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  • Book Overview & Buying Mastering Python for Finance
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Mastering Python for Finance

Mastering Python for Finance

3.3 (7)
Buy this Book
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Mastering Python for Finance

Mastering Python for Finance

3.3 (7)
Buy this Book

Overview of this book

If you are an undergraduate or graduate student, a beginner to algorithmic development and research, or a software developer in the financial industry who is interested in using Python for quantitative methods in finance, this is the book for you. It would be helpful to have a bit of familiarity with basic Python usage, but no prior experience is required.
Table of Contents (12 chapters)
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Preface
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Preface
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What this book covers
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What you need for this book
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Who this book is for
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Conventions
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Reader feedback
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Customer support
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1
1. Python for Financial Applications
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1. Python for Financial Applications
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Is Python for me?
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Objected-oriented versus functional programming
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Which Python version should I use?
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Introducing IPython
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Summary
2
2. The Importance of Linearity in Finance
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2. The Importance of Linearity in Finance
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The capital asset pricing model and the security market line
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The Arbitrage Pricing Theory model
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Multivariate linear regression of factor models
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Linear optimization
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Solving linear equations using matrices
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The LU decomposition
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The Cholesky decomposition
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The QR decomposition
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Summary
3
3. Nonlinearity in Finance
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3. Nonlinearity in Finance
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Nonlinearity modeling
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Examples of nonlinear models
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An introduction to root-finding
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Incremental search
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The bisection method
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Newton's method
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The secant method
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Combining root-finding methods
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SciPy implementations
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Summary
4
4. Numerical Procedures
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4. Numerical Procedures
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Introduction to options
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Binomial trees in options pricing
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The Greeks for free
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Trinomial trees in options pricing
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Lattices in options pricing
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Finite differences in options pricing
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Putting it all together – implied volatility modeling
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Summary
5
5. Interest Rates and Derivatives
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5. Interest Rates and Derivatives
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Fixed-income securities
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Yield curves
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Valuing a zero-coupon bond
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Bootstrapping a yield curve
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Forward rates
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Calculating the yield to maturity
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Calculating the price of a bond
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Bond duration
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Bond convexity
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Short-rate modeling
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Bond options
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Pricing a callable bond option
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Summary
6
6. Interactive Financial Analytics with Python and VSTOXX
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6. Interactive Financial Analytics with Python and VSTOXX
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Volatility derivatives
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Gathering the EUROX STOXX 50 Index and VSTOXX data
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Merging the data
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Financial analytics of SX5E and V2TX
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Correlation between SX5E and V2TX
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Calculating the VSTOXX sub-indices
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Calculating the VSTOXX main index
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Summary
7
7. Big Data with Python
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7. Big Data with Python
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Introducing big data
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Hadoop for big data
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Is big data for me?
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Getting Apache Hadoop
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A word count program in Hadoop
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Going deeper – Hadoop for finance
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Introducing NoSQL
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Summary
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8. Algorithmic Trading
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8. Algorithmic Trading
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Introduction to algorithmic trading
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List of trading platforms with public API
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Which is the best programming language to use?
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System functionalities
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Algorithmic trading with Interactive Brokers and IbPy
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Building a mean-reverting algorithmic trading system
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Forex trading with OANDA API
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Building a trend-following forex trading platform
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VaR for risk management
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Summary
9
9. Backtesting
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9. Backtesting
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An introduction to backtesting
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Designing and implementing a backtesting system
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Ten considerations for a backtesting model
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Discussion of algorithms in backtesting
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Summary
10
10. Excel with Python
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10. Excel with Python
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Overview of COM
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Excel for finance
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Building a COM server
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Building the COM client in Excel
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What else can I do with COM?
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Summary
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Index
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Index

Algorithmic trading with Interactive Brokers and IbPy

In this section, we will build a working algorithmic trading platform that will authenticate with Interactive Brokers (IB) and log in, retrieve the market data, and send orders. IB is one of the most popular brokers in the trading community and has a long history of API development. There are plenty of articles on the use of the API available on the Web. IB serves clients ranging from hedge funds to retail traders. Although the API does not support Python directly, Python wrappers such as IbPy are available to make the API calls to the IB interface. The IB API is unique to its own implementation, and every broker has its own API handling methods. Nevertheless, the documents and sample applications provided by your broker would demonstrate the core functionality of every API interface that can be easily integrated into an algorithmic trading system if designed properly.

Getting Interactive Brokers' Trader WorkStation

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Tech Concepts
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Programming languages
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Mastering Python for Finance
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