我現在編程也常常讓AI過目一下，能查到不少漏洞或弱點。。。

Key nits (easy fixes/notes):

1. Function/name mismatch – you use ['Close'] but the function is called get_adj_close_data.

   • If you truly want adjusted prices (usually better for returns): change to ['Adj Close'] (or rename the function). [yf.download default prices to adjusted prices]

2. Month-end pick – your groupby(...).tail(1) works; resample('M').last() is a common alternative. Either is fine. [resample('M').last() would show month-end date instead of original date]

3. Benchmark total return – you already noted this: ^GSPC is price index (no dividends). Beta/vol are fine, but long-run return comps will be lower than total return. [Unfortunately ^SP500TR does not work here, don't know why]

4. Excel save – you create the workbook but don’t save it. Add something like wb.save('beta_report.xlsx') (and optionally wb.close()).

5. Column formatting – your ranges match the output layout:

   • B (beta), C (rho), F (vol ratio) → 0.0000

   • D (sigma_s), E (sigma_m) → 0.0000% (those are decimals; percent format is appropriate)

6. Type hints – list[str] needs Python 3.9+. For broader compatibility: from typing import List then tickers: List[str].

7. Data gaps/NaNs – pandas’ corr skips NaNs pairwise, so you’re fine. Just be aware early months can be sparse for some tickers.

8. yf.download end date – yfinance usually treats end as inclusive for daily data, but behavior can vary; your 5y+15d window gives enough buffer either way.