Book description
Understand, design, and implement stateoftheart mathematical and statistical applications used in finance with Python
In Detail
Built initially for scientific computing, Python quickly found its place in finance. Its flexibility and robustness can be easily incorporated into applications for mathematical studies, research, and software development.
With this book, you will learn about all the tools you need to successfully perform research studies and modeling, improve your trading strategies, and effectively manage risks. You will explore the various tools and techniques used in solving complex problems commonly faced in finance.
You will learn how to price financial instruments such as stocks, options, interest rate derivatives, and futures using computational methods. Also, you will learn how you can perform data analytics on market indexes and use NoSQL to store tick data.
What You Will Learn
 Perform interactive computing with IPython Notebook
 Solve linear equations of financial models and perform ordinary least squares regression
 Explore nonlinear modeling and solutions for optimum points using rootfinding algorithms and solvers
 Discover different types of numerical procedures used in pricing options
 Model fixedincome instruments with bonds and interest rates
 Manage big data with NoSQL and perform analytics with Hadoop
 Build a highfrequency algorithmic trading platform with Python
 Create an eventdriven backtesting tool and measure your strategies
Publisher resources
Table of contents

Mastering Python for Finance
 Table of Contents
 Mastering Python for Finance
 Credits
 About the Author
 About the Reviewers
 www.PacktPub.com
 Preface
 1. Python for Financial Applications
 2. The Importance of Linearity in Finance
 3. Nonlinearity in Finance

4. Numerical Procedures
 Introduction to options
 Binomial trees in options pricing
 The Greeks for free
 Trinomial trees in options pricing
 Lattices in options pricing
 Finite differences in options pricing
 Putting it all together – implied volatility modeling
 Summary
 5. Interest Rates and Derivatives
 6. Interactive Financial Analytics with Python and VSTOXX

7. Big Data with Python
 Introducing big data
 Hadoop for big data
 Is big data for me?
 Getting Apache Hadoop
 A word count program in Hadoop
 Going deeper – Hadoop for finance

Introducing NoSQL
 Getting MongoDB
 Creating the data directory and running MongoDB
 Getting PyMongo
 Running a test connection
 Getting a database
 Getting a collection
 Inserting a document
 Fetching a single document
 Deleting documents
 Batchinserting 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
 Building a meanreverting algorithmic trading system
 Forex trading with OANDA API
 Building a trendfollowing forex trading platform
 VaR for risk management
 Summary

9. Backtesting
 An introduction to backtesting
 Designing and implementing a 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 insample 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
 Summary
 10. Excel with Python
 Index
Product information
 Title: Mastering Python for Finance
 Author(s):
 Release date: April 2015
 Publisher(s): Packt Publishing
 ISBN: 9781784394516
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