Source code for zipline.utils.run_algo

import click
import os
import sys
import warnings

try:
    from pygments import highlight
    from pygments.lexers import PythonLexer
    from pygments.formatters import TerminalFormatter

    PYGMENTS = True
except ImportError:
    PYGMENTS = False
import logbook
import pandas as pd
from toolz import concatv
from trading_calendars import get_calendar

from zipline.data import bundles
from zipline.data.benchmarks import get_benchmark_returns_from_file
from zipline.data.data_portal import DataPortal
from zipline.finance import metrics
from zipline.finance.trading import SimulationParameters
from zipline.pipeline.data import USEquityPricing
from zipline.pipeline.loaders import USEquityPricingLoader

import zipline.utils.paths as pth
from zipline.extensions import load
from zipline.errors import SymbolNotFound
from zipline.algorithm import TradingAlgorithm, NoBenchmark
from zipline.finance.blotter import Blotter

log = logbook.Logger(__name__)


class _RunAlgoError(click.ClickException, ValueError):
    """Signal an error that should have a different message if invoked from
    the cli.

    Parameters
    ----------
    pyfunc_msg : str
        The message that will be shown when called as a python function.
    cmdline_msg : str, optional
        The message that will be shown on the command line. If not provided,
        this will be the same as ``pyfunc_msg`
    """

    exit_code = 1

    def __init__(self, pyfunc_msg, cmdline_msg=None):
        if cmdline_msg is None:
            cmdline_msg = pyfunc_msg

        super(_RunAlgoError, self).__init__(cmdline_msg)
        self.pyfunc_msg = pyfunc_msg

    def __str__(self):
        return self.pyfunc_msg


# TODO: simplify
# flake8: noqa: C901
def _run(
    handle_data,
    initialize,
    before_trading_start,
    analyze,
    algofile,
    algotext,
    defines,
    data_frequency,
    capital_base,
    bundle,
    bundle_timestamp,
    start,
    end,
    output,
    trading_calendar,
    print_algo,
    metrics_set,
    local_namespace,
    environ,
    blotter,
    custom_loader,
    benchmark_spec,
):
    """Run a backtest for the given algorithm.

    This is shared between the cli and :func:`zipline.run_algo`.
    """

    bundle_data = bundles.load(
        bundle,
        environ,
        bundle_timestamp,
    )

    if trading_calendar is None:
        trading_calendar = get_calendar("XNYS")

    # date parameter validation
    if trading_calendar.session_distance(start, end) < 1:
        raise _RunAlgoError(
            "There are no trading days between %s and %s"
            % (
                start.date(),
                end.date(),
            ),
        )

    benchmark_sid, benchmark_returns = benchmark_spec.resolve(
        asset_finder=bundle_data.asset_finder,
        start_date=start,
        end_date=end,
    )

    if algotext is not None:
        if local_namespace:
            ip = get_ipython()  # noqa
            namespace = ip.user_ns
        else:
            namespace = {}

        for assign in defines:
            try:
                name, value = assign.split("=", 2)
            except ValueError:
                raise ValueError(
                    "invalid define %r, should be of the form name=value" % assign,
                )
            try:
                # evaluate in the same namespace so names may refer to
                # eachother
                namespace[name] = eval(value, namespace)
            except Exception as e:
                raise ValueError(
                    "failed to execute definition for name %r: %s" % (name, e),
                )
    elif defines:
        raise _RunAlgoError(
            "cannot pass define without `algotext`",
            "cannot pass '-D' / '--define' without '-t' / '--algotext'",
        )
    else:
        namespace = {}
        if algofile is not None:
            algotext = algofile.read()

    if print_algo:
        if PYGMENTS:
            highlight(
                algotext,
                PythonLexer(),
                TerminalFormatter(),
                outfile=sys.stdout,
            )
        else:
            click.echo(algotext)

    first_trading_day = bundle_data.equity_minute_bar_reader.first_trading_day

    data = DataPortal(
        bundle_data.asset_finder,
        trading_calendar=trading_calendar,
        first_trading_day=first_trading_day,
        equity_minute_reader=bundle_data.equity_minute_bar_reader,
        equity_daily_reader=bundle_data.equity_daily_bar_reader,
        adjustment_reader=bundle_data.adjustment_reader,
        future_minute_reader=bundle_data.equity_minute_bar_reader,
        future_daily_reader=bundle_data.equity_daily_bar_reader,
    )

    pipeline_loader = USEquityPricingLoader.without_fx(
        bundle_data.equity_daily_bar_reader,
        bundle_data.adjustment_reader,
    )

    def choose_loader(column):
        if column in USEquityPricing.columns:
            return pipeline_loader
        try:
            return custom_loader.get(column)
        except KeyError:
            raise ValueError("No PipelineLoader registered for column %s." % column)

    if isinstance(metrics_set, str):
        try:
            metrics_set = metrics.load(metrics_set)
        except ValueError as e:
            raise _RunAlgoError(str(e))

    if isinstance(blotter, str):
        try:
            blotter = load(Blotter, blotter)
        except ValueError as e:
            raise _RunAlgoError(str(e))

    try:
        perf = TradingAlgorithm(
            namespace=namespace,
            data_portal=data,
            get_pipeline_loader=choose_loader,
            trading_calendar=trading_calendar,
            sim_params=SimulationParameters(
                start_session=start,
                end_session=end,
                trading_calendar=trading_calendar,
                capital_base=capital_base,
                data_frequency=data_frequency,
            ),
            metrics_set=metrics_set,
            blotter=blotter,
            benchmark_returns=benchmark_returns,
            benchmark_sid=benchmark_sid,
            **{
                "initialize": initialize,
                "handle_data": handle_data,
                "before_trading_start": before_trading_start,
                "analyze": analyze,
            }
            if algotext is None
            else {
                "algo_filename": getattr(algofile, "name", "<algorithm>"),
                "script": algotext,
            },
        ).run()
    except NoBenchmark:
        raise _RunAlgoError(
            (
                "No ``benchmark_spec`` was provided, and"
                " ``zipline.api.set_benchmark`` was not called in"
                " ``initialize``."
            ),
            (
                "Neither '--benchmark-symbol' nor '--benchmark-sid' was"
                " provided, and ``zipline.api.set_benchmark`` was not called"
                " in ``initialize``. Did you mean to pass '--no-benchmark'?"
            ),
        )

    if output == "-":
        click.echo(str(perf))
    elif output != os.devnull:  # make the zipline magic not write any data
        perf.to_pickle(output)

    return perf


# All of the loaded extensions. We don't want to load an extension twice.
_loaded_extensions = set()


def load_extensions(default, extensions, strict, environ, reload=False):
    """Load all of the given extensions. This should be called by run_algo
    or the cli.

    Parameters
    ----------
    default : bool
        Load the default exension (~/.zipline/extension.py)?
    extension : iterable[str]
        The paths to the extensions to load. If the path ends in ``.py`` it is
        treated as a script and executed. If it does not end in ``.py`` it is
        treated as a module to be imported.
    strict : bool
        Should failure to load an extension raise. If this is false it will
        still warn.
    environ : mapping
        The environment to use to find the default extension path.
    reload : bool, optional
        Reload any extensions that have already been loaded.
    """
    if default:
        default_extension_path = pth.default_extension(environ=environ)
        pth.ensure_file(default_extension_path)
        # put the default extension first so other extensions can depend on
        # the order they are loaded
        extensions = concatv([default_extension_path], extensions)

    for ext in extensions:
        if ext in _loaded_extensions and not reload:
            continue
        try:
            # load all of the zipline extensionss
            if ext.endswith(".py"):
                with open(ext) as f:
                    ns = {}
                    exec(compile(f.read(), ext, "exec"), ns, ns)
            else:
                __import__(ext)
        except Exception as e:
            if strict:
                # if `strict` we should raise the actual exception and fail
                raise
            # without `strict` we should just log the failure
            warnings.warn("Failed to load extension: %r\n%s" % (ext, e), stacklevel=2)
        else:
            _loaded_extensions.add(ext)


[docs]def run_algorithm( start, end, initialize, capital_base, handle_data=None, before_trading_start=None, analyze=None, data_frequency="daily", bundle="quantopian-quandl", bundle_timestamp=None, trading_calendar=None, metrics_set="default", benchmark_returns=None, default_extension=True, extensions=(), strict_extensions=True, environ=os.environ, custom_loader=None, blotter="default", ): """ Run a trading algorithm. Parameters ---------- start : datetime The start date of the backtest. end : datetime The end date of the backtest.. initialize : callable[context -> None] The initialize function to use for the algorithm. This is called once at the very begining of the backtest and should be used to set up any state needed by the algorithm. capital_base : float The starting capital for the backtest. handle_data : callable[(context, BarData) -> None], optional The handle_data function to use for the algorithm. This is called every minute when ``data_frequency == 'minute'`` or every day when ``data_frequency == 'daily'``. before_trading_start : callable[(context, BarData) -> None], optional The before_trading_start function for the algorithm. This is called once before each trading day (after initialize on the first day). analyze : callable[(context, pd.DataFrame) -> None], optional The analyze function to use for the algorithm. This function is called once at the end of the backtest and is passed the context and the performance data. data_frequency : {'daily', 'minute'}, optional The data frequency to run the algorithm at. bundle : str, optional The name of the data bundle to use to load the data to run the backtest with. This defaults to 'quantopian-quandl'. bundle_timestamp : datetime, optional The datetime to lookup the bundle data for. This defaults to the current time. trading_calendar : TradingCalendar, optional The trading calendar to use for your backtest. metrics_set : iterable[Metric] or str, optional The set of metrics to compute in the simulation. If a string is passed, resolve the set with :func:`zipline.finance.metrics.load`. benchmark_returns : pd.Series, optional Series of returns to use as the benchmark. default_extension : bool, optional Should the default zipline extension be loaded. This is found at ``$ZIPLINE_ROOT/extension.py`` extensions : iterable[str], optional The names of any other extensions to load. Each element may either be a dotted module path like ``a.b.c`` or a path to a python file ending in ``.py`` like ``a/b/c.py``. strict_extensions : bool, optional Should the run fail if any extensions fail to load. If this is false, a warning will be raised instead. environ : mapping[str -> str], optional The os environment to use. Many extensions use this to get parameters. This defaults to ``os.environ``. blotter : str or zipline.finance.blotter.Blotter, optional Blotter to use with this algorithm. If passed as a string, we look for a blotter construction function registered with ``zipline.extensions.register`` and call it with no parameters. Default is a :class:`zipline.finance.blotter.SimulationBlotter` that never cancels orders. Returns ------- perf : pd.DataFrame The daily performance of the algorithm. See Also -------- zipline.data.bundles.bundles : The available data bundles. """ load_extensions(default_extension, extensions, strict_extensions, environ) benchmark_spec = BenchmarkSpec.from_returns(benchmark_returns) return _run( handle_data=handle_data, initialize=initialize, before_trading_start=before_trading_start, analyze=analyze, algofile=None, algotext=None, defines=(), data_frequency=data_frequency, capital_base=capital_base, bundle=bundle, bundle_timestamp=bundle_timestamp, start=start, end=end, output=os.devnull, trading_calendar=trading_calendar, print_algo=False, metrics_set=metrics_set, local_namespace=False, environ=environ, blotter=blotter, custom_loader=custom_loader, benchmark_spec=benchmark_spec, )
class BenchmarkSpec(object): """ Helper for different ways we can get benchmark data for the Zipline CLI and zipline.utils.run_algo.run_algorithm. Parameters ---------- benchmark_returns : pd.Series, optional Series of returns to use as the benchmark. benchmark_file : str or file File containing a csv with `date` and `return` columns, to be read as the benchmark. benchmark_sid : int, optional Sid of the asset to use as a benchmark. benchmark_symbol : str, optional Symbol of the asset to use as a benchmark. Symbol will be looked up as of the end date of the backtest. no_benchmark : bool Flag indicating that no benchmark is configured. Benchmark-dependent metrics will be calculated using a dummy benchmark of all-zero returns. """ def __init__( self, benchmark_returns, benchmark_file, benchmark_sid, benchmark_symbol, no_benchmark, ): self.benchmark_returns = benchmark_returns self.benchmark_file = benchmark_file self.benchmark_sid = benchmark_sid self.benchmark_symbol = benchmark_symbol self.no_benchmark = no_benchmark @classmethod def from_cli_params( cls, benchmark_sid, benchmark_symbol, benchmark_file, no_benchmark ): return cls( benchmark_returns=None, benchmark_sid=benchmark_sid, benchmark_symbol=benchmark_symbol, benchmark_file=benchmark_file, no_benchmark=no_benchmark, ) @classmethod def from_returns(cls, benchmark_returns): return cls( benchmark_returns=benchmark_returns, benchmark_file=None, benchmark_sid=None, benchmark_symbol=None, no_benchmark=benchmark_returns is None, ) def resolve(self, asset_finder, start_date, end_date): """ Resolve inputs into values to be passed to TradingAlgorithm. Returns a pair of ``(benchmark_sid, benchmark_returns)`` with at most one non-None value. Both values may be None if no benchmark source has been configured. Parameters ---------- asset_finder : zipline.assets.AssetFinder Asset finder for the algorithm to be run. start_date : pd.Timestamp Start date of the algorithm to be run. end_date : pd.Timestamp End date of the algorithm to be run. Returns ------- benchmark_sid : int Sid to use as benchmark. benchmark_returns : pd.Series Series of returns to use as benchmark. """ if self.benchmark_returns is not None: benchmark_sid = None benchmark_returns = self.benchmark_returns elif self.benchmark_file is not None: benchmark_sid = None benchmark_returns = get_benchmark_returns_from_file( self.benchmark_file, ) elif self.benchmark_sid is not None: benchmark_sid = self.benchmark_sid benchmark_returns = None elif self.benchmark_symbol is not None: try: asset = asset_finder.lookup_symbol( self.benchmark_symbol, as_of_date=end_date, ) benchmark_sid = asset.sid benchmark_returns = None except SymbolNotFound: raise _RunAlgoError( "Symbol %r as a benchmark not found in this bundle." % self.benchmark_symbol ) elif self.no_benchmark: benchmark_sid = None benchmark_returns = self._zero_benchmark_returns( start_date=start_date, end_date=end_date, ) else: log.warn( "No benchmark configured. " "Assuming algorithm calls set_benchmark." ) log.warn( "Pass --benchmark-sid, --benchmark-symbol, or" " --benchmark-file to set a source of benchmark returns." ) log.warn( "Pass --no-benchmark to use a dummy benchmark " "of zero returns.", ) benchmark_sid = None benchmark_returns = None return benchmark_sid, benchmark_returns @staticmethod def _zero_benchmark_returns(start_date, end_date): return pd.Series( index=pd.date_range(start_date, end_date, tz="utc"), data=0.0, )