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

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

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

Get it from:

• http://www.amazon.com/Mastering-Python-Finance-James-Weiming/dp/1784394513
• https://www.packtpub.com/big-data-and-business-intelligence/mastering-python-finance

Check out these awesome books too:

• Python for Finance by Dr. Yves Hilpisch: http://shop.oreilly.com/product/0636920032441.do
• Python for Quants. Volume I. by Dr. Pawel Lachowicz: http://www.quantatrisk.com/python-for-quants-volume-i/

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