Option (a) from the exit-policy question: the certified number must be the traded
number. Plus the modularity correction the user called for on 778b6c0.
1. VOTE COMBINING BELONGS TO THE ORCHESTRATOR, NOT TO A MEMBER.
778b6c0 fixed the last-writer-wins bug on g_LiveAISignedConfidence by having a
member average its siblings through g_warriorEnsemble. That trades a scheduling bug
for a coupling bug, and it is the wrong shape for this EA: every signal runs in its
own instance, minds its own state, and VOTES to the orchestrator, which is the only
thing allowed to combine opinions.
Replaced with a publish/aggregate pair in Variables\ConfidenceBridge.mqh whose split
is enforced by shape rather than by convention:
- PublishAIVote(slot, conf) - a member writes ONLY its own slot, reads nobody's;
- AggregateAIVotes() - called by CExpertSignalCustom::LiveSignedConfidence.
CExpertSignalAIBase::EnsembleLiveSignedConfidence is gone. The orchestrator also
republishes the aggregate into g_LiveAISignedConfidence, because the intelligent
trailing reads that global directly and must act on the same number the exit route
does rather than on a leftover from whichever member ticked last. A solo AI signal
owns slot 0, so the non-ensemble path is unchanged.
2. THE GATE NOW REPLAYS THE REAL EXIT RULE.
SimulateTradeOutcome() walks the same price series with the same fill/barrier/spread
convention as ComputeLabelForBar - deliberately by copy, so a disagreement between
the two can only be a policy effect and never a discrepancy between two pieces of our
own arithmetic - and terminates at the FIRST of stop / target / vote reversal /
horizon. Barriers are tested before the vote on the same bar: intrabar we cannot know
which came first, and the barrier is what the broker executes automatically, so
checking the vote first would credit the exit policy with escapes a real stop would
have taken out of its hands.
It runs AFTER pass 3, not inside it. A vote-flip exit for a trade entered at bar r is
decided by the model's output at bars r-1, r-2, ... - NEWER bars - and pass 3 walks
oldest-to-newest, so at the moment r is graded its own exit does not exist yet. Only
once m_oosDecisionSeries is complete over the whole OOS window can the replay run.
In ensemble mode that series carries the member's adjusted decision and the live exit
reads the ensemble aggregate, which is the coupling the user identified: an LSTM entry
really can be closed by the ensemble turning against it.
3. THE STATISTIC HAS TO CHANGE WITH THE POLICY, AND THAT IS THE REAL FINDING.
A barrier exit pays a fixed R. A vote exit pays whatever the close happens to be. So
the moment vote exits are enabled the payoff is CONTINUOUS, and "win rate vs
break-even" stops being a meaningful test - there is no fixed break-even for a
variable payoff. SimulateTradeOutcome therefore returns R rather than a bool, and the
replay reports expectancy in R with its SE taken from the R distribution (overlap-
deflated on the same EffectiveSampleSize doctrine as every other SE here), not from a
binomial.
This is the same class of error as win-based scoring in 2026-08-09: measuring a
variable-payoff process with a fixed-payoff statistic. Naming it now, while vote exits
are still off, is much cheaper than discovering it after they go on.
4. WHY THIS IS SAFE TO SHIP TODAY.
Min_Vote_Close ships Disabled (101, unreachable on both scales it drives) and reaches
the AI signal through the new ExitPolicy() setter as 1.01, which the setter turns into
0.0 = no vote exit. Under that policy every replayed trade resolves at a barrier and
the simulation is arithmetically the same trade the deploy gate already certifies -
they cannot drift. The report says so explicitly, and prints ONCE per run in that
state; when vote exits are on it prints every era, because then the divergence is the
thing to watch. Nothing about today's numbers moves.
The gate switchover is wired but dormant by construction: it becomes exit-aware the
moment the input is enabled, which is exactly what "the certified number is the traded
number" has to mean.
KNOWN LIMIT, stated rather than hidden: only the AI early-exit route is replayed. The
rule-based averaged-vote close (m_threshold_close) depends on every other filter's
live vote, which pass 3 does not reproduce, so a position the classic filters would
have closed is held to its barrier here. The replay therefore holds LONGER than live
and overstates barrier-reached outcomes. Faithful only while the AI is the dominant
vote - which is the configuration this is being built for.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
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>
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>
In AI/Network.mqh, return early from InitDirectML during
tester/optimization/forward runs to prevent agent-side file-lock
failures caused by rapid stop/restart cycles accessing DLL imports.
In Expert/ExpertSignalAIBase.mqh, add MathIsValidNumber checks in
CalibratedConfidenceMagnitude and SignaledConfidence to safely handle
NaN values, and refactor ShutdownChartCleanup to accept a preserve
flag, avoiding unnecessary chart purges during tester runs for faster
shutdowns. Also add m_purgeChartOnDestruct member.
In AI/NeuronDirectML.mqh, clean up a minor comment formatting issue.
Replace old ATR_MULTIPLIER, THRESHOLDS_PRESET enums with new
STOP_LOSS_MODE, TAKE_PROFIT_MODE, AI_EXIT_MODE enums that support
ATR-based, intelligent confidence-scaled, and swing-anchored modes.
Also fix LSTM signal identity string.
Optimize() scaled lot size off account trade-history streaks with no Magic-number
filter (picked up other EAs'/manual trades) and an unconfigurable m_factor stuck at
1.0 (Factor() was never wired from an input), so a 3-trade streak could triple lot
size or send it negative. It was also entirely disconnected from what the AI model
actually knows about the current setup.
Replaced both AdjustRiskAmount()'s linear confidence-only scale and Optimize()'s
streak multiplier with one edge-based model: p from the empirically calibrated
AI/DB confidence magnitude, b from the trade's real reward:risk ratio (newly
bridged from OpenParams() via g_TradeRewardRiskRatio), quarter-Kelly applied and
clamped so risk% can only ever scale down from its configured ceiling, never above it.
- Define MAX_WEIGHT constant (1.0e6) for weight limits in clusters
- Remove redundant barrier from FeedForward kernel (prevents sync issues)
- Port FeedForwardProof and CalcInputGradientProof kernels for max-pooling (no weights, sliding max)
- Port FeedForwardConv kernel for convolution layers (shared weights, multiple output channels)
- Remove unused code and refactor signal condition logic (CSignalPAI)
