## 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.
**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.
**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.
**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.
## 2026-02-04 - Optimized Lot Normalization and Margin Clamping
**Learning:** In MQL5 `CalculateLots` functions, applying margin-based clamping before the final normalization and volume limit checks (`MathFloor`, `MathMax`, `MathMin`) is more efficient. This ensures that the rounding and clamping logic is applied only once to the final lot value, avoiding redundant calculations if the initial lot size exceeds available margin. Additionally, replacing division by `SYMBOL_MARGIN_INITIAL` with multiplication by a cached inverse value further improves performance in the trade execution path.
**Action:** Consolidate lot size normalization into a single execution path by applying all constraints (risk-based, margin-based, and broker limits) before the final rounding step. Use cached inverse values for margin calculations.
