MQL5-Google-Onedrive/.jules/bolt.md

# 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.

## 2026-02-04 - Single-Path Lot Normalization and Margin Clamping
**Learning:** Redundant calculations in `CalculateLots()` can be eliminated by applying margin constraints to the raw lot size before any rounding or volume limit checks. This ensures that `MathFloor`, `MathMax`, `MathMin`, and `NormalizeDouble` are executed exactly once. Additionally, pre-calculating the inverse of `SYMBOL_MARGIN_INITIAL` in `OnInit` allows replacing an expensive division with a fast multiplication in the margin clamping path.
**Action:** Always refactor lot calculation functions to follow a "raw-calculate -> clamp-by-margin -> normalize-and-limit" flow, using cached inverse constants for any divisions by fixed symbol properties.

## 2026-02-05 - Optimization of EA Execution Flow and Log Throttling
**Learning:** Major performance gains in high-frequency trading EAs can be achieved by reordering gatekeeper logic in `OnTick`. Placing cheap local math (like time filters or daily limit checks) before expensive cross-process API calls (`TerminalInfoInteger`, `MQLInfoInteger`) saves significant overhead. Additionally, throttling repetitive error logs (like "AutoTrading disabled" or "Daily limit reached") using `static datetime` timers prevents log flooding, which is a common performance bottleneck during market volatility.
**Action:** Always prioritize internal state and arithmetic checks over environment API calls in `OnTick` and implement time-based throttling for any logs or alerts that could be triggered repeatedly on every price tick. In `CheckDailyLimits`, using a `static datetime` flag to remember a reached limit for the day allows for a near-instant exit on subsequent ticks.

## 2026-02-11 - Flask Dashboard Markdown Caching and Syscall Reduction
**Learning:** Rendering Markdown files on every request in a web dashboard is a significant CPU/IO bottleneck. Efficiency can be further improved by consolidating file metadata checks. Using `os.stat()` once is faster than calling `os.path.exists()` and `os.path.getmtime()` separately, as it retrieves all metadata in a single system call. Additionally, extracting large HTML templates to module-level constants avoids repeated memory allocations and string concatenations within the request lifecycle.
**Action:** In Python web scripts, consolidate file metadata retrieval into a single `os.stat()` call and move static template strings outside of request handler functions.

## 2026-02-12 - Single-Pass CI Validation and Chunked I/O
**Learning:** Consolidating multiple file list passes and I/O operations (stat and read) into a single-pass loop in CI validation scripts significantly reduces overhead. Performing size checks before reading content and using chunked reading for NUL byte detection prevents memory pressure and allows for early exit on invalid files, making the validation process both faster and more robust.
**Action:** Always combine file discovery and validation checks into a single loop when possible. Prioritize cheap metadata checks (like size) before expensive content reads, and use chunked I/O for large file analysis to maintain a low memory footprint.