Description:
decimalpy 0.101
decimalpy - long description
In financial calculations it is recommended to use Decimal as a numerical base.
At the same time there is a need to have the same functionality as in numpy or R.
So the goal of this package is to fill this gap, i.e. implementing Decimal based
structures to ease eg. financial calculations.
In the creation of the finance package. it was decided to use Decimal
based structures.
There were 2 reasons for this:
In finance decimals matters and when other financial systems use the IEEE
standard 854-1987 the package finance need to do the same
For valuation purposes it is important that the financial calculations are
the exact same as those performed in eg spreadsheets who use the IEEE
standard 854-1987
See also the chapter that examplifies the reasons for this..
After a while it was clear that there were a lot of code with a life or purpose of
its own. And that was how the decimalpy package was born.
The Package decimalpy is inspired by numpy and eg the vector concept of The R package.
The key difference from numpy is that in decimalpy the only number type is
decimal.
The Package contains:
An n-dimensional array of decimals, a decimalvector
An n-dimensional array of decimals where the keys can be of a specific
type and not just integers as in a decimalvector, a
SortedKeysDecimalValuedDict
A decorator decimalvector_function that converts a simpel function into a
function that given a decimalvector as an argument returns a decimalvector
of function values. This makes it fairly easy to extend the number of
decimalvector functions. Also decimalvector functions makes it fairly easy
to use other packages like eg matplotlib
A set of decimalvector (typically financial) functions
Meta functions (functions on functions) for numerical first
(NumericalFirstOrder) and second (NumericalSecondOrder) order differention
A meta function for finding the inverse value of a function
The package will be extended in order to support the needs in the package
finance .
The decimal package is open source under the Python Software Foundation
License
How to install
Just run setup.py install command. Or in windows use the windows installer.
Documentation, etc
Visit my homepage to see more on how
to use and the research behind the code. It’s a blog like place on finance, math
and scientific computing.
Planned added contents
The planned development so far is:
Planned added content of version 0.2:
Implementation of matrix and more decimalbased functions
Planned added content of version 0.3:
Implementation af a statistical test package
License
For personal and professional use. You cannot resell or redistribute these repositories in their original state.
Files:
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