- 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
- 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
- Building a mean-reverting algorithmic trading system
- Forex trading with OANDA API
- Building a trend-following 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 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
- Summary
- 10. Excel with Python
- Index
Author
James Ma Weiming
Reviewers
Namit Kewat
Marco Marchioro
Jiri Pik
Steven E. Sommer, MD, MBA
Commissioning Editor
Usha Iyer
Acquisition Editor
Usha Iyer
Content Development Editor
Susmita Sabat
Technical Editor
Prajakta Mhatre
Copy Editor
Rashmi Sawant
Project Coordinator
Milton Dsouza
Proofreaders
Stephen Copestake
Safis Editing
Paul Hindle
Indexer
Hemangini Bari
Graphics
Sheetal Aute
Valentina D'silva
Disha Haria
Abhinash Sahu
Production Coordinator
Aparna Bhagat
Cover Work
Aparna Bhagat
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