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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

Index

As this ebook edition doesn't have fixed pagination, the page numbers below are hyperlinked for reference only, based on the printed edition of this book.

A

aggregates

used, for creating columns 32-34

alerts

sending, via SMS 361, 362

algorithmic trading app

building 248-251

class 253

inheritance and overriding 252

request-callback pattern 252

testing 253, 254

alpha factors 86

AlphaLens

backtest results, preparing 194-199

factor return performance, examining 204-210

factor turnover, evaluating 210-215

IC, evaluating 199-204

apply function 69

ArcticDB

reference link 357

ArcticDB DataFrame database

using, for storage 350-355

asfreq method

reference link 63

asset returns

calculating, with pandas 47-51

autocommit mode 283

Average True Range (ATR) 163

B

backtest results

preparing 194-199

bag 329

basic lower band 163

basic upper band 163

basis trade 9

...
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