- Replace the hand-rolled fractal/deviation-%/ATR-trend-context ZigZag
approximation with the actual MQL5 ZigZag indicator (rebranded as
CustomIndicators/ADZigZag.mq5, logic untouched) as the training label
source; bump the settle/confirmation window from 20 to 100 bars so a
proper leg can form before being trusted, and drop the now-dead
ZigZagDeviationPct/MinTrendATRMultiple/TrendContextBars inputs.
- Rebrand the stock Volumes indicator the same way (ADVolume.mq5).
- Fix HYBRID mode (AIType=HYBRID) oversubscribing the CPU fallback tier:
every concurrent CNet instance was independently sizing its worker
pool off the same global TargetCPULoad input. g_netPeerCount/
PeerNetworkCount() now split it across however many CNet instances
(live+shadow x active signals) are actually sharing the CPU.
- Fix NEURONS_REDUCTION_FACTOR's confusing retention-vs-reduction
semantics so RF_70 means an actual 70% reduction; default to 4 hidden
layers / RF_70.
- Migrate remaining free-form training/indicator inputs to enums for UI
consistency; default news filter lookback to 1h, disable every-tick.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
- Tighten MAX_WEIGHT from 1.0e6 to 100.0 to prevent unbounded weight growth.
- Add MIN_ACTIVATION_DERIVATIVE (1e-4) to avoid zero gradients for saturated units.
- Introduce WEIGHT_DECAY (0.01) to decouple decay from Adam updates.
- Add MAX_WEIGHT_DELTA (0.1) to clamp per-step Adam updates and prevent overshoot.
- Apply sign-agreement gate on weight updates in Adam kernel to only apply steps aligned with current gradient direction.
- Fix tanh/sigmoid derivative calculations to use the new floor instead of hard-coded edge-case values.
Warrior_EA.mq5: clear Comment() and destroy the control panel before
Expert.Deinit()'s object-purge cascade runs out from under it; stop
re-registering the same signal filters on every DB retry (was causing a
double-delete of the same pointer on shutdown).
DirectML/WarriorCPU.cpp: bound the worker-thread join in ThreadPool::Stop()
instead of blocking forever - CPU_Shutdown() held g_mutex across an unbounded
join, so a watchdog-killed calling thread could leave it locked forever,
poisoning every future call into the DLL (matches reports of the EA getting
stuck on "initializing" after being removed and re-added to a chart).
Also includes prior era-0 label-cache prebuild and pullback/reversal
label-quality work in AI/Network.mqh, Expert/ExpertSignalAIBase.mqh, and
Variables/Inputs.mqh.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
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.
Changed default HiddenLayersCount from LAYERS_5 to LAYERS_3 and NeuronsReduction from RF_80 to RF_60 to improve model generalization and reduce overfitting.
Add #resource directives for WarriorDML.dll and WarriorCPU.dll, and ExtractComputeDlls() to extract them on first run, enabling MQL Cloud Protector builds to ship DLLs without manual copy. Extend CNet::Save and Load with trainingComplete and indicatorParams arrays to persist AutoTune indicator param values.
- 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)
