How to Backtest a Support/Resistance Strategy on SPY with Python

In this video you will run a full 3-year backtest of the GotEyes support/resistance strategy on SPY — and see exactly which years the strategy worked, which it did not, and what the Sharpe ratio actually says about taking it live. This is bt04 in the Mastering Backtesting for Algo Traders series. By this point you have real options data loaded (bt03) — now we put the strategy under the microscope across 2022, 2023, 2024, and 2025 data. What you will learn in this video: • How the GotEyes strategy uses support and resistance levels to identify reversal entries on SPY • Why this backtest uses a hybrid approach — bar-by-bar with stateful S/R (event-driven accuracy at vectorized speed) • How to load multi-year options data with version fallbacks per year and why that matters for clean results • What the Sharpe ratio, trade count, and drawdown look like across 4 years of real SPY data • How to read backtest output that is honest — not curve-fitted This is part of the Mastering Backtesting for Algo Traders series. Each video builds toward a fully automated 0DTE SPY/QQQ options bot running live on Alpaca. Tools used: • ThetaData — historical options data: https://www.thetadata.net/ • DuckDB — fast in-process SQL analytics: https://duckdb.org • Alpaca — commission-free broker API: https://alpaca.markets If you are building algo trading systems in Python and want to validate a strategy before going live, this is the workflow. → Subscribe for the full build: https://www.youtube.com/@kreativekodr ⚠️ DISCLAIMER: Educational content ONLY. NOT financial advice. I am NOT a financial advisor. Trading involves substantial risk. You are solely responsible for your own financial decisions and any losses incurred.