Refactoring of TradingEnvironment to isolate the global state: index symbol
and exchange timezone. Parameters that define the simulation (start, end, and
capital base) were put in a new class, SimulationParameters.

Global state for the financial simulation environment is accessed through the
zipline.finance.trading module, which now contains a module variable:
environment.

Parameters are passed into an algorithm as a keyword argument, sim_params.
SimulationParameters creates a trading day index for the test period that
can be used to find trading days, calculate distance between trading days,
and other common operations. The sim params index is just selected from the
global state.

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Details:

    - adding delorean to the requirements.
    - made index symbol a parameter for loading the benchmark data. changed
    messagepack storage to be symbol specific.
    - ported risk, performance, algorithm, transforms, batch transforms
    and associated tests to use simulation parameters and global environment
    - factory and sim factory use global state and sim params
    - factory method parameter names now reflect the class expected
TradingEnvironment allows the specification of a benchmark index and a
local timezone for the exchange. This commit adds tests to verify the
TradingEnvironment properly handles London Stock Exchange index, FTSE.

    - added LSE reference rrules calendar (thanks to Edward Johns)
    - added tests to verify LSE environment matches rrule calendar
    - added a test to verify global environment behavior can be set.
    - moved DailyReturn class to trading to eliminate circularity from
    risk <-> trading.
    - updated TradingEnvironment to be a context manager. This allows users
    to run algorithms in individually isolated environments in one python
    process. This is useful for managing multiple algorithms in a single
    ipython notebook.
    - added comments to explain behavior and useage of the global environment