This is a pre-production deployment of Warehouse. Changes made here affect the production instance of PyPI (
Help us improve Python packaging - Donate today!

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.

## 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.



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


easy_install qtk

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

Release History

Release History

This version
History Node


History Node


History Node


History Node


History Node


Download Files

Download Files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

File Name & Checksum SHA256 Checksum Help Version File Type Upload Date (45.8 kB) Copy SHA256 Checksum SHA256 Source Aug 26, 2016

Supported By

WebFaction WebFaction Technical Writing Elastic Elastic Search Pingdom Pingdom Monitoring Dyn Dyn DNS Sentry Sentry Error Logging CloudAMQP CloudAMQP RabbitMQ Heroku Heroku PaaS Kabu Creative Kabu Creative UX & Design Fastly Fastly CDN DigiCert DigiCert EV Certificate Rackspace Rackspace Cloud Servers DreamHost DreamHost Log Hosting