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jamesmawm / Mastering-Python-for-Finance-source-codes

Accompanying source codes for my book 'Mastering Python for Finance'.
MIT License
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This is the accompanying source codes for my book 'Mastering Python for Finance'.

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ISBN-10: 1784394513, ISBN-13: 978-1784394516

Available on major sales channels including Amazon, Safari Online and Barnes & Noble, in paperback, Kindle and ebook.

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Table of Contents

  1. Python for Financial Applications
    • Is Python for me?
      • Free and open source
      • High-level, powerful, and flexible
      • A wealth of standard libraries
    • Objected-oriented versus functional programming
      • The object-oriented approach
      • The functional approach
      • Which approach should I use?
    • Which Python version should I use?
    • Introducing IPython
      • Getting IPython
      • Using pip
      • The IPython Notebook
        • Notebook documents
        • Running the IPython Notebook
        • Creating a new notebook
      • Notebook cells
        • Code cell
        • Markdown cell
        • Raw NBConvert cell
        • Heading cells
      • Simple exercises with IPython Notebook
        • Creating a notebook with heading and Markdown cells
        • Saving notebooks
        • Mathematical operations in cells
        • Displaying graphs
        • Inserting equations
        • Displaying images
        • Inserting YouTube videos
        • Working with HTML
        • The pandas DataFrame object as an HTML table
      • Notebook for finance
    • Summary
  2. The Importance of Linearity in Finance
    • The capital asset pricing model and the security market line
    • The Arbitrage Pricing Theory model
    • Multivariate linear regression of factor models
    • Linear optimization
      • Getting PuLP
      • A simple linear optimization problem
      • Outcomes of linear programs
      • Integer programming
        • An example of an integer programming model with binary conditions
        • A different approach with binary conditions
    • Solving linear equations using matrices
    • The LU decomposition
    • The Cholesky decomposition
    • The QR decomposition
      • Solving with other matrix algebra methods
        • The Jacobi method
        • The Gauss-Seidel method
    • Summary
  3. Nonlinearity in Finance
    • Nonlinearity modeling
    • Examples of nonlinear models
      • The implied volatility model
      • The Markov regime-switching model
      • The threshold autoregressive model
      • Smooth transition models
    • An introduction to root-finding
    • Incremental search
    • The bisection method
    • Newton's method
    • The secant method
    • Combining root-finding methods
    • SciPy implementations
      • Root-finding scalar functions
      • General nonlinear solvers
    • Summary
  4. Numerical Procedures
    • Introduction to options
    • Binomial trees in options pricing
      • Pricing European options
        • Are these formulas relevant to stocks? What about futures?
      • Writing the StockOption class
      • Writing the BinomialEuropeanOption class
      • Pricing American options with the BinomialTreeOption class
      • The Cox-Ross-Rubinstein model
        • Writing the BinomialCRROption class
      • Using a Leisen-Reimer tree
        • Writing the BinomialLROption class
    • The Greeks for free
      • Writing the BinomialLRWithGreeks class
    • Trinomial trees in options pricing
      • Writing the TrinomialTreeOption class
    • Lattices in options pricing
      • Using a binomial lattice
      • Writing the BinomialCRROption class
      • Using the trinomial lattice
        • Writing the TrinomialLattice class
    • Finite differences in options pricing
      • The explicit method
        • Writing the FiniteDifferences class
        • Writing the FDExplicitEu class
      • The implicit method
        • Writing the FDImplicitEu class
      • The Crank-Nicolson method
        • Writing the FDCnEu class
      • Pricing exotic barrier options
        • A down-and-out option
        • Writing the FDCnDo class
      • American options pricing with finite differences
        • Writing the FDCnAm class
    • Putting it all together – implied volatility modeling
      • Implied volatilities of AAPL American put option
    • Summary
  5. Interest Rates and Derivatives
    • Fixed-income securities
    • Yield curves
    • Valuing a zero-coupon bond
      • Spot and zero rates
    • Bootstrapping a yield curve
    • Forward rates
    • Calculating the yield to maturity
    • Calculating the price of a bond
    • Bond duration
    • Bond convexity
    • Short-rate modeling
      • The Vasicek model
      • The Cox-Ingersoll-Ross model
      • The Rendleman and Bartter model
      • The Brennan and Schwartz model
    • Bond options
      • Callable bonds
      • Puttable bonds
      • Convertible bonds
      • Preferred stocks
    • Pricing a callable bond option
      • Pricing a zero-coupon bond by the Vasicek model
      • Value of early-exercise
      • Policy iteration by finite differences
      • Other considerations in callable bond pricing
    • Summary
  6. Interactive Financial Analytics with Python and VSTOXX
    • Volatility derivatives
      • STOXX and the Eurex
      • The EURO STOXX 50 Index
      • The VSTOXX
      • The VIX
    • Gathering the EUROX STOXX 50 Index and VSTOXX data
    • Merging the data
    • Financial analytics of SX5E and V2TX
    • Correlation between SX5E and V2TX
    • Calculating the VSTOXX sub-indices
      • Getting the OESX data
      • Formulas to calculate the VSTOXX sub-index
      • Implementation of the VSTOXX sub-index value
      • Analyzing the results
    • Calculating the VSTOXX main index
    • Summary
  7. Big Data with Python
    • Introducing big data
    • Hadoop for big data
      • HDFS
      • YARN
      • MapReduce
    • Is big data for me?
    • Getting Apache Hadoop
      • Getting a QuickStart VM from Cloudera
      • Getting VirtualBox
      • Running Cloudera VM on VirtualBox
    • A word count program in Hadoop
      • Downloading sample data
      • The map program
      • The reduce program
      • Testing our scripts
      • Running MapReduce on Hadoop
      • Hue for browsing HDFS
    • Going deeper – Hadoop for finance
      • Obtaining IBM stock prices from Yahoo! Finance
      • Modifying the map program
      • Testing our map program with IBM stock prices
      • Running MapReduce to count intraday price changes
      • Performing analysis on our MapReduce results
    • Introducing NoSQL
      • Getting MongoDB
      • Creating the data directory and running MongoDB
        • Running MongoDB from Windows
        • Running MongoDB from Mac OS X
      • Getting PyMongo
      • Running a test connection
      • Getting a database
      • Getting a collection
      • Inserting a document
      • Fetching a single document
      • Deleting documents
      • Batch-inserting documents
      • Counting documents in the collection
      • Finding documents
      • Sorting documents
      • Conclusion
    • Summary
  8. Algorithmic Trading
    • Introduction to algorithmic trading
    • List of trading platforms with public API
    • Which is the best programming language to use?
    • System functionalities
    • Algorithmic trading with Interactive Brokers and IbPy
      • Getting Interactive Brokers' Trader WorkStation
      • Getting IbPy – the IB API wrapper
      • A simple order routing mechanism
    • Building a mean-reverting algorithmic trading system
      • Setting up the main program
      • Handling events
      • Implementing the mean-reverting algorithm
      • Tracking our positions
    • Forex trading with OANDA API
      • What is REST?
      • Setting up an OANDA account
      • Exploring the API
      • Getting oandapy – the OANDA REST API wrapper
      • Getting and parsing rates data
      • Sending an order
    • Building a trend-following forex trading platform
      • Setting up the main program Handling events
      • Implementing the trend-following algorithm
      • Tracking our positions
    • VaR for risk management
    • Summary
  9. Backtesting
    • An introduction to backtesting
      • Concerns in backtesting
      • Concept of an event-driven backtesting system
    • Designing and implementing a backtesting system
      • The TickData class
      • The MarketData class
      • The MarketDataSource class
      • The Order class
      • The Position class
      • The Strategy class
      • The MeanRevertingStrategy class
      • The Backtester class
      • Running our backtesting system
      • Improving your backtesting system
    • Ten considerations for a backtesting model
      • Resources restricting your model
      • Criteria of evaluation of the model
      • Estimating the quality of backtest parameters
      • Be prepared to face model risk
      • Performance of a backtest with in-sample data
      • Addressing common pitfalls in backtesting
      • Have a common sense idea of your model
      • Understanding the context for the model
      • Make sure you have the right data
      • Data mine your results
    • Discussion of algorithms in backtesting
      • K-means clustering
      • K-nearest neighbor machine learning algorithm
      • Classification and regression tree analysis
      • The 2k factorial design
      • The genetic algorithm
    • Summary
  10. Excel with Python
    • Overview of COM
    • Excel for finance
    • Building a COM server
      • Prerequisites
      • Getting the pythoncom module
      • Building the Black-Scholes model COM server
      • Registering and unregistering the COM server
      • Building the Cox-Ross-Rubinstein binomial tree model COM server
      • Building the trinomial lattice model COM server
    • Building the COM client in Excel
      • Setting up the VBA code
      • Setting up the cells
    • What else can I do with COM?
    • Summary