Build and backtest your algorithmic trading strategies to gain a true advantage in the market
Key Features
Get quality insights from market data, stock analysis, and create your own data visualisations Learn how to navigate the different features in Pythons data analysis libraries Start systematically approaching quantitative research and strategy generation/backtesting in algorithmic trading
Book DescriptionCreating an effective system to automate your trading can help you achieve two of every traders key goals; saving time and making money. But to devise a system that will work for you, you need guidance to show you the ropes around building a system and monitoring its performance. This is where Hands-on Financial Trading with Python can give you the advantage.
This practical Python book will introduce you to Python and tell you exactly why its the best platform for developing trading strategies. Youll then cover quantitative analysis using Python, and learn how to build algorithmic trading strategies with Zipline using various market data sources.
Using Zipline as the backtesting library allows access to complimentary US historical daily market data until 2018. As you advance, you will gain an in-depth understanding of Python libraries such as NumPy and pandas for analyzing financial datasets, and explore Matplotlib, statsmodels, and scikit-learn libraries for advanced analytics.
As you progress, youll pick up lots of skills like time series forecasting, covering pmdarima and Facebook Prophet.
By the end of this trading book, you will be able to build predictive trading signals, adopt basic and advanced algorithmic trading strategies, and perform portfolio optimization to help you get and stayahead of the markets.
What you will learn
Discover how quantitative analysis works by covering financial statistics and ARIMA Use core Python libraries to perform quantitative research and strategy development using real datasets Understand how to access financial and economic data in Python Implement effective data visualization with Matplotlib Apply scientific computing and data visualization with popular Python libraries Build and deploy backtesting algorithmic trading strategies
Who this book is forIf youre a financial trader or a data analyst who wants a hands-on introduction to designing algorithmic trading strategies, then this book is for you. You dont have to be a fully-fledged programmer to dive into this book, but knowing how to use Pythons core libraries and a solid grasp on statistics will help you get the most out of this book.
Table of Contents
Introduction to algorithmic trading
Exploratory Data Analysis in Python
High-speed Scientific Computing using NumPy
Data Manipulation and Analysis with Pandas
Data Visualization using Matplotlib
Statistical Estimation, Inference, and Prediction
Financial Market Data Access in Python
Introduction to Zipline and PyFolio
Fundamental algorithmic trading strategies
Jiri Pik is an artificial intelligence architect & strategist who works with major investment banks, hedge funds, and other players. He has architected and delivered breakthrough trading, portfolio, and risk management systems, as well as decision support systems, across numerous industries. Jiri's consulting firm, Jiri PikRocketEdge, provides its clients with certified expertise, judgment, and execution at the speed of light. Sourav Ghosh has worked in several proprietary high-frequency algorithmic trading firms over the last decade. He has built and deployed extremely low latency, high-throughput automated trading systems for trading exchanges around the world, across multiple asset classes. He specializes in statistical arbitrage market-making and pairs trading strategies for the most liquid global futures contracts. Sourav works as a Senior Quantitative Developer at a trading firm in Chicago. He holds a Masters in Computer Science from the University of Southern California. His areas of interest include Computer Architecture, FinTech, Probability Theory and Stochastic Processes, Statistical Learning and Inference Methods, and Natural Language Processing.
