I want to turn my option-trading ideas into numbers, so I need a Python command-line tool that can back test U.S. equity and index options against historical data. I will supply the raw data from Polygon in whatever format is most convenient—CSV, JSON, direct API pull, you name it—so the project can focus on the analytics rather than data collection. Core requirement The platform must ingest that data and run thorough historical data analysis based on my parameter sets (entry rules, exits, position sizing, date ranges, etc.). Speed and code clarity matter more than a flashy interface; everything should run cleanly from the terminal with clear, well-documented commands and flags. Desired workflow 1. I point the script to a local file path or live API key. 2. I pass strategy parameters via CLI arguments or a simple config file. 3. The program outputs summary stats—P&L, drawdown, Sharpe, individual trade log—and, if possible, saves them to CSV. Deliverables • Fully commented Python source code (preferably modular for future feature additions). • A short README that explains installation, required libraries, and example command usage. • Unit tests covering the main analytics functions so I can extend the tool confidently. Tech notes – Pandas, NumPy, and either Backtrader or Zipline are fine as long as setup stays light. – Please follow PEP 8. – Keep external dependencies minimal; virtual-env or conda instructions are enough. Acceptance criteria The script should process at least one year of SPY option chain data without errors and reproduce a sample performance report I will share during development. If you have questions about the data schema or edge-case handling, let me know early so we can keep the build smooth.