forked from LengKundee/MQL5-Google-Onedrive
Optimized the `CalculateLots` function in `SMC_TrendBreakout_MTF_EA.mq5` by: 1. Replacing division by `g_marginInitial` with multiplication by a pre-calculated `g_invMarginInitial`. 2. Consolidating lot normalization and clamping into a single execution path at the end of the function. 3. Applying margin constraints before normalization to avoid redundant calculations. These changes improve performance in the trade execution path and ensure DRY principles are followed. Verified with `ci_validate_repo.py` and `test_automation.py`.
32 lines
4 KiB
Markdown
32 lines
4 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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## 2026-01-20 - MQL5 OnTick Execution Flow Optimization
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**Learning:** Significant performance gains in MQL5 EAs can be achieved by carefully ordering the logic in `OnTick`. Moving the `PositionSelect` check before `CopyRates` and `CopyBuffer` avoids expensive data operations when a trade is already active. Additionally, reducing the requested bar count in data fetching functions to the absolute minimum (e.g., 2 instead of 3) and using `SymbolInfoTick` for atomic, lazy price retrieval further reduces overhead.
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**Action:** Always place 'gatekeeper' checks (new bar, position existence, terminal trading allowed) at the top of `OnTick` and minimize the data payload for indicator and price fetching to only what is strictly necessary for the current bar's logic.
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## 2026-02-05 - Optimized Lot Calculation Path
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**Learning:** In MQL5 lot size calculations, redundant normalization and clamping can be avoided by reordering the logic. Applying margin constraints *before* the final normalization ensures a single execution path. Additionally, replacing division by `SYMBOL_MARGIN_INITIAL` with multiplication by a pre-calculated inverse in `OnInit` provides a measurable micro-optimization in the trade execution path.
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**Action:** Consolidate lot normalization and clamping to a single step at the end of `CalculateLots`. Pre-calculate inverses for any constant denominators (like initial margin) in `OnInit` after verifying they are greater than zero.
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