A QuantLib Python ToolKit
Project description
# 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.
## 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.
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.
## 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.
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