How to Backtest a Trading Strategy in Python (Step-by-Step Quant Tutorial)

📊 How to Backtest a Trading Strategy in Python In this video, we walk through how to build and backtest a systematic trading strategy in Python using a clean and reproducible research workflow. Backtesting is a fundamental step in quantitative finance. Instead of relying on intuition or anecdotal performance, we use historical data and programmatic rules to evaluate whether a strategy would have worked in the past. You’ll learn how to structure a research-grade backtesting pipeline that allows you to test signals, simulate portfolio weights, and analyze performance. This workflow is designed for quantitative researchers, algorithmic traders, and data scientists who want to move from ideas to testable investment strategies. 🧠 What you’ll learn • How to structure a simple but robust backtesting pipeline • How to transform a signal into portfolio weights • How to simulate portfolio returns using historical price data • How to calculate performance metrics such as cumulative returns and Sharpe ratio • How to organize research code for repeatable strategy testing 🛠️ Tech stack used • Python • pandas • numpy • plotly (for visualization) 📂 Code & Resources All code used in this video is available on GitHub. You can clone the repository and use it as a starting framework for building your own quantitative trading strategies and research pipelines. ⏱ Timestamps 00:00 - Introduction 02:03 - Data Import 03:03 - Introduction to bt 06:08 - Backtesting a Buy & Hold Strategy 13:03 - Performance evaluation 15:45 - Backtesting a Risk Parity Strategy 22:45 - Backtesting a Combined Strategy 25:28 - Conclusion