## 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.
**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.
**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.
