b2653ebaa3
Parallelize independent AI analysis calls in `scripts/market_research.py` to reduce total execution time.
1.8 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.
2026-01-24 - Sequential API Calls in Scripts
Learning: The automation scripts (e.g., market_research.py) execute independent API calls (Gemini, Jules) sequentially, unnecessarily accumulating latency.
Action: Use concurrent.futures.ThreadPoolExecutor to parallelize independent network-bound tasks in Python automation scripts to significantly reduce execution time.