5672ffdd21
- Implement lazy loading for MTF direction confirmation to avoid redundant data copying. - Use native `ArrayFill` for faster indicator buffer clearing in `OnCalculate`. - Replace `CopyTime` with `iTime` for more efficient lower timeframe bar checks. - Declare fractal buffers as `static` to reduce memory allocation overhead. - All changes verified with repository validation and integration tests. Co-authored-by: Mouy-leng <199350297+Mouy-leng@users.noreply.github.com>
4.5 KiB
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.
2026-01-20 - MQL5 OnTick Execution Flow Optimization
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.
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.
2026-02-05 - Lazy Loading MTF Confirmation in Indicators
Learning: Cross-timeframe data access (using iTime, CopyTime, or CopyBuffer on a different timeframe) is one of the more expensive operations in MQL5 OnCalculate. Moving these checks to a "lazy loading" pattern—where they only execute if a primary signal has already been confirmed on the current timeframe—can save significant CPU resources, as signals typically occur on less than 5% of bars. Action: Always defer Multi-Timeframe (MTF) confirmation logic until after all primary single-timeframe signal conditions are met.
2026-02-05 - Native ArrayFill vs. Scripted Loops for Buffer Clearing
Learning: Native functions like ArrayFill and ArrayInitialize are implemented in optimized C++ within the MetaTrader terminal and are significantly faster than manual for-loops in MQL5 for clearing or initializing large indicator buffers. This is especially noticeable when calculating indicators on charts with thousands of bars. Action: Use ArrayFill() to clear specific ranges of indicator buffers in OnCalculate instead of manual loops.