def initialize(self, *args, **kwargs):
"""
Call self._initialize with `self` made available to Zipline API
functions.
"""
with ZiplineAPI(self):
self._initialize(self)
def initialize(self, *args, **kwargs):
self._context_persistence_excludes = (list(self.__dict__.keys()) +
['trading_client'])
if os.path.isfile(self.state_filename):
log.info("Loading state from {}".format(self.state_filename))
load_context(self.state_filename,
context=self,
checksum=self.algo_filename)
return
with ZiplineAPI(self):
super(self.__class__, self).initialize(*args, **kwargs)
store_context(self.state_filename,
context=self,
checksum=self.algo_filename,
exclude_list=self._context_persistence_excludes)
def test_zipline_api_resolves_dynamically(self):
# Make a dummy algo.
algo = TradingAlgorithm(
initialize=lambda context: None,
handle_data=lambda context, data: None,
sim_params=self.sim_params,
)
# Verify that api methods get resolved dynamically by patching them out
# and then calling them
for method in algo.all_api_methods():
name = method.__name__
sentinel = object()
def fake_method(*args, **kwargs):
return sentinel
setattr(algo, name, fake_method)
with ZiplineAPI(algo):
self.assertIs(sentinel, getattr(zipline.api, name)())
def initialize(self, *args, **kwargs):
self._context_persistence_excludes = \
self._context_persistence_blacklist + \
[e for e in self.__dict__.keys()
if e not in self._context_persistence_whitelist]
if os.path.isfile(self.state_filename):
log.info("Loading state from {}".format(self.state_filename))
load_context(self.state_filename,
context=self,
checksum=self.algo_filename)
return
with ZiplineAPI(self):
super(self.__class__, self).initialize(*args, **kwargs)
store_context(self.state_filename,
context=self,
checksum=self.algo_filename,
exclude_list=self._context_persistence_excludes)
def transform(self):
"""
Main generator work loop.
"""
algo = self.algo
metrics_tracker = algo.metrics_tracker
emission_rate = metrics_tracker.emission_rate
def every_bar(
dt_to_use,
current_data=self.current_data,
handle_data=algo.event_manager.handle_data,
):
for capital_change in calculate_minute_capital_changes(dt_to_use):
yield capital_change
self.simulation_dt = dt_to_use
# called every tick (minute or day).
algo.on_dt_changed(dt_to_use)
blotter = algo.blotter
# handle any transactions and commissions coming out new orders
# placed in the last bar
new_transactions, new_commissions, closed_orders = blotter.get_transactions(
current_data)
blotter.prune_orders(closed_orders)
for transaction in new_transactions:
metrics_tracker.process_transaction(transaction)
# since this order was modified, record it
order = blotter.orders[transaction.order_id]
metrics_tracker.process_order(order)
for commission in new_commissions:
metrics_tracker.process_commission(commission)
handle_data(algo, current_data, dt_to_use)
# grab any new orders from the blotter, then clear the list.
# this includes cancelled orders.
new_orders = blotter.new_orders
blotter.new_orders = []
# if we have any new orders, record them so that we know
# in what perf period they were placed.
for new_order in new_orders:
metrics_tracker.process_order(new_order)
def once_a_day(midnight_dt,
current_data=self.current_data,
data_portal=self.data_portal):
# process any capital changes that came overnight
for capital_change in algo.calculate_capital_changes(
midnight_dt, emission_rate=emission_rate,
is_interday=True):
yield capital_change
# set all the timestamps
self.simulation_dt = midnight_dt
algo.on_dt_changed(midnight_dt)
metrics_tracker.handle_market_open(
midnight_dt,
algo.data_portal,
)
# handle any splits that impact any positions or any open orders.
assets_we_care_about = (metrics_tracker.positions.keys()
| algo.blotter.open_orders.keys())
if assets_we_care_about:
splits = data_portal.get_splits(assets_we_care_about,
midnight_dt)
if splits:
algo.blotter.process_splits(splits)
metrics_tracker.handle_splits(splits)
def on_exit():
# Remove references to algo, data portal, et al to break cycles
# and ensure deterministic cleanup of these objects when the
# simulation finishes.
self.algo = None
self.benchmark_source = self.current_data = self.data_portal = None
with ExitStack() as stack:
stack.callback(on_exit)
stack.enter_context(self.processor)
stack.enter_context(ZiplineAPI(self.algo))
if algo.data_frequency == "minute":
def execute_order_cancellation_policy():
algo.blotter.execute_cancel_policy(SESSION_END)
def calculate_minute_capital_changes(dt):
# process any capital changes that came between the last
# and current minutes
return algo.calculate_capital_changes(
dt, emission_rate=emission_rate, is_interday=False)
else:
def execute_order_cancellation_policy():
pass
def calculate_minute_capital_changes(dt):
return []
for dt, action in self.clock:
if action == BAR:
for capital_change_packet in every_bar(dt):
yield capital_change_packet
elif action == SESSION_START:
for capital_change_packet in once_a_day(dt):
yield capital_change_packet
elif action == SESSION_END:
# End of the session.
positions = metrics_tracker.positions
position_assets = algo.asset_finder.retrieve_all(positions)
self._cleanup_expired_assets(dt, position_assets)
execute_order_cancellation_policy()
algo.validate_account_controls()
yield self._get_daily_message(dt, algo, metrics_tracker)
elif action == BEFORE_TRADING_START_BAR:
self.simulation_dt = dt
algo.on_dt_changed(dt)
algo.before_trading_start(self.current_data)
elif action == MINUTE_END:
minute_msg = self._get_minute_message(
dt,
algo,
metrics_tracker,
)
yield minute_msg
risk_message = metrics_tracker.handle_simulation_end(
self.data_portal, )
yield risk_message
def run(self, source, overwrite_sim_params=True,
benchmark_return_source=None):
"""Run the algorithm.
:Arguments:
source : can be either:
- pandas.DataFrame
- zipline source
- list of sources
If pandas.DataFrame is provided, it must have the
following structure:
* column names must consist of ints representing the
different sids
* index must be DatetimeIndex
* array contents should be price info.
:Returns:
daily_stats : pandas.DataFrame
Daily performance metrics such as returns, alpha etc.
"""
if isinstance(source, list):
if overwrite_sim_params:
warnings.warn("""List of sources passed, will not attempt to extract sids, and start and end
dates. Make sure to set the correct fields in sim_params passed to
__init__().""", UserWarning)
overwrite_sim_params = False
elif isinstance(source, pd.DataFrame):
# if DataFrame provided, wrap in DataFrameSource
source = DataFrameSource(source)
elif isinstance(source, pd.Panel):
source = DataPanelSource(source)
if isinstance(source, list):
self.set_sources(source)
else:
self.set_sources([source])
# Override sim_params if params are provided by the source.
if overwrite_sim_params:
if hasattr(source, 'start'):
self.sim_params.period_start = source.start
if hasattr(source, 'end'):
self.sim_params.period_end = source.end
all_sids = [sid for s in self.sources for sid in s.sids]
self.sim_params.sids = set(all_sids)
# Changing period_start and period_close might require updating
# of first_open and last_close.
self.sim_params._update_internal()
# Create history containers
if len(self.history_specs) != 0:
self.history_container = HistoryContainer(
self.history_specs,
self.sim_params.sids,
self.sim_params.first_open)
# Create transforms by wrapping them into StatefulTransforms
self.transforms = []
for namestring, trans_descr in iteritems(self.registered_transforms):
sf = StatefulTransform(
trans_descr['class'],
*trans_descr['args'],
**trans_descr['kwargs']
)
sf.namestring = namestring
self.transforms.append(sf)
# force a reset of the performance tracker, in case
# this is a repeat run of the algorithm.
self.perf_tracker = None
# create transforms and zipline
self.gen = self._create_generator(self.sim_params)
with ZiplineAPI(self):
# loop through simulated_trading, each iteration returns a
# perf dictionary
perfs = []
for perf in self.gen:
perfs.append(perf)
# convert perf dict to pandas dataframe
daily_stats = self._create_daily_stats(perfs)
self.analyze(daily_stats)
return daily_stats
def analyze(self, perf):
if self._analyze is None:
return
with ZiplineAPI(self):
self._analyze(self, perf)
def transform(self):
"""
Main generator work loop.
"""
algo = self.algo
algo.data_portal = self.data_portal
handle_data = algo.event_manager.handle_data
current_data = self.current_data
data_portal = self.data_portal
# can't cache a pointer to algo.perf_tracker because we're not
# guaranteed that the algo doesn't swap out perf trackers during
# its lifetime.
# likewise, we can't cache a pointer to the blotter.
algo.perf_tracker.position_tracker.data_portal = data_portal
def every_bar(dt_to_use):
# called every tick (minute or day).
self.simulation_dt = dt_to_use
algo.on_dt_changed(dt_to_use)
blotter = algo.blotter
perf_tracker = algo.perf_tracker
# handle any transactions and commissions coming out new orders
# placed in the last bar
new_transactions, new_commissions = \
blotter.get_transactions(current_data)
for transaction in new_transactions:
perf_tracker.process_transaction(transaction)
# since this order was modified, record it
order = blotter.orders[transaction.order_id]
perf_tracker.process_order(order)
if new_commissions:
for commission in new_commissions:
perf_tracker.process_commission(commission)
handle_data(algo, current_data, dt_to_use)
# grab any new orders from the blotter, then clear the list.
# this includes cancelled orders.
new_orders = blotter.new_orders
blotter.new_orders = []
# if we have any new orders, record them so that we know
# in what perf period they were placed.
if new_orders:
for new_order in new_orders:
perf_tracker.process_order(new_order)
self.algo.portfolio_needs_update = True
self.algo.account_needs_update = True
self.algo.performance_needs_update = True
def once_a_day(midnight_dt):
# Get the positions before updating the date so that prices are
# fetched for trading close instead of midnight
positions = algo.perf_tracker.position_tracker.positions
position_assets = algo.asset_finder.retrieve_all(positions)
# set all the timestamps
self.simulation_dt = midnight_dt
algo.on_dt_changed(midnight_dt)
# we want to wait until the clock rolls over to the next day
# before cleaning up expired assets.
self._cleanup_expired_assets(midnight_dt, position_assets)
perf_tracker = algo.perf_tracker
# handle any splits that impact any positions or any open orders.
assets_we_care_about = \
viewkeys(perf_tracker.position_tracker.positions) | \
viewkeys(algo.blotter.open_orders)
if assets_we_care_about:
splits = data_portal.get_splits(assets_we_care_about,
midnight_dt)
if splits:
algo.blotter.process_splits(splits)
perf_tracker.position_tracker.handle_splits(splits)
# call before trading start
algo.before_trading_start(current_data)
def handle_benchmark(date):
algo.perf_tracker.all_benchmark_returns[date] = \
self.benchmark_source.get_value(date)
with ExitStack() as stack:
stack.enter_context(self.processor)
stack.enter_context(ZiplineAPI(self.algo))
if algo.data_frequency == 'minute':
def execute_order_cancellation_policy():
algo.blotter.execute_cancel_policy(DAY_END)
else:
def execute_order_cancellation_policy():
pass
for dt, action in self.clock:
if action == BAR:
every_bar(dt)
elif action == DAY_START:
once_a_day(dt)
elif action == DAY_END:
# End of the day.
execute_order_cancellation_policy()
handle_benchmark(normalize_date(dt))
yield self._get_daily_message(dt, algo, algo.perf_tracker)
elif action == MINUTE_END:
handle_benchmark(dt)
minute_msg, daily_msg = \
self._get_minute_message(dt, algo, algo.perf_tracker)
yield minute_msg
if daily_msg:
yield daily_msg
risk_message = algo.perf_tracker.handle_simulation_end()
yield risk_message
def transform(self):
"""
Main generator work loop.
"""
algo = self.algo
emission_rate = algo.perf_tracker.emission_rate
def every_bar(dt_to_use,
current_data=self.current_data,
handle_data=algo.event_manager.handle_data):
# called every tick (minute or day).
algo.on_dt_changed(dt_to_use)
for capital_change in calculate_minute_capital_changes(dt_to_use):
yield capital_change
self.simulation_dt = dt_to_use
blotter = algo.blotter
perf_tracker = algo.perf_tracker
# handle any transactions and commissions coming out new orders
# placed in the last bar
new_transactions, new_commissions, closed_orders = \
blotter.get_transactions(current_data)
blotter.prune_orders(closed_orders)
for transaction in new_transactions:
perf_tracker.process_transaction(transaction)
# since this order was modified, record it
order = blotter.orders[transaction.order_id]
perf_tracker.process_order(order)
if new_commissions:
for commission in new_commissions:
perf_tracker.process_commission(commission)
handle_data(algo, current_data, dt_to_use)
# grab any new orders from the blotter, then clear the list.
# this includes cancelled orders.
new_orders = blotter.new_orders
blotter.new_orders = []
# if we have any new orders, record them so that we know
# in what perf period they were placed.
if new_orders:
for new_order in new_orders:
perf_tracker.process_order(new_order)
algo.portfolio_needs_update = True
algo.account_needs_update = True
algo.performance_needs_update = True
def once_a_day(midnight_dt,
current_data=self.current_data,
data_portal=self.data_portal):
perf_tracker = algo.perf_tracker
# Get the positions before updating the date so that prices are
# fetched for trading close instead of midnight
positions = algo.perf_tracker.position_tracker.positions
position_assets = algo.asset_finder.retrieve_all(positions)
# set all the timestamps
self.simulation_dt = midnight_dt
algo.on_dt_changed(midnight_dt)
# process any capital changes that came overnight
for capital_change in algo.calculate_capital_changes(
midnight_dt, emission_rate=emission_rate,
is_interday=True):
yield capital_change
# we want to wait until the clock rolls over to the next day
# before cleaning up expired assets.
self._cleanup_expired_assets(midnight_dt, position_assets)
# handle any splits that impact any positions or any open orders.
assets_we_care_about = \
viewkeys(perf_tracker.position_tracker.positions) | \
viewkeys(algo.blotter.open_orders)
if assets_we_care_about:
splits = data_portal.get_splits(assets_we_care_about,
midnight_dt)
if splits:
algo.blotter.process_splits(splits)
perf_tracker.position_tracker.handle_splits(splits)
def handle_benchmark(date, benchmark_source=self.benchmark_source):
try:
algo.perf_tracker.all_benchmark_returns[date] = \
benchmark_source.get_value(date)
except Exception as e:
# algo.perf_tracker.all_benchmark_returns[date] = 1
pass
def on_exit():
# Remove references to algo, data portal, et al to break cycles
# and ensure deterministic cleanup of these objects when the
# simulation finishes.
self.algo = None
self.benchmark_source = self.current_data = self.data_portal = None
with ExitStack() as stack:
stack.callback(on_exit)
stack.enter_context(self.processor)
stack.enter_context(ZiplineAPI(self.algo))
if algo.data_frequency == 'minute':
def execute_order_cancellation_policy():
algo.blotter.execute_cancel_policy(SESSION_END)
def calculate_minute_capital_changes(dt):
# process any capital changes that came between the last
# and current minutes
return algo.calculate_capital_changes(
dt, emission_rate=emission_rate, is_interday=False)
else:
def execute_order_cancellation_policy():
pass
def calculate_minute_capital_changes(dt):
return []
for dt, action in self.clock:
if action == BAR:
for capital_change_packet in every_bar(dt):
yield capital_change_packet
elif action == SESSION_START:
for capital_change_packet in once_a_day(dt):
yield capital_change_packet
elif action == SESSION_END:
# End of the session.
if emission_rate == 'daily':
handle_benchmark(normalize_date(dt))
execute_order_cancellation_policy()
yield self._get_daily_message(dt, algo, algo.perf_tracker)
elif action == BEFORE_TRADING_START_BAR:
self.simulation_dt = dt
algo.on_dt_changed(dt)
algo.before_trading_start(self.current_data)
elif action == MINUTE_END:
handle_benchmark(dt)
minute_msg = \
self._get_minute_message(dt, algo, algo.perf_tracker)
yield minute_msg
risk_message = algo.perf_tracker.handle_simulation_end()
yield risk_message
def run(self, source, sim_params=None, benchmark_return_source=None):
"""Run the algorithm.
:Arguments:
source : can be either:
- pandas.DataFrame
- zipline source
- list of zipline sources
If pandas.DataFrame is provided, it must have the
following structure:
* column names must consist of ints representing the
different sids
* index must be DatetimeIndex
* array contents should be price info.
:Returns:
daily_stats : pandas.DataFrame
Daily performance metrics such as returns, alpha etc.
"""
if isinstance(source, (list, tuple)):
assert self.sim_params is not None or sim_params is not None, \
"""When providing a list of sources, \
sim_params have to be specified as a parameter
or in the constructor."""
elif isinstance(source, pd.DataFrame):
# if DataFrame provided, wrap in DataFrameSource
source = DataFrameSource(source)
elif isinstance(source, pd.Panel):
source = DataPanelSource(source)
if not isinstance(source, (list, tuple)):
self.sources = [source]
else:
self.sources = source
# Check for override of sim_params.
# If it isn't passed to this function,
# use the default params set with the algorithm.
# Else, we create simulation parameters using the start and end of the
# source provided.
if sim_params is None:
if self.sim_params is None:
start = source.start
end = source.end
sim_params = create_simulation_parameters(
start=start,
end=end,
capital_base=self.capital_base,
)
else:
sim_params = self.sim_params
# update sim params to ensure it's set
self.sim_params = sim_params
if self.sim_params.sids is None:
all_sids = [sid for s in self.sources for sid in s.sids]
self.sim_params.sids = set(all_sids)
# Create history containers
if len(self.history_specs) != 0:
self.history_container = HistoryContainer(
self.history_specs, self.sim_params.sids,
self.sim_params.first_open)
# Create transforms by wrapping them into StatefulTransforms
self.transforms = []
for namestring, trans_descr in iteritems(self.registered_transforms):
sf = StatefulTransform(trans_descr['class'], *trans_descr['args'],
**trans_descr['kwargs'])
sf.namestring = namestring
self.transforms.append(sf)
# force a reset of the performance tracker, in case
# this is a repeat run of the algorithm.
self.perf_tracker = None
# create transforms and zipline
self.gen = self._create_generator(sim_params)
with ZiplineAPI(self):
# loop through simulated_trading, each iteration returns a
# perf dictionary
perfs = []
for perf in self.gen:
perfs.append(perf)
# convert perf dict to pandas dataframe
daily_stats = self._create_daily_stats(perfs)
self.analyze(daily_stats)
return daily_stats
def run(self,
source,
overwrite_sim_params=True,
benchmark_return_source=None):
"""Run the algorithm.
:Arguments:
source : can be either:
- pandas.DataFrame
- zipline source
- list of sources
If pandas.DataFrame is provided, it must have the
following structure:
* column names must be the different asset identifiers
* index must be DatetimeIndex
* array contents should be price info.
:Returns:
daily_stats : pandas.DataFrame
Daily performance metrics such as returns, alpha etc.
"""
# Ensure that source is a DataSource object
if isinstance(source, list):
if overwrite_sim_params:
warnings.warn(
"""List of sources passed, will not attempt to extract start and end
dates. Make sure to set the correct fields in sim_params passed to
__init__().""", UserWarning)
overwrite_sim_params = False
elif isinstance(source, pd.DataFrame):
# if DataFrame provided, wrap in DataFrameSource
source = DataFrameSource(source)
elif isinstance(source, pd.Panel):
source = DataPanelSource(source)
if isinstance(source, list):
self.set_sources(source)
else:
self.set_sources([source])
# Override sim_params if params are provided by the source.
if overwrite_sim_params:
if hasattr(source, 'start'):
self.sim_params.period_start = source.start
if hasattr(source, 'end'):
self.sim_params.period_end = source.end
# The sids field of the source is the canonical reference for
# sids in this run
all_sids = [sid for s in self.sources for sid in s.sids]
self.sim_params.sids = set(all_sids)
# Check that all sids from the source are accounted for in
# the AssetFinder
for sid in self.sim_params.sids:
try:
self.asset_finder.retrieve_asset(sid)
except SidNotFound:
warnings.warn("No Asset found for sid '%s'. Make sure "
"that the correct identifiers and asset "
"metadata are passed to __init__()." % sid)
# Changing period_start and period_close might require updating
# of first_open and last_close.
self.sim_params._update_internal()
# force a reset of the performance tracker, in case
# this is a repeat run of the algorithm.
self.perf_tracker = None
# create zipline
self.gen = self._create_generator(self.sim_params)
# Create history containers
if self.history_specs:
self.history_container = self.history_container_class(
self.history_specs,
self.sim_params.sids,
self.sim_params.first_open,
self.sim_params.data_frequency,
)
with ZiplineAPI(self):
# loop through simulated_trading, each iteration returns a
# perf dictionary
perfs = []
for perf in self.gen:
perfs.append(perf)
# convert perf dict to pandas dataframe
daily_stats = self._create_daily_stats(perfs)
self.analyze(daily_stats)
return daily_stats
def transform(self, stream_in):
"""
Main generator work loop.
"""
# Initialize the mkt_close
mkt_open = self.algo.perf_tracker.market_open
mkt_close = self.algo.perf_tracker.market_close
# inject the current algo
# snapshot time to any log record generated.
with ExitStack() as stack:
stack.enter_context(self.processor)
stack.enter_context(ZiplineAPI(self.algo))
data_frequency = self.sim_params.data_frequency
self._call_before_trading_start(mkt_open)
for date, snapshot in stream_in:
self.simulation_dt = date
self.on_dt_changed(date)
# If we're still in the warmup period. Use the event to
# update our universe, but don't yield any perf messages,
# and don't send a snapshot to handle_data.
if date < self.algo_start:
for event in snapshot:
if event.type == DATASOURCE_TYPE.SPLIT:
self.algo.blotter.process_split(event)
elif event.type == DATASOURCE_TYPE.TRADE:
self.update_universe(event)
self.algo.perf_tracker.process_trade(event)
elif event.type == DATASOURCE_TYPE.CUSTOM:
self.update_universe(event)
else:
messages = self._process_snapshot(
date,
snapshot,
self.algo.instant_fill,
)
# Perf messages are only emitted if the snapshot contained
# a benchmark event.
for message in messages:
yield message
# When emitting minutely, we need to call
# before_trading_start before the next trading day begins
if date == mkt_close:
if mkt_close <= self.algo.perf_tracker.last_close:
before_last_close = \
mkt_close < self.algo.perf_tracker.last_close
try:
mkt_open, mkt_close = \
self.env.next_open_and_close(mkt_close)
except NoFurtherDataError:
# If at the end of backtest history,
# skip advancing market close.
pass
if before_last_close:
self._call_before_trading_start(mkt_open)
elif data_frequency == 'daily':
next_day = self.env.next_trading_day(date)
if next_day is not None and \
next_day < self.algo.perf_tracker.last_close:
self._call_before_trading_start(next_day)
self.algo.portfolio_needs_update = True
self.algo.account_needs_update = True
self.algo.performance_needs_update = True
risk_message = self.algo.perf_tracker.handle_simulation_end()
yield risk_message
def transform(self):
"""
Main generator work loop.
"""
algo = self.algo
def every_bar(dt_to_use, current_data=self.current_data,
handle_data=algo.event_manager.handle_data):
# called every tick (minute or day).
if dt_to_use in algo.capital_changes:
process_minute_capital_changes(dt_to_use)
self.simulation_dt = dt_to_use
algo.on_dt_changed(dt_to_use)
blotter = algo.blotter
perf_tracker = algo.perf_tracker
# handle any transactions and commissions coming out new orders
# placed in the last bar
new_transactions, new_commissions, closed_orders = \
blotter.get_transactions(current_data)
blotter.prune_orders(closed_orders)
for transaction in new_transactions:
perf_tracker.process_transaction(transaction)
# since this order was modified, record it
order = blotter.orders[transaction.order_id]
perf_tracker.process_order(order)
if new_commissions:
for commission in new_commissions:
perf_tracker.process_commission(commission)
handle_data(algo, current_data, dt_to_use)
# grab any new orders from the blotter, then clear the list.
# this includes cancelled orders.
new_orders = blotter.new_orders
blotter.new_orders = []
# if we have any new orders, record them so that we know
# in what perf period they were placed.
if new_orders:
for new_order in new_orders:
perf_tracker.process_order(new_order)
algo.portfolio_needs_update = True
algo.account_needs_update = True
algo.performance_needs_update = True
def once_a_day(midnight_dt, current_data=self.current_data,
data_portal=self.data_portal):
perf_tracker = algo.perf_tracker
if midnight_dt in algo.capital_changes:
# process any capital changes that came overnight
change = algo.capital_changes[midnight_dt]
log.info('Processing capital change of %s at %s' %
(change, midnight_dt))
perf_tracker.process_capital_changes(change, is_interday=True)
# Get the positions before updating the date so that prices are
# fetched for trading close instead of midnight
positions = algo.perf_tracker.position_tracker.positions
position_assets = algo.asset_finder.retrieve_all(positions)
# set all the timestamps
self.simulation_dt = midnight_dt
algo.on_dt_changed(midnight_dt)
# we want to wait until the clock rolls over to the next day
# before cleaning up expired assets.
self._cleanup_expired_assets(midnight_dt, position_assets)
# handle any splits that impact any positions or any open orders.
assets_we_care_about = \
viewkeys(perf_tracker.position_tracker.positions) | \
viewkeys(algo.blotter.open_orders)
if assets_we_care_about:
splits = data_portal.get_splits(assets_we_care_about,
midnight_dt)
if splits:
algo.blotter.process_splits(splits)
perf_tracker.position_tracker.handle_splits(splits)
# call before trading start
algo.before_trading_start(current_data)
def handle_benchmark(date, benchmark_source=self.benchmark_source):
algo.perf_tracker.all_benchmark_returns[date] = \
benchmark_source.get_value(date)
def on_exit():
# Remove references to algo, data portal, et al to break cycles
# and ensure deterministic cleanup of these objects when the
# simulation finishes.
self.algo = None
self.benchmark_source = self.current_data = self.data_portal = None
with ExitStack() as stack:
stack.callback(on_exit)
stack.enter_context(self.processor)
stack.enter_context(ZiplineAPI(self.algo))
if algo.data_frequency == 'minute':
def execute_order_cancellation_policy():
algo.blotter.execute_cancel_policy(DAY_END)
def process_minute_capital_changes(dt):
# If we are running daily emission, prices won't
# necessarily be synced at the end of every minute, and we
# need the up-to-date prices for capital change
# calculations. We want to sync the prices as of the
# last market minute, and this is okay from a data portal
# perspective as we have technically not "advanced" to the
# current dt yet.
algo.perf_tracker.position_tracker.sync_last_sale_prices(
self.env.previous_market_minute(dt),
False,
self.data_portal
)
# process any capital changes that came between the last
# and current minutes
change = algo.capital_changes[dt]
log.info('Processing capital change of %s at %s' %
(change, dt))
algo.perf_tracker.process_capital_changes(
change,
is_interday=False
)
else:
def execute_order_cancellation_policy():
pass
def process_minute_capital_changes(dt):
pass
for dt, action in self.clock:
if action == BAR:
every_bar(dt)
elif action == DAY_START:
once_a_day(dt)
elif action == DAY_END:
# End of the day.
if algo.perf_tracker.emission_rate == 'daily':
handle_benchmark(normalize_date(dt))
execute_order_cancellation_policy()
yield self._get_daily_message(dt, algo, algo.perf_tracker)
elif action == MINUTE_END:
handle_benchmark(dt)
minute_msg = \
self._get_minute_message(dt, algo, algo.perf_tracker)
yield minute_msg
risk_message = algo.perf_tracker.handle_simulation_end()
yield risk_message
def transform(self, stream_in):
"""
Main generator work loop.
"""
# Initialize the mkt_close
mkt_open = self.algo.perf_tracker.market_open
mkt_close = self.algo.perf_tracker.market_close
# inject the current algo
# snapshot time to any log record generated.
# with 。。。as 是一种上下文管理器，打开与关闭。exitstack（）是一个语法糖
with ExitStack() as stack:
stack.enter_context(self.processor)
stack.enter_context(ZiplineAPI(self.algo))
data_frequency = self.sim_params.data_frequency
self._call_before_trading_start(mkt_open)
for date, snapshot in stream_in:
#print date,u'在主循环之内的date',self.algo_start
#raw_input()
#for i in snapshot:
# print i
#raw_input()
# snapshot,为迭代的数据系统，包括时间，股票数据等
# 进入主循环，跟随日期进行循环
self.simulation_dt = date #模拟日期
self.on_dt_changed(date)
# If we're still in the warmup period. Use the event to
# update our universe, but don't yield any perf messages,
# and don't send a snapshot to handle_data.
# 如果在热身阶段
# 判断是否进入交易日期，若开始了则handle_data发送给
# 判断是否进行到达模拟开始的时间
if date < self.algo_start:
for event in snapshot:
if event.type == DATASOURCE_TYPE.SPLIT:
self.algo.blotter.process_split(event)
elif event.type == DATASOURCE_TYPE.TRADE:
self.update_universe(event)
self.algo.perf_tracker.process_trade(event)
elif event.type == DATASOURCE_TYPE.CUSTOM:
self.update_universe(event)
if self.algo.history_container:
#print self.current_data
self.algo.history_container.update(
self.current_data, date)
else:
# 进入每日信息的处理，
messages = self._process_snapshot(
date,
snapshot,
self.algo.instant_fill,
)
# Perf messages are only emitted if the snapshot contained
# a benchmark event.
for message in messages:
yield message
# When emitting minutely, we need to call
# before_trading_start before the next trading day begins
if date == mkt_close:
if mkt_close <= self.algo.perf_tracker.last_close:
before_last_close = \
mkt_close < self.algo.perf_tracker.last_close
try:
mkt_open, mkt_close = \
self.env.next_open_and_close(mkt_close)
except NoFurtherDataError:
# If at the end of backtest history,
# skip advancing market close.
pass
if before_last_close:
self._call_before_trading_start(mkt_open)
elif data_frequency == 'daily':
next_day = self.env.next_trading_day(date)
if next_day is not None and \
next_day < self.algo.perf_tracker.last_close:
self._call_before_trading_start(
next_day
) #如果下一天非空，并且next不是表现的最后一天。last_close,就
self.algo.portfolio_needs_update = True
self.algo.account_needs_update = True
self.algo.performance_needs_update = True
risk_message = self.algo.perf_tracker.handle_simulation_end()
yield risk_message