too early to read,
but it is the number to check once 17ae has run comparable eras.
Compile-verified: 0 errors, 0 warnings.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
|
||
|
AnimateDread
|
b6736fd40b |
fix: the DB backfill could never run, and HEAD did not compile
Four defects in 64c5dd5/1a05e63, found by review + a baseline compile. Goals 1-8 of that session are unchanged; this makes 6 and 8 actually reachable. 1. HEAD DID NOT COMPILE - 6 errors. CControlPanel::Minimize/Maximize were declared `virtual bool ... override`, but CAppDialog declares both as `virtual void` (Controls\Dialog.mqh). errors 265 + 404 on each, plus 151 on `bool ok = CAppDialog::Minimize()`. Return type is void now; there was never a success flag to forward. Verified: 0 errors, 0 warnings. 2. THE BACKFILL COULD NEVER ADVANCE, and neither could the OOS continual simulation (that one has been dead since it was written). Both are armed at the instant convergence is declared, and both advance only from inside Train(), one chunk per call. But ScheduleTrainingIfNeeded's only per-tick ArmStudyEvent site sits in the `else` of a branch taken whenever m_trainingComplete is set and m_trainRunActive is clear - which is exactly the state FinalizeTrainRun() leaves behind one line before they are armed. Train() was never called again, so the walks sat at their start index forever: no "simulation complete" line, and not one row written to the DB this feature exists to fill. Only a manual Resume/Retrain unstuck them. Both flags now keep the model schedulable. 3. IN AI_HYBRID - the mode this ships in - the backfill was never even armed. Ensemble members deploy at Train() ENTRY and return immediately (so no era is wasted), which skips the era-end block the backfill was started from. All four members were a no-op for a second, independent reason. Armed on the ensemble deploy path too, from m_resumeBars/m_resumeOosCutoff. 4. RE-RUNS DUPLICATED ROWS. RegisterSignal inserts unconditionally - no key, no duplicate check - and m_dbBackfillDone is in-memory, so every later attach that retrained to convergence wrote a second full set of rows for the same bars. The ranking would count one bar once per model that ever deployed, weighting superseded opinions as heavily as the live one. A .dbfill marker stamps the deployed era; written only on completion (an interrupted walk redoes itself rather than ranking a partial window) and deleted with the other sidecars on reset-weights. Also: WarmBlocking's timeout was silent, which restored the exact silent pin failure it was added to prevent - it now says so in the journal, and returns true for "no reference pairs to wait for" so the warning stays rare enough to be read. Not addressed, needs a decision: the backfill scores the OOS window with the checkpoint that was SELECTED as best on that same window, then writes those win rates into the table filter weights rank on - the selection set consumed twice, undiscounted, while the deploy gate right next to it applies a family-wise correction for exactly that effect. The rows are also simulated triple-barrier outcomes at today's spread sharing a table with realised fills. The completion log line now states both plainly. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> |
||
|
AnimateDread
|
64c5dd55d3 | feat: implement one-shot pattern-database backfill and enhance accuracy tracking for ensemble models | ||
|
AnimateDread
|
5a5be8999e | fix(altdata): add late warning for alt data arrival after model build | ||
|
AnimateDread
|
e049b624ba |
feat(ensemble): deploy gate on the COMBINED VOTE, with a joint checkpoint
The unit of evaluation in ensemble mode becomes the vote, because the
vote is what trades (user: "at the end of the day they will vote
together during live trading so that would make sense").
Four decisions move from the member to the ensemble:
* which era is "best" -> the era whose COMBINED VOTE scored best
* what is checkpointed -> a JOINT snapshot: every member's weights
at that one era
* when the run gives up -> one shared plateau ladder
* whether it may deploy -> family-wise gate on the vote
WHY THE JOINT CHECKPOINT IS THE POINT: per-member selection picks each
net's own best era, and those eras differ. The resulting quartet was
never measured together at any instant, so the vote it casts live is a
configuration no OOS number ever described. Capturing all four at the
era whose vote won makes the deployed ensemble exactly the measured one.
Correct because of the era barrier (
|
||
|
AnimateDread
|
b77e7b4766 |
fix(ensemble): responsive panel + synchronized eras + combined-vote accuracy
Four user-reported/requested items, one root cause chain:
1) DEAD CONTROL PANEL in AI_HYBRID mode. All members posted custom event
id 1 and handled id 1001, and CExpertCustom broadcasts every chart
event to every filter - so each posted event ran a train chunk in ALL
N members (N*N chunks per round) and the chart thread never idled
long enough to deliver clicks/drags. profiling.csv: 99.45% of time in
OnChartEventHandler. Fix: per-instance study-event ids
(STUDY_EVENT_ID_BASE + construction order, offset above the Controls
library's ON_* codes - id 1 was also ON_DBL_CLICK, so panel
double-clicks fired training chunks). ArmStudyEvent() is the single
post site; lost-event watchdog replaces the accidental
sibling-clears-my-flag rescue.
2) WARM-UP DUPLICATION. The auto-tune sweep is deterministic over
identical features/labels, and it ends in the full MI diagnostic
suite, which the MI-share gate never intercepted on the sweep path -
four members ran four identical ~36s sweep+report blocks. First
member publishes outcome (g_ensembleChartTuneDone/Installed/Settings);
the rest apply it and skip both.
3) DEINIT STRANDED PANEL+ARROWS (user repro 18:52). Root cause from the
log: the 4,500ms budget runs from MetaTrader's stop REQUEST - a heavy
autosave in flight ate it, OnDeinit got ~430ms and died in the first
member's arrow persist ("Abnormal termination" 432ms in). Fix: early
visible-UI sweep (native prefix deletes for status/panel/dialog)
right after ClearStatusLabel, and a fast path for still-training
models - their arrows are re-rendered every era, so they get one bulk
purge instead of scan+atomic-write in the death window.
4) ENSEMBLE FEATURES (user requests): era BARRIER - members advance era
by era together; a member ahead of the slowest still-training member
declines Train() calls and its chunk budget is donated
(TRAIN_TIME_BUDGET_MS = 120/activeTrainers, UI headroom constant).
COMBINED-VOTE OOS SCORE - each member's pass-3 scan contributes its
adjusted per-bar decision (0.0 on abstain) to a shared row buffer;
the last member to finish the era scores the averaged vote vs the
mirrored Min_Vote_Open against the same target-before-stop outcomes
members grade themselves on, publishing an "Ensemble vote" line on
the aggregated panel. Member headlines now carry their lifetime win
rate with break-even.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
|
||
|
AnimateDread
|
0788238c00 |
feat(inputs): unify ALL indicator periods under the tuner; EnableAltData input; AI-first defaults
- PeriodMA/MA_Type/PeriodRSI: input -> const seeds (closing the set: every
indicator parameter is now tuner-owned)
- Variables\TunedPeriods.mqh: chart-level tuned-period state. A gated
install writes TunedPeriods_{SYM}_{TF}.cfg; next attach reads it BEFORE
the DB fingerprint and classic-signal config, so classic votes, DB key,
and tuner seeds always describe the same indicators regardless of
classic/AI/hybrid use. Restart-grained adoption by design (no mid-run
handle churn); new periods re-key the signal DB (semantics rule).
- EnableAltData input in AI Input Features (consumption gate only;
collection keeps running); |ALT DB-fingerprint token; opt-out on an
alt-trained model correctly starts fresh via the width compare.
- Defaults: all four classic votes OFF (AI-first; WARRIOR_MARKET_BUILD
branches collapsed with the marketplace pivot), order-flow/Wyckoff NN
features OFF (alt data is the default information diet; toggles stay).
Compiles 0 errors / 0 warnings.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
|
||
|
AnimateDread
|
8ce635ce70 |
feat(altdata): external feature block wired into the NN feature window
- System\AltData.mqh: CAltDataPanel - publication-stamped CSV panel
(Common\Files\Warrior_EA\AltData\{SYM}_{TF}.csv), as-of lookup by bar
open, 0-fill degradation (mirrors cross-asset), hourly live refresh
- Topology: width block AFTER the .cfg name-list pin is pre-read
(ReadAltDataPinFromCfg) so a grown export can never mismatch a resumed
model's width or shift its slots
- Persistence: alt pin appended to the .cfg (append-and-length-guard
convention), adopt-don't-compare on load
- Features: emit block after Wyckoff SBI; EnsureFresh probe in
BuildFeatureWindow (never fires in tester)
- export.py: fixed a-priori scale constants (never data-fitted)
Widths change SP500 +4 / USDJPY +3 / XAUUSD +1 (fingerprint re-keys ->
fresh models on redeploy); EURUSD exports nothing and resumes unchanged.
Compiles 0 errors / 0 warnings.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
|
||
|
AnimateDread
|
430cdbe650 |
feat(ai): conditional barrier geometry for the fractal target - MFE/MAE measured leg-by-leg at labeled bars
The derived stop/target were quantiles of EVERY bar''s excursions over a fixed horizon - q75 adverse gave a 2.6-3.5*ATR stop against a ~1.7*ATR target (user: "looks limiting"). That pooled measurement was correct when direction was dead (any subset of bars had the same distribution) and is provably mis-sized now that the gate certifies the label carries information: the bars the model trades are the labeled bars, and their excursions differ from the pool. FractalDirectionLabel now records, for every Buy/Sell-labeled IS bar during the prebuild, the favourable and adverse travel in ATR units over exactly the LEG the label points at - entry close through the next fractal extreme (user request: "from a fractal to the next for maximum accuracy"). DeriveBarrierGeometry reads the same q75-adverse/q50- favourable quantiles off that conditional sample instead of the pool, with a logged fallback to pooled when fewer than the minimum legs exist. Quantiles kept over averages deliberately: a mean MFE is dominated by runaway legs and would set an unreachable target. No circularity: the fractal label does not depend on SL/TP (the barrier label does - this path must never feed it). Recording stops the moment geometry is derived and pinned, so pass 2 relabels and later bars cannot silently re-shape a certified pair. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> |
||
|
AnimateDread
|
a734b91e80 |
feat(ui): one aggregated status panel for the AI_HYBRID ensemble
All four ensemble members previously wrote their full multi-line panels
to the SAME global label objects - an ensemble chart would flicker
between four stacked panels covering the chart side (user request:
aggregate). Every AI-side SetStatusLabel call site now routes through
CExpertSignalAIBase::PublishStatus - solo charts draw the full panel
exactly as before; an ensemble member claims a slot and contributes
only its HEADLINE to one combined block ("HYBRID ensemble - N models",
then one line per model; the live line leads with the model current
signal). The combined render skips unchanged text and enforces its own
minimum redraw interval so four publishers cannot multiply
ChartRedraw() cost.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
|
||
|
AnimateDread
|
609be10391 |
feat(ai): AI_HYBRID = ensemble preset (all NNs, one chart); conv+recurrent renamed AI_CONVLSTM
The user is right that no special combination logic is needed: the AI signals are ordinary voting filters, and the aggregate already has union semantics - abstaining filters do not dilute the average, so an ensemble chart trades whenever ANY deployed member clears the vote threshold and disagreeing members net out. What the ensemble preset actually adds: - AI_CHOICE value 4 renamed AI_CONVLSTM (the name says the front-end); enum VALUES stable, CSignalHYBRID class and State\HYBRID\ folder kept, so saved configs and trained models keep their identity. - New AI_HYBRID = 6: enables PAI+CONV+LSTM+CONVLSTM together on one chart - replaces four separate charts of the same symbol. Each member trains and self-gates independently; only certified members ever vote. - |ENS1 fingerprint token on every member, so an ensemble member's weight files can never collide with a solo model of identical settings on another chart of the same symbol (the duplicate-chart guard would otherwise correctly fight over one .nnw). - Private default AIType = AI_HYBRID: one D1 drop now yields every topology's gate verdict for that symbol. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> |
||
|
AnimateDread
|
0adaea48b6 |
fix(resume): model reload stalled training - three hardenings on the resume path
A resumed META model hot-looped pass 1 (0->100% scan oscillation, silent for
3 minutes until the stall reporter fired) because EVERY window failed at the
first AD/Wyckoff feature: the init-time param adoption called
ReInitADIndicators unconditionally, destroying five freshly-calculating
indicator instances to recreate them with BYTE-IDENTICAL params (verified by
parsing the .nnw header - the MI tuner had kept the configured settings), at
process start, on a box with 1 GB free of 31. The replacements sat cold for
6+ minutes while full-history resweeps starved the indicator threads harder.
- AdoptIndicatorParams: installs a loaded param set into the tuner and
rebuilds handles ONLY when the set actually differs from what the live
indicators run. Both call sites (resume init + panel reload) use it.
- Resumed models get the same 3 warm-up passes as fresh ones. The skip was
the shared root cause of the cold-ATR (
|
||
|
AnimateDread
|
444909d0a3 |
feat: S2 meta-labeling head - binary trade-quality model over the classic-candidate corpus
The NN now has a target that is not per-bar direction (closed, best-of-999 p=1.0000): P(win | this journaled candidate, at the EA's own SL/TP, net of cost). One net for all 52 pattern-sides, AIType=AI_META. - NetForward.mqh: the host-side softmax+CE gradient generalized total==3 -> 2||3 on both backprop paths; a 2-class softmax IS a logistic head, and no compute backend changes. - SignalMETA.mqh (new): corpus loaded read-only from the LARGEST signal DB on disk (decoupled from the config fingerprint that burned four S1 runs); the GMT->server offset is measured PER ROW against entryPrice vs bar open (DST-immune, histogram logged); a window-span regime filter drops the pre-2017 daily-backfill rows; 31-feature setup descriptor appended at the input (26 one-hot + side + tanh netVote + SL/TP ATR + spread/ATR). - Training.mqh: candidate-queued pass 1, binary-target pass 2, per-candidate calibration (2.5) and OOS (3) walks. Counter mapping win->Buy / loss->Sell lets checkpoint selection, the edge floor, the plateau ladder and the family-wise deploy gate run UNCHANGED: precision reads as win rate among traded candidates, chance as the base win rate, recalls as sensitivity/ specificity. Era-end META line: coverage x (p - break-even) vs the null. - Labels are the side-conditional triple-barrier win caches - never the DB's stop-and-reverse outcome. Logit adjustment deliberately skipped (~40% base rate). Live inference + online learning guarded off until S3. - Fingerprint: conditional |TGT:META1; State\META\ folder + 2-output filename slot keep meta models fully separate from direction models. Compiles clean (0 errors, 0 warnings). S2 run = attach a chart with AIType=AI_META; S3 wires the votes via the per-side hooks. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> |
||
|
AnimateDread
|
923addf574 |
feat: pin the cross-asset pair set train->serve + warm the sync at init
The reference-pair set was re-discovered from Market Watch on every build, so adding or removing a terminal symbol silently changed what a trained model's six cross-asset features meant - the last open train/serve parity gap from the 2026-08-11 audit. The set a model's FIRST successful build actually used is now stamped into its .cfg (append-and-length-guard, adopt-don't-compare - the derived-barrier pattern) and every later build constructs the panel from exactly that list; a pinned pair that is temporarily unavailable is skipped, never substituted. Also warms SymbolSelect/SeriesInfo for every reference symbol at InitNeuralNetwork, so the terminal's ~minute of async cross-symbol download starts at init instead of when the first Build() trips over an unselected symbol - the source of the startup 'only 0 usable reference pairs' console failures. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> |
||
|
AnimateDread
|
e2c959331f |
perf: the excursion head cost 3.6x era time - cut its dispatches ~250x
Measured on exc-race-v3: LSTM era 300s -> 1087s (net 272->748s, "other" 30->337s). My estimate had been "single-digit percent". The cost is per-DISPATCH, not per-FLOP, and therefore hits EVERY backend: the head is 19k weights and ~2.4 GFLOP an era - seconds of arithmetic - but ~48k forward/backward calls x several layer submits each, and its 760-wide layer exceeds the CPU DLL's inline threshold so each one pays a real handoff. The classifier's own net time tripled too, from contention with a second pool on an already-full box. Three changes, all backend-neutral because they remove submits rather than tune threads: SCORE ONLY DISJOINT WINDOWS (~64x). Adjacent bars share all but one bar of their horizon, so 16k consecutive bars were always ~250 independent observations - the full-sample tally was never worth more than the disjoint one, it just quoted an n that was ~64x too large. Dropping it costs nothing statistically and removes 63 of every 64 forward passes. The two parallel tallies collapse into one, which is also less code. The trailing ring still advances on every bar: it needs the outcome SEQUENCE, and that is array lookups, not a forward pass. TRAIN ON EVERY 4th PRIMARY BAR (4x). The target is low-dimensional and strongly autocorrelated - neighbouring bars carry near-identical excursion information - so per-bar training buys resolution the target does not have. Strided on ATTEMPTS, not acceptances, so a stretch of unlabelled bars cannot silently change the spacing. OWN TIMING COLUMN. The head's passes were landing in the era line's "other" bucket, which is how a 3.6x regression read as an unexplained jump in the one column nobody attributes. A cost that cannot be seen in the timing line cannot be traded off against anything. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> |
||
|
AnimateDread
|
4cfbb82634 |
feat: race the excursion head against a trailing-quantile incumbent
Beating a frozen global constant is the weakest admissible bar for replacing a global constant. The honest incumbent is a rolling rung frequency: it adapts to the volatility regime - exactly what the head claims to predict - and needs no model, no 760 inputs and no training. Implemented as a ring of per-bar outcome bitmasks (32 rungs fit one ulong), sized horizon + EXCURSION_TRAIL_WINDOW. The newest `horizon` entries are held back UNRESOLVED: a bar's rung outcomes are only known one horizon later, so using them would be lookahead and would flatter the incumbent into an opponent the head could never fairly beat. Pass 3 walks oldest-to-newest, so "pushed more than horizon bars ago" is exactly "resolved by now". Each push is O(rungs), not O(window). The head's decision-rung Brier is pro-rated to the trailing estimate's coverage before the ratio, since the incumbent only scores bars where its window is warm. This line is worth reading on its own, independently of the head: if the trailing quantile beats the global constant, that is a cheap risk-control win available with no machine learning at all - and it is the same number either way, so the run answers both questions in one pass. The ring is deliberately NOT reset per era - it estimates the market, not the era, and re-warming 500 bars every era would leave the incumbent unusable over the first chunk of every scoring pass, handing the head a free win on exactly those bars. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> |
||
|
AnimateDread
|
06d4785e39 |
fix: the excursion gate would have passed Stage 2 on an artifact I made
Second-opinion review killed the +4.2% far-rung result, correctly, and
the mechanism is my own bug. A head trained toward {0.05,0.9} converges
to 0.05+0.85p, so its bias is 0.05-0.15p: negative where p is near 1,
POSITIVE where p < 1/3, growing monotonically as the rung gets farther.
Against a baseline frozen at the IS rate, an upward-biased head scores
positive Brier skill whenever the OOS rate merely sits above the IS rate.
Predicted signature: huge negatives near, ~zero at p=1/3, growing
positives far. Observed: -82% ... -0.6% ... +1.2/+2.7/+4.2. The far rungs
were not the clean end of a distorted measurement, they were the other
face of the same artifact. Everything before
|
||
|
AnimateDread
|
950b0fdab0 |
diag: name the cause when every feature window fails, and enforce the width contract
Era 0 stalls with "NOT ONE of 54681 scanned bars produced a usable feature window, windows ok=0 failed=54681" and nothing else. That line reads identically for a cold ATR, a conditionally-missing optional feature block and an out-of-range index, so it cannot be diagnosed without one restart per hypothesis. Two changes: 1. WIDTH CONTRACT in BufferTempData. Every enabled block must emit exactly m_neuronsCount values on EVERY bar. A block that emits its values on some bars and skips them on others (indicator, panel or series unavailable for that bar) does not merely shorten the window - it SHIFTS every feature after it into the wrong slot, and the net then trains on silently misaligned inputs that still look like a valid window to everything downstream. Now rejected, rolled back and reported once, naming the optional blocks (XA / SPR / swing context) as the ones carrying an availability test. Worth having independently of the current stall. 2. BuildFeatureWindow records WHICH lookback slot rejected and how much of the window was assembled, and the pass-1 stall report renders it: "slot 0 of 20 REJECTED (window had 0 of 760)" is an indicator warm-up or history-edge read; "every lookback bar ACCEPTED and the window was still short: 640 of 760" is a missing 6-value block. No behaviour change on a healthy run: the width check is an equality that already holds, and the diagnostics render only inside the total-failure branch. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> |
||
|
AnimateDread
|
2d28f6542b |
feat: excursion-size head (Stage 1, measurement only)
Direction is closed - normalised asymmetry fails on three instruments with a working positive control, and the classifier's own best-of-999 era-cap test agrees (+0.9pp = 1.48 sigma, family-wise p=1.0000). SIZE is a different question and RANGE clears at ~4x its null. Checked the denomination before building on that, since the source memo warns to: m_excUpCache holds (maxHigh - fill)/ATR, so "RANGE is predictable" is a claim about travel RELATIVE to current ATR, not a restatement of "ATR is autocorrelated". It is exactly the part a fixed multiple (stop 3.31*ATR, target 1.64*ATR) discards. A second small CNet, 760 -> 24 -> 32 sigmoid outputs = P(price reaches ladder rung k) upward and downward. Survival parameterisation rather than regressing the multiple, because it needs nothing new from CNet: sigmoid outputs and the per-neuron delta the `total != 3` branch already applies (a quantile head would need a linear activation and a pinball gradient in Network.mqh, Network.cl and the DirectML path, on a class four topologies share). Targets are free - m_ladderUpAt already records first-touch age per rung with 0 meaning never reached. Separate net, not extra outputs on the classifier: more outputs would change m_outputNeuronsCount, the .nnw shape and the fingerprint, and push the count off 3 - the exact condition backProp uses to select the joint softmax gradient the 3-class head depends on. The classifier is bit-for-bit unaffected and this is removable without trace. STAGE 1 PLACES NO ORDERS. It reports a Brier skill score against the constant per-rung base rate - the baseline a fixed ATR multiple already assumes - with both predictors fitted IS and evaluated OOS, so neither gets a look at the test set. Positive skill justifies Stage 2 (drive SL/TP and sizing off ExcursionQuantile, which is defined and deliberately uncalled). Zero or negative means ATR already carries everything and Stage 2 must not be built. Trains only on primary occurrences: the replay queue oversamples for CLASS balance, and a direction-balanced sample is a biased SIZE sample. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> |
||
|
AnimateDread
|
2189316c35 |
fix: the operating point was fitted on bars the net had memorized
FitDirConfThreshold harvested its margin histogram from pass 2's own
backprop samples. Pairing every fit against the same era's OOS result
shows what that measured:
PAI era 1 IS 25% cov @ 66.1% (-0.8pp) -> OOS 64% (-3pp) gap +2.1pp
PAI era 76 IS 90% cov @ 79.6% (+12.7pp) -> OOS 65% (-2pp) gap +14.6pp
LSTM era 9 IS 77% cov @ 81.6% (+14.6pp) -> OOS 63% (-4pp) gap +18.6pp
The gap grows monotonically while OOS stays flat, so within a handful of
eras the curve stops describing behaviour on unseen bars. That is fatal
here specifically, because the objective branches on the SIGN of
(p - break-even): the memorized curve reads +12pp at 95% coverage, so
coverage x (p - p0) correctly maximises coverage and returns ~0.02 - fire
on every bar. The "p < p0 -> get more selective" branch, which is the
actual regime and the entire point of
|
||
|
AnimateDread
|
ccfbc62561 |
fix: the recall gate was unsatisfiable and the LR decay was a spiral
Both made the run structurally unable to succeed, independently of any
signal in the data. Found by reading the 13:01 log.
RECALL GATE. m_objectiveMet required Buy, Sell AND Neutral OOS recall
each >= 40%. First-touch resolution (
|
||
|
AnimateDread
|
ba13eefecc |
fix: a resumed model cached a cold ATR as permanent, so it never trained
BufferTempData cached EVERY failure - m_featureCacheHasValue[idx]=true with m_featureCacheValid[idx]=false - and the cache never re-tries a miss. So a single feature read taken before the terminal had finished calculating the indicator buffers marked those bars unusable for the rest of the process, even though the data arrived milliseconds later. MT5 fills an indicator's buffers asynchronously after the handle is created, and a cold ATR returns 0 for EVERY index, not just its warm-up tail. BufferTempDataCompute rejects a bar with no ATR (correctly - the price features would be meaningless), so the whole window failed, and the whole cache was poisoned. Only resumed models were hit, because only they read features that early. Topology.mqh sets m_warmupPassesRemaining = netLoaded ? 0 : 3: a fresh start sits through three separately-scheduled Train() calls before anything touches a feature, which is exactly what those passes are for. A resumed one skips them and TuneIndicatorsAndTrain drives StartLabelCachePrebuild and the MI report from the first chart event. Its rationale - "a restart already has a proven-synced history" - holds for HISTORY and not for INDICATORS, which are recreated every process start. Downstream: BuildFeatureWindow failed on every bar of every era, so add_loop never went true, so pass 2, pass 3, the era counter and the checkpoint were all skipped and pass 1 swept 0->100% forever. The "0 samples" MI report line at startup was the same failure, four seconds earlier, already visible in the log. - a miss is now cached only when it is PERMANENT; the two "not ready yet" guards mark m_featureFailTransient and are recomputed on the next visit. Steady-state cost is ~ind_Periods bars per era, not 54k. - an era that discards itself now drops the feature cache before restarting, so any remaining cause of this state self-heals instead of looping. Deleting the .nnw "fixed" this only by turning the model back into a fresh one. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> |
||
|
AnimateDread
|
464a0fe19d |
diag: an era that discards itself now says so instead of scanning forever
add_loop is exactly "at least one bar produced a usable feature
window". When it stays false, pass 2, pass 3, the era counter, the
checkpoint and every log line in the era-end block are ALL skipped:
Train() returns having done nothing, m_eraResumePending is still false,
and the next call restarts the SAME era from bar 0. That is an
infinite 0->100% "scan" loop that prints absolutely nothing - the only
remaining silent restart path in Train(), and it matches the reported
symptom exactly.
Pass 1 now counts usable vs unusable windows and reports at the pass
boundary, which demonstrably executes:
- total failure routes through ReportTrainStall (already capped at
one line a minute, and carries the run-state flags) naming the
counts, the required window width and the bar count
- success prints how long the scan took and how many samples it
handed to pass 2, but only once the era has passed 10s - a fast
era stays as quiet as before, a slow one distinguishes "advancing"
from "sweeping the same bars forever"
A PARTIAL failure is normal and deliberately does not shout: pass 1
walks oldest-to-newest and the deepest bars predate the indicators'
warm-up, so those windows fail and are cached as misses.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
|
||
|
AnimateDread
|
1a5157befc |
fix: training could only advance one 120ms chunk per bar
ScheduleTrainingIfNeeded() armed the next Train() call only when dtStudied < lastBarDate. That watermark test is right for a CONVERGED model - one inference refresh per new bar - and wrong for a training run, because Train() is chunked: it does ~120ms of work and yields, needing thousands of calls to finish one era, and every one of those calls has to be armed from there. dtStudied is two incompatible things. Train() sets it to the training WINDOW START (~2008); FinalizeTrainRun() sets it to the last bar SCANNED (~now). So the moment any run finalized, the scheduler went silent until the next candle closed. On H1 that is one chunk per hour. The symptom was indistinguishable from a hang: no era lines, no heartbeats, not one of the six instrumented stall branches - because Train() was not being CALLED. The TRAIN STALL line that caught it reported runActive=Y only because m_trainRunActive had been set microseconds earlier in that same call, and eraResume=N proved no era was in flight. Two log bursts, 28 minutes apart, exactly one H1 bar. Before |
||
|
AnimateDread
|
3855d4666a |
diag: the heartbeat could be outrun by the condition it watched for
It fired only on 4096-item boundaries once an era had already run 60s. Those boundaries are all crossed in the first few chunks of pass 1, so an era that became slow AFTER them printed nothing at all - which is precisely what happened: 20 minutes, four pegged cores, zero heartbeats. I read that silence as "the era loop is never reached" and went looking for a wedge above it. The silence may simply have meant "past the last boundary". A diagnostic whose trigger can be outrun by the condition it watches for is worse than no diagnostic, because it produces confident wrong conclusions. Now time-gated: checked every 256 items (the mask only keeps GetTickCount off the hot path), prints when the era has run >60s and >30s since the last line, up to 12 per era. Progress/phase for the panel is still published on every call, before any gate. Both build variants compile 0 errors, 0 warnings. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> |
||
|
AnimateDread
|
b461844767 |
fix: prebuild and era sized different windows; diag: Train() names its branch
TWO things, one incident. 1) THE BUG I SHIPPED IN |