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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

Manage Orders, Positions, and Portfolios with the IB API

In algorithmic trading, efficient management of orders, positions, and portfolio data is critical. Luckily for us, we can do it all using Python. Managing orders encompasses a range of activities, including executing new trades, canceling existing orders, and updating orders to adapt to changing market conditions or shifts in trading strategies. Managing positions involves monitoring and analyzing live position data to track profit and loss (PnL) in real time. This immediate insight into the performance of individual trades enables traders to make informed decisions on whether to hold, sell, or adjust positions. Further, real-time (or near real-time) portfolio data can generate real-time (or near real-time) risk statistics to improve overall risk management. Portfolio data management involves a comprehensive analysis of the portfolio to assess its performance, understand risk exposure, and make strategic adjustments for optimizing...

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