How to Build & Backtest Option Bull Call Spread Trading Algorithm with Python
About this lesson
@MattMacarty #lumibot #optionstrading #algotrading #python #bullcallspread **Automate complex multi-leg options strategies using Python and LumiBot.** In Part 4 of our LumiBot series, we transition from equity trading to automated options strategies. This tutorial focuses on building a Bull Call Spread algorithm for the Gold ETF (GLD). We move beyond simple orders to handle professional-grade requirements: calculating the 3rd Friday expiration, managing multi-leg assets, and benchmarking performance against the S&P 500. ### ⏱️ Key Technical Milestones 0:00 - Series Introduction & LumiWealth Overview 1:06 - Strategy Concept: The Bull Call Spread Framework 2:50 - Parameter Architecture: Defining Strikes and Spreads 4:34 - **Trading Iteration:** Normalizing dates for options cycles 7:42 - **Asset Definition:** Configuring Long vs. Short calls in LumiBot 9:00 - Multi-Leg Execution: Processing list-based orders 11:00 - Strike Selection: Rounding logic for standardized options 13:05 - **Expiration Engineering:** Calculating the 3rd Friday with the `calendar` module 16:45 - Backtesting: Initial $5 Spread results on GLD 18:50 - Optimization: Increasing spread size for higher volatility capture 19:30 - Final Analysis: Outperforming the SPY benchmark --- ### 🏛️ Implementation Highlights This strategy demonstrates advanced derivative management in Python: **1. Dynamic Expiration Logic:** * Unlike stocks, options require a temporal component. We implement custom logic using Python’s `calendar` module to automatically identify the next 3rd-Friday expiration, ensuring the bot always trades the correct monthly cycle. **2. Multi-Leg Order Management:** * Learn how to define separate `Asset` objects for the long and short legs of a vertical spread and execute them as a synchronized strategic unit within the LumiBot lifecycle. **3. Quantitative Analysis:** * We utilize **QuantStats** to generate a performance tear sheet, comparing the risk-adjusted returns of our automated s
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