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. Message to see a preview to the data