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NumPy Cookbook

You're reading from  NumPy Cookbook

Product type Book
Published in Oct 2012
Publisher Packt
ISBN-13 9781849518925
Pages 226 pages
Edition 1st Edition
Languages

Table of Contents (17) Chapters

NumPy Cookbook
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
1. Winding Along with IPython 2. Advanced Indexing and Array Concepts 3. Get to Grips with Commonly Used Functions 4. Connecting NumPy with the Rest of the World 5. Audio and Image Processing 6. Special Arrays and Universal Functions 7. Profiling and Debugging 8. Quality Assurance 9. Speed Up Code with Cython 10. Fun with Scikits Index

The steady state vector determination


A Markov chain is a system that has at least two states. For detailed information on Markov chains, please refer to http://en.wikipedia.org/wiki/Markov_chain.The state at time t depends on the state at time t-1, and only the state at t-1. The system switches at random between these states. I would like to define a Markov chain for a stock. Let's say that we have the states flat F, up U, and down D. We can determine the steady state based on end of day close prices.

Far into the distant future or in theory infinite time, the state of our Markov chain system will not change anymore. This is also called a steady state (http://en.wikipedia.org/wiki/Steady_state). The stochastic matrix (http://en.wikipedia.org/wiki/Stochastic_matrix) A, which contains the state transition probabilities, and when applied to the steady state, will yield the same state x. The mathematical notation for this will be as follows:

Another way to look at this is as the eigenvector...

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