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REL: Release notes file for 0.6.1
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Eddie Hebert
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# Zipline 0.6.1 Release Notes
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**Highlights**
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- **Major fixes to risk calculations, see BUG section.**
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- **Port of `history()` function, see ENH section**
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- **Start of support for Quantopian algorithm script-syntax, see ENH section.**
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- **conda package manager support, see BLD section.**
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## Enchancements (ENH)
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### Always process new orders.
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i.e. on bars where `handle_data` isn't called, but there is 'clock' data e.g. a
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consistent benchmark, process orders.
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### Empty positions are now filtered from the portfolio container.
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To help prevent algorithms from operating on positions that are not in the
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existing universe of stocks.
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Formerly, iterating over positions would return positions for stocks which had
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zero shares held. (Where an explicit check in algorithm code for `pos.amount !=
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0` could prevent from using a non-existent position.)
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### Add trading calendar for BMF&Bovespa.
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### Add beginning of algo script support.
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Starts on the path of parity with the script syntax in Quantopian's IDE on
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<https://quantopian.com>
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Example:
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from datetime import datetime
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import pytz
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from zipline import TradingAlgorithm
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from zipline.utils.factory import load_from_yahoo
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from zipline.api import order
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def initialize(context):
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context.test = 10
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def handle_date(context, data):
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order('AAPL', 10)
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print(context.test)
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if __name__ == '__main__':
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import pylab as pl
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start = datetime(2008, 1, 1, 0, 0, 0, 0, pytz.utc)
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end = datetime(2010, 1, 1, 0, 0, 0, 0, pytz.utc)
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data = load_from_yahoo(
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stocks=['AAPL'],
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indexes={},
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start=start,
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end=end)
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data = data.dropna()
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algo = TradingAlgorithm(
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initialize=initialize,
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handle_data=handle_date)
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results = algo.run(data)
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results.portfolio_value.plot()
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pl.show()
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### Add HDF5 and CSV sources.
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### Limit `handle_data` to times with market data.
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To prevent cases where custom data types had unaligned timestamps, only call
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`handle_data` when market data passes through.
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Custom data that comes before market data will still update the data bar. But
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the handling of that data will only be done when there is actionable market
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data.
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### Extended commission PerShare method to allow a minimum cost per trade.
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### Add symbol api function
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A `symbol()` lookup feature was added to Quantopian. By adding the same API
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function to zipline we can make copy&pasting of a Zipline algo to Quantopian
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easier.
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### Add simulated random trade source.
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Added a new data source that emits events with certain user-specified
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frequency (minute or daily).
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This allows users to backtest and debug an algorithm in minute mode to
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provide a cleaner path towards Quantopian.
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### Remove dependency on benchmark for trading day calendar.
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Instead of the benchmarks' index, the trading calendar is now used to populate
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the environment's trading days.
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Remove `extra_date` field, since unlike the benchmarks list, the trading
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calendar can generate future dates, so dates for current day trading do not need
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to be appended.
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Motivations:
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- The source for the open and close/early close calendar and the trading day
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calendar is now the same, which should help prevent potential issues due to
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misalignment.
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- Allows configurations where the benchmark is provided as a generator based
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data source to need to supply a second benchmark list just to populate dates.
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### Port `history()` API method from Quantopian.
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Opens the core of the `history()` function that was previously only available on
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the Quantopian platform.
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The history method is analoguous to the `batch_transform` function/decorator,
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but with a hopefully more precise specification of the frequency and period of
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the previous bar data that is captured.
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Example usage:
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from zipline.api import history, add_history
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def initialize(context):
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add_history(bar_count=2, frequency='1d', field='price')
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def handle_data(context, data):
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prices = history(bar_count=2, frequency='1d', field='price')
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context.last_prices = prices
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N.B. this version of history lacks the backfilling capability that allows the
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return a full DataFrame on the first bar.
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## Bug Fixes (BUG)
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### Adjust benchmark events to match market hours (#241)
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Previously benchmark events were emitted at 0:00 on the day the
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benchmark related to: in 'minute' emission mode this meant that
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the benchmarks were emitted before any intra-day trades were
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processed.
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### Ensure perf stats are generated for all days
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When running with minutely emissions the simulator would report to the
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user that it simulated 'n - 1' days (where n is the number of days
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specified in the simulation params). Now the correct number of trading
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days are reported as being simulated.
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### Fix repr for cumulative risk metrics.
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The `__repr__` for RiskMetricsCumulative was referring to an older structure of
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the class, causing an exception when printed.
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Also, now prints the last values in the metrics DataFrame.
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### Prevent minute emission from crashing at end of available data.
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The next day calculation was causing an error when a minute emission algorithm
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reached the end of available data.
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Instead of a generic exception when available data is reached, raise and catch a
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named exception so that the tradesimulation loop can skip over, since the next
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market close is not needed at the end.
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### Fix pandas indexing in trading calendar.
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This could alternatively be filed under PERF. Index using loc instead of the
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inefficient index-ing of day, then time.
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### Prevent crash in vwap transform due to non-existent member.
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The WrongDataForTransform was referencing a `self.fields` member,
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which did not exist.
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Add a self.fields member set to `price` and `volume` and use
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it to iterate over during the check.
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### Fix max drawdown calculation.
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The input into max drawdown was incorrect, causing the bad results. i.e. the
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`compounded_log_returns` were not values representative of the algorithms total
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return at a given time, though `calculate_max_drawdown` was treating the values
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as if they were. Instead, the `algorithm_period_returns` series is now used,
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which does provide the total return.
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### Fix cost basis calculation.
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Cost basis calculation now takes direction of txn into account.
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Closing a long position or covering a short shouldn't affect the cost basis.
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### Fix floating point error in order()
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Where order amounts that were near an integer could accidentally be floored or
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ceilinged (depending on being postive or negative) to the wrong integer.
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e.g. an amount stored internally as -27.99999 was converted to -27 instead of
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-28.
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### Update perf period state when positions are changed by splits
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Otherwise, `self._position_amounts` will be out of sync with position.amount,
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etc.
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### Fix misalignment of downside series calc when using exact dates.
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An oddity that was exposed while working on making the return series passed to
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the risk module more exact, the series comparison between the returns and mean
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returns was unbalanced, because the mean returns were not masked down to the
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downside data points; however, in most, if not all cases this was papered over
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by the call to `.valid()` which was removed in this change set.
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### Check that self.logger exists before using it.
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`self.logger` is initialized as `None` and there is no guarantee that users have
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set it, so check that it exists before trying to pass messages to it.
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### Prevent out of sync market closes in performance tracker.
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In situations where the performance tracker has been reset or patched to handle
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state juggling with warming up live data, the `market_close` member of the
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performance tracker could end up out of sync with the current algo time as
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determined by the
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The symptom was dividends never triggering, because the end of day checks would
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not match the current time.
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Fix by having the tradesimulation loop be responsible, in minute/minute mode,
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for advancing the market close and passing that value to the performance
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tracker, instead of having the market close advanced by the performance tracker
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as well.
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### Fix numerous cumulative and period risk calculations.
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The calculations that are expected to change are:
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- cumulative.beta
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- cumulative.alpha
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- cumulative.information
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- cumulative.sharpe
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- period.sortino
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#### How Risk Calculations Are Changing
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##### Risk Fixes for Both Period and Cumulative
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###### Downside Risk
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Use sample instead of population for standard deviation.
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Add a rounding factor, so that if the two values are close for a given dt, that
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they do not count as a downside value, which would throw off the denominator of
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the standard deviation of the downside diffs.
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###### Standard Deviation Type
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Across the board the standard deviation has been standardized to using a
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'sample' calculation, whereas before cumulative risk was mostly using
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'population'. Using `ddof=1` with `np.std` calculates as if the values are a
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sample.
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##### Cumulative Risk Fixes
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###### Beta
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Use the daily algorithm returns and benchmarks instead of annualized mean
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returns.
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###### Volatility
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Use sample instead of population with standard deviation.
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The volatility is an input to other calculations so this change affects Sharpe
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and Information ratio calculations.
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###### Information Ratio
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The benchmark returns input is changed from annualized benchmark returns to the
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annualized mean returns.
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###### Alpha
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The benchmark returns input is changed from annualized benchmark returns to the
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annualized mean returns.
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##### Period Risk Fixes
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###### Sortino
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Now uses the downside risk of the daily return vs. the mean algorithm returns
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for the minimum acceptable return instead of the treasury return.
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The above required adding the calculation of the mean algorithm returns for
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period risk.
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Also, uses `algorithm_period_returns` and `tresaury_period_return` as the
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cumulative Sortino does, instead of using algorithm returns for both inputs into
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the Sortino calculation.
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## Performance (PERF)
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### Removed `alias_dt` transform in favor of property on SIDData.
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Adding a copy of the Event's dt field as datetime via the `alias_dt` generator,
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so that the API was forgiving and allowed both datetime and dt on a SIDData
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object, was creating noticeable overhead, even on an noop algorithms.
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Instead of incurring the cost of copying the datetime value and assigning it
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to the Event object on every event that is passed through the system, add a
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property to SIDData which acts as an alias `datetime` to `dt`.
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Eventually support for `data['foo'].datetime` may be removed, and could be
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considered deprecated.
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### Remove the drop of 'null return' from cumulative returns.
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The check of existence of the null return key, and the drop of said return
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on every single bar was adding unneeded CPU time when an algorithm was run
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with minute emissions.
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Instead, add the 0.0 return with an index of the trading day before the
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start date.
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The removal of the `null return` was mainly in place so that the period
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calculation was not crashing on a non-date index value; with the index as a
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date, the period return can also approximate volatility (even though the
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that volatility has high noise-to-signal strength because it uses only two
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values as an input.)
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## Maintenance and Refactorings (MAINT)
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### Allow `sim_params` to provide data frequency for the algorithm.
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In the case that `data_frequency` of the algorithm is None, allow the
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`sim_params` to provide the `data_frequency`.
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Also, defer to the algorithms data frequency, if provided.
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## Build (BLD)
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### Added support for building and releasing via conda
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For those who prefer building with <http://conda.pydata.org/> to compiling
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locally with pip.
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The following should install Zipline on many systems.
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conda install -c quantopian zipline
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# Contributors
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- Eddie Hebert \<ehebert@quantopian.com\>, @ehebert, 49
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- Thomas Wiecki \<thomas.wiecki@gmail.com\>, @twiecki, 28
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- Richard Frank \<rich@quantopian.com\>, @richafrank, 11
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- Jamie Kirkpatrick \<jkp@spotify.com\>, @jkp, 2
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- Jeremiah Lowin \<jlowin@gmail.com\>, @jlowin, 2
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- Colin Alexander \<colin.1.alexander@gmail.com\>, @colin1alexander, 1
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- Michael Schatzow \<michael.schatzow@gmail.com\>, @MichaelWS, 1
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- Moises Trovo \<moises.trovo@gmail.com\>, @mtrovo, 1
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- Suminda Dharmasena \<sirinath1978m@gmail.com\>, @sirinath, 1
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