フォーク元 animatedread/Warrior_EA
720 のコミット
| 作成者 | SHA1 | メッセージ | 日付 | |
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AnimateDread
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afe1038d11 |
fix(topology): stop a training-alone size becoming permanent, and stop the keep-screen latching underpowered
1. THE POOL FIX WAS LANDING ON A TOPOLOGY THAT COULD NOT SEE IT. ComputeFirstLayerWidth budgets against EstimatedInSampleBars, which counts this chart's own bars PLUS the training pool. On a COLD fleet start every chart derives and pins its topology BEFORE any chart has published a pool file - measured on the 18:13 start, model creation at 18:13:21 against a first publish at 18:13:48. All six sized as if training alone, wrote that into .cfg, and adopted it back on every later start even with the pool full. SP500 ran a first layer floored to 16 while adopting 30229 peer rows. Adopt-don't-compare exists to protect weights shaped by those sizes. It was also running for a model with NO .nnw, where there is nothing to protect and the .cfg is just a record of one unlucky moment. The four derived sizes are now re-measured when no weights exist. Safe on all three counts that matter: free (nothing to discard), cannot loop (once weights exist the .cfg is authoritative again), and cannot fragment the pool - the derived width is NOT in BuildModelFingerprint, which keys only on the FEATURE layout. Verified: field 2 of the fingerprint is LEGACY_HISTORY_BARS_SLOT, not the first-layer width. TO TAKE EFFECT the weights must be wiped while the TrainPool is KEPT - the census has to be non-empty at derivation time. A full wipe empties the pool and reproduces the original condition exactly. 2. THE KEEP-SCREEN LATCHED ON AN UNDERPOWERED SAMPLE. MI_MIN_SAMPLES is a floor for "can this be computed", and it was being used as the bar for "is this answer final". The screen fired on the first era clearing 200 rows and latched, measuring at 202-773 samples where a warm chart gives ~2065. Columns kept then tracked SAMPLE SIZE rather than information - EURUSD kept 0 of 49 at n=202, SP500 kept 15 at n=773, and the ordering across all six charts was very nearly monotone in n. A thin sample is still measured and printed, but it no longer closes the question: below MI_GOOD_SAMPLE_FRACTION of the target the result is labelled underpowered and a later era supersedes it, bounded by the same attempt budget. An underpowered screen that latches is worse than one that waits, because it looks like a result. Build tag -> fleet-pool-v2. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> |
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AnimateDread
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a970405042 |
feat(pool,mi): one feature layout fleet-wide, and the keep-screen stops self-disabling on a cold start
TWO CHANGES, BOTH RETRAIN-FORCING BY INTENT.
1. SP500 was training alone, and one alt-data column was the reason.
The alt block's width joins the model fingerprint, and the pool reader only
adopts peer rows whose fingerprint and width match. The exporter gives each
instrument the series that apply to it - FX 15 columns, metals/oil 14, SP500
13 - so the fleet ran as three incompatible pools:
EURUSD/USDJPY/USDCAD adopt ~57-60k peer rows each
XAUUSD/XTIUSD adopt 6.4k / 20.3k
SP500 "EVERY peer file was REJECTED, so this chart is
training alone" - 0 rows
SP500 therefore trained on 2279 independent observations against a 600-wide
input with its first layer floored at 16, printing its own "expect
overfitting" warning. It is the one chart with no pool and the worst
capacity ratio in the fleet by a factor of three.
Fresh models now pin ALTDATA_FLEET_COLUMNS - the 12-column intersection -
instead of their own file header. An existing model still adopts its .cfg
pin, so this re-keys nothing that is already trained.
Intersection rather than union: filling an absent series with its median
makes that column constant per instrument, which lets a pooled model
identify the source instrument and stop learning the shared mechanism. It
is also 6 columns narrower. Cost is six columns whose retained information
is UNMEASURED - the keep-screen reports a bitmask nothing has mapped back
to names.
2. The MI keep-screen disabled itself for the whole run on any cold start.
ReportFeatureLabelInformation set m_miReportDone on ENTRY. On a cold start
the label cache is allocated before it is filled, so BuildMiSample finds no
row carrying a resolved label and returns 0 - a sixth exit, and the only
one the
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AnimateDread
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d9092a2408 |
fix(vote): persist the member's skill verdict - a converged model was ruled no-skill on every restart
SP500 resumed converged at era 136 with its tier ladder correctly restored and still swept 4999 bars reporting "0 had a snapshot, drew 0 arrow(s)" while the other five charts drew 221-312. HasDemonstratedEdge() - added with the no-skill exclusion - compares m_eraStatPrecPct against m_eraStatChancePct. Both are written once per era by EnsembleStashEraStats. A converged model runs no eras, so after a restart both sat at their -1 ctor defaults, every member was ruled no-skill, ReconstructionWeight() returned 0 for all four, and the overlay divisor was zero on every bar. Exactly the failure the WST7 ladder persistence fixed one level down: the ladder says how much a member votes, this says whether it may. RankTiersFromOos already computes the pair (pooled holdout precision and the zero-skill reference rate) and now records it as the CERTIFIED edge. That path is reached by the era end AND by the deployed replay, which is the only measurement a converged model will ever make. Persisted as WST8; HasDemonstratedEdge() prefers the era pair and falls back to it. The census line also had to be fixed: it reported "NOT ONE of those bars had a single member snapshot ... no enrolled member has published m_overlaySigSnap" for a condition that was purely a skill verdict. The snapshots were there. It now counts the two causes separately and names the one that fired. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> |
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AnimateDread
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15b028450b |
fix(vote): follow the derived rung until a checkpoint exists, pin thereafter
A LIVE DEFECT from combining today's two changes. The threshold pins ON CHECKPOINT ( |
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AnimateDread
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326e314b3e |
diag(features): emit the keep-set as a comparable hex mask
The keep-screen answered whether pruning is worth doing - consistently, across
all six charts:
chart kept width first-layer budget
EURUSD 17/52 624 -> 204 11.3 -> 34.3
USDCAD 19/52 624 -> 228 10.0 -> 27.2
USDJPY 18/52 624 -> 216 11.2 -> 32.4
XAUUSD 17/51 612 -> 204 7.8 -> 23.2
SP500 18/50 600 -> 216 3.8 -> 10.5
XTIUSD 16/51 612 -> 192 3.8 -> 12.1
~1 column in 3 carries the association and the rate is stable across six
independent charts - noise would not reproduce that tightly. Pruning nearly
triples the capacity budget and lifts XAUUSD off the 16-wide floor. SP500 and
XTIUSD (the two pool-poor charts) improve ~2.8x and still miss it; they need the
12-bar window cut as well, which is a separate lever costing nothing in feature
semantics and not touching pool compatibility.
Headline MI is strong everywhere under the pivot-event label: 0.008-0.0099 nats
against a ~0.002 null, strongest column 0.047-0.077 against a ~0.006 null-max
(8-13x).
WHAT THIS COMMIT ADDS is the last fact needed before a mask can be built: WHICH
columns, as a hex bitmask, so two charts' masks can be compared by eye and by
grep. Identical masks across the fleet mean ONE fleet-wide mask keeps every chart
in a single pool group; divergent masks would split six charts into six groups of
one, and pooling is the only thing currently holding the FX charts above the
capacity floor - so a per-chart prune could cost more capacity than it buys.
Still report-only. No fingerprint change, no retrain forced.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
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AnimateDread
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8c1266db0b |
diag(mi): name which BuildMiSample exit abandoned the sample
The MI screen collapsed to "-1.00000 nats/feature over 0 permutations" on the
first COLD start after a wipe, taking the new per-column keep-screen with it. On
the same chart seconds earlier the auto-tuner had scored the same function fine:
auto-tune complete - 12 candidates scored, mutual information 0.00843 nats
feature/label information - -1.00000 nats/feature ... over 0 permutations
So the data exists and something between the two collapses the sample window.
Cold-start only - every successful report today came from a warm start where the
models loaded from disk, and wiping is what exposed it.
I formed three explanations (label-cache invalidation by the tuner, a shift pad
scaled off an unmeasured label resolution, a zero feature width) and each failed
against the log. Three failed explanations is the point where guessing stops and
instrumenting starts.
BuildMiSample has five distinct -1 exits and the caller can only observe the
collapsed result. Each now names itself and prints the terms that would explain
it: bars, lo/hi, MI_MIN_SAMPLES, OOS split, history window, shift pad and the
measured label resolution the pad scales from. Throttled via TCLog.
Deliberately NOT also "fixing" the latch that makes this stick
(ReportFeatureLabelInformation sets m_miReportDone at ENTRY regardless of
outcome, and the first member then sets g_ensembleChartMiReportDone, so one
failed attempt disables the screen for every member on the chart for the whole
run). If the cause is a genuine cold-start ordering problem, making it retry
would paper over it - the instrumentation decides which fix is correct.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
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AnimateDread
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a9e941d7ee |
feat(features): per-column MI keep-screen (report only)
Step 1 of the prune, stopping deliberately short of pruning - two blockers make
an immediate mask the wrong move, and this is the measurement that decides
whether pruning is worth doing at all.
WHY NOT PRUNE YET:
* the screen runs with cross-asset ABSENT - its own log line says the numbers
"describe a NARROWER vector than training will use". A mask built from it
would have no evidence either way about the cross-asset block.
* a per-chart mask FRAGMENTS THE POOL. The mask must participate in the
fingerprint, and the pool only accepts peers with an identical feature
layout. Pooling is currently the only thing keeping the FX trio off the
capacity floor - the three pool-poor charts (SP500, XAUUSD, XTIUSD) are
exactly the three still floored. Six per-chart masks = six pool groups of
one, and pruning could cost more capacity than it buys.
WHAT THIS ADDS: the per-column MI was always computed inside ScoreMiSample and
thrown away except for the sum and the max. It is retained now, and the same
permutation draws that build the headline null also accumulate a PER-COLUMN null,
which is what a per-column p-value needs - distinct from the null-of-the-max,
which answers the single family-wise question "is the strongest column real".
Selection uses Benjamini-Hochberg at q=0.10, NOT the family-wise bar. FWER
controls the chance of one false positive, which is right for a verdict and far
too conservative for selection - it would discard every genuinely weak-but-useful
feature. BH bounds the expected SHARE of kept columns that are noise, which is
what a feature set cares about.
The report prints the decision in capacity units: columns kept, the resulting
input width, and the first-layer budget before and after against the 16-wide
floor. 3 of 52 is not a feature set; 45 of 52 is not worth a fingerprint re-key.
The cross-asset caveat prints itself when it applies.
Context that makes this worth doing at all: under the pivot-event label the MI
screen now reads "above the noise floor - a real association" - mean 4x the null
(p=0.005), strongest column 7.7x the null-max, excess 0.80% of label entropy,
against 1.3x / 1.15x / ~0.1% under the old label. The noise-floor verdict that
closed several earlier directions was a property of the OLD label.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
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AnimateDread
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32eb5c5f58 |
feat(vote): edge-over-chance currency, no-skill exclusion, checkpoint burn-in
RETRAIN-FORCING and deliberately so. Two independent fixes for the same symptom - charts that go quiet while others overtrade. 1. THE VOTE CURRENCY IS NOW EDGE OVER CHANCE, not an absolute win rate. A tier weight is a raw win rate and a raw win rate means nothing without the chance rate behind it: 30% is strong under a 14% base rate and catastrophic under 50%, yet both entered the mean as "30". That is why the threshold needed re-tuning every time the label changed - 25 was permissive at ~70% win rates under the old direction label and a near-unanimity rule at ~30% under the pivot-event one - and why one chart's 25% was never the same statement as another's. Subtracting the member's own chance rate makes the units percentage points of demonstrated edge, comparable across charts, labels and regimes. Clamped at zero: a below-chance tier is anti-informative, and contributing negatively would act on a broken model as an inverted oracle rather than discarding it. 2. A NO-SKILL MEMBER IS NOW ABSENT, NOT ABSTAINING. Measured on XTIUSD: a Perceptron collapsed to B97/S6/N3, pooled win rate 11.5% against a 14% chance rate - worse than guessing - and still voting. Three healthy members voting Sell scored -21.06/0.77 = -27.4 and cleared; with the dead one voting Buy it became (-21.06+1.44)/0.89 = -22.0 and was BLOCKED. It vetoed its own ensemble on ~95% of bars, and that WAS the chart's 3.3% coverage. Neither existing guard caught it: it IS self-ranked and its tier weights were 11-14. The fix has to remove it from the DIVISOR, not just the sum - an abstainer contributes weight by design, so zeroing only the contribution makes the dilution worse. VoteCapableWeight() already means exactly "may this member's weight sit in the denominator", so the skill test belongs there. ReconstructionWeight() and the OOS scorer's divisor move with it or the scorer certifies a vote live does not cast. The skill test reads the PREVIOUS era's measurement - gating this era's vote on this era's own outcome would be circular. 3. CHECKPOINT BURN-IN (ENSEMBLE_CHECKPOINT_MIN_ERA 20). XAUUSD deployed the checkpoint from ERA 2, XTIUSD from ERA 4, each after 69 and 65 further eras failed to beat it. Ensemble coverage measures AGREEMENT, and four models that have barely moved off their initialisation agree almost by construction - so coverage is inflated exactly when the models know least and decays as they differentiate (XAUUSD 6.6% at era 8 -> 0.4% at era 75). Since selectionScore is precision discounted by coverage, an early era outscores every mature one and the ladder freezes on it. INTENDED CONSEQUENCE: a chart whose MATURE coverage cannot clear the floor now refuses to deploy rather than shipping era-2 weights. Fewer deploys, honest ones. Burn-in eras are also kept out of g_ensCandidateEras (they could not have won, so counting them inflates the family-wise N and raises the bar for nothing) and out of g_ensErasSinceBest (or the run reaches "no better vote for N eras" with no best to beat, exhausting the escalation ladder before the first era may compete). Every pinned threshold and .stats record is in the OLD currency and is now meaningless - this forces a fresh start on its own. Nothing needs re-tuning because the threshold is DERIVED: the sweep re-picks the rung by itself. Compiled clean; NOT yet run. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> |
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AnimateDread
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d059780c22 |
fix(io): stage atomic writes to a PER-CHART temp, not a shared one
A DATA-INTEGRITY BUG, pre-existing, surfaced by the clearer failure message in
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AnimateDread
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869cd1b40c |
fix(pool): defer the atomic promotion to the timer instead of spinning on the tick
REPLACES the in-line retry from
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AnimateDread
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72dba892cc |
fix(chart): configure the vote-arrow layer with the PINNED threshold
Second instance of the same regression |
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AnimateDread
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b1c3a898aa |
fix(persist): adopt the pinned threshold on load; trim the accuracy label
THE REGRESSION, mine, from |
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AnimateDread
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ad4ae58814 |
feat(vote): exit-on-reversal boolean, pin the threshold, retry the atomic rename
THE EXIT KNOB. Exit_On_Reversal_Vote (default false) replaces the deleted Signal_ThresholdClose with one boolean: false pins the close threshold to an arithmetically unreachable 101, true pins it to the SAME threshold the entry uses - the seed at first, then the derived value, republished together whenever it moves. A second threshold was always redundant; "the bot now says the other way" is one question. It also arms CExpertSignalCustom::m_holdToBarrier, which was DEAD CODE: HoldToBarrier(bool) had no caller anywhere in the build, so the flag had been permanently false and the disabled close threshold was carrying the whole hold-to-barrier policy alone. Both halves now move together. Default stays false because the reason is statistical: the gate certifies P(label agrees | vote fired) against a label that runs to the barrier, so an early close trades something never measured. Turning it on is a different strategy, not a tightening of this one. THE PIN. The live threshold now moves only when an era's weights become the checkpoint, and freezes once g_ensDeployApproved. Every era still derives its own rung - that is how the best one is found - but the rung that TRADES belongs to the checkpoint, exactly as the weights do. Two reasons, one measured and one structural: the per-era rung moves on 6-34% of steps (the live run flapped SP500 15 -> 10 -> 15 within a minute of starting), and without the pin a later era's rung could end up applied to an earlier era's deployed model. A ladder restart releases the pin, since clearing the checkpoint clears what it pinned. The era line now prints the rung its own numbers came from, so it stays honest when that differs from the pinned one. THE ATOMIC RENAME retried zero times. Six charts share the TrainPool and AltData directories, so a publish regularly lands while a peer chart holds the destination open and FileMove returns 5004 - 27 times in one day on the live fleet. Nothing was lost (the temp keeps the new content, the old file stays intact) but the row did not update until the next publish. Now four attempts at 25ms, on the FAILURE PATH ONLY - a successful rename never sleeps - and skipped in the tester, where the contention cannot happen and Sleep would distort a pass. A rescued retry is logged, so worsening contention is visible. Retrain-neutral. Compiled clean; NOT yet run. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> |
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AnimateDread
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34f1e09372 |
feat(magic): assign the magic number once, then remember it
Expert_MagicNumber = 0 (the new default) means "draw one and write it down".
On first attach the EA picks a random magic in a distinctive band, persists it
to MQL5\Files\Warrior_<symbol>_<period>.magic, and reads that same value back on
every later start. Unique without anyone typing it, and STABLE.
Stability is the whole point. The magic is how the EA recognises its own
positions - a fresh one per start would leave every open position invisible to
the scheduled close-all, the risk-budget flatten and the journal's MAE/MFE walk:
trades still running that no code would ever manage again. So the value is
persisted before it is ever used to trade.
Stored TERMINAL-LOCAL rather than in Common\Files\Warrior_EA, on purpose: that
folder is the one wiped for a retrain, and positions outlive retrains. It also
gives two terminals on the same symbol different magics, which a chart-identity
hash could not.
Fallbacks, both of which stay stable without a file:
* tester/optimizer/forward use a magic derived from chart identity, so two
identical passes cannot differ.
* an unwritable file falls back to that same derived value, and says so.
Books occupy EVEN slots only, so one chart's short book (base+1) can never land
on another chart's long book.
WarriorOwnsMagic() now also recognises the legacy 2024/2025 pair permanently.
Without it, switching an existing chart to 0 while a position was open would
orphan that position. Every caller also matches the symbol, so claiming those
values can only reach positions on this EA's own chart.
Existing charts are untouched: MT5 stores inputs per chart, so the six live
charts keep the 2024 they already have and keep managing what they hold.
Compiled clean; NOT yet run.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
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AnimateDread
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17270ab308 |
feat(trade): two books per symbol, and delete the vote exit
Allow_Hedging (default ON, live only on a RETAIL_HEDGING account) gives the EA an independent long book and short book on its symbol: at most one long and at most one short, each opened on its own side's vote and each held to its own barrier. On a netting account, or with the input off, the original single-position path runs bit-for-bit unchanged and init says which one is live. WHY THIS INSTEAD OF A VOTE EXIT. The deploy gate certifies P(label agrees | vote fired) and the label runs to the barrier, so closing early on a reversal makes the realised outcome stop being the labelled one - the certified precision no longer describes what is traded. Opening the other side acts on the new signal and leaves the old position's certification intact, and costs no more than reversing: both pay the new side's spread, the difference is only that the existing position runs on to a barrier already measured as positive-expectancy. So Signal_ThresholdClose is DELETED rather than tuned, along with its SIGNAL_CLOSE_PRESETS enum; the threshold is pinned to an arithmetically unreachable 101 (the stock default of 100 is reachable by a weighted mean of values capped at 100). Note the two books can never both fill from one signal: CheckOpenLong and CheckOpenShort test opposite signs of the same m_direction, so at most one clears per tick. A hedge only forms when a LATER opposite vote fires - which is what keeps it from being a guaranteed-loss wash pair. The mechanism is a SelectPosition() override keyed on the active book's magic; every inherited close/trail path then operates on that book untouched. The long book keeps Expert_MagicNumber, so no existing position, journal row or risk-budget state file is re-addressed. Short book is +1. Four ownership filters had to widen from "== m_magic" to WarriorOwnsMagic(), or the short book would have been invisible to the code that must reach it: the scheduled close-all (positions and orders), the risk budget's emergency flatten, and the journal's MAE/MFE walk. WarriorOwnsMagic() is deliberately NOT gated on Allow_Hedging - turning the input off while a short-book position is open would otherwise orphan it with nothing left to close it. Risk sizing needed no change: CapRiskAmount already subtracts OpenRiskAtStops(), which counts every position regardless of magic, so the second book is sized inside what the first one left. Conservative for a hedged pair, which cannot lose both stops - the safe direction. Retrain-neutral: neither input is in BuildModelFingerprint() or ComputeDbConfigFingerprint(). Compiled clean; NOT yet run. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> |
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AnimateDread
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c6eb9085d5 |
feat(vote): derive the threshold instead of configuring it
Signal_ThresholdOpen becomes a seed. The era verdict now picks the HIGHEST
sweep rung whose vote still clears the whole deploy gate - coverage floor,
exact-binomial precision bar and two-sidedness together - computes the era's
verdict AT that rung, and publishes it to the live signal's m_threshold_open
so the bar the gate certifies is the bar the EA trades.
Measured on 619 era verdicts across all six live charts:
* every era on every symbol had at least one rung clearing the full gate.
At the fixed 25% the fleet was actually running, four of six symbols had
none, ever. The threshold, not the models, was the blocker.
* walk-forward (rung derived on era N, scored on era N+1): 10.2% coverage /
31.8% precision, against an oracle re-picking on N+1 of 10.3% / 31.7%.
Near-zero shrinkage - a measurement, not a fit. It holds because the
binding constraint is COVERAGE, a near-deterministic step function of the
vote distribution, not precision.
* vs a fixed 15% (best global value): +0.6pp precision, 3.4pp less coverage.
vs a fixed 20%: deployable on all six rather than four of six.
Selection on the highest PASSING rung, never on the best-precision rung - that
is a best-of-6 on a noisy statistic and this project has crowned noise that way
four times. The multiplicity that remains is paid for: nTried in
EnsembleSurvivesSelection is now eras x rungs. Costs nothing - all six charts
clear it by 6.5-12 sigma even forming z on effective rather than raw calls.
Also fixes, in the same path: the direction-policy gate is hoisted above the
per-rung tally so every rung is scored on the population the gate certifies.
Retrain-neutral: not in BuildModelFingerprint(), no .nnw re-keyed.
Compiled clean; NOT yet run.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
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AnimateDread
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51620fe9e0 |
fix(vote): Signal_ThresholdOpen 25 -> 15, from the sweep's own numbers
The quorum model that said 20 was wrong. It reasoned from the vote's quantisation - 4 members at tier weight ~30, so 3-of-4 agreeing gives 22.5 and PCT_20 admits it - and simultaneous agreement turns out to be rarer than a per-member coverage of ~27% implies. Measured on real rows by the threshold sweep added in the previous commit: symbol floor 15% cov/prec 20% cov/prec 25% cov/prec (active) EURUSD 6.7% 15.7 / 32.6 12.5 / 33.7 4.1 / 35.0 SP500 6.9% 9.8 / 32.7 4.0 / 34.4 X 1.0 / 34.7 X USDCAD 7.2% 18.9 / 32.2 12.8 / 32.6 4.0 / 34.7 X XAUUSD 6.7% 12.8 / 29.2 4.4 / 33.9 X 1.0 / 26.5 X XTIUSD 6.9% 11.4 / 34.3 7.8 / 35.7 1.7 / 38.4 X 15 clears the coverage floor on every symbol; 20 fails SP500 and XAUUSD; 25 fails all of them. The precision surrendered is about 2pp, because precision is nearly flat across these rungs while coverage moves 10-20x - the high thresholds were buying almost nothing for the coverage they cost. The rule this encodes is: take the cheapest rung whose coverage clears the floor, not the best precision. Precision above the bar earns nothing extra; coverage below the floor makes the era undeployable regardless. Not in BuildModelFingerprint(), so every trained .nnw survives. DOES NOT MOVE THE RUNNING FLEET. MT5 stores input values per chart in profiles\Charts\*\chart*.chr, so this only takes effect on a fresh attach; the live charts have to be changed in each one's EA properties. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> |
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AnimateDread
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b9da557e4e |
diag(gate): report what the vote would score at every threshold rung
The gate could say "coverage too low" but never "and here is what it would be one rung down", so the single parameter most responsible for a refusal was the one its own output said least about. Working it out by hand needed a model of the vote's quantisation (a weighted mean of member tier weights, so the threshold is really a quorum) and that model could not be checked: MT5 stores the input PER CHART in profiles\Charts\* \chart*.chr, so an already-attached EA ignores a changed source default - confirmed by a full close/recompile/relaunch after which the log still read "fired at vote>=25%". There was no cheap A/B available. Each era now reports coverage and precision at every PERCENTAGE_PRESETS rung from 5% to 30%, measured on the same rows the verdict just scored, marking the active rung and any rung that clears the coverage floor. It is accumulated before the live threshold test so the sweep sees every scored row, and gated by the same direction policy so its numbers are comparable with what the gate certifies. Nothing reads it to decide anything. Motivation, measured overnight across 534 eras with zero runtime errors: every symbol clears its precision bar and every symbol fails on coverage (0.0-3.3% against a ~6.7-7.2% floor), while the members stay healthy throughout at 22-27% precision against a 13-14% chance rate on 25-38% of bars. Only the aggregation fails. SP500 was DEPLOYABLE at era 5 with 7.5% coverage and sits at 0.7% by era 536 with precision unchanged - more training is proven not to help, because a 25% threshold against ~30 tier weights demands unanimity and the models diverge as they specialise. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> |
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AnimateDread
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1533365a85 |
diag(gate): the coverage refusal contradicted itself
The message I added one commit ago printed, verbatim: "Precision was 28.4% against a 39.1% bar, so the calls it DID make were NOT good enough: the vote is too selective, not too weak." Those two clauses say opposite things. Only the "NOT" was conditional; the diagnosis after the colon was hardcoded, so whenever precision missed its bar the line asserted and denied the same thing in one sentence. The two cases are opposite diagnoses and must not share a sentence: - Precision CLEARED its bar -> the calls were good and there were too few of them. The vote is too selective. - Precision MISSED its bar -> this is still not "the model is weak", because the exact-binomial floor is computed from the INDEPENDENT call count, so thin coverage inflates the very bar it is judged against. Reporting that as a second, separate failure sends a reader off to fix the model when coverage is what moved the target. Caught by reading the diagnostic's own first live firing rather than by review - the same way the two regressions before it were found. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> |
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AnimateDread
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0f756faf2a |
diag(gate): name the deployability condition that actually failed
The stage-3 refusal read "no era's combined vote ever cleared the deployability floor" and then listed all three conditions in one parenthesis - fires on a quarter of the base rate, both directions alive, precision above the reference by 2 sigma - without saying which one fired. The three have nothing in common as fixes, so the list was not a diagnosis. It cost real time to work out by hand tonight, and the answer was coverage every time. Keeps the best era's coverage, its floor and its precision bar alongside the win rate already retained, and names the failing condition. The coverage branch also states whether the calls it DID make cleared the precision bar, because "too selective" and "too weak" are opposite problems that the old message could not distinguish, and points at Signal_ThresholdOpen being a quorum rather than at the models. Cleared at both existing reset sites so a refusal can never describe an era that is no longer the best. Context: SP500 reached stage 3 at era 67 and was refused on coverage 0.5% against a 6.9% floor while its precision was 62.5% against a 61.4% bar - i.e. the vote was too selective, not too weak. Same doctrine as CTrainPoolReader::Announce's reject list. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> |
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AnimateDread
|
2e22e714c6 |
fix(pool): length-prefix the fingerprint - the cross-instrument pool was inert
STrainPoolHeader wrote its fingerprint into a FILE_BIN stream as FileWriteString(h, fingerprint + "\n") and read it back with FileReadString(h) - no length argument. In binary mode FileWriteString emits the characters raw: no length prefix, no terminator, and "\n" is just another character rather than a delimiter anything honours. The reader had nothing to stop at, over-read into the float rows that follow, and returned the fingerprint plus a few bytes of binary garbage - so `fingerprint != wantFp` could never succeed between two genuinely identical models. Verified in the bytes rather than inferred: xxd on a v1 file shows three ints then the fingerprint starting immediately at offset 12 with no count in front of it, and EURUSD/USDJPY/USDCAD all stored width 624 with byte-identical fingerprints while each one's log rejected the other two as "different model fingerprint". The StringReplace on "\n" is the tell that a delimiter was intended. Cross-asset-class peers really are incompatible and always will be - FX majors carry XA:6, indices/metals/oil carry XA:6:IDX2, giving widths 600/612/624 - which is why the reject list looked plausible and this went unread. The three FX majors were always poolable and never pooled. Length-prefixes the string, bounds-checks the count before sizing a read from it, and bumps TRAINPOOL_RECORD_VERSION 1 -> 2 so existing files are refused by the version gate with a reason instead of being misread. Also documents, without changing, why Signal_ThresholdOpen is now a unanimity rule: the vote is a weighted mean of tier weights, those fell from ~70 to ~30 with the pivot-event label, so PCT_25 went from ~36% of the reachable ceiling to ~83%. Measured: all 6 symbols clear their precision bar, 4 of 6 fail only on coverage, and coverage decays 6.8% -> 2.2% over 35 eras as the models specialise - which shrinks effN and so RAISES the deploy bar at flat precision. PCT_20 (a 3-of-4 quorum) is the indicated change but is left unmade: MT5 stores input values per chart in profiles\Charts\*\chart*.chr, so an already-attached EA ignores this default entirely - confirmed by a full close/recompile/relaunch cycle after which the log still read "fired at vote>=25%". Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> |
||
|
AnimateDread
|
994fe3899c |
feat(label): pivot-EVENT target replaces direction-to-next-pivot
The old target asked "which way is the next pivot", which every bar of a
~13-20 bar leg answers identically - so the net could not tell a fresh turn
from mid-trend and learned the prevailing direction instead. Its own
zero-skill reference showed it: chance sat at 56/44, i.e. the label WAS the
drift, and the gate's standing warning ("a model that only reproduces it has
found the drift, not an edge") applied to the target itself.
Buy now means a swing LOW commits within PIVOT_LABEL_TOLERANCE_BARS bars,
Sell a swing HIGH, Neutral no turn that close. Pivot type is read from
ZigZagBuffer[p] == Low[p], exact by construction in ZigZag.mq5. The existing
P1-final-once-P2-commits rule is kept and now also settles the NEGATIVE
verdict, so the Neutral majority is permanent rather than provisional.
Measured on a full fresh run, all 6 charts:
class balance 56/44/~0 -> 13.7/13.7/72.6 (imbalance 5.3:1)
label overlap ~31 bars -> 5 bars
independent obs 368-1086 -> 2331-7032
weights/obs 9.2-26.2 -> 1.1-4.2
coverage 100% of bars -> 17-48%
23 of 24 models fire all three classes at precision 18-32% vs 13-15%
chance; SP500's ensemble reaches DEPLOYABLE (32.3% vs a 24.0% bar).
Two bindings had to move with the label:
- The capacity deflator. m_swingLifespan fed EstimatedInSampleBars() as
raw/31, measured from the legs. Overlap is now a property of the LABEL -
one turn is callable by exactly the tolerance window - so it is the
window, not a leg measurement. Missing this would have kept every model
sized for a sixth of its real evidence.
- A dormant cold-start seed. Labels.mqh seeds the output bias toward the
dominant class above COLD_START_SEED_MIN_DOMINANCE (0.70); at 56/44 it
never armed, at 72.6% Neutral it does - writing a fixed +-3.0 against a
true prior spread of ~1.75, which would start every net predicting Neutral
~95% of the time. Now seeds the measured log-prior, zero-centred and
capped by the same guard rail the logit adjustment uses (Lin et al. 2017).
TGT:SWG1 -> TGT:PVT1:<tolerance>, with the window in the token because it is
part of the label: every .nnw is invalidated and the fleet retrains.
Depth is still gated, and now for a precise reason: the first dense layer
stays at FIRST_LAYER_MIN_WIDTH because budget = effN/(inputWidth+1) is 11.2
at input 624. Reaching the next rung needs inputWidth <= ~218, i.e. feature
pruning - not architecture.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
|
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AnimateDread
|
6adb710a79 | fix(binomial): correct tail calculation in BinomialUpperTailP and add tests for accuracy | ||
|
AnimateDread
|
0fddaeea12 | fix: correct edge floor percentage calculation and logging for model training | ||
|
AnimateDread
|
b2784b5a4d |
Enhance Feature and Topology Interfaces with Bulk Operations and Cache Management
- Added bulk read/write methods for feature caches in IFeaturesView and its implementations to optimize performance. - Introduced LabelCacheInvalidateAll method to manage label cache invalidation alongside feature cache. - Implemented PooledIndependentBars method in topology interfaces to account for additional independent observations. - Enhanced risk budget management with throttling for peak-equity updates to reduce unnecessary file operations. - Improved error handling and logging for ATR trailing stops to ensure better visibility of issues. - Updated alt-data handling to prevent unnecessary operations during testing and optimization phases. |
||
|
AnimateDread
|
ccfe5563e3 |
perf(tester,bn): no sub-second timer in the tester + BN kernels on the DLL tier
THE OPTIMIZER ("0.1% an hour per agent", 0 of 39 passes in 78 min,
12 agents): the tester fires OnTimer on SIMULATED time, so the live
chart's 500ms EventSetMillisecondTimer over a 2016-2026 pass is ~600
MILLION OnTimer calls - each walking 4x PollTraining, the vote
readout's string build, the overlay advance and the deployed census.
None of it serves an inference-only pass: training never runs, per-bar
inference is driven by OnTickHandler off the tick stream, the risk
budget re-checks in OnTick, and there is no chart to keep fresh.
StepSetTimer now arms EventSetTimer(3600) in tester/optimizer/forward
(~2,600 calls per pass) and keeps the 500ms timer for live charts.
Plus a TESTER PASS SELF-PROFILE: per-tick buckets (pre / Expert.OnTick
/ journal) and the timer total, printed once at the pass's OnDeinit -
so if a pass is still slow it names its own consumer instead of being
diagnosed from outside.
OFFLOAD (operator: "as much calculation as possible to DLL/OpenCL"):
batch norm was the ONE stage still host-side on the DLL tier - the
device path was OpenCL-only, so every sample crossed the bus twice per
BN layer and normalized in interpreted MQL5 (and every model runs
batchnorm ON). Four new exports mirror AI\Network.cl's BatchNorm*
kernels 1:1 in DOUBLE precision (closer to the host reference than
the float OpenCL kernels): forward with running stats + frozen flag,
hidden gradient with the clamp derivative, gamma/beta accumulate, and
the batch-mean apply (no weight decay, moments-before-skip ordering,
sqrt-stored v). BnDeviceEligible/EnsureBnDeviceBuffers/all four
Dispatch* now route by backend; the EXISTING in-situ self-checks
(host-vs-device on the first real sample, latch-off + host fallback on
mismatch) verify the DLL kernels exactly as they verified OpenCL ones.
batch_accum_check regression: ALL CHECKS PASSED on the rebuilt DLL.
Same deployment coupling as
|
||
|
AnimateDread
|
bd46374954 |
perf(train): DLL-side mini-batch apply + 300ms slice - the era bottleneck
"Hundreds of times slower than a regular EA" decomposed into two
multiplied factors, both measured:
1. THE OPTIMIZER STEP RAN IN INTERPRETED MQL5. The CPU tier shipped
the F4 accumulate exports with deliberately no matching apply
(WarriorCPU.h said so), so on the DLL backend - this box - every
TRAIN_BATCH_SIZE=8 batch fell to the host loop in ApplyAccumToBlock:
a per-weight MQL5 pass through CBufferDouble.At()/Update() plus four
full weight-matrix BufferRead/Write round trips. The 2026-07-26
profile had already shown the per-sample Adam step at 81% of ALL
runtime (feedForward: 8%; feature building: 0.35%) - sqrt+divide
per weight vs one multiply-add; moving it into MQL5 made it worse.
New CPU_ApplyAccumAdam / CPU_ApplyAccumMomentum: one element-wise
ParallelFor takes the batch-mean step and zeroes the accumulator
DLL-side, generic over any flat block (dense/conv/LSTM/batch-norm -
all apply paths funnel through ApplyAccumToBlock, which now tries
the DLL first, with the same one-warning failure latch as the
OpenCL fast path). Math is the shipped step to the last clamp:
sqrt-stored v, ClampDelta, AdamW decay, ClampWeight.
batch_accum_check extended (check 6) and ALL PASS: apply == host
reference at B=8/B=4, accumulator zeroed, and B=1 accumulate+apply
== the unbatched Adam kernel BIT-EXACTLY (kernel-vs-kernel, no
transcription). DLL rebuilt with the shipped /fp:fast recipe.
2. A 24% DUTY CYCLE. Train sliced 120ms per 500ms timer period
(30ms/member x4), leaving the chart thread idle 76% of the time.
Now 300ms total (75ms/member): ~60% duty, ~2.5x, click latency
bounded at ~300ms while training runs - between the fully-reactive
120 and the documented "sticky drag" 480.
DEPLOYMENT COUPLING: the new .ex5 #imports the new exports, so it will
NOT LOAD against the old WarriorCPU.dll ("cannot find function"). Copy
DirectML\WarriorCPU.dll into MQL5\Libraries (terminal closed) in the
same step as deploying the new .ex5.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
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||
|
AnimateDread
|
781ae3a702 |
perf(deinit): I/O-free chart cleanup, dead-panel purge, skip clean weight saves
The 18:23 terminal close (20260825.log) killed two of six charts inside OnDeinit: they printed "shutting down" then nothing for 5.9 s until "Abnormal termination", stranding ~700 objects each - including the one family no prefix sweep can reach, the control panel (CAppDialog names its 15 objects <numeric instance id><control>, and a re-attach mints a new id, so a killed panel is a permanent ghost; XTIUSD carried one across sessions). The stall sat in the two file writes that preceded all visible cleanup while the four sibling charts flooded the same 2013-era disk - the ~4x18MB-per-chart shutdown weight saves. Three changes: 1. OnDeinit touches no file until the chart is clean. CVoteArrowStore splits Save() into Snapshot() (the chart scan, in memory) and WriteSnapshot() (the disk half, consuming). New order: status label, vote-arrow snapshot, prefix sweep, panel destroy - all object ops - then member sidecars, final sweep, timings, and only then the visibility file, the vote-arrow write and the weight saves. 2. PurgeOrphanedPanelObjects() at OnInit: deletes numeric-prefix CAppDialog ghosts by name (6 chrome + 9 buttons), qualifying a prefix only when >=4 of OUR button names carry it, so a foreign dialog sharing stock chrome names is never touched. 3. m_netDirty: set by every net mutation (both backProp sites, both RestoreWeights sites, online learning conservatively, panel reset), cleared only on a successful Net.Save. Shutdown AND the per-bar autosave now skip the ~18MB write when the net is provably unchanged - for converged ensembles that is every save - which removes the very flood that starved the sibling charts. .stats still writes every time (small; carries the vote record and calibration). A skipped save leaves the .nnw header dtStudied stale, which is the already-handled attach-after-offline-gap case. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> |
||
|
AnimateDread
|
4e4bff51d4 |
feat(vote): backfill the ensemble win-rate record from the overlay sweep
"Vote win rate: measuring..." never resolved on a deployed chart whose .stats predate the WST7 ensemble record: g_ensCumOosTotal is fed only by the era-end combined-vote scorer (Training.mqh), and a deployed ensemble runs no further eras. The replay pass rebuilt every MEMBER's ladder (64-71% each, per the 16:12 log) but nothing ever scored the COMBINED vote, so the aggregate line sat on "measuring" while 300+ arrows drew. The overlay sweep already reconstructs the vote per bar with the live threshold and direction policy - so it now also tallies, BEFORE declustering (NMS thins arrows, not calls), each threshold-clearing bar against the inline swing-pivot label (same resolution ScoreReplayFromCache uses, same window-mismatch reason). On sweep completion Warrior_EA.mq5 harvests the tally through a consuming one-shot read and adopts it ONLY when the record is empty and the models are deployed - a training-time sweep can never pre-empt the era scorer, and a restored record always wins. The result is persisted immediately into every member's .stats. Also verified against the same log: the sweep does NOT ignore DrawUnfilteredSignals - 4986 voter bars -> ~300 arrows, all gated on the 25% open threshold. The arrow increase vs the restored set (41-312 saved) is the replay-minted ladder reading stronger (partly in-sample), plus the reconstruction deliberately not replaying order validation/session hours (tooltip says so); the backfilled record carries the same caveat and is labelled so in the log. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> |
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|
AnimateDread
|
b13a68e411 |
chore(panel): trim the live vote line to direction, magnitude, voters, verdict
Drops "peak N%", "need N%" and the "armed (bar still open)" middle verdict state - three pieces that were useful while tuning Signal_ThresholdOpen but add nothing once a chart is settled and running. m_votePeak is still tracked (nothing programmatic reads it via this line), just no longer printed. The verdict collapses back to two states: "training, not tradable yet" (undeployed) or TRADE/no trade (deployed) - fires is already forced false on a prospective vote, so "no trade" falls out for a bar that hasn't closed without a separate word for it. Compile-verified in _claude_stage: 0 errors, 0 warnings. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> |
||
|
AnimateDread
|
26fc9a1217 |
fix(replay): resolve labels inline - the prebuilt cache's window never overlapped the rescan
The 15:13 session proved the replay pass ran end-to-end on all 24 models and scored ZERO labelled bars on every one of them, while each rescan sat on ~5000 scored predictions (~2755 Buy / ~2232 Sell). The two windows never overlapped: StartLabelCachePrebuild deliberately keeps a CONVERGED model's dtStudied watermark (it gates inference recency and must not move), so the prebuild's window was the handful of bars since the last studied bar - all with uncommitted pivots, hence "label cache pre-built - Buy: 0 | Sell: 0 | Neutral: 0" on every member. The label never needed a cache. SwingPivotDirectionLabel(idx) is a pure function of the ZigZag/Close/ATR buffers the rescan itself refreshes over exactly the scoring window, and m_lastLabelLifespan == 0 is its own unresolved flag - the same finality gate the cache applies, applied directly. ScoreReplayFromCache now resolves each bar's label inline and the label-prebuild stage is deleted from the rebuild state machine outright; going through a cache built for a different window was indirection that changed the answer. Also splits the empty-result diagnostics: "no resolved labels" (a windowing/data fault) is now distinguished from "labels present, every call Neutral" (a calibration verdict). The first version reported the second message for both, which mislabelled this very bug as a calibration outcome in the same breath as reporting scored=0. Honest limitation, stated in the code too: the replay window includes bars the model trained on, so a replay-minted ladder is measured partly in-sample and will read stronger than a holdout-measured one. It is replaced by the genuine article at the next completed scoring pass; until then it is what makes a restarted deployed model able to vote at all. Compile-verified in _claude_stage: 0 errors, 0 warnings. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> |
||
|
AnimateDread
|
5aec69fe4c |
feat(vote): replay pass rebuilds a deployed model's ladder without retraining
The previous commit persisted the tier ladder, which fixes this going forward but did nothing for models whose .stats predates WST7 - they still had to retrain to mint one. They never did. Every number a converged model needs in order to vote is a pure function of weights already on disk plus labels derivable from the chart, so replay them: stage 1 build the label cache (existing chunked prebuild) stage 2 rescan history (existing chunked rescan, deployed net) stage 3 score + rank + persist (one walk over two arrays) ScoreReplayFromCache() walks m_arrowSignalCache against m_labelCacheBuy/Sell, fills the same m_oosTierFired/Hits and per-class totals pass 3 fills, and hands them to RankTiersFromOos() - deliberately feeding the existing ranker rather than reimplementing it. The shrinkage, the chance reference and the module trust weight are subtle enough that a second copy would drift, and a ladder measured by a slightly different rule would be silently incomparable with every ladder training produced. AdvanceDeployedRebuild() sequences the three stages off the timer. It has to be a sequence: stages 1 and 2 are each minutes of work draining in time-boxed slices, and stage 2's output is meaningless until stage 1 has labels to score against. The previous version ran the rescan with no labels at all, which is why it could only ever rebuild arrows and never the ladder - the thing actually blocking the vote. The result is written to .stats immediately. The failure being repaired is state that lived in memory and was never written down; recomputing it and not saving it would repeat that exactly. Also routes every rescan completion through one hook, so there is a single place that knows what a finished rescan means - republish for a manual one, score and rank for a rebuild. Compile-verified in _claude_stage: 0 errors, 0 warnings. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> |
||
|
AnimateDread
|
cb1d86e477 |
fix(vote): persist the tier ladder - a converged model was mute after every restart
THIS IS NOT A DISPLAY BUG. A deployed model could not vote, or trade, at
any point after a terminal restart, and never would have.
LiveVoteContribution() returns 0 for every call until m_tiersSelfRanked
is set - deliberately, and correctly: before RankTiersFromOos() runs,
m_pattern_0..3 hold the constructor's stock 25/50/75/100, which since the
2026-08-18 currency change is the WRONG UNIT rather than a weak opinion,
and one unranked member would drag the whole ensemble over any threshold.
But that ladder is produced ONLY by a completed pass 3, and it was never
persisted - the code comment at LiveVoteContribution says so outright.
A converged model runs no further passes. So on every restart it lost its
entire vote permanently:
LiveVoteContribution -> 0 => no live vote ("0 vote/4 flat")
ReconstructionWeight -> 0 => overlay divisor 0 ("0 had a snapshot")
=> no arrows
=> no fired bars, so g_ensCumOosTotal stays 0
=> "measuring..." forever
Every symptom reported over the last three exchanges is that one cause.
The log is unambiguous: six H4 charts resumed at era 70/71, all 24
rescans completed with ~2700 Buy / ~2200 Sell per model, and the overlay
then swept 4999 bars finding "0 had a snapshot". The calls were there;
nothing was permitted to count them.
WST7 now stores the four tier weights, the module trust weight and the
self-ranked flag beside the model. Restored only when the stored flag
says the ladder was MEASURED - a .stats written before a model's first
pass 3 holds the stock ladder, and adopting that as if measured is the
exact error the flag exists to prevent.
A .stats predating WST7 has no ladder, so existing converged models stay
silent until their next scoring pass mints one. That case now prints a
warning naming all three of its symptoms, because each one independently
looks like a different bug.
Compile-verified in _claude_stage: 0 errors, 0 warnings.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
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|
AnimateDread
|
b92e233b88 |
fix(chart): a deployed model rescans history to rebuild its vote arrows
The sidecar added in
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|
AnimateDread
|
484a9d8b0f |
fix(panel,arrows): one deploy predicate, a deployed-only readout, and persist the vote arrows
Four reported symptoms, three of them one root cause: the ensemble's certified record was session-scoped and written ONLY at pass-3 completion. A deployed ensemble runs no further eras, so every restart lost the aggregate win rate, the aggregate panel line and the overlay snapshots - and could never regenerate them, because regeneration only happens at an era end that will never come. THE SELF-CONTRADICTION. Member rows read "Live - learning from new bars" (from m_trainingComplete) while the line under them read "training, not tradable yet" (from `prospective`, which means "this number came from ProspectiveVote() rather than a real Direction() call" - what happens on any bar where every member abstains, and which says nothing whatever about training state). Both now resolve through one predicate: WarriorChartModelsDeployed(), fed by members publishing their own state on the same slot and cadence as their vote. Adds a third verdict word, "armed (bar still open)", for a deployed model on a prospective recompute - the case that used to claim it was training. DEPLOYED PANEL. Once every published model is converged the per-member rows are dropped: what ships is the aggregate vote win rate, the live vote, and the verdict. While training the rows stay - they are the only way a collapsed or lagging member is visible, since a collapsed member abstains and so is invisible in the aggregate by construction. ACCURACY NOW RESPECTS THE ENTRY THRESHOLD. The panel's "precision 65%" came from m_cumOosCorrect/m_cumOosTotal, which counts every bar a model called Buy or Sell - threshold-blind, and per-model rather than per-vote. The correct number already existed (votePrecPct: bars where |vote| >= threshold and the direction policy allows) and is now what the panel shows, with the threshold named in the text because the number is meaningless without it. VOTE ARROWS PERSIST. With DrawUnfilteredSignals off - the default - the chart shows SIG_VOTE_PREFIX arrows, and nothing saved them: CChartUI's .arrows sidecar is member-scoped and never saw that layer. New CVoteArrowStore mirrors them to a chart-keyed sidecar and restores them progressively at init, on the same budgeted non-blocking path. The header stores the open/close thresholds; a mismatch on load DISCARDS the arrows rather than redrawing a picture of a strategy no longer configured - stale arrows are worse than none, because none is visibly empty and stale is confidently wrong. Also: .stats bumped to WST7 carrying the ensemble record (guarded on threshold match, most-complete-copy-wins), and the loader's version tests collapsed from an or-chain to ">=" - the magics are ASCII 'WST1'.. 'WST7' so they are already ordered, and a missed arm in that chain reads the NEXT field's bytes into this one, which fails as plausible numbers rather than as an error. Compile-verified in _claude_stage: 0 errors, 0 warnings. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> |
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|
AnimateDread
|
ee0dc78382 |
chore(repo): move research/ and references/ out to ..\Warrior_Research
This repo now holds only EA (MQL5) sources. The Python research scripts and the third-party MQL5/PDF reference material live in a sibling workspace folder, ..\Warrior_Research\, with their own git repo (initial commit ec2214a there). Nothing in the EA depends on either folder at build or run time, and the research scripts address ..\Market Data\ and the MetaTrader Common\Files directory by absolute path, so the relocation breaks no path. EA comments that cite scripts by name (research/edge.py, research/altdata/export.py, research/test_spread.py, ...) stay accurate - only the parent folder moved. .gitignore drops the two rules that only existed for the moved trees (references/*.pdf, research/edge_rows.npy); they were carried over to the new repo. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> |
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|
AnimateDread
|
34180b8c9c |
fix(exits): never let a declined tick leave an open position unchecked
Refresh() returning false skipped the whole of Processing(), and
Processing() is where CheckClose() and CheckTrailingStop() live. So on
any tick with unusable quote history, a failed RefreshRates(), or a
period-flag mismatch, an already-open position got no exit check at all -
it rode. Invisible by construction: nothing logged, no order sent, and
next tick the position looks exactly as it should. The only trace is a
stop that should have moved and didn't.
ProtectOpenPosition() now runs the CLOSING half of Processing() on those
ticks. Only the closing half, on purpose: CheckReverse() and the
pending-order block both OPEN exposure, and opening on data just declared
unfit to trade on is the opposite of the point. Closing on an imperfect
quote reduces risk even when the quote is wrong; opening on it does not.
When it acts, it says so in the journal - a degraded-path exit should
never be silent.
Also pins the invariant at the Expert_EveryTick gate: that input
throttles how often the EA forms an OPINION, never how often it can act
on a position it already holds. Exits stay above the gate, and the
comment now says so to the next person editing it.
Pre-existing hole, not introduced by the EveryTick work in
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|
AnimateDread
|
5f7bbd5d6d |
docs(overview): trade-management enums are placement-only, and pin ordinals
Records the rule the removal exposed: validation catches an enum value that no longer exists, but not one that silently now means something else. Also drops the stale claim that SL_Mode/TP_Mode define the training target - the swing-pivot label is geometry-free. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> |
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|
AnimateDread
|
15827a6b77 |
refactor(trade-mgmt): remove all confidence-scaled trade management
Five modes went, all of them staking real risk on the model's confidence: Intelligent entry (ENTRY_INTELLIGENT), stop (SL_INTELLIGENT), target (TP_INTELLIGENT), trailing (CTrailingIntelligent) and lot size (CMoneyIntelligent's quarter-Kelly). With them, the Confidence_Source input and the CONFIDENCE_SOURCE enum, whose only job was choosing which number those five read. The reason is calibration, not correctness: the confidence magnitude is known to be miscalibrated against the label prior, so every one of these modes multiplied money by a quantity whose units were never established. The DB arm had a second, independent defect - since the tester DB guard (SignalDatabaseActive) it reads 0 in tester and optimizer but non-zero live, so any backtest of CONF_DB/CONF_BLENDED could not reproduce live trading. And what the DB produces is a filter-RANKING win rate, not a per-trade win probability. Both confidence numbers are still recorded per trade (aiConfidence / dbConfidence) and still bucketed against outcome in TradeJournalReport. Recording is what keeps the question answerable; acting on it was the part with no evidence behind it. ConfidenceBridge.mqh now carries an explicit telemetry-only rule at the top. ENUM ORDINALS PINNED. Removing a member vacated a value in four enums at once and MT5 does not validate an enum input replayed from a saved .set or a stored optimization pass. TRAILING_STRATEGY and MONEY_MANAGEMENT_STRATEGY now carry explicit values so the survivors keep the numbers they were saved as, and ValidateBarrierInputs is widened into ValidateTradeManagementInputs covering SL_Mode, TP_Mode, Entry_Multiplier, TrailingStrategy and MM_STRATEGY. Without that gate a chart saved with the Intelligent stop would feed SL_Mode = -1 into a multiplier now used verbatim, placing the stop on the wrong side of entry. RETRAIN-NEUTRAL: neither SL_Mode nor TP_Mode appears in BuildModelFingerprint() or ComputeDbConfigFingerprint() since the swing-pivot target replaced the barrier labels. No .nnw, .cfg or .db re-keys. Also drops the now-dead g_TradeRewardRiskRatio bridge, the CMoneyRiskBase::AdjustRiskAmount hook and the unsigned AIConfidence(). Compile-verified in _claude_stage: 0 errors, 0 warnings. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> |
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|
AnimateDread
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6d48fdb4cd |
perf(tester): stop agents doing chart work on deinit; enforce Expert_EveryTick
Three related changes, all aimed at work being repeated at a frequency nobody chose. 1. OnDeinit gets a tester/optimizer fast path. Everything in the live teardown exists to leave a CHART clean and a live model's state on disk. An optimization agent has neither. It was still running, on EVERY pass: a per-signal arrow-sidecar WRITE (ShutdownChartCleanup -> PersistAndClearChartSignals) plus two full chart-object scans plus a ChartRedraw. At optimization scale that is hundreds of thousands of pointless file writes per agent, against a ~4,500 ms budget MetaTrader force-terminates on - the shape of thing that stalls an agent rather than failing it. The fast path keeps MarkShutdown() and FlushTrainRun() (so a killed pass never leaves a half-written era) and still calls dbm.Deinit() and Expert.Deinit() - leaking the signal tree or a handle across passes is its own way to accumulate into a stall. The two now-unreachable !isTesterRun guards further down are folded away. 2. All four tester handlers are present and documented by WHERE THEY RUN. OnTesterInit/OnTesterPass/OnTesterDeinit run in the CONTROLLING TERMINAL once per session; only OnTester runs on the agent, per pass. OnTesterPass was missing entirely - added empty and deliberately so: it only fires for passes that shipped FrameAdd() data, which this EA never sends, and reading frames there would put per-pass work on the terminal's critical path. Declared so that adding frame-sending later fails loudly instead of silently dropping every frame. 3. Expert_EveryTick is now actually enforced. It was passed to Expert.Init() and only ever reached StartIndex() - which bar a signal READS. The whole pipeline still ran on every quote. It now gates m_signal.SetDirection() in CExpertCustom::Processing(): that call drives Direction(), which is a TRANSACTION (NN forward passes, DB rows, chart arrows, one-shot vote state), and re-running it on every tick of a 4-hour bar repeats all of it. Scoped deliberately. Everything after that line still runs per tick - CheckReverse/CheckClose/CheckTrailingStop and pending-order maintenance are risk management, and a stop that only trails at bar boundaries is a different strategy, not a faster one. The scheduled close-all in OnTick() matches a +-1 MINUTE window, so bar-gating it on H4 would step straight over the thing 100% of label timeouts already resolve against. g_riskBudget.Update() also stays at quote frequency, by design. System/NewBar.mqh becomes CNewBar, a class. The free function it replaced had zero callers and kept its watermark in a `static`: ONE watermark shared by every caller, so the first caller each tick consumed the transition and every other caller was told "no new bar" for a bar that had just opened. Per-instance state fixes that; first observation counts as new, so a fresh attach acts immediately instead of idling up to a full bar. Compile-verified in _claude_stage: 0 errors, 0 warnings. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> |
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AnimateDread
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1baa13c5b4 |
refactor(meta): remove meta-labeling entirely - RETRAIN-NEUTRAL
~2,300 lines. META had real, repeatedly measured ranking skill and ZERO
operating points that ever cleared break-even (0/350 H1 eras, 1/999 H4
pre-2-sigma, 0/8 pooled fitted points). The clinching arithmetic was edge x
width = 0.095 ATR/trade against spread 0.099 ATR/trade, and the
dose-response showed the high-conviction tail is temporally unstable -
the precision-vs-threshold slope flips sign between calib and test on 3 of
4 symbols, so no ex-ante threshold rule exists. It shipped default-off and
never gated a live entry. The self-measured tier weights are what actually
rank the vote, and all six H4 instruments converged on them alone.
RETRAIN-NEUTRAL, and that is the property that made this safe:
- The weights fingerprint emitted "|TGT:META2" or "|TGT:SWG1" from an
if/else. Every direction model already took the SWG1 arm, so
collapsing it to an unconditional append is byte-identical. No .nnw or
.cfg is orphaned or re-keyed.
- NetInputWidth() lost its "+ MetaDescWidth()" term. MetaDescWidth()
returned 0 for every direction model, so the input layer is unchanged.
- DbLegacyAiSlot()'s slot 5 was reachable only with all four Use_* NNs
off AND meta on - a config that never shipped. Every existing .db keeps
its filename.
Deleted outright: Signals/SignalMETA.mqh, Expert/Trading/MetaGate.mqh (the
directory is now empty), Expert/Training/{MetaCorpus,MetaCandidateStore,
MetaFamilies}.mqh, Tests/Test_MetaFamilies.mq5, Meta_Labeling_Design.md.
Unwound in place, the delicate part: Training.mqh carried four
IsMetaTarget() branches whose else-arm WRAPPED the direction body (pass 1
queueing, pass 2 backprop, pass 2.5 calibration, pass 3 OOS scoring). Each
wrapper is removed and the direction body promoted back to its original
nesting - the bodies were never re-indented when the wrappers were added,
so the promoted code is byte-identical to what ran before META existed.
Also gone: the ensemble verdict's meta-veto replay and its
approved/vetoed/unscored counters, the per-family/per-side OOS
decomposition arrays, the m_isTrainQueueCand parallel queue and its
lockstep shuffle, and the S2 era report.
Also removed: the CMetaGate abstraction and the live CheckOpenPosition
veto; m_gates plus AddFilter's non-voter routing and IsVotingSignal()
(META was the only non-voting child, so m_gates was always empty);
m_parentSignal/SetParentSignal (existed only to reach the root's gate);
SweepPrepare/SweepPrepareIndicator (only caller was the corpus sweep);
IsMetaTarget() from all four view interfaces and their adapters;
Use_MetaLabeling, EnableMETA, Meta_ExportDataset, m_trainTarget.
EvalShift is KEPT - HistoricalNetVote() uses it for the filtered overlay,
not just the corpus sweep; only its comment changed. The 2-output softmax
arm in NetForward.mqh is kept too: it costs nothing and is the reusable
binary-head path, now commented as unclaimed rather than as META's.
Compile-verified in _claude_stage: 0 errors, 0 warnings, matching the
pre-edit baseline.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
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AnimateDread
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ad5c2542ec |
perf(tester): skip the signal DB in tester/optimizer, drop ExportFeaturesOnly
Two removals of work that a backtest was paying for and never using. 1. SignalDatabaseActive() gates the signal DB off in tester/optimizer. A backtest opened the fingerprinted SQLite DB under FILE_COMMON - and so did every parallel optimization agent, against the same file, with the per-tick journal Update() behind them. Measured 2026-08-25 on a 12-agent SP500 H4 run: zero passes completed in 75 minutes. It bought nothing, for a reason specific to this EA's current shape: the DB's only effect on a trading decision is ApplyPatternWeight overriding a filter's module weight, and that is declined for any self-ranking filter (CExpertSignalCustom's !filter.SelfRanked() guard). The AI members self-rank once their tiers are measured, and the classic votes that DID consume the ranking are gone - so a tester run's DB was written and never read. Skipping it changes no decision. One predicate, not two inline guards: OnInit asks the question twice (InitDatabaseAndJournal, then VerifyDatabaseTransactionCycle) and a run where those disagreed would try to open a database it never initialised. The tester now takes journal.InitTrackingOnly(), so close detection, MAE/MFE and the expectancy-stop feed still run - only the SQLite half is dropped, and Update() already skipped its INSERT when there is no DB. Caveat recorded at the predicate: if a future filter consumes DB ranking WITHOUT self-ranking, this needs revisiting - a backtest would then stop reproducing live. 2. ExportFeaturesOnly and its two exporters are gone. Research-only CSV dumps (feature matrix + a hardcoded 8-symbol x 5-TF raw rates grid), superseded by the research/ python path that reads its own data. Removed the input, m_exportFeaturesOnly, the setter, both method declarations, ExportFeatureMatrix()/ExportRawRates() (111 lines in AutoTune.mqh), the OnTick early-return, and the ctor initialiser. The config-lock bypass it owned collapses to the plain tester test: `if(!inTesterOrOpt && !AcquireConfigLock())`. Shared helpers it called - ServableBars, EnsureBarCachesCapacity, ResizeBuffers, RefreshData - all have other callers and are untouched. Compile-verified in _claude_stage: 0 errors, 0 warnings, identical to the baseline taken before either edit. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> |
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AnimateDread
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e035a3076d |
feat(pool): default cross-instrument training pooling on
Use_Training_Pool gates a fully-built, fully-wired mechanism (Expert\Training\TrainingPool.mqh + the Add/Adopt/Publish call sites already in Training.mqh) that shipped false. Nothing to build - the writer/reader/atomic-file/compat-gate/age-gate/lookahead-purge were all already there, measured +2.02pp of paired skill at H4 (research/ edge.py, 2026-08-24). Flipping the default is the whole change. Compile: 0 errors, 0 warnings (stage). Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> |
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AnimateDread
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b5d34a82b4 |
feat(panel): one live vote line, no stale era count, no per-model HUD
Two chart-display fixes reported after watching a converged 4-model ensemble: the ensemble panel's trailing "(era 69, 4 models, DEPLOYING)" was frozen at whatever era the ensemble happened to deploy on, and the separate top-right HUD (one line per model, raw B/S/N + weight + era + error) was clutter once the vote itself is what matters. Root cause of the freeze: g_ensembleVoteLine is written once per era, at pass-3 completion. A deployed/converged ensemble runs no further eras (ScheduleTrainingIfNeeded's trainingComplete branch skips Train() entirely), so that line could never update again - the era count and "DEPLOYING" marker were permanent set-dressing from the deploying era, not a live reading. - EnsembleScoreCombinedVote() drops the era/DEPLOYING tail once g_ensDeployApproved - nothing left there worth freezing. - UpdateVoteReadout() (the aggregate "VOTE ..." line, previously its own top-right chart object) now writes g_liveVoteLine instead of drawing anything. Both status-label builders - PublishEnsembleStatus for the ensemble panel, PublishStatus's choke point for the solo panel - append it as one line, refreshed every tick/timer exactly as the old HUD was, so the live vote replaces the frozen era tail in the same visual slot. - RefreshVoteReadout()'s per-member loop (DisplayHudLine, one ObjectLabel per model) is deleted outright rather than folded in - the operator asked for the aggregate only, "without telling me each individual network". Follow-on dead-code removal, since DisplayHudLine was the only caller: the DispProb/DispSignal/MetaGateArmedNow/MetaHasScore/ MetaLastP/MetaLastBe/MetaApproved/MetaVetoed leg of IChartView (and its AIBaseChartView/AIBaseChartViewImpl/ExpertSignalAIBase forwards) had no other reader. The underlying data survives untouched - m_metaTelemetry is still populated live by SignalMETA.mqh, m_dispSignal still feeds ProspectiveVote - only the chart-view forwarding that existed solely to reach the deleted HUD is gone. Compile: 0 errors, 0 warnings (stage). Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> |
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AnimateDread
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12d9871650 |
chore(comments): drop two references to the deleted drift verdict
DIRECTION_INTELLIGENT and the drift verdict it fed were removed in
the step-3 demolition (
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AnimateDread
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cdc8b2d1ec | fix(enumerations): remove duplicate MARKET entry and maintain trailing strategy consistency | ||
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AnimateDread
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4ad079aaed |
fix(topology): size the network against observations, not bars
The capacity budget is stated in weights per INDEPENDENT observation and divides by the mean label lifespan to get there. It never once did: EstimatedInSampleBars() deflates via m_labelOverlap, but it is only ever called from InitNeuralNetwork, where the label cache does not exist yet (that same function sets m_labelCachePrebuilt = false a few lines below), so MeanLifespan() returned its "nothing measured" default of 1.0 at every call. Every fresh model was sized as though its labels did not overlap - over-budgeting the first dense layer by a factor of L, which is several rungs of a power-of-two ladder. The "expect overfitting, reduce the feature set or pool instruments" warning is the branch that should fire on H1 and structurally could not. Fixed at the source rather than by reordering the boot sequence (the prebuild is chunked across Train() calls and cannot complete inside init): MeasureSwingGeometry() walks the ZigZag ONCE at init and answers both questions from it - the median leg gives the window, and the leg series gives the mean label lifespan analytically. SwingPivotDirectionLabel resolves bar i when the SECOND pivot after it commits, so a bar d bars before pivot P waits d + (the leg leaving P); summed over every bar of every leg that is exactly the mean the label walk accumulates. That also closes the coherence gap the swing target opened: the window was measured with a private +/-12-bar fractal while the label aimed at ZigZag(12,5,3) pivots, so it was sized against a leg distribution the label never used. One pivot source now, the label's. Also: - ResetWeights() re-derives the shape. It rebuilt from the members a history-starved init had pinned and re-saved them - so the "let history download, then reset from the panel" advice in both fallback warnings did nothing at all. - The CAPACITY line prints the measured lifespan beside the one the topology was sized for, and warns when they differ by more than a ladder rung. That is the check that makes the estimator falsifiable. - Topology reads the view's symbol, not _Symbol (latent for pooling). - Unmeasured geometry defaults to HISTORY_BARS_FALLBACK, never 1.0: under-sizing is recoverable, over-sizing silently is not. Compile: 0 errors, 0 warnings (stage). Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> |
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AnimateDread
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ec1692f348 |
feat(mi): the screen is an alarm, not a gate
The MI suite kept its one irreplaceable job - the label-alignment lookahead scan, whose margin is priced by the headline permutation null and whose validity is proven by the positive control. Everything that judged or vetoed on top of that measurement is gone: - m_dirEvidence deploy veto deleted from all four deploy sites. The policy is that screens are priors, not gates; the family-wise selection test on held-out precision is the deploy protection, and a marginal per-bar MI test cannot veto a model that reads the window jointly (the report itself said so on every print). - Per-column CFeatureSelector deleted; BlockPermuteLabels (the null engine ScoreMiSample depends on, ragged-tail fix intact) moves to AutoTune.mqh as a free function. - Feature-lag profile deleted, with its MI_LAG_* constants and BuildMiSample's featureBarOffset; MiShiftPad no longer pads by m_historyBars. Compile: 0 errors, 0 warnings (stage). Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> |
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AnimateDread
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8f2164698b |
feat(target): delete the barrier/geometry stack - the label is the verdict
Step 3 of the swing-pivot plan, whole-hog. The swing label is now the ONE target and the era verdict is precision + recall per class against the label's own base rate - no win rate, no break-even, no expectancy, no geometry anywhere in training. DELETED - Expert/Excursion/ (4), Expert/BarrierHorizon/ (4), GeometrySweep, FirstPassageLadder, Labeling/TripleBarrier.mqh (CLabelOverlap survives in Labeling/LabelOverlap.mqh), 3 test EAs. - TripleBarrierLabel + walk, fractal label, geometry derivation/scan/ adoption, exit-policy replay, excursion MI targets, the drift verdict (DIRECTION_INTELLIGENT), the recall floor, balanced-accuracy telemetry, the barrier defines, the .cfg geometry adopt (slots kept as zeros for the positional layout), the derived-geometry live-order override. - TRAINING_TARGET input/enum: direction models are always swing; META2 re-keys the meta head onto label agreement (descriptor loses its two geometry slots). REWORKED - Labels.mqh (1795 -> ~370 lines): AdvanceSwingLabelState with FINALITY-GATED CACHING - an unresolved bar (pivot pair uncommitted) is never cached, so it can never freeze as a false Neutral; training, calibration, OOS scoring and online learning all skip unresolved bars. - SDeployVerdict: significance-only; SOosTally chance = larger directional class share; pooled gate poolability = timeframe (record v2). - Purge/embargo/declustering gaps: the measured mean label resolution lag (LabelResolutionBars), not a barrier horizon. - Pool purge key + backfill DB rows: marked at the bar the label resolved on (m_labelResolveAge), not a fabricated barrier touch. - Online learning frontier: finality, not a horizon delay. - m_bestBalancedOos -> m_bestSelectionScore, m_erasSinceBestBalanced -> m_erasSinceBest, ensemble vote outcome arrays -> label arrays. STEP 4 folded in: Entry_Multiplier / SL_Mode / TP_Mode / tradingdirection are inputs again - trade management is the tester GA's search space. Fingerprints: every direction model re-keys (TGT:SWG1 now unconditional, CUT token gone); META1 -> META2. Full retrain, as planned. Compile-verified in _claude_stage: Warrior_EA + both surviving test EAs, 0 errors, 0 warnings each. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> |
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AnimateDread
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8c945bf752 |
feat(target): swing is the default, and tau is measured, not chosen
- TrainingTarget defaults to TARGET_SWING.
- LogitAdjustTau input, preset enum and all plumbing deleted: tau is fixed
at 1.0 (the full log-prior, Menon et al.'s consistent value); the
delivered strength is capped to the head's usable logit range from the
priors the prebuild measures. The CAPPED journal line is the step-1
measurement. |LA💯BS becomes a frozen legacy fingerprint slot, so no
existing model re-keys.
- The swing label measures its own resolution lag (idx - P2, the earliest
bar P1 can be final on) into the overlap/SE machinery, capped at
SWING_SCAN_CAP_BARS instead of a barrier horizon it does not have.
- The prebuild line is target-aware: both-won, timeout and horizon-lifespan
fragments are barrier-walk facts and no longer decorate swing counts.
Compile-verified in _claude_stage: 0 errors, 0 warnings.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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