Zipline, a Pythonic Algorithmic Trading Library.zip

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_clo