I have a clear goal—turn my stock-options ideas into a fully automated Python algorithm that can run end-to-end, from data ingestion to live order routing. The focus is strictly on options trading in the equity market; I’m not looking for an off-the-shelf trend-following or mean-reversion template but a purpose-built rules engine that reflects my own entry, exit, and risk parameters. Here’s what I need from you: • Clean, modular Python code that pulls real-time and historical options chains, computes my signals, sizes positions, and routes orders through a broker API (Interactive Brokers, Alpaca, or similar—happy to decide together). • A back-testing layer that lets me stress-test the strategy across multiple years of stock-level options data and outputs key metrics—win rate, max drawdown, Sharpe, and P/L curves. • Simple configuration files so I can tweak strikes, expiries, filters, and risk caps without rewriting code. • Clear documentation plus a short read-me showing setup, required libraries, and how to flip between paper-trading and live execution. Acceptance criteria 1. Back-test report shows the strategy running without errors on at least five years of SPY options data. 2. Paper-trading mode submits and tracks orders correctly in a sandbox environment for one trading day. 3. All functions and classes are commented; hand-off includes the code, config files, and back-test results. If you enjoy crafting production-ready trading software and can point to previous Python/stock-options work, let’s talk.