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

Hands-On Financial Trading with Python
Contributors
About the authors
About the reviewer
Preface
    Who this book is for
    What this book covers
    To get the most out of this book
    Download the example code files
    Download the color images
    Conventions used
    Get in touch
    Reviews
Section 1: Introduction to Algorithmic Trading
Chapter 1: Introduction to Algorithmic Trading
    Walking through the evolution of algorithmic trading
    Understanding financial asset classes
    Going through the modern electronic trading exchange
        Order types
        Limit order books
        The exchange matching engine
    Understanding the components of an algorithmic trading system
        The core infrastructure of an algorithmic trading system
        The quantitative infrastructure of an algorithmic trading system
    Summary
Section 2: In-Depth Look at Python Libraries for the Analysis of Financial Datasets
Chapter 2: Exploratory Data Analysis in Python
    Technical requirements
    Introduction to EDA
        Steps in EDA
        Revelation of the identity of A, B, and C and EDA's conclusions
    Special Python libraries for EDA
    Summary
Chapter 3: High-Speed Scientific Computing Using NumPy
    Technical requirements
    Introduction to NumPy
    Creating NumPy ndarrays
        Creating 1D ndarrays
        Creating 2D ndarrays
        Creating any-dimension ndarrays
        Creating an ndarray with np.zeros(...)
        Creating an ndarray with np.ones(...)
        Creating an ndarray with np.identity(...)
        Creating an ndarray with np.arange(...)
        Creating an ndarray with np.random.randn(…)
    Data types used with NumPy ndarrays
        Creating a numpy.float64 array
        Creating a numpy.bool array
        ndarrays' dtype attribute
        Converting underlying data types of ndarray with numpy.ndarrays.astype(...) 
    Indexing of ndarrays
        Direct access to an ndarray's element
        ndarray slicing
        Boolean indexing
        Indexing with arrays
    Basic ndarray operations
        Scalar multiplication with an ndarray
        Linear combinations of ndarrays
        Exponentiation of ndarrays
        Addition of an ndarray with a scalar
        Transposing a matrix
        Changing the layout of an ndarray
        Finding the minimum value in an ndarray
        Calculating the absolute value
        Calculating the mean of an ndarray
        Finding the index of the maximum value in an ndarray
        Calculating the cumulative sum of elements of an ndarray
        Finding NaNs in an ndarray
        Finding the truth values of x1>x2 of two ndarrays
        any and all Boolean operations on ndarrays
        Sorting ndarrays
        Searching within ndarrays
    File operations on ndarrays
        File operations with text files
        File operations with binary files
    Summary
Chapter 4: Data Manipulation and Analysis with pandas
    Technical requirements
    Introducing pandas Series, pandas DataFrames, and pandas Indexes
        pandas.Series
        pandas.DataFrame
        pandas.Index
    Learning essential pandas.DataFrame operations
        Indexing, selection, and filtering of DataFrames
        Dropping rows and columns from a DataFrame
        Sorting values and ranking the values' order within a DataFrame
        Arithmetic operations on DataFrames
        Merging and combining multiple DataFrames into a single DataFrame
        Hierarchical indexing
        Grouping operations in DataFrames
        Transforming values in DataFrames' axis indices
        Handling missing data in DataFrames
        The transformation of DataFrames with functions and mappings
        Discretization/bucketing of DataFrame values
        Permuting and sampling DataFrame values to generate new DataFrames
    Exploring file operations with pandas.DataFrames
        CSV files
        JSON files
    Summary
Chapter 5: Data Visualization Using Matplotlib
    Technical requirements
    Creating figures and subplots
        Defining figures' subplots
        Plotting in subplots
    Enriching plots with colors, markers, and line styles
    Enriching axes with ticks, labels, and legends
    Enriching data points with annotations
    Saving plots to files
    Charting a pandas DataFrame with Matplotlib
        Creating line plots of a DataFrame column
        Creating bar plots of a DataFrame column
        Creating histogram and density plots of a DataFrame column
        Creating scatter plots of two DataFrame columns
        Plotting time series data
    Summary
Chapter 6: Statistical Estimation, Inference, and Prediction
    Technical requirements
    Introduction to statsmodels
        Normal distribution test with Q-Q plots
        Time series modeling with statsmodels
        ETS analysis of a time series
        Augmented Dickey-Fuller test for stationarity of a time series
        Autocorrelation and partial autocorrelation of a time series
        ARIMA time series model
    Using a SARIMAX time series model with pmdarima
    Time series forecasting with Facebook's Prophet library
    Introduction to scikit-learn regression and classification
        Generating the dataset
        Running RidgeCV regression on the dataset
        Running a classification method on the dataset
    Summary
Section 3: Algorithmic Trading in Python
Chapter 7: Financial Market Data Access in Python
    Technical requirements
    Exploring the yahoofinancials Python library
        Single-ticker retrieval
        Multiple-tickers retrieval
    Exploring the pandas_datareader Python library
        Access to Yahoo Finance
        Access to EconDB
        Access to the Federal Reserve Bank of St Louis' FRED
        Caching queries
    Exploring the Quandl data source
    Exploring the IEX Cloud data source
    Exploring the MarketStack data source
    Summary
Chapter 8: Introduction to Zipline and PyFolio
    Technical requirements
    Introduction to Zipline and PyFolio
    Installing Zipline and PyFolio
        Installing Zipline
        Installing PyFolio
    Importing market data into a Zipline/PyFolio backtesting system
        Importing data from the historical Quandl bundle
        Importing data from the CSV files bundle
        Importing data from custom bundles
    Structuring Zipline/PyFolio backtesting modules
        Trading happens every day
        Trading happens on a custom schedule
    Reviewing the key Zipline API reference
        Types of orders
        Commission models
        Slippage models
    Running Zipline backtesting from the command line
    Introduction to risk management with PyFolio
        Market volatility, PnL variance, and PnL standard deviation
        Trade-level Sharpe ratio
        Maximum drawdown
    Summary
Chapter 9: Fundamental Algorithmic Trading Strategies
    Technical requirements 
    What is an algorithmic trading strategy?
    Learning momentum-based/trend-following strategies
        Rolling window mean strategy
        Simple moving averages strategy
        Exponentially weighted moving averages strategy
        RSI strategy
        MACD crossover strategy
        RSI and MACD strategies
        Triple exponential average strategy
        Williams R% strategy
    Learning mean-reversion strategies
        Bollinger band strategy
        Pairs trading strategy
    Learning mathematical model-based strategies
        Minimization of the portfolio volatility strategy with monthly trading
        Maximum Sharpe ratio strategy with monthly trading
    Learning time series prediction-based strategies
        SARIMAX strategy
        Prophet strategy
    Summary
Appendix A: How to Setup a Python Environment
    Technical requirements
    Initial setup
    Downloading the complimentary Quandl data bundle
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