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) before expensive cross-process API calls (TerminalInfoInteger, MQLInfoInteger) saves significant overhead. Additionally, throttling repetitive error logs (like "AutoTrading disabled") 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 that could be triggered repeatedly on every price tick.

2026-02-05 - Local Fixed-Size Arrays and Lazy Account Metric Retrieval

Learning: Significant micro-optimizations in MQL5 can be achieved by using local fixed-size arrays (e.g., double buffer[2]) for CopyBuffer instead of static dynamic arrays for small datasets. This avoids dynamic memory management and ArraySetAsSeries overhead. Furthermore, fetching only the specific account metrics needed (ACCOUNT_EQUITY vs ACCOUNT_BALANCE) based on risk settings reduces the number of terminal API calls in the confirmed signal path. Action: Use local fixed-size arrays for fetching small indicator data sets and refactor trade functions to lazily retrieve only the required account metrics.