MQL5-Google-Onedrive/.jules/bolt.md
google-labs-jules[bot] 4f0ee6bc89 feat: add Gzip compression to web dashboard
- Implements Gzip compression for Flask responses > 500 bytes.
- Adds Content-Encoding, Content-Length, and Vary headers.
- Reduces network payload for text-based content.
- Adds comprehensive tests for compression logic.
- Updates Bolt's Journal.
2026-02-21 05:30:54 +00:00

3 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.

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.

2026-03-09 - Web Response Compression

Learning: Gzip compression reduces response size significantly (up to ~70%) for text-based content, especially rendered HTML/Markdown. Implementing it via flask.after_request is simpler and more performant than adding a heavy dependency like Flask-Compress, keeping the footprint minimal. Action: Always verify if text-based responses are compressed, and use flask.after_request with gzip for simple, dependency-free compression.