def set_bundle_data(bundle_name='alpaca_api'):
global BUNDLE_DATA, PRICING_LOADER
BUNDLE_DATA = bundles.load(bundle_name)
PRICING_LOADER = USEquityPricingLoader.without_fx(
BUNDLE_DATA.equity_daily_bar_reader, BUNDLE_DATA.adjustment_reader)
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, 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,
)
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
raise ValueError("No PipelineLoader registered for column %s." %
column)
if isinstance(metrics_set, six.string_types):
try:
metrics_set = metrics.load(metrics_set)
except ValueError as e:
raise _RunAlgoError(str(e))
if isinstance(blotter, six.string_types):
try:
blotter = load(Blotter, blotter)
except ValueError as e:
raise _RunAlgoError(str(e))
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()
if output == '-':
click.echo(str(perf))
elif output != os.devnull: # make the zipline magic not write any data
perf.to_pickle(output)
return perf
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
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,
benchmark_spec,
broker,
state_filename,
realtime_bar_target,
performance_callback,
stop_execution_callback,
teardown,
execution_id):
"""Run a backtest for the given algorithm.
This is shared between the cli and :func:`zipline.run_algo`.
zipline-trader additions:
broker - wrapper to connect to a real broker
state_filename - saving the context of the algo to be able to restart
performance_callback - a callback to send performance results everyday and not only at the end of the backtest.
this allows to run live, and monitor the performance of the algorithm
stop_execution_callback - A callback to check if execution should be stopped. it is used to be able to stop live
trading (also simulation could be stopped using this) execution. if the callback returns True, then algo
execution will be aborted.
teardown - algo method like handle_data() or before_trading_start() that is called when the algo execution stops
execution_id - unique id to identify this execution (backtest or live instance)
"""
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,
)
emission_rate = 'daily'
if broker:
emission_rate = 'minute'
# if we run zipline as a command line tool, these will probably not be initiated
if not start:
start = pd.Timestamp.utcnow()
if not end:
# in cli mode, sessions are 1 day only. and it will be re-ran each day by user
end = start + pd.Timedelta('1 day')
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
DataPortalClass = (partial(DataPortalLive, broker)
if broker
else DataPortal)
data = DataPortalClass(
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,
)
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
raise ValueError(
"No PipelineLoader registered for column %s." % column
)
if isinstance(metrics_set, six.string_types):
try:
metrics_set = metrics.load(metrics_set)
except ValueError as e:
raise _RunAlgoError(str(e))
if isinstance(blotter, six.string_types):
try:
blotter = load(Blotter, blotter)
except ValueError as e:
raise _RunAlgoError(str(e))
TradingAlgorithmClass = (partial(LiveTradingAlgorithm,
broker=broker,
state_filename=state_filename,
realtime_bar_target=realtime_bar_target)
if broker else TradingAlgorithm)
perf = TradingAlgorithmClass(
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,
emission_rate=emission_rate,
data_frequency=data_frequency,
execution_id=execution_id
),
metrics_set=metrics_set,
blotter=blotter,
benchmark_returns=benchmark_returns,
benchmark_sid=benchmark_sid,
performance_callback=performance_callback,
stop_execution_callback=stop_execution_callback,
**{
'initialize': initialize,
'handle_data': handle_data,
'before_trading_start': before_trading_start,
'analyze': analyze,
'teardown': teardown,
} if algotext is None else {
'algo_filename': getattr(algofile, 'name', '<algorithm>'),
'script': algotext,
}
).run()
if output == '-':
click.echo(str(perf))
elif output != os.devnull: # make the zipline magic not write any data
perf.to_pickle(output)
return perf
from zipline.pipeline.loaders import USEquityPricingLoader
from zipline.utils.calendars import get_calendar
from zipline.pipeline.data import USEquityPricing
from zipline.data.data_portal import DataPortal
from sharadar.pipeline.engine import symbols, make_pipeline_engine, load_sharadar_bundle
import pandas as pd
bundle = load_sharadar_bundle()
# Set the dataloader
pricing_loader = USEquityPricingLoader.without_fx(
bundle.equity_daily_bar_reader, bundle.adjustment_reader)
# Define the function for the get_loader parameter
def choose_loader(column):
if column not in USEquityPricing.columns:
raise Exception('Column not in USEquityPricing')
return pricing_loader
# Set the trading calendar
trading_calendar = get_calendar('NYSE')
start_date = pd.to_datetime('2020-08-26', utc=True)
end_date = pd.to_datetime('2020-09-02', utc=True)
bar_count = trading_calendar.session_distance(start_date, end_date)
# Create a data portal
data_portal = DataPortal(bundle.asset_finder,
trading_calendar=trading_calendar,
first_trading_day=start_date,