MQL5-Google-Onedrive/.jules/bolt.md

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# Bolt's Journal ⚡
This journal is for CRITICAL, non-routine performance learnings ONLY.
- Codebase-specific bottlenecks
- Failed optimizations (and why)
- Surprising performance patterns
- Rejected changes with valuable lessons
## 2024-07-25 - MQL5 Native Functions vs. Scripted Loops
**Learning:** My assumption that a manual MQL5 loop over a pre-cached array would be faster than built-in functions like `iHighest()` and `iLowest()` was incorrect. The code review pointed out that MQL5's native, built-in functions are implemented in highly optimized C++ and are significantly faster than loops executed in the MQL5 scripting layer. The original comment stating this was correct.
**Action:** Always prefer using MQL5's built-in, native functions for calculations like finding highs/lows over manual loops, even if the data is already in a local array. The performance gain from the native implementation outweighs the overhead of the function call.
## 2024-07-26 - Native ArrayMaximum/ArrayMinimum Efficiency
**Learning:** Confirmed that native `ArrayMaximum()` and `ArrayMinimum()` are the preferred way to find extreme values in price arrays. Also, when using these functions, it's important to check if they return `-1` to avoid invalid array access, especially if the `count` or `start` parameters might be dynamic.
**Action:** When replacing manual loops with native array functions, always include a check for the `-1` return value to ensure robustness while gaining performance.
## 2026-01-19 - Native Object Cleanup in MQL5
**Learning:** While iterating through chart objects manually is flexible, it becomes a major bottleneck if the chart has thousands of objects. For simple prefix-based cleanup (often used in indicators), the native `ObjectsDeleteAll(0, prefix)` is significantly more efficient than a scripted loop calling `ObjectName()` and `StringFind()` for every object on the chart.
**Action:** Use `ObjectsDeleteAll()` for bulk object removal by prefix whenever the "keep N latest" logic is not strictly required or can be safely bypassed for performance.
## 2026-01-20 - Robust New Bar Check in MQL5 OnCalculate
**Learning:** An early exit in `OnCalculate` based on bar time MUST check `prev_calculated > 0`. If `prev_calculated == 0`, the terminal is requesting a full recalculation (e.g., after a history sync or data gap fill), and exiting early would result in stale data. Also, using `iTime()` is more robust than indexing into the `time[]` array if the array's series state is unknown.
**Action:** Always wrap "new bar" early exits in indicators with `if(prev_calculated > 0 && ...)` and prefer `iTime()` for the current bar's timestamp.
## 2026-01-21 - MQL5 Local Array Sizing and Shadowing
**Learning:** Declaring large or dynamic arrays as `static` in MQL5's `OnTick` is good for memory reuse. However, for small, occasional data (like confirmation EMAs or ATR), fixed-size local arrays (e.g., `double buffer[2]`) are safer and more efficient. They avoid the overhead of dynamic resizing by `CopyBuffer` and eliminate the risk of "shadowing" or "uninitialized state" bugs that can occur with static dynamic arrays if they are not consistently populated on every execution path.
**Action:** Use fixed-size local arrays for small data sets (1-5 elements) that are only needed conditionally. Reserve `static` dynamic arrays for large data (like price history) that is required on every tick.