def _create_generator(self, sim_params, source_filter=None):
"""
Create a basic generator setup using the sources to this algorithm.
::source_filter:: is a method that receives events in date
sorted order, and returns True for those events that should be
processed by the zipline, and False for those that should be
skipped.
"""
if not self.initialized:
self.initialize(*self.initialize_args, **self.initialize_kwargs)
self.initialized = True
if self.perf_tracker is None:
# HACK: When running with the `run` method, we set perf_tracker to
# None so that it will be overwritten here.
self.perf_tracker = PerformanceTracker(sim_params)
self.portfolio_needs_update = True
self.account_needs_update = True
self.performance_needs_update = True
self.data_gen = self._create_data_generator(source_filter, sim_params)
self.trading_client = AlgorithmSimulator(self, sim_params)
transact_method = transact_partial(self.slippage, self.commission)
self.set_transact(transact_method)
return self.trading_client.transform(self.data_gen)
def test_bts_simulation_dt(self):
code = """
def initialize(context):
pass
"""
algo = TradingAlgorithm(
script=code,
sim_params=self.sim_params,
env=self.env,
metrics=metrics.load('none'),
)
algo.metrics_tracker = algo._create_metrics_tracker()
benchmark_source = algo._create_benchmark_source()
algo.metrics_tracker.handle_start_of_simulation(benchmark_source)
dt = pd.Timestamp("2016-08-04 9:13:14", tz='US/Eastern')
algo_simulator = AlgorithmSimulator(
algo,
self.sim_params,
self.data_portal,
BeforeTradingStartsOnlyClock(dt),
benchmark_source,
NoRestrictions(),
None
)
# run through the algo's simulation
list(algo_simulator.transform())
# since the clock only ever emitted a single before_trading_start
# event, we can check that the simulation_dt was properly set
self.assertEqual(dt, algo_simulator.simulation_dt)
def test_bts_simulation_dt(self):
code = """
def initialize(context):
pass
"""
algo = self.make_algo(script=code, metrics=metrics.load("none"))
algo.metrics_tracker = algo._create_metrics_tracker()
benchmark_source = algo._create_benchmark_source()
algo.metrics_tracker.handle_start_of_simulation(benchmark_source)
dt = pd.Timestamp("2016-08-04 9:13:14", tz="US/Eastern")
algo_simulator = AlgorithmSimulator(
algo,
self.sim_params,
self.data_portal,
BeforeTradingStartsOnlyClock(dt),
benchmark_source,
NoRestrictions(),
None,
)
# run through the algo's simulation
list(algo_simulator.transform())
# since the clock only ever emitted a single before_trading_start
# event, we can check that the simulation_dt was properly set
assert dt == algo_simulator.simulation_dt
def test_bts_simulation_dt(self):
code = """
def initialize(context):
pass
"""
algo = TradingAlgorithm(script=code,
sim_params=self.sim_params,
env=self.env)
algo.perf_tracker = PerformanceTracker(
sim_params=self.sim_params,
trading_calendar=self.trading_calendar,
asset_finder=self.asset_finder,
)
dt = pd.Timestamp("2016-08-04 9:13:14", tz='US/Eastern')
algo_simulator = AlgorithmSimulator(
algo,
self.sim_params,
self.data_portal,
BeforeTradingStartsOnlyClock(dt),
algo._create_benchmark_source(),
NoRestrictions(),
None
)
# run through the algo's simulation
list(algo_simulator.transform())
# since the clock only ever emitted a single before_trading_start
# event, we can check that the simulation_dt was properly set
self.assertEqual(dt, algo_simulator.simulation_dt)
def _create_generator(self, sim_params, source_filter=None):
"""
Create a basic generator setup using the sources and
transforms attached to this algorithm.
::source_filter:: is a method that receives events in date
sorted order, and returns True for those events that should be
processed by the zipline, and False for those that should be
skipped.
"""
sim_params.data_frequency = self.data_frequency
# perf_tracker will be instantiated in __init__ if a sim_params
# is passed to the constructor. If not, we instantiate here.
if self.perf_tracker is None:
self.perf_tracker = PerformanceTracker(sim_params)
self.data_gen = self._create_data_generator(source_filter, sim_params)
self.trading_client = AlgorithmSimulator(self, sim_params)
transact_method = transact_partial(self.slippage, self.commission)
self.set_transact(transact_method)
return self.trading_client.transform(self.data_gen)
def _create_generator(self, sim_params, source_filter=None):
"""
Create a basic generator setup using the sources to this algorithm.
::source_filter:: is a method that receives events in date
sorted order, and returns True for those events that should be
processed by the zipline, and False for those that should be
skipped.
"""
if not self.initialized:
self.initialize(*self.initialize_args, **self.initialize_kwargs)
self.initialized = True
if self.perf_tracker is None:
# HACK: When running with the `run` method, we set perf_tracker to
# None so that it will be overwritten here.
self.perf_tracker = PerformanceTracker(
sim_params=sim_params, env=self.trading_environment
)
self.portfolio_needs_update = True
self.account_needs_update = True
self.performance_needs_update = True
self.data_gen = self._create_data_generator(source_filter, sim_params)
self.trading_client = AlgorithmSimulator(self, sim_params)
transact_method = transact_partial(self.slippage, self.commission)
self.set_transact(transact_method)
return self.trading_client.transform(self.data_gen)
def _create_generator(self, sim_params, source_filter=None):
"""
Create a basic generator setup using the sources and
transforms attached to this algorithm.
::source_filter:: is a method that receives events in date
sorted order, and returns True for those events that should be
processed by the zipline, and False for those that should be
skipped.
"""
sim_params.data_frequency = self.data_frequency
# perf_tracker will be instantiated in __init__ if a sim_params
# is passed to the constructor. If not, we instantiate here.
if self.perf_tracker is None:
self.perf_tracker = PerformanceTracker(sim_params)
self.data_gen = self._create_data_generator(source_filter,
sim_params)
self.trading_client = AlgorithmSimulator(self, sim_params)
transact_method = transact_partial(self.slippage, self.commission)
self.set_transact(transact_method)
return self.trading_client.transform(self.data_gen)
def _create_generator(self, sim_params, source_filter=None):
"""
Create a basic generator setup using the sources to this algorithm.
::source_filter:: is a method that receives events in date
sorted order, and returns True for those events that should be
processed by the zipline, and False for those that should be
skipped.
"""
if not self.initialized:
self.initialize(*self.initialize_args, **self.initialize_kwargs)
self.initialized = True
if self.perf_tracker is None:
# HACK: When running with the `run` method, we set perf_tracker to
# None so that it will be overwritten here.
#self.perf_tracker = CustomPerfTracker(
self.perf_tracker = PerformanceTracker(
sim_params=sim_params, env=self.trading_environment
)
self.portfolio_needs_update = True
self.account_needs_update = True
self.performance_needs_update = True
self.data_gen = self._create_data_generator(source_filter, sim_params)
# Zipline uses a lot of composition object oriented design.
# How does composition help keep the system flexible and allow for object to be
# substituted at run-time.
# Zipline is a backtester that runs on historical data but it's also
# the engine that drives Quantopion's live trading.
# What objects might be modified to allow for live trading and greater functionality?
# What Are the other key objects? (hint, look at the blotter)
self.trading_client = AlgorithmSimulator(self, sim_params)
transact_method = transact_partial(self.slippage, self.commission)
self.set_transact(transact_method)
# The transform method does the heavy lifting of the main zipline event loop
return self.trading_client.transform(self.data_gen)
def _create_generator(self, sim_params, source_filter=None):
"""
Create a basic generator setup using the sources and
transforms attached to this algorithm.
::source_filter:: is a method that receives events in date
sorted order, and returns True for those events that should be
processed by the zipline, and False for those that should be
skipped.
"""
# Instantiate perf_tracker
self.perf_tracker = PerformanceTracker(sim_params)
self.portfolio_needs_update = True
self.data_gen = self._create_data_generator(source_filter, sim_params)
self.trading_client = AlgorithmSimulator(self, sim_params)
transact_method = transact_partial(self.slippage, self.commission)
self.set_transact(transact_method)
return self.trading_client.transform(self.data_gen)
def _create_generator(self, sim_params, source_filter=None):
"""
Create a basic generator setup using the sources and
transforms attached to this algorithm.
::source_filter:: is a method that receives events in date
sorted order, and returns True for those events that should be
processed by the zipline, and False for those that should be
skipped.
"""
self.data_gen = self._create_data_generator(source_filter, sim_params)
self.trading_client = AlgorithmSimulator(self, sim_params)
transact_method = transact_partial(self.slippage, self.commission)
self.set_transact(transact_method)
return self.trading_client.transform(self.data_gen)
class TradingAlgorithm(object):
"""
Base class for trading algorithms. Inherit and overload
initialize() and handle_data(data).
A new algorithm could look like this:
```
from zipline.api import order, symbol
def initialize(context):
context.sid = symbol('AAPL')
context.amount = 100
def handle_data(context, data):
sid = context.sid
amount = context.amount
order(sid, amount)
```
To then to run this algorithm pass these functions to
TradingAlgorithm:
my_algo = TradingAlgorithm(initialize, handle_data)
stats = my_algo.run(data)
"""
def __init__(self, *args, **kwargs):
"""Initialize sids and other state variables.
:Arguments:
:Optional:
initialize : function
Function that is called with a single
argument at the begninning of the simulation.
handle_data : function
Function that is called with 2 arguments
(context and data) on every bar.
script : str
Algoscript that contains initialize and
handle_data function definition.
data_frequency : {'daily', 'minute'}
The duration of the bars.
capital_base : float <default: 1.0e5>
How much capital to start with.
instant_fill : bool <default: False>
Whether to fill orders immediately or on next bar.
asset_finder : An AssetFinder object
A new AssetFinder object to be used in this TradingEnvironment
equities_metadata : can be either:
- dict
- pandas.DataFrame
- object with 'read' property
If dict is provided, it must have the following structure:
* keys are the identifiers
* values are dicts containing the metadata, with the metadata
field name as the key
If pandas.DataFrame is provided, it must have the
following structure:
* column names must be the metadata fields
* index must be the different asset identifiers
* array contents should be the metadata value
If an object with a 'read' property is provided, 'read' must
return rows containing at least one of 'sid' or 'symbol' along
with the other metadata fields.
identifiers : List
Any asset identifiers that are not provided in the
equities_metadata, but will be traded by this TradingAlgorithm
"""
self.sources = []
# List of trading controls to be used to validate orders.
self.trading_controls = []
# List of account controls to be checked on each bar.
self.account_controls = []
self._recorded_vars = {}
self.namespace = kwargs.pop('namespace', {})
self._platform = kwargs.pop('platform', 'zipline')
self.logger = None
self.benchmark_return_source = None
# default components for transact
self.slippage = VolumeShareSlippage()
self.commission = PerShare()
self.instant_fill = kwargs.pop('instant_fill', False)
# If an env has been provided, pop it
self.trading_environment = kwargs.pop('env', None)
if self.trading_environment is None:
self.trading_environment = TradingEnvironment()
# Update the TradingEnvironment with the provided asset metadata
self.trading_environment.write_data(
equities_data=kwargs.pop('equities_metadata', {}),
equities_identifiers=kwargs.pop('identifiers', []),
futures_data=kwargs.pop('futures_metadata', {}),
)
# set the capital base
self.capital_base = kwargs.pop('capital_base', DEFAULT_CAPITAL_BASE)
self.sim_params = kwargs.pop('sim_params', None)
if self.sim_params is None:
self.sim_params = create_simulation_parameters(
capital_base=self.capital_base,
start=kwargs.pop('start', None),
end=kwargs.pop('end', None),
env=self.trading_environment,
)
else:
self.sim_params.update_internal_from_env(self.trading_environment)
# Build a perf_tracker
self.perf_tracker = PerformanceTracker(sim_params=self.sim_params,
env=self.trading_environment)
# Pull in the environment's new AssetFinder for quick reference
self.asset_finder = self.trading_environment.asset_finder
self.init_engine(kwargs.pop('ffc_loader', None))
# Maps from name to Term
self._filters = {}
self._factors = {}
self._classifiers = {}
self.blotter = kwargs.pop('blotter', None)
if not self.blotter:
self.blotter = Blotter()
# Set the dt initally to the period start by forcing it to change
self.on_dt_changed(self.sim_params.period_start)
# The symbol lookup date specifies the date to use when resolving
# symbols to sids, and can be set using set_symbol_lookup_date()
self._symbol_lookup_date = None
self.portfolio_needs_update = True
self.account_needs_update = True
self.performance_needs_update = True
self._portfolio = None
self._account = None
self.history_container_class = kwargs.pop(
'history_container_class', HistoryContainer,
)
self.history_container = None
self.history_specs = {}
# If string is passed in, execute and get reference to
# functions.
self.algoscript = kwargs.pop('script', None)
self._initialize = None
self._before_trading_start = None
self._analyze = None
self.event_manager = EventManager()
if self.algoscript is not None:
filename = kwargs.pop('algo_filename', None)
if filename is None:
filename = '<string>'
code = compile(self.algoscript, filename, 'exec')
exec_(code, self.namespace)
self._initialize = self.namespace.get('initialize')
if 'handle_data' not in self.namespace:
raise ValueError('You must define a handle_data function.')
else:
self._handle_data = self.namespace['handle_data']
self._before_trading_start = \
self.namespace.get('before_trading_start')
# Optional analyze function, gets called after run
self._analyze = self.namespace.get('analyze')
elif kwargs.get('initialize') and kwargs.get('handle_data'):
if self.algoscript is not None:
raise ValueError('You can not set script and \
initialize/handle_data.')
self._initialize = kwargs.pop('initialize')
self._handle_data = kwargs.pop('handle_data')
self._before_trading_start = kwargs.pop('before_trading_start',
None)
self.event_manager.add_event(
zipline.utils.events.Event(
zipline.utils.events.Always(),
# We pass handle_data.__func__ to get the unbound method.
# We will explicitly pass the algorithm to bind it again.
self.handle_data.__func__,
),
prepend=True,
)
# If method not defined, NOOP
if self._initialize is None:
self._initialize = lambda x: None
# Alternative way of setting data_frequency for backwards
# compatibility.
if 'data_frequency' in kwargs:
self.data_frequency = kwargs.pop('data_frequency')
self._most_recent_data = None
# Prepare the algo for initialization
self.initialized = False
self.initialize_args = args
self.initialize_kwargs = kwargs
def init_engine(self, loader):
"""
Construct and save an FFCEngine from loader.
If loader is None, constructs a NoOpFFCEngine.
"""
if loader is not None:
self.engine = SimpleFFCEngine(
loader,
self.trading_environment.trading_days,
self.asset_finder,
)
else:
self.engine = NoOpFFCEngine()
def initialize(self, *args, **kwargs):
"""
Call self._initialize with `self` made available to Zipline API
functions.
"""
with ZiplineAPI(self):
self._initialize(self, *args, **kwargs)
def before_trading_start(self, data):
if self._before_trading_start is None:
return
self._before_trading_start(self, data)
def handle_data(self, data):
self._most_recent_data = data
if self.history_container:
self.history_container.update(data, self.datetime)
self._handle_data(self, data)
# Unlike trading controls which remain constant unless placing an
# order, account controls can change each bar. Thus, must check
# every bar no matter if the algorithm places an order or not.
self.validate_account_controls()
def analyze(self, perf):
if self._analyze is None:
return
with ZiplineAPI(self):
self._analyze(self, perf)
def __repr__(self):
"""
N.B. this does not yet represent a string that can be used
to instantiate an exact copy of an algorithm.
However, it is getting close, and provides some value as something
that can be inspected interactively.
"""
return """
{class_name}(
capital_base={capital_base}
sim_params={sim_params},
initialized={initialized},
slippage={slippage},
commission={commission},
blotter={blotter},
recorded_vars={recorded_vars})
""".strip().format(class_name=self.__class__.__name__,
capital_base=self.capital_base,
sim_params=repr(self.sim_params),
initialized=self.initialized,
slippage=repr(self.slippage),
commission=repr(self.commission),
blotter=repr(self.blotter),
recorded_vars=repr(self.recorded_vars))
def _create_data_generator(self, source_filter, sim_params=None):
"""
Create a merged data generator using the sources attached to this
algorithm.
::source_filter:: is a method that receives events in date
sorted order, and returns True for those events that should be
processed by the zipline, and False for those that should be
skipped.
"""
if sim_params is None:
sim_params = self.sim_params
if self.benchmark_return_source is None:
if sim_params.data_frequency == 'minute' or \
sim_params.emission_rate == 'minute':
def update_time(date):
return self.trading_environment.get_open_and_close(date)[1]
else:
def update_time(date):
return date
benchmark_return_source = [
Event({'dt': update_time(dt),
'returns': ret,
'type': zipline.protocol.DATASOURCE_TYPE.BENCHMARK,
'source_id': 'benchmarks'})
for dt, ret in
self.trading_environment.benchmark_returns.iteritems()
if dt.date() >= sim_params.period_start.date() and
dt.date() <= sim_params.period_end.date()
]
else:
benchmark_return_source = self.benchmark_return_source
date_sorted = date_sorted_sources(*self.sources)
if source_filter:
date_sorted = filter(source_filter, date_sorted)
with_benchmarks = date_sorted_sources(benchmark_return_source,
date_sorted)
# Group together events with the same dt field. This depends on the
# events already being sorted.
return groupby(with_benchmarks, attrgetter('dt'))
def _create_generator(self, sim_params, source_filter=None):
"""
Create a basic generator setup using the sources to this algorithm.
::source_filter:: is a method that receives events in date
sorted order, and returns True for those events that should be
processed by the zipline, and False for those that should be
skipped.
"""
if not self.initialized:
self.initialize(*self.initialize_args, **self.initialize_kwargs)
self.initialized = True
if self.perf_tracker is None:
# HACK: When running with the `run` method, we set perf_tracker to
# None so that it will be overwritten here.
self.perf_tracker = PerformanceTracker(
sim_params=sim_params, env=self.trading_environment
)
self.portfolio_needs_update = True
self.account_needs_update = True
self.performance_needs_update = True
self.data_gen = self._create_data_generator(source_filter, sim_params)
self.trading_client = AlgorithmSimulator(self, sim_params)
transact_method = transact_partial(self.slippage, self.commission)
self.set_transact(transact_method)
return self.trading_client.transform(self.data_gen)
def get_generator(self):
"""
Override this method to add new logic to the construction
of the generator. Overrides can use the _create_generator
method to get a standard construction generator.
"""
return self._create_generator(self.sim_params)
# TODO: make a new subclass, e.g. BatchAlgorithm, and move
# the run method to the subclass, and refactor to put the
# generator creation logic into get_generator.
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, map columns to sids and wrap
# in DataFrameSource
copy_frame = source.copy()
copy_frame.columns = self._write_and_map_id_index_to_sids(
source.columns, source.index[0],
)
source = DataFrameSource(copy_frame)
elif isinstance(source, pd.Panel):
# If Panel provided, map items to sids and wrap
# in DataPanelSource
copy_panel = source.copy()
copy_panel.items = self._write_and_map_id_index_to_sids(
source.items, source.major_axis[0],
)
source = DataPanelSource(copy_panel)
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
# Changing period_start and period_close might require updating
# of first_open and last_close.
self.sim_params.update_internal_from_env(
env=self.trading_environment
)
# The sids field of the source is the reference for the universe at
# the start of the run
self._current_universe = set()
for source in self.sources:
for sid in source.sids:
self._current_universe.add(sid)
# Check that all sids from the source are accounted for in
# the AssetFinder. This retrieve call will raise an exception if the
# sid is not found.
for sid in self._current_universe:
self.asset_finder.retrieve_asset(sid)
# 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.current_universe(),
self.sim_params.first_open,
self.sim_params.data_frequency,
self.trading_environment,
)
# 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 _write_and_map_id_index_to_sids(self, identifiers, as_of_date):
# Build new Assets for identifiers that can't be resolved as
# sids/Assets
identifiers_to_build = []
for identifier in identifiers:
asset = None
if isinstance(identifier, Asset):
asset = self.asset_finder.retrieve_asset(sid=identifier.sid,
default_none=True)
elif hasattr(identifier, '__int__'):
asset = self.asset_finder.retrieve_asset(sid=identifier,
default_none=True)
if asset is None:
identifiers_to_build.append(identifier)
self.trading_environment.write_data(
equities_identifiers=identifiers_to_build)
return self.asset_finder.map_identifier_index_to_sids(
identifiers, as_of_date,
)
def _create_daily_stats(self, perfs):
# create daily and cumulative stats dataframe
daily_perfs = []
# TODO: the loop here could overwrite expected properties
# of daily_perf. Could potentially raise or log a
# warning.
for perf in perfs:
if 'daily_perf' in perf:
perf['daily_perf'].update(
perf['daily_perf'].pop('recorded_vars')
)
perf['daily_perf'].update(perf['cumulative_risk_metrics'])
daily_perfs.append(perf['daily_perf'])
else:
self.risk_report = perf
daily_dts = [np.datetime64(perf['period_close'], utc=True)
for perf in daily_perfs]
daily_stats = pd.DataFrame(daily_perfs, index=daily_dts)
return daily_stats
@api_method
def add_transform(self, transform, days=None):
"""
Ensures that the history container will have enough size to service
a simple transform.
:Arguments:
transform : string
The transform to add. must be an element of:
{'mavg', 'stddev', 'vwap', 'returns'}.
days : int <default=None>
The maximum amount of days you will want for this transform.
This is not needed for 'returns'.
"""
if transform not in {'mavg', 'stddev', 'vwap', 'returns'}:
raise ValueError('Invalid transform')
if transform == 'returns':
if days is not None:
raise ValueError('returns does use days')
self.add_history(2, '1d', 'price')
return
elif days is None:
raise ValueError('no number of days specified')
if self.sim_params.data_frequency == 'daily':
mult = 1
freq = '1d'
else:
mult = 390
freq = '1m'
bars = mult * days
self.add_history(bars, freq, 'price')
if transform == 'vwap':
self.add_history(bars, freq, 'volume')
@api_method
def get_environment(self, field='platform'):
env = {
'arena': self.sim_params.arena,
'data_frequency': self.sim_params.data_frequency,
'start': self.sim_params.first_open,
'end': self.sim_params.last_close,
'capital_base': self.sim_params.capital_base,
'platform': self._platform
}
if field == '*':
return env
else:
return env[field]
def add_event(self, rule=None, callback=None):
"""
Adds an event to the algorithm's EventManager.
"""
self.event_manager.add_event(
zipline.utils.events.Event(rule, callback),
)
@api_method
def schedule_function(self,
func,
date_rule=None,
time_rule=None,
half_days=True):
"""
Schedules a function to be called with some timed rules.
"""
date_rule = date_rule or DateRuleFactory.every_day()
time_rule = ((time_rule or TimeRuleFactory.market_open())
if self.sim_params.data_frequency == 'minute' else
# If we are in daily mode the time_rule is ignored.
zipline.utils.events.Always())
self.add_event(
make_eventrule(date_rule, time_rule, half_days),
func,
)
@api_method
def record(self, *args, **kwargs):
"""
Track and record local variable (i.e. attributes) each day.
"""
# Make 2 objects both referencing the same iterator
args = [iter(args)] * 2
# Zip generates list entries by calling `next` on each iterator it
# receives. In this case the two iterators are the same object, so the
# call to next on args[0] will also advance args[1], resulting in zip
# returning (a,b) (c,d) (e,f) rather than (a,a) (b,b) (c,c) etc.
positionals = zip(*args)
for name, value in chain(positionals, iteritems(kwargs)):
self._recorded_vars[name] = value
@api_method
def symbol(self, symbol_str):
"""
Default symbol lookup for any source that directly maps the
symbol to the Asset (e.g. yahoo finance).
"""
# If the user has not set the symbol lookup date,
# use the period_end as the date for sybmol->sid resolution.
_lookup_date = self._symbol_lookup_date if self._symbol_lookup_date is not None \
else self.sim_params.period_end
return self.asset_finder.lookup_symbol(
symbol_str,
as_of_date=_lookup_date,
)
@api_method
def symbols(self, *args):
"""
Default symbols lookup for any source that directly maps the
symbol to the Asset (e.g. yahoo finance).
"""
return [self.symbol(identifier) for identifier in args]
@api_method
def sid(self, a_sid):
"""
Default sid lookup for any source that directly maps the integer sid
to the Asset.
"""
return self.asset_finder.retrieve_asset(a_sid)
@api_method
def future_chain(self, root_symbol, as_of_date=None):
""" Look up a future chain with the specified parameters.
Parameters
----------
root_symbol : str
The root symbol of a future chain.
as_of_date : datetime.datetime or pandas.Timestamp or str, optional
Date at which the chain determination is rooted. I.e. the
existing contract whose notice date is first after this date is
the primary contract, etc.
Returns
-------
FutureChain
The future chain matching the specified parameters.
Raises
------
RootSymbolNotFound
If a future chain could not be found for the given root symbol.
"""
if as_of_date:
try:
as_of_date = pd.Timestamp(as_of_date, tz='UTC')
except ValueError:
raise UnsupportedDatetimeFormat(input=as_of_date,
method='future_chain')
return FutureChain(
asset_finder=self.asset_finder,
get_datetime=self.get_datetime,
root_symbol=root_symbol.upper(),
as_of_date=as_of_date
)
def _calculate_order_value_amount(self, asset, value):
"""
Calculates how many shares/contracts to order based on the type of
asset being ordered.
"""
last_price = self.trading_client.current_data[asset].price
if tolerant_equals(last_price, 0):
zero_message = "Price of 0 for {psid}; can't infer value".format(
psid=asset
)
if self.logger:
self.logger.debug(zero_message)
# Don't place any order
return 0
if isinstance(asset, Future):
value_multiplier = asset.contract_multiplier
else:
value_multiplier = 1
return value / (last_price * value_multiplier)
@api_method
def order(self, sid, amount,
limit_price=None,
stop_price=None,
style=None):
"""
Place an order using the specified parameters.
"""
def round_if_near_integer(a, epsilon=1e-4):
"""
Round a to the nearest integer if that integer is within an epsilon
of a.
"""
if abs(a - round(a)) <= epsilon:
return round(a)
else:
return a
# Truncate to the integer share count that's either within .0001 of
# amount or closer to zero.
# E.g. 3.9999 -> 4.0; 5.5 -> 5.0; -5.5 -> -5.0
amount = int(round_if_near_integer(amount))
# Raises a ZiplineError if invalid parameters are detected.
self.validate_order_params(sid,
amount,
limit_price,
stop_price,
style)
# Convert deprecated limit_price and stop_price parameters to use
# ExecutionStyle objects.
style = self.__convert_order_params_for_blotter(limit_price,
stop_price,
style)
return self.blotter.order(sid, amount, style)
def validate_order_params(self,
asset,
amount,
limit_price,
stop_price,
style):
"""
Helper method for validating parameters to the order API function.
Raises an UnsupportedOrderParameters if invalid arguments are found.
"""
if not self.initialized:
raise OrderDuringInitialize(
msg="order() can only be called from within handle_data()"
)
if style:
if limit_price:
raise UnsupportedOrderParameters(
msg="Passing both limit_price and style is not supported."
)
if stop_price:
raise UnsupportedOrderParameters(
msg="Passing both stop_price and style is not supported."
)
if not isinstance(asset, Asset):
raise UnsupportedOrderParameters(
msg="Passing non-Asset argument to 'order()' is not supported."
" Use 'sid()' or 'symbol()' methods to look up an Asset."
)
for control in self.trading_controls:
control.validate(asset,
amount,
self.updated_portfolio(),
self.get_datetime(),
self.trading_client.current_data)
@staticmethod
def __convert_order_params_for_blotter(limit_price, stop_price, style):
"""
Helper method for converting deprecated limit_price and stop_price
arguments into ExecutionStyle instances.
This function assumes that either style == None or (limit_price,
stop_price) == (None, None).
"""
# TODO_SS: DeprecationWarning for usage of limit_price and stop_price.
if style:
assert (limit_price, stop_price) == (None, None)
return style
if limit_price and stop_price:
return StopLimitOrder(limit_price, stop_price)
if limit_price:
return LimitOrder(limit_price)
if stop_price:
return StopOrder(stop_price)
else:
return MarketOrder()
@api_method
def order_value(self, sid, value,
limit_price=None, stop_price=None, style=None):
"""
Place an order by desired value rather than desired number of shares.
If the requested sid is found in the universe, the requested value is
divided by its price to imply the number of shares to transact.
If the Asset being ordered is a Future, the 'value' calculated
is actually the exposure, as Futures have no 'value'.
value > 0 :: Buy/Cover
value < 0 :: Sell/Short
Market order: order(sid, value)
Limit order: order(sid, value, limit_price)
Stop order: order(sid, value, None, stop_price)
StopLimit order: order(sid, value, limit_price, stop_price)
"""
amount = self._calculate_order_value_amount(sid, value)
return self.order(sid, amount,
limit_price=limit_price,
stop_price=stop_price,
style=style)
@property
def recorded_vars(self):
return copy(self._recorded_vars)
@property
def portfolio(self):
return self.updated_portfolio()
def updated_portfolio(self):
if self.portfolio_needs_update:
self._portfolio = \
self.perf_tracker.get_portfolio(self.performance_needs_update)
self.portfolio_needs_update = False
self.performance_needs_update = False
return self._portfolio
@property
def account(self):
return self.updated_account()
def updated_account(self):
if self.account_needs_update:
self._account = \
self.perf_tracker.get_account(self.performance_needs_update)
self.account_needs_update = False
self.performance_needs_update = False
return self._account
def set_logger(self, logger):
self.logger = logger
def on_dt_changed(self, dt):
"""
Callback triggered by the simulation loop whenever the current dt
changes.
Any logic that should happen exactly once at the start of each datetime
group should happen here.
"""
assert isinstance(dt, datetime), \
"Attempt to set algorithm's current time with non-datetime"
assert dt.tzinfo == pytz.utc, \
"Algorithm expects a utc datetime"
self.datetime = dt
self.perf_tracker.set_date(dt)
self.blotter.set_date(dt)
@api_method
def get_datetime(self, tz=None):
"""
Returns the simulation datetime.
"""
dt = self.datetime
assert dt.tzinfo == pytz.utc, "Algorithm should have a utc datetime"
if tz is not None:
# Convert to the given timezone passed as a string or tzinfo.
if isinstance(tz, string_types):
tz = pytz.timezone(tz)
dt = dt.astimezone(tz)
return dt # datetime.datetime objects are immutable.
def set_transact(self, transact):
"""
Set the method that will be called to create a
transaction from open orders and trade events.
"""
self.blotter.transact = transact
def update_dividends(self, dividend_frame):
"""
Set DataFrame used to process dividends. DataFrame columns should
contain at least the entries in zp.DIVIDEND_FIELDS.
"""
self.perf_tracker.update_dividends(dividend_frame)
@api_method
def set_slippage(self, slippage):
if not isinstance(slippage, SlippageModel):
raise UnsupportedSlippageModel()
if self.initialized:
raise OverrideSlippagePostInit()
self.slippage = slippage
@api_method
def set_commission(self, commission):
if not isinstance(commission, (PerShare, PerTrade, PerDollar)):
raise UnsupportedCommissionModel()
if self.initialized:
raise OverrideCommissionPostInit()
self.commission = commission
@api_method
def set_symbol_lookup_date(self, dt):
"""
Set the date for which symbols will be resolved to their sids
(symbols may map to different firms or underlying assets at
different times)
"""
try:
self._symbol_lookup_date = pd.Timestamp(dt, tz='UTC')
except ValueError:
raise UnsupportedDatetimeFormat(input=dt,
method='set_symbol_lookup_date')
def set_sources(self, sources):
assert isinstance(sources, list)
self.sources = sources
# Remain backwards compatibility
@property
def data_frequency(self):
return self.sim_params.data_frequency
@data_frequency.setter
def data_frequency(self, value):
assert value in ('daily', 'minute')
self.sim_params.data_frequency = value
@api_method
def order_percent(self, sid, percent,
limit_price=None, stop_price=None, style=None):
"""
Place an order in the specified asset corresponding to the given
percent of the current portfolio value.
Note that percent must expressed as a decimal (0.50 means 50\%).
"""
value = self.portfolio.portfolio_value * percent
return self.order_value(sid, value,
limit_price=limit_price,
stop_price=stop_price,
style=style)
@api_method
def order_target(self, sid, target,
limit_price=None, stop_price=None, style=None):
"""
Place an order to adjust a position to a target number of shares. If
the position doesn't already exist, this is equivalent to placing a new
order. If the position does exist, this is equivalent to placing an
order for the difference between the target number of shares and the
current number of shares.
"""
if sid in self.portfolio.positions:
current_position = self.portfolio.positions[sid].amount
req_shares = target - current_position
return self.order(sid, req_shares,
limit_price=limit_price,
stop_price=stop_price,
style=style)
else:
return self.order(sid, target,
limit_price=limit_price,
stop_price=stop_price,
style=style)
@api_method
def order_target_value(self, sid, target,
limit_price=None, stop_price=None, style=None):
"""
Place an order to adjust a position to a target value. If
the position doesn't already exist, this is equivalent to placing a new
order. If the position does exist, this is equivalent to placing an
order for the difference between the target value and the
current value.
If the Asset being ordered is a Future, the 'target value' calculated
is actually the target exposure, as Futures have no 'value'.
"""
target_amount = self._calculate_order_value_amount(sid, target)
return self.order_target(sid, target_amount,
limit_price=limit_price,
stop_price=stop_price,
style=style)
@api_method
def order_target_percent(self, sid, target,
limit_price=None, stop_price=None, style=None):
"""
Place an order to adjust a position to a target percent of the
current portfolio value. If the position doesn't already exist, this is
equivalent to placing a new order. If the position does exist, this is
equivalent to placing an order for the difference between the target
percent and the current percent.
Note that target must expressed as a decimal (0.50 means 50\%).
"""
target_value = self.portfolio.portfolio_value * target
return self.order_target_value(sid, target_value,
limit_price=limit_price,
stop_price=stop_price,
style=style)
@api_method
def get_open_orders(self, sid=None):
if sid is None:
return {
key: [order.to_api_obj() for order in orders]
for key, orders in iteritems(self.blotter.open_orders)
if orders
}
if sid in self.blotter.open_orders:
orders = self.blotter.open_orders[sid]
return [order.to_api_obj() for order in orders]
return []
@api_method
def get_order(self, order_id):
if order_id in self.blotter.orders:
return self.blotter.orders[order_id].to_api_obj()
@api_method
def cancel_order(self, order_param):
order_id = order_param
if isinstance(order_param, zipline.protocol.Order):
order_id = order_param.id
self.blotter.cancel(order_id)
@api_method
def add_history(self, bar_count, frequency, field, ffill=True):
data_frequency = self.sim_params.data_frequency
history_spec = HistorySpec(bar_count, frequency, field, ffill,
data_frequency=data_frequency,
env=self.trading_environment)
self.history_specs[history_spec.key_str] = history_spec
if self.initialized:
if self.history_container:
self.history_container.ensure_spec(
history_spec, self.datetime, self._most_recent_data,
)
else:
self.history_container = self.history_container_class(
self.history_specs,
self.current_universe(),
self.sim_params.first_open,
self.sim_params.data_frequency,
env=self.trading_environment,
)
def get_history_spec(self, bar_count, frequency, field, ffill):
spec_key = HistorySpec.spec_key(bar_count, frequency, field, ffill)
if spec_key not in self.history_specs:
data_freq = self.sim_params.data_frequency
spec = HistorySpec(
bar_count,
frequency,
field,
ffill,
data_frequency=data_freq,
env=self.trading_environment,
)
self.history_specs[spec_key] = spec
if not self.history_container:
self.history_container = self.history_container_class(
self.history_specs,
self.current_universe(),
self.datetime,
self.sim_params.data_frequency,
bar_data=self._most_recent_data,
env=self.trading_environment,
)
self.history_container.ensure_spec(
spec, self.datetime, self._most_recent_data,
)
return self.history_specs[spec_key]
@api_method
def history(self, bar_count, frequency, field, ffill=True):
history_spec = self.get_history_spec(
bar_count,
frequency,
field,
ffill,
)
return self.history_container.get_history(history_spec, self.datetime)
####################
# Account Controls #
####################
def register_account_control(self, control):
"""
Register a new AccountControl to be checked on each bar.
"""
if self.initialized:
raise RegisterAccountControlPostInit()
self.account_controls.append(control)
def validate_account_controls(self):
for control in self.account_controls:
control.validate(self.updated_portfolio(),
self.updated_account(),
self.get_datetime(),
self.trading_client.current_data)
@api_method
def set_max_leverage(self, max_leverage=None):
"""
Set a limit on the maximum leverage of the algorithm.
"""
control = MaxLeverage(max_leverage)
self.register_account_control(control)
####################
# Trading Controls #
####################
def register_trading_control(self, control):
"""
Register a new TradingControl to be checked prior to order calls.
"""
if self.initialized:
raise RegisterTradingControlPostInit()
self.trading_controls.append(control)
@api_method
def set_max_position_size(self,
sid=None,
max_shares=None,
max_notional=None):
"""
Set a limit on the number of shares and/or dollar value held for the
given sid. Limits are treated as absolute values and are enforced at
the time that the algo attempts to place an order for sid. This means
that it's possible to end up with more than the max number of shares
due to splits/dividends, and more than the max notional due to price
improvement.
If an algorithm attempts to place an order that would result in
increasing the absolute value of shares/dollar value exceeding one of
these limits, raise a TradingControlException.
"""
control = MaxPositionSize(asset=sid,
max_shares=max_shares,
max_notional=max_notional)
self.register_trading_control(control)
@api_method
def set_max_order_size(self, sid=None, max_shares=None, max_notional=None):
"""
Set a limit on the number of shares and/or dollar value of any single
order placed for sid. Limits are treated as absolute values and are
enforced at the time that the algo attempts to place an order for sid.
If an algorithm attempts to place an order that would result in
exceeding one of these limits, raise a TradingControlException.
"""
control = MaxOrderSize(asset=sid,
max_shares=max_shares,
max_notional=max_notional)
self.register_trading_control(control)
@api_method
def set_max_order_count(self, max_count):
"""
Set a limit on the number of orders that can be placed within the given
time interval.
"""
control = MaxOrderCount(max_count)
self.register_trading_control(control)
@api_method
def set_do_not_order_list(self, restricted_list):
"""
Set a restriction on which sids can be ordered.
"""
control = RestrictedListOrder(restricted_list)
self.register_trading_control(control)
@api_method
def set_long_only(self):
"""
Set a rule specifying that this algorithm cannot take short positions.
"""
self.register_trading_control(LongOnly())
###########
# FFC API #
###########
@api_method
@require_not_initialized(AddTermPostInit())
def add_factor(self, factor, name):
if name in self._factors:
raise ValueError("Name %r is already a factor!" % name)
self._factors[name] = factor
@api_method
@require_not_initialized(AddTermPostInit())
def add_filter(self, filter):
name = "anon_filter_%d" % len(self._filters)
self._filters[name] = filter
# Note: add_classifier is not yet implemented since you can't do anything
# useful with classifiers yet.
def _all_terms(self):
# Merge all three dicts.
return dict(
chain.from_iterable(
iteritems(terms)
for terms in (self._filters, self._factors, self._classifiers)
)
)
def compute_factor_matrix(self, start_date):
"""
Compute a factor matrix containing at least the data necessary to
provide values for `start_date`.
Loads a factor matrix with data extending from `start_date` until a
year from `start_date`, or until the end of the simulation.
"""
days = self.trading_environment.trading_days
# Load data starting from the previous trading day...
start_date_loc = days.get_loc(start_date)
# ...continuing until either the day before the simulation end, or
# until 252 days of data have been loaded. 252 is a totally arbitrary
# choice that seemed reasonable based on napkin math.
sim_end = self.sim_params.last_close.normalize()
end_loc = min(start_date_loc + 252, days.get_loc(sim_end))
end_date = days[end_loc]
return self.engine.factor_matrix(
self._all_terms(),
start_date,
end_date,
), end_date
def current_universe(self):
return self._current_universe
@classmethod
def all_api_methods(cls):
"""
Return a list of all the TradingAlgorithm API methods.
"""
return [
fn for fn in itervalues(vars(cls))
if getattr(fn, 'is_api_method', False)
]
class TradingAlgorithm(object):
"""
Base class for trading algorithms. Inherit and overload
initialize() and handle_data(data).
A new algorithm could look like this:
```
from zipline.api import order, symbol
def initialize(context):
context.sid = symbol('AAPL')
context.amount = 100
def handle_data(context, data):
sid = context.sid
amount = context.amount
order(sid, amount)
```
To then to run this algorithm pass these functions to
TradingAlgorithm:
my_algo = TradingAlgorithm(initialize, handle_data)
stats = my_algo.run(data)
"""
def __init__(self, *args, **kwargs):
"""Initialize sids and other state variables.
:Arguments:
:Optional:
initialize : function
Function that is called with a single
argument at the begninning of the simulation.
handle_data : function
Function that is called with 2 arguments
(context and data) on every bar.
script : str
Algoscript that contains initialize and
handle_data function definition.
data_frequency : {'daily', 'minute'}
The duration of the bars.
capital_base : float <default: 1.0e5>
How much capital to start with.
instant_fill : bool <default: False>
Whether to fill orders immediately or on next bar.
asset_finder : An AssetFinder object
A new AssetFinder object to be used in this TradingEnvironment
asset_metadata: can be either:
- dict
- pandas.DataFrame
- object with 'read' property
If dict is provided, it must have the following structure:
* keys are the identifiers
* values are dicts containing the metadata, with the metadata
field name as the key
If pandas.DataFrame is provided, it must have the
following structure:
* column names must be the metadata fields
* index must be the different asset identifiers
* array contents should be the metadata value
If an object with a 'read' property is provided, 'read' must
return rows containing at least one of 'sid' or 'symbol' along
with the other metadata fields.
identifiers : List
Any asset identifiers that are not provided in the
asset_metadata, but will be traded by this TradingAlgorithm
"""
self.sources = []
# List of trading controls to be used to validate orders.
self.trading_controls = []
# List of account controls to be checked on each bar.
self.account_controls = []
self._recorded_vars = {}
self.namespace = kwargs.get('namespace', {})
self._platform = kwargs.pop('platform', 'zipline')
self.logger = None
self.benchmark_return_source = None
# default components for transact
self.slippage = VolumeShareSlippage()
self.commission = PerShare()
self.instant_fill = kwargs.pop('instant_fill', False)
# set the capital base
self.capital_base = kwargs.pop('capital_base', DEFAULT_CAPITAL_BASE)
self.sim_params = kwargs.pop('sim_params', None)
if self.sim_params is None:
self.sim_params = create_simulation_parameters(
capital_base=self.capital_base,
start=kwargs.pop('start', None),
end=kwargs.pop('end', None))
self.perf_tracker = PerformanceTracker(self.sim_params)
# Update the TradingEnvironment with the provided asset metadata
self.trading_environment = kwargs.pop('env',
TradingEnvironment.instance())
self.trading_environment.update_asset_finder(
asset_finder=kwargs.pop('asset_finder', None),
asset_metadata=kwargs.pop('asset_metadata', None),
identifiers=kwargs.pop('identifiers', None))
# Pull in the environment's new AssetFinder for quick reference
self.asset_finder = self.trading_environment.asset_finder
self.init_engine(kwargs.pop('ffc_loader', None))
# Maps from name to Term
self._filters = {}
self._factors = {}
self._classifiers = {}
self.blotter = kwargs.pop('blotter', None)
if not self.blotter:
self.blotter = Blotter()
# Set the dt initally to the period start by forcing it to change
self.on_dt_changed(self.sim_params.period_start)
self.portfolio_needs_update = True
self.account_needs_update = True
self.performance_needs_update = True
self._portfolio = None
self._account = None
self.history_container_class = kwargs.pop(
'history_container_class',
HistoryContainer,
)
self.history_container = None
self.history_specs = {}
# If string is passed in, execute and get reference to
# functions.
self.algoscript = kwargs.pop('script', None)
self._initialize = None
self._before_trading_start = None
self._analyze = None
self.event_manager = EventManager()
if self.algoscript is not None:
filename = kwargs.pop('algo_filename', None)
if filename is None:
filename = '<string>'
code = compile(self.algoscript, filename, 'exec')
exec_(code, self.namespace)
self._initialize = self.namespace.get('initialize')
if 'handle_data' not in self.namespace:
raise ValueError('You must define a handle_data function.')
else:
self._handle_data = self.namespace['handle_data']
self._before_trading_start = \
self.namespace.get('before_trading_start')
# Optional analyze function, gets called after run
self._analyze = self.namespace.get('analyze')
elif kwargs.get('initialize') and kwargs.get('handle_data'):
if self.algoscript is not None:
raise ValueError('You can not set script and \
initialize/handle_data.')
self._initialize = kwargs.pop('initialize')
self._handle_data = kwargs.pop('handle_data')
self._before_trading_start = kwargs.pop('before_trading_start',
None)
self.event_manager.add_event(
zipline.utils.events.Event(
zipline.utils.events.Always(),
# We pass handle_data.__func__ to get the unbound method.
# We will explicitly pass the algorithm to bind it again.
self.handle_data.__func__,
),
prepend=True,
)
# If method not defined, NOOP
if self._initialize is None:
self._initialize = lambda x: None
# Alternative way of setting data_frequency for backwards
# compatibility.
if 'data_frequency' in kwargs:
self.data_frequency = kwargs.pop('data_frequency')
self._most_recent_data = None
# Prepare the algo for initialization
self.initialized = False
self.initialize_args = args
self.initialize_kwargs = kwargs
def init_engine(self, loader):
"""
Construct and save an FFCEngine from loader.
If loader is None, constructs a NoOpFFCEngine.
"""
if loader is not None:
self.engine = SimpleFFCEngine(
loader,
self.trading_environment.trading_days,
self.asset_finder,
)
else:
self.engine = NoOpFFCEngine()
def initialize(self, *args, **kwargs):
"""
Call self._initialize with `self` made available to Zipline API
functions.
"""
with ZiplineAPI(self):
self._initialize(self)
def before_trading_start(self):
if self._before_trading_start is None:
return
self._before_trading_start(self)
def handle_data(self, data):
self._most_recent_data = data
if self.history_container:
self.history_container.update(data, self.datetime)
self._handle_data(self, data)
# Unlike trading controls which remain constant unless placing an
# order, account controls can change each bar. Thus, must check
# every bar no matter if the algorithm places an order or not.
self.validate_account_controls()
def analyze(self, perf):
if self._analyze is None:
return
with ZiplineAPI(self):
self._analyze(self, perf)
def __repr__(self):
"""
N.B. this does not yet represent a string that can be used
to instantiate an exact copy of an algorithm.
However, it is getting close, and provides some value as something
that can be inspected interactively.
"""
return """
{class_name}(
capital_base={capital_base}
sim_params={sim_params},
initialized={initialized},
slippage={slippage},
commission={commission},
blotter={blotter},
recorded_vars={recorded_vars})
""".strip().format(class_name=self.__class__.__name__,
capital_base=self.capital_base,
sim_params=repr(self.sim_params),
initialized=self.initialized,
slippage=repr(self.slippage),
commission=repr(self.commission),
blotter=repr(self.blotter),
recorded_vars=repr(self.recorded_vars))
def _create_data_generator(self, source_filter, sim_params=None):
"""
Create a merged data generator using the sources attached to this
algorithm.
::source_filter:: is a method that receives events in date
sorted order, and returns True for those events that should be
processed by the zipline, and False for those that should be
skipped.
"""
if sim_params is None:
sim_params = self.sim_params
if self.benchmark_return_source is None:
if sim_params.data_frequency == 'minute' or \
sim_params.emission_rate == 'minute':
def update_time(date):
return self.trading_environment.get_open_and_close(date)[1]
else:
def update_time(date):
return date
benchmark_return_source = [
Event({
'dt': update_time(dt),
'returns': ret,
'type': zipline.protocol.DATASOURCE_TYPE.BENCHMARK,
'source_id': 'benchmarks'
}) for dt, ret in
self.trading_environment.benchmark_returns.iteritems()
if dt.date() >= sim_params.period_start.date()
and dt.date() <= sim_params.period_end.date()
]
else:
benchmark_return_source = self.benchmark_return_source
date_sorted = date_sorted_sources(*self.sources)
if source_filter:
date_sorted = filter(source_filter, date_sorted)
with_benchmarks = date_sorted_sources(benchmark_return_source,
date_sorted)
# Group together events with the same dt field. This depends on the
# events already being sorted.
return groupby(with_benchmarks, attrgetter('dt'))
def _create_generator(self, sim_params, source_filter=None):
"""
Create a basic generator setup using the sources to this algorithm.
::source_filter:: is a method that receives events in date
sorted order, and returns True for those events that should be
processed by the zipline, and False for those that should be
skipped.
"""
if not self.initialized:
self.initialize(*self.initialize_args, **self.initialize_kwargs)
self.initialized = True
if self.perf_tracker is None:
# HACK: When running with the `run` method, we set perf_tracker to
# None so that it will be overwritten here.
self.perf_tracker = PerformanceTracker(sim_params)
self.portfolio_needs_update = True
self.account_needs_update = True
self.performance_needs_update = True
self.data_gen = self._create_data_generator(source_filter, sim_params)
self.trading_client = AlgorithmSimulator(self, sim_params)
transact_method = transact_partial(self.slippage, self.commission)
self.set_transact(transact_method)
return self.trading_client.transform(self.data_gen)
def get_generator(self):
"""
Override this method to add new logic to the construction
of the generator. Overrides can use the _create_generator
method to get a standard construction generator.
"""
return self._create_generator(self.sim_params)
# TODO: make a new subclass, e.g. BatchAlgorithm, and move
# the run method to the subclass, and refactor to put the
# generator creation logic into get_generator.
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, map columns to sids and wrap
# in DataFrameSource
copy_frame = source.copy()
copy_frame.columns = \
self.asset_finder.map_identifier_index_to_sids(
source.columns, source.index[0]
)
source = DataFrameSource(copy_frame)
elif isinstance(source, pd.Panel):
# If Panel provided, map items to sids and wrap
# in DataPanelSource
copy_panel = source.copy()
copy_panel.items = self.asset_finder.map_identifier_index_to_sids(
source.items, source.major_axis[0])
source = DataPanelSource(copy_panel)
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
# Changing period_start and period_close might require updating
# of first_open and last_close.
self.sim_params._update_internal()
# The sids field of the source is the reference for the universe at
# the start of the run
self._current_universe = set()
for source in self.sources:
for sid in source.sids:
self._current_universe.add(sid)
# Check that all sids from the source are accounted for in
# the AssetFinder. This retrieve call will raise an exception if the
# sid is not found.
for sid in self._current_universe:
self.asset_finder.retrieve_asset(sid)
# 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.current_universe(),
self.sim_params.first_open,
self.sim_params.data_frequency,
)
# 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 _create_daily_stats(self, perfs):
# create daily and cumulative stats dataframe
daily_perfs = []
# TODO: the loop here could overwrite expected properties
# of daily_perf. Could potentially raise or log a
# warning.
for perf in perfs:
if 'daily_perf' in perf:
perf['daily_perf'].update(
perf['daily_perf'].pop('recorded_vars'))
perf['daily_perf'].update(perf['cumulative_risk_metrics'])
daily_perfs.append(perf['daily_perf'])
else:
self.risk_report = perf
daily_dts = [
np.datetime64(perf['period_close'], utc=True)
for perf in daily_perfs
]
daily_stats = pd.DataFrame(daily_perfs, index=daily_dts)
return daily_stats
@api_method
def add_transform(self, transform, days=None):
"""
Ensures that the history container will have enough size to service
a simple transform.
:Arguments:
transform : string
The transform to add. must be an element of:
{'mavg', 'stddev', 'vwap', 'returns'}.
days : int <default=None>
The maximum amount of days you will want for this transform.
This is not needed for 'returns'.
"""
if transform not in {'mavg', 'stddev', 'vwap', 'returns'}:
raise ValueError('Invalid transform')
if transform == 'returns':
if days is not None:
raise ValueError('returns does use days')
self.add_history(2, '1d', 'price')
return
elif days is None:
raise ValueError('no number of days specified')
if self.sim_params.data_frequency == 'daily':
mult = 1
freq = '1d'
else:
mult = 390
freq = '1m'
bars = mult * days
self.add_history(bars, freq, 'price')
if transform == 'vwap':
self.add_history(bars, freq, 'volume')
@api_method
def get_environment(self, field='platform'):
env = {
'arena': self.sim_params.arena,
'data_frequency': self.sim_params.data_frequency,
'start': self.sim_params.first_open,
'end': self.sim_params.last_close,
'capital_base': self.sim_params.capital_base,
'platform': self._platform
}
if field == '*':
return env
else:
return env[field]
def add_event(self, rule=None, callback=None):
"""
Adds an event to the algorithm's EventManager.
"""
self.event_manager.add_event(
zipline.utils.events.Event(rule, callback), )
@api_method
def schedule_function(self,
func,
date_rule=None,
time_rule=None,
half_days=True):
"""
Schedules a function to be called with some timed rules.
"""
date_rule = date_rule or DateRuleFactory.every_day()
time_rule = ((time_rule or TimeRuleFactory.market_open())
if self.sim_params.data_frequency == 'minute' else
# If we are in daily mode the time_rule is ignored.
zipline.utils.events.Always())
self.add_event(
make_eventrule(date_rule, time_rule, half_days),
func,
)
@api_method
def record(self, *args, **kwargs):
"""
Track and record local variable (i.e. attributes) each day.
"""
# Make 2 objects both referencing the same iterator
args = [iter(args)] * 2
# Zip generates list entries by calling `next` on each iterator it
# receives. In this case the two iterators are the same object, so the
# call to next on args[0] will also advance args[1], resulting in zip
# returning (a,b) (c,d) (e,f) rather than (a,a) (b,b) (c,c) etc.
positionals = zip(*args)
for name, value in chain(positionals, iteritems(kwargs)):
self._recorded_vars[name] = value
@api_method
def symbol(self, symbol_str):
"""
Default symbol lookup for any source that directly maps the
symbol to the Asset (e.g. yahoo finance).
"""
return self.asset_finder.lookup_symbol_resolve_multiple(
symbol_str, as_of_date=self.datetime)
@api_method
def symbols(self, *args):
"""
Default symbols lookup for any source that directly maps the
symbol to the Asset (e.g. yahoo finance).
"""
return [self.symbol(identifier) for identifier in args]
@api_method
def sid(self, a_sid):
"""
Default sid lookup for any source that directly maps the integer sid
to the Asset.
"""
return self.asset_finder.retrieve_asset(a_sid)
@api_method
def future_chain(self, root_symbol, as_of_date=None):
""" Look up a future chain with the specified parameters.
Parameters
----------
root_symbol : str
The root symbol of a future chain.
as_of_date : datetime.datetime or pandas.Timestamp or str, optional
Date at which the chain determination is rooted. I.e. the
existing contract whose notice date is first after this date is
the primary contract, etc.
Returns
-------
FutureChain
The future chain matching the specified parameters.
Raises
------
RootSymbolNotFound
If a future chain could not be found for the given root symbol.
"""
return FutureChain(asset_finder=self.asset_finder,
get_datetime=self.get_datetime,
root_symbol=root_symbol.upper(),
as_of_date=as_of_date)
def _calculate_order_value_amount(self, asset, value):
"""
Calculates how many shares/contracts to order based on the type of
asset being ordered.
"""
last_price = self.trading_client.current_data[asset].price
if tolerant_equals(last_price, 0):
zero_message = "Price of 0 for {psid}; can't infer value".format(
psid=asset)
if self.logger:
self.logger.debug(zero_message)
# Don't place any order
return 0
if isinstance(asset, Future):
value_multiplier = asset.contract_multiplier
else:
value_multiplier = 1
return value / (last_price * value_multiplier)
@api_method
def order(self,
sid,
amount,
limit_price=None,
stop_price=None,
style=None):
"""
Place an order using the specified parameters.
"""
def round_if_near_integer(a, epsilon=1e-4):
"""
Round a to the nearest integer if that integer is within an epsilon
of a.
"""
if abs(a - round(a)) <= epsilon:
return round(a)
else:
return a
# Truncate to the integer share count that's either within .0001 of
# amount or closer to zero.
# E.g. 3.9999 -> 4.0; 5.5 -> 5.0; -5.5 -> -5.0
amount = int(round_if_near_integer(amount))
# Raises a ZiplineError if invalid parameters are detected.
self.validate_order_params(sid, amount, limit_price, stop_price, style)
# Convert deprecated limit_price and stop_price parameters to use
# ExecutionStyle objects.
style = self.__convert_order_params_for_blotter(
limit_price, stop_price, style)
return self.blotter.order(sid, amount, style)
def validate_order_params(self, asset, amount, limit_price, stop_price,
style):
"""
Helper method for validating parameters to the order API function.
Raises an UnsupportedOrderParameters if invalid arguments are found.
"""
if not self.initialized:
raise OrderDuringInitialize(
msg="order() can only be called from within handle_data()")
if style:
if limit_price:
raise UnsupportedOrderParameters(
msg="Passing both limit_price and style is not supported.")
if stop_price:
raise UnsupportedOrderParameters(
msg="Passing both stop_price and style is not supported.")
if not isinstance(asset, Asset):
raise UnsupportedOrderParameters(
msg="Passing non-Asset argument to 'order()' is not supported."
" Use 'sid()' or 'symbol()' methods to look up an Asset.")
for control in self.trading_controls:
control.validate(asset, amount, self.updated_portfolio(),
self.get_datetime(),
self.trading_client.current_data)
@staticmethod
def __convert_order_params_for_blotter(limit_price, stop_price, style):
"""
Helper method for converting deprecated limit_price and stop_price
arguments into ExecutionStyle instances.
This function assumes that either style == None or (limit_price,
stop_price) == (None, None).
"""
# TODO_SS: DeprecationWarning for usage of limit_price and stop_price.
if style:
assert (limit_price, stop_price) == (None, None)
return style
if limit_price and stop_price:
return StopLimitOrder(limit_price, stop_price)
if limit_price:
return LimitOrder(limit_price)
if stop_price:
return StopOrder(stop_price)
else:
return MarketOrder()
@api_method
def order_value(self,
sid,
value,
limit_price=None,
stop_price=None,
style=None):
"""
Place an order by desired value rather than desired number of shares.
If the requested sid is found in the universe, the requested value is
divided by its price to imply the number of shares to transact.
If the Asset being ordered is a Future, the 'value' calculated
is actually the exposure, as Futures have no 'value'.
value > 0 :: Buy/Cover
value < 0 :: Sell/Short
Market order: order(sid, value)
Limit order: order(sid, value, limit_price)
Stop order: order(sid, value, None, stop_price)
StopLimit order: order(sid, value, limit_price, stop_price)
"""
amount = self._calculate_order_value_amount(sid, value)
return self.order(sid,
amount,
limit_price=limit_price,
stop_price=stop_price,
style=style)
@property
def recorded_vars(self):
return copy(self._recorded_vars)
@property
def portfolio(self):
return self.updated_portfolio()
def updated_portfolio(self):
if self.portfolio_needs_update:
self._portfolio = \
self.perf_tracker.get_portfolio(self.performance_needs_update)
self.portfolio_needs_update = False
self.performance_needs_update = False
return self._portfolio
@property
def account(self):
return self.updated_account()
def updated_account(self):
if self.account_needs_update:
self._account = \
self.perf_tracker.get_account(self.performance_needs_update)
self.account_needs_update = False
self.performance_needs_update = False
return self._account
def set_logger(self, logger):
self.logger = logger
def on_dt_changed(self, dt):
"""
Callback triggered by the simulation loop whenever the current dt
changes.
Any logic that should happen exactly once at the start of each datetime
group should happen here.
"""
assert isinstance(dt, datetime), \
"Attempt to set algorithm's current time with non-datetime"
assert dt.tzinfo == pytz.utc, \
"Algorithm expects a utc datetime"
self.datetime = dt
self.perf_tracker.set_date(dt)
self.blotter.set_date(dt)
@api_method
def get_datetime(self, tz=None):
"""
Returns the simulation datetime.
"""
dt = self.datetime
assert dt.tzinfo == pytz.utc, "Algorithm should have a utc datetime"
if tz is not None:
# Convert to the given timezone passed as a string or tzinfo.
if isinstance(tz, string_types):
tz = pytz.timezone(tz)
dt = dt.astimezone(tz)
return dt # datetime.datetime objects are immutable.
def set_transact(self, transact):
"""
Set the method that will be called to create a
transaction from open orders and trade events.
"""
self.blotter.transact = transact
def update_dividends(self, dividend_frame):
"""
Set DataFrame used to process dividends. DataFrame columns should
contain at least the entries in zp.DIVIDEND_FIELDS.
"""
self.perf_tracker.update_dividends(dividend_frame)
@api_method
def set_slippage(self, slippage):
if not isinstance(slippage, SlippageModel):
raise UnsupportedSlippageModel()
if self.initialized:
raise OverrideSlippagePostInit()
self.slippage = slippage
@api_method
def set_commission(self, commission):
if not isinstance(commission, (PerShare, PerTrade, PerDollar)):
raise UnsupportedCommissionModel()
if self.initialized:
raise OverrideCommissionPostInit()
self.commission = commission
def set_sources(self, sources):
assert isinstance(sources, list)
self.sources = sources
# Remain backwards compatibility
@property
def data_frequency(self):
return self.sim_params.data_frequency
@data_frequency.setter
def data_frequency(self, value):
assert value in ('daily', 'minute')
self.sim_params.data_frequency = value
@api_method
def order_percent(self,
sid,
percent,
limit_price=None,
stop_price=None,
style=None):
"""
Place an order in the specified asset corresponding to the given
percent of the current portfolio value.
Note that percent must expressed as a decimal (0.50 means 50\%).
"""
value = self.portfolio.portfolio_value * percent
return self.order_value(sid,
value,
limit_price=limit_price,
stop_price=stop_price,
style=style)
@api_method
def order_target(self,
sid,
target,
limit_price=None,
stop_price=None,
style=None):
"""
Place an order to adjust a position to a target number of shares. If
the position doesn't already exist, this is equivalent to placing a new
order. If the position does exist, this is equivalent to placing an
order for the difference between the target number of shares and the
current number of shares.
"""
if sid in self.portfolio.positions:
current_position = self.portfolio.positions[sid].amount
req_shares = target - current_position
return self.order(sid,
req_shares,
limit_price=limit_price,
stop_price=stop_price,
style=style)
else:
return self.order(sid,
target,
limit_price=limit_price,
stop_price=stop_price,
style=style)
@api_method
def order_target_value(self,
sid,
target,
limit_price=None,
stop_price=None,
style=None):
"""
Place an order to adjust a position to a target value. If
the position doesn't already exist, this is equivalent to placing a new
order. If the position does exist, this is equivalent to placing an
order for the difference between the target value and the
current value.
If the Asset being ordered is a Future, the 'target value' calculated
is actually the target exposure, as Futures have no 'value'.
"""
target_amount = self._calculate_order_value_amount(sid, target)
return self.order_target(sid,
target_amount,
limit_price=limit_price,
stop_price=stop_price,
style=style)
@api_method
def order_target_percent(self,
sid,
target,
limit_price=None,
stop_price=None,
style=None):
"""
Place an order to adjust a position to a target percent of the
current portfolio value. If the position doesn't already exist, this is
equivalent to placing a new order. If the position does exist, this is
equivalent to placing an order for the difference between the target
percent and the current percent.
Note that target must expressed as a decimal (0.50 means 50\%).
"""
target_value = self.portfolio.portfolio_value * target
return self.order_target_value(sid,
target_value,
limit_price=limit_price,
stop_price=stop_price,
style=style)
@api_method
def get_open_orders(self, sid=None):
if sid is None:
return {
key: [order.to_api_obj() for order in orders]
for key, orders in iteritems(self.blotter.open_orders)
if orders
}
if sid in self.blotter.open_orders:
orders = self.blotter.open_orders[sid]
return [order.to_api_obj() for order in orders]
return []
@api_method
def get_order(self, order_id):
if order_id in self.blotter.orders:
return self.blotter.orders[order_id].to_api_obj()
@api_method
def cancel_order(self, order_param):
order_id = order_param
if isinstance(order_param, zipline.protocol.Order):
order_id = order_param.id
self.blotter.cancel(order_id)
@api_method
def add_history(self, bar_count, frequency, field, ffill=True):
data_frequency = self.sim_params.data_frequency
history_spec = HistorySpec(bar_count,
frequency,
field,
ffill,
data_frequency=data_frequency)
self.history_specs[history_spec.key_str] = history_spec
if self.initialized:
if self.history_container:
self.history_container.ensure_spec(
history_spec,
self.datetime,
self._most_recent_data,
)
else:
self.history_container = self.history_container_class(
self.history_specs,
self.current_universe(),
self.sim_params.first_open,
self.sim_params.data_frequency,
)
def get_history_spec(self, bar_count, frequency, field, ffill):
spec_key = HistorySpec.spec_key(bar_count, frequency, field, ffill)
if spec_key not in self.history_specs:
data_freq = self.sim_params.data_frequency
spec = HistorySpec(
bar_count,
frequency,
field,
ffill,
data_frequency=data_freq,
)
self.history_specs[spec_key] = spec
if not self.history_container:
self.history_container = self.history_container_class(
self.history_specs,
self.current_universe(),
self.datetime,
self.sim_params.data_frequency,
bar_data=self._most_recent_data,
)
self.history_container.ensure_spec(
spec,
self.datetime,
self._most_recent_data,
)
return self.history_specs[spec_key]
@api_method
def history(self, bar_count, frequency, field, ffill=True):
history_spec = self.get_history_spec(
bar_count,
frequency,
field,
ffill,
)
return self.history_container.get_history(history_spec, self.datetime)
####################
# Account Controls #
####################
def register_account_control(self, control):
"""
Register a new AccountControl to be checked on each bar.
"""
if self.initialized:
raise RegisterAccountControlPostInit()
self.account_controls.append(control)
def validate_account_controls(self):
for control in self.account_controls:
control.validate(self.updated_portfolio(), self.updated_account(),
self.get_datetime(),
self.trading_client.current_data)
@api_method
def set_max_leverage(self, max_leverage=None):
"""
Set a limit on the maximum leverage of the algorithm.
"""
control = MaxLeverage(max_leverage)
self.register_account_control(control)
####################
# Trading Controls #
####################
def register_trading_control(self, control):
"""
Register a new TradingControl to be checked prior to order calls.
"""
if self.initialized:
raise RegisterTradingControlPostInit()
self.trading_controls.append(control)
@api_method
def set_max_position_size(self,
sid=None,
max_shares=None,
max_notional=None):
"""
Set a limit on the number of shares and/or dollar value held for the
given sid. Limits are treated as absolute values and are enforced at
the time that the algo attempts to place an order for sid. This means
that it's possible to end up with more than the max number of shares
due to splits/dividends, and more than the max notional due to price
improvement.
If an algorithm attempts to place an order that would result in
increasing the absolute value of shares/dollar value exceeding one of
these limits, raise a TradingControlException.
"""
control = MaxPositionSize(asset=sid,
max_shares=max_shares,
max_notional=max_notional)
self.register_trading_control(control)
@api_method
def set_max_order_size(self, sid=None, max_shares=None, max_notional=None):
"""
Set a limit on the number of shares and/or dollar value of any single
order placed for sid. Limits are treated as absolute values and are
enforced at the time that the algo attempts to place an order for sid.
If an algorithm attempts to place an order that would result in
exceeding one of these limits, raise a TradingControlException.
"""
control = MaxOrderSize(asset=sid,
max_shares=max_shares,
max_notional=max_notional)
self.register_trading_control(control)
@api_method
def set_max_order_count(self, max_count):
"""
Set a limit on the number of orders that can be placed within the given
time interval.
"""
control = MaxOrderCount(max_count)
self.register_trading_control(control)
@api_method
def set_do_not_order_list(self, restricted_list):
"""
Set a restriction on which sids can be ordered.
"""
control = RestrictedListOrder(restricted_list)
self.register_trading_control(control)
@api_method
def set_long_only(self):
"""
Set a rule specifying that this algorithm cannot take short positions.
"""
self.register_trading_control(LongOnly())
###########
# FFC API #
###########
@api_method
@require_not_initialized(AddTermPostInit())
def add_factor(self, factor, name):
if name in self._factors:
raise ValueError("Name %r is already a factor!" % name)
self._factors[name] = factor
@api_method
@require_not_initialized(AddTermPostInit())
def add_filter(self, filter):
name = "anon_filter_%d" % len(self._filters)
self._filters[name] = filter
# Note: add_classifier is not yet implemented since you can't do anything
# useful with classifiers yet.
def _all_terms(self):
# Merge all three dicts.
return dict(
chain.from_iterable(
iteritems(terms) for terms in (self._filters, self._factors,
self._classifiers)))
def compute_factor_matrix(self, start_date):
"""
Compute a factor matrix starting at start_date.
"""
days = self.trading_environment.trading_days
start_date_loc = days.get_loc(start_date)
sim_end = self.sim_params.last_close.normalize()
end_loc = min(start_date_loc + 252, days.get_loc(sim_end))
end_date = days[end_loc]
return self.engine.factor_matrix(
self._all_terms(),
start_date,
end_date,
), end_date
def current_universe(self):
return self._current_universe
@classmethod
def all_api_methods(cls):
"""
Return a list of all the TradingAlgorithm API methods.
"""
return [
fn for fn in itervalues(vars(cls))
if getattr(fn, 'is_api_method', False)
]
class TradingAlgorithm(object):
"""
Base class for trading algorithms. Inherit and overload
initialize() and handle_data(data).
A new algorithm could look like this:
```
from zipline.api import order
def initialize(context):
context.sid = 'AAPL'
context.amount = 100
def handle_data(self, data):
sid = context.sid
amount = context.amount
order(sid, amount)
```
To then to run this algorithm pass these functions to
TradingAlgorithm:
my_algo = TradingAlgorithm(initialize, handle_data)
stats = my_algo.run(data)
"""
# If this is set to false then it is the responsibility
# of the overriding subclass to set initialized = true
AUTO_INITIALIZE = True
def __init__(self, *args, **kwargs):
"""Initialize sids and other state variables.
:Arguments:
:Optional:
initialize : function
Function that is called with a single
argument at the begninning of the simulation.
handle_data : function
Function that is called with 2 arguments
(context and data) on every bar.
script : str
Algoscript that contains initialize and
handle_data function definition.
data_frequency : str (daily, hourly or minutely)
The duration of the bars.
capital_base : float <default: 1.0e5>
How much capital to start with.
instant_fill : bool <default: False>
Whether to fill orders immediately or on next bar.
environment : str <default: 'zipline'>
The environment that this algorithm is running in.
"""
self.datetime = None
self.registered_transforms = {}
self.transforms = []
self.sources = []
# List of trading controls to be used to validate orders.
self.trading_controls = []
self._recorded_vars = {}
self.namespace = kwargs.get('namespace', {})
self._environment = kwargs.pop('environment', 'zipline')
self.logger = None
self.benchmark_return_source = None
# default components for transact
self.slippage = VolumeShareSlippage()
self.commission = PerShare()
self.instant_fill = kwargs.pop('instant_fill', False)
# set the capital base
self.capital_base = kwargs.pop('capital_base', DEFAULT_CAPITAL_BASE)
self.sim_params = kwargs.pop('sim_params', None)
if self.sim_params is None:
self.sim_params = create_simulation_parameters(
capital_base=self.capital_base
)
self.perf_tracker = PerformanceTracker(self.sim_params)
self.blotter = kwargs.pop('blotter', None)
if not self.blotter:
self.blotter = Blotter()
self.portfolio_needs_update = True
self.account_needs_update = True
self.performance_needs_update = True
self._portfolio = None
self._account = None
self.history_container = None
self.history_specs = {}
# If string is passed in, execute and get reference to
# functions.
self.algoscript = kwargs.pop('script', None)
self._initialize = None
self._before_trading_start = None
self._analyze = None
self.event_manager = EventManager()
if self.algoscript is not None:
exec_(self.algoscript, self.namespace)
self._initialize = self.namespace.get('initialize')
if 'handle_data' not in self.namespace:
raise ValueError('You must define a handle_data function.')
else:
self._handle_data = self.namespace['handle_data']
self._before_trading_start = \
self.namespace.get('before_trading_start')
# Optional analyze function, gets called after run
self._analyze = self.namespace.get('analyze')
elif kwargs.get('initialize') and kwargs.get('handle_data'):
if self.algoscript is not None:
raise ValueError('You can not set script and \
initialize/handle_data.')
self._initialize = kwargs.pop('initialize')
self._handle_data = kwargs.pop('handle_data')
self._before_trading_start = kwargs.pop('before_trading_start',
None)
self.event_manager.add_event(
zipline.utils.events.Event(
zipline.utils.events.Always(),
# We pass handle_data.__func__ to get the unbound method.
# We will explicitly pass the algorithm to bind it again.
self.handle_data.__func__,
),
prepend=True,
)
# If method not defined, NOOP
if self._initialize is None:
self._initialize = lambda x: None
# Alternative way of setting data_frequency for backwards
# compatibility.
if 'data_frequency' in kwargs:
self.data_frequency = kwargs.pop('data_frequency')
# Subclasses that override initialize should only worry about
# setting self.initialized = True if AUTO_INITIALIZE is
# is manually set to False.
self.initialized = False
self.initialize(*args, **kwargs)
if self.AUTO_INITIALIZE:
self.initialized = True
def initialize(self, *args, **kwargs):
"""
Call self._initialize with `self` made available to Zipline API
functions.
"""
with ZiplineAPI(self):
self._initialize(self)
def before_trading_start(self):
if self._before_trading_start is None:
return
self._before_trading_start(self)
def handle_data(self, data):
if self.history_container:
self.history_container.update(data, self.datetime)
self._handle_data(self, data)
def analyze(self, perf):
if self._analyze is None:
return
with ZiplineAPI(self):
self._analyze(self, perf)
def __repr__(self):
"""
N.B. this does not yet represent a string that can be used
to instantiate an exact copy of an algorithm.
However, it is getting close, and provides some value as something
that can be inspected interactively.
"""
return """
{class_name}(
capital_base={capital_base}
sim_params={sim_params},
initialized={initialized},
slippage={slippage},
commission={commission},
blotter={blotter},
recorded_vars={recorded_vars})
""".strip().format(class_name=self.__class__.__name__,
capital_base=self.capital_base,
sim_params=repr(self.sim_params),
initialized=self.initialized,
slippage=repr(self.slippage),
commission=repr(self.commission),
blotter=repr(self.blotter),
recorded_vars=repr(self.recorded_vars))
def _create_data_generator(self, source_filter, sim_params=None):
"""
Create a merged data generator using the sources and
transforms attached to this algorithm.
::source_filter:: is a method that receives events in date
sorted order, and returns True for those events that should be
processed by the zipline, and False for those that should be
skipped.
"""
if sim_params is None:
sim_params = self.sim_params
if self.benchmark_return_source is None:
env = trading.environment
if (sim_params.data_frequency == 'minute'
or sim_params.emission_rate == 'minute'):
update_time = lambda date: env.get_open_and_close(date)[1]
else:
update_time = lambda date: date
benchmark_return_source = [
Event({'dt': update_time(dt),
'returns': ret,
'type': zipline.protocol.DATASOURCE_TYPE.BENCHMARK,
'source_id': 'benchmarks'})
for dt, ret in trading.environment.benchmark_returns.iterkv()
if dt.date() >= sim_params.period_start.date()
and dt.date() <= sim_params.period_end.date()
]
else:
benchmark_return_source = self.benchmark_return_source
date_sorted = date_sorted_sources(*self.sources)
if source_filter:
date_sorted = filter(source_filter, date_sorted)
with_tnfms = sequential_transforms(date_sorted,
*self.transforms)
with_benchmarks = date_sorted_sources(benchmark_return_source,
with_tnfms)
# Group together events with the same dt field. This depends on the
# events already being sorted.
return groupby(with_benchmarks, attrgetter('dt'))
def _create_generator(self, sim_params, source_filter=None):
"""
Create a basic generator setup using the sources and
transforms attached to this algorithm.
::source_filter:: is a method that receives events in date
sorted order, and returns True for those events that should be
processed by the zipline, and False for those that should be
skipped.
"""
if self.perf_tracker is None:
# HACK: When running with the `run` method, we set perf_tracker to
# None so that it will be overwritten here.
self.perf_tracker = PerformanceTracker(sim_params)
self.portfolio_needs_update = True
self.account_needs_update = True
self.performance_needs_update = True
self.data_gen = self._create_data_generator(source_filter, sim_params)
self.trading_client = AlgorithmSimulator(self, sim_params)
transact_method = transact_partial(self.slippage, self.commission)
self.set_transact(transact_method)
return self.trading_client.transform(self.data_gen)
def get_generator(self):
"""
Override this method to add new logic to the construction
of the generator. Overrides can use the _create_generator
method to get a standard construction generator.
"""
return self._create_generator(self.sim_params)
# TODO: make a new subclass, e.g. BatchAlgorithm, and move
# the run method to the subclass, and refactor to put the
# generator creation logic into get_generator.
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 _create_daily_stats(self, perfs):
# create daily and cumulative stats dataframe
daily_perfs = []
# TODO: the loop here could overwrite expected properties
# of daily_perf. Could potentially raise or log a
# warning.
for perf in perfs:
if 'daily_perf' in perf:
perf['daily_perf'].update(
perf['daily_perf'].pop('recorded_vars')
)
daily_perfs.append(perf['daily_perf'])
else:
self.risk_report = perf
daily_dts = [np.datetime64(perf['period_close'], utc=True)
for perf in daily_perfs]
daily_stats = pd.DataFrame(daily_perfs, index=daily_dts)
return daily_stats
def add_transform(self, transform_class, tag, *args, **kwargs):
"""Add a single-sid, sequential transform to the model.
:Arguments:
transform_class : class
Which transform to use. E.g. mavg.
tag : str
How to name the transform. Can later be access via:
data[sid].tag()
Extra args and kwargs will be forwarded to the transform
instantiation.
"""
self.registered_transforms[tag] = {'class': transform_class,
'args': args,
'kwargs': kwargs}
@api_method
def get_environment(self):
return self._environment
def add_event(self, rule=None, callback=None):
"""
Adds an event to the algorithm's EventManager.
"""
self.event_manager.add_event(
zipline.utils.events.Event(rule, callback),
)
@api_method
def schedule_function(self,
func,
date_rule=None,
time_rule=None,
half_days=True):
"""
Schedules a function to be called with some timed rules.
"""
if self.sim_params.data_frequency != 'minute':
raise IncompatibleScheduleFunctionDataFrequency()
date_rule = date_rule or DateRuleFactory.every_day()
time_rule = time_rule or TimeRuleFactory.market_open()
self.add_event(
make_eventrule(date_rule, time_rule, half_days),
func,
)
@api_method
def record(self, *args, **kwargs):
"""
Track and record local variable (i.e. attributes) each day.
"""
# Make 2 objects both referencing the same iterator
args = [iter(args)] * 2
# Zip generates list entries by calling `next` on each iterator it
# receives. In this case the two iterators are the same object, so the
# call to next on args[0] will also advance args[1], resulting in zip
# returning (a,b) (c,d) (e,f) rather than (a,a) (b,b) (c,c) etc.
positionals = zip(*args)
for name, value in chain(positionals, iteritems(kwargs)):
self._recorded_vars[name] = value
@api_method
def symbol(self, symbol_str, as_of_date=None):
"""
Default symbol lookup for any source that directly maps the
symbol to the identifier (e.g. yahoo finance).
Keyword argument as_of_date is ignored.
"""
return symbol_str
@api_method
def order(self, sid, amount,
limit_price=None,
stop_price=None,
style=None):
"""
Place an order using the specified parameters.
"""
def round_if_near_integer(a, epsilon=1e-4):
"""
Round a to the nearest integer if that integer is within an epsilon
of a.
"""
if abs(a - round(a)) <= epsilon:
return round(a)
else:
return a
# Truncate to the integer share count that's either within .0001 of
# amount or closer to zero.
# E.g. 3.9999 -> 4.0; 5.5 -> 5.0; -5.5 -> -5.0
amount = int(round_if_near_integer(amount))
# Raises a ZiplineError if invalid parameters are detected.
self.validate_order_params(sid,
amount,
limit_price,
stop_price,
style)
# Convert deprecated limit_price and stop_price parameters to use
# ExecutionStyle objects.
style = self.__convert_order_params_for_blotter(limit_price,
stop_price,
style)
return self.blotter.order(sid, amount, style)
def validate_order_params(self,
sid,
amount,
limit_price,
stop_price,
style):
"""
Helper method for validating parameters to the order API function.
Raises an UnsupportedOrderParameters if invalid arguments are found.
"""
if not self.initialized:
raise OrderDuringInitialize(
msg="order() can only be called from within handle_data()"
)
if style:
if limit_price:
raise UnsupportedOrderParameters(
msg="Passing both limit_price and style is not supported."
)
if stop_price:
raise UnsupportedOrderParameters(
msg="Passing both stop_price and style is not supported."
)
for control in self.trading_controls:
control.validate(sid,
amount,
self.updated_portfolio(),
self.get_datetime(),
self.trading_client.current_data)
@staticmethod
def __convert_order_params_for_blotter(limit_price, stop_price, style):
"""
Helper method for converting deprecated limit_price and stop_price
arguments into ExecutionStyle instances.
This function assumes that either style == None or (limit_price,
stop_price) == (None, None).
"""
# TODO_SS: DeprecationWarning for usage of limit_price and stop_price.
if style:
assert (limit_price, stop_price) == (None, None)
return style
if limit_price and stop_price:
return StopLimitOrder(limit_price, stop_price)
if limit_price:
return LimitOrder(limit_price)
if stop_price:
return StopOrder(stop_price)
else:
return MarketOrder()
@api_method
def order_value(self, sid, value,
limit_price=None, stop_price=None, style=None):
"""
Place an order by desired value rather than desired number of shares.
If the requested sid is found in the universe, the requested value is
divided by its price to imply the number of shares to transact.
value > 0 :: Buy/Cover
value < 0 :: Sell/Short
Market order: order(sid, value)
Limit order: order(sid, value, limit_price)
Stop order: order(sid, value, None, stop_price)
StopLimit order: order(sid, value, limit_price, stop_price)
"""
last_price = self.trading_client.current_data[sid].price
if np.allclose(last_price, 0):
zero_message = "Price of 0 for {psid}; can't infer value".format(
psid=sid
)
if self.logger:
self.logger.debug(zero_message)
# Don't place any order
return
else:
amount = value / last_price
return self.order(sid, amount,
limit_price=limit_price,
stop_price=stop_price,
style=style)
@property
def recorded_vars(self):
return copy(self._recorded_vars)
@property
def portfolio(self):
return self.updated_portfolio()
def updated_portfolio(self):
if self.portfolio_needs_update:
self._portfolio = \
self.perf_tracker.get_portfolio(self.performance_needs_update)
self.portfolio_needs_update = False
self.performance_needs_update = False
return self._portfolio
@property
def account(self):
return self.updated_account()
def updated_account(self):
if self.account_needs_update:
self._account = \
self.perf_tracker.get_account(self.performance_needs_update)
self.account_needs_update = False
self.performance_needs_update = False
return self._account
def set_logger(self, logger):
self.logger = logger
def on_dt_changed(self, dt):
"""
Callback triggered by the simulation loop whenever the current dt
changes.
Any logic that should happen exactly once at the start of each datetime
group should happen here.
"""
assert isinstance(dt, datetime), \
"Attempt to set algorithm's current time with non-datetime"
assert dt.tzinfo == pytz.utc, \
"Algorithm expects a utc datetime"
self.datetime = dt
self.perf_tracker.set_date(dt)
self.blotter.set_date(dt)
@api_method
def get_datetime(self):
"""
Returns a copy of the datetime.
"""
date_copy = copy(self.datetime)
assert date_copy.tzinfo == pytz.utc, \
"Algorithm should have a utc datetime"
return date_copy
def set_transact(self, transact):
"""
Set the method that will be called to create a
transaction from open orders and trade events.
"""
self.blotter.transact = transact
def update_dividends(self, dividend_frame):
"""
Set DataFrame used to process dividends. DataFrame columns should
contain at least the entries in zp.DIVIDEND_FIELDS.
"""
self.perf_tracker.update_dividends(dividend_frame)
@api_method
def set_slippage(self, slippage):
if not isinstance(slippage, SlippageModel):
raise UnsupportedSlippageModel()
if self.initialized:
raise OverrideSlippagePostInit()
self.slippage = slippage
@api_method
def set_commission(self, commission):
if not isinstance(commission, (PerShare, PerTrade, PerDollar)):
raise UnsupportedCommissionModel()
if self.initialized:
raise OverrideCommissionPostInit()
self.commission = commission
def set_sources(self, sources):
assert isinstance(sources, list)
self.sources = sources
def set_transforms(self, transforms):
assert isinstance(transforms, list)
self.transforms = transforms
# Remain backwards compatibility
@property
def data_frequency(self):
return self.sim_params.data_frequency
@data_frequency.setter
def data_frequency(self, value):
assert value in ('daily', 'minute')
self.sim_params.data_frequency = value
@api_method
def order_percent(self, sid, percent,
limit_price=None, stop_price=None, style=None):
"""
Place an order in the specified security corresponding to the given
percent of the current portfolio value.
Note that percent must expressed as a decimal (0.50 means 50\%).
"""
value = self.portfolio.portfolio_value * percent
return self.order_value(sid, value,
limit_price=limit_price,
stop_price=stop_price,
style=style)
@api_method
def order_target(self, sid, target,
limit_price=None, stop_price=None, style=None):
"""
Place an order to adjust a position to a target number of shares. If
the position doesn't already exist, this is equivalent to placing a new
order. If the position does exist, this is equivalent to placing an
order for the difference between the target number of shares and the
current number of shares.
"""
if sid in self.portfolio.positions:
current_position = self.portfolio.positions[sid].amount
req_shares = target - current_position
return self.order(sid, req_shares,
limit_price=limit_price,
stop_price=stop_price,
style=style)
else:
return self.order(sid, target,
limit_price=limit_price,
stop_price=stop_price,
style=style)
@api_method
def order_target_value(self, sid, target,
limit_price=None, stop_price=None, style=None):
"""
Place an order to adjust a position to a target value. If
the position doesn't already exist, this is equivalent to placing a new
order. If the position does exist, this is equivalent to placing an
order for the difference between the target value and the
current value.
"""
last_price = self.trading_client.current_data[sid].price
if np.allclose(last_price, 0):
# Don't place an order
if self.logger:
zero_message = "Price of 0 for {psid}; can't infer value"
self.logger.debug(zero_message.format(psid=sid))
return
target_amount = target / last_price
return self.order_target(sid, target_amount,
limit_price=limit_price,
stop_price=stop_price,
style=style)
@api_method
def order_target_percent(self, sid, target,
limit_price=None, stop_price=None, style=None):
"""
Place an order to adjust a position to a target percent of the
current portfolio value. If the position doesn't already exist, this is
equivalent to placing a new order. If the position does exist, this is
equivalent to placing an order for the difference between the target
percent and the current percent.
Note that target must expressed as a decimal (0.50 means 50\%).
"""
target_value = self.portfolio.portfolio_value * target
return self.order_target_value(sid, target_value,
limit_price=limit_price,
stop_price=stop_price,
style=style)
@api_method
def get_open_orders(self, sid=None):
if sid is None:
return {
key: [order.to_api_obj() for order in orders]
for key, orders in iteritems(self.blotter.open_orders)
if orders
}
if sid in self.blotter.open_orders:
orders = self.blotter.open_orders[sid]
return [order.to_api_obj() for order in orders]
return []
@api_method
def get_order(self, order_id):
if order_id in self.blotter.orders:
return self.blotter.orders[order_id].to_api_obj()
@api_method
def cancel_order(self, order_param):
order_id = order_param
if isinstance(order_param, zipline.protocol.Order):
order_id = order_param.id
self.blotter.cancel(order_id)
@api_method
def add_history(self, bar_count, frequency, field,
ffill=True):
data_frequency = self.sim_params.data_frequency
daily_at_midnight = (data_frequency == 'daily')
history_spec = HistorySpec(bar_count, frequency, field, ffill,
daily_at_midnight=daily_at_midnight,
data_frequency=data_frequency)
self.history_specs[history_spec.key_str] = history_spec
@api_method
def history(self, bar_count, frequency, field, ffill=True):
spec_key_str = HistorySpec.spec_key(
bar_count, frequency, field, ffill)
history_spec = self.history_specs[spec_key_str]
return self.history_container.get_history(history_spec, self.datetime)
####################
# Trading Controls #
####################
def register_trading_control(self, control):
"""
Register a new TradingControl to be checked prior to order calls.
"""
if self.initialized:
raise RegisterTradingControlPostInit()
self.trading_controls.append(control)
@api_method
def set_max_position_size(self,
sid=None,
max_shares=None,
max_notional=None):
"""
Set a limit on the number of shares and/or dollar value held for the
given sid. Limits are treated as absolute values and are enforced at
the time that the algo attempts to place an order for sid. This means
that it's possible to end up with more than the max number of shares
due to splits/dividends, and more than the max notional due to price
improvement.
If an algorithm attempts to place an order that would result in
increasing the absolute value of shares/dollar value exceeding one of
these limits, raise a TradingControlException.
"""
control = MaxPositionSize(sid=sid,
max_shares=max_shares,
max_notional=max_notional)
self.register_trading_control(control)
@api_method
def set_max_order_size(self, sid=None, max_shares=None, max_notional=None):
"""
Set a limit on the number of shares and/or dollar value of any single
order placed for sid. Limits are treated as absolute values and are
enforced at the time that the algo attempts to place an order for sid.
If an algorithm attempts to place an order that would result in
exceeding one of these limits, raise a TradingControlException.
"""
control = MaxOrderSize(sid=sid,
max_shares=max_shares,
max_notional=max_notional)
self.register_trading_control(control)
@api_method
def set_max_order_count(self, max_count):
"""
Set a limit on the number of orders that can be placed within the given
time interval.
"""
control = MaxOrderCount(max_count)
self.register_trading_control(control)
@api_method
def set_long_only(self):
"""
Set a rule specifying that this algorithm cannot take short positions.
"""
self.register_trading_control(LongOnly())
@classmethod
def all_api_methods(cls):
"""
Return a list of all the TradingAlgorithm API methods.
"""
return [fn for fn in cls.__dict__.itervalues()
if getattr(fn, 'is_api_method', False)]
class TradingAlgorithm(object):
"""
Base class for trading algorithms. Inherit and overload
initialize() and handle_data(data).
A new algorithm could look like this:
```
class MyAlgo(TradingAlgorithm):
def initialize(self, sids, amount):
self.sids = sids
self.amount = amount
def handle_data(self, data):
sid = self.sids[0]
amount = self.amount
self.order(sid, amount)
```
To then to run this algorithm:
my_algo = MyAlgo([0], 100) # first argument has to be list of sids
stats = my_algo.run(data)
"""
def __init__(self, *args, **kwargs):
"""Initialize sids and other state variables.
:Arguments:
data_frequency : str (daily, hourly or minutely)
The duration of the bars.
annualizer : int <optional>
Which constant to use for annualizing risk metrics.
If not provided, will extract from data_frequency.
capital_base : float <default: 1.0e5>
How much capital to start with.
"""
self._portfolio = None
self.datetime = None
self.registered_transforms = {}
self.transforms = []
self.sources = []
self._recorded_vars = {}
self.logger = None
self.benchmark_return_source = None
# default components for transact
self.slippage = VolumeShareSlippage()
self.commission = PerShare()
if 'data_frequency' in kwargs:
self.set_data_frequency(kwargs.pop('data_frequency'))
else:
self.data_frequency = None
# Override annualizer if set
if 'annualizer' in kwargs:
self.annualizer = kwargs['annualizer']
# set the capital base
self.capital_base = kwargs.pop('capital_base', DEFAULT_CAPITAL_BASE)
self.sim_params = kwargs.pop('sim_params', None)
if self.sim_params:
self.sim_params.data_frequency = self.data_frequency
self.blotter = kwargs.pop('blotter', None)
if not self.blotter:
self.blotter = Blotter()
# an algorithm subclass needs to set initialized to True when
# it is fully initialized.
self.initialized = False
# call to user-defined constructor method
self.initialize(*args, **kwargs)
def __repr__(self):
"""
N.B. this does not yet represent a string that can be used
to instantiate an exact copy of an algorithm.
However, it is getting close, and provides some value as something
that can be inspected interactively.
"""
return """
{class_name}(
capital_base={capital_base}
sim_params={sim_params},
initialized={initialized},
slippage={slippage},
commission={commission},
blotter={blotter},
recorded_vars={recorded_vars})
""".strip().format(class_name=self.__class__.__name__,
capital_base=self.capital_base,
sim_params=repr(self.sim_params),
initialized=self.initialized,
slippage=repr(self.slippage),
commission=repr(self.commission),
blotter=repr(self.blotter),
recorded_vars=repr(self.recorded_vars))
def _create_data_generator(self, source_filter, sim_params):
"""
Create a merged data generator using the sources and
transforms attached to this algorithm.
::source_filter:: is a method that receives events in date
sorted order, and returns True for those events that should be
processed by the zipline, and False for those that should be
skipped.
"""
if self.benchmark_return_source is None:
benchmark_return_source = [
Event({'dt': ret.date,
'returns': ret.returns,
'type': zipline.protocol.DATASOURCE_TYPE.BENCHMARK,
'source_id': 'benchmarks'})
for ret in trading.environment.benchmark_returns
if ret.date.date() >= sim_params.period_start.date()
and ret.date.date() <= sim_params.period_end.date()
]
else:
benchmark_return_source = self.benchmark_return_source
date_sorted = date_sorted_sources(*self.sources)
if source_filter:
date_sorted = ifilter(source_filter, date_sorted)
with_tnfms = sequential_transforms(date_sorted,
*self.transforms)
with_alias_dt = alias_dt(with_tnfms)
with_benchmarks = date_sorted_sources(benchmark_return_source,
with_alias_dt)
# Group together events with the same dt field. This depends on the
# events already being sorted.
return groupby(with_benchmarks, attrgetter('dt'))
def _create_generator(self, sim_params, source_filter=None):
"""
Create a basic generator setup using the sources and
transforms attached to this algorithm.
::source_filter:: is a method that receives events in date
sorted order, and returns True for those events that should be
processed by the zipline, and False for those that should be
skipped.
"""
sim_params.data_frequency = self.data_frequency
self.data_gen = self._create_data_generator(source_filter,
sim_params)
self.perf_tracker = PerformanceTracker(sim_params)
self.trading_client = AlgorithmSimulator(self, sim_params)
transact_method = transact_partial(self.slippage, self.commission)
self.set_transact(transact_method)
return self.trading_client.transform(self.data_gen)
def get_generator(self):
"""
Override this method to add new logic to the construction
of the generator. Overrides can use the _create_generator
method to get a standard construction generator.
"""
return self._create_generator(self.sim_params)
def initialize(self, *args, **kwargs):
pass
# TODO: make a new subclass, e.g. BatchAlgorithm, and move
# the run method to the subclass, and refactor to put the
# generator creation logic into get_generator.
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 not sim_params:
if not self.sim_params:
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
# Create transforms by wrapping them into StatefulTransforms
self.transforms = []
for namestring, trans_descr in self.registered_transforms.iteritems():
sf = StatefulTransform(
trans_descr['class'],
*trans_descr['args'],
**trans_descr['kwargs']
)
sf.namestring = namestring
self.transforms.append(sf)
# create transforms and zipline
self.gen = self._create_generator(sim_params)
# 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)
return daily_stats
def _create_daily_stats(self, perfs):
# create daily and cumulative stats dataframe
daily_perfs = []
# TODO: the loop here could overwrite expected properties
# of daily_perf. Could potentially raise or log a
# warning.
for perf in perfs:
if 'daily_perf' in perf:
perf['daily_perf'].update(
perf['daily_perf'].pop('recorded_vars')
)
daily_perfs.append(perf['daily_perf'])
else:
self.risk_report = perf
daily_dts = [np.datetime64(perf['period_close'], utc=True)
for perf in daily_perfs]
daily_stats = pd.DataFrame(daily_perfs, index=daily_dts)
return daily_stats
def add_transform(self, transform_class, tag, *args, **kwargs):
"""Add a single-sid, sequential transform to the model.
:Arguments:
transform_class : class
Which transform to use. E.g. mavg.
tag : str
How to name the transform. Can later be access via:
data[sid].tag()
Extra args and kwargs will be forwarded to the transform
instantiation.
"""
self.registered_transforms[tag] = {'class': transform_class,
'args': args,
'kwargs': kwargs}
def record(self, **kwargs):
"""
Track and record local variable (i.e. attributes) each day.
"""
for name, value in kwargs.items():
self._recorded_vars[name] = value
def order(self, sid, amount, limit_price=None, stop_price=None):
return self.blotter.order(sid, amount, limit_price, stop_price)
def order_value(self, sid, value, limit_price=None, stop_price=None):
last_price = self.trading_client.current_data[sid].price
return self.blotter.order_value(sid, value, last_price,
limit_price=limit_price,
stop_price=stop_price)
@property
def recorded_vars(self):
return copy(self._recorded_vars)
@property
def portfolio(self):
return self._portfolio
def set_portfolio(self, portfolio):
self._portfolio = portfolio
def set_logger(self, logger):
self.logger = logger
def set_datetime(self, dt):
assert isinstance(dt, datetime), \
"Attempt to set algorithm's current time with non-datetime"
assert dt.tzinfo == pytz.utc, \
"Algorithm expects a utc datetime"
self.datetime = dt
def get_datetime(self):
"""
Returns a copy of the datetime.
"""
date_copy = copy(self.datetime)
assert date_copy.tzinfo == pytz.utc, \
"Algorithm should have a utc datetime"
return date_copy
def set_transact(self, transact):
"""
Set the method that will be called to create a
transaction from open orders and trade events.
"""
self.blotter.transact = transact
def set_slippage(self, slippage):
if not isinstance(slippage, SlippageModel):
raise UnsupportedSlippageModel()
if self.initialized:
raise OverrideSlippagePostInit()
self.slippage = slippage
def set_commission(self, commission):
if not isinstance(commission, (PerShare, PerTrade)):
raise UnsupportedCommissionModel()
if self.initialized:
raise OverrideCommissionPostInit()
self.commission = commission
def set_sources(self, sources):
assert isinstance(sources, list)
self.sources = sources
def set_transforms(self, transforms):
assert isinstance(transforms, list)
self.transforms = transforms
def set_data_frequency(self, data_frequency):
assert data_frequency in ('daily', 'minute')
self.data_frequency = data_frequency
self.annualizer = ANNUALIZER[self.data_frequency]
def order_percent(self, sid, percent, limit_price=None, stop_price=None):
"""
Place an order in the specified security corresponding to the given
percent of the current portfolio value.
Note that percent must expressed as a decimal (0.50 means 50\%).
"""
value = self.portfolio.portfolio_value * percent
return self.order_value(sid, value, limit_price, stop_price)
def target(self, sid, target, limit_price=None, stop_price=None):
"""
Place an order to adjust a position to a target number of shares. If
the position doesn't already exist, this is equivalent to placing a new
order. If the position does exist, this is equivalent to placing an
order for the difference between the target number of shares and the
current number of shares.
"""
if sid in self.portfolio.positions:
current_position = self.portfolio.positions[sid].amount
req_shares = target - current_position
return self.order(sid, req_shares, limit_price, stop_price)
else:
return self.order(sid, target, limit_price, stop_price)
def target_value(self, sid, target, limit_price=None, stop_price=None):
"""
Place an order to adjust a position to a target value. If
the position doesn't already exist, this is equivalent to placing a new
order. If the position does exist, this is equivalent to placing an
order for the difference between the target value and the
current value.
"""
if sid in self.portfolio.positions:
current_position = self.portfolio.positions[sid].amount
current_price = self.portfolio.positions[sid].last_sale_price
current_value = current_position * current_price
req_value = target - current_value
return self.order_value(sid, req_value, limit_price, stop_price)
else:
return self.order_value(sid, target, limit_price, stop_price)
def target_percent(self, sid, target, limit_price=None, stop_price=None):
"""
Place an order to adjust a position to a target percent of the
current portfolio value. If the position doesn't already exist, this is
equivalent to placing a new order. If the position does exist, this is
equivalent to placing an order for the difference between the target
percent and the current percent.
Note that target must expressed as a decimal (0.50 means 50\%).
"""
if sid in self.portfolio.positions:
current_position = self.portfolio.positions[sid].amount
current_price = self.portfolio.positions[sid].last_sale_price
current_value = current_position * current_price
else:
current_value = 0
target_value = self.portfolio.portfolio_value * target
req_value = target_value - current_value
return self.order_value(sid, req_value, limit_price, stop_price)
class TradingAlgorithm(object):
"""
Base class for trading algorithms. Inherit and overload
initialize() and handle_data(data).
A new algorithm could look like this:
```
from zipline.api import order
def initialize(context):
context.sid = 'AAPL'
context.amount = 100
def handle_data(self, data):
sid = context.sid
amount = context.amount
order(sid, amount)
```
To then to run this algorithm pass these functions to
TradingAlgorithm:
my_algo = TradingAlgorithm(initialize, handle_data)
stats = my_algo.run(data)
"""
def __init__(self, *args, **kwargs):
"""Initialize sids and other state variables.
:Arguments:
:Optional:
initialize : function
Function that is called with a single
argument at the begninning of the simulation.
handle_data : function
Function that is called with 2 arguments
(context and data) on every bar.
script : str
Algoscript that contains initialize and
handle_data function definition.
data_frequency : str (daily, hourly or minutely)
The duration of the bars.
annualizer : int <optional>
Which constant to use for annualizing risk metrics.
If not provided, will extract from data_frequency.
capital_base : float <default: 1.0e5>
How much capital to start with.
instant_fill : bool <default: False>
Whether to fill orders immediately or on next bar.
"""
self.datetime = None
self.registered_transforms = {}
self.transforms = []
self.sources = []
self._recorded_vars = {}
self.logger = None
self.benchmark_return_source = None
self.perf_tracker = None
# default components for transact
self.slippage = VolumeShareSlippage()
self.commission = PerShare()
if 'data_frequency' in kwargs:
self.set_data_frequency(kwargs.pop('data_frequency'))
else:
self.data_frequency = None
self.instant_fill = kwargs.pop('instant_fill', False)
# Override annualizer if set
if 'annualizer' in kwargs:
self.annualizer = kwargs['annualizer']
# set the capital base
self.capital_base = kwargs.pop('capital_base', DEFAULT_CAPITAL_BASE)
self.sim_params = kwargs.pop('sim_params', None)
if self.sim_params:
if self.data_frequency is None:
self.data_frequency = self.sim_params.data_frequency
else:
self.sim_params.data_frequency = self.data_frequency
self.perf_tracker = PerformanceTracker(self.sim_params)
self.blotter = kwargs.pop('blotter', None)
if not self.blotter:
self.blotter = Blotter()
self.portfolio_needs_update = True
self._portfolio = None
# If string is passed in, execute and get reference to
# functions.
self.algoscript = kwargs.pop('script', None)
self._initialize = None
if self.algoscript is not None:
self.ns = {}
exec_(self.algoscript, self.ns)
if 'initialize' not in self.ns:
raise ValueError('You must define an initialze function.')
if 'handle_data' not in self.ns:
raise ValueError('You must define a handle_data function.')
self._initialize = self.ns['initialize']
self._handle_data = self.ns['handle_data']
# If two functions are passed in assume initialize and
# handle_data are passed in.
elif kwargs.get('initialize', False) and kwargs.get('handle_data'):
if self.algoscript is not None:
raise ValueError('You can not set script and \
initialize/handle_data.')
self._initialize = kwargs.pop('initialize')
self._handle_data = kwargs.pop('handle_data')
if self._initialize is None:
self._initialize = lambda x: None
# an algorithm subclass needs to set initialized to True when
# it is fully initialized.
self.initialized = False
self.initialize(*args, **kwargs)
def initialize(self, *args, **kwargs):
# store algo reference in global space
set_algo_instance(self)
try:
self._initialize(self)
finally:
set_algo_instance(None)
def handle_data(self, data):
self._handle_data(self, data)
def __repr__(self):
"""
N.B. this does not yet represent a string that can be used
to instantiate an exact copy of an algorithm.
However, it is getting close, and provides some value as something
that can be inspected interactively.
"""
return """
{class_name}(
capital_base={capital_base}
sim_params={sim_params},
initialized={initialized},
slippage={slippage},
commission={commission},
blotter={blotter},
recorded_vars={recorded_vars})
""".strip().format(class_name=self.__class__.__name__,
capital_base=self.capital_base,
sim_params=repr(self.sim_params),
initialized=self.initialized,
slippage=repr(self.slippage),
commission=repr(self.commission),
blotter=repr(self.blotter),
recorded_vars=repr(self.recorded_vars))
def _create_data_generator(self, source_filter, sim_params):
"""
Create a merged data generator using the sources and
transforms attached to this algorithm.
::source_filter:: is a method that receives events in date
sorted order, and returns True for those events that should be
processed by the zipline, and False for those that should be
skipped.
"""
if self.benchmark_return_source is None:
env = trading.environment
if (self.data_frequency == 'minute'
or sim_params.emission_rate == 'minute'):
update_time = lambda date: env.get_open_and_close(date)[1]
else:
update_time = lambda date: date
benchmark_return_source = [
Event({
'dt': update_time(dt),
'returns': ret,
'type': zipline.protocol.DATASOURCE_TYPE.BENCHMARK,
'source_id': 'benchmarks'
})
for dt, ret in trading.environment.benchmark_returns.iterkv()
if dt.date() >= sim_params.period_start.date()
and dt.date() <= sim_params.period_end.date()
]
else:
benchmark_return_source = self.benchmark_return_source
date_sorted = date_sorted_sources(*self.sources)
if source_filter:
date_sorted = filter(source_filter, date_sorted)
with_tnfms = sequential_transforms(date_sorted, *self.transforms)
with_alias_dt = alias_dt(with_tnfms)
with_benchmarks = date_sorted_sources(benchmark_return_source,
with_alias_dt)
# Group together events with the same dt field. This depends on the
# events already being sorted.
return groupby(with_benchmarks, attrgetter('dt'))
def _create_generator(self, sim_params, source_filter=None):
"""
Create a basic generator setup using the sources and
transforms attached to this algorithm.
::source_filter:: is a method that receives events in date
sorted order, and returns True for those events that should be
processed by the zipline, and False for those that should be
skipped.
"""
sim_params.data_frequency = self.data_frequency
# perf_tracker will be instantiated in __init__ if a sim_params
# is passed to the constructor. If not, we instantiate here.
if self.perf_tracker is None:
self.perf_tracker = PerformanceTracker(sim_params)
self.data_gen = self._create_data_generator(source_filter, sim_params)
self.trading_client = AlgorithmSimulator(self, sim_params)
transact_method = transact_partial(self.slippage, self.commission)
self.set_transact(transact_method)
return self.trading_client.transform(self.data_gen)
def get_generator(self):
"""
Override this method to add new logic to the construction
of the generator. Overrides can use the _create_generator
method to get a standard construction generator.
"""
return self._create_generator(self.sim_params)
# TODO: make a new subclass, e.g. BatchAlgorithm, and move
# the run method to the subclass, and refactor to put the
# generator creation logic into get_generator.
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 not sim_params:
if not self.sim_params:
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
# 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)
# store algo reference in global space
set_algo_instance(self)
try:
# 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)
finally:
# remove algo from global space
set_algo_instance(None)
return daily_stats
def _create_daily_stats(self, perfs):
# create daily and cumulative stats dataframe
daily_perfs = []
# TODO: the loop here could overwrite expected properties
# of daily_perf. Could potentially raise or log a
# warning.
for perf in perfs:
if 'daily_perf' in perf:
perf['daily_perf'].update(
perf['daily_perf'].pop('recorded_vars'))
daily_perfs.append(perf['daily_perf'])
else:
self.risk_report = perf
daily_dts = [
np.datetime64(perf['period_close'], utc=True)
for perf in daily_perfs
]
daily_stats = pd.DataFrame(daily_perfs, index=daily_dts)
return daily_stats
def add_transform(self, transform_class, tag, *args, **kwargs):
"""Add a single-sid, sequential transform to the model.
:Arguments:
transform_class : class
Which transform to use. E.g. mavg.
tag : str
How to name the transform. Can later be access via:
data[sid].tag()
Extra args and kwargs will be forwarded to the transform
instantiation.
"""
self.registered_transforms[tag] = {
'class': transform_class,
'args': args,
'kwargs': kwargs
}
@api_method
def record(self, **kwargs):
"""
Track and record local variable (i.e. attributes) each day.
"""
for name, value in kwargs.items():
self._recorded_vars[name] = value
@api_method
def order(self, sid, amount, limit_price=None, stop_price=None):
return self.blotter.order(sid, amount, limit_price, stop_price)
@api_method
def order_value(self, sid, value, limit_price=None, stop_price=None):
"""
Place an order by desired value rather than desired number of shares.
If the requested sid is found in the universe, the requested value is
divided by its price to imply the number of shares to transact.
value > 0 :: Buy/Cover
value < 0 :: Sell/Short
Market order: order(sid, value)
Limit order: order(sid, value, limit_price)
Stop order: order(sid, value, None, stop_price)
StopLimit order: order(sid, value, limit_price, stop_price)
"""
last_price = self.trading_client.current_data[sid].price
if np.allclose(last_price, 0):
zero_message = "Price of 0 for {psid}; can't infer value".format(
psid=sid)
self.logger.debug(zero_message)
# Don't place any order
return
else:
amount = value / last_price
return self.order(sid, amount, limit_price, stop_price)
@property
def recorded_vars(self):
return copy(self._recorded_vars)
@property
def portfolio(self):
# internally this will cause a refresh of the
# period performance calculations.
return self.perf_tracker.get_portfolio()
def updated_portfolio(self):
# internally this will cause a refresh of the
# period performance calculations.
if self.portfolio_needs_update:
self._portfolio = self.perf_tracker.get_portfolio()
self.portfolio_needs_update = False
return self._portfolio
def set_logger(self, logger):
self.logger = logger
def set_datetime(self, dt):
assert isinstance(dt, datetime), \
"Attempt to set algorithm's current time with non-datetime"
assert dt.tzinfo == pytz.utc, \
"Algorithm expects a utc datetime"
self.datetime = dt
@api_method
def get_datetime(self):
"""
Returns a copy of the datetime.
"""
date_copy = copy(self.datetime)
assert date_copy.tzinfo == pytz.utc, \
"Algorithm should have a utc datetime"
return date_copy
def set_transact(self, transact):
"""
Set the method that will be called to create a
transaction from open orders and trade events.
"""
self.blotter.transact = transact
@api_method
def set_slippage(self, slippage):
if not isinstance(slippage, SlippageModel):
raise UnsupportedSlippageModel()
if self.initialized:
raise OverrideSlippagePostInit()
self.slippage = slippage
@api_method
def set_commission(self, commission):
if not isinstance(commission, (PerShare, PerTrade, PerDollar)):
raise UnsupportedCommissionModel()
if self.initialized:
raise OverrideCommissionPostInit()
self.commission = commission
def set_sources(self, sources):
assert isinstance(sources, list)
self.sources = sources
def set_transforms(self, transforms):
assert isinstance(transforms, list)
self.transforms = transforms
def set_data_frequency(self, data_frequency):
assert data_frequency in ('daily', 'minute')
self.data_frequency = data_frequency
self.annualizer = ANNUALIZER[self.data_frequency]
@api_method
def order_percent(self, sid, percent, limit_price=None, stop_price=None):
"""
Place an order in the specified security corresponding to the given
percent of the current portfolio value.
Note that percent must expressed as a decimal (0.50 means 50\%).
"""
value = self.portfolio.portfolio_value * percent
return self.order_value(sid, value, limit_price, stop_price)
@api_method
def order_target(self, sid, target, limit_price=None, stop_price=None):
"""
Place an order to adjust a position to a target number of shares. If
the position doesn't already exist, this is equivalent to placing a new
order. If the position does exist, this is equivalent to placing an
order for the difference between the target number of shares and the
current number of shares.
"""
if sid in self.portfolio.positions:
current_position = self.portfolio.positions[sid].amount
req_shares = target - current_position
return self.order(sid, req_shares, limit_price, stop_price)
else:
return self.order(sid, target, limit_price, stop_price)
@api_method
def order_target_value(self,
sid,
target,
limit_price=None,
stop_price=None):
"""
Place an order to adjust a position to a target value. If
the position doesn't already exist, this is equivalent to placing a new
order. If the position does exist, this is equivalent to placing an
order for the difference between the target value and the
current value.
"""
if sid in self.portfolio.positions:
current_position = self.portfolio.positions[sid].amount
current_price = self.portfolio.positions[sid].last_sale_price
current_value = current_position * current_price
req_value = target - current_value
return self.order_value(sid, req_value, limit_price, stop_price)
else:
return self.order_value(sid, target, limit_price, stop_price)
@api_method
def order_target_percent(self,
sid,
target,
limit_price=None,
stop_price=None):
"""
Place an order to adjust a position to a target percent of the
current portfolio value. If the position doesn't already exist, this is
equivalent to placing a new order. If the position does exist, this is
equivalent to placing an order for the difference between the target
percent and the current percent.
Note that target must expressed as a decimal (0.50 means 50\%).
"""
if sid in self.portfolio.positions:
current_position = self.portfolio.positions[sid].amount
current_price = self.portfolio.positions[sid].last_sale_price
current_value = current_position * current_price
else:
current_value = 0
target_value = self.portfolio.portfolio_value * target
req_value = target_value - current_value
return self.order_value(sid, req_value, limit_price, stop_price)
@api_method
def get_open_orders(self, sid=None):
if sid is None:
return {
key: [order.to_api_obj() for order in orders]
for key, orders in self.blotter.open_orders.iteritems()
}
if sid in self.blotter.open_orders:
orders = self.blotter.open_orders[sid]
return [order.to_api_obj() for order in orders]
return []
@api_method
def get_order(self, order_id):
if order_id in self.blotter.orders:
return self.blotter.orders[order_id].to_api_obj()
@api_method
def cancel_order(self, order_param):
order_id = order_param
if isinstance(order_param, zipline.protocol.Order):
order_id = order_param.id
self.blotter.cancel(order_id)
def raw_positions(self):
"""
Returns the current portfolio for the algorithm.
N.B. this is not done as a property, so that the function can be
passed and called from within a source.
"""
# Return the 'internal' positions object, as in the one that is
# not passed to the algo, and thus should not have tainted keys.
return self.perf_tracker.cumulative_performance.positions
def raw_orders(self):
"""
Returns the current open orders from the blotter.
N.B. this is not a property, so that the function can be passed
and called back from within a source.
"""
return self.blotter.open_orders
class TradingAlgorithm(object):
"""
Base class for trading algorithms. Inherit and overload
initialize() and handle_data(data).
A new algorithm could look like this:
```
from zipline.api import order
def initialize(context):
context.sid = 'AAPL'
context.amount = 100
def handle_data(self, data):
sid = context.sid
amount = context.amount
order(sid, amount)
```
To then to run this algorithm pass these functions to
TradingAlgorithm:
my_algo = TradingAlgorithm(initialize, handle_data)
stats = my_algo.run(data)
"""
# If this is set to false then it is the responsibility
# of the overriding subclass to set initialized = true
AUTO_INITIALIZE = True
def __init__(self, *args, **kwargs):
"""Initialize sids and other state variables.
:Arguments:
:Optional:
initialize : function
Function that is called with a single
argument at the begninning of the simulation.
handle_data : function
Function that is called with 2 arguments
(context and data) on every bar.
script : str
Algoscript that contains initialize and
handle_data function definition.
data_frequency : str (daily, hourly or minutely)
The duration of the bars.
capital_base : float <default: 1.0e5>
How much capital to start with.
instant_fill : bool <default: False>
Whether to fill orders immediately or on next bar.
"""
self.datetime = None
self.registered_transforms = {}
self.transforms = []
self.sources = []
# List of trading controls to be used to validate orders.
self.trading_controls = []
self._recorded_vars = {}
self.namespace = kwargs.get('namespace', {})
self.logger = None
self.benchmark_return_source = None
# default components for transact
self.slippage = VolumeShareSlippage()
self.commission = PerShare()
self.instant_fill = kwargs.pop('instant_fill', False)
# set the capital base
self.capital_base = kwargs.pop('capital_base', DEFAULT_CAPITAL_BASE)
self.sim_params = kwargs.pop('sim_params', None)
if self.sim_params is None:
self.sim_params = create_simulation_parameters(
capital_base=self.capital_base
)
self.perf_tracker = PerformanceTracker(self.sim_params)
self.blotter = kwargs.pop('blotter', None)
if not self.blotter:
self.blotter = Blotter()
self.portfolio_needs_update = True
self._portfolio = None
self.history_container = None
self.history_specs = {}
# If string is passed in, execute and get reference to
# functions.
self.algoscript = kwargs.pop('script', None)
self._initialize = None
self._analyze = None
if self.algoscript is not None:
exec_(self.algoscript, self.namespace)
self._initialize = self.namespace.get('initialize', None)
if 'handle_data' not in self.namespace:
raise ValueError('You must define a handle_data function.')
else:
self._handle_data = self.namespace['handle_data']
# Optional analyze function, gets called after run
self._analyze = self.namespace.get('analyze', None)
elif kwargs.get('initialize', False) and kwargs.get('handle_data'):
if self.algoscript is not None:
raise ValueError('You can not set script and \
initialize/handle_data.')
self._initialize = kwargs.pop('initialize')
self._handle_data = kwargs.pop('handle_data')
# If method not defined, NOOP
if self._initialize is None:
self._initialize = lambda x: None
# Alternative way of setting data_frequency for backwards
# compatibility.
if 'data_frequency' in kwargs:
self.data_frequency = kwargs.pop('data_frequency')
# Subclasses that override initialize should only worry about
# setting self.initialized = True if AUTO_INITIALIZE is
# is manually set to False.
self.initialized = False
self.initialize(*args, **kwargs)
if self.AUTO_INITIALIZE:
self.initialized = True
def initialize(self, *args, **kwargs):
"""
Call self._initialize with `self` made available to Zipline API
functions.
"""
with ZiplineAPI(self):
self._initialize(self)
def handle_data(self, data):
if self.history_container:
self.history_container.update(data, self.datetime)
self._handle_data(self, data)
def analyze(self, perf):
if self._analyze is None:
return
with ZiplineAPI(self):
self._analyze(self, perf)
def __repr__(self):
"""
N.B. this does not yet represent a string that can be used
to instantiate an exact copy of an algorithm.
However, it is getting close, and provides some value as something
that can be inspected interactively.
"""
return """
{class_name}(
capital_base={capital_base}
sim_params={sim_params},
initialized={initialized},
slippage={slippage},
commission={commission},
blotter={blotter},
recorded_vars={recorded_vars})
""".strip().format(class_name=self.__class__.__name__,
capital_base=self.capital_base,
sim_params=repr(self.sim_params),
initialized=self.initialized,
slippage=repr(self.slippage),
commission=repr(self.commission),
blotter=repr(self.blotter),
recorded_vars=repr(self.recorded_vars))
def _create_data_generator(self, source_filter, sim_params=None):
"""
Create a merged data generator using the sources and
transforms attached to this algorithm.
::source_filter:: is a method that receives events in date
sorted order, and returns True for those events that should be
processed by the zipline, and False for those that should be
skipped.
"""
if sim_params is None:
sim_params = self.sim_params
if self.benchmark_return_source is None:
env = trading.environment
if (sim_params.data_frequency == 'minute'
or sim_params.emission_rate == 'minute'):
update_time = lambda date: env.get_open_and_close(date)[1]
else:
update_time = lambda date: date
benchmark_return_source = [
Event({'dt': update_time(dt),
'returns': ret,
'type': zipline.protocol.DATASOURCE_TYPE.BENCHMARK,
'source_id': 'benchmarks'})
for dt, ret in trading.environment.benchmark_returns.iterkv()
if dt.date() >= sim_params.period_start.date()
and dt.date() <= sim_params.period_end.date()
]
else:
benchmark_return_source = self.benchmark_return_source
date_sorted = date_sorted_sources(*self.sources)
if source_filter:
date_sorted = filter(source_filter, date_sorted)
with_tnfms = sequential_transforms(date_sorted,
*self.transforms)
with_benchmarks = date_sorted_sources(benchmark_return_source,
with_tnfms)
# Group together events with the same dt field. This depends on the
# events already being sorted.
return groupby(with_benchmarks, attrgetter('dt'))
def _create_generator(self, sim_params, source_filter=None):
"""
Create a basic generator setup using the sources and
transforms attached to this algorithm.
::source_filter:: is a method that receives events in date
sorted order, and returns True for those events that should be
processed by the zipline, and False for those that should be
skipped.
"""
# Instantiate perf_tracker
self.perf_tracker = PerformanceTracker(sim_params)
self.portfolio_needs_update = True
self.data_gen = self._create_data_generator(source_filter, sim_params)
self.trading_client = AlgorithmSimulator(self, sim_params)
transact_method = transact_partial(self.slippage, self.commission)
self.set_transact(transact_method)
return self.trading_client.transform(self.data_gen)
def get_generator(self):
"""
Override this method to add new logic to the construction
of the generator. Overrides can use the _create_generator
method to get a standard construction generator.
"""
return self._create_generator(self.sim_params)
# TODO: make a new subclass, e.g. BatchAlgorithm, and move
# the run method to the subclass, and refactor to put the
# generator creation logic into get_generator.
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 _create_daily_stats(self, perfs):
# create daily and cumulative stats dataframe
daily_perfs = []
# TODO: the loop here could overwrite expected properties
# of daily_perf. Could potentially raise or log a
# warning.
for perf in perfs:
if 'daily_perf' in perf:
perf['daily_perf'].update(
perf['daily_perf'].pop('recorded_vars')
)
daily_perfs.append(perf['daily_perf'])
else:
self.risk_report = perf
daily_dts = [np.datetime64(perf['period_close'], utc=True)
for perf in daily_perfs]
daily_stats = pd.DataFrame(daily_perfs, index=daily_dts)
return daily_stats
def add_transform(self, transform_class, tag, *args, **kwargs):
"""Add a single-sid, sequential transform to the model.
:Arguments:
transform_class : class
Which transform to use. E.g. mavg.
tag : str
How to name the transform. Can later be access via:
data[sid].tag()
Extra args and kwargs will be forwarded to the transform
instantiation.
"""
self.registered_transforms[tag] = {'class': transform_class,
'args': args,
'kwargs': kwargs}
@api_method
def record(self, *args, **kwargs):
"""
Track and record local variable (i.e. attributes) each day.
"""
# Make 2 objects both referencing the same iterator
args = [iter(args)] * 2
# Zip generates list entries by calling `next` on each iterator it
# receives. In this case the two iterators are the same object, so the
# call to next on args[0] will also advance args[1], resulting in zip
# returning (a,b) (c,d) (e,f) rather than (a,a) (b,b) (c,c) etc.
positionals = zip(*args)
for name, value in chain(positionals, iteritems(kwargs)):
self._recorded_vars[name] = value
@api_method
def symbol(self, symbol_str, as_of_date=None):
"""
Default symbol lookup for any source that directly maps the
symbol to the identifier (e.g. yahoo finance).
Keyword argument as_of_date is ignored.
"""
return symbol_str
@api_method
def order(self, sid, amount,
limit_price=None,
stop_price=None,
style=None):
"""
Place an order using the specified parameters.
"""
def round_if_near_integer(a, epsilon=1e-4):
"""
Round a to the nearest integer if that integer is within an epsilon
of a.
"""
if abs(a - round(a)) <= epsilon:
return round(a)
else:
return a
# Truncate to the integer share count that's either within .0001 of
# amount or closer to zero.
# E.g. 3.9999 -> 4.0; 5.5 -> 5.0; -5.5 -> -5.0
amount = int(round_if_near_integer(amount))
# Raises a ZiplineError if invalid parameters are detected.
self.validate_order_params(sid,
amount,
limit_price,
stop_price,
style)
# Convert deprecated limit_price and stop_price parameters to use
# ExecutionStyle objects.
style = self.__convert_order_params_for_blotter(limit_price,
stop_price,
style)
return self.blotter.order(sid, amount, style)
def validate_order_params(self,
sid,
amount,
limit_price,
stop_price,
style):
"""
Helper method for validating parameters to the order API function.
Raises an UnsupportedOrderParameters if invalid arguments are found.
"""
if not self.initialized:
raise OrderDuringInitialize(
msg="order() can only be called from within handle_data()"
)
if style:
if limit_price:
raise UnsupportedOrderParameters(
msg="Passing both limit_price and style is not supported."
)
if stop_price:
raise UnsupportedOrderParameters(
msg="Passing both stop_price and style is not supported."
)
for control in self.trading_controls:
control.validate(sid,
amount,
self.updated_portfolio(),
self.get_datetime(),
self.trading_client.current_data)
@staticmethod
def __convert_order_params_for_blotter(limit_price, stop_price, style):
"""
Helper method for converting deprecated limit_price and stop_price
arguments into ExecutionStyle instances.
This function assumes that either style == None or (limit_price,
stop_price) == (None, None).
"""
# TODO_SS: DeprecationWarning for usage of limit_price and stop_price.
if style:
assert (limit_price, stop_price) == (None, None)
return style
if limit_price and stop_price:
return StopLimitOrder(limit_price, stop_price)
if limit_price:
return LimitOrder(limit_price)
if stop_price:
return StopOrder(stop_price)
else:
return MarketOrder()
@api_method
def order_value(self, sid, value,
limit_price=None, stop_price=None, style=None):
"""
Place an order by desired value rather than desired number of shares.
If the requested sid is found in the universe, the requested value is
divided by its price to imply the number of shares to transact.
value > 0 :: Buy/Cover
value < 0 :: Sell/Short
Market order: order(sid, value)
Limit order: order(sid, value, limit_price)
Stop order: order(sid, value, None, stop_price)
StopLimit order: order(sid, value, limit_price, stop_price)
"""
last_price = self.trading_client.current_data[sid].price
if np.allclose(last_price, 0):
zero_message = "Price of 0 for {psid}; can't infer value".format(
psid=sid
)
if self.logger:
self.logger.debug(zero_message)
# Don't place any order
return
else:
amount = value / last_price
return self.order(sid, amount,
limit_price=limit_price,
stop_price=stop_price,
style=style)
@property
def recorded_vars(self):
return copy(self._recorded_vars)
@property
def portfolio(self):
return self.updated_portfolio()
def updated_portfolio(self):
if self.portfolio_needs_update:
self._portfolio = self.perf_tracker.get_portfolio()
self.portfolio_needs_update = False
return self._portfolio
def set_logger(self, logger):
self.logger = logger
def on_dt_changed(self, dt):
"""
Callback triggered by the simulation loop whenever the current dt
changes.
Any logic that should happen exactly once at the start of each datetime
group should happen here.
"""
assert isinstance(dt, datetime), \
"Attempt to set algorithm's current time with non-datetime"
assert dt.tzinfo == pytz.utc, \
"Algorithm expects a utc datetime"
self.datetime = dt
self.perf_tracker.set_date(dt)
self.blotter.set_date(dt)
@api_method
def get_datetime(self):
"""
Returns a copy of the datetime.
"""
date_copy = copy(self.datetime)
assert date_copy.tzinfo == pytz.utc, \
"Algorithm should have a utc datetime"
return date_copy
def set_transact(self, transact):
"""
Set the method that will be called to create a
transaction from open orders and trade events.
"""
self.blotter.transact = transact
def update_dividends(self, dividend_frame):
"""
Set DataFrame used to process dividends. DataFrame columns should
contain at least the entries in zp.DIVIDEND_FIELDS.
"""
self.perf_tracker.update_dividends(dividend_frame)
@api_method
def set_slippage(self, slippage):
if not isinstance(slippage, SlippageModel):
raise UnsupportedSlippageModel()
if self.initialized:
raise OverrideSlippagePostInit()
self.slippage = slippage
@api_method
def set_commission(self, commission):
if not isinstance(commission, (PerShare, PerTrade, PerDollar)):
raise UnsupportedCommissionModel()
if self.initialized:
raise OverrideCommissionPostInit()
self.commission = commission
def set_sources(self, sources):
assert isinstance(sources, list)
self.sources = sources
def set_transforms(self, transforms):
assert isinstance(transforms, list)
self.transforms = transforms
# Remain backwards compatibility
@property
def data_frequency(self):
return self.sim_params.data_frequency
@data_frequency.setter
def data_frequency(self, value):
assert value in ('daily', 'minute')
self.sim_params.data_frequency = value
@api_method
def order_percent(self, sid, percent,
limit_price=None, stop_price=None, style=None):
"""
Place an order in the specified security corresponding to the given
percent of the current portfolio value.
Note that percent must expressed as a decimal (0.50 means 50\%).
"""
value = self.portfolio.portfolio_value * percent
return self.order_value(sid, value,
limit_price=limit_price,
stop_price=stop_price,
style=style)
@api_method
def order_target(self, sid, target,
limit_price=None, stop_price=None, style=None):
"""
Place an order to adjust a position to a target number of shares. If
the position doesn't already exist, this is equivalent to placing a new
order. If the position does exist, this is equivalent to placing an
order for the difference between the target number of shares and the
current number of shares.
"""
if sid in self.portfolio.positions:
current_position = self.portfolio.positions[sid].amount
req_shares = target - current_position
return self.order(sid, req_shares,
limit_price=limit_price,
stop_price=stop_price,
style=style)
else:
return self.order(sid, target,
limit_price=limit_price,
stop_price=stop_price,
style=style)
@api_method
def order_target_value(self, sid, target,
limit_price=None, stop_price=None, style=None):
"""
Place an order to adjust a position to a target value. If
the position doesn't already exist, this is equivalent to placing a new
order. If the position does exist, this is equivalent to placing an
order for the difference between the target value and the
current value.
"""
last_price = self.trading_client.current_data[sid].price
if np.allclose(last_price, 0):
# Don't place an order
if self.logger:
zero_message = "Price of 0 for {psid}; can't infer value"
self.logger.debug(zero_message.format(psid=sid))
return
target_amount = target / last_price
return self.order_target(sid, target_amount,
limit_price=limit_price,
stop_price=stop_price,
style=style)
@api_method
def order_target_percent(self, sid, target,
limit_price=None, stop_price=None, style=None):
"""
Place an order to adjust a position to a target percent of the
current portfolio value. If the position doesn't already exist, this is
equivalent to placing a new order. If the position does exist, this is
equivalent to placing an order for the difference between the target
percent and the current percent.
Note that target must expressed as a decimal (0.50 means 50\%).
"""
target_value = self.portfolio.portfolio_value * target
return self.order_target_value(sid, target_value,
limit_price=limit_price,
stop_price=stop_price,
style=style)
@api_method
def get_open_orders(self, sid=None):
if sid is None:
return {
key: [order.to_api_obj() for order in orders]
for key, orders in iteritems(self.blotter.open_orders)
if orders
}
if sid in self.blotter.open_orders:
orders = self.blotter.open_orders[sid]
return [order.to_api_obj() for order in orders]
return []
@api_method
def get_order(self, order_id):
if order_id in self.blotter.orders:
return self.blotter.orders[order_id].to_api_obj()
@api_method
def cancel_order(self, order_param):
order_id = order_param
if isinstance(order_param, zipline.protocol.Order):
order_id = order_param.id
self.blotter.cancel(order_id)
@api_method
def add_history(self, bar_count, frequency, field,
ffill=True):
daily_at_midnight = (self.sim_params.data_frequency == 'daily')
history_spec = HistorySpec(bar_count, frequency, field, ffill,
daily_at_midnight=daily_at_midnight)
self.history_specs[history_spec.key_str] = history_spec
@api_method
def history(self, bar_count, frequency, field, ffill=True):
spec_key_str = HistorySpec.spec_key(
bar_count, frequency, field, ffill)
history_spec = self.history_specs[spec_key_str]
return self.history_container.get_history(history_spec, self.datetime)
####################
# Trading Controls #
####################
def register_trading_control(self, control):
"""
Register a new TradingControl to be checked prior to order calls.
"""
if self.initialized:
raise RegisterTradingControlPostInit()
self.trading_controls.append(control)
@api_method
def set_max_position_size(self,
sid=None,
max_shares=None,
max_notional=None):
"""
Set a limit on the number of shares and/or dollar value held for the
given sid. Limits are treated as absolute values and are enforced at
the time that the algo attempts to place an order for sid. This means
that it's possible to end up with more than the max number of shares
due to splits/dividends, and more than the max notional due to price
improvement.
If an algorithm attempts to place an order that would result in
increasing the absolute value of shares/dollar value exceeding one of
these limits, raise a TradingControlException.
"""
control = MaxPositionSize(sid=sid,
max_shares=max_shares,
max_notional=max_notional)
self.register_trading_control(control)
@api_method
def set_max_order_size(self, sid=None, max_shares=None, max_notional=None):
"""
Set a limit on the number of shares and/or dollar value of any single
order placed for sid. Limits are treated as absolute values and are
enforced at the time that the algo attempts to place an order for sid.
If an algorithm attempts to place an order that would result in
exceeding one of these limits, raise a TradingControlException.
"""
control = MaxOrderSize(sid=sid,
max_shares=max_shares,
max_notional=max_notional)
self.register_trading_control(control)
@api_method
def set_max_order_count(self, max_count):
"""
Set a limit on the number of orders that can be placed within the given
time interval.
"""
control = MaxOrderCount(max_count)
self.register_trading_control(control)
@api_method
def set_long_only(self):
"""
Set a rule specifying that this algorithm cannot take short positions.
"""
self.register_trading_control(LongOnly())
@classmethod
def all_api_methods(cls):
"""
Return a list of all the TradingAlgorithm API methods.
"""
return [fn for fn in cls.__dict__.itervalues()
if getattr(fn, 'is_api_method', False)]
class TradingAlgorithm(object):
"""
Base class for trading algorithms. Inherit and overload
initialize() and handle_data(data).
A new algorithm could look like this:
```
from zipline.api import order
def initialize(context):
context.sid = 'AAPL'
context.amount = 100
def handle_data(self, data):
sid = context.sid
amount = context.amount
order(sid, amount)
```
To then to run this algorithm pass these functions to
TradingAlgorithm:
my_algo = TradingAlgorithm(initialize, handle_data)
stats = my_algo.run(data)
"""
def __init__(self, *args, **kwargs):
"""Initialize sids and other state variables.
:Arguments:
:Optional:
initialize : function
Function that is called with a single
argument at the begninning of the simulation.
handle_data : function
Function that is called with 2 arguments
(context and data) on every bar.
script : str
Algoscript that contains initialize and
handle_data function definition.
data_frequency : str (daily, hourly or minutely)
The duration of the bars.
annualizer : int <optional>
Which constant to use for annualizing risk metrics.
If not provided, will extract from data_frequency.
capital_base : float <default: 1.0e5>
How much capital to start with.
instant_fill : bool <default: False>
Whether to fill orders immediately or on next bar.
"""
self.datetime = None
self.registered_transforms = {}
self.transforms = []
self.sources = []
self._recorded_vars = {}
self.logger = None
self.benchmark_return_source = None
self.perf_tracker = None
# default components for transact
self.slippage = VolumeShareSlippage()
self.commission = PerShare()
if 'data_frequency' in kwargs:
self.set_data_frequency(kwargs.pop('data_frequency'))
else:
self.data_frequency = None
self.instant_fill = kwargs.pop('instant_fill', False)
# Override annualizer if set
if 'annualizer' in kwargs:
self.annualizer = kwargs['annualizer']
# set the capital base
self.capital_base = kwargs.pop('capital_base', DEFAULT_CAPITAL_BASE)
self.sim_params = kwargs.pop('sim_params', None)
if self.sim_params:
if self.data_frequency is None:
self.data_frequency = self.sim_params.data_frequency
else:
self.sim_params.data_frequency = self.data_frequency
self.perf_tracker = PerformanceTracker(self.sim_params)
self.blotter = kwargs.pop('blotter', None)
if not self.blotter:
self.blotter = Blotter()
self.portfolio_needs_update = True
self._portfolio = None
# If string is passed in, execute and get reference to
# functions.
self.algoscript = kwargs.pop('script', None)
if self.algoscript is not None:
self.ns = {}
exec_(self.algoscript, self.ns)
if 'initialize' not in self.ns:
raise ValueError('You must define an initialze function.')
if 'handle_data' not in self.ns:
raise ValueError('You must define a handle_data function.')
self._initialize = self.ns['initialize']
self._handle_data = self.ns['handle_data']
# If two functions are passed in assume initialize and
# handle_data are passed in.
elif kwargs.get('initialize', False) and kwargs.get('handle_data'):
if self.algoscript is not None:
raise ValueError('You can not set script and \
initialize/handle_data.')
self._initialize = kwargs.pop('initialize')
self._handle_data = kwargs.pop('handle_data')
# an algorithm subclass needs to set initialized to True when
# it is fully initialized.
self.initialized = False
self.initialize(*args, **kwargs)
def initialize(self, *args, **kwargs):
# store algo reference in global space
set_algo_instance(self)
try:
self._initialize(self)
finally:
set_algo_instance(None)
def handle_data(self, data):
self._handle_data(self, data)
def __repr__(self):
"""
N.B. this does not yet represent a string that can be used
to instantiate an exact copy of an algorithm.
However, it is getting close, and provides some value as something
that can be inspected interactively.
"""
return """
{class_name}(
capital_base={capital_base}
sim_params={sim_params},
initialized={initialized},
slippage={slippage},
commission={commission},
blotter={blotter},
recorded_vars={recorded_vars})
""".strip().format(class_name=self.__class__.__name__,
capital_base=self.capital_base,
sim_params=repr(self.sim_params),
initialized=self.initialized,
slippage=repr(self.slippage),
commission=repr(self.commission),
blotter=repr(self.blotter),
recorded_vars=repr(self.recorded_vars))
def _create_data_generator(self, source_filter, sim_params):
"""
Create a merged data generator using the sources and
transforms attached to this algorithm.
::source_filter:: is a method that receives events in date
sorted order, and returns True for those events that should be
processed by the zipline, and False for those that should be
skipped.
"""
if self.benchmark_return_source is None:
env = trading.environment
if (self.data_frequency == 'minute'
or sim_params.emission_rate == 'minute'):
update_time = lambda date: env.get_open_and_close(date)[1]
else:
update_time = lambda date: date
benchmark_return_source = [
Event({'dt': update_time(dt),
'returns': ret,
'type': zipline.protocol.DATASOURCE_TYPE.BENCHMARK,
'source_id': 'benchmarks'})
for dt, ret in trading.environment.benchmark_returns.iterkv()
if dt.date() >= sim_params.period_start.date()
and dt.date() <= sim_params.period_end.date()
]
else:
benchmark_return_source = self.benchmark_return_source
date_sorted = date_sorted_sources(*self.sources)
if source_filter:
date_sorted = filter(source_filter, date_sorted)
with_tnfms = sequential_transforms(date_sorted,
*self.transforms)
with_alias_dt = alias_dt(with_tnfms)
with_benchmarks = date_sorted_sources(benchmark_return_source,
with_alias_dt)
# Group together events with the same dt field. This depends on the
# events already being sorted.
return groupby(with_benchmarks, attrgetter('dt'))
def _create_generator(self, sim_params, source_filter=None):
"""
Create a basic generator setup using the sources and
transforms attached to this algorithm.
::source_filter:: is a method that receives events in date
sorted order, and returns True for those events that should be
processed by the zipline, and False for those that should be
skipped.
"""
sim_params.data_frequency = self.data_frequency
# perf_tracker will be instantiated in __init__ if a sim_params
# is passed to the constructor. If not, we instantiate here.
if self.perf_tracker is None:
self.perf_tracker = PerformanceTracker(sim_params)
self.data_gen = self._create_data_generator(source_filter,
sim_params)
self.trading_client = AlgorithmSimulator(self, sim_params)
transact_method = transact_partial(self.slippage, self.commission)
self.set_transact(transact_method)
return self.trading_client.transform(self.data_gen)
def get_generator(self):
"""
Override this method to add new logic to the construction
of the generator. Overrides can use the _create_generator
method to get a standard construction generator.
"""
return self._create_generator(self.sim_params)
# TODO: make a new subclass, e.g. BatchAlgorithm, and move
# the run method to the subclass, and refactor to put the
# generator creation logic into get_generator.
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 not sim_params:
if not self.sim_params:
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
# 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)
# store algo reference in global space
set_algo_instance(self)
try:
# 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)
finally:
# remove algo from global space
set_algo_instance(None)
return daily_stats
def _create_daily_stats(self, perfs):
# create daily and cumulative stats dataframe
daily_perfs = []
# TODO: the loop here could overwrite expected properties
# of daily_perf. Could potentially raise or log a
# warning.
for perf in perfs:
if 'daily_perf' in perf:
perf['daily_perf'].update(
perf['daily_perf'].pop('recorded_vars')
)
daily_perfs.append(perf['daily_perf'])
else:
self.risk_report = perf
daily_dts = [np.datetime64(perf['period_close'], utc=True)
for perf in daily_perfs]
daily_stats = pd.DataFrame(daily_perfs, index=daily_dts)
return daily_stats
def add_transform(self, transform_class, tag, *args, **kwargs):
"""Add a single-sid, sequential transform to the model.
:Arguments:
transform_class : class
Which transform to use. E.g. mavg.
tag : str
How to name the transform. Can later be access via:
data[sid].tag()
Extra args and kwargs will be forwarded to the transform
instantiation.
"""
self.registered_transforms[tag] = {'class': transform_class,
'args': args,
'kwargs': kwargs}
@api_method
def record(self, **kwargs):
"""
Track and record local variable (i.e. attributes) each day.
"""
for name, value in kwargs.items():
self._recorded_vars[name] = value
@api_method
def order(self, sid, amount, limit_price=None, stop_price=None):
return self.blotter.order(sid, amount, limit_price, stop_price)
@api_method
def order_value(self, sid, value, limit_price=None, stop_price=None):
"""
Place an order by desired value rather than desired number of shares.
If the requested sid is found in the universe, the requested value is
divided by its price to imply the number of shares to transact.
value > 0 :: Buy/Cover
value < 0 :: Sell/Short
Market order: order(sid, value)
Limit order: order(sid, value, limit_price)
Stop order: order(sid, value, None, stop_price)
StopLimit order: order(sid, value, limit_price, stop_price)
"""
last_price = self.trading_client.current_data[sid].price
if np.allclose(last_price, 0):
zero_message = "Price of 0 for {psid}; can't infer value".format(
psid=sid
)
self.logger.debug(zero_message)
# Don't place any order
return
else:
amount = value / last_price
return self.order(sid, amount, limit_price, stop_price)
@property
def recorded_vars(self):
return copy(self._recorded_vars)
@property
def portfolio(self):
# internally this will cause a refresh of the
# period performance calculations.
return self.perf_tracker.get_portfolio()
def updated_portfolio(self):
# internally this will cause a refresh of the
# period performance calculations.
if self.portfolio_needs_update:
self._portfolio = self.perf_tracker.get_portfolio()
self.portfolio_needs_update = False
return self._portfolio
def set_logger(self, logger):
self.logger = logger
def set_datetime(self, dt):
assert isinstance(dt, datetime), \
"Attempt to set algorithm's current time with non-datetime"
assert dt.tzinfo == pytz.utc, \
"Algorithm expects a utc datetime"
self.datetime = dt
@api_method
def get_datetime(self):
"""
Returns a copy of the datetime.
"""
date_copy = copy(self.datetime)
assert date_copy.tzinfo == pytz.utc, \
"Algorithm should have a utc datetime"
return date_copy
def set_transact(self, transact):
"""
Set the method that will be called to create a
transaction from open orders and trade events.
"""
self.blotter.transact = transact
@api_method
def set_slippage(self, slippage):
if not isinstance(slippage, SlippageModel):
raise UnsupportedSlippageModel()
if self.initialized:
raise OverrideSlippagePostInit()
self.slippage = slippage
@api_method
def set_commission(self, commission):
if not isinstance(commission, (PerShare, PerTrade, PerDollar)):
raise UnsupportedCommissionModel()
if self.initialized:
raise OverrideCommissionPostInit()
self.commission = commission
def set_sources(self, sources):
assert isinstance(sources, list)
self.sources = sources
def set_transforms(self, transforms):
assert isinstance(transforms, list)
self.transforms = transforms
def set_data_frequency(self, data_frequency):
assert data_frequency in ('daily', 'minute')
self.data_frequency = data_frequency
self.annualizer = ANNUALIZER[self.data_frequency]
@api_method
def order_percent(self, sid, percent, limit_price=None, stop_price=None):
"""
Place an order in the specified security corresponding to the given
percent of the current portfolio value.
Note that percent must expressed as a decimal (0.50 means 50\%).
"""
value = self.portfolio.portfolio_value * percent
return self.order_value(sid, value, limit_price, stop_price)
@api_method
def order_target(self, sid, target, limit_price=None, stop_price=None):
"""
Place an order to adjust a position to a target number of shares. If
the position doesn't already exist, this is equivalent to placing a new
order. If the position does exist, this is equivalent to placing an
order for the difference between the target number of shares and the
current number of shares.
"""
if sid in self.portfolio.positions:
current_position = self.portfolio.positions[sid].amount
req_shares = target - current_position
return self.order(sid, req_shares, limit_price, stop_price)
else:
return self.order(sid, target, limit_price, stop_price)
@api_method
def order_target_value(self, sid, target, limit_price=None,
stop_price=None):
"""
Place an order to adjust a position to a target value. If
the position doesn't already exist, this is equivalent to placing a new
order. If the position does exist, this is equivalent to placing an
order for the difference between the target value and the
current value.
"""
if sid in self.portfolio.positions:
current_position = self.portfolio.positions[sid].amount
current_price = self.portfolio.positions[sid].last_sale_price
current_value = current_position * current_price
req_value = target - current_value
return self.order_value(sid, req_value, limit_price, stop_price)
else:
return self.order_value(sid, target, limit_price, stop_price)
@api_method
def order_target_percent(self, sid, target, limit_price=None,
stop_price=None):
"""
Place an order to adjust a position to a target percent of the
current portfolio value. If the position doesn't already exist, this is
equivalent to placing a new order. If the position does exist, this is
equivalent to placing an order for the difference between the target
percent and the current percent.
Note that target must expressed as a decimal (0.50 means 50\%).
"""
if sid in self.portfolio.positions:
current_position = self.portfolio.positions[sid].amount
current_price = self.portfolio.positions[sid].last_sale_price
current_value = current_position * current_price
else:
current_value = 0
target_value = self.portfolio.portfolio_value * target
req_value = target_value - current_value
return self.order_value(sid, req_value, limit_price, stop_price)
@api_method
def get_open_orders(self, sid=None):
if sid is None:
return {key: [order.to_api_obj() for order in orders]
for key, orders
in self.blotter.open_orders.iteritems()}
if sid in self.blotter.open_orders:
orders = self.blotter.open_orders[sid]
return [order.to_api_obj() for order in orders]
return []
@api_method
def get_order(self, order_id):
if order_id in self.blotter.orders:
return self.blotter.orders[order_id].to_api_obj()
@api_method
def cancel_order(self, order_param):
order_id = order_param
if isinstance(order_param, zipline.protocol.Order):
order_id = order_param.id
self.blotter.cancel(order_id)
def raw_positions(self):
"""
Returns the current portfolio for the algorithm.
N.B. this is not done as a property, so that the function can be
passed and called from within a source.
"""
# Return the 'internal' positions object, as in the one that is
# not passed to the algo, and thus should not have tainted keys.
return self.perf_tracker.cumulative_performance.positions
def raw_orders(self):
"""
Returns the current open orders from the blotter.
N.B. this is not a property, so that the function can be passed
and called back from within a source.
"""
return self.blotter.open_orders
class TradingAlgorithm(object):
"""
Base class for trading algorithms. Inherit and overload
initialize() and handle_data(data).
A new algorithm could look like this:
```
class MyAlgo(TradingAlgorithm):
def initialize(self, sids, amount):
self.sids = sids
self.amount = amount
def handle_data(self, data):
sid = self.sids[0]
amount = self.amount
self.order(sid, amount)
```
To then to run this algorithm:
my_algo = MyAlgo([0], 100) # first argument has to be list of sids
stats = my_algo.run(data)
"""
def __init__(self, *args, **kwargs):
"""Initialize sids and other state variables.
:Arguments:
data_frequency : str (daily, hourly or minutely)
The duration of the bars.
annualizer : int <optional>
Which constant to use for annualizing risk metrics.
If not provided, will extract from data_frequency.
fill_delay : datetime.timedelta
Delay between placing an order and filling an order.
capital_base : float <default: 1.0e5>
How much capital to start with.
"""
self._portfolio = None
self.datetime = None
self.registered_transforms = {}
self.transforms = []
self.sources = []
self._recorded_vars = {}
self.logger = None
self.benchmark_return_source = None
# default components for transact
self.slippage = VolumeShareSlippage()
self.commission = PerShare()
self.set_data_frequency(kwargs.pop('data_frequency', 'daily'))
# Override annualizer if set
if 'annualizer' in kwargs:
self.annualizer = kwargs['annualizer']
if 'fill_delay' in kwargs:
self.fill_delay = kwargs['fill_delay']
# set the capital base
self.capital_base = kwargs.pop('capital_base', DEFAULT_CAPITAL_BASE)
self.sim_params = kwargs.pop('sim_params', None)
if self.sim_params:
self.sim_params.data_frequency = self.data_frequency
self.blotter = kwargs.pop('blotter', None)
if not self.blotter:
self.blotter = Blotter(fill_delay=self.fill_delay)
# an algorithm subclass needs to set initialized to True when
# it is fully initialized.
self.initialized = False
# call to user-defined constructor method
self.initialize(*args, **kwargs)
def __repr__(self):
"""
N.B. this does not yet represent a string that can be used
to instantiate an exact copy of an algorithm.
However, it is getting close, and provides some value as something
that can be inspected interactively.
"""
return """
{class_name}(
captial_base={capital_base}
sim_params={sim_params},
initialized={initialized},
slippage={slippage},
commission={commission},
blotter={blotter},
recorded_vars={recorded_vars})
""".strip().format(class_name=self.__class__.__name__,
capital_base=self.capital_base,
sim_params=repr(self.sim_params),
initialized=self.initialized,
slippage=repr(self.slippage),
commission=repr(self.commission),
blotter=repr(self.blotter),
recorded_vars=repr(self.recorded_vars))
def _create_data_generator(self, source_filter, sim_params):
"""
Create a merged data generator using the sources and
transforms attached to this algorithm.
::source_filter:: is a method that receives events in date
sorted order, and returns True for those events that should be
processed by the zipline, and False for those that should be
skipped.
"""
if self.benchmark_return_source is None:
benchmark_return_source = [
Event({'dt': ret.date,
'returns': ret.returns,
'type': zipline.protocol.DATASOURCE_TYPE.BENCHMARK,
'source_id': 'benchmarks'})
for ret in trading.environment.benchmark_returns
if ret.date.date() >= sim_params.period_start.date()
and ret.date.date() <= sim_params.period_end.date()
]
else:
benchmark_return_source = self.benchmark_return_source
date_sorted = date_sorted_sources(*self.sources)
if source_filter:
date_sorted = ifilter(source_filter, date_sorted)
with_tnfms = sequential_transforms(date_sorted,
*self.transforms)
with_alias_dt = alias_dt(with_tnfms)
with_benchmarks = date_sorted_sources(benchmark_return_source,
with_alias_dt)
# Group together events with the same dt field. This depends on the
# events already being sorted.
return groupby(with_benchmarks, attrgetter('dt'))
def _create_generator(self, sim_params, source_filter=None):
"""
Create a basic generator setup using the sources and
transforms attached to this algorithm.
::source_filter:: is a method that receives events in date
sorted order, and returns True for those events that should be
processed by the zipline, and False for those that should be
skipped.
"""
sim_params.data_frequency = self.data_frequency
self.data_gen = self._create_data_generator(source_filter,
sim_params)
self.perf_tracker = PerformanceTracker(sim_params)
self.trading_client = AlgorithmSimulator(self, sim_params)
transact_method = transact_partial(self.slippage, self.commission)
self.set_transact(transact_method)
return self.trading_client.transform(self.data_gen)
def get_generator(self):
"""
Override this method to add new logic to the construction
of the generator. Overrides can use the _create_generator
method to get a standard construction generator.
"""
return self._create_generator(self.sim_params)
def initialize(self, *args, **kwargs):
pass
# TODO: make a new subclass, e.g. BatchAlgorithm, and move
# the run method to the subclass, and refactor to put the
# generator creation logic into get_generator.
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 not sim_params:
if not self.sim_params:
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
# Create transforms by wrapping them into StatefulTransforms
self.transforms = []
for namestring, trans_descr in self.registered_transforms.iteritems():
sf = StatefulTransform(
trans_descr['class'],
*trans_descr['args'],
**trans_descr['kwargs']
)
sf.namestring = namestring
self.transforms.append(sf)
# create transforms and zipline
self.gen = self._create_generator(sim_params)
# 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)
return daily_stats
def _create_daily_stats(self, perfs):
# create daily and cumulative stats dataframe
daily_perfs = []
cum_perfs = []
# TODO: the loop here could overwrite expected properties
# of daily_perf. Could potentially raise or log a
# warning.
for perf in perfs:
if 'daily_perf' in perf:
perf['daily_perf'].update(
perf['daily_perf'].pop('recorded_vars')
)
daily_perfs.append(perf['daily_perf'])
else:
cum_perfs.append(perf)
daily_dts = [np.datetime64(perf['period_close'], utc=True)
for perf in daily_perfs]
daily_stats = pd.DataFrame(daily_perfs, index=daily_dts)
return daily_stats
def add_transform(self, transform_class, tag, *args, **kwargs):
"""Add a single-sid, sequential transform to the model.
:Arguments:
transform_class : class
Which transform to use. E.g. mavg.
tag : str
How to name the transform. Can later be access via:
data[sid].tag()
Extra args and kwargs will be forwarded to the transform
instantiation.
"""
self.registered_transforms[tag] = {'class': transform_class,
'args': args,
'kwargs': kwargs}
def record(self, **kwargs):
"""
Track and record local variable (i.e. attributes) each day.
"""
for name, value in kwargs.items():
self._recorded_vars[name] = value
def order(self, sid, amount, limit_price=None, stop_price=None):
return self.blotter.order(sid, amount, limit_price, stop_price)
@property
def recorded_vars(self):
return copy(self._recorded_vars)
@property
def portfolio(self):
return self._portfolio
def set_portfolio(self, portfolio):
self._portfolio = portfolio
def set_logger(self, logger):
self.logger = logger
def set_datetime(self, dt):
assert isinstance(dt, datetime), \
"Attempt to set algorithm's current time with non-datetime"
assert dt.tzinfo == pytz.utc, \
"Algorithm expects a utc datetime"
self.datetime = dt
def get_datetime(self):
"""
Returns a copy of the datetime.
"""
date_copy = copy(self.datetime)
assert date_copy.tzinfo == pytz.utc, \
"Algorithm should have a utc datetime"
return date_copy
def set_transact(self, transact):
"""
Set the method that will be called to create a
transaction from open orders and trade events.
"""
self.blotter.transact = transact
def set_slippage(self, slippage):
if not isinstance(slippage, SlippageModel):
raise UnsupportedSlippageModel()
if self.initialized:
raise OverrideSlippagePostInit()
self.slippage = slippage
def set_commission(self, commission):
if not isinstance(commission, (PerShare, PerTrade)):
raise UnsupportedCommissionModel()
if self.initialized:
raise OverrideCommissionPostInit()
self.commission = commission
def set_sources(self, sources):
assert isinstance(sources, list)
self.sources = sources
def set_transforms(self, transforms):
assert isinstance(transforms, list)
self.transforms = transforms
def set_data_frequency(self, data_frequency):
assert data_frequency in ('daily', 'minute')
self.data_frequency = data_frequency
self.annualizer = ANNUALIZER[self.data_frequency]
self.fill_delay = FILL_DELAYS[self.data_frequency]
class ZMQAlgorithm(TradingAlgorithm):
def _create_data_generator(self, source_filter, sim_params=None):
'''
Allow a client to supply prices at the command line to control the
algrithms dataset.
Returns a tuple (timestamp, generator of prices)
'''
# This is overriden from the base TradingAlgorithm
# What else happens in this method?
# What's the point of the benchmark?
return self.zmq_event_gen()
def _create_generator(self, sim_params, source_filter=None):
"""
Create a basic generator setup using the sources to this algorithm.
::source_filter:: is a method that receives events in date
sorted order, and returns True for those events that should be
processed by the zipline, and False for those that should be
skipped.
"""
if not self.initialized:
self.initialize(*self.initialize_args, **self.initialize_kwargs)
self.initialized = True
if self.perf_tracker is None:
# HACK: When running with the `run` method, we set perf_tracker to
# None so that it will be overwritten here.
#self.perf_tracker = CustomPerfTracker(
self.perf_tracker = PerformanceTracker(
sim_params=sim_params, env=self.trading_environment
)
self.portfolio_needs_update = True
self.account_needs_update = True
self.performance_needs_update = True
self.data_gen = self._create_data_generator(source_filter, sim_params)
# Zipline uses a lot of composition object oriented design.
# How does composition help keep the system flexible and allow for object to be
# substituted at run-time.
# Zipline is a backtester that runs on historical data but it's also
# the engine that drives Quantopion's live trading.
# What objects might be modified to allow for live trading and greater functionality?
# What Are the other key objects? (hint, look at the blotter)
self.trading_client = AlgorithmSimulator(self, sim_params)
transact_method = transact_partial(self.slippage, self.commission)
self.set_transact(transact_method)
# The transform method does the heavy lifting of the main zipline event loop
return self.trading_client.transform(self.data_gen)
def zmq_event_gen(self, port=5555):
context = zmq.Context()
price_socket = context.socket(zmq.REQ)
price_socket.connect('tcp://localhost:%s' % port)
orders_socket = context.socket(zmq.PUB)
orders_socket.bind('tcp://*:%s' % (port+1))
for dt in self.sim_params.trading_days:
prices = []
# Investigate the asset_finder class. How might data be stored?
for sid_id in self.trading_environment.asset_finder.sids:
prompt = "{} [{}]".format(dt, sid(sid_id))
price_socket.send_string(prompt)
data = price_socket.recv_string()
price = float(data)
# Look at DataFrameSource to see that the dataframe input
# quickly gets turned into a series of events (that are yielded)
event = {
'dt': dt,
'sid': sid(sid_id),
'price': float(price),
'volume': 1e9,
'type': DATASOURCE_TYPE.TRADE,
}
event = Event(event)
prices.append(event)
# We return a generator. Zipline makes heavy use of `yield` and generators
# to build an event-driven model that runs syncronously].
# How could we modify an algoithm to run async?
yield dt, prices
# What columns are being displayed?
# Why might some orders have a commission and others are NAN?
# 'orders' is a collection of all orders placed.
# How could we change this to include open_orders?
show_orders(self.blotter.orders, orders_socket)
