Python for Finance: Analyze Big Financial Data

(Elle) #1

simulation of financial models, Simulation of Financial Models–A Use Case


valuation framework, Valuation Framework–Market Environments


volatility options, Volatility Options–The Options Portfolio


financial


definition of, The Rise of Real-Time Analytics


implied volatilities example, Implied Volatilities–Implied Volatilities


Monte Carlo simulation example, Monte Carlo Simulation–Graphical Analysis


retrieving data, Financial Data–Financial Data


size of data sets, Input/Output Operations


technical analysis example, Technical Analysis–Technical Analysis


interactive


benefits of Python for, Shorter time-to-results–Ensuring high performance


publishing platform for sharing, Basic usage


tools for, Tools–Spyder


real-time, The Rise of Real-Time Analytics


annualized performance, The Data


antithetic paths, Random Number Generation


antithetic variates, Variance Reduction


application development


benefits of Python for end-to-end, From Prototyping to Production


documentation best practices, Documentation


rapid web applications, Rapid Web Applications–Styling


syntax best practices, Python Syntax


tools for, Tools–Spyder


unit testing best practices, Unit Testing


approximation of functions, Approximation–Interpolation


interpolation, Interpolation


regression, Regression–Multiple dimensions


arbitrary precision floats, Floats


arrays


DataFrames and, Second Steps with DataFrame Class


input-output operations with PyTables, Working with Arrays


memory layout and, Memory Layout


regular NumPy arrays, Regular NumPy Arrays–Regular NumPy Arrays

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