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

Cover
Copyright
Contributors
Table of Contents
Preface
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
	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
	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
	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
	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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