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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

Discussion of algorithms in backtesting

After taking into consideration the designing of a backtesting model, one or more algorithms may be used to improve the model on a continuous basis. This section briefly covers some of the algorithmic techniques used in areas of backtesting, such as data mining and machine learning.

K-means clustering

The k-means clustering algorithm is a method of clustering analysis in data mining. From the backtest results of n observations, the k-means algorithm is designed to classify the data into k clusters based on their relative distance from each other. The center point of each cluster is computed. The objective then is to find the within-cluster sum of squares that gives us a model averaged point. The model averaged point indicates the likely average performance of the model, which can be used for further comparison with the performance of other models.

K-nearest neighbor machine learning algorithm

The k-nearest neighbor (KNN) is a lazy learning technique that...

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Tech Concepts
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Programming languages
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Mastering Python for Finance
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