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

View More author details

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

Why subscribe?

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...