catalyst/docs/release-notes/zipline-0.6.1.md

Zipline 0.6.1 Release Notes

Highlights

• Major fixes to risk calculations, see BUG section.
• Port of history() function, see ENH section
• Start of support for Quantopian algorithm script-syntax, see ENH section.
• conda package manager support, see BLD section.

Enhancements (ENH)

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: 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:

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 (BUG)

Adjust benchmark events to match market hours (#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 PERF. 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

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 (PERF)

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 (MAINT)

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 (BLD)

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

• Eddie Hebert <ehebert@quantopian.com>, @ehebert, 49
• Thomas Wiecki <thomas.wiecki@gmail.com>, @twiecki, 28
• Richard Frank <rich@quantopian.com>, @richafrank, 11
• Jamie Kirkpatrick <jkp@spotify.com>, @jkp, 2
• Jeremiah Lowin <jlowin@gmail.com>, @jlowin, 2
• Colin Alexander <colin.1.alexander@gmail.com>, @colin1alexander, 1
• Michael Schatzow <michael.schatzow@gmail.com>, @MichaelWS, 1
• Moises Trovo <moises.trovo@gmail.com>, @mtrovo, 1
• Suminda Dharmasena <sirinath1978m@gmail.com>, @sirinath, 1