Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Save more on your purchases now! discount-offer-chevron-icon
Savings automatically calculated. No voucher code required.
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
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

Arrow left icon
Product type Paperback
Published in Aug 2024
Publisher Packt
ISBN-13 9781835084700
Length 404 pages
Edition 1st Edition
Languages
Tools
Arrow right icon
Author (1):
Arrow left icon
Jason Strimpel Jason Strimpel
Author Profile Icon Jason Strimpel
Jason Strimpel
Arrow right icon
View More author details
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

Calculating real-time key performance and risk indicators

Real-time performance and risk metrics are important for maintaining robust trading strategies. They allow us to compare real-life performance to the performance of our backtests. They provide immediate feedback on the effectiveness of our trading algorithms and let us make adjustments in response to market volatility or unexpected events. By continuously monitoring risk metrics such as drawdowns, volatility, and value at risk, we can effectively manage exposure and mitigate potential losses. Most professional algorithmic traders spend their time analyzing and explaining deviations from the performance that they expect in their backtests to the performance that they observe during live trading. This recipe will introduce the tools we need to do the same.

Getting ready

We’ll use empyrical-reloaded to compute performance and risk statistics. To install it, use pip:

pip install empyrical-reloaded

At the top of...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime