Creates a container object for averages in mavg window.

Moving another data type off of ndict on the path to making
it easier to discern between types when
Creates a new averages container for each call to get_averages.

Ideally, we'd want to avoid multiple object creation,
but currently not playing well with generator chain.
Moves slippage transactions off of ndict.

So that the datatype is unique.
Converts slippage orders to use a simple object.

Reducing the number of ndicts that are created, so that profiling
has less noise.
Changes use of ndict in transforms to be name object.

So that when inspecting memory, it's easier to find each datatype.
Uses an object for sid data to be passed to handle data.

Moving another datatype off of ndict, to make it easier to inspect
objects in memory when debugging.
Adds a contains method to SIDData to support `in` statement.
Upgrade to Pandas 0.10.1
Removes unused parameters in test trade info creation.

This code hasn't used `random` in a while, removing the parameter
left as an artifact.
Adds dictionary style access to Position object.
Adds dictionary style access to Portfolio object.
Fixes dictionary-style lookup on Position and Portfolio.

Typo on method name, /__getattr__/__getitem__
Add the Sortino ratio for downside risk
Force float value, and compare result against boundary
Merge pull request #66 from rday/sortino_ratio

Add the Sortino ratio for downside risk
Removes unused method.
Corrects wording on event_window test, with regards to empty values.

These tests ensure that there are three, not two, empty values
at the beginning of the transform.

Also, ensures that we are using a window length of 3 on the tests.
So that wordings of errors,etc. match the window length.
Enriches message returned for test batch transform.

Adds formatting of the failing values when ReturnPriceBatchTransform's
data shape does not match the window_length.
Fixes dates used in test factory for batch transforms.

The test factory was creating non-market days.
i.e. the date range spanned the weekend.

Using pandas' BDay frequency so that only business days are created.

This specific date range doesn't have holidays, so not accounting
for holidays in the factory.

Also, widens the range of the trading calendar to cover the test dates
generated by the factory which include 1990.
Previously the trading calendar began with 2002, meaning that holiday
and weekend adjustments with the data exercised by the factory did
not trigger when run with data in 1990.

This does increase the memory footprint of the tradingcalendar module.
However, only by a couple MB, so taking the hit there to enable
correct behavior.
Moves creation of non_trading_days into a function.

So that we're not leaving rules used for configuration hanging in
module space.