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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
8
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
11
Index
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Index

Chapter 3. Nonlinearity in Finance

In recent years, there has been a growing interest in research on nonlinear phenomena in economic and financial theory. With nonlinear serial dependence playing a significant role in the returns of many financial time series, this makes security valuation and pricing very important, leading to an increase in studies of nonlinear modeling of financial products.

Practitioners in the financial industry use nonlinear models to forecast volatility, price derivatives, and compute Value at Risk (VAR). Unlike linear models, where linear algebra is used to find a solution, nonlinear models do not necessarily infer a global optimal solution. Numerical root-finding methods are usually employed to converge toward the nearest local optimal solution, which is a root.

In this chapter, we will discuss the following topics to explore some methods that will help us extract information from nonlinear models:

  • Examining the definition of nonlinearity
  • Discussing the volatility...
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Tech Concepts
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Programming languages
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Mastering Python for Finance
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