forked from mnbvc188199/Warrior_EA
AI Expert Advisor
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| Filename | Latest commit message | Latest commit date |
|---|---|---|
1a900a0a35
Era-680 report, all three observations one equation: "peak 29, no arrows at
threshold 30" / "at 20, arrows on EVERY bar" / "label at 12 while arrows
everywhere". Under the voters-only divisor, any bar with at least one
directional voter read the weighted mean of the firing tiers' weights - and
once the tiers self-ranked to each model's pooled win rate (~28-31), that
mean was NEAR-CONSTANT regardless of headcount. One member alone: ~29. Four
unanimous: ~29. Min_Vote_Open was a step function around that constant -
above it nothing ever fired, below it everything did - and the label's 12
was a 3v1 split netting through the same divisor. Not three display bugs:
one arithmetic that could not express agreement.
The divisor is now the CAPABLE weight - every filter that could vote,
whether it did or not:
* live (Direction): VoteCapableWeight() - classic pattern ladders always,
veto filters never, AI members once past the same readiness test
LongCondition gates on. A model still training must not dilute an
ensemble it cannot join: four trainees + one deployed model is a solo
chart wearing an ensemble label, and the solo vote reads full strength.
* gate (EnsembleEraVerdict): g_ensVoteWeightSum accumulates for every
member that EVALUATED the bar, Neutral included.
* overlay sweep + prospective readout: weight counts whenever the member
has data; a snapshotted Neutral dilutes.
One arithmetic, four sites, same numbers everywhere.
What the numbers become (four members, w~0.29, tiers~29): unanimous ~29 -
the CEILING, which is the pooled win rate and is what the peak displays;
3-of-4 ~22; 2-of-4 ~14.5; 3v1 ~14.5. Min_Vote_Open 20 now means "roughly
three-quarters of the ensemble's trust agrees, net". It MUST sit below the
ceiling to ever fire - the census/peak states the ceiling.
This is the ensemble the user specified in the original design discussion
("if the perceptron also votes, both together reach the threshold; if
another NN votes the other side, the threshold is not reached") - union
semantics was the pre-ensemble behaviour, kept until measurement showed its
vote magnitude was a constant.
Plus overlay DECLUSTERING, the other half of "arrows on every bar": the
same three NMS rules as the per-member arrows (same-direction runs collapse
to their first bar, cross-direction flicker keeps the stronger side), online
over the sweep's strictly oldest->newest walk. Suppression is a verdict and
deletes a standing arrow; the den==0 no-data skip still never does.
NOT COMPILED - user compiles in MetaEditor.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
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| .claude | ||
| .clinerules | ||
| AI | ||
| Database | ||
| DirectML | ||
| docs | ||
| Enumerations | ||
| Expert | ||
| Market Descriptions | ||
| Marketing/Logo | ||
| Money | ||
| Panel | ||
| references | ||
| research | ||
| Scripts | ||
| Signals | ||
| Structures | ||
| System | ||
| Trailing | ||
| Variables | ||
| .gitignore | ||
| AI_NETWORK.md | ||
| cpu_directml.log | ||
| DATABASE.md | ||
| EXPERIMENTS.md | ||
| Meta_Labeling_Design.md | ||
| opencl.log | ||
| profiling.csv | ||
| README.md | ||
| REFACTOR_NOTES.md | ||
| SIGNALS.md | ||
| Warrior_EA.md | ||
| Warrior_EA.mq5 | ||
| Warrior_EA.mqproj | ||
| Warrior_EA_System_Overview.md | ||
Warrior_EA Project Overview
Description
Warrior_EA is a modular, AI/ML-ready MetaTrader 5 Expert Advisor designed for robust, production-grade trading. It integrates traditional and AI-driven signals, advanced money management, trailing stops, and a database/statistics subsystem for adaptive optimization.
Key Features
- AI/ML Integration: LSTM, PAI, and CONV neural network signals, with configurable feature pipelines and training options.
- Traditional Signals: Modular support for classic indicators (MA, MACD, RSI, etc.) and price action patterns.
- Money Management: Fixed lot, fixed risk, and intelligent/adaptive strategies.
- Trailing Stops: ATR-based, MA-based, Parabolic SAR, and more.
- Database/Statistics: Tracks trades, signals, and performance for optimization and research.
- Configurable Inputs: All major features and strategies are user-configurable via Inputs.mqh.
- Robust Initialization: Retry logic and error handling for all critical subsystems.
- Production-Ready: Designed for institutional and advanced retail use, with a focus on maintainability and extensibility.
Directory Structure
- AI/: Neural network and ML logic
- Database/: Database and statistics management
- Enumerations/: Enum and type definitions
- Expert/: Main EA orchestration and custom logic
- Money/: Money management strategies
- Signals/: Signal generation (AI and traditional)
- Structures/: Data structures for signals and trades
- System/: Utility and infrastructure modules
- Trailing/: Trailing stop strategies
- Variables/: Global input parameters and runtime variables
Getting Started
- Configure your desired strategies and features in
Variables/Inputs.mqh. - Compile
Warrior_EA.mq5in MetaEditor. - Attach to a chart and enable Algo Trading.
- Monitor logs and database/statistics for performance and optimization.
Modernization & AI/ML Roadmap
- Migrate all hard-coded signals to a configurable, feature-driven pipeline.
- Expand AI/ML subsystem with new models and training options.
- Enhance database/statistics for deeper analytics and automated optimization.
- Introduce unit and integration tests for all modules.
Documented April 2026. For subsystem details, see each directory's README.md and AI_NETWORK.md.