What this book covers
Chapter 1, Introduction to Algorithmic Trading and Python, introduces the key financial trading concepts and explains why Python is best suited for algorithmic trading.
Chapter 2, Exploratory Data Analysis in Python, provides an overview of the first step in processing any dataset, exploratory data analysis.
Chapter 3, High-Speed Scientific Computing Using NumPy, takes a detailed look at NumPy, a library for fast and scalable structured arrays and vectorized computations.
Chapter 4, Data Manipulation and Analysis with pandas, introduces the pandas library, built on top of NumPy, which provides data manipulation and analysis methods to structured DataFrames.
Chapter 5, Data Visualization Using Matplotlib, focuses on one of the primary visualization libraries in Python, Matplotlib.
Chapter 6, Statistical Estimation, Inference, and Prediction, discusses the statsmodels and scikit-learn libraries for advanced statistical analysis techniques, time series analysis techniques, as well as training and validating machine learning models.
Chapter 7, Financial Market Data Access in Python, describes alternative ways to retrieve market data in Python.
Chapter 8, Introduction to Zipline and PyFolio, covers Zipline and PyFolio, which are Python libraries that abstract away the complexities of actual backtesting and performance/risk analysis of algorithmic trading strategies. They allow you to entirely focus on the trading logic.
Chapter 9, Fundamental Algorithmic Trading Strategies, introduces the concept of an algorithmic strategy, and eight different trading algorithms representing the most used algorithms.
