Mastering Python for Finance电子书

Mastering Python for Finance

Table of Contents

Mastering Python for Finance

Credits

About the Author

About the Reviewers

www.PacktPub.com

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Why subscribe?

Free access for Packt account holders

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Downloading the example code

Errata

Piracy

Questions

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

Index