- 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
The following table lists some brokers and trading platform vendors who have their API documentation publicly available:
|
Broker/vendor |
URL |
Programming languages supported |
|---|---|---|
|
Interactive Brokers |
C++, Posix C++, Java, and Visual Basic for ActiveX | |
|
E*Trade |
Java, PHP, and C++ | |
|
IG |
REST, Java, FIX, and Microsoft .NET Framework 4.0 | |
|
Tradier |
Java, Perl, Python, and Ruby | |
|
TradeKing |
Java, Node.js, PHP, R, and Ruby | |
|
Cunningham trading systems |
Microsoft .NET Framework 4.0 | |
|
CQG |
C#, C++, Excel, MATLAB, and VB.NET | |
|
Trading technologies |
Microsoft .NET Framework 4.0 | |
|
OANDA |
REST, Java, FIX, and MT4 |
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