Hands-On Financial Trading with Python: A practical guide to using Zipline and other Python libraries for backtesting trading strategies

by Jiri Pik, Sourav Ghosh

Overview

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

Book Description:

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

Who this book is for:

This book is for data analysts and financial traders who want to explore how to design algorithmic trading strategies using Python's core libraries. If you are looking for a practical guide to backtesting algorithmic trading strategies and building your own strategies, then this book is for you. Beginner-level working knowledge of Python programming and statistics will be helpful.

Product Details

ISBN-13:	9781838982881
Publisher:	Packt Publishing
Publication date:	04/29/2021
Pages:	360
Sales rank:	763,059
Product dimensions:	7.50(w) x 9.25(h) x 0.75(d)

About the Author

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 Pik-RocketEdge, 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. He 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.

Table of Contents

Table of Contents

1. Introduction to algorithmic trading
2. Exploratory Data Analysis in Python
3. High-speed Scientific Computing using NumPy
4. Data Manipulation and Analysis with Pandas
5. Data Visualization using Matplotlib
6. Statistical Estimation, Inference, and Prediction
7. Financial Market Data Access in Python
8. Introduction to Zipline and PyFolio
9. Fundamental algorithmic trading strategies