Hands-On Financial Trading with Python: A practical guide to using Zipline and other Python libraries for backtesting trading strategies
by: Jiri Pik and Sourav Ghosh
Publisher finelybook 出版社: Packt Publishing (April 29,2021)
Language 语言: English
Print Length 页数: 360 pages
ISBN-10: 1838982884
ISBN-13: 9781838982881
Book Description
By finelybook
Discover how to build and backtest algorithmic trading strategies with Zipline
Key Features
Get to grips with market data and stock analysis and visualize data to gain quality insights
Find out how to systematically approach quantitative research and strategy generation/backtesting in algorithmic trading
Learn how to navigate the different features in Python’s data analysis libraries
Algorithmic trading helps you stay ahead of the markets by devising strategies in quantitative analysis to gain profits and cut losses.
The book starts by introducing you to algorithmic trading and explaining why Python is the best platform for developing trading strategies. You’ll 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. You’ll also focus on 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.
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
