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MQL4/.github/instructions/mql5-scripts.instructions.md
Rahul Dhangar 669ecde7b7 Initial commit: Documentation and MT4 EA Phases 1-3 complete 
- 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
2025-11-04 01:38:41 +05:30

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**/MQL5/Scripts/_Thivyam/**/*.mq5

Instructions for _Thivyam MQL5 Scripts

CRITICAL: MQL5 Syntax Compliance

Before writing any MQL5 code, read and follow: mql5-syntax-critical.instructions.md

Key mandatory rules:

  • Use dot operator (.) for pointer member access, NOT arrow (->)
  • Declare loop variables BEFORE for-loop: int i; for(i=0; i<n; i++)
  • Use dynamic arrays (Type[]) for ArraySetAsSeries() and CopyRates()
  • Verify property constant names in official MQL5 documentation

Script-Specific Instructions

  • Purpose: Scripts must perform a single, well-defined task (e.g., order cleanup, data export) and exit cleanly after execution.
  • Entry Point: Use OnStart() as the sole entry point. Do not rely on OnTick() or OnChartEvent() in scripts.
  • Input Parameters: Expose configurable behaviour through clearly named input variables. Validate parameters up front and abort with descriptive logs when invalid.
  • Risk Controls: When scripts place or close trades, reuse shared _Thivyam risk and execution helpers to honour firm-wide limits.
  • Error Handling: Wrap calls to terminal APIs in conditionals, check results, and print GetLastError() codes for any failures.
  • Logging: Emit concise status updates to the Experts log so automation can trace script progress.
  • Cleanup: Release indicator handles, chart objects, or file handles before returning from OnStart().
  • Agent Workflow: Add short comments explaining non-obvious terminal interactions so future automated agents can maintain behaviour safely.