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...
