669ecde7b7
- Added comprehensive MQ4/MQ5 comparison documentation (60+ pages) - Added MT4 EA implementation plan and tracking documents (60+ pages) - Phase 1: Core infrastructure with input parameters and data structures - Phase 2: Bar detection system for H4, M30, M15 timeframes - Phase 3: Pattern detection logic (Regular/Irregular Buy/Sell patterns) - Reference files: FINAL_H4_ZONES.mq4, MultiTimeframeZoneEA.mq5, Strategy.md
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1.3 KiB
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17 lines
No EOL
1.3 KiB
Markdown
---
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applyTo: "**/*.py"
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---
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# Instructions for _Thivyam Python Scripts
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- **Purpose:** Python scripts are used for tasks like advanced back-testing, data analysis, machine learning model training, and generating reports from MetaTrader data.
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- **Style:** Follow the PEP 8 style guide for all Python code.
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- **Libraries:**
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- For data manipulation, use `pandas`.
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- For numerical operations, use `numpy`.
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- For plotting and visualization, use `matplotlib` or `plotly`.
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- **Type Hinting:** Use Python type hints for all function signatures to improve code clarity and maintainability.
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- **Documentation:** All functions and classes must have clear docstrings explaining their purpose, arguments, and return values.
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- **Logging:** Use the standardised `_Thivyam` logging helpers (import from `thivyam.logging`) or Python's `logging` module configured with UTC timestamps for reproducibility.
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- **Configuration:** Load credentials and environment-specific settings from `.env` files or OS-level secrets; never hardcode API keys.
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- **Testing:** Provide lightweight unit tests (pytest preferred) for data transforms, and include sample datasets when possible.
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- **Agent Workflow:** Include a short `README.md` in each script directory explaining inputs, outputs, and any scheduled automation so future agents can run them without guesswork. |