Python Stock Options Backtesting & Analysis

I have six months of stock options data already saved as CSV files. I want a clean, modular Python script—built with Pandas and NumPy—that back-tests every trade I flagged and tells me:

• Whether each contract hit in-the-money before expiry.
• Overall hit-rate percentage broken down by call buys, put buys, put sells (bullish), and call sells (bearish).
• A profit-and-loss summary plus any notable anomalies you detect.

What I envision for this first phase:
1. Load and parse my CSV files, mapping timestamps, strikes, and expiries.
2. Run the back-test logic, outputting results to a tidy DataFrame and a concise visual/CSV report I can drop into a dashboard.
3. Keep functions reusable so we can later plug in live data—either by scraping daily highs/lows or wiring up to an options data API.

Please write clear comments, let me tweak parameters (expiry windows, strike distances, fee assumptions), and include a quick README that walks me through running the script end-to-end. I’ll sign off once I can reproduce your numbers on my side and the hit-rate aligns with a few manual spot checks.
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