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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 404 pages
Edition 1st Edition
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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

Streaming real-time options data with ThetaData

The Options Price Reporting Authority (OPRA) functions as a securities information processor, aggregating options quotes and transaction details from predominant U.S. exchanges. Approximately 1.4 million active options contracts are traded, generating in excess of 3 terabytes of data on a daily basis. OPRA is responsible for the real-time consolidation and dissemination of this data. ThetaData, through its connection to OPRA, facilitates the distribution of this data in an unfiltered format to non-professional users. Furthermore, ThetaData’s Python API is capable of streaming quotes and trades with a latency measured in milliseconds. This efficiency is achieved by compressing the data to approximately 1/30th of its original volume.

The Theta Terminal is an intermediate layer that bridges our data-providing server with the Python API. The terminal runs as a background process. It hosts a local server on your machine, to which...

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