- Ensure `tick_volume` array is set as series in ADShorteningOfThrust.mq5 to prevent future-data leak in volume calculations.
- Ensure `open` array is set as series in ADWyckoffFailedStructure.mq5 to prevent future-data leak in structure detection.
- Add missing PReLU gradient scaling (multiply by 0.01 for negative outputs) in CPU_CalcHiddenGradient and DirectML shader to match expected derivative behavior across all backends.
- Extend `CNet::getResults` and `CLayer::Load` to support `defNeuronConvOCL`, `defNeuronPoolOCL`, and `defNeuronLSTMOCL` types (previously only `defNeuronBaseOCL` was handled), preventing potential crashes or incorrect outputs with those neuron types.
- Increase `ind_Periods` default from `PERIOD_5` to `PERIOD_20` because `PERIOD_5` was smaller than ADZigZag's `InpDepth=12`, making it structurally impossible for the model to see enough bars to recognize swing pivots, likely causing the erratic Buy/Sell OOS recall observed during training runs.
- Added MAX_CLASS_SAMPLE_WEIGHT (3.0) to cap the sample weight multiplier, preventing anti-correlated Buy/Sell recall whipsaw observed when per-era ratio overwrites previous separability.
- Added MIN_OOS_CLASS_SAMPLES_FOR_GATE (10) to require a minimum number of true OOS samples before trusting class recall, avoiding degenerate false-convergence cases.
- Increased TRAIN_TIME_BUDGET_MS from 80 to 500 to improve throughput under MT5's custom event dispatch model (which determines actual pacing); trades UI responsiveness (~500ms lag) for roughly 6× more bars processed per event.
- 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)
- Updated README.md with project overview, key features, directory structure, getting started guide, and modernization roadmap.
- Added AI_NETWORK.md detailing the neural network and AI/ML infrastructure, including architecture, components, usage patterns, and next steps.
- Introduced DATABASE.md for the Database module, outlining key components, design highlights, usage patterns, and future enhancements.
- Created README.md files for Enumerations, Expert, Money, Signals, Structures, System, Trailing, Variables directories, detailing their purpose, key components, and integration notes.
- Documented the Signals subsystem, emphasizing modularity, extensibility, and AI/ML readiness.
- Added comprehensive descriptions for individual signal modules in Signals/ directory.
- Established clear integration notes and recommendations for future improvements across all modules.
