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.

2026-01-23 - Python File System Checks

Learning: Checking for file existence (os.path.exists) before getting metadata (os.path.getmtime) introduces a redundant syscall. os.stat() provides both pieces of information in a single syscall and uses the EAFP (Easier to Ask for Forgiveness than Permission) pattern, which is more Pythonic and slightly faster, especially in high-frequency loops or handlers. Action: Use os.stat() when both existence and metadata are needed, wrapping it in a try...except OSError block.

2026-01-26 - yfinance Bulk Download

Learning: yfinance Ticker.history in a loop is significantly slower than yf.download with a list of tickers due to sequential HTTP requests. yf.download with group_by='ticker' provides a consistent MultiIndex structure even for single tickers, simplifying bulk processing. Action: Always prefer yf.download(tickers) over iterating yf.Ticker(t) when fetching data for multiple symbols.

2026-02-09 - Git Command Performance

Learning: git for-each-ref is a powerful tool for batch data retrieval, but without filtering, it processes all refs, including thousands of stale merged branches in older repositories. Calculating ahead-behind counts for these stale branches is O(N) where N is total branches, which can be significantly slower than O(M) where M is active branches. Action: Always filter git for-each-ref with --no-merged (or --merged depending on use case) when only interested in a subset of branches, especially when expensive formatting options like ahead-behind are used.

2026-02-12 - Python Test Parallelization Overhead

Learning: Using concurrent.futures.ProcessPoolExecutor for a small suite of short-running tests (like those executing quick subprocess calls) can be slower than sequential execution. The overhead of spawning processes and managing inter-process communication for output capturing exceeds the performance gain from parallelism when the individual tasks are extremely short. Action: Prefer sequential execution for lightweight test suites. Parallelism should only be introduced when individual tests have significant execution time that outweighs the process/thread management overhead.

2026-02-12 - MQL5 Tick Path Caching

Learning: Environment API calls in MQL5 (e.g., TerminalInfoInteger, MQLInfoInteger) and logic that uses TimeToStruct/StructToTime are relatively expensive when called on every single price tick. Implementing a simple 1-second cache using a static datetime flag significantly reduces this overhead with no impact on trading logic accuracy, as terminal states rarely change more than once per second. Action: Implement 1-second caching for environment and limit checks in OnTick hot paths. Prioritize cheaper internal logic checks before expensive API-based environment checks.