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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
Tools
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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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Toc

Table of Contents (16) Chapters Close

Preface 1. Chapter 1: Acquire Free Financial Market Data with Cutting-Edge Python Libraries 2. Chapter 2: Analyze and Transform Financial Market Data with pandas FREE CHAPTER 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

Quickly visualizing data using pandas

pandas is an all-purpose data manipulation library. Not only can you use it for data acquisition and manipulation as we saw in Chapter 1, Acquire Free Financial Market Data with Cutting-edge Python Libraries and Chapter 2, Analyze and Transform Financial Market Data with pandas, but you can use it for plotting too. pandas offers various “backends” that are used while plotting through a common method. In this recipe, you’ll learn how to use the default backend, Matplotlib, to quickly plot financial market data using a line plot, bar chart, histogram, and others.

How to do it…

You can use the Matplotlib plots through pandas by importing them.

  1. Import the libraries:
    import matplotlib as plt
    import pandas as pd
    from openbb import obb
    from pandas.plotting import bootstrap_plot, scatter_matrix
    obb.user.preferences.output_type = "dataframe"
  2. Download stock price data:
    df = obb.equity.price.historical...
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