forked from animatedread/Warrior_EA
| Filename | Latest commit message | Latest commit date |
|---|---|---|
d20058fc1b
Meta_ExportDataset input: with AIType=META the chart writes its complete training set once per attach - every resolved+labeled candidate as [barTime|family|pattern|side|won|NetInputWidth floats] using the SAME window builder, descriptor and label caches pass 2 trains on, so offline examples are byte-equivalent to the EA's own. Sidecar .meta.csv carries layout + the geometry/BE the labels were computed at. Files land in Common\Files\Warrior_EA\MetaExport\<sym>_<period>.f32. This is the pooling architecture decision: multi-symbol training INSIDE the per-chart God-class would be the riskiest surgery this codebase has seen; instead each chart exports, the pooled head trains offline (small dense+BN net, minutes on this box), is validated per-symbol under the same chronological splits and coverage x (p - BE) gate, and only a WINNER gets written back into a .nnw for the EA to load natively (format fully mapped). Also turns every future meta experiment from a 20-minute tester cycle into minutes of offline iteration. Cost-model note for the record (user challenge, verified): spread is 0.099 ATR = ~2% of the 4.74 ATR trade width - tiny per bar, but expressed in win-rate points it is 0.099/4.74 = 2.1pp, which is the measured base-vs-BE gap and the size of the entire observed skill lift. Zero-spread relabeling would put base == BE by construction. Multi-day holds additionally pay swap, which the label does NOT charge - the true bar is higher, not lower. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> |
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| .. | ||
| README.md | ||
| SignalCONV.mqh | ||
| SignalHYBRID.mqh | ||
| SignalIchimoku.mqh | ||
| SignalLSTM.mqh | ||
| SignalMA.mqh | ||
| SignalMACD.mqh | ||
| SignalMETA.mqh | ||
| SignalNewsFilter.mqh | ||
| SignalPAI.mqh | ||
| SignalRiskGuard.mqh | ||
| SignalRSI.mqh | ||
| Signals.mqh | ||
| SignalSessionFilter.mqh | ||
Signals Subsystem (Signals/)
Overview
The Signals/ directory contains all trade signal generation logic for the Warrior EA. It includes both traditional indicator-based signals and advanced AI/ML-driven signals. Each signal is encapsulated in its own class, supporting modularity and extensibility.
Key Components
Signals.mqh
- Main orchestration file for signal modules.
- Includes both traditional and AI/ML signal classes.
- Facilitates integration of filters (news, session, etc.) and advanced signals.
AI/ML-Driven Signals
- SignalLSTM.mqh: Implements an LSTM-based neural network signal generator. Integrates with the AI subsystem, supports model training, loading, and inference. Designed for advanced, data-driven strategies.
- SignalPAI.mqh: Implements a Perceptron AI-based signal generator. Inherits from
CExpertSignalAIBase. Provides methods for initializing, training, and using a perceptron neural network for trade signal generation. Supports dynamic configuration, indicator integration, and modular AI/ML pipeline features. Designed for advanced, data-driven strategies and easy integration into the EA's AI subsystem.
Traditional Indicator-Based Signals
- SignalMA.mqh: Moving Average signal generator.
- SignalMACD.mqh: MACD oscillator signal generator.
- SignalRSI.mqh: Relative Strength Index signal generator.
- SignalStoch.mqh: Stochastic oscillator signal generator.
- SignalPB.mqh: Pin Bar pattern signal generator.
- SignalNewsFilter.mqh: News event filter for signals, configurable by impact and lookback period.
- SignalSessionFilter.mqh: Session-based filter (London, New York, Tokyo sessions).
Individual Signal Modules
Below is a comprehensive list of all signal modules in the Signals/ directory, with a brief description of each:
- SignalAC.mqh: (Removed)
- SignalAO.mqh: (Removed)
- SignalCCI.mqh: (Removed)
- SignalCONV.mqh: Convolutional AI signal. Uses a neural network for advanced pattern recognition.
- SignalDTDB.mqh: (Removed)
- SignalEB.mqh: (Removed)
- SignalIB.mqh: (Removed)
- SignalIchimoku.mqh: Ichimoku Kinko Hyo classic vote, written from scratch (no standard-library module exists). 12 patterns numbered weakest-to-strongest, covering the full repertoire: price/cloud bias, projected cloud colour and full Chikou Span confirmation (models 0-2, all at the standard-library floor weight of 10 since each is a standing state rather than a trigger); the TK cross graded weak/neutral/strong by cloud position (3/5/10, weights 10/40/90); Kumo twist (4); Kijun-sen cross and bounce (6/7); Kumo breakout and thin-cloud breakout (8/9); and Sanyaku Kōten/Gyakuten at 100 (11). Its class comment documents MT5's draw-shift-only buffer convention and the resulting lookahead hazard.
- SignalITF.mqh: Intraday Time Filter. Filters signals based on time-of-day and day-of-week.
- SignalLSTM.mqh: LSTM AI signal. Uses a recurrent neural network for sequence-based prediction.
- SignalMA.mqh: Moving Average classic vote (unified
ADMovingAveragecustom indicator; 4 patterns). - SignalMACD.mqh: MACD oscillator classic vote, ported from the MQL5 standard library. 6 patterns including single and double price/oscillator divergence — the only divergence model in the classic set.
- SignalNewsFilter.mqh: (Filters trading signals based on economic news events and impact levels. Configurable lookback window and impact threshold.)
- SignalPAI.mqh: Implements a Perceptron AI-based signal generator. Inherits from
CExpertSignalAIBase. Provides methods for initializing, training, and using a perceptron neural network for trade signal generation. Supports dynamic configuration, indicator integration, and modular AI/ML pipeline features. Designed for advanced, data-driven strategies and easy integration into the EA's AI subsystem. - SignalPB.mqh: (Removed)
- SignalRSI.mqh: RSI classic vote (4 patterns).
- SignalRVI.mqh: (Removed)
- Signals.mqh: Main orchestration file for all signals.
- SignalSAR.mqh: (Removed)
- SignalSessionFilter.mqh: (Removed)
- SignalStoch.mqh: (Removed)
- SignalWPR.mqh: (Removed)
Integration Notes
- All signals derive from a common base (typically
CExpertSignalCustomorCExpertSignalAIBase). - Modular design allows for easy addition/removal of signals and filters.
- Migration to a fully AI/ML-driven pipeline is recommended for future-proofing and improved performance.
- Some files (e.g., SignalPAI.mqh) may require conversion or external review due to non-text format.
Documented April 2026. For AI/ML migration and modernization, see AI_NETWORK.md and project roadmap.