finance 0.2502

Part 1 - Simple time dependent assets

Time is generic like a period such as eg 1 month and non-generic like a specific date. In part both types are implemented with a heavy use of operator overload.

This means that questions like: How many days are there between a date 2009-12-27 and 3 months ahead can be calculated like:

>>> from finance import bankdate
>>> t1 = bankdate('2009-12-27')
>>> print t1 + '3m'
2010-03-27
>>> print t1 + '3m' - t1
90

• To see more on bankdate

• To see more on timeperiods

Further a vector-like structure handling future payments - a dateflow - is implemented as a class.

Through method overload it is easy to build even very complex cashflows (= dateflow)

• To see more on dateflows

Generators of standard dateflows is also a part of the package.

• To see more on daterange

• To see more on daterangeiter

• To see more on standarddateflowgenerator

Before any calculations on a dateflow can be made dates has to be converted into times. For this the class datetotime is created.

• To see more on datetotime

Finally simpel calculations like present value and different sorts of duration can be made though the class timeflow

• To see more on timeflow

Changes in 0.2502:

There were still some problems with ultimo dates which now should be solved. Thank you to Johan Uys for bringing it to my attention.

Changes in 0.2501:

Problems with generating ultimo dates has been solved. Thank you to Ankush Sahai for bringing it to my attention.

Changes in 0.25:

Code has been rewritten to isolate strickt mathematical strucktures like e.g. DecimalVector in separate packages. There have been slight modifications to yieldcurves.

Changes in 0.20:

Now discount curves based on benchmark zero bonds where the rates are continous forward rates. It is possible to get standard yieldcalculations done like:

Instantiate:

>>> import finance
>>> ns = finance.yieldcurves.NelsonSiegel(0.061, -0.01, -0.0241, 0.275)

See the settings:

>>> ns
Nelson Siegel (level=0.061, slope=-0.01, curvature=-0.0241, scale=0.275)

Get the discountfactors at times 1, 2, 5, 10:

>>> times = [1, 2, 5, 10]
>>> ns(times)
DecimalVector([0.9517121708497056177816078083, 0.9072377300179418172521412527, 0.7844132592062346545344544940, 0.6008958407659500402742872859])

Get the zero coupon rate at time 5 and 7

>>> r5, r7 = ns.zero_coupon_rate([5, 7])
>>> r5, r7
(Decimal('0.049762403554685553400657196'), Decimal('0.050625188777310061599365592'))

Get the forward rate between time 5 and 7

>>> f5_7 = ns.discrete_forward_rate(5, 7)
>>> f5_7
Decimal('0.052785255470657667493924028')

As shown above yieldcurves are made using the DecimalVector concept. Especially all outputs will be Decimal or DecimalVector.

For now there are 3 different yield curve types:

• The Nelson Siegel

• The natural cubic spline

• The financial cubic spline

This way the finance package covers a large part of yieldcurves in use. Since it is easy to add more yieldcurves due to the design more will come.

Yieldcurves are of course integrated into the timeflow. So now it is possible to do most fixed income calculations.

A tutorial on fixed income calculations in the finance package is on its way.

Risk calculations based on linearily decomposable discountcurves is postponed intil later.