- Hands-On Financial Trading with Python
- Contributors
- About the authors
- About the reviewer
- 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
- 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
- Other Books You May Enjoy
Section 3: Algorithmic Trading in Python
This section teaches you how to retrieve market data in Python, how to run basic algorithmic trading backtesting, and describes in detail the key algorithmic trading algorithms.
This section comprises the following chapters:
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