Release 0.6.1
-------------

:Release: 0.6.1
:Date: April 23, 2014


Highlights
~~~~~~~~~~

* Major fixes to risk calculations, see Bug Fixes section.
* Port of ``history()`` function, see Enhancements section
* Start of support for Quantopian algorithm script-syntax, see ENH
  section.
* conda package manager support, see Build section.

Enhancements
~~~~~~~~~~~~

* Always process new orders
  i.e. on bars where ``handle_data`` isn't called, but there is 'clock'
  data e.g. a consistent benchmark, process orders.
* Empty positions are now filtered from the portfolio container.
  To help prevent algorithms from operating on positions that are not in
  the existing universe of stocks.
  Formerly, iterating over positions would return positions for stocks
  which had zero shares held. (Where an explicit check in algorithm code
  for ``pos.amount != 0`` could prevent from using a non-existent
  position.)
* Add trading calendar for BMF&Bovespa.
* Add beginning of algo script support.
* Starts on the path of parity with the script syntax in Quantopian's IDE
  on https://quantopian.com
  Example:

  .. code-block:: python

     from datetime import datetime import pytz
     from zipline import TradingAlgorithm
     from zipline.utils.factory import load_from_yahoo

     from zipline.api import order

     def initialize(context):
         context.test = 10

     def handle_date(context, data):
         order('AAPL', 10)
         print(context.test)

     if __name__ == '__main__':
         import pylab as pl
         start = datetime(2008, 1, 1, 0, 0, 0, 0, pytz.utc)
         end = datetime(2010, 1, 1, 0, 0, 0, 0, pytz.utc)
         data = load_from_yahoo(
             stocks=['AAPL'],
             indexes={},
             start=start,
             end=end)
         data = data.dropna()
         algo = TradingAlgorithm(
             initialize=initialize,
             handle_data=handle_date)
         results = algo.run(data)
         results.portfolio_value.plot()
         pl.show()

* Add HDF5 and CSV sources.
* Limit ``handle_data`` to times with market data.
  To prevent cases where custom data types had unaligned timestamps, only
  call ``handle_data`` when market data passes through.
  Custom data that comes before market data will still update the data
  bar. But the handling of that data will only be done when there is
  actionable market data.
* Extended commission PerShare method to allow a minimum cost per trade.
* Add symbol api function
  A ``symbol()`` lookup feature was added to Quantopian. By adding the
  same API function to zipline we can make copy&pasting of a Zipline algo
  to Quantopian easier.
* Add simulated random trade source.
  Added a new data source that emits events with certain user-specified
  frequency (minute or daily).
  This allows users to backtest and debug an algorithm in minute mode to
  provide a cleaner path towards Quantopian.
* Remove dependency on benchmark for trading day calendar.
  Instead of the benchmarks' index, the trading calendar is now used to
  populate the environment's trading days.
  Remove ``extra_date`` field, since unlike the benchmarks list, the
  trading calendar can generate future dates, so dates for current day
  trading do not need to be appended.
  Motivations:

  -  The source for the open and close/early close calendar and the
     trading day calendar is now the same, which should help prevent
     potential issues due to misalignment.
  -  Allows configurations where the benchmark is provided as a generator
     based data source to need to supply a second benchmark list just to
     populate dates.
* Port ``history()`` API method from Quantopian.
  Opens the core of the ``history()`` function that was previously only
  available on the Quantopian platform.

  The history method is analoguous to the ``batch_transform``
  function/decorator, but with a hopefully more precise specification of
  the frequency and period of the previous bar data that is captured.
  Example usage:

  .. code-block:: python

     from zipline.api import history, add_history

     def initialize(context):
         add_history(bar_count=2, frequency='1d', field='price')

     def handle_data(context, data):
         prices = history(bar_count=2, frequency='1d', field='price')
         context.last_prices = prices

  N.B. this version of history lacks the backfilling capability that
  allows the return a full DataFrame on the first bar.


Bug Fixes
~~~~~~~~~

* Adjust benchmark events to match market hours (:issue:`241`).
  Previously benchmark events were emitted at 0:00 on the day the
  benchmark related to: in 'minute' emission mode this meant that the
  benchmarks were emitted before any intra-day trades were processed.
* Ensure perf stats are generated for all days
  When running with minutely emissions the simulator would report to the
  user that it simulated 'n - 1' days (where n is the number of days
  specified in the simulation params). Now the correct number of trading
  days are reported as being simulated.
* Fix repr for cumulative risk metrics.
  The ``__repr__`` for RiskMetricsCumulative was referring to an older
  structure of the class, causing an exception when printed.
  Also, now prints the last values in the metrics DataFrame.
* Prevent minute emission from crashing at end of available data.
  The next day calculation was causing an error when a minute emission
  algorithm reached the end of available data.
  Instead of a generic exception when available data is reached, raise and
  catch a named exception so that the tradesimulation loop can skip over,
  since the next market close is not needed at the end.
* Fix pandas indexing in trading calendar. This could alternatively be filed
  under Performance. Index using loc instead of the inefficient index-ing of
  day, then time.
* Prevent crash in vwap transform due to non-existent member.
  The WrongDataForTransform was referencing a ``self.fields`` member,
  which did not exist.
  Add a self.fields member set to ``price`` and ``volume`` and use it to
  iterate over during the check.
* Fix max drawdown calculation.
  The input into max drawdown was incorrect, causing the bad results. i.e.
  the ``compounded_log_returns`` were not values representative of the
  algorithms total return at a given time, though
  ``calculate_max_drawdown`` was treating the values as if they were.
  Instead, the ``algorithm_period_returns`` series is now used, which does
  provide the total return.
* Fix cost basis calculation.
  Cost basis calculation now takes direction of txn into account.
  Closing a long position or covering a short shouldn't affect the cost
  basis.
* Fix floating point error in ``order()``.
  Where order amounts that were near an integer could accidentally be
  floored or ceilinged (depending on being postive or negative) to the
  wrong integer.
  e.g. an amount stored internally as -27.99999 was converted to -27
  instead of -28.
* Update perf period state when positions are changed by splits.
  Otherwise, ``self._position_amounts`` will be out of sync with
  position.amount, etc.
* Fix misalignment of downside series calc when using exact dates.
  An oddity that was exposed while working on making the return series
  passed to the risk module more exact, the series comparison between the
  returns and mean returns was unbalanced, because the mean returns were
  not masked down to the downside data points; however, in most, if not
  all cases this was papered over by the call to ``.valid()`` which was
  removed in this change set.
* Check that self.logger exists before using it.
  ``self.logger`` is initialized as ``None`` and there is no guarantee
  that users have set it, so check that it exists before trying to pass
  messages to it.
* Prevent out of sync market closes in performance tracker.
  In situations where the performance tracker has been reset or patched to
  handle state juggling with warming up live data, the ``market_close``
  member of the performance tracker could end up out of sync with the
  current algo time as determined by the performance tracker.
  The symptom was dividends never triggering, because the end of day
  checks would not match the current time.
  Fix by having the tradesimulation loop be responsible, in minute/minute
  mode, for advancing the market close and passing that value to the
  performance tracker, instead of having the market close advanced by the
  performance tracker as well.
* Fix numerous cumulative and period risk calculations.
  The calculations that are expected to change are:

  - ``cumulative.beta``
  - ``cumulative.alpha``
  - ``cumulative.information``
  - ``cumulative.sharpe``
  - ``period.sortino``

  How Risk Calculations Are Changing
  Risk Fixes for Both Period and Cumulative

  Downside Risk

  Use sample instead of population for standard deviation.

  Add a rounding factor, so that if the two values are close for a given
  dt, that they do not count as a downside value, which would throw off
  the denominator of the standard deviation of the downside diffs.

  Standard Deviation Type

  Across the board the standard deviation has been standardized to using a
  'sample' calculation, whereas before cumulative risk was mostly using
  'population'. Using ``ddof=1`` with ``np.std`` calculates as if the
  values are a sample.

  Cumulative Risk Fixes

  Beta

  Use the daily algorithm returns and benchmarks instead of annualized
  mean returns.

  Volatility

  Use sample instead of population with standard deviation.

  The volatility is an input to other calculations so this change affects
  Sharpe and Information ratio calculations.

  Information Ratio

  The benchmark returns input is changed from annualized benchmark returns
  to the annualized mean returns.

  Alpha

  The benchmark returns input is changed from annualized benchmark returns
  to the annualized mean returns.

  Period Risk Fixes

  Sortino

  Now uses the downside risk of the daily return vs. the mean algorithm
  returns for the minimum acceptable return instead of the treasury
  return.

  The above required adding the calculation of the mean algorithm returns
  for period risk.

  Also, uses ``algorithm_period_returns`` and ``tresaury_period_return``
  as the cumulative Sortino does, instead of using algorithm returns for
  both inputs into the Sortino calculation.


Performance
~~~~~~~~~~~

* Removed ``alias_dt`` transform in favor of property on SIDData.
  Adding a copy of the Event's dt field as datetime via the ``alias_dt``
  generator, so that the API was forgiving and allowed both datetime and
  dt on a SIDData object, was creating noticeable overhead, even on an
  noop algorithms.
  Instead of incurring the cost of copying the datetime value and
  assigning it to the Event object on every event that is passed through
  the system, add a property to SIDData which acts as an alias
  ``datetime`` to ``dt``.
  Eventually support for ``data['foo'].datetime`` may be removed, and
  could be considered deprecated.
* Remove the drop of 'null return' from cumulative returns.
  The check of existence of the null return key, and the drop of said
  return on every single bar was adding unneeded CPU time when an
  algorithm was run with minute emissions.
  Instead, add the 0.0 return with an index of the trading day before the
  start date.
  The removal of the ``null return`` was mainly in place so that the
  period calculation was not crashing on a non-date index value; with the
  index as a date, the period return can also approximate volatility (even
  though the that volatility has high noise-to-signal strength because it
  uses only two values as an input.)


Maintenance and Refactorings
~~~~~~~~~~~~~~~~~~~~~~~~~~~~

* Allow ``sim_params`` to provide data frequency for the algorithm.
  In the case that ``data_frequency`` of the algorithm is None, allow the
  ``sim_params`` to provide the ``data_frequency``.

  Also, defer to the algorithms data frequency, if provided.


Build
~~~~~

* Added support for building and releasing via conda
  For those who prefer building with http://conda.pydata.org/ to compiling
  locally with pip.
  The following should install Zipline on many systems.

  ::

    conda install -c quantopian zipline

Contributors
~~~~~~~~~~~~

The following people have contributed to this release, ordered by
numbers of commit:

::

        49  Eddie Hebert
        28  Thomas Wiecki
        11  Richard Frank
         2  Jamie Kirkpatrick
         2  Jeremiah Lowin
         1  Colin Alexander
         1  Michael Schatzow
         1  Moises Trovo
         1  Suminda Dharmasena
