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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
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

Getting a market data snapshot

In the previous recipe, we learned how to get historical data. In some situations, we may need the current market price. In Chapter 12, Deploy Strategies to a Live Environment, we’ll use the current market price to create methods to target specific values or percentage allocations in our portfolio.

The API uses tick types, each representing a specific category of market data, such as last trade price, volume, or bid and ask. These tick types let us access real-time pricing information, which is important for making informed trading decisions. This recipe will show you how to get real-time market data.

Getting ready…

We assume you’ve created the app.py, client.py, and wrapper.py files in the trading-app directory. If not, do it now.

How to do it…

We’ll update app.py, client.py, and wrapper.py to request the last price for a contract.

  1. Open client.py and include the following method in the IBClient...
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