The control panel drove training by looping g_aiSignals[] - a
hand-maintained, MAX_AI_SIGNALS-capped, AI-only registry that had
already dropped an ensemble member on the floor once (609be10). A model
missing from it still trains and still votes, it just cannot be paused,
stopped, deployed or reset, and every button label is computed from the
same short list, so the panel described one set of models while acting
on another. Classic signals could not respond to a panel action at all.
Commands now walk the signal tree CExpert already owns:
Expert.DispatchSignalCommand(cmd) -> root signal -> every filter,
recursively, returning how many actually acted.
CExpertSignalCustom carries the seam (OnSignalCommand / HasSignalTrait,
both no-ops by default), so a classic signal opts in by overriding two
methods and needs no registration and no cap. CExpertSignalAIBase
implements the training commands over its existing Pause/Stop/Deploy/
Reset methods - the behaviour is unchanged, only its reach reported.
Button labels ask the same tree via CountSignalTrait, with
SIGTRAIT_TRAINABLE as an explicit denominator: "all paused" is
meaningless without knowing how many could be paused. Pause/Stop resolve
their toggle direction ONCE in the EA and hand every model the same
plain command, instead of each re-deriving the direction from its own
local state - which is how a mixed set ends up half paused. The alerts
now report the count acted on rather than assuming it.
Two dispatch bugs found on the way, both from a database guard copied
onto event delivery: CExpertSignalCustom::OnTickHandler and
::OnChartEventHandler each skipped any filter whose GetFilterID() is
"NULL". That id is a DB folder name, and CSignalNewsFilter,
CSignalSessionFilter and CSignalRiskGuard never set one - so all three
were silently receiving neither ticks nor chart events. The guard stays
where it belongs, on the paths that write pattern tables.
ENUM_CP_ACTION moves to Enumerations\GlobalEnums.mqh (now include-
guarded) because the Expert bases have to name it and the panel is
included long after them.
The AI-only lifecycle loops - PollTraining, the weight autosave,
AltDataReload, OnDeinit's shutdown cascade - still use g_aiSignals[] and
are untouched here.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Four defects in 64c5dd5/1a05e63, found by review + a baseline compile.
Goals 1-8 of that session are unchanged; this makes 6 and 8 actually reachable.
1. HEAD DID NOT COMPILE - 6 errors. CControlPanel::Minimize/Maximize were
declared `virtual bool ... override`, but CAppDialog declares both as
`virtual void` (Controls\Dialog.mqh). errors 265 + 404 on each, plus 151
on `bool ok = CAppDialog::Minimize()`. Return type is void now; there was
never a success flag to forward. Verified: 0 errors, 0 warnings.
2. THE BACKFILL COULD NEVER ADVANCE, and neither could the OOS continual
simulation (that one has been dead since it was written). Both are armed
at the instant convergence is declared, and both advance only from inside
Train(), one chunk per call. But ScheduleTrainingIfNeeded's only per-tick
ArmStudyEvent site sits in the `else` of a branch taken whenever
m_trainingComplete is set and m_trainRunActive is clear - which is exactly
the state FinalizeTrainRun() leaves behind one line before they are armed.
Train() was never called again, so the walks sat at their start index
forever: no "simulation complete" line, and not one row written to the DB
this feature exists to fill. Only a manual Resume/Retrain unstuck them.
Both flags now keep the model schedulable.
3. IN AI_HYBRID - the mode this ships in - the backfill was never even armed.
Ensemble members deploy at Train() ENTRY and return immediately (so no era
is wasted), which skips the era-end block the backfill was started from.
All four members were a no-op for a second, independent reason. Armed on
the ensemble deploy path too, from m_resumeBars/m_resumeOosCutoff.
4. RE-RUNS DUPLICATED ROWS. RegisterSignal inserts unconditionally - no key,
no duplicate check - and m_dbBackfillDone is in-memory, so every later
attach that retrained to convergence wrote a second full set of rows for
the same bars. The ranking would count one bar once per model that ever
deployed, weighting superseded opinions as heavily as the live one. A
.dbfill marker stamps the deployed era; written only on completion (an
interrupted walk redoes itself rather than ranking a partial window) and
deleted with the other sidecars on reset-weights.
Also: WarmBlocking's timeout was silent, which restored the exact silent
pin failure it was added to prevent - it now says so in the journal, and
returns true for "no reference pairs to wait for" so the warning stays rare
enough to be read.
Not addressed, needs a decision: the backfill scores the OOS window with the
checkpoint that was SELECTED as best on that same window, then writes those
win rates into the table filter weights rank on - the selection set consumed
twice, undiscounted, while the deploy gate right next to it applies a
family-wise correction for exactly that effect. The rows are also simulated
triple-barrier outcomes at today's spread sharing a table with realised
fills. The completion log line now states both plainly.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Extract shared persistence logic into PersistDeployedModel() to guarantee consistency across all deploy paths (ladder, era-cap, stop, and manual). Add DeployNow() to let the panel button finalize the current best checkpoint as the live model, and RetrainDeployed() to revert deployment and resume training from the deployed weights without starting from scratch. This enables operator-controlled deployment while preserving online continual learning.
Add CaptureWeights and RestoreWeights methods that snapshot every neuron's weights into host arrays (CArrayDouble per neuron) and restore them in-place via setWeights. This replaces the file-based SaveCheckpoint/LoadCheckpoint for the mid-run best-era rollback, because the file path re-creates neurons (CLayer+Init) which fails on the multithreaded CPU-DLL backend (CDirectMLMy/WarriorCPU.dll) that cannot allocate a second full set of neuron tensors while the live set exists. In-memory weight copy uses only getWeights/setWeights, already proven by the per-era shadow blend. Snapshots weights only (not Adam moments); the regression handler decays eta on restore and clips per-step deltas to prevent stale-moment overshoot. Snapshot is valid only within a single Train() run. Also adds HaveWeightSnapshot() query and the m_weightSnapshot / m_haveWeightSnapshot member variables.
- Added ConfirmDestructiveAction() function that shows a warning MessageBox before executing destructive reset/delete operations.
- Used confirmation for "Reset" (AI weights) and "ResetDB" (database) actions to prevent accidental data loss.
- Moved "Report" button creation before the "Signals" button so that the two red (destructive) buttons appear adjacent at the bottom of the panel.
- Added optional `weighScale` parameter (default -1.0) to `CNeuronBase::Init` and `CLayer::CreateElement`.
- Updated `CNeuronPool::Init` to use LeCun-uniform scaling (1/sqrt(window+1)) for its base initialization.
- Updated `CNet::CNet` to use He-scaled initialization (sqrt(2/neurons)) for dense layers.
- These changes enable more flexible and statistically sound weight initialization, matching the rationale used in OCL-based implementations, leading to better training stability and convergence.
Replace the absolute thread count input (CpuDllThreads) with a percentage-based CPU_LOAD_PRESET enum (TargetCPULoad). This allows users to specify a percentage of detected cores to use when the CPU DLL fallback is active, improving flexibility and preventing issues when multiple instances share the same CPU DLL pool. Also adds CPU_GetHardwareConcurrency() for accurate core detection.
