Python for Algorithmic Trading Cookbook E-Book

Jason Strimpel

• Herausgeber: Packt Publishing
• Kategorie: Fachliteratur
• Sprache: Englisch

Seitenzahl: 399

Veröffentlichungsjahr: 2024

Beschreibung

Discover how Python has made algorithmic trading accessible to non-professionals with unparalleled expertise and practical insights from Jason Strimpel, founder of PyQuant News and a seasoned professional with global experience in trading and risk management. This book guides you through from the basics of quantitative finance and data acquisition to advanced stages of backtesting and live trading.
Detailed recipes will help you leverage the cutting-edge OpenBB SDK to gather freely available data for stocks, options, and futures, and build your own research environment using lightning-fast storage techniques like SQLite, HDF5, and ArcticDB. This book shows you how to use SciPy and statsmodels to identify alpha factors and hedge risk, and construct momentum and mean-reversion factors. You’ll optimize strategy parameters with walk-forward optimization using VectorBT and construct a production-ready backtest using Zipline Reloaded. Implementing all that you’ve learned, you’ll set up and deploy your algorithmic trading strategies in a live trading environment using the Interactive Brokers API, allowing you to stream tick-level data, submit orders, and retrieve portfolio details.
By the end of this algorithmic trading book, you'll not only have grasped the essential concepts but also the practical skills needed to implement and execute sophisticated trading strategies using Python.

Leseprobe

Python for Algorithmic Trading Cookbook

Recipes for designing, building, and deploying algorithmic trading strategies with Python

Jason Strimpel

Python for Algorithmic Trading Cookbook

Copyright © 2024 Packt Publishing

All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.

Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the author, nor Packt Publishing or its dealers and distributors, will be held liable for any damages caused or alleged to have been caused directly or indirectly by this book.

Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.

Group Product Manager: Apeksha Shetty

Publishing Product Manager: Nilesh Kowadkar

Book Project Manager: Hemangi Lotlikar

Senior Editor: Nazia Shaikh

Technical Editor: Sweety Pagaria

Copy Editor: Safis Editing

Proofreader: Nazia Shaikh

Indexer: Rekha Nair

Production Designer: Ponraj Dhandapani

Senior DevRel Marketing Coordinator: Nivedita Singh

First published: August 2024

Production reference: 2251024

Published by Packt Publishing Ltd.

Grosvenor House

11 St Paul’s Square

Birmingham

B3 1RB, UK.

ISBN 978-1-83508-470-0

www.packtpub.com

Contributors

About the author

Jason Strimpel is the founder of PyQuant News and co-founder of Trade Blotter. His career America, Europe, and Asia over the last 20+ years. He previously traded for a Chicago-based hedge fund, was a risk manager at JPMorgan, and managed production risk technology for an energy derivatives trading firm in London. In Singapore, he served as APAC CIO for an agricultural trading firm and built the data science team for a global metals trading firm. Jason holds degrees in finance and economics and a Master’s in Capitalize Quantitative Finance from the Illinois Institute of Technology. He shares his expertise through the PyQuant Newsletter, social media, and teaches Getting Started With Python for Quant Finance.

About the reviewers

Sudarshan Sawal specializes in quantitative finance with a background in economics and engineering. As Head of Products (Quant) at PredictNow.ai, he enhanced trading models and boosted client portfolios, with notable research in portfolio optimization using Conditional Portfolio Optimization (CPO) methodology. Previously at QTS Capital Management, Sudarshan researched trading algorithms and assisted clients with machine learning models. He’s represented PredictNow.ai at various financial conferences.

Brian Schneider holds a Bachelor of Science degree in electrical engineering and a Master of Science in financial engineering. He has 25 years of experience in systems engineering, software design and architecture, algorithm performance, data science, and analytics. He has a passion for financial markets and automating algorithmic trading systems and co-founded an investment management company in that space.

Chris Conlan is the founder and CEO of Conlan Scientific, a financial data science consultancy based out of Charlotte, North Carolina. He works with his team of data scientists to build machine learning solutions for banks, lenders, investors, traders, and fintech companies. Chris holds a Bachelor’s in statistics from the University of Virginia where he later taught data science capstone courses at the undergraduate and graduate levels. Chris serves on the Board of Advisors for the Masters of Science in Finance and Analytics degree program at Queens University of Charlotte.

Table of Contents

Preface

1

Acquire Free Financial Market Data with Cutting-Edge Python Libraries

Technical requirements

Working with stock market data with the OpenBB Platform

Getting ready…

How to do it…

How it works…

There’s more…

See also

Fetching historic futures data with the OpenBB Platform

Getting ready…

How to do it…

There’s more…

See also

Navigating options market data with the OpenBB Platform

Getting ready…

How to do it…

How it works…

There’s more…

See also

Harnessing factor data using pandas_datareader

Getting ready…

How to do it…

How it works…

There’s more…

See also

2

Analyze and Transform Financial Market Data with pandas

Diving into pandas index types

How to do it…

How it works…

There’s more…

See also

Building pandas Series and DataFrames

Getting ready

How to do it…

How it works…

There’s more…

See also

Manipulating and transforming DataFrames

Getting ready…

How to do it…

How it works…

There’s more…

See also

Examining and selecting data from DataFrames

How to do it…

How it works…

There’s more…

See also

Calculating asset returns using pandas

How to do it…

How it works…

There’s more…

See also

Measuring the volatility of a return series

How to do it…

How it works…

There’s more…

See also

Generating a cumulative return series

Getting ready…

How to do it…

How it works…

See also

Resampling data for different time frames

How to do it…

How it works…

There’s more…

See also

Addressing missing data issues

Getting ready…

How to do it…

How it works…

There’s more…

See also

Applying custom functions to analyze time series data

Getting ready…

How to do it…

How it works…

There’s more…

See also

3

Visualize Financial Market Data with Matplotlib, Seaborn, and Plotly Dash

Quickly visualizing data using pandas

How to do it…

How it works…

There’s more…

See also

Animating the evolution of the yield curve with Matplotlib

How to do it…

How it works…

There’s more…

See also

Plotting options implied volatility surfaces with Matplotlib

Getting ready…

How to do it…

How it works…

There’s more…

See also

Visualizing statistical relationships with Seaborn

How to do it…

How it works…

There’s more…

See also

Creating an interactive PCA analytics dashboard with Plotly Dash

Getting ready…

How to do it…

How it works…

There’s more…

See also

4

Store Financial Market Data on Your Computer

Storing data on disk in CSV format

How to do it…

How it works…

There’s more…

See also…

Storing data on disk with SQLite

Getting ready…

How to do it…

How it works…

There’s more…

See also…

Storing data in a PostgreSQL database server

Getting ready…

How to do it…

How it works…

There’s more…

See also…

Storing data in ultra-fast HDF5 format

Getting ready…

How to do it…

How it works…

There’s more…

See also…

5

Build Alpha Factors for Stock Portfolios

Identifying latent return drivers using principal component analysis

Getting ready

How to do it…

How it works…

There’s more…

See also

Finding and hedging portfolio beta using linear regression

Getting ready

How to do it…

How it works…

There’s more…

See also

Analyzing portfolio sensitivities to the Fama-French factors

Getting ready

How to do it…

How it works…

There’s more…

See also

Assessing market inefficiency based on volatility

How to do it…

How it works…

There’s more…

See also

Preparing a factor ranking model using Zipline Pipelines

Getting ready

How to do it…

How it works…

There’s more…

See also

6

Vector-Based Backtesting with VectorBT

Building technical strategies with VectorBT

Getting ready

How to do it…

How it works…

There’s more…

See also

Conducting walk-forward optimization with VectorBT

Getting ready

How to do it…

How it works…

There’s more…

See also

Optimizing the SuperTrend strategy with VectorBT Pro

Getting ready

How to do it…

How it works…

There’s more…

See also

7

Event-Based Backtesting Factor Portfolios with Zipline Reloaded

Technical Requirements

For Windows, Unix/Linux, and Mac Intel users

For Mac M1/M2 users

Backtesting a momentum factor strategy with Zipline Reloaded

Getting ready

How to do it…

How it works…

There’s more…

See also

Exploring a mean reversion strategy with Zipline Reloaded

Getting ready

How to do it…

How it works…

There’s more…

See also

8

Evaluate Factor Risk and Performance with Alphalens Reloaded

Preparing backtest results

Getting ready…

How to do it…

How it works…

There’s more…

See also

Evaluating the information coefficient

Getting ready…

How to do it…

How it works…

There’s more…

See also

Examining factor return performance

How to do it…

How it works…

There’s more…

See also

Evaluating factor turnover

How to do it…

How it works…

There’s more…

See also

9

Assess Backtest Risk and Performance Metrics with Pyfolio

Preparing Zipline backtest results for Pyfolio Reloaded

Getting ready…

How to do it…

How it works…

There’s more…

See also

Generating strategy performance and return analytics

Getting ready…

How to do it…

How it works…

There’s more…

See also

Building a drawdown and rolling risk analysis

Getting ready…

How to do it…

How it works…

There’s more…

See also

Analyzing strategy holdings, leverage, exposure, and sector allocations

Getting ready…

How to do it…

How it works…

There’s more…

See also

Breaking Down Strategy Performance to Trade Level

Getting ready…

How to do it…

How it works…

There’s more…

See also

10

Set Up the Interactive Brokers Python API

Building an algorithmic trading app

Getting ready…

How to do it…

How it works…

There’s more…

See also

Creating a Contract object with the IB API

Getting ready…

How to do it…

How it works…

There’s more…

See also

Creating an Order object with the IB API

Getting ready…

How to do it…

How it works…

There’s more…

See also

Fetching historical market data

Getting ready…

How to do it…

How it works…

There’s more…

See also

Getting a market data snapshot

Getting ready…

How to do it…

How it works…

There’s more…

See also

Streaming live market data

Getting ready…

How to do it…

How it works…

There’s more…

See also

Storing live tick data in a local SQL database

Getting ready…

How to do it…

How it works…

There’s more…

See also

11

Manage Orders, Positions, and Portfolios with the IB API

Executing orders with the IB API

Getting ready

How to do it…

How it works…

There’s more…

See also

Managing orders once they’re placed

Getting ready

How to do it…

How it works…

There’s more…

See also

Getting details about your portfolio

Getting ready

How to do it…

How it works…

There’s more…

See also

Inspecting positions and position details

Getting ready

How to do it…

How it works…

There’s more…

See also

Computing portfolio profit and loss

Getting ready

How to do it…

How it works…

There’s more…

See also

12

Deploy Strategies to a Live Environment

Calculating real-time key performance and risk indicators

Getting ready

How to do it…

How it works…

There’s more…

See also

Sending orders based on portfolio targets

Getting ready

How to do it…

How it works…

There’s more…

See also

Deploying a monthly factor portfolio strategy

Getting ready

How to do it…

How it works…

There’s more…

See also

Deploying an options combo strategy

Getting ready

How to do it…

How it works…

There’s more…

See also

Deploying an intraday multi-asset mean reversion strategy

Getting ready

How to do it…

How it works…

There’s more…

See also

13

Advanced Recipes for Market Data and Strategy Management

Streaming real-time options data with ThetaData

Getting ready

How to do it…

How it works…

There’s more…

See also

Using the ArcticDB DataFrame database for tick storage

Getting ready

How to do it…

How it works…

There’s more…

See also

Triggering real-time risk limit alerts

Getting ready

How to do it…

How it works…

There’s more…

See also

Storing trade execution details in a SQL database

Getting ready

How to do it…

How it works…

There’s more…

See also

Index

Other Books You May Enjoy

Preface

Algorithmic trading is the art of using statistical models, programming, and math to trade financial assets. With the vast volumes of data available in today’s markets, it is essential to have powerful tools at your disposal to remain competitive. In Python for Algorithmic Trading Cookbook, you’ll get code to design, backtest, and deploy your own algorithmic trading strategies with Python. Python is not only accessible and easy to learn, but also boasts thousands of powerful libraries that can help you implement sophisticated trading strategies.

Many resources available today cover basic trading strategies largely focused on technical analysis. Unfortunately, these strategies often fail over the long run. This book aims to bring professional techniques and tools to non-professionals through small, digestible recipes. These recipes will not only guide you in creating, testing, and deploying algorithmic trading strategies, but build a strong foundation in the tools and techniques to prepare for creating, testing, and deploying algorithmic trading strategies.

In this book, I will share insights and methodologies drawn from my 20+ years of experience in algorithmic trading and Python programming. I have taught the techniques presented in this book to over 1,000+ students in my course, Getting Started with Python for Quant Finance. The course has been praised for its practical application of Python to algorithmic trading. Whether you are a seasoned programmer looking to expand into trading or a trader aiming to enhance your technical skills, this book is built to provide you with the tools and knowledge you need.

With Python, you can easily access financial data, perform complex calculations, and backtest your models efficiently. This book will guide you through the entire process, from setting up your Python environment to implementing advanced trading algorithms.

By the end of this journey, you will have a robust toolkit to develop and refine your own trading strategies. As the financial markets evolve, so too must our approaches. With the right knowledge and tools, you can navigate these changes and find opportunities that others might miss.

Who this book is for

Traders, investors, and Python developers can gain practical insights into designing, backtesting, and deploying algorithmic trading strategies from this book. The three main personas who are the target audience of this content are as follows:

Active traders and investors: Individuals who are already investing in the stock market and want to leverage algorithmic strategies to enhance their trading performance. They will learn to use Python to develop, test, and implement advanced trading models, including acquiring and processing freely available market data with OpenBB and building a research environment populated with financial market data.

Python developers with market interest: Developers with a solid understanding of Python data structures and libraries, such as pandas, who are looking to apply their programming skills to the financial markets. This book will help them bridge the gap between coding and trading by providing practical recipes and techniques used in algorithmic trading. They will learn to identify alpha factors, engineer them into signals, and use VectorBT for walk-forward optimization to find strategy parameters.

Aspiring algorithmic traders: For those who aspire to enter the field of algorithmic trading and have basic experience in Python programming, this book will provide them with the foundational knowledge and tools to start designing and deploying their trading strategies. They will learn to build production-ready backtests with Zipline, evaluate factor performance, set up the code framework to connect and send orders to Interactive Brokers, and deploy trading strategies to a live trading environment using the IB API.

This book will equip you with the skills to acquire and analyze financial data and build and refine algorithmic trading strategies using Python. Whether you are an experienced market participant looking to enhance your technical capabilities or a Python programmer with a keen interest in the financial markets, this book provides actionable insights and techniques to succeed in algorithmic trading.

What this book covers

Chapter 1, Acquire Free Financial Market Data with Cutting-edge Python Libraries, provides an in-depth exploration of acquiring various types of financial market data. You will learn to work with stock market, historic futures, and options market data using the OpenBB Platform, and harness factor data using pandas-datareader.

Chapter 2, Analyze and Transform Financial Market Data with pandas, dives into the powerful pandas library for data manipulation. This chapter explains pandas index types, building series and DataFrames, and transforming data. You will learn to calculate asset returns, measure volatility, generate cumulative returns, resample data, address missing data issues, and apply custom functions to analyze time series data.

Chapter 3, Visualize Financial Market Data with Matplotlib, Seaborn, and Plotly Dash, covers techniques for visualizing financial data. You will quickly visualize data using pandas, animate yield curve evolution with Matplotlib, plot options implied volatility surfaces, visualize statistical relationships with Seaborn, and create an interactive PCA analytics dashboard with Plotly Dash.

Chapter 4, Store Financial Market Data on Your Computer, discusses methods for efficiently storing financial data. You will learn to store data in CSV format, SQLite, a networked Postgres database, and the ultra-fast HDF5 format, ensuring your data is easily accessible and well organized for analysis and backtesting.

Chapter 5, Build Alpha Factors for Stock Portfolios, focuses on creating alpha factors. It covers identifying latent return drivers with principal component analysis, hedging portfolio beta using linear regression, analyzing portfolio sensitivities to Fama-French factors, assessing market inefficiency based on volatility, and preparing a factor ranking model using Zipline pipelines.

Chapter 6, Vector-Based Backtesting with VectorBT, introduces vector-based backtesting. This chapter guides you through experimenting with millions of strategy combinations, conducting walk-forward optimization, and implementing a risk parity backtest using VectorBT, providing a robust framework for strategy evaluation.

Chapter 7, Event-Based Backtesting Factor Portfolios with Zipline Reloaded, explores event-based backtesting. You will backtest a momentum factor strategy and explore a mean reversion strategy using Zipline Reloaded, helping you understand the dynamics and performance of various trading strategies.

Chapter 8, Evaluate Factor Risk and Performance with Alphalens Reloaded, examines factor risk and performance. You will prepare backtest results, evaluate the information coefficient, examine factor return performance, and evaluate factor turnover, ensuring a comprehensive analysis of your trading strategies.

Chapter 9, Assess Backtest Risk and Performance Metrics with Pyfolio, covers risk and performance assessment. This chapter explains preparing Zipline backtest results for pyfolio, generating strategy performance analytics, building a drawdown and rolling risk analysis, analyzing strategy holdings, leverage, exposure, sector allocations, and breaking down performance to the trade level.

Chapter 10, Set Up the Interactive Brokers Python API, provides a guide to building an algorithmic trading app. You will create contract and order objects with the IB API, fetch historical market data, get market data snapshots, stream live tick data, and store live tick data in a local SQL database, enabling real-time trading and data management.

Chapter 11, Manage Orders, Positions, and Portfolios with the IB API, explains managing trades and portfolios. You will learn to execute orders, manage placed orders, get portfolio details, inspect positions, and compute portfolio profit and loss, providing comprehensive tools to manage your trading operations.

Chapter 12, Deploy Strategies to a Live Environment, focuses on live trading strategy deployment. This chapter covers calculating real-time performance and risk indicators, sending orders based on portfolio targets, and deploying monthly factor, options combo, and intraday multi-asset mean reversion strategies, ensuring your strategies are effective and responsive in live markets.

Chapter 13, Advanced Recipes for Market Data and Strategy Management, offers advanced techniques for managing market data and strategies. You will learn to stream real-time options data with ThetaData, use the ArcticDB DataFrame database for tick storage, trigger real-time risk limit alerts, and store trade execution details in a SQL database, enhancing your data management and strategy implementation capabilities.

To get the most out of this book

You will need Python version 3.10 to work with the examples in this book. Ideally, you will install Python using the Anaconda distribution. All code examples have been tested using Python version 3.10 on macOS with an M2 chip.

Software/hardware covered in the book

Operating system requirements

Python version 3.10

Windows, macOS, or Linux

pandas version 2+

OpenBB Platform version 4+

PostgreSQL

Where necessary, specific installation instructions are included in the given chapters.

If you are using the digital version of this book, we advise you to type the code yourself or access the code from the book’s GitHub repository (a link is available in the next section). Doing so will help you avoid any potential errors related to the copying and pasting of code.

Download the example code files

You can download the example code files for this book from GitHub at https://github.com/PacktPublishing/Algorithmic-Trading-with-Python-Cookbook. If there’s an update to the code, it will be updated in the GitHub repository.

We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!

Conventions used

There are a number of text conventions used throughout this book.

Code in text: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: “save_data_range uses the pandas to_csv method to store the data to disk using GZIP compression in the CSV format.”

A block of code is set as follows:

import datetime as dt import pandas as pd from openbb_terminal.sdk import openbb

Any command-line input or output is written as follows:

pip install thetadata

Bold: Indicates a new term, an important word, or words that you see onscreen. For instance, words in menus or dialog boxes appear in bold. Here is an example: “In the Actions pane, click on Create Basic Task.”

Tips or important notes

Appear like this.

Get in touch

Feedback from our readers is always welcome.

General feedback: If you have questions about any aspect of this book, email us at [email protected] and mention the book title in the subject of your message.

Errata: Although we have taken every care to ensure the accuracy of our content, mistakes do happen. If you have found a mistake in this book, we would be grateful if you would report this to us. Please visit www.packtpub.com/support/errata and fill in the form.

Piracy: If you come across any illegal copies of our works in any form on the internet, we would be grateful if you would provide us with the location address or website name. Please contact us at [email protected] with a link to the material.

If you are interested in becoming an author: If there is a topic that you have expertise in and you are interested in either writing or contributing to a book, please visit authors.packtpub.com.

Share Your Thoughts

Once you’ve read Python for Algorithmic Trading Cookbook, we’d love to hear your thoughts! Please click here to go straight to the Amazon review page for this book and share your feedback.

Your review is important to us and the tech community and will help us make sure we’re delivering excellent quality content.

Download a free PDF copy of this book

Thanks for purchasing this book!

Do you like to read on the go but are unable to carry your print books everywhere?

Is your eBook purchase not compatible with the device of your choice?

Don’t worry, now with every Packt book you get a DRM-free PDF version of that book at no cost.

Read anywhere, any place, on any device. Search, copy, and paste code from your favorite technical books directly into your application.

The perks don’t stop there, you can get exclusive access to discounts, newsletters, and great free content in your inbox daily

Follow these simple steps to get the benefits:

Scan the QR code or visit the link below

https://packt.link/free-ebook/9781835084700

Submit your proof of purchaseThat’s it! We’ll send your free PDF and other benefits to your email directly

2

Analyze and Transform Financial Market Data with pandas

The pandas library was invented by Wes McKinney while at the investment management firm AQR Capital Management, where he researched macro and credit trading strategies. He built pandas to provide flexible, easy-to-use data structures for data analysis. Since it was open sourced in 2009, pandas has become the standard tool to analyze and transform data using Python.

pandas is well-suited for working with tabular data, like that stored in spreadsheets or databases, and it integrates well with many other data analysis libraries in the Python ecosystem. Its capabilities extend to handling missing data, reshaping datasets, and merging and joining datasets, and it also provides robust tools for loading data from flat files, Excel files, databases, and HDF5 file formats. It’s widely used in academia, finance, and many areas of business due to its rich features and ease of use.

This chapter will begin by covering recipes to help you build pandas data structures, primarily focusing on DataFrames and Series, the two primary data structures of pandas. You will learn how these structures allow for a wide variety of operations, such as slicing, indexing, and subsetting large datasets, all of which are crucial in algorithmic trading. From there, you will learn how to inspect and select data from DataFrames.

After you have a firm understanding of manipulating data using pandas, we’ll cover recipes for analyses that are common in algorithmic trading, including how to compute asset returns and the volatility of a return series. The chapter will teach you how to generate a cumulative return series and resample data to different time frames, providing you with the flexibility to analyze data at various granularities. You will also learn how to handle missing data, a common issue in real-world datasets.

Lastly, this chapter will show you how to apply custom functions to time series data. Throughout this chapter, you will see how pandas integrates with other libraries in the scientific Python ecosystem, such as Matplotlib for data visualization, NumPy for numerical operations, and Scikit-Learn for machine learning.

In this chapter, we’ll cover the following recipes:

Diving into pandas index typesBuilding pandas Series and DataFramesManipulating and transforming DataFramesExamining and selecting data from DataFramesCalculating asset returns using pandasMeasuring the volatility of a return seriesGenerating a cumulative return seriesResampling data for different time framesAddressing missing data issuesApplying custom functions to analyze time series data

Diving into pandas index types

The Index is an immutable sequence that’s used for indexing and alignment that serves as the label or key for rows in the DataFrame or elements in a series. It allows for fast lookup and relational operations and as of pandas version 2, it can contain values of any type, including integers, strings, and even tuples. Indexes in pandas are immutable, which makes them safe to share across multiple DataFrames or Series. They also have several built-in methods for common operations, such as sorting, grouping, and set operations such as union and intersection. pandas supports multiple indexes, allowing for complex, hierarchical data organization. This feature is particularly useful when dealing with high-dimensional data such as option chains. We’ll see examples of MultiIndexes later in this chapter.

There are seven types of pandas indexes. The differences are dependent on the type of data used to create the index. For example, an Int64Index is an index that’s made up of 64-bit integers. pandas is smart enough to create the right type of index, depending on the data used to instantiate it.

How to do it…

We’ll start by creating a simple index with a series of integers:

Import pandas as the common alias, pd: import pandas as pdInstantiate the Index class: