8157c42314
- 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.
46 行
3.4 KiB
Markdown
46 行
3.4 KiB
Markdown
# Expert/ Directory Documentation
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## ExpertCustom.mqh
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- **Purpose:** Defines the `CExpertCustom` class, a base class for custom expert advisors in the EA framework.
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- **Inheritance:** Inherits from `CExpert` (not shown here, likely a core MQL5/MetaQuotes class or defined elsewhere in the project).
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- **Key Features:**
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- Provides virtual event handlers for MQL5 events: `OnTick`, `OnTimer`, `OnChartEvent`, etc., allowing derived classes to implement custom trading logic.
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- Contains methods for initializing trading objects, handling series data, and managing trading events.
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- Designed for extensibility: users can derive their own expert logic by subclassing and overriding the provided virtual methods.
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- **Modernization Note:**
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- This class is a good candidate for further abstraction to support AI/ML-driven strategies. Consider introducing interfaces or abstract base classes for signal generation, money management, and risk control, allowing plug-and-play of traditional and AI/ML modules.
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- Ensure all event handlers are unit-testable and decoupled from hard-coded logic.
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## ExpertMoneyCustom.mqh
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- **Purpose:** Implements a custom money management class (`CExpertMoneyCustom`) for the EA, extending `CExpertMoney`.
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- **Key Features:**
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- `CheckAndCorrectVolumeValue`: Ensures trade volume is within broker constraints (min/max/step), adjusting and describing corrections.
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- `CheckAndAdjustMoneyForTrade`: Dynamically adjusts lot size to fit available margin, reducing lots if margin is insufficient, and handles errors gracefully.
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- **Modernization Note:**
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- This class is a good candidate for AI/ML-driven position sizing. Consider abstracting the logic to allow for ML-based risk and lot size optimization.
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- Ensure all adjustments are logged for transparency and auditability.
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## ExpertSignalAIBase.mqh
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- **Purpose:** Provides a base class (`CExpertSignalAIBase`) for AI/ML-driven signal generation, extending `CExpertSignalCustom`.
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- **Key Features:**
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- Integrates with the neural network/AI subsystem (`Network.mqh`).
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- Manages a wide range of indicator objects (Open, Close, High, Low, Volumes, AD, ADX, MACD, etc.) for feature extraction.
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- Supports dynamic configuration of neural network topology, training, and feature selection.
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- Implements methods for indicator initialization, data buffering, training, and topology persistence.
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- **Modernization Note:**
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- This is the main integration point for advanced ML/AI logic. Ensure all feature selection and training parameters are externally configurable.
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- Refactor to support plug-and-play feature pipelines and automated hyperparameter optimization.
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## ExpertSignalCustom.mqh
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- **Purpose:** Implements a custom signal logic class (`CExpertSignalCustom`) for the EA, extending `CExpertSignal`.
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- **Key Features:**
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- Adds database-driven signal tracking, pattern recognition, and trade record management.
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- Supports dynamic filter addition, ATR-based entry/exit logic, and advanced signal buffering.
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- Implements robust duplicate detection, trade status management, and event-driven signal processing.
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- **Modernization Note:**
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- This class is central to integrating traditional and AI/ML signals. Refactor to decouple hard-coded logic and support dynamic, testable signal pipelines.
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- Ensure all database operations are abstracted for testability and future migration to more advanced data stores.
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---
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(Other files in Expert/ will be documented as they are processed.)
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