Python for Finance: Analyze Big Financial Data

line plot of DataFrame object, Basic Analytics

parameters of DataFrame function, Second Steps with DataFrame Class, I/O with

pandas

parameters of date-range function, Second Steps with DataFrame Class

similarity to SQL database table, First Steps with DataFrame Class

vectorization with, Financial Data

DataNitro

benefits of, Scripting Excel with Python

cell attributes, Scripting with DataNitro

cell methods, Scripting with DataNitro

cell typesetting options, Scripting with DataNitro

combining with Excel, Working with DataNitro

installing, Installing DataNitro

optimizing performance, Scripting with DataNitro

plotting with, Plotting with DataNitro

scripting with, Scripting with DataNitro

user-defined functions, User-defined functions

DataReader function, Financial Data

dates and times

described by regular expressions, Strings

implied volatilities example, Implied Volatilities–Implied Volatilities

in risk-neutral discounting, Modeling and Handling Dates

Monte Carlo simulation example, Monte Carlo Simulation–Graphical Analysis

NumPy support for, NumPy–pandas

pandas support for, pandas–pandas

Python datetime module, Python–NumPy

technical analysis example, Technical Analysis–Technical Analysis

(see also financial time series data)

datetime module, Python–NumPy

datetime64 class, NumPy–pandas

date_range function, Second Steps with DataFrame Class

default, probability of, Credit Value Adjustments

Deltas, Generic Valuation Class

dependent observations, Regression