Description:

qtk 0.1.3

# Quant Python ToolKit

This package is intended to be a layer above QuantLib Python and a few other quantitative libraries
to be more accessible for quantitative finance calculations.

## Minimal Example
Here is a minimal example for valuing a bond using a provided zero rates.

from qtk import Controller, Field as F, Template as T

data = [{
'Compounding': 'Compounded',
'CompoundingFrequency': 'Annual',
'Currency': 'USD',
'DiscountBasis': '30/360',
'DiscountCalendar': 'UnitedStates',
'ListOfDate': ['1/15/2015', '7/15/2015', '1/15/2016'],
'ListOfZeroRate': [0.0, 0.005, 0.007],
'ObjectId': 'USD.Zero.Curve',
'Template': 'TermStructure.Yield.ZeroCurve'},
{
'DiscountCurve': '->USD.Zero.Curve',
'ObjectId': 'BondEngine',
'Template': 'Engine.Bond.Discounting'},
{
'AccrualCalendar': 'UnitedStates',
'AccrualDayConvention': 'Unadjusted',
'AsOfDate': '2016-01-15',
'Coupon': 0.06,
'CouponFrequency': 'Semiannual',
'Currency': 'USD',
'DateGeneration': 'Backward',
'EndOfMonth': False,
'IssueDate': '2015-01-15',
'MaturityDate': '2016-01-15',
'ObjectId': 'USD.TBond',
'PaymentBasis': '30/360',
'PricingEngine': '->BondEngine',
'Template': 'Instrument.Bond.TreasuryBond'}]

res = Controller(data)
asof_date = "1/15/2015"
ret = res.process(asof_date)
tbond = res.object("USD.TBond")
print tbond.NPV()


The basic idea here is that once you have the data prepared, the `Controller` can be invoked to do the calculations.
A few points that are worth noting here.

- All the data is textual and rather intuitive. For instance, the coupon
frequency is just stated as `Annual` or `Semiannual`. The same is true for a lot of other fields. For dates,
the `dateutil` package is used to parse and covers a wide variety of formats.

- The `data` is essentially a `list` of `dict` with each `dict` corresponding to a specific `object` as determined
by the value to the key `Template` in each `dict`. Each `object` here has a name as specified by the value of the
key `ObjectId`

- One of the values can refer to another object described by a `dict` by using the `reference` syntax. For instance,
the first `dict` in the `data` list (with `ObjectId` given as *USD.Zero.Curve* ) variable refers to an interest
rate term structure of zero rates. The next object is a discounting bond engine, and require an yield curve as
input for the discount curve. Here the yield curve is refered by using the prefix `->` along with the name of the
object we are referring to.

- Here, the `Controller` parses the data, and figures out the dependency and processes the object in the correct order
and fulfills the dependencies behind the scenes.

## Introspection

There are a few convenience methods that provide help on how to construct the data packet. For example,
the `help` method in the template prints out the summary and list of fields on how to construct
the data packet for the template.

> T.TS_YIELD_BOND.help()

**Description**

A template for creating yield curve by stripping bond quotes.

**Required Fields**

- `Template` [*Template*]: 'TermStructure.Yield.BondCurve'
- `InstrumentCollection` [*List*]: Collection of instruments
- `AsOfDate` [*Date*]: Reference date or as of date
- `Country` [*String*]: Country
- `Currency` [*String*]: Currency

**Optional Fields**

- `ObjectId` [*String*]: A unique name or identifier to refer to this dictionary data
- `InterpolationMethod` [*String*]: The interpolation method can be one of the following choices: LinearZero, CubicZero, FlatForward, LinearForward,LogCubicDiscount.
- `DiscountBasis` [*DayCount*]: Discount Basis
- `SettlementDays` [*Integer*]: Settlement days
- `DiscountCalendar` [*Calendar*]: Discount Calendar

The `help` method prints the description in `info` method in Markdown format. While using IPython/Jupyter notebooks, the description
prints in a nice looking format. One can start with a sample data packet to fill out the input fields using the `sample_data` method.

> T.TS_YIELD_BOND.sample_data()

{'AsOfDate': 'Required (Date)',
'Country': 'Required (String)',
'Currency': 'Required (String)',
'DiscountBasis': 'Optional (DayCount)',
'DiscountCalendar': 'Optional (Calendar)',
'InstrumentCollection': 'Required (List)',
'InterpolationMethod': 'Optional (String)',
'ObjectId': 'Optional (String)',
'SettlementDays': 'Optional (Integer)',
'Template': 'TermStructure.Yield.BondCurve'}


## Installation

You can install qtk using `pip` or `easy_install`

pip install qtk

or

easy_install qtk

`qtk` has a dependency on `QuantLib-Python` which needs to be installed as well.

License

For personal and professional use. You cannot resell or redistribute these repositories in their original state.

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