How to Backtest Trading Algorithms and Portfolio Metrics with Python and QuantStats

Matt Macarty · Beginner ·🌐 Frontend Engineering ·3y ago

About this lesson

**Unlock professional-grade financial analysis and backtesting in Python using the powerful QuantStats library!** This comprehensive tutorial provides a deep dive into QuantStats, the all-in-one portfolio profiling tool. We'll be covering how to generate comprehensive portfolio metrics and backtest trading algorithms in Python. QuantStats allows you to easily measure your portfolio's performance and identify potential areas of improvement, allowing you to make informed investment decisions. In this video, you'll discover: * Essential Python libraries: NumPy & Pandas. * The QuantStats library: A comprehensive tool for backtesting and performance analysis. * Step-by-step guidance: From data preparation to result interpretation. * Real-world examples: To solidify your understanding. --- ### ⏱️ Video Chapters (QuantStats Masterclass) Master the three core modules and key analysis steps: 0:00 - Intro to QuantStats: Overview and HTML Report Example 2:02 - Step 1: **Data Preparation** (Downloading Returns with `yfinance` & `qs.utils`) 3:31 - Module 1: **QuantStats `stats`** - Overview of Available Statistics 4:16 - Calculating Key Metrics: **Sharpe Ratio**, Best Day/Month, Max Drawdown 5:08 - Extending Pandas for Simpler Method Calls (e.g., `portfolio.sharpe()`) 6:51 - Building a Sample Leveraged Portfolio (Long SPY, Short Bonds) 8:25 - Module 2: **QuantStats `plots`** - Saving Earnings & **Heatmap** Charts 12:21 - Module 3: **QuantStats `reports`** - Building a Full Trading Algorithm 15:26 - Generating the **Comprehensive HTML Report (The "Tear Sheet")** 15:55 - Interpreting the Full Financial Report, Metrics, and **Drawdown** Plots --- ### 📈 Key Skills You Will Learn: * Build a trading algorithm using Python NumPy and Pandas. * Backtest your strategies to assess historical performance and risk. * Output key portfolio metrics like **Sharpe ratio**, Sortino ratio, and **Maximum Drawdown**. * Generate visualizations of your results and professional-grade reports.

Original Description

**Unlock professional-grade financial analysis and backtesting in Python using the powerful QuantStats library!** This comprehensive tutorial provides a deep dive into QuantStats, the all-in-one portfolio profiling tool. We'll be covering how to generate comprehensive portfolio metrics and backtest trading algorithms in Python. QuantStats allows you to easily measure your portfolio's performance and identify potential areas of improvement, allowing you to make informed investment decisions. In this video, you'll discover: * Essential Python libraries: NumPy & Pandas. * The QuantStats library: A comprehensive tool for backtesting and performance analysis. * Step-by-step guidance: From data preparation to result interpretation. * Real-world examples: To solidify your understanding. --- ### ⏱️ Video Chapters (QuantStats Masterclass) Master the three core modules and key analysis steps: 0:00 - Intro to QuantStats: Overview and HTML Report Example 2:02 - Step 1: **Data Preparation** (Downloading Returns with `yfinance` & `qs.utils`) 3:31 - Module 1: **QuantStats `stats`** - Overview of Available Statistics 4:16 - Calculating Key Metrics: **Sharpe Ratio**, Best Day/Month, Max Drawdown 5:08 - Extending Pandas for Simpler Method Calls (e.g., `portfolio.sharpe()`) 6:51 - Building a Sample Leveraged Portfolio (Long SPY, Short Bonds) 8:25 - Module 2: **QuantStats `plots`** - Saving Earnings & **Heatmap** Charts 12:21 - Module 3: **QuantStats `reports`** - Building a Full Trading Algorithm 15:26 - Generating the **Comprehensive HTML Report (The "Tear Sheet")** 15:55 - Interpreting the Full Financial Report, Metrics, and **Drawdown** Plots --- ### 📈 Key Skills You Will Learn: * Build a trading algorithm using Python NumPy and Pandas. * Backtest your strategies to assess historical performance and risk. * Output key portfolio metrics like **Sharpe ratio**, Sortino ratio, and **Maximum Drawdown**. * Generate visualizations of your results and professional-grade reports.

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Chapters (10)

Intro to QuantStats: Overview and HTML Report Example

2:02 Step 1: **Data Preparation** (Downloading Returns with `yfinance` & `qs.utils`

3:31 Module 1: **QuantStats `stats`** - Overview of Available Statistics

4:16 Calculating Key Metrics: **Sharpe Ratio**, Best Day/Month, Max Drawdown

5:08 Extending Pandas for Simpler Method Calls (e.g., `portfolio.sharpe()`)

6:51 Building a Sample Leveraged Portfolio (Long SPY, Short Bonds)

8:25 Module 2: **QuantStats `plots`** - Saving Earnings & **Heatmap** Charts

12:21 Module 3: **QuantStats `reports`** - Building a Full Trading Algorithm

15:26 Generating the **Comprehensive HTML Report (The "Tear Sheet")**

15:55 Interpreting the Full Financial Report, Metrics, and **Drawdown** Plots

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