forked from animatedread/Warrior_EA
| Author | SHA1 | Message | Date | |
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AnimateDread
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320f13253f |
feat: first-passage ladder + expectancy scan - price every geometry, not just the chosen one
Corrects the premise of the previous plan. Break-even is NOT a ceiling. If the model shifts the win probability on the bars it selects from p0 = m/(m+k) to p0 + d, then EV = (p0+d)*k - (1-p0-d)*m = d*(k+m) because p0*k - (1-p0)*m is zero by construction. The stop:target RATIO is expectancy-neutral - a punishing break-even is exactly repaid by the payoff - and only the real edge d and the TOTAL WIDTH (k+m) move EV. Width matters because the spread is charged once per trade however wide the barriers are, so a narrow barrier spends much of its own range on costs. DeriveBarrierGeometry's own comment already said the ratio buys nothing; the objective just never followed from it. Blocker this had to solve first: m_excUpCache/m_excDownCache hold only MAXIMUM travel each way, and a maximum cannot say which side was reached FIRST - so any geometry other than the walked one was undecidable on precisely the bars where both barriers were touched, ~28% of the sample. - BARRIER_LADDER: per bar, the first-touch AGE for 8 travel distances in each direction, filled during the walk the labels already run. Cursors keep it O(1) amortised per walked bar rather than 16 comparisons. Levels are travel FROM ENTRY, not barrier prices, so one ladder serves both directions and the spread is applied analytically when a level converts back to an SL/TP multiple - storing prices would need four ladders and bake today's spread into the cache. Sized, invalidated and validity-gated with the label caches. - ReportGeometryExpectancyScan: every ladder pair priced exactly off that cache - width in ATR and in SPREADS (cost efficiency, knowable without knowing d), break-even, both base rates, the share of bars resolved inside the horizon, and EV per unit of edge. Compares the widest resolvable pair against the quantile rule's pick. MEASUREMENT ONLY - the quantile rule still chooses. Nothing here can measure d, and width buys nothing if the wider target is less predictable. Base rates are printed beside each break-even because a persistent gap is DRIFT and must not be credited to the model. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> |
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AnimateDread
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f656914d62 |
fix: the panel presented a chance win rate as skill
"Buy/Sell calls correct: 65% (unseen data, lifetime)" is a WIN RATE - m_cumOosCorrect advances on oTradeWon, did the implied trade reach its target before its stop - not label agreement. A win rate means nothing without the barrier that produced it. With the measured geometry a trade risks slMult*ATR to make tpMult*ATR, so under a driftless walk ANY directional call wins slMult/(slMult+tpMult) of the time for free. On the shipped 3.33/1.62 pair that is 67.3%, and the empirical long-win base rate on this window is ~65.7%. All four topologies read 65%: at chance, and below break-even, while the panel announced "65% correct". Four models with completely different trade counts agreeing on one number was the tell - a win rate fixed by the geometry rather than produced by the network. The deploy gate already benchmarks against this null (chancePrecPct); the panel did not, and the panel is what a buyer reads. The line now carries its own break-even and the risk/reward that sets it, so the number can never again be read as edge on its own. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> |
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AnimateDread
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ba13eefecc |
fix: a resumed model cached a cold ATR as permanent, so it never trained
BufferTempData cached EVERY failure - m_featureCacheHasValue[idx]=true with m_featureCacheValid[idx]=false - and the cache never re-tries a miss. So a single feature read taken before the terminal had finished calculating the indicator buffers marked those bars unusable for the rest of the process, even though the data arrived milliseconds later. MT5 fills an indicator's buffers asynchronously after the handle is created, and a cold ATR returns 0 for EVERY index, not just its warm-up tail. BufferTempDataCompute rejects a bar with no ATR (correctly - the price features would be meaningless), so the whole window failed, and the whole cache was poisoned. Only resumed models were hit, because only they read features that early. Topology.mqh sets m_warmupPassesRemaining = netLoaded ? 0 : 3: a fresh start sits through three separately-scheduled Train() calls before anything touches a feature, which is exactly what those passes are for. A resumed one skips them and TuneIndicatorsAndTrain drives StartLabelCachePrebuild and the MI report from the first chart event. Its rationale - "a restart already has a proven-synced history" - holds for HISTORY and not for INDICATORS, which are recreated every process start. Downstream: BuildFeatureWindow failed on every bar of every era, so add_loop never went true, so pass 2, pass 3, the era counter and the checkpoint were all skipped and pass 1 swept 0->100% forever. The "0 samples" MI report line at startup was the same failure, four seconds earlier, already visible in the log. - a miss is now cached only when it is PERMANENT; the two "not ready yet" guards mark m_featureFailTransient and are recomputed on the next visit. Steady-state cost is ~ind_Periods bars per era, not 54k. - an era that discards itself now drops the feature cache before restarting, so any remaining cause of this state self-heals instead of looping. Deleting the .nnw "fixed" this only by turning the model back into a fresh one. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> |
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AnimateDread
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464a0fe19d |
diag: an era that discards itself now says so instead of scanning forever
add_loop is exactly "at least one bar produced a usable feature
window". When it stays false, pass 2, pass 3, the era counter, the
checkpoint and every log line in the era-end block are ALL skipped:
Train() returns having done nothing, m_eraResumePending is still false,
and the next call restarts the SAME era from bar 0. That is an
infinite 0->100% "scan" loop that prints absolutely nothing - the only
remaining silent restart path in Train(), and it matches the reported
symptom exactly.
Pass 1 now counts usable vs unusable windows and reports at the pass
boundary, which demonstrably executes:
- total failure routes through ReportTrainStall (already capped at
one line a minute, and carries the run-state flags) naming the
counts, the required window width and the bar count
- success prints how long the scan took and how many samples it
handed to pass 2, but only once the era has passed 10s - a fast
era stays as quiet as before, a slow one distinguishes "advancing"
from "sweeping the same bars forever"
A PARTIAL failure is normal and deliberately does not shout: pass 1
walks oldest-to-newest and the deepest bars predate the indicators'
warm-up, so those windows fail and are cached as misses.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
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AnimateDread
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1a5157befc |
fix: training could only advance one 120ms chunk per bar
ScheduleTrainingIfNeeded() armed the next Train() call only when dtStudied < lastBarDate. That watermark test is right for a CONVERGED model - one inference refresh per new bar - and wrong for a training run, because Train() is chunked: it does ~120ms of work and yields, needing thousands of calls to finish one era, and every one of those calls has to be armed from there. dtStudied is two incompatible things. Train() sets it to the training WINDOW START (~2008); FinalizeTrainRun() sets it to the last bar SCANNED (~now). So the moment any run finalized, the scheduler went silent until the next candle closed. On H1 that is one chunk per hour. The symptom was indistinguishable from a hang: no era lines, no heartbeats, not one of the six instrumented stall branches - because Train() was not being CALLED. The TRAIN STALL line that caught it reported runActive=Y only because m_trainRunActive had been set microseconds earlier in that same call, and eraResume=N proved no era was in flight. Two log bursts, 28 minutes apart, exactly one H1 bar. Before |
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AnimateDread
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3855d4666a |
diag: the heartbeat could be outrun by the condition it watched for
It fired only on 4096-item boundaries once an era had already run 60s. Those boundaries are all crossed in the first few chunks of pass 1, so an era that became slow AFTER them printed nothing at all - which is precisely what happened: 20 minutes, four pegged cores, zero heartbeats. I read that silence as "the era loop is never reached" and went looking for a wedge above it. The silence may simply have meant "past the last boundary". A diagnostic whose trigger can be outrun by the condition it watches for is worse than no diagnostic, because it produces confident wrong conclusions. Now time-gated: checked every 256 items (the mask only keeps GetTickCount off the hot path), prints when the era has run >60s and >30s since the last line, up to 12 per era. Progress/phase for the panel is still published on every call, before any gate. Both build variants compile 0 errors, 0 warnings. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> |
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AnimateDread
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b461844767 |
fix: prebuild and era sized different windows; diag: Train() names its branch
TWO things, one incident. 1) THE BUG I SHIPPED IN |
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AnimateDread
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783fd9e7a6 |
fix: the panel showed "100%" for the whole of pass 1
The simple panel derived its percentage from pass 2's counters: (m_isTrainCursor+1) / max(m_isTrainQueueCount,1). During pass 1 those are 0 and 0, so the expression is (0+1)/max(0,1) = 100%. An era spends its first pass scanning ~38k bars - minutes of work - and the panel reported that phase as finished the entire time. Observed by the user as "started learning at 100% of their era and are stuck there", and it actively misled the diagnosis: the one number on screen said the opposite of what was happening. The UI cannot fix this on its own - it can see pass 2's counters but has no way to know which pass owns them. So each pass now PUBLISHES its own progress and a short phase name through TrainHeartbeat (which every pass already calls per item), and the panel just displays them: "learning (era 45, scan 34%)". Published before the heartbeat's 4096-item journal gate, so the panel updates continuously while the journal stays quiet. Both build variants compile 0 errors, 0 warnings. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> |
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AnimateDread
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694b75686e |
diag: slow eras must explain themselves - heartbeat + era time split + pass-1 paint
The 23:42 restart left all four charts grinding ~25x slower than the 18:01 baseline (era lines in 86 seconds there; 20+ minutes of nothing here), and NOTHING could say why from outside: pass 1 logs nothing, its status paint sat inside the !wouldQueue branch so the IS sweep - 80% of the pass, processed FIRST - painted nothing either, the VPS has no debugger for a thread stack, and the hourly new-bar cache invalidation cancels and restarts an unfinished era, so a slow era can stay invisible FOREVER. Externals gave: four chart threads at ~95% pure user-mode compute, DLL pool idle, no file writes. That narrows it to "MQL5-side per-item work in the era passes" and no further. So training now explains itself: - TrainHeartbeat: one line per 4096 processed items, only after an era has already run 60s, at most 6 lines per era - a healthy era stays exactly as quiet as before. Reports position and the cumulative split: feature-window builds vs net forward/backprop vs everything else. Hooked into all three passes. - The era summary line gains "| ERA TOOK Ns (feature windows X, net fwd/back Y, other Z)" whenever an era exceeded 120s. - Pass 1 paints its progress for QUEUED bars too, not just the OOS slice, so the panel shows "learning (era N)" instead of sitting on the idle writer's "Getting ready..." for the entire IS sweep. The label is throttled internally; painting per bar costs nothing. Both build variants compile 0 errors, 0 warnings. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> |
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AnimateDread
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0c85c54a5b |
fix: a restart no longer loses the measured geometry or the training window
Terminal restart, 22:25: all four resumed models sat on empty windows with enum 2:6 barriers. Three interlocking causes, all visible in one log excerpt: 1) THE PRE-SCAN WINDOW WAS SIZED BY THE SAVED WATERMARK. A resumed model's dtStudied sits at its last studied bar, so Bars(dtStudied, now) ~ 0 and the resumed-model MI pre-scan built a zero-bar "complete" label cache - logged as "Buy: 0 | Sell: 0 | Neutral: 0". Train()'s own era start RESETS dtStudied to the training-window rule before computing its window; the pre-scan did not. The rule is now factored into TrainWindowStart() and both use it. The scan also refuses to arm before SERIES_SYNCHRONIZED (it ran in the same second as OnInit), and deployed models keep their watermark - for them it gates inference recency, not a training window. 2) THE HORIZON LATCHED ON AN INDICATOR WARM-UP. ComputeBarrierHorizonBars ran against a ZigZag with 0 calculated legs, fell back, and EnsureBarrierHorizon latched fallback(32) x slMult x tpMult = 384 for the process lifetime. A leg-starved horizon is now PROVISIONAL: re-resolved on the next rebuild, the label cache wiped if it moved (labels from two horizons answer different questions), and the geometry deriver refuses to run from it - a pair derived over a warm-up window would get PINNED. 3) THE DERIVED GEOMETRY WAS NEVER PERSISTED. The .cfg is written at model creation and at weights-reset - both BEFORE era 0 derives - so the measured pair lived only in memory: every restart read back zeros, adopted nothing, fell back to the enum barriers, and the era-0-only gate meant a resumed model could NEVER re-derive. A full day of training on 3.33/1.62 resumed as 2:6. Now: the settled pair is pinned to the .cfg the moment derivation completes (one-shot, atomic write), and the derive gate accepts any model with no pinned pair, not just era 0 - mid-run stability is carried by m_geometryDerived itself, which never allows a second derivation. Both build variants compile 0 errors, 0 warnings. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> |
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AnimateDread
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199726f651 |
fix: a one-sided era can no longer become the best checkpoint
Measured on HYBRID, era 29 of the first win-scored run: the model collapsed
to always-Buy and was crowned "new best selection score 67.1%". Under
win-based scoring that is not a coincidence - the always-call-the-drift-side
model IS the chance reference, so it scores exactly chance (P(winLong) ~ 67%
on SP500), while every honest two-sided era scores 63-66% because shorts win
less often against the drift. Raw score ranking therefore actively prefers
the degenerate model, every regression restores back to it, and live NMS
collapses its near-constant signal to ~25 trades per era - observed as
"hybrid barely trades".
bothSidesLive already blocked one-sided eras from DEPLOYING (tradeableOK,
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AnimateDread
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9a7c37f334 |
fix: live trades now use the geometry the gate certifies; perf: BN kernels
Three changes, one theme: the trade placed, the trade graded, and the trade computed are now the same trade. 1) GEOMETRY WIRE (correctness, the ranked #1 open issue). The measured barrier pair reached the LABELS only - OpenParams still placed orders at the enum geometry (2*ATR/6*ATR), so the deploy gate certified "reaches 1.62*ATR before 3.33*ATR above break-even" about trades the EA never placed. Published via g_DerivedSlAtrMult/g_DerivedTpAtrMult (ConfidenceBridge, same same-tick contract as the confidence globals, because OpenParams runs on the root signal which has no pointer to the AI filter). Two writers: DeriveBarrierGeometry at era 0, and the .cfg adoption a deployed model takes. Overrides both legs and both Intelligent modes - the certificate is exact or it is nothing. TP is ATR-anchored like the label, NOT risk-relative, so a floor-widened stop cannot reshape the certified target. 2) BATCH NORM RUNS DEVICE-SIDE ON OPENCL. Four kernels in Network.cl - forward, hidden gradient, gamma/beta accumulate, gamma/beta apply - each a line-for-line transcription of the host implementation (NormalizeHost / HiddenGradHost / StepGammaBeta) including every NaN guard, clamp, and the exact moment-write ordering. The host copies remain the runtime for the DLL and pure-MQL5 tiers and the reference the kernels must match. Because this box has no OpenCL platform, the safety story is layered: - shim validation: kernels compiled as C and driven against a fp64 host transcription over NaN-poisoned stats, NaN gamma, over-clamp inputs, the frozen path, both optimizers, 3 batches - ALL PASS, worst normalized diff 0.132 vs tolerance 1.0 - in-situ self-check: each kernel is compared against its host twin ON FIRST USE on the real device (SelfCheckBn*), covering what the shim cannot - arg indices and buffer bindings. Any disagreement resyncs from the good copy, latches all BN kernels off process-wide, and training continues host-side. A transcription bug costs a warning and some speed, never a poisoned .nnw. - sync discipline: BatchOptions is now a CBufferDouble with explicit authority tracking (m_bnDeviceAuthoritative). Checkpoints/saves pull read-only; restores/loads/resets push; a mid-batch handover drains the device gamma/beta accumulator into the host arrays so no sample is lost. 3) SMALL FIXES. Apply-kernel build failure now latches the dispatch path at init (one warning instead of warning + failed Execute). Build tag bumped to win-scoring-gpu-v1 - first tag change since expectancy-stop-v1 despite five binary-changing commits. Both build variants compile 0 errors, 0 warnings. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> |
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AnimateDread
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5cef0947f4 |
fix: the deploy gate was benchmarking a win rate against a label frequency
The gate rests on an invariant stated at ExpertSignalAIBase.mqh:199 - under a
driftless walk P(touch +k before -m) is m/(m+k), and break-even for a k:m trade
is ALSO m/(m+k), so "beats chance" and "is profitable" are the same test.
That invariant needs reward >= risk, and the measured geometry no longer
satisfies it. With target 1.62*ATR and stop 3.33*ATR, break-even is 67.3%, but
both-won bars were stripped out of Buy and Sell so the label base rate read
37.5%. chancePrecPct is max(BuyTotal,SellTotal)/bars, so the gate was clearing
models nearly 30pp short of break-even: 42% "directional precision" is +4 sigma
against 37.5% and loses money on every single trade. Live since
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AnimateDread
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ce5265488e |
fix: both-won bars were labelled "do not trade" - resolve by first touch
Removing the min-reward:risk raise let the MEASURED geometry come back with the target NEARER than the stop (SP500 H1: target 1.62*ATR at q50 of favourable, stop 3.33*ATR at q75 of adverse). That reopened a branch the code called unreachable: price can reach +target and -target inside one horizon, winning in BOTH directions, and those bars fell through to Neutral. Neutral has only three producers, both-lost is unreachable (you cannot touch -3.33 without crossing -1.62 first, which wins the short), and timeouts logged at 1.0% of Neutral - so ~27% of ALL bars were being handed to the model as the abstain class when a trade either way would have collected its target. The cleanest positives in the sample, labelled "do not trade", while the fitted confidence threshold was being asked to find selectivity in what was left. Resolved by FIRST TOUCH: the target reached earlier is the trade that would have closed first. Same forward window, no extra lookahead. Same-bar ties stay Neutral - OHLC cannot order two touches, and unlike an intrabar stop tie there is no pessimistic side to fall to, so a guess would inject a coin-flip direction into the target. Also: - count both-won and its same-bar tie subset in the prebuild line, so the share is measured rather than inferred from arithmetic on a log line - scope the timeout counter to IS, matching the tally it is reported as a percentage OF; it was incremented over the whole scan and divided by an in-sample denominator - clear m_lastBarrierTimedOut at the top of the walk with the excursions, not at the bottom - the two early returns published the previous bar's verdict - mark the pass-1 label line PROVISIONAL. It prints the enum fallback because geometry can only be derived from excursions that do not exist yet, and it reads exactly like a config change that failed to take effect FORCES RETRAIN. Both build variants compile 0 errors, 0 warnings. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> |
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AnimateDread
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19dfb91108 |
feat: fitted directional confidence threshold - selectivity gets a mechanism
The training loss and the selection metric wanted different things and only the second one knew it. Logit-adjusted cross-entropy has no term for "how often should I trade", so the head calls a direction on 87-91% of bars. The selection metric is precision x coverage credit, saturating at the coverage floor - above the floor extra calls earn NOTHING and only precision counts. So selection wanted few good calls, the loss produced many mediocre ones, and all selection could do was pick the least-bad era out of what it was handed. Nothing pushed the model toward selectivity. This gives the decision RULE the policy instead of distorting the loss (which is estimating class probabilities correctly, and a probability estimate should not be bent to encode a trading policy - Elkan 2001: estimate, then choose the operating point separately). AdjustedSignalFromSoftmax now abstains unless the winning direction's softmax margin over its best rival clears a fitted threshold. Margin, not the winning probability: the latter moves with overall calibration rather than with how close the decision actually was. Fitted on IS, applied to OOS and live. Pass 2 already forward-passes every IS sample, so the margin histogram is harvested there for free (primary occurrences only, so the oversampled replay queue cannot skew the operating point); the fit runs at the end of pass 2, BEFORE pass 3, so the deploy gate grades the thresholded model on bars the threshold never saw. Fitting on pass 3's own predictions would be choosing the operating point on the data being graded - the best-of-N error corrected in five other places here. Objective: maximise IS directional precision subject to still clearing the SAME coverage floor the deploy gate uses (base rate x 0.25, re-derived locally so the two cannot drift apart). Swept top-down in one pass; ties go to the LOWER threshold, since equal precision for less coverage is strictly worse. Under DIR_CONF_MIN_FIT_CALLS (200) it runs unthresholded rather than on a guess. The threshold is part of the MODEL, not the run: captured with Net.CaptureWeights(), restored with the weights at both restore sites, and appended to the .cfg under the same length-guard convention so a deployed model reloads at the operating point its gate actually cleared. A pre-2026-08-09 .cfg reads 0.0, which is exactly the behaviour it was trained under. Per-era line now prints "@margin>=X.XX" next to coverage, so a coverage drop can be attributed to the operating point rather than guessed at. Both build variants compile 0 errors / 0 warnings. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> |
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AnimateDread
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217b9bc9bf |
feat: remove Min_Risk_Reward_Ratio - a guess was overriding a measurement
The barrier geometry is derived from the instrument's own excursion distribution (stop at q75 of adverse travel, target at q50 of favourable), and then a 1:2 floor was applied on top, raising the target to twice whatever the stop happened to be. On SP500 H1 that pushed the target to 6.66*ATR, reached on 3.3% of bars inside the horizon - so the label became "almost never a win" and every topology was trained to predict an event that essentially does not occur. A measured target has to stay measured. The ratio never bought what it was believed to buy. A reward:risk floor does not create expectancy; it trades hit rate against payoff at a break-even the geometry already fixes - which this project has separately MEASURED (payoff 0.92 -> 5.72 with expectancy flat). What it did buy was two outages: four consecutive Market validation rejections for "no trading operations" when it rejected 100% of setups, and the label corruption above. Removed: - the input and the RISK_REWARD_RATIO enum (deleted, not left dangling - a live enum with no input behind it is the shape of the stale-.set incident that trained ~250 eras on the wrong target) - the forced target raise in the label geometry - the rrOK eligibility gate in the barrier-geometry scan, so every unclamped pairing now competes on the measurement alone. Clamping stays disqualifying for its own unrelated reason. - the reward < minRR*risk veto in OpenParams Kept: g_TradeRewardRiskRatio still computed and still bridged to Kelly sizing in MoneyIntelligent - the ratio as a SIZING input was always the sound use. Risk stays bounded where it actually is - account risk % and CRiskBudget. The low-reachability warning survives but is re-aimed: with nothing inflating the target, a target the market rarely reaches can only mean the horizon is truncating the excursions the geometry is derived from. Both build variants compile 0 errors / 0 warnings. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> |
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AnimateDread
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371f8aaecd |
fix: the Adam second moment was never Adam - all four tiers
Root cause of the B=32 regression, and it predates F4 entirely. Every Adam
kernel stored v already square-rooted and then fed that stored value back in
as if it were the variance:
v_new = sqrt(b2 * v_old + (1 - b2) * g^2)
That recursion has a fixed point at v ~= b2 = 0.999 for ANY gradient below
unit scale, so the denominator stops tracking the gradient and Adam degrades
into plain SGD with lr = lt. Measured against the shipped WarriorCPU.dll
(batch_accum_check.cpp, TestOptimizerScaleInvariance), 4000 steps of a
constant gradient: 3285x less displacement at |g|=1e-5 than at |g|=1, where
a scale-invariant optimizer gives the same distance for both. After the fix
all six magnitudes read 1.199 and v tracks |g| exactly.
It hit conv/LSTM specifically because they sit behind a batch-norm with
running variance ~2.6e+05, so their gradients arrive divided by ~500 - deep
in the degraded regime - while the dense stack near the loss stayed in the
working one. In situ on SP500 H1: lstm1 dW/W 2.62/10.0/7.14% -> 0.024/0.022/
0.003%, conv1 decaying to 0.000% by era 30. NeuronBatchNorm.mqh already
squared v back for gamma/beta and its comment named the kernels as wrong,
which is exactly why gamma/beta kept training while the stages behind froze.
Persisted .nnw needs no migration - v keeps its std-dev meaning.
Also, the two ways F4 exposed it, both mine:
- No LR compensation for B fewer steps per era. sqrt(B) for adaptive methods
(Krizhevsky 2014; Granziol et al. 2022), applied once in
InitialEtaForOptimizer(). Linear scaling (Goyal et al. 2017) is for SGD.
- Plateau patience denominated in eras, so raising B made the ladder 32x more
impatient in its only unit. PAI converged at era 41 on ~49k updates where
the same config had been finding new bests at era 1028.
TrainPlateauPatienceEras() stretches it by the same sqrt(B).
TRAIN_BATCH_SIZE 32 -> 8 so the patience stretch stays affordable (8 -> 23
eras per stage, not 8 -> 45). Both helpers are identities at B=1.
Deploy gate: DEPLOY_MIN_SIDE_RECALL_PCT (10%) folded into tradeableOK. The
perceptron reported Sell:0% recall in all 41 eras, cleared the floor on Buy
alone at 36.6% vs 34% chance, deployed, and sprayed buy arrows. Folded into
the ranking key rather than checked at deploy time so a one-sided era cannot
become best-so-far in the first place.
Deinit: the arrow purge now runs BEFORE ExtPanel.Destroy(), an unbounded
CAppDialog teardown that sat ahead of it - the same ordering inversion the
rule there exists to prevent. CONV was force-terminated 4.8 s into OnDeinit
(vs ~1.1 s for the three that finished) having reached none of its cleanup,
so its arrows stayed on the chart. Steps are now timed in the log.
PurgeChart's verification rescan filtered on OBJ_ARROW, the same blind spot
as the bulk delete, so "persisted 10 ... cleared 0" passed silently. It now
walks every object type and reports the object counts when both are zero.
Both build variants compile 0 errors / 0 warnings; both DLLs rebuilt.
FORCES A RETRAIN (already forced by N1) and both DLLs must ship with the .ex5.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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AnimateDread
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0c01dc279b |
feat: mini-batch gradient accumulation (F4), front-end-aware capacity budget (F6), split Wyckoff categoricals (N1)
Completes the 2026-08-09 training audit. FORCES A RETRAIN of every
Wyckoff-enabled config (N1 re-keys the fingerprint), and BOTH DLLs must be
redeployed alongside the .ex5 - they carry new exports.
F4 - mini-batch accumulation, TRAIN_BATCH_SIZE=32. Training was pure online
SGD (one weight update per bar), which is the mechanical source of the
era-to-era whipsaw every downstream guard was built to cope with. The O(n^2)
outer product is native - AccumulateWeightGrad / AccumulateWeightGradConv /
AccumulateBufferInto in Network.cl, WarriorCPU and WarriorDML - while the
optimizer step is host-side MQL5 shared by all tiers (ApplyAccumToBlock), so
there is one Adam/SGD implementation instead of four that can drift.
- the LSTM needs no outer-product kernel (WeightsGradient already holds the
sample's full dW) but could NOT simply be left un-zeroed between samples:
CPU_LSTMSeqBackward/DML_LSTMSeqBackward memset it on entry. Hence a
separate accumulator plus an elementwise add.
- batch-norm gamma/beta accumulate in host arrays, not new BatchOptions
slots - BN_OPT_STRIDE is baked into every persisted .nnw.
- scoped to pass 2; online learning keeps immediate updates. Every save /
checkpoint / scoring boundary flushes, scaling by the real sample count.
- degrades to per-sample updates (one log line) on a tier that cannot
accumulate, so old devices and DLL-free builds are unaffected.
- verified offline: DirectML/batch_accum_check.cpp drives the real exports
against an independent reference; at B=1 the accumulator matches the
shipped unbatched kernel's own gradient to 1.1e-16. Math only - the
in-situ check remains the per-layer dW/W report on a real era.
F6 - ComputeFirstLayerWidth budgeted against the RAW input width even where a
conv/LSTM front end had already reduced it, so an LSTM's dense stack was
charged for 1,280 inputs when it receives 64. Confirmed from the deployed
.cfg files: CONV, LSTM and HYBRID were all pinned at the 16-unit floor. Now
budgeted against the front-end output and capped at it (never fan out), with
the derivation reordered so both stages settle first.
N1 - EventCode/EventPhase/StructuralPhase are signed categoricals packing
direction and Wyckoff stage into one scalar across a sign discontinuity. Split
into direction + [0,1] magnitude, the same convention the base OHLC block uses.
Information-preserving; 13 readings now occupy 16 inputs.
Compiled clean (0 errors, 0 warnings); both DLLs rebuilt.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
|
||
|
AnimateDread
|
274630f802 |
fix: training-stability audit fixes F1/F2/F3/F5 - unbiased shuffle, real plateau escapes, fresh optimizer state on restore, pure OOS metric
Four of the six findings from research/training_pipeline_audit_2026-08-09.md (F4 mini-batching and F6 feature re-encode deliberately deferred - see the report's implementation-status section for why): - F1: pass-2 Fisher-Yates (and AutoTune's MI block shuffle) used MathRand()%, which is 15-bit - provably non-uniform on every full-history era over 32,768 queued samples. New 30-bit ShuffleRandomIndex(). - F2: plateau warm restarts were a no-op whenever eta already sat at its ceiling (the normal state of a non-regressing plateau) - the ladder was just a 24-era countdown. Restarts now overshoot to 5x the ceiling (PLATEAU_RESTART_BOOST) and anneal geometrically back over the patience window, SGDR-style; ETA_MIN widened 1e-4 -> 1e-5 so the decay schedule has real range. - F3: checkpoint restores put weights back but kept the rejected trajectory's Adam moments, so the optimizer immediately pushed back toward the rolled-back state (the restore->regress->restore oscillation). CNet::ResetOptimizerState() zeroes moments/momentum/step counters (weights, BN statistics, gamma/beta untouched) on every mid-run restore, every boosted restart, and the deploy-time restore that online learning continues from. - F5: batch-norm running statistics now freeze for the pass-3 OOS scoring walk, so the selection metric the checkpoint ranking and deploy gate read is a pure function of the checkpoint instead of partly measuring BN drift. Defensive unfreeze in FinalizeTrainRun covers stop-mid-pass; live/online adaptation and the OOS continual-learning simulation stay adaptive by design. Compiled clean (0 errors, 0 warnings) via the staged-tree recipe. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> |
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|
AnimateDread
|
ee48381cbd |
fix: NMS gates the TRADE, not just the arrow - one arrow is now one trade
NmsLiveAccept() appeared in exactly one place: wrapped around DrawObject(). It never touched dPrevSignal, and dPrevSignal is what LongCondition() / ShortCondition() / SignedAIConfidence() read. So a declustered bar lost its arrow and still opened a position. Measured on SP500 H1 2026-08-09: CONV called a direction on 64% of bars, so the ~500 bars visible on screen held ~320 decisions - and ~40 arrows were drawn. Roughly one arrow per eight positions the EA would take. And the survivors are not a random eighth. Rule 2 of the declustering keeps the HIGHER-CONFIDENCE side of a cluster, so the visible set is systematically the best member of each run. A chart showing the best of every eight decisions and hiding the rest reads far better than the model is - the same best-of-N selection error already corrected in the geometry scan, the indicator tuner, the lag profile and the deploy gate, this time on the display layer, where it is most likely to mislead the person deciding whether to trade. Fixed by neutralising dPrevSignal when NMS rejects, rather than adding a "may trade" flag consulted at each read site: that leaves exactly ONE definition of what the model decided this bar, so the arrow, the panel's "Current signal", the confidence feeding sizing/SL/TP/trailing, the refresh tally and the order itself cannot drift apart again. Also reports the consequence instead of hiding it. Every OOS counter on the era line still scores every directional call - a population ~8x larger than what now trades - so the line carries a second figure: | TRADED (declustered) NN% on N calls (edge +Npp) replaying the identical rule over pass 3 (which walks OOS bars oldest to newest, the same order the live sweep sees). Its cursors are separate members from the live ones so a training pass can never disturb the live chart's declustering. Deliberately NOT switched into selectionScore yet. Declustering cuts coverage from ~64% of bars to ~8%, well under MIN_COVERAGE_FRACTION_OF_BASE_RATE, which would make every checkpoint undeployable overnight - the minRR collision and the recall-floor catch-22 twice over. The floor gets re-derived from these measurements first. Compiles clean: 0 errors, 0 warnings. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> |
||
|
AnimateDread
|
922484e8d9 |
feat: expose the AD/Wyckoff parameters; default the indicator tuner off
AutoTuneIndicators now defaults to FALSE, and the 33 AD/Wyckoff parameters
it used to search are now inputs.
WHY THE DEFAULT FLIPPED - not because the search is broken. It is correct,
and its own Sidak gate is what proves it: 324 candidates per model on
SP500 H1, "no improvement" on all four topologies (0.00236 -> 0.00236 on
the AD configs, 0.00370 -> 0.00370 on PAI), winner rejected at p=1.0000.
It cannot do better here by construction - it ranks candidates by MARGINAL
MI, and the headline MI is 0.00370 nats against a shuffled null of
0.00379 +/- 0.00061 (p=0.4975), so every candidate is a noise draw and the
maximum over N of them is noise too. The cost is 45-56 min per model in
one synchronous call with no yield, and it was the amplifier for the
handle leak fixed in
|
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|
AnimateDread
|
1df305431d |
feat: gate deployment on the null of the MAXIMUM, not the per-era null
EDGE_MIN_SIGMAS is a PER-ERA test and the deployed model is the MAXIMUM
over every era a run ranks. A 2-sigma one-sided test passes on noise with
probability 0.0228 per era, so over N eras the chance at least one clears
it is 1-(1-0.0228)^N: 34% by era 18, 80% by era 70, 93% by era 112. The
gate was near-certain to open on a long run whatever the data held.
It did. HYBRID deployed 2026-08-08 at dir-precision 35.5% vs 34% chance -
+1.5pp, best of 112 eras whose per-era values wandered 30%..35.5%. At the
call counts these runs produce that is p_family 0.92..0.9999.
Every OTHER best-of-N decision here already carries this correction, and
every one REJECTS on this data: the barrier-geometry winner (null of the
maximum over 6, p=0.3902), the indicator tuner (Sidak, p=1.0000), the MI
lag profile (null of the maximum over 21 lags). The one decision that
ships a model to a live account had none.
BestCheckpointSurvivesSelection() re-tests the checkpoint that is about to
deploy:
z = (precision - chance)/SE, SE = sqrt(p0(1-p0)/n)
p_single = P(Z >= z)
p_family = 1 - (1-p_single)^N
against DEPLOY_FAMILY_WISE_ALPHA. It uses the checkpoint's OWN
snapshotted precision/chance/call-count, not the latest era's, because
the model that ships is the one that has to clear the bar.
N counts CANDIDATE eras (coverage measurable, at least one directional
call) - an era that called nothing directional could never have become
the best, so counting it would make the gate stricter than the search
that actually happened.
Conservative on purpose: consecutive eras share OOS bars and differ by
one gradient step, so they are nowhere near N independent draws and the
true family-wise error is below this bound. This gate decides what trades
real money and the house posture is reject-unless-demonstrated.
Effect at 2900 directional calls / N=112: required edge goes 1.76pp ->
2.92pp. A real edge clears it; +1.5pp does not.
Applied to BOTH automatic paths - the plateau ladder's stage-3 deploy and
the m_trainingComplete assignment - which must stay identical or the flag
persisted into the .nnw disagrees with the decision to stop, and a reload
runs inference on a model the ladder refused.
NOT applied to the two operator paths (era-cap deploy, panel Deploy
button). Those stay the operator's call; ReportSelectionGateVerdict()
logs the verdict beside them so an authorised deploy can never later be
misread as a validated one.
NormalUpperTail() is A&S 26.2.17 (|err| < 7.5e-8), self-contained rather
than pulling in Math\Stat. Verified against reference values to 6dp:
Q(1.645)=0.049985, Q(1.96)=0.024998, Q(3.0)=0.001350. Its locals are
ntB1..ntB5 because AI\Network.mqh line 79 does "#define b1 AdamBeta1".
Compiles clean: 0 errors, 0 warnings.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
|
||
|
AnimateDread
|
33f106d99d |
fix: the indicator re-init leaked a terminal handle per candidate
This is what killed CONV and LSTM on 2026-08-07. Terminal journal:
19:19:40 6664 x "VirtualAlloc failed in large allocator"
19:19:40.829 expert Warrior_EA (SP500,H1) removed <- CONV
19:29:55 2048 x "VirtualAlloc failed in large allocator"
19:29:55.359 expert Warrior_EA (SP500,H1) removed <- LSTM
50ms and 71ms after each printed its "logit adjustment" line, i.e. the
instant era 0 tried to allocate its training queues. They did not hang -
MT5 shot them for running out of memory.
ReInitADIndicators() re-Create()s every enabled indicator and released
nothing. The comment above it asserted "CiCustom.Create() already
releases its old handle"; MQL5's CIndicator::Create is
m_handle = IndicatorCreate(symbol, period, type, num_params, params);
a plain overwrite, whose only success-path IndicatorRelease is in
~CIndicator. IndicatorRelease appeared nowhere in this codebase.
That function is the indicator tuner's inner loop. AutoTuneIndicators
scored 324 candidates per model on SP500 H1, so ~324 x 6 orphaned
terminal-side instances, each holding a full-history buffer set -
ADWyckoffEventStream is 14 buffers x ~38k bars x 8 bytes = ~4.3 MB each.
Gigabytes. Confirmed in the shutdown teardown, where a single surviving
expert still held 70 x ADWES, 53 x ADWyckoffEventStream, 23 x
WFS(48,3,1.80,1.10), 19 x ADMovingAverage, 18 x WYSB(162). Those
parameter sets are the tuner's candidate grids.
Release is UNCONDITIONAL, not gated on the handle having changed: MT5
refcounts instances by (symbol, period, params), so re-creating with
IDENTICAL params returns the SAME handle with the count incremented -
the "23 x WFS(48,3,1.80,1.10)" pattern. Either way Create() added one
reference and we hold one handle, so one release is owed.
Released AFTER the re-creates, never before: dropping the terminal's
last reference first would tear the instance down, so an identical-params
Create() would rebuild it from scratch instead of reusing the live one -
turning a refcount bump into a full recalculation over all history, 324
times over.
PAI was unaffected because it has no AD/Wyckoff indicators enabled (35
candidates, built-ins only). HYBRID survived on luck: CONV and LSTM died
2 and 12 minutes before its own sweep finished, freeing the memory.
Compiles clean: 0 errors, 0 warnings.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
|
||
|
AnimateDread
|
bfc1da9de1 |
fix: the sequence models were reading the window backwards
BuildFeatureWindow() replaces eight hand-rolled copies of the same loop
and feeds the window OLDEST BAR FIRST. Every copy fed it newest-first,
because MQL5 timeseries indices run backwards and `r + b` with b ascending
walks into the past.
Harmless for PAI and CONV - a dense layer learns a weight per position
either way, a conv learns time-mirrored kernels. Not harmless for the
recurrent stacks:
- LSTM_SeqStepForward reads `inputs + t*Iw`, so step t is block t.
- It writes output[] only when t == steps-1: the visible output IS the
last hidden state.
- c_t = f*c_{t-1} + i*g decays toward the start of the sequence.
lstm_seq_flowcheck.cpp measured block 0's influence on the output at
1.2e-2 of block T-1's, at the shipped forget bias of 1.0.
So the bar being PREDICTED sat at the far end of the decay and the output
was handed to the OLDEST bar in the window - the exact inverse of what the
window is for. ~80x backwards on LSTM and HYBRID, on all three tiers
(OpenCL kernel, CPU DLL, pure-MQL5 inference), which is why it never
surfaced as a backend discrepancy.
This does not create edge - the MI diagnostics read at the noise floor
(p=0.4975) with a working positive control. It makes the one hypothesis
those diagnostics explicitly do NOT cover testable: they are marginal and
per-bar, and state they "cannot rule out one that only exists in
combination or across time". The sequence model is the instrument for
across-time structure and it has been crippled, so that hypothesis has
never been honestly tested.
Fingerprint gets an unconditional |WIN:2 - the vector keeps its shape and
its features, so a stale .nnw would load cleanly and run a model fitted to
one ordering against the other, silently. Re-keying every config is the
point, not collateral damage. FORCES A FULL RETRAIN.
Also: the now-relative bar caches are re-keyed on the two live paths.
EnsureBarCachesCapacity() was only ever called from training paths, but
once m_trainingComplete is set ScheduleTrainingIfNeeded() routes every bar
to RefreshConvergedSignal() and Train() is never re-entered - so nothing
cleared the feature cache again for the life of the process. A chart that
trained to convergence kept replaying the rows computed for the last
training era's bar grid: the live signal froze at its convergence-time
value, and OnlineLearnStep() backpropped those stale features against
freshly resolved labels. Backtests were never affected (an inference-only
process never allocates the arrays, so every read recomputes).
Compiles clean: 0 errors, 0 warnings.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
|
||
|
AnimateDread
|
b3b7e7bceb |
fix: excursion window must not depend on the barrier it sizes
DIRECTION IS NOT THERE, and this run is what establishes it. Three symbols:
raw ASYMMETRY clears on all three (p=0.0199 / 0.0050 / 0.0050)
norm ASYMMETRY collapses on all three (p=0.3433 / 0.5075 / 0.2736),
USDCAD landing BELOW its own null
RANGE control strengthens to 3-5x its null everywhere
Divide sigma out and the apparent directional signal vanishes entirely. What
cleared was volatility leaking through an unnormalised difference. Note this
would have passed any replication test: three instruments at p=0.005 is exactly
the evidence one would accept before committing to a rebuild, and the confound
reproduces perfectly. Replication was never going to catch it - only the
normalisation could.
Two defects of mine, both surfaced by the same run.
1. THE GEOMETRY DERIVATION WAS DIVERGING, NOT CONVERGING. It produced a
14.57*ATR stop and a 29.14*ATR target that only 5.7% of bars ever reach.
Excursions were measured over the barrier horizon; the horizon scales with
the target; the target is a quantile of the excursions - so target ->
horizon -> excursions -> target ran away, and "settled" only because the
horizon ladder caps at 384 bars. A saturated runaway, which the iteration
guard could not catch because it watches for OSCILLATION.
Fixed at the root: excursions now accumulate only over m_swingMedianBars -
the UNSCALED median ZigZag leg, a property of the instrument that owes
nothing to the barrier. The barrier walk still runs the full horizon,
because that is how long the trade is held; only the MEASUREMENT used to
size the barrier is confined to a geometry-independent window.
(The Min_Risk_Reward_Ratio warning fired correctly and is what flagged it -
the diagnostic worked while the derivation behind it did not.)
2. THE CONFOUND VERDICT WAS UNREACHABLE. `sizeCleared && !asymCleared` was
tested first and is true whenever size clears - i.e. always - so the branch
that NAMES the volatility confound never printed; all three symbols showed
the generic size-not-direction message instead. Verdict chain rewritten with
the specific case first, and the dangling elses my first patch introduced
removed.
FORCES A FULL RETRAIN (the excursion window changes every derived barrier).
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
|
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|
AnimateDread
|
32ffeb99f3 |
fix: normalise the asymmetry target - the raw one is confounded by volatility
Three symbols ran the excursion test. RANGE/UP/DOWN cleared on all three;
raw ASYMMETRY cleared on EURUSD and USDCAD at p=0.0050 and not on SP500
(p=0.1045). That looked like the first directional signal this project has
found. It probably is not, and the test as built could not tell.
(up-dn) IS NOT SCALE-FREE. If sigma is predictable - and RANGE clears at ~4x its
null on every instrument - and the directional part is symmetric noise eps, then
up-dn ~ sigma*eps, so a large sigma pushes the value into BOTH outer terciles. A
pure volatility predictor scores positive MI against a 3-bin (up-dn) while
carrying no directional information at all. Crucially that confound REPLICATES,
so reproducing on two instruments is not evidence against it - and the effect
sizes fit it: asymmetry runs 1.3-1.6x its null where RANGE runs ~4x, and carries
~0.1% of the target's entropy against RANGE's ~0.9%. That is the shape of a
leaked fraction of the volatility signal, not an independent one.
So add (up-dn)/(up+dn): bounded in [-1,+1], volatility divided out, and the only
target a directional claim may rest on. The verdict now separates the cases and
NAMES the confound when raw clears while normalised does not, instead of
reporting the raw line as a finding.
Two bugs of mine in the same block, both caught by output rather than review:
- The derived-geometry line had a MISORDERED argument list: it printed
"stop 25.00*ATR (q3 of adverse travel)" - the quantile percentage as the
multiple and the multiple as the quantile. Real values were 2.61 stop /
8.03 target. A 25*ATR stop is absurd on its face, which is why it was seen.
- THE STOP QUANTILE WAS BACKWARDS, and this one changes labels. It was 0.25
"so ordinary noise does not reach it", but q25 means 75% of bars EXCEED the
stop - hit three times in four. The printed reachability said exactly that
("stop on 75.0% of bars"). Now 0.75. A quantile is a threshold, not a rate.
This is the entire reason reachability is measured and printed rather than
assumed.
Also raises BARRIER_DERIVE_MAX_PASSES 3 -> 5: SP500 did not settle in 3 (stop
still moving ~14% per pass) while EURUSD and USDCAD converged on pass 2. And
bounds both quantile indices with MathMin(..., n-1) so q=1.0 cannot run off the
end of the sorted array.
The geometry from the previous run is NOT usable and the asymmetry result is
unresolved, not established. Both are decided by the next run.
FORCES A FULL RETRAIN (the stop quantile changes every label).
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
|
||
|
AnimateDread
|
a7701f032b |
feat: derive the ATR multiples from measured excursions - no hardcoded geometry
The barrier was still two constants. SL_Mode/TP_Mode left the Inputs tab in |
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|
AnimateDread
|
2c78f3b90d |
diag: is "optimal SL/TP" learnable? Score the features against excursions
Proposed direction: train the net to predict entry/SL/TP that maximise return
and minimise drawdown, rather than to classify direction. Before rebuilding a
head, measure whether the target is learnable at all.
That question splits into two that behave nothing alike:
HOW FAR price travels (MFE/MAE) - essentially volatility, and volatility
clustering is about the most robust regularity in markets.
WHICH WAY it goes first (the asymmetry) - direction, which is what every
noise-floor verdict in this project has been about.
Expectancy comes ONLY from the second. The first buys position sizing and
drawdown control - worth having under prop-firm limits, but not an edge: exit
management on RANDOM entries already moved the payoff ratio 0.92 -> 5.72 with
expectancy FLAT.
Crucially this is NOT already answered. Every MI figure here scored the
triple-barrier label, i.e. one specific question at one fixed geometry. A
noise-floor result there says nothing about whether excursion MAGNITUDE is
learnable - different target, different answer.
Four targets, and the verdict is the CONTRAST, printed explicitly because the
dangerous misreading of "UP clears" is "we can predict profitable trades":
RANGE (up+dn) - realised volatility, included as a POSITIVE CONTROL that
SHOULD clear. Every prior verdict here lacked a control
expected to pass; a range target at the floor indicts the
measurement, not the market.
UP / DOWN - MFE / MAE.
ASYMMETRY - up-dn, the only one that can pay.
Collected inside the walk the label already does (one max, one min per bar).
The early-out when both barriers resolved is GONE: it would have truncated the
excursions at whichever bar tripped the last barrier, making the measurement a
function of the CURRENT SL/TP - the circularity this is trying to escape. The
loop was already bounded by the horizon, so only the average cost moves.
Discretised into 3 EQUAL-FREQUENCY bins, so every downstream piece (block
permutation, null, p-value) is reused unchanged. Equal-frequency because MFE is
fat-tailed and fixed-width bins would put nearly every row in bin 0; it also
pins H(Y) at ln(3)=1.099 for all four, making them comparable to each other and
to the barrier label's ~1.02 instead of confounded by class balance.
Two bugs fixed in this code before it ever ran, both of which would have
produced a plausible quiet wrong answer rather than an error:
- TripleBarrierLabel early-returns on invalid ATR/close BEFORE the point the
accumulators were reset, so one bar's excursions would be cached under
another bar's index. Cleared at the top now, ahead of every return.
- An unresolvable bar is still flagged as labelled but carries excursions of
exactly 0. Under equal-frequency binning a block of identical zeros drags
the lowest cut onto zero and a third of the sample lands in one
uninformative bin - a depressed score that reads as "not predictable", a
false negative in the direction that would wrongly kill the idea. Rows
where both excursions are zero are dropped; price cannot travel zero both
ways over a whole horizon.
Read-only diagnostic. No topology or label change: no retrain of its own.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
|
||
|
AnimateDread
|
3482b6c238 |
feat: entry/SL/TP stop being inputs - the barrier geometry is measured
Three enums left the Inputs tab. They were three things a user had to pick and, in the tester, three more axes for a genetic optimization to overfit. Entry_Multiplier is pinned to MARKET. Its pending modes place the entry at a LEVEL while the rest of the pipeline measures from the bar open - the exact mismatch that manufactured the +0.097 R "retail fade" result later retracted as a fill artifact. This codebase's fill model cannot honestly simulate a pending entry, so it is no longer offered. SL_Mode/TP_Mode become a STARTING pair. ReportBarrierGeometryScan now ADOPTS its winner instead of printing "set SL_Mode/TP_Mode to X and retrain": - only when it clears the family-wise gate from |
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|
AnimateDread
|
9e1c72aacc |
fix: make the indicator tuner actually measure, and gate what it installs
ROOT CAUSE of the zero spread measured on SP500 H1 2026-08-07 (all 17 candidates
returned exactly 0.00359 nats): the tune loop re-inits the indicators and then
scores, with no RefreshData() between.
ReInitADIndicators() does its part - Create() builds a NEW handle carrying the
new parameters, and the feature cache is flagged stale so features really are
recomputed. But BufferTempDataCompute() reads the CIndicatorBuffer objects, and
only Refresh() copies data out of a handle into those. So every candidate was
scored on values still held from the PREVIOUS handle. My earlier guess in the
diagnostic ("suspect the feature cache") was wrong: the cache invalidation works.
Two things land together, because neither is safe alone:
1. RefreshData() after the re-init, so a candidate is scored on its own features.
2. A SELECTION GATE on the install. bestScore is a MAXIMUM over candidates, and
the maximum of N draws from a null beats its incumbent almost every time - so
"it beat the incumbent" installs noise. This selector is the highest-stakes of
the three found in this audit because it ACTS: it overwrites the user's
configured indicator settings and forces BuildFreshTopology(), so the network
then trains on whatever the noise picked. Fixing (1) without (2) would have
made a dormant bug actively harmful.
The gate draws the winner's own permutation null once, then corrects the p-value
for having chosen it out of N with Sidak: p_family = 1 - (1-p)^N. Sidak rather
than the max-of-N resample used by the geometry scan because each candidate here
has a DIFFERENT feature set, so their draws cannot be pooled; Sidak needs only
the one null. Exact under independence, mildly anti-conservative under positive
dependence - stated in the comment rather than hidden. A rejected winner restores
the configured settings, which best[] cannot do since the descent mutates it.
Also reports the least-ready tunable handle's BarsCalculated(). IndicatorCreate()
calculates asynchronously, so if the spread is STILL zero the handles simply are
not done and the tuner needs to yield between candidates rather than score them
back to back - a state machine like the label prebuild. That distinction is now
readable from the log instead of requiring another guess.
No input, topology or label change: no retrain.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
|
||
|
AnimateDread
|
e5ceed6466 |
fix: MI diagnostics never ran on a resumed model - the stated intent was never achieved
A comment above the diagnostic branch says it "runs even when the sweep does not: on a resumed model ... tying it to that gate meant the only way to see the answer on a running model was to delete the model." It does not. Moving the diagnostic out of the tuner's gate left it behind m_labelCachePrebuilt, which has the same effect: the eager label pre-scan runs only on a FRESH start, because a net loaded from disk labels lazily per bar. So on a resumed model the flag is false forever and the whole MI block - headline, positive control, alignment scan, lag profile, geometry scan, winner test, and the auto-tune line - silently never runs. Measured on SP500 H1 2026-08-07: attached at era 271, still nothing by era 314, zero MI lines in the day's log, and the only "label cache pre-built" entry predates the attach. It also explains the shape of every capture on 08-05/06: each one came directly after a weights reset. The situation the comment was written to eliminate is exactly the situation that persisted. So drive the pre-scan when it is the only thing missing. Safe on a trained net: its one fresh-net side effect, pushing the output-layer bias toward the dominant class, is already gated on m_eraCount == 0, and the advance gate in Train() sits ABOVE if(!m_trainRunActive), so the era loop keeps its state - training pauses for the scan (~1s at 38k bars) and continues from where it was, not from 0. Announced only on a start that actually armed, since StartLabelCachePrebuild() returns unarmed when history is not ready and is retried per bar event. NOT sampled from the lazily-filled cache instead: BuildMiSample skips bars with no cached label, so that would score whichever subset training happened to have visited - a biased subsample presented as a measurement, which is the failure this diagnostic exists to catch. Also corrects a claim in 0d58923's comment. It argued four consecutive "no improvement" runs were ~1-in-100,000 evidence the indicator tuner is inert, by multiplying 5.6% across four runs. They are not independent trials: the MI scorer is deterministic and all four covered nearly the same bars, so an incumbent that is the maximum on this data is the maximum on every run. One ~1-in-18 observation with three correlated repeats, ~5.6% - unremarkable. The same independence assumption that made the uncorrected lag profile star four lags. The candidate-spread line stands: it settles inert-vs-live directly. No input, topology or label change: no retrain. Training in flight stays valid. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> |
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|
AnimateDread
|
0d5892357b |
diag: report the indicator tuner's candidate spread - "no improvement" is ambiguous
Auditing the other best-of-N scans after
|
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|
AnimateDread
|
cccf94f9ca |
fix: correct the lag profile across lags too - it contradicted itself
|
||
|
AnimateDread
|
04ee2e113a |
fix: gate the barrier-geometry winner on a family-wise null, not its own
The scan ends by printing "set SL_Mode/TP_Mode to <winner> and retrain".
That advisory fired on `bestExcess > cfgExcess * 1.5` - a ratio between two
numbers, with no test that either is distinguishable from zero.
bestExcess is a MAXIMUM over the eligible candidates. The maximum of several
draws from a null sits well above any single draw from it, so a max-shaped
statistic tested against a single-candidate null crowns a winner on noise
almost every time. On SP500 H1 the winner is 2:8 at +0.00081 nats - and the
lag profile committed in
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AnimateDread
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3271f1ea93 |
diag: MI feature-lag profile - close the blind spot in every MI verdict so far
BuildMiSample samples features from ONE bar. So every "MI is at the noise floor" result this codebase has produced - including yesterday's p=0.18 on SP500 H1 - described the ENTRY BAR's 31 features only, while the network is fed 20 bars of them. If information lived at lag 7 and not lag 0, the report would have said "no signal" while the model could still learn. The diagnostic we have been making decisions on had a blind spot exactly the width of the input vector. Adds a FEATURE-side offset to BuildMiSample, which is not the same thing as the existing labelBarOffset and is not interchangeable with it. Shifting the LABEL changes which trade is predicted, so at any non-zero offset the features sit inside the labelled window and the score is lookahead - that is precisely what the alignment scan measures and correctly reports (4.7x more knowable 5 bars into a 128-bar window). Shifting the FEATURES keeps the label pinned to the entry bar, so every row stays causal. ReportFeatureLagProfile() then scores k = 0..historyBars against the same block-permutation null and reports the deepest lag that clears it - the lookback the data supports, versus the 20 that was picked by hand and never measured. The null is redrawn PER LAG: finite-sample MI bias moves with the realised class counts and bin occupancy, and different rows survive the validity checks at each lag, so one shared floor would be right for lag 0 and wrong everywhere else. Draw count is reduced accordingly (40, not 200) since cost is draws x historyBars; this figure decides a lookback, never a trade. MiShiftPad now also covers historyBars, keeping the fixed-pad invariant that makes two builds comparable row by row. Read-only - no input, topology or label change, so no retrain. Both builds 0/0. Build tag lag-profile-v1. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> |
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AnimateDread
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8c5ea639ee | feat: extend ADWyckoffEventStream with new range-lifecycle parameters and update related features | ||
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AnimateDread
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8ccbddb051 |
Add new research scripts for trading strategy analysis
- Implemented sqx_audit.py to audit StrategyQuant X trade lists, focusing on performance metrics and cost analysis. - Created sqx_portfolio.py to evaluate portfolio performance based on uncorrelated components and their impact on risk and return. - Developed swing.py to analyze cost ratios across different holding periods and assess swing trading structures. - Introduced test_management.py to investigate the effectiveness of exit rules on random entries and their impact on expectancy. |
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AnimateDread
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f1b7dcf7f3 |
fix: correct MI sample alignment and improve BN weight diagnostic report
The MI sample builder used `MathAbs(labelBarOffset)` as a padding, causing rows from offset and non-offset builds to be paired with a double shift. This broke the positive control, failed the 5× gate, and voided all reported mutual‑information figures. Replace with the fixed `MiShiftPad` constant to ensure builds enumerate the same set of bars and row-k alignment is preserved. Add `BatchOptionsTotal()` to `CNeuronBatchNormOCL` and split the packed BN weight array in the learning report into separate norms for the outgoing dense matrix, gamma, beta, running statistics, and Adam moment buffers. This turns an ambiguous single‑norm reading into precise diagnostics that distinguish weight divergence from scaling issues. |
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AnimateDread
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5f647ba5db |
fix: improve error messages and suppress false sharing-violation logs
- BufferDouble: replace hardcoded "DirectML/CPU-DLL" with dynamic backend name and add buffer index/element count to all error prints for easier debugging. - NetPersistence: distinguish missing file from transient lock by probing FileIsExist before logging, eliminating false "sharing violation" warnings when no saved model exists on first run. |
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AnimateDread
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ceb6342dfd |
feat(ai): spread as a volatility-regime feature, and fix a stale-index cache in both new blocks
Adds spread/ATR and the spread change ratio as network inputs (EnableSpreadFeature,
default on). Spread is the one microstructure channel that is both FX-available and
genuinely historical in the Strategy Tester - "during testing, the spread is not modeled
but is taken from historical data" - so unlike swap, signed tick flow or depth of market it
is something a backtest can honestly validate.
What it encodes, stated precisely because the raw measurement overstates it.
research/test_spread.py found spr/atr the strongest single feature in this codebase, on 5
of 8 instrument/geometry cells at 2-4x any volume feature. But the barrier LABEL charges
the spread inside its own barriers, so a wide-spread bar is mechanically likelier to
resolve as a loss and the feature would partly be predicting its own cost model. Relabelling
at zero cost and re-measuring the identical feature showed 20-40% of it WAS that tautology
and the majority was not (XAUUSD retained 97%). What survives is a volatility-regime
reading: spread is near-fixed while ATR is not, so the ratio runs high exactly when
realised volatility is below its own ATR estimate, which genuinely predicts whether
ATR-scaled barriers get reached. It is UNSIGNED - Neutral-vs-directional only, never a side.
Also fixes a stale-index bug I introduced with the cross-asset panel and had just repeated
in the spread series. Both cached on length alone:
if(m_crossAsset.Bars() >= bars) return true;
MQL5 series indices are relative to NOW, so one new closed candle shifts every index by
one. Keyed only on length, the panel keeps serving its index 0 as a bar that is no longer
the newest, and every cross-asset value is read one bar out of step with the price features
sitting beside it in the same vector - silently, with no error and no shape change. This is
the same class of defect as the dtStudied watermark behind the zero-direction backtests.
Both now carry a datetime anchor on m_Time.GetData(0), the same invalidation key the
label/feature bar caches already use.
And a performance fix that fell out of it: with correct invalidation the panel rebuilds on
every new bar, and RefreshConvergedSignal runs per bar - which in the tester would mean one
full multi-symbol resample per simulated bar at training depth. Inference only reads bars
0..m_historyBars-1 plus the panel's own slow window, so it now requests exactly that. The
cache check is >=, so a deeper panel left from training still satisfies it.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
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AnimateDread
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d80d9444a5 |
feat(ai): widen the volume feature block from 1 value to 4
The block fed exactly one number: (v[i] - v[i-1]) / v[i-1]. That is the first difference, and it cannot express three things that matter - the LEVEL relative to a baseline (two dead bars and two frantic bars both read ~0 change), and the two volume-vs-range interactions, where heavy participation that went NOWHERE (absorption) and heavy participation that travelled (continuation) mean opposite things and currently collapse onto the same value. research/test_volume.py measures each candidate's mutual information with the triple- barrier label across 3 instruments x 2 geometries, against a BLOCK-permutation null - blocks sized to the barrier horizon, because adjacent labels share almost their entire outcome window and a free shuffle yields a null so tight that everything looks significant. Finite-sample MI bias (~7/n here) is reported alongside rather than subtracted, since the permutation null already absorbs it. Result: volLevel beats the shipped change ratio outright on 4 of 6 cells (EURUSD 2:3 +0.000118 excess at p=0.006, USDJPY 1:2 +0.000284 at p=0.002); absorption is the single strongest reading anywhere in the sweep at EURUSD 1:2 (+0.000404, p=0.002) though it is null on XAUUSD; vol x range clears on 4 of 6. The shipped change ratio is itself significant on 5 of 6, so it stays. Kept OUT: a session-relative z-score against the same hour-of-day's own recent history. It was the weakest candidate - null on both EURUSD cells - and it is the only one needing per-hour rolling bookkeeping in MQL5. Not worth the state for a reading that did not survive its own null on the primary instrument. Magnitudes, stated plainly because they are the point: the excess MI is ~2e-4 nats against a label entropy near 1.05. That is under a tenth of one percent of the label's uncertainty. It is real, it repeats across instruments, and it is nowhere near an edge - this is worth having because it costs one 50-bar loop, not because it changes the answer. Prior work stands: the whole single-series feature family measured at the noise floor. m_neuronsCount is already in the fingerprint, so the width change re-keys existing caches by itself, which is correct - the input vector genuinely changed shape. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> |
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AnimateDread
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8710240cd5 |
fix(signals): revive a dead MA model, and demote Sanyaku from state to event
Two defects surfaced by research/test_classic.py, both verified fixed by re-running the
transcription against 178k bars of EURUSD H1.
CSignalMA model 1 could never fire. For any recursive average - and MA_TYPE_EMA is the
shipped default - MA(i) = a*Close(i) + (1-a)*MA(i+1), so
DiffMA(i) = a * (Close(i) - MA(i+1))
DiffCloseMA(i) = (1-a) * (Close(i) - MA(i+1))
are positive multiples of one quantity and always share a sign. Model 1 asks for a close
BELOW a RISING average, which is precisely the combination that identity forbids: 0.000%
of bars, either direction, any symbol. The MQL5 standard library this was ported from
defaults to MODE_SMA, where the two are merely correlated - the bug arrived with the EMA
default, not with the port. Reading the slope one bar back (DiffMAPrev) breaks the tie for
every MA type while keeping the model's stated meaning. Now fires on 7.92% of bars.
CSignalIchimoku model 11 fired on 27% of bars at weight 100. Sanyaku is three standing
STATES conjoined with no transition term, so it held across long stretches - and being
last in the if-chain at the top weight, the module's highest-conviction reading was also
its most common one, overwriting all eight event models below it on a quarter of all bars.
The old comment rejected an event form because "demanding all three flip on the same bar
would fire almost never" - true, but that is not the alternative. Kouten is the TURN: the
ALIGNMENT transitions, and only one role need change for it to. Testing !Sanyaku(idx+1)
fires once per aligned stretch. Now 2.17%, in line with Kumo breakout (2.4%) and the
strong TK cross (1.1%). DataReady() extended one bar deeper to cover the lookback.
Neither pattern showed edge before or after; this is about the models meaning what they
say and the vote not being dominated by a constant.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
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AnimateDread
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a77ff64b13 |
fix(deinit): a full model write was running ahead of the cheap cleanup
"Abnormal termination" is back, and this time it is not the arrows. The timing names the culprit exactly: 16:02:31.547 OnDeinit: shutting down 16:02:36.003 Abnormal termination <- 4.46 s, MetaTrader gave up 16:02:36.226 chart signals - persisted <- cleanup finished 0.2 s LATE OnDeinit called StopTraining() BEFORE the chart cleanup. StopTraining() finalises an in-flight run, and FinalizeTrainRun() restores the best checkpoint and then persists it - a full ~1MB model write per signal. So the expensive step ran ahead of the cheap bounded one, which is precisely the inversion the shutdown ordering exists to prevent. The previous fix put PersistWeightsOnShutdown last and missed that StopTraining smuggles a second save in at the front. Two changes: Cleanup now runs FIRST, then StopTraining, then the weight save. The visible teardown is cheap and bounded, so it always completes even when everything after it is killed. And the deploy-persist inside FinalizeTrainRun is suppressed during shutdown. RestoreWeights() is an in-MEMORY swap, so the best checkpoint is already the live net by that line, and PersistWeightsOnShutdown writes exactly those weights moments later. The old path wrote the same model twice per signal - eight full writes across four charts - for no benefit. A user-pressed Stop still persists immediately, because nothing else would. Compiles 0 errors / 0 warnings. Build tag deinit-order-v2. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> |
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AnimateDread
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7d038df749 |
research: export the feature matrix and a raw OHLCV grid for offline work
The bottleneck on this project has never been the modelling - it is that
every hypothesis costs a compile, a deploy, an attach and a log read, and
answers exactly one question. Days have gone into questions that are
seconds of arithmetic once the data is in hand.
Adds a RESEARCH-ONLY build, gated behind WARRIOR_EXPORT_FEATURES and
never compiled into a shipped binary, which writes two things to
Common\Files\Warrior_EA\Research\ and then does nothing at all:
<symbol>_<tf>_features.csv - one row per bar: index, time, OHLC, ATR,
and the m_neuronsCount feature values. Exactly what the network sees.
The raw bars ride along on purpose: with OHLC and ATR offline, every
barrier geometry, horizon and in-trade target is recomputable without
MetaTrader in the loop.
<symbol>_<tf>_rates.csv - raw OHLCV across a grid of 8 symbols x 5
timeframes. The 26 engineered features only exist for the attached
chart (indicator handles bind to PERIOD_CURRENT); raw rates do not, so
ONE attach yields the whole research grid. The bar time also makes
session/hour/day-of-week derivable - the only inputs in play that are
not a transform of the same OHLCV series.
Safety, because this binary gets attached to a chart on a LIVE ACCOUNT to
reach real history:
- OnTick returns immediately, so Expert.OnTick() - the entire trading
path - is unreachable regardless of the AlgoTrading toggle, the
signal state or the inputs. Structurally incapable of sending an
order, not merely unlikely to.
- No config lock. It never trains and never saves a model, so it has
nothing to protect against a concurrent chart - and taking the lock
would make it refuse to start exactly when the config it wants to
read is already open, which is when it is most useful.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
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AnimateDread
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004f2a04f7 |
fix(diag): the symbol sweep was measuring its own sampling, not the market
Twelve cells came back with higher-timeframe "signal" 5-9x anything on H1, at p=0.005. It was an artifact, and the sweep's own columns gave it away: excess tracked the sampling STRIDE almost monotonically, and the three D1 cells - stride collapsed to 1-5 bars against a 128-bar horizon, i.e. ~99% window overlap - were the three highest. Three flaws, all the same family: comparing numbers without the spread that belongs to them. 1. THE NULL ASSUMED INDEPENDENCE THE LABELS DO NOT HAVE. Triple-barrier labels overlap; two rows less than one horizon apart share most of their outcome window. A free Fisher-Yates shuffle destroys that dependence along with the association, making the null far narrower than the truth and handing out significance that isn't there - Lopez de Prado ch. 4 arriving through the back door of the significance test. Now permutes contiguous BLOCKS of at least one horizon, so the null keeps the autocorrelation and the p-value means what it says. It degrades honestly: severe overlap leaves few blocks, the null widens, nothing reaches significance. The block count is now printed, because THAT - not the row count - is the sample size a p-value rests on, and a warning fires under 30 blocks so "not significant" is not misread as "no signal" when it means "not enough independent history to tell". 2. THE POSITIVE CONTROL'S STRENGTH DEPENDED ON THE DATASET. It paired each row's label with the NEXT SAMPLE ROW's, whose distance is the stride - so on M5, where stride ran 160-717 bars against a 128-bar horizon, it was pairing two windows that never overlap. All three M5 cells duly reported a FAILED estimator and voided their own results with nothing wrong. A control whose strength varies with the cell cannot certify the cell. Now pinned to a quarter of the horizon, where ~75% overlap is guaranteed by construction. 3. THE LOOKAHEAD VERDICT HAD NO MARGIN. It flagged 7 of 12 cells on gaps of 0.00008-0.00040 nats against a measured null sd of ~0.00030 - noise, every one. Now requires 3 sd, the same discipline the deploy floor applies to precision. Compiles 0 errors / 0 warnings, standard and Market. Build tag blockperm-v1. Supersedes every number from the sweep. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> |
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AnimateDread
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168422ff7a |
fix(labels): the 128-bar horizon ceiling was truncating the shipped label
The corrected geometry scan exposed something bigger than the geometry
question it was asked. Every pairing from 2:6 upward came back CLAMPED -
including 2:6, the SHIPPED configuration.
First-passage time for a driftless walk leaving [-m,+k] goes as m*k, and
the measured swing median here is ~12 bars at m*k=1, so 2:6 wants ~144
bars and 3:10 wants ~360. The ladder stopped at 128. A clamped label
stops meaning "does the target come before the stop" and quietly becomes
"...within 128 bars", while the deployed EA holds until SL or TP with no
bar limit. So the target the models have been trained on all along was
not the strategy the EA executes, and the trades it silently reclassified
as Neutral were the SLOW WINNERS - precisely the ones a 1:3 barrier
exists to capture. Timeout share stayed ~0% throughout, which is why this
never showed up: the truncation lands in Neutral, not in the timeout
counter that was watching for it.
Ladder extended to 384 (12..128, 192, 256, 384) so every selectable
geometry gets an honest horizon. Cost is one embargo of at most 384 bars
out of ~38k.
Second fix, same class of error as the H(Y) one: the scan's "best
eligible" was 2:2, a 1:1 barrier, against a shipped Min_Risk_Reward_Ratio
of 1:2. Training four topologies on that target would have produced a
model whose every setup is rejected at the door - the exact failure
behind four consecutive Market rejections for "no trading operations".
Sub-minRR geometries are now ineligible and marked [<minRR], printed
rather than hidden.
Also drops the dense-depth tag from the display name ("Perceptron 3L" ->
"Perceptron"). Depth is derived, so it names nothing a user chose; the
config tag [PAI-0be2] already disambiguates concurrent charts and does it
for every input rather than one. Full topology still logged by "config -".
Compiles 0 errors / 0 warnings, standard and Market. Build tag
horizon-384-v1. Changes the LABEL for every geometry, so the next scan
supersedes the previous numbers - and a retrain is required before any
model trained under the truncated target means anything.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
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AnimateDread
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40af4a4b5b |
fix(labels): the geometry scan rewarded the labels it should reject
First run named 3:10 on all four charts, at 2.3x the configured 2:6. That answer was wrong and the fault was the ranking statistic. 3:10 wants a horizon of ~swingMedian*30 (~320 bars) and gets BARRIER_HORIZON_MAX. Clamped, most trades never resolve, the unresolved remainder all lands in Neutral, and H(Y) collapses. The old statistic divided the excess BY H(Y) - so a collapsing denominator made the most degenerate label look like the most predictable one. Every geometry from 2:6 upward was already showing the clamped h128, and the two widest scored highest, which is the fingerprint of the artefact rather than of signal. Two fixes: Rank on the raw excess in nats. Subtracting each geometry's OWN measured null already removes the class-balance bias, which is the only thing the normalisation was ever needed for. Disqualify clamped geometries outright rather than ranking them down. The deployed EA holds until SL or TP with no bar limit, so a truncated label trains the model on a question the strategy never asks. They are still printed, marked '!', so the disqualification is visible instead of a silent omission - and the scan now says so explicitly when nothing eligible is left, because "the limit is the feature set, not the target" is itself the finding in that case. The scan also reports each geometry's directional share and timeout share now. A label nobody can trade is not a candidate however well it scores, and that has to be visible in the same line as the score. Compiles 0 errors / 0 warnings. Build tag geometry-scan-v2. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> |
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AnimateDread
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f97ab9f1d6 |
feat(labels): measure which barrier is predictable at entry, don't guess
The alignment scan settled the shape of the problem: 4.7x more is knowable 5 bars into a 128-bar window than at the entry the model actually trades. A 6xATR target reached over 128 bars is decided overwhelmingly by what happens DURING the window, so whatever the entry state knows is buried under 128 bars of later noise. That is a property of the TARGET, and it is why four different architectures all landed on precision exactly equal to the base rate - no topology can undo it. So measure the target. For each SL/TP pairing a user can actually select, relabel the same sampled bars and score how much the SAME features say about THAT outcome at entry. Seconds, no training, no topology, and it runs on the diagnostic path that already exists. Ranked on excess over its OWN null as a share of its OWN H(Y), never on raw nats: each geometry has a different class balance, hence a different finite-sample bias and a different amount of information there to find, so raw MI would rank the most BALANCED label rather than the most PREDICTABLE one. The break-even win rate m/(m+k) is printed beside each so the ranking is read next to the bar the model must clear. Stated in the output because it is the easy thing to get wrong: chance precision EQUALS break-even at every geometry, so a tighter target does not hand you expectancy. It buys predictability - less noise piled on top of what the entry state knows - which is the one thing changing topology cannot do. Read-only by construction: it relabels a sampled copy via TripleBarrierLabel(), never writes the label cache (which belongs to the configured geometry), and restores the horizon and overrides it borrowed. The overrides apply only when BOTH are positive, so a half-set pair can never silently relabel a live run. Compiles 0 errors / 0 warnings, standard and Market. Build tag geometry-scan-v1. Redeploy only - no retrain to READ the ranking. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> |
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AnimateDread
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4443ce85c1 |
fix(diag): the alignment scan cried misalignment at its own arithmetic
First run came back "WARNING - peak at k=+5, NOT 0 ... a feature/label
misalignment upstream of every topology". That was a false alarm produced
by the diagnostic's own design, and exactly the kind of plausible-looking
output this project has lost days to.
Bar indices are MQL5 SERIES indices - HIGHER index = OLDER bar
(TripleBarrierLabel walks its window as `for(t = idx-1; t >= idx-horizon;
t--)`, decreasing index = forward in time). The two directions therefore
mean opposite things and the scan treated them as symmetric:
k < 0 label belongs to a NEWER bar, its barrier window opens AFTER the
features exist. Nothing at bar i can legitimately know it, so a
peak here is real lookahead and a bug.
k > 0 label belongs to an OLDER bar, already k bars into its window by
the time bar i happens - so the features hold the realised first
k bars of that outcome. MI MUST rise with k. Arithmetic.
Only the k<0 side can indict the pipeline, and on the observed data it is
clean: -5/-3/-2/-1 all sit at or below the k=0 value and the noise floor,
so there is no lookahead - a real negative result, not an absence of
evidence.
The k>0 side is now reported as what it is, a second positive control,
with its gradient as the finding: 0.01881 at k=+5 against 0.00401 at k=0
means ~4.7x more is knowable 5 bars into a 128-bar window than at the
entry the model actually trades on.
Compiles 0 errors / 0 warnings. Build tag mi-align-v2. Redeploy only.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
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AnimateDread
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87c8656b53 |
diag(autotune): a positive control, and a scan that separates "no signal"
from "signal knocked out of step" Four architecturally different networks landed on the same precision - Buy 23-25% against a 25.4% base rate, Sell 19-22% against 22.0% - while making completely different calls (HYBRID votes Sell on 69% of bars, PAI on 41%). Precision equal to the base rate is what INDEPENDENCE looks like, and precision under independence is fixed by the label distribution, not by the architecture, so all four converging on it is arithmetic rather than coincidence. Accuracy meanwhile tracks coverage exactly as independence predicts (31.1/30.3/25.0 predicted vs 31.8/28.9/24.6 observed for PAI/CONV/HYB). But "no information in the data" and "information destroyed upstream of every topology" produce that identical picture, and the MI test alone cannot tell them apart either. Two additions: POSITIVE CONTROL. Three "measurements" in this codebase have turned out to be silent no-ops that produced plausible numbers - the MI scorer reading an array nobody filled, the eval-mode guard that switched off the imbalance correction, the alternation gate whose premise was never true. So the estimator now has to prove it responds to a signal known to be present before any floor reading is believed: the label of a neighbouring sample row, ~19 bars away and far inside the 128-bar barrier horizon, so the two outcome windows overlap heavily and MUST be associated. Same binning, same estimator. Near the floor => every MI figure is void. ALIGNMENT SCAN. Re-scores against the label taken from bar i+k for k in -5..+5. A peak at k != 0 is a feature/label misalignment - an off-by-one in the label index, a horizon applied to the wrong bar, a feature window that lags what it claims - which would destroy the information before any topology saw it and would look identical in every accuracy number this EA prints. A flat profile says the features simply do not carry this target. The sampled range is trimmed by |k| at both ends so a shift is measured rather than an edge effect, and both bars must carry a real label. Also: BuildMiSample publishes its stride instead of the report recomputing that arithmetic (it would drift), and the control sizes its buffers from its own sample count rather than the caller's. Compiles 0 errors / 0 warnings, standard and Market. Build tag mi-control-align-v1. Redeploy only - no retrain, no model deletion; the diagnostic runs on resumed models. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> |