Uses a Portfolio object instead of an ndict.

Gains some performance by using a 'regular' object instead of
an ndict.

Also, directly sets up the values that we return, instead of going in
between with __core_dict and then removing values.

In it's entirety performanc.as_portfolio is the current
highest bottleneck, working on reducing time spent in that function.
Recycles the portfolio container to be passed to handle_data.

The creation of a new portfolio ndict on each call of handle_data
was creating a very high performance overhead.

Instead, we use the same the portfolio object for each event,
and replace the values contained within.
Recycles objects for positions.

Instead of creating a new ndict for each position on every event,
we change the values in the object that held the previous position.

The creation of new objects on each event was incurring too much
overhead.

Changes the position type returned by performance module.

For improved speed, changes from ndict to a simple Python object,
since the cost of setting ndict values is too expensive for the
number of times that positions are returned.

Also, changes the containing type of the positions to be dictionary
with the __missing__ overloaded, instead of the ndict that had that
behavior, to reduce the penalty of using ndicts.
Merge pull request #58 from quantopian/placeholder-object-in-performance-inner-loop

Recycles objects for portfolio and positions.
Upgrade requests module to 1.1.0
Rolls over existing PerformancePeriod.

Instead of doing the rollover by creating a new PerformancePeriod,
introduces a `rollover` method that resets the values that need
to be fresh in a new period, and moves the ending values to starting
values, and leaves positions intact.

This isn't a major runtime improvement in of itself, but it does
allow us to more easily keep track of position values from period
to period, which other improvements will use.
Uses vdot and numpy arrays for position totals.

Gets almost 100x speed up over iterating over the values and
summing up the values in Python.

Farms out the work to numpy and atlas by using the vector dot
product of the amounts and last sale prices.

Adds some wiring of keeping track of an index into the numpy arrays
for each position, so that value can be overwritten as events update
those amounts and sale prices.
Merge branch 'master' of github.com:quantopian/zipline into batch_to_pandas
Converted lookups and dict creation.