forked from LengKundee/MQL5-Google-Onedrive
afaa6c2d73
- Pre-calculated `g_riskMultiplier` and `g_lotValuePerUnit` in `OnInit()` to optimize `CalculateLots()` math. - Declared data arrays (`rates`, `upperBand`, `lowerBand`, `emaFast`, `emaSlow`, `atr`) as `static` in `OnTick()` to reuse memory allocations. - Added performance documentation in `.jules/bolt.md`. Co-authored-by: Mouy-leng <199350297+Mouy-leng@users.noreply.github.com>
28 lines
3.3 KiB
Markdown
28 lines
3.3 KiB
Markdown
# Bolt's Journal ⚡
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This journal is for CRITICAL, non-routine performance learnings ONLY.
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- Codebase-specific bottlenecks
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- Failed optimizations (and why)
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- Surprising performance patterns
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- Rejected changes with valuable lessons
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## 2024-07-25 - MQL5 Native Functions vs. Scripted Loops
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**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.
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**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.
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## 2024-07-26 - Native ArrayMaximum/ArrayMinimum Efficiency
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**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.
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**Action:** When replacing manual loops with native array functions, always include a check for the `-1` return value to ensure robustness while gaining performance.
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## 2026-01-19 - Native Object Cleanup in MQL5
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**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.
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**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.
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## 2026-01-20 - Robust New Bar Check in MQL5 OnCalculate
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**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.
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**Action:** Always wrap "new bar" early exits in indicators with `if(prev_calculated > 0 && ...)` and prefer `iTime()` for the current bar's timestamp.
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## 2026-01-21 - MQL5 Performance: Static Arrays and Multiplier Pre-calculation
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**Learning:** In high-frequency MQL5 code like `OnTick`, repeated memory allocations for arrays (e.g., `MqlRates`, `double` buffers) create measurable overhead. Using the `static` keyword allows memory reuse across calls. Furthermore, complex mathematical formulas (like lot size calculations involving multiple divisions/multiplications) can often be simplified into a single pre-calculated multiplier in `OnInit`, significantly reducing the CPU cycles required per tick.
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**Action:** Always declare data-fetching arrays as `static` in `OnTick`. Pre-calculate constant portions of trading formulas in `OnInit` and store them in cached global variables.
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