forked from animatedread/Warrior_EA
| Author | SHA1 | Message | Date | |
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AnimateDread
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844aac653a |
fix(train): the OOS final pass ran a full epoch at an undecayed rate
Capping the pass at m_etaCeiling was not enough. Measured on the first two live runs: USDCAD 0.00085 over 13,335 bars, EURUSD 0.00242 over 15,041 - a 3x spread across charts, because a chart whose plateau ladder reset recently still carries a high eta and the cap never bound. The slice turns out to be roughly HALF the data, not a tail, so one pass over it at the model's own rate is a full training epoch on a model that has already been selected and certified. That is materially more than the 'just a bit finer weights' this was asked for. OOS_FINAL_PASS_ETA_SCALE (0.25) now scales the rate. Scaling rather than shortening the pass keeps the whole slice in play - seeing the held-out bars at all is the point - while making the step proportionate to an already-selected model. USDCAD and EURUSD have already taken the unscaled pass; that is not reversible without a retrain. USDJPY has not converged yet and will get the corrected one. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> |
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AnimateDread
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3218db4a38 |
feat(train): ONE pass over the held-out slice at deploy, on the restored checkpoint
The OOS slice is the newest history and the model never trains on it, while online learning adapts to every bar resolving AFTER deployment. That leaves a gap exactly at the handover, over the most regime-relevant data there is. This closes it: select on validation, then refit on everything, which is standard practice. Placed AFTER Net.RestoreWeights() and ResetOptimizerState() and BEFORE PersistDeployedModel(), so it refines the weights that were actually SELECTED rather than whatever the run happened to end on, and what it produces is what gets written down. THE COST IS REAL AND IS NOW STATED IN THE LOG. The deploy line promises "every model reverts to the weights it held at the era whose combined vote scored best, so the ensemble that trades is exactly the one that was measured". After this pass that is no longer literally true, so the pass prints that the certified numbers belong to the PRE-PASS weights and must be quoted that way. Set EnableOosFinalPass=false to keep certified == traded exactly. Guards: * ONE-SHOT PER RUN, and the flag is set BEFORE the loop so no early return inside it can leave the pass eligible to fire twice over bars it already trained on. Reset at m_trainRunActive=true, because a retrain is a fresh selection and earns a fresh pass. * THE CONVERGED RATE, never a plateau-boosted one: m_modelEta can still carry PLATEAU_RESTART_BOOST from an escape attempt, and this is a refinement of a selected model, not another warm restart. g_eta is what backProp reads, so that is what is capped and restored. * OLDEST -> NEWEST. Series indices count backwards, so decreasing i moves forward in time - the order the bars happened in. * A failed feedForward is never followed by backProp; the output layer would still hold the previous sample's activations and the update would be this bar's label against another bar's prediction. * m_oosFinalPassCutoff records the newest bar consumed and is deliberately NOT cleared on a new run, so a later run can say plainly that its out-of-sample window reaches back into bars this model has already seen. Expect the gain to come from CURRENCY rather than finer weights: OOS precision was measured flat from era 20 while in-sample error kept falling, so the data this model can already see is exhausted. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> |
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AnimateDread
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6308a19f27 |
feat(signal): make the signal cooldown tunable, and add a hard any-direction gate
The declustering the charts needed already existed - NmsLiveAccept, per-direction run-collapse plus cross-direction resolution plus strict alternation - and it was already set to 10 bars. It could not be TUNED: SignalClusterWindow was a compile- time const, so finding the right value needed a rebuild. That is the actual gap. Now three inputs, as enum dropdowns: Signal_CooldownScope per-direction, or a hard any-direction gate on top Signal_CooldownBars SCB_OFF..SCB_50, default 10 Signal_CooldownMinutes SCM_OFF..SCM_1440, overrides bars when set Minutes resolve against the CHART period and round UP, so a cooldown asked for in wall-clock is never silently shorter than requested and survives a timeframe change. SCB_/SCM_ prefixes are deliberately unique. M15/M30/M60 are ALREADY members of NF_LOOKBACK_PRESETS, and MQL5 binds a duplicated enum member to the first-declared enum silently - the obvious names would have compiled straight into the news filter's values. THE ANY-DIRECTION GATE IS ADDITIVE, NOT A REPLACEMENT, and the first cut of this had it backwards. Measured on the live log: the current rules draw 222 arrows over 4999 bars, while a BARE 10-bar cooldown permits up to 454 - because ALTERNATION is what declutters today, not the window. Swapping the rules out would have roughly doubled the clutter it was asked to remove. Layered, it can only ever suppress more. Suppressed bars still advance the per-direction last-SEEN cursors, so a run straddling the boundary does not restart as if it were fresh. Applied at all THREE sites that must agree - live inference, OOS pass-3 scoring and the chart renderer. Their own comments say why: an arrow set that does not obey the same rule as the traded set shows calls the EA would never take. Also corrects a stale comment that called this window "display only". It is not: when it suppresses, the live path zeroes the signal outright - no arrow, no vote, no position. Training never sees it, so these cost no retrain and are correctly absent from the fingerprint. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> |
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AnimateDread
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8083a31754 |
diag(gate): move the conviction curve to the horizon that has value, and add mean-d per rung
The 5-bar conviction curve cannot answer the question it was built for. The oracle measures ~0 at 5 bars across three charts (+0.012, -0.054, +0.064), so PERFECT foresight earns nothing there and no rung can show payoff either. Every reading it produced was null by construction. It was placed at 5 bars for statistical power, before the oracle showed what that horizon is worth. Kept as a control; the hold-horizon curve is the one to read. Also adds MEAN DISTANCE-TO-PIVOT PER RUNG, which is the high-power form of the same question. Payoff falls ~0.34 ATR for every bar of distance to the pivot (fleet-pooled: d=1 +2.095, d=2 +1.743, d=3 +1.300, d=4 +0.969, d=5 +0.769, wrong calls -0.668). So a rung that selects NEARER pivots is worth more per call even at unchanged precision - and mean-d is a far tighter statistic than mean-payoff, because d spans five bars where payoff spans several ATR. That matters because it can REOPEN a lever I closed. Precision does not rise with the rung - every 15-vs-10 comparison across six charts sits below 0.71 sigma - so the threshold looked exhausted. But precision is not the only thing a threshold can select for. If conviction correlates with proximity to the pivot, raising it buys payoff without buying precision. Directional labels only: an incorrect call has no pivot and therefore no distance, and folding those in as zero would read as "this rung picks pivots that are imminent" when it means the opposite. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> |
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AnimateDread
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0d9320cc87 |
diag(gate): the ORACLE - what a perfect caller of this label would earn
The ceiling on the target, and the measurement that decides where the work goes. Same payoff arithmetic, signed by the LABEL's direction instead of the vote's, over every directionally-labelled shared bar. If a model that got EVERY pivot right still earns nothing over the holding horizon then the target carries no money and no amount of model improvement reaches any - the label, not the network, is what has to change. If the oracle earns well the target is sound and the shortfall is the model's. Those are completely different programmes and nothing so far distinguishes them. It uses no forecast, so it is not a leak: it is the value of perfect foresight OF THIS LABEL, reported as a benchmark. Nothing may trade on it. Accumulated above the voter and direction-policy filters, like the zero-skill book, because it is a property of the bars and their labels rather than of what the vote did with them. A bar with no directional label offers a perfect caller nothing to take and is skipped rather than counted as zero - the benchmark is "every call it COULD make". Motivated by the first skill-by-distance row, which already reframes the day: correct calls earn +0.75 to +1.90 ATR against a spread of 0.005-0.042, and incorrect ones cost -0.66. That puts break-even precision near 32% against a measured 33-37% - thin, but on the right side, and utterly unlike the "no payoff" reading the confounded 5-bar window suggested. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> |
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AnimateDread
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1a9b56e3b0 |
diag(label): expose bars-to-pivot - the confound the payoff test was missing
CORRECTION to what the payoff instrument was measuring. The 5-bar horizon looked like the powered test and it is confounded. SwingPivotDirectionLabel returns Buy when a swing LOW lands up to PIVOT_LABEL_TOLERANCE_BARS bars AHEAD, and says the quiet part itself: gating on where the pivot sits relative to entry "would drop exactly the bars where the turn has not finished coming to us", and how much adverse move remains before the turn "is a trade-management question". So on a CORRECT Buy call price is often still falling for d more bars. A window shorter than d measures the APPROACH, not the leg, and its negative contribution is expected on the calls that are RIGHT. The tight null at 5 bars (-0.012 +/- 0.074) is therefore not evidence of no payoff. Neither horizon is both clean and powered: 5 bars is powered and confounded, 18-19 is clean and has an SE of 0.277. (idx - P1) was computed in the label and thrown away. Now cached beside m_labelResolveAge under the same validity flag, and bucketed in the era verdict. DELIBERATELY NOT USED AS A PER-CALL HORIZON, which is the trap sitting right next to this: d exists only on bars the label found a pivot for, so a horizon that varied with d would hand correct and incorrect calls different windows and bias the comparison outright. The horizon stays fixed; d only buckets. The bucket for "the label called no pivot here" is reported by name rather than folded in, because it is the control the others are read against. Buckets 1..N condition on the label, so they describe the MECHANISM, not what a book earns. Reads: rising with d means the edge is in EARLY calls and the tolerance window is spending it - fixable by reweighting the loss, not by a new label. Flat means that hypothesis dies. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> |
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AnimateDread
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3372b82dfa |
diag(gate): the conviction curve - does payoff rise with vote magnitude?
The practical question behind "can I just trade the strongest signals" is whether payoff rises with vote magnitude. The threshold sweep already visits every rung, so the whole curve costs four arrays and no extra pass. Reported as the DRIFT-FREE statistic per rung - long plus short, both sign corrected - with the two halves alongside. The halves alone invite reading a drift-fed long side as skill, which is exactly the error the zero-skill book caught at the certified rung: an always-long book earns MORE than the vote on two of three charts. Taken at the SHORT horizon, which is the one with the power. Pooled across the three training charts the certified rung reads -0.012 +/- 0.074 ATR - a tight null, 95% interval [-0.16, +0.13], with the long/short pattern (+0.030 against -0.041) being the drift signature exactly. The hold horizon agrees and is 3.7x noisier, so the answer is not a horizon artifact. Precision is already known not to rise significantly with the rung (every 15-vs-10 comparison across six charts sits below 0.71 sigma). If payoff rises anyway that is a surprise worth having; if it does not, the two agree and the threshold lever is closed on both counts. Still gates nothing. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> |
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AnimateDread
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feaadd80a2 |
diag(gate): split the payoff by side at the horizon that can actually resolve it
The by-side test is the one that separates directional skill from drift, but at
the HOLD horizon it cannot answer: payoff overlap is the horizon itself, so an
18-bar window leaves ~65 independent observations per chart and a standard error
of 0.25-0.45 ATR against an effect that would matter at 0.1.
The 5-bar window carries ~3.8x the independent observations and roughly half the
standard error. It buys that power by risking a window that ends before the
pivot has committed - which is exactly why the horizon was widened in
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AnimateDread
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ce4f74fe2c |
diag(ensemble): measure how much the four members actually disagree
The ensemble beats its best single member by +2.2 to +6.8pp on all six charts - sign-stable across six instruments, so the ensemble is doing real work rather than diluting. How much MORE is available depends entirely on how decorrelated the members are: the variance of an m-member average scales as (1+(m-1)r)/m, so at r=0.8 four models are worth about 1.2 independent ones and at r=0.3 nearly 3. Nothing measured that, so the obvious next lever - different feature subsets per member, or a fifth architecture - could not be costed. Both force a full retrain of 24 models, which is not a price to pay on a guess. Measured on the SIGNED VOTE, which is what actually gets averaged: not accuracy, not raw confidence. Two members can agree on direction almost always and still contribute independently through magnitude. Accumulated over every SHARED row rather than fired ones - restricting to fired rows would measure agreement only where the members already agreed enough to fire, which is the sample most biased toward agreement. A member whose signed vote never varies (all abstentions, a dead tier) is SKIPPED rather than counted as r=0, which would drag the mean toward "decorrelated" using a member carrying no information at all. Reported as an effective member count, which is the honest way to say what four models are worth. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> |
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AnimateDread
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7500e08e17 |
feat(gate): the payoff number needed a zero-skill book and a by-side split
payoff-v1 reported what a call was worth and nothing to compare it against. A
positive mean R is not a finding on its own: if the instrument drifts, an
ALWAYS-LONG book earns a positive mean too, and drift is the one anomaly family
this project has found that survives cost - so the vote would be reporting the
market's own move as if it were its own.
Two comparisons, and the second is the one that decides it:
ZERO-SKILL BOOK - the same forward move accumulated with a fixed long sign over
every SHARED row, not only fired ones. Accumulated above the voter and
direction-policy filters deliberately: restricting it to bars the vote fired on
would compare the vote against a baseline the vote itself selected. Always-short
is exactly its negative, so one pass covers both.
BY SIDE - the vote's own payoff split by the direction it took, still sign
corrected, at the rung the live signal is actually trading:
both sides positive -> directional skill, it pays going either way
one positive, one negative
and roughly cancelling -> it found the drift, and the pooled mean is
saying nothing about skill
This is drift-free BY CONSTRUCTION - drift enters both sides with opposite sign
after the correction, so it cannot manufacture a two-sided positive. That is
precisely what a pooled mean cannot tell you and what no baseline subtraction
fully recovers.
The split is taken at the CHECKPOINTED rung, not this era's derived one: the
derived rung is not known until after the row loop that accumulates the split,
and the checkpointed rung is the operating point the question is actually about.
Still gates nothing.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
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AnimateDread
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98f485b901 |
fix(gate): the payoff horizon ended before the pivot it was measuring
The first cut measured payoff over SwingLifespanEstimate() bars. That is
PIVOT_LABEL_TOLERANCE_BARS - a constant of the TARGET describing how many bars
share one pivot event - and it is the wrong horizon for what a call is worth.
The label fires when a pivot lands WITHIN that window. So at that horizon the
pivot may only just have committed, and a perfectly correct call can still show
a negative forward move because the turn it predicted has not had one bar to
run. Measuring only there would understate the payoff of a signal working
exactly as designed, and could inflip its sign.
Measures two horizons and reports both:
SHORT = PIVOT_LABEL_TOLERANCE_BARS "has the pivot arrived" - a control
HOLD = that + the median ZigZag leg the pivot PLUS the leg it opens,
which is how long a trade on this
call would actually be held
Adds CTopology::SwingLegMedianBars(). It is deliberately NOT the same thing as
SwingLifespanEstimate() and the declaration says so: the lifespan is a constant
of the target and is what the effective-sample-size deflation divides by, while
the leg median is a measurement of the chart and is how long the move runs.
Conflating them is what produced the wrong horizon in the first place.
Non-const and lazily measured, because a model that adopted its .cfg never
walked the chart and would otherwise report HISTORY_BARS_FALLBACK as if it were
a measurement - the same lazy pattern DeriveHistoryBars() already uses.
Reporting both horizons is also the guard against picking one and calling it
the truth. A break-even conclusion here has already been overturned once purely
by getting a horizon wrong.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
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AnimateDread
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43c1b27654 |
feat(gate): measure what a call was WORTH, not only how often it was right
The ensemble deploy gate certifies PRECISION against a chance rate and has never known whether a correct call pays for its own spread. Every verdict this project has recorded - 33% precision against a 14% chance rate, an edge that clears its exact-binomial bar comfortably - is silent on the one question that decides whether any of it is tradeable, and the cost boundary is exactly where several earlier edges died with their precision already believed. Adds a per-row payoff measurement, taken once per ROW (a chart property, not a member one) at the same time the label is written: * forward close move over K = round(SwingLifespanEstimate()) bars, * the up and down extreme excursions over the same window, each divided by the bar's own ATR. K is deliberately the label lifespan the effective-sample-size deflation already uses, so precision and payoff describe the same window and can be read in one sentence. POLICY-FREE: no stop, no target, no trailing rule. It measures the SIGNAL, not a trade-management choice layered on top - exit shaping moves payoff around without creating any, so mixing the two would hide which was responsible. Stored unsigned by direction; the sign comes from the vote at verdict time, and a short's excursions SWAP rather than negate - negating them would report a short's worst case as a negative best case. The newest K bars of the OOS slice have no forward window and are dropped from the tally with their own denominator, never counted as a zero move: that is the leading-edge trap that made the lag profile's first run a false positive. The era verdict now prints mean R, MFE and MAE at the certified rung against the spread in the same ATR units. It GATES NOTHING - wiring a policy to an unvalidated payoff number is how a measurement becomes a decision before anyone has checked it. Build tag payoff-v1. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> |
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AnimateDread
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1e6d00e602 |
fix(vote): a resumed converged model passed the eligibility test and then abstained on every bar
Found by restarting the terminal against three charts that had just deployed - the exact scenario |
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AnimateDread
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6c2959dc32 |
feat(topology): re-derive capacity once when the training pool appears
A cold fleet start sizes every model BEFORE any chart has published a pool
file, so the first layer is budgeted as if the chart trains alone and then
pinned to .cfg. This is not a rare race - it is what happens EVERY time the
feature layout changes, because that invalidates the pool and forces a wipe.
Correcting it by hand needs a two-phase start: run the fleet to fill the pool,
stop, wipe the weights while KEEPING the pool, restart so derivation sees it.
That is not something an unattended fleet can do for itself, and getting it
wrong is silent - the models simply stay narrow.
TuneIndicatorsAndTrain now notices that the pool has appeared and re-derives
once, reusing ResetWeights() - the existing tested path that re-measures all
four sizes, rebuilds and rewrites the .cfg. No second copy of that logic.
Bounded on every axis that could make it a loop:
- once per model (the flag is set BEFORE the reset, because ResetWeights
zeroes m_eraCount and the model would otherwise re-qualify forever)
- only while era <= CAPACITY_RESIZE_MAX_ERA, so the discarded eras are worth
nothing
- only on CAPACITY_RESIZE_MIN_GROWTH real growth
- only if the recomputed width actually differs; if it does not, the check
settles itself rather than re-running the census every era
Safe against the one thing that would make it self-defeating: the derived width
is NOT part of BuildModelFingerprint, so a model that resizes does not leave
the pool it resized for.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
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AnimateDread
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00699e1af8 |
fix(chart): stale combined-vote arrows survived every wipe, because two files lived outside Warrior_EA\
Operator report: arrows labelled as restored from a previous session on a fleet training from era 0. Confirmed - all six charts restored 115-431 combined-vote arrows drawn by models that no longer exist. TWO INDEPENDENT DEFECTS, either of which alone causes it. 1. CVoteArrowStore::Discard() HAD NO CALLER. The member-scoped .arrows file is cleared by ClearPersistedChartSignals on a fresh topology. The CHART-scoped .votearrows store has an equivalent Discard(), written for exactly this, and nothing ever called it. The store is keyed on the DB config fingerprint, which does not move when a model is wiped, so it reloaded across any reset - fresh topology, panel weight reset, or a model-file wipe. A vote is a claim made by a specific set of members. If any member rebuilt from scratch this run, the whole stored history is void, so g_warriorFreshTopologyThisRun is now raised wherever a member discards weights or builds a fresh topology, and the store Discards instead of Loads. 2. TWO WARRIOR FILES LIVED OUTSIDE Warrior_EA\. .sigvis and .votearrows were written to the ROOT of Common\Files, outside the one directory that "wipe the Warrior EA files" has always meant. Two consecutive wipes this session left them standing untouched, and neither wipe was as fresh as reported. Both now live under Warrior_EA\ChartState\. A wipe that does not remove all of a program's state is not a wipe, and nothing in the log told the operator which files were missed. NOTE for anyone re-running the wipe: pre-existing WarriorVote_*.votearrows and Warrior_EA_*.sigvis in the Common\Files ROOT are orphaned by this change and should be deleted once. Build tag -> fleet-pool-v3. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> |
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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
|
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
|
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
|
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
|
b1c3a898aa |
fix(persist): adopt the pinned threshold on load; trim the accuracy label
THE REGRESSION, mine, from |
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AnimateDread
|
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
|
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
|
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
|
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
|
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
|
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 | ||
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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. |
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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> |
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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
|
26fc9a1217 |
fix(replay): resolve labels inline - the prebuilt cache's window never overlapped the rescan
The 15:13 session proved the replay pass ran end-to-end on all 24 models and scored ZERO labelled bars on every one of them, while each rescan sat on ~5000 scored predictions (~2755 Buy / ~2232 Sell). The two windows never overlapped: StartLabelCachePrebuild deliberately keeps a CONVERGED model's dtStudied watermark (it gates inference recency and must not move), so the prebuild's window was the handful of bars since the last studied bar - all with uncommitted pivots, hence "label cache pre-built - Buy: 0 | Sell: 0 | Neutral: 0" on every member. The label never needed a cache. SwingPivotDirectionLabel(idx) is a pure function of the ZigZag/Close/ATR buffers the rescan itself refreshes over exactly the scoring window, and m_lastLabelLifespan == 0 is its own unresolved flag - the same finality gate the cache applies, applied directly. ScoreReplayFromCache now resolves each bar's label inline and the label-prebuild stage is deleted from the rebuild state machine outright; going through a cache built for a different window was indirection that changed the answer. Also splits the empty-result diagnostics: "no resolved labels" (a windowing/data fault) is now distinguished from "labels present, every call Neutral" (a calibration verdict). The first version reported the second message for both, which mislabelled this very bug as a calibration outcome in the same breath as reporting scored=0. Honest limitation, stated in the code too: the replay window includes bars the model trained on, so a replay-minted ladder is measured partly in-sample and will read stronger than a holdout-measured one. It is replaced by the genuine article at the next completed scoring pass; until then it is what makes a restarted deployed model able to vote at all. Compile-verified in _claude_stage: 0 errors, 0 warnings. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> |
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AnimateDread
|
5aec69fe4c |
feat(vote): replay pass rebuilds a deployed model's ladder without retraining
The previous commit persisted the tier ladder, which fixes this going forward but did nothing for models whose .stats predates WST7 - they still had to retrain to mint one. They never did. Every number a converged model needs in order to vote is a pure function of weights already on disk plus labels derivable from the chart, so replay them: stage 1 build the label cache (existing chunked prebuild) stage 2 rescan history (existing chunked rescan, deployed net) stage 3 score + rank + persist (one walk over two arrays) ScoreReplayFromCache() walks m_arrowSignalCache against m_labelCacheBuy/Sell, fills the same m_oosTierFired/Hits and per-class totals pass 3 fills, and hands them to RankTiersFromOos() - deliberately feeding the existing ranker rather than reimplementing it. The shrinkage, the chance reference and the module trust weight are subtle enough that a second copy would drift, and a ladder measured by a slightly different rule would be silently incomparable with every ladder training produced. AdvanceDeployedRebuild() sequences the three stages off the timer. It has to be a sequence: stages 1 and 2 are each minutes of work draining in time-boxed slices, and stage 2's output is meaningless until stage 1 has labels to score against. The previous version ran the rescan with no labels at all, which is why it could only ever rebuild arrows and never the ladder - the thing actually blocking the vote. The result is written to .stats immediately. The failure being repaired is state that lived in memory and was never written down; recomputing it and not saving it would repeat that exactly. Also routes every rescan completion through one hook, so there is a single place that knows what a finished rescan means - republish for a manual one, score and rank for a rebuild. Compile-verified in _claude_stage: 0 errors, 0 warnings. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> |
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AnimateDread
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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
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b92e233b88 |
fix(chart): a deployed model rescans history to rebuild its vote arrows
The sidecar added in
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AnimateDread
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484a9d8b0f |
fix(panel,arrows): one deploy predicate, a deployed-only readout, and persist the vote arrows
Four reported symptoms, three of them one root cause: the ensemble's certified record was session-scoped and written ONLY at pass-3 completion. A deployed ensemble runs no further eras, so every restart lost the aggregate win rate, the aggregate panel line and the overlay snapshots - and could never regenerate them, because regeneration only happens at an era end that will never come. THE SELF-CONTRADICTION. Member rows read "Live - learning from new bars" (from m_trainingComplete) while the line under them read "training, not tradable yet" (from `prospective`, which means "this number came from ProspectiveVote() rather than a real Direction() call" - what happens on any bar where every member abstains, and which says nothing whatever about training state). Both now resolve through one predicate: WarriorChartModelsDeployed(), fed by members publishing their own state on the same slot and cadence as their vote. Adds a third verdict word, "armed (bar still open)", for a deployed model on a prospective recompute - the case that used to claim it was training. DEPLOYED PANEL. Once every published model is converged the per-member rows are dropped: what ships is the aggregate vote win rate, the live vote, and the verdict. While training the rows stay - they are the only way a collapsed or lagging member is visible, since a collapsed member abstains and so is invisible in the aggregate by construction. ACCURACY NOW RESPECTS THE ENTRY THRESHOLD. The panel's "precision 65%" came from m_cumOosCorrect/m_cumOosTotal, which counts every bar a model called Buy or Sell - threshold-blind, and per-model rather than per-vote. The correct number already existed (votePrecPct: bars where |vote| >= threshold and the direction policy allows) and is now what the panel shows, with the threshold named in the text because the number is meaningless without it. VOTE ARROWS PERSIST. With DrawUnfilteredSignals off - the default - the chart shows SIG_VOTE_PREFIX arrows, and nothing saved them: CChartUI's .arrows sidecar is member-scoped and never saw that layer. New CVoteArrowStore mirrors them to a chart-keyed sidecar and restores them progressively at init, on the same budgeted non-blocking path. The header stores the open/close thresholds; a mismatch on load DISCARDS the arrows rather than redrawing a picture of a strategy no longer configured - stale arrows are worse than none, because none is visibly empty and stale is confidently wrong. Also: .stats bumped to WST7 carrying the ensemble record (guarded on threshold match, most-complete-copy-wins), and the loader's version tests collapsed from an or-chain to ">=" - the magics are ASCII 'WST1'.. 'WST7' so they are already ordered, and a missed arm in that chain reads the NEXT field's bytes into this one, which fails as plausible numbers rather than as an error. Compile-verified in _claude_stage: 0 errors, 0 warnings. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> |
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AnimateDread
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15827a6b77 |
refactor(trade-mgmt): remove all confidence-scaled trade management
Five modes went, all of them staking real risk on the model's confidence: Intelligent entry (ENTRY_INTELLIGENT), stop (SL_INTELLIGENT), target (TP_INTELLIGENT), trailing (CTrailingIntelligent) and lot size (CMoneyIntelligent's quarter-Kelly). With them, the Confidence_Source input and the CONFIDENCE_SOURCE enum, whose only job was choosing which number those five read. The reason is calibration, not correctness: the confidence magnitude is known to be miscalibrated against the label prior, so every one of these modes multiplied money by a quantity whose units were never established. The DB arm had a second, independent defect - since the tester DB guard (SignalDatabaseActive) it reads 0 in tester and optimizer but non-zero live, so any backtest of CONF_DB/CONF_BLENDED could not reproduce live trading. And what the DB produces is a filter-RANKING win rate, not a per-trade win probability. Both confidence numbers are still recorded per trade (aiConfidence / dbConfidence) and still bucketed against outcome in TradeJournalReport. Recording is what keeps the question answerable; acting on it was the part with no evidence behind it. ConfidenceBridge.mqh now carries an explicit telemetry-only rule at the top. ENUM ORDINALS PINNED. Removing a member vacated a value in four enums at once and MT5 does not validate an enum input replayed from a saved .set or a stored optimization pass. TRAILING_STRATEGY and MONEY_MANAGEMENT_STRATEGY now carry explicit values so the survivors keep the numbers they were saved as, and ValidateBarrierInputs is widened into ValidateTradeManagementInputs covering SL_Mode, TP_Mode, Entry_Multiplier, TrailingStrategy and MM_STRATEGY. Without that gate a chart saved with the Intelligent stop would feed SL_Mode = -1 into a multiplier now used verbatim, placing the stop on the wrong side of entry. RETRAIN-NEUTRAL: neither SL_Mode nor TP_Mode appears in BuildModelFingerprint() or ComputeDbConfigFingerprint() since the swing-pivot target replaced the barrier labels. No .nnw, .cfg or .db re-keys. Also drops the now-dead g_TradeRewardRiskRatio bridge, the CMoneyRiskBase::AdjustRiskAmount hook and the unsigned AIConfidence(). Compile-verified in _claude_stage: 0 errors, 0 warnings. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> |
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AnimateDread
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1baa13c5b4 |
refactor(meta): remove meta-labeling entirely - RETRAIN-NEUTRAL
~2,300 lines. META had real, repeatedly measured ranking skill and ZERO
operating points that ever cleared break-even (0/350 H1 eras, 1/999 H4
pre-2-sigma, 0/8 pooled fitted points). The clinching arithmetic was edge x
width = 0.095 ATR/trade against spread 0.099 ATR/trade, and the
dose-response showed the high-conviction tail is temporally unstable -
the precision-vs-threshold slope flips sign between calib and test on 3 of
4 symbols, so no ex-ante threshold rule exists. It shipped default-off and
never gated a live entry. The self-measured tier weights are what actually
rank the vote, and all six H4 instruments converged on them alone.
RETRAIN-NEUTRAL, and that is the property that made this safe:
- The weights fingerprint emitted "|TGT:META2" or "|TGT:SWG1" from an
if/else. Every direction model already took the SWG1 arm, so
collapsing it to an unconditional append is byte-identical. No .nnw or
.cfg is orphaned or re-keyed.
- NetInputWidth() lost its "+ MetaDescWidth()" term. MetaDescWidth()
returned 0 for every direction model, so the input layer is unchanged.
- DbLegacyAiSlot()'s slot 5 was reachable only with all four Use_* NNs
off AND meta on - a config that never shipped. Every existing .db keeps
its filename.
Deleted outright: Signals/SignalMETA.mqh, Expert/Trading/MetaGate.mqh (the
directory is now empty), Expert/Training/{MetaCorpus,MetaCandidateStore,
MetaFamilies}.mqh, Tests/Test_MetaFamilies.mq5, Meta_Labeling_Design.md.
Unwound in place, the delicate part: Training.mqh carried four
IsMetaTarget() branches whose else-arm WRAPPED the direction body (pass 1
queueing, pass 2 backprop, pass 2.5 calibration, pass 3 OOS scoring). Each
wrapper is removed and the direction body promoted back to its original
nesting - the bodies were never re-indented when the wrappers were added,
so the promoted code is byte-identical to what ran before META existed.
Also gone: the ensemble verdict's meta-veto replay and its
approved/vetoed/unscored counters, the per-family/per-side OOS
decomposition arrays, the m_isTrainQueueCand parallel queue and its
lockstep shuffle, and the S2 era report.
Also removed: the CMetaGate abstraction and the live CheckOpenPosition
veto; m_gates plus AddFilter's non-voter routing and IsVotingSignal()
(META was the only non-voting child, so m_gates was always empty);
m_parentSignal/SetParentSignal (existed only to reach the root's gate);
SweepPrepare/SweepPrepareIndicator (only caller was the corpus sweep);
IsMetaTarget() from all four view interfaces and their adapters;
Use_MetaLabeling, EnableMETA, Meta_ExportDataset, m_trainTarget.
EvalShift is KEPT - HistoricalNetVote() uses it for the filtered overlay,
not just the corpus sweep; only its comment changed. The 2-output softmax
arm in NetForward.mqh is kept too: it costs nothing and is the reusable
binary-head path, now commented as unclaimed rather than as META's.
Compile-verified in _claude_stage: 0 errors, 0 warnings, matching the
pre-edit baseline.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
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AnimateDread
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ad5c2542ec |
perf(tester): skip the signal DB in tester/optimizer, drop ExportFeaturesOnly
Two removals of work that a backtest was paying for and never using. 1. SignalDatabaseActive() gates the signal DB off in tester/optimizer. A backtest opened the fingerprinted SQLite DB under FILE_COMMON - and so did every parallel optimization agent, against the same file, with the per-tick journal Update() behind them. Measured 2026-08-25 on a 12-agent SP500 H4 run: zero passes completed in 75 minutes. It bought nothing, for a reason specific to this EA's current shape: the DB's only effect on a trading decision is ApplyPatternWeight overriding a filter's module weight, and that is declined for any self-ranking filter (CExpertSignalCustom's !filter.SelfRanked() guard). The AI members self-rank once their tiers are measured, and the classic votes that DID consume the ranking are gone - so a tester run's DB was written and never read. Skipping it changes no decision. One predicate, not two inline guards: OnInit asks the question twice (InitDatabaseAndJournal, then VerifyDatabaseTransactionCycle) and a run where those disagreed would try to open a database it never initialised. The tester now takes journal.InitTrackingOnly(), so close detection, MAE/MFE and the expectancy-stop feed still run - only the SQLite half is dropped, and Update() already skipped its INSERT when there is no DB. Caveat recorded at the predicate: if a future filter consumes DB ranking WITHOUT self-ranking, this needs revisiting - a backtest would then stop reproducing live. 2. ExportFeaturesOnly and its two exporters are gone. Research-only CSV dumps (feature matrix + a hardcoded 8-symbol x 5-TF raw rates grid), superseded by the research/ python path that reads its own data. Removed the input, m_exportFeaturesOnly, the setter, both method declarations, ExportFeatureMatrix()/ExportRawRates() (111 lines in AutoTune.mqh), the OnTick early-return, and the ctor initialiser. The config-lock bypass it owned collapses to the plain tester test: `if(!inTesterOrOpt && !AcquireConfigLock())`. Shared helpers it called - ServableBars, EnsureBarCachesCapacity, ResizeBuffers, RefreshData - all have other callers and are untouched. Compile-verified in _claude_stage: 0 errors, 0 warnings, identical to the baseline taken before either edit. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> |
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AnimateDread
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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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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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AnimateDread
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95503c2101 |
fix(target): pivot finality is an event, not a waiting period
Operator's observation, verified against Examples/ZigZag.mq5's selection loop: the only erasures it performs are ZigZagBuffer[last_high_pos] while hunting a bottom and ZigZagBuffer[last_low_pos] while hunting a peak. A pivot therefore leaves the erasable slot permanently the moment the OPPOSITE pivot is committed, and can never move again - the opposite pivot does not itself need to be final. SwingPivotDirectionLabel now waits for that event instead of for m_swingConfirmationBars. The bar aims at P1, so it becomes trainable once P2 exists; pivots alternate by construction, so P2 is the next non-zero bar and needs no type test. Until then the label is not knowable and the bar is Neutral. Exact rather than a guess, and it removes the need to measure a repaint-lag distribution at all. SwingConfirmationBars keeps its other uses; it is no longer this target's lookahead control. Compile-verified in the staging copy: 0 errors, 0 warnings. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> |