forked from animatedread/Warrior_EA
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
AnimateDread
|
0adaea48b6 |
fix(resume): model reload stalled training - three hardenings on the resume path
A resumed META model hot-looped pass 1 (0->100% scan oscillation, silent for
3 minutes until the stall reporter fired) because EVERY window failed at the
first AD/Wyckoff feature: the init-time param adoption called
ReInitADIndicators unconditionally, destroying five freshly-calculating
indicator instances to recreate them with BYTE-IDENTICAL params (verified by
parsing the .nnw header - the MI tuner had kept the configured settings), at
process start, on a box with 1 GB free of 31. The replacements sat cold for
6+ minutes while full-history resweeps starved the indicator threads harder.
- AdoptIndicatorParams: installs a loaded param set into the tuner and
rebuilds handles ONLY when the set actually differs from what the live
indicators run. Both call sites (resume init + panel reload) use it.
- Resumed models get the same 3 warm-up passes as fresh ones. The skip was
the shared root cause of the cold-ATR (
|
||
|
AnimateDread
|
df48c37f65 |
feat: per-family x per-side OOS breakdown in the META era report
350-era S2 verdict on SP500 H1: the meta head carries REAL ranking skill (+1.0-1.3pp mean over base, 101/350 eras clear their own 2-sigma bar, traded subset wins 66.1% at <30% coverage vs 64.5% base) but 0/350 eras produced a positive cov x (p - BE): the candidate stream sits 3pp under the derived geometry's 67.5% break-even and ~2.6pp of recovered skill cannot bridge it. Skill plateaued by mid-run (1.28pp -> 1.05pp), so more eras only buy multiplicity, and the deploy gate correctly shipped nothing. The aggregate can hide a deployable subset (one family/side clearing BE blended with junk), so the META era line now decomposes the SAME traded population into MA/RSI/MACD/Ichimoku x LONG/SHORT cells, each as traded/candidates base->traded win rate. 32 cells is a best-of-N search by construction - any candidate cell faces the family-wise rule before belief. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> |
||
|
AnimateDread
|
444909d0a3 |
feat: S2 meta-labeling head - binary trade-quality model over the classic-candidate corpus
The NN now has a target that is not per-bar direction (closed, best-of-999 p=1.0000): P(win | this journaled candidate, at the EA's own SL/TP, net of cost). One net for all 52 pattern-sides, AIType=AI_META. - NetForward.mqh: the host-side softmax+CE gradient generalized total==3 -> 2||3 on both backprop paths; a 2-class softmax IS a logistic head, and no compute backend changes. - SignalMETA.mqh (new): corpus loaded read-only from the LARGEST signal DB on disk (decoupled from the config fingerprint that burned four S1 runs); the GMT->server offset is measured PER ROW against entryPrice vs bar open (DST-immune, histogram logged); a window-span regime filter drops the pre-2017 daily-backfill rows; 31-feature setup descriptor appended at the input (26 one-hot + side + tanh netVote + SL/TP ATR + spread/ATR). - Training.mqh: candidate-queued pass 1, binary-target pass 2, per-candidate calibration (2.5) and OOS (3) walks. Counter mapping win->Buy / loss->Sell lets checkpoint selection, the edge floor, the plateau ladder and the family-wise deploy gate run UNCHANGED: precision reads as win rate among traded candidates, chance as the base win rate, recalls as sensitivity/ specificity. Era-end META line: coverage x (p - break-even) vs the null. - Labels are the side-conditional triple-barrier win caches - never the DB's stop-and-reverse outcome. Logit adjustment deliberately skipped (~40% base rate). Live inference + online learning guarded off until S3. - Fingerprint: conditional |TGT:META1; State\META\ folder + 2-output filename slot keep meta models fully separate from direction models. Compiles clean (0 errors, 0 warnings). S2 run = attach a chart with AIType=AI_META; S3 wires the votes via the per-side hooks. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> |
||
|
AnimateDread
|
36e8463310 | refactor: derive history bars for input sequences and update related configurations | ||
|
AnimateDread
|
923addf574 |
feat: pin the cross-asset pair set train->serve + warm the sync at init
The reference-pair set was re-discovered from Market Watch on every build, so adding or removing a terminal symbol silently changed what a trained model's six cross-asset features meant - the last open train/serve parity gap from the 2026-08-11 audit. The set a model's FIRST successful build actually used is now stamped into its .cfg (append-and-length-guard, adopt-don't-compare - the derived-barrier pattern) and every later build constructs the panel from exactly that list; a pinned pair that is temporarily unavailable is skipped, never substituted. Also warms SymbolSelect/SeriesInfo for every reference symbol at InitNeuralNetwork, so the terminal's ~minute of async cross-symbol download starts at init instead of when the first Build() trips over an unselected symbol - the source of the startup 'only 0 usable reference pairs' console failures. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> |
||
|
AnimateDread
|
bd1037975a |
fix: the trailing incumbent read the future across eras; cold AD blocks cached zeros as truth
Three findings from the 2026-08-11 audit:
1. The excursion head's trailing-quantile ring was deliberately never cleared
between eras ("a rolling estimate of the market, not of the era") - but
pass 3 re-walks the SAME OOS window every era, so at each walk's restart
the ring still held the outcome masks of the newest OOS bars from the
previous walk: the chronological FUTURE of the bars about to be scored.
For the first ~window+horizon pushes of every era the "trailing" incumbent
was partly a leading one - conservative for the gate (an informed incumbent
is a harder hurdle) but exactly the self-made-artifact class
|
||
|
AnimateDread
|
0848c8a16c |
fix: live inference queried the 1-tick forming bar - a window training never built
RefreshLatestSignal ran at the first tick after a bar opens and built its window at r=0: series index 0 at that instant is a candle with one tick of data - (close-open)/atr ~ 0, high ~ low, degenerate volume, indicators on a 1-tick bar. Training never produces such a window (every labeled bar is fully closed, entry at that bar's CLOSE), so the deployed model's final timestep - the one the LSTM/HYBRID output is keyed to - was out-of-distribution on every live decision, and pass 3's deploy-gate OOS scores measured a different query than live executed. The parity index is r=1: the newest CLOSED bar, whose close IS the current price - the exact instant the label's hypothetical entry happens. Single backtests shared the old skew (same r=0), which is why the tester agreed with live while both disagreed with training. Bookkeeping split that the index change forces: m_lastBarTime/dtStudied stay anchored to the FORMING bar's open (they gate against SERIES_LASTBAR_DATE; anchoring at bar 1 would re-fire the refresh every tick), while bt - the arrow, its High/Low placement, and NMS declustering - anchors to the decision bar, now matching the rescan path's convention. Also: a failed refresh no longer trades the previous bar's signal for the whole bar. RefreshLatestSignal returns success, zeroes dPrevSignal on failure (no opinion beats a stale one), and RefreshConvergedSignal advances dtStudied only on success so the next tick retries - the tester path (m_lastBarTime) already worked this way; this is the live path catching up. FORCES RE-VALIDATION of deployed models: the effective live query distribution changes. Bundled with the backprop transpose fix's retrain. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> |
||
|
AnimateDread
|
e2c959331f |
perf: the excursion head cost 3.6x era time - cut its dispatches ~250x
Measured on exc-race-v3: LSTM era 300s -> 1087s (net 272->748s, "other" 30->337s). My estimate had been "single-digit percent". The cost is per-DISPATCH, not per-FLOP, and therefore hits EVERY backend: the head is 19k weights and ~2.4 GFLOP an era - seconds of arithmetic - but ~48k forward/backward calls x several layer submits each, and its 760-wide layer exceeds the CPU DLL's inline threshold so each one pays a real handoff. The classifier's own net time tripled too, from contention with a second pool on an already-full box. Three changes, all backend-neutral because they remove submits rather than tune threads: SCORE ONLY DISJOINT WINDOWS (~64x). Adjacent bars share all but one bar of their horizon, so 16k consecutive bars were always ~250 independent observations - the full-sample tally was never worth more than the disjoint one, it just quoted an n that was ~64x too large. Dropping it costs nothing statistically and removes 63 of every 64 forward passes. The two parallel tallies collapse into one, which is also less code. The trailing ring still advances on every bar: it needs the outcome SEQUENCE, and that is array lookups, not a forward pass. TRAIN ON EVERY 4th PRIMARY BAR (4x). The target is low-dimensional and strongly autocorrelated - neighbouring bars carry near-identical excursion information - so per-bar training buys resolution the target does not have. Strided on ATTEMPTS, not acceptances, so a stretch of unlabelled bars cannot silently change the spacing. OWN TIMING COLUMN. The head's passes were landing in the era line's "other" bucket, which is how a 3.6x regression read as an unexplained jump in the one column nobody attributes. A cost that cannot be seen in the timing line cannot be traded off against anything. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> |
||
|
AnimateDread
|
345a672500 |
fix: purge every EA object namespace on init and after deinit teardown
Leftover objects survived deinit because the cleanup list had drifted. PurgeChart()'s own comment said it removed "our namespaced signal arrows plus the status-label objects" while the code removed arrows ONLY, and the panel prefix was swept at OnInit and nowhere else - so an ordinary deinit left the status line, and any panel straggler, on the chart. Three scattered call sites and a comment cannot be kept in step. There is now ONE list - WarriorChartPrefixes() - covering arrows, status label and panel, and one sweep, WarriorPurgeChartObjects(), used by every path. Add a prefix there when a new object family appears and every cleanup picks it up. Two call sites added: OnInit, before ANYTHING is drawn (including the status label it would otherwise delete). Chart objects live in the chart PROFILE, not in the EA, so they outlive the process: a deinit force-terminated at MetaTrader's ~4,500 ms budget, a crash, a terminal kill, or an .ex5 replaced while attached all strand objects no later deinit will ever own - and deleting the EA's files does not remove them, which is why they read as corruption. Arrows are included: LoadChartSignals restores them from their sidecar moments later and already opens with its own arrow sweep, so this only removes orphans the sidecar does not account for - the ones SaveChartSignals would otherwise ADOPT, since it rebuilds that sidecar by scanning the chart. OnDeinit, after ExtPanel.Destroy. Destroy walks an unbounded control tree and ClearStatusLabel clears text rather than guaranteeing object removal; either can leave a straggler and nothing looked afterwards. Bounded work - three prefix deletes and one object-list scan - so it respects the ordering rule that keeps the cheap visible cleanup ahead of the heavy save. Arrows excluded: ShutdownChartCleanup already persisted and removed them and re-deleting would race that write. The two are complementary: the deinit sweep closes the ordinary case, the OnInit purge closes the case where MetaTrader never let us finish. Only the second can help after a starved shutdown. Both sweeps rescan by name across EVERY object type and delete what the bulk call missed. ObjectsDeleteAll's return has already been observed disagreeing with a by-name scan of the same chart microseconds apart, and object commands are queued on the chart rather than applied inline, so a returned count is not evidence the objects are gone. Panel create site now uses WARRIOR_PANEL_PREFIX instead of a literal, so the name cannot drift away from the list that cleans it up. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> |
||
|
AnimateDread
|
4cfbb82634 |
feat: race the excursion head against a trailing-quantile incumbent
Beating a frozen global constant is the weakest admissible bar for replacing a global constant. The honest incumbent is a rolling rung frequency: it adapts to the volatility regime - exactly what the head claims to predict - and needs no model, no 760 inputs and no training. Implemented as a ring of per-bar outcome bitmasks (32 rungs fit one ulong), sized horizon + EXCURSION_TRAIL_WINDOW. The newest `horizon` entries are held back UNRESOLVED: a bar's rung outcomes are only known one horizon later, so using them would be lookahead and would flatter the incumbent into an opponent the head could never fairly beat. Pass 3 walks oldest-to-newest, so "pushed more than horizon bars ago" is exactly "resolved by now". Each push is O(rungs), not O(window). The head's decision-rung Brier is pro-rated to the trailing estimate's coverage before the ratio, since the incumbent only scores bars where its window is warm. This line is worth reading on its own, independently of the head: if the trailing quantile beats the global constant, that is a cheap risk-control win available with no machine learning at all - and it is the same number either way, so the run answers both questions in one pass. The ring is deliberately NOT reset per era - it estimates the market, not the era, and re-warming 500 bars every era would leave the incumbent unusable over the first chunk of every scoring pass, handing the head a free win on exactly those bars. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> |
||
|
AnimateDread
|
06d4785e39 |
fix: the excursion gate would have passed Stage 2 on an artifact I made
Second-opinion review killed the +4.2% far-rung result, correctly, and
the mechanism is my own bug. A head trained toward {0.05,0.9} converges
to 0.05+0.85p, so its bias is 0.05-0.15p: negative where p is near 1,
POSITIVE where p < 1/3, growing monotonically as the rung gets farther.
Against a baseline frozen at the IS rate, an upward-biased head scores
positive Brier skill whenever the OOS rate merely sits above the IS rate.
Predicted signature: huge negatives near, ~zero at p=1/3, growing
positives far. Observed: -82% ... -0.6% ... +1.2/+2.7/+4.2. The far rungs
were not the clean end of a distorted measurement, they were the other
face of the same artifact. Everything before
|
||
|
AnimateDread
|
950b0fdab0 |
diag: name the cause when every feature window fails, and enforce the width contract
Era 0 stalls with "NOT ONE of 54681 scanned bars produced a usable feature window, windows ok=0 failed=54681" and nothing else. That line reads identically for a cold ATR, a conditionally-missing optional feature block and an out-of-range index, so it cannot be diagnosed without one restart per hypothesis. Two changes: 1. WIDTH CONTRACT in BufferTempData. Every enabled block must emit exactly m_neuronsCount values on EVERY bar. A block that emits its values on some bars and skips them on others (indicator, panel or series unavailable for that bar) does not merely shorten the window - it SHIFTS every feature after it into the wrong slot, and the net then trains on silently misaligned inputs that still look like a valid window to everything downstream. Now rejected, rolled back and reported once, naming the optional blocks (XA / SPR / swing context) as the ones carrying an availability test. Worth having independently of the current stall. 2. BuildFeatureWindow records WHICH lookback slot rejected and how much of the window was assembled, and the pass-1 stall report renders it: "slot 0 of 20 REJECTED (window had 0 of 760)" is an indicator warm-up or history-edge read; "every lookback bar ACCEPTED and the window was still short: 640 of 760" is a missing 6-value block. No behaviour change on a healthy run: the width check is an equality that already holds, and the diagnostics render only inside the total-failure branch. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> |
||
|
AnimateDread
|
2d28f6542b |
feat: excursion-size head (Stage 1, measurement only)
Direction is closed - normalised asymmetry fails on three instruments with a working positive control, and the classifier's own best-of-999 era-cap test agrees (+0.9pp = 1.48 sigma, family-wise p=1.0000). SIZE is a different question and RANGE clears at ~4x its null. Checked the denomination before building on that, since the source memo warns to: m_excUpCache holds (maxHigh - fill)/ATR, so "RANGE is predictable" is a claim about travel RELATIVE to current ATR, not a restatement of "ATR is autocorrelated". It is exactly the part a fixed multiple (stop 3.31*ATR, target 1.64*ATR) discards. A second small CNet, 760 -> 24 -> 32 sigmoid outputs = P(price reaches ladder rung k) upward and downward. Survival parameterisation rather than regressing the multiple, because it needs nothing new from CNet: sigmoid outputs and the per-neuron delta the `total != 3` branch already applies (a quantile head would need a linear activation and a pinball gradient in Network.mqh, Network.cl and the DirectML path, on a class four topologies share). Targets are free - m_ladderUpAt already records first-touch age per rung with 0 meaning never reached. Separate net, not extra outputs on the classifier: more outputs would change m_outputNeuronsCount, the .nnw shape and the fingerprint, and push the count off 3 - the exact condition backProp uses to select the joint softmax gradient the 3-class head depends on. The classifier is bit-for-bit unaffected and this is removable without trace. STAGE 1 PLACES NO ORDERS. It reports a Brier skill score against the constant per-rung base rate - the baseline a fixed ATR multiple already assumes - with both predictors fitted IS and evaluated OOS, so neither gets a look at the test set. Positive skill justifies Stage 2 (drive SL/TP and sizing off ExcursionQuantile, which is defined and deliberately uncalled). Zero or negative means ATR already carries everything and Stage 2 must not be built. Trains only on primary occurrences: the replay queue oversamples for CLASS balance, and a direction-balanced sample is a biased SIZE sample. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> |
||
|
AnimateDread
|
2189316c35 |
fix: the operating point was fitted on bars the net had memorized
FitDirConfThreshold harvested its margin histogram from pass 2's own
backprop samples. Pairing every fit against the same era's OOS result
shows what that measured:
PAI era 1 IS 25% cov @ 66.1% (-0.8pp) -> OOS 64% (-3pp) gap +2.1pp
PAI era 76 IS 90% cov @ 79.6% (+12.7pp) -> OOS 65% (-2pp) gap +14.6pp
LSTM era 9 IS 77% cov @ 81.6% (+14.6pp) -> OOS 63% (-4pp) gap +18.6pp
The gap grows monotonically while OOS stays flat, so within a handful of
eras the curve stops describing behaviour on unseen bars. That is fatal
here specifically, because the objective branches on the SIGN of
(p - break-even): the memorized curve reads +12pp at 95% coverage, so
coverage x (p - p0) correctly maximises coverage and returns ~0.02 - fire
on every bar. The "p < p0 -> get more selective" branch, which is the
actual regime and the entire point of
|
||
|
AnimateDread
|
d919a4aea2 |
feat: 10-bar decluster window + alternation on every signal consumer
SignalClusterWindow 3 -> 10 for all topologies. On H1 a 3-bar window
collapsed only the tightest runs and left visible clusters at every
turn; 10 bars is closer to the spacing of genuinely distinct setups.
ALTERNATION. Rule 1 only collapses a same-direction run INSIDE the
window; past it a second Buy is emitted with no Sell between, giving
Buy/Buy/Buy/Sell. With both directions tradeable that sequence is the
model re-entering a move it is already in rather than finding a new
one. The kept sequence must now alternate: the first signal passes,
and after that a direction passes only if the last KEPT signal was the
opposite one.
Added to ALL THREE consumers, with identical logic, because they must
agree:
- NmsLiveAccept -> the live trade
- pass 3's OOS replay -> the tally the deploy gate grades
- PruneDirectionalClusters -> the drawn history
A rule applied to only some of these certifies one strategy and trades
another - the same defect class as the geometry the gate certified
while OpenParams placed something else (
|
||
|
AnimateDread
|
ccfbc62561 |
fix: the recall gate was unsatisfiable and the LR decay was a spiral
Both made the run structurally unable to succeed, independently of any
signal in the data. Found by reading the 13:01 log.
RECALL GATE. m_objectiveMet required Buy, Sell AND Neutral OOS recall
each >= 40%. First-touch resolution (
|
||
|
AnimateDread
|
ece2154102 |
fix: flush the in-flight era on shutdown; sweep orphaned chart objects on attach
Chart objects live in the MT5 chart PROFILE, not in this EA's files. They survive a terminal restart, a recompile, and deleting every .nnw/.cfg/.stats/.arrows on disk. Only a deinit that RUNS TO COMPLETION removes them - and MetaTrader force-terminates OnDeinit at roughly 4,500 ms, so a run killed mid-cleanup orphans them permanently with no owner left to clean up after. That is the "deleted every file, recompiled, restarted, old arrows and a stale panel still there" report: nothing was wrong with the files and deleting them could not have helped. Both halves are fixed. STOP OVERRUNNING THE BUDGET. OnDeinit used to finalise the in-flight run (StopTraining -> FinalizeTrainRun: checkpoint restore, live-state re-seed) and then write two full nets per chart. On four charts that is the bulk of the budget, spent to preserve a PARTIAL era that was never scored, never checkpointed and never deployable. FlushTrainRun() discards it instead - drop the resumable bookkeeping, leave the net neutral (unfreeze BN, flush the batch, batch size 1), skip the save - and training resumes from the last completed era, which the era-end save and the periodic autosave have already put on disk. What is discarded is bounded by one era. A CONVERGED model keeps the old finalise-and-save path: its weights can carry online-learning updates made since the last era boundary, and for a deployed model no further era boundary is coming to persist them. MAKE CLEANUP SELF-HEALING. Every purge sat behind a branch - no model loaded, sidecar missing - so the common paths returned leaving whatever the previous instance stranded. LoadChartSignals now sweeps the arrow namespace unconditionally before restoring, so the post-init chart holds exactly what the sidecar holds whichever branch runs, and the panel gets the same treatment before Create() (CAppDialog namespaces its controls, so a killed Destroy strands the lot and the next attach draws a second panel on the corpse). Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> |
||
|
AnimateDread
|
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> |
||
|
AnimateDread
|
ba13eefecc |
fix: a resumed model cached a cold ATR as permanent, so it never trained
BufferTempData cached EVERY failure - m_featureCacheHasValue[idx]=true with m_featureCacheValid[idx]=false - and the cache never re-tries a miss. So a single feature read taken before the terminal had finished calculating the indicator buffers marked those bars unusable for the rest of the process, even though the data arrived milliseconds later. MT5 fills an indicator's buffers asynchronously after the handle is created, and a cold ATR returns 0 for EVERY index, not just its warm-up tail. BufferTempDataCompute rejects a bar with no ATR (correctly - the price features would be meaningless), so the whole window failed, and the whole cache was poisoned. Only resumed models were hit, because only they read features that early. Topology.mqh sets m_warmupPassesRemaining = netLoaded ? 0 : 3: a fresh start sits through three separately-scheduled Train() calls before anything touches a feature, which is exactly what those passes are for. A resumed one skips them and TuneIndicatorsAndTrain drives StartLabelCachePrebuild and the MI report from the first chart event. Its rationale - "a restart already has a proven-synced history" - holds for HISTORY and not for INDICATORS, which are recreated every process start. Downstream: BuildFeatureWindow failed on every bar of every era, so add_loop never went true, so pass 2, pass 3, the era counter and the checkpoint were all skipped and pass 1 swept 0->100% forever. The "0 samples" MI report line at startup was the same failure, four seconds earlier, already visible in the log. - a miss is now cached only when it is PERMANENT; the two "not ready yet" guards mark m_featureFailTransient and are recomputed on the next visit. Steady-state cost is ~ind_Periods bars per era, not 54k. - an era that discards itself now drops the feature cache before restarting, so any remaining cause of this state self-heals instead of looping. Deleting the .nnw "fixed" this only by turning the model back into a fresh one. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> |
||
|
AnimateDread
|
464a0fe19d |
diag: an era that discards itself now says so instead of scanning forever
add_loop is exactly "at least one bar produced a usable feature
window". When it stays false, pass 2, pass 3, the era counter, the
checkpoint and every log line in the era-end block are ALL skipped:
Train() returns having done nothing, m_eraResumePending is still false,
and the next call restarts the SAME era from bar 0. That is an
infinite 0->100% "scan" loop that prints absolutely nothing - the only
remaining silent restart path in Train(), and it matches the reported
symptom exactly.
Pass 1 now counts usable vs unusable windows and reports at the pass
boundary, which demonstrably executes:
- total failure routes through ReportTrainStall (already capped at
one line a minute, and carries the run-state flags) naming the
counts, the required window width and the bar count
- success prints how long the scan took and how many samples it
handed to pass 2, but only once the era has passed 10s - a fast
era stays as quiet as before, a slow one distinguishes "advancing"
from "sweeping the same bars forever"
A PARTIAL failure is normal and deliberately does not shout: pass 1
walks oldest-to-newest and the deepest bars predate the indicators'
warm-up, so those windows fail and are cached as misses.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
|
||
|
AnimateDread
|
3855d4666a |
diag: the heartbeat could be outrun by the condition it watched for
It fired only on 4096-item boundaries once an era had already run 60s. Those boundaries are all crossed in the first few chunks of pass 1, so an era that became slow AFTER them printed nothing at all - which is precisely what happened: 20 minutes, four pegged cores, zero heartbeats. I read that silence as "the era loop is never reached" and went looking for a wedge above it. The silence may simply have meant "past the last boundary". A diagnostic whose trigger can be outrun by the condition it watches for is worse than no diagnostic, because it produces confident wrong conclusions. Now time-gated: checked every 256 items (the mask only keeps GetTickCount off the hot path), prints when the era has run >60s and >30s since the last line, up to 12 per era. Progress/phase for the panel is still published on every call, before any gate. Both build variants compile 0 errors, 0 warnings. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> |
||
|
AnimateDread
|
b461844767 |
fix: prebuild and era sized different windows; diag: Train() names its branch
TWO things, one incident. 1) THE BUG I SHIPPED IN |
||
|
AnimateDread
|
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> |
||
|
AnimateDread
|
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> |
||
|
AnimateDread
|
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> |
||
|
AnimateDread
|
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,
|
||
|
AnimateDread
|
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
|
||
|
AnimateDread
|
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> |
||
|
AnimateDread
|
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> |
||
|
AnimateDread
|
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>
|
||
|
AnimateDread
|
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> |
||
|
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
|
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
|
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>
|
||
|
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 |
||
|
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
|
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
|
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
|
||
|
AnimateDread
|
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> |
||
|
AnimateDread
|
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. |
||
|
AnimateDread
|
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. |
||
|
AnimateDread
|
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>
|
||
|
AnimateDread
|
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>
|
||
|
AnimateDread
|
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> |
||
|
AnimateDread
|
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>
|
||
|
AnimateDread
|
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> |