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Python for Algorithmic Trading Cookbook

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

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Product type Paperback
Published in Aug 2024
Publisher Packt
ISBN-13 9781835084700
Length 406 pages
Edition 1st Edition
Languages
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Author (1):
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Jason Strimpel Jason Strimpel
Author Profile Icon Jason Strimpel
Jason Strimpel
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Table of Contents (16) Chapters Close

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 15. Other Books You May Enjoy

Storing data on disk with SQLite

SQLite offers a bridge between the simplicity of flat files and the robustness of relational databases. As a serverless, self-contained database, SQLite provides algorithmic traders with a lightweight yet powerful tool to store and query data with SQL but without the complexity of setting up a full-scale database system. Its integration with Python is seamless, and its compact nature makes it an excellent choice for applications where portability and minimal configuration are priorities. For traders who require more structure than CSVs, or prefer to use SQL, but without the overhead of larger database systems, SQLite is the optimal choice.

Getting ready…

We’ll build a script that can be set to run automatically using a CRON job (Mac, Linux, Unix) or Task Scheduler (Windows). For this recipe, we will create a Python script called market_data_sqlite.py and run it from the command line. We’ll also introduce the exchange_calendars...

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