You're reading from Python for Algorithmic Trading Cookbook Recipes for designing, building, and deploying algorithmic trading strategies with Python

Product type Paperback

Published in Aug 2024

Publisher Packt

ISBN-13 9781835084700

Length 404 pages

Edition 1st Edition

Languages

Python

Tools

Zipline

Concepts

Data Analysis

Author (1):

Jason Strimpel

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Table of Contents (16) Chapters

Preface

1. Chapter 1: Acquire Free Financial Market Data with Cutting-Edge Python Libraries FREE CHAPTER

2. Chapter 2: Analyze and Transform Financial Market Data with pandas

3. Chapter 3: Visualize Financial Market Data with Matplotlib, Seaborn, and Plotly Dash

4. Chapter 4: Store Financial Market Data on Your Computer

5. Chapter 5: Build Alpha Factors for Stock Portfolios

6. Chapter 6: Vector-Based Backtesting with VectorBT

7. Chapter 7: Event-Based Backtesting Factor Portfolios with Zipline Reloaded

8. Chapter 8: Evaluate Factor Risk and Performance with Alphalens Reloaded

9. Chapter 9: Assess Backtest Risk and Performance Metrics with Pyfolio

10. Chapter 10: Set Up the Interactive Brokers Python API

11. Chapter 11: Manage Orders, Positions, and Portfolios with the IB API

12. Chapter 12: Deploy Strategies to a Live Environment

13. Chapter 13: Advanced Recipes for Market Data and Strategy Management

14. Index

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Preparing backtest results

Zipline Reloaded is a robust backtesting library that has an integrated ecosystem of tools designed to assess trading strategy performance. This ecosystem makes it easier for traders to transition from strategy development to evaluation. An example of an integrated tool is Alphalens Reloaded which is the focus of this chapter.

We learned in Chapter 7, Event-Based Backtesting Factor Portfolios with Zipline Reloaded that the output DataFrame of a Zipline backtest provides a detailed analysis of a trading strategy’s performance over a specified historical data period. The output includes metrics like cumulative returns, alpha, beta, Sharpe ratio, and maximum drawdown, among many others. We need to manipulate the output DataFrame to extract some of the data so it’s suitable for use with Alphalens Reloaded.

This recipe will walk through the process of extracting the relevant information.