Best PYTHON book for Finance

Whats the best Python book for Finance (Risk or Quant)?

Python for Finance, by Yves is probably worth checking out. Other than that, i’d search on Amazon and look at reviews.

I think it depends specifically on what you are doing with the code. Usually, these books will have some theme like options pricing or some statistics stuff, and have code that supplements the material. Programming language is not as important as what you are trying to achieve.

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And keep in mind that if it’s statistics material, it better be written by a PhD or MS statistician, or it’ll be pretty wrong, usually.

I glance through all of these “Data Science” and “Big Data” books for finance and other subjects and apparently any bozo with a degree can publish. (and Big data and data science are generally bastardizations of statistics unless taught by a real statistician.)

Anything from Wes McKinney or Travis Oliphant is gonna hit the mark

what about Python for Finance by Yves Hilpisch is it any good?

honestly I am not sure. I want apply my finance background with some coding to get into back office of a Bank/Hedge fund/ financial service/consulting. What would you suggest

"I think it depends specifically on what you are doing with the code. Usually, these books will have some theme like options pricing or some statistics stuff, and have code that supplements the material. Programming language is not as important as what you are trying to achieve. "

Shameless plug but I write a blog at www.pythonforfinance.net which you may find of some interest…

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Nothing to be ashamed about S666, it’s right in front of the topic and you’ve done a terrific job.

There’s a book and the name happens to be Python for finance. It includes:

  • Fundamentals: Python data structures, NumPy array handling, time series analysis with pandas, visualization with matplotlib, high performance I/O operations with PyTables, date/time information handling, and selected best practices
  • Financial topics: mathematical techniques with NumPy, SciPy and SymPy such as regression and optimization; stochastics for Monte Carlo simulation, Value-at-Risk, and Credit-Value-at-Risk calculations; statistics for normality tests, mean-variance portfolio optimization, principal component analysis (PCA), and Bayesian regression
  • Special topics: performance Python for financial algorithms, such as vectorization and parallelization, integrating Python with Excel, and building financial applications based on Web technologies.

Python for finance seems apt.

Though, not so sure about the reviews though!

Top 4:

Head-First Python (2nd edition)

Learn Python the Hard Way (3rd Edition)

Python Programming: An Introduction to Computer Science (3rd Edition)

Learning with Python: How to Think Like a Computer Scientist

Fantastic resource. Exactly what I was looking for. Cheers!