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>
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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 Performance: Static Arrays and Multiplier Pre-calculation
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.
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.