forked from animatedread/Warrior_EA
| Filename | Latest commit message | Latest commit date |
|---|---|---|
The labelMatchesVote gate compared a single last-writer-wins label (LongCondition then ShortCondition) against the net vote sign, which structurally censored the pattern tables: a long event co-occurring with any short-side state model lost its label to the later writer and was dropped, while the mirrored short event journaled fine. Ichimoku models 0/3 and MA model 1 could not produce a row at all by construction (MA model 1 was "revived" in |
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| .. | ||
| README.md | ||
| signalInfoStructure.mqh | ||
| TradeRecordStructure.mqh | ||
Structures Subsystem (Structures/)
Overview
The Structures/ directory contains data structure definitions used throughout the Warrior EA for organizing and passing trading-related information. These structures are essential for maintaining clean, modular, and testable code, especially as the system evolves toward AI/ML-driven logic.
Key Components
signalInfoStructure.mqh
- Struct:
SignalInfo - Purpose: Encapsulates all relevant information about a generated trading signal.
- Fields:
year,month,day,DOW,hour,minutes: Timestamp of the signal.tableName: Source or context table for the signal.pattern: Name or type of the detected pattern.direction: Trade direction (e.g., buy/sell).entryPrice: Price at which the signal was generated.
TradeRecordStructure.mqh
- Struct:
TradeRecord - Purpose: Stores all relevant information about a completed trade.
- Fields:
year,month,day,day_of_week,hour,minutes: Timestamp of the trade.pattern: Pattern or strategy used for the trade.direction: Trade direction (buy/sell).entryPrice: Entry price for the trade.exitPrice: Exit price for the trade.result: Outcome/result of the trade (e.g., win/loss).
Integration Notes
- These structures are used for logging, analytics, and passing data between subsystems (signals, database, AI/ML modules).
- Consistent use of well-defined structures improves maintainability and supports future AI/ML integration.
Documented April 2026. For further details, see the main project documentation and AI_NETWORK.md.