wassname/catalyst
1
0
Fork 0
mirror of https://github.com/wassname/catalyst.git synced 2026-06-28 04:53:20 +08:00
Code Issues Packages Projects Releases Wiki Activity
Files
fb85e4a2bd4347db69bd46ffb76eb3d92de9666c
catalyst/zipline/algorithm.py
T
dmichalowicz dd78bfa4e8 Change to a 10.5 hour futures calendar
2017-02-06 11:41:29 -05:00

2510 lines
88 KiB
Python
Raw Blame History

#
# Copyright 2015 Quantopian, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from collections import Iterable
try:
# optional cython based OrderedDict
from cyordereddict import OrderedDict
except ImportError:
from collections import OrderedDict
from copy import copy
import operator as op
import warnings
from datetime import tzinfo, time
import logbook
import pytz
import pandas as pd
from contextlib2 import ExitStack
from pandas.tseries.tools import normalize_date
import numpy as np
from itertools import chain, repeat
from numbers import Integral
from six import (
exec_,
iteritems,
itervalues,
string_types,
viewkeys,
viewvalues,
)
from zipline._protocol import handle_non_market_minutes
from zipline.assets.synthetic import make_simple_equity_info
from zipline.data.data_portal import DataPortal
from zipline.data.us_equity_pricing import PanelBarReader
from zipline.errors import (
AttachPipelineAfterInitialize,
HistoryInInitialize,
NoSuchPipeline,
OrderDuringInitialize,
PipelineOutputDuringInitialize,
RegisterAccountControlPostInit,
RegisterTradingControlPostInit,
SetBenchmarkOutsideInitialize,
SetCommissionPostInit,
SetSlippagePostInit,
UnsupportedCommissionModel,
UnsupportedDatetimeFormat,
UnsupportedOrderParameters,
UnsupportedSlippageModel,
CannotOrderDelistedAsset,
UnsupportedCancelPolicy,
SetCancelPolicyPostInit,
OrderInBeforeTradingStart
)
from zipline.finance.trading import TradingEnvironment
from zipline.finance.blotter import Blotter
from zipline.finance.commission import PerShare, CommissionModel
from zipline.finance.controls import (
LongOnly,
MaxOrderCount,
MaxOrderSize,
MaxPositionSize,
MaxLeverage,
RestrictedListOrder
)
from zipline.finance.execution import (
LimitOrder,
MarketOrder,
StopLimitOrder,
StopOrder,
)
from zipline.finance.performance import PerformanceTracker
from zipline.finance.asset_restrictions import Restrictions
from zipline.finance.slippage import (
VolumeShareSlippage,
SlippageModel
)
from zipline.finance.cancel_policy import NeverCancel, CancelPolicy
from zipline.finance.asset_restrictions import (
NoRestrictions,
StaticRestrictions,
SecurityListRestrictions,
)
from zipline.assets import Asset, Future
from zipline.gens.tradesimulation import AlgorithmSimulator
from zipline.pipeline import Pipeline
from zipline.pipeline.engine import (
ExplodingPipelineEngine,
SimplePipelineEngine,
)
from zipline.utils.api_support import (
api_method,
require_initialized,
require_not_initialized,
ZiplineAPI,
disallowed_in_before_trading_start)
from zipline.utils.input_validation import (
coerce_string,
ensure_upper_case,
error_keywords,
expect_types,
optional,
)
from zipline.utils.calendars.trading_calendar import days_at_time
from zipline.utils.cache import CachedObject, Expired
from zipline.utils.calendars import get_calendar
from zipline.utils.compat import exc_clear
import zipline.utils.events
from zipline.utils.events import (
EventManager,
make_eventrule,
date_rules,
time_rules,
AfterOpen,
BeforeClose
)
from zipline.utils.factory import create_simulation_parameters
from zipline.utils.math_utils import (
tolerant_equals,
round_if_near_integer
)
from zipline.utils.pandas_utils import clear_dataframe_indexer_caches
from zipline.utils.preprocess import preprocess
from zipline.utils.security_list import SecurityList
import zipline.protocol
from zipline.sources.requests_csv import PandasRequestsCSV
from zipline.gens.sim_engine import MinuteSimulationClock
from zipline.sources.benchmark_source import BenchmarkSource
from zipline.zipline_warnings import ZiplineDeprecationWarning
DEFAULT_CAPITAL_BASE = 1e5
log = logbook.Logger("ZiplineLog")
class TradingAlgorithm(object):
"""A class that represents a trading strategy and parameters to execute
the strategy.
Parameters
----------
*args, **kwargs
Forwarded to ``initialize`` unless listed below.
initialize : callable[context -> None], optional
Function that is called at the start of the simulation to
setup the initial context.
handle_data : callable[(context, data) -> None], optional
Function called on every bar. This is where most logic should be
implemented.
before_trading_start : callable[(context, data) -> None], optional
Function that is called before any bars have been processed each
day.
analyze : callable[(context, DataFrame) -> None], optional
Function that is called at the end of the backtest. This is passed
the context and the performance results for the backtest.
script : str, optional
Algoscript that contains the definitions for the four algorithm
lifecycle functions and any supporting code.
namespace : dict, optional
The namespace to execute the algoscript in. By default this is an
empty namespace that will include only python built ins.
algo_filename : str, optional
The filename for the algoscript. This will be used in exception
tracebacks. default: '<string>'.
data_frequency : {'daily', 'minute'}, optional
The duration of the bars.
capital_base : float, optional
How much capital to start with. default: 1.0e5
instant_fill : bool, optional
Whether to fill orders immediately or on next bar. default: False
equities_metadata : dict or DataFrame or file-like object, optional
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`` method is provided, ``read`` must
return rows containing at least one of 'sid' or 'symbol' along
with the other metadata fields.
futures_metadata : dict or DataFrame or file-like object, optional
The same layout as ``equities_metadata`` except that it is used
for futures information.
identifiers : list, optional
Any asset identifiers that are not provided in the
equities_metadata, but will be traded by this TradingAlgorithm.
get_pipeline_loader : callable[BoundColumn -> PipelineLoader], optional
The function that maps pipeline columns to their loaders.
create_event_context : callable[BarData -> context manager], optional
A function used to create a context mananger that wraps the
execution of all events that are scheduled for a bar.
This function will be passed the data for the bar and should
return the actual context manager that will be entered.
history_container_class : type, optional
The type of history container to use. default: HistoryContainer
platform : str, optional
The platform the simulation is running on. This can be queried for
in the simulation with ``get_environment``. This allows algorithms
to conditionally execute code based on platform it is running on.
default: 'zipline'
"""
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.
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.data_portal = kwargs.pop('data_portal', None)
# 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
if 'equities_metadata' in kwargs or 'futures_metadata' in kwargs:
warnings.warn(
'passing metadata to TradingAlgorithm is deprecated; please'
' write this data into the asset db before passing it to the'
' trading environment',
DeprecationWarning,
stacklevel=1,
)
self.trading_environment.write_data(
equities=kwargs.pop('equities_metadata', None),
futures=kwargs.pop('futures_metadata', None),
)
# If a schedule has been provided, pop it. Otherwise, use NYSE.
self.trading_calendar = kwargs.pop(
'trading_calendar',
get_calendar("NYSE")
)
# 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),
trading_calendar=self.trading_calendar,
)
self.perf_tracker = None
# Pull in the environment's new AssetFinder for quick reference
self.asset_finder = self.trading_environment.asset_finder
# Initialize Pipeline API data.
self.init_engine(kwargs.pop('get_pipeline_loader', None))
self._pipelines = {}
# Create an always-expired cache so that we compute the first time data
# is requested.
self._pipeline_cache = CachedObject(None, pd.Timestamp(0, tz='UTC'))
self.blotter = kwargs.pop('blotter', None)
self.cancel_policy = kwargs.pop('cancel_policy', NeverCancel())
if not self.blotter:
self.blotter = Blotter(
data_frequency=self.data_frequency,
asset_finder=self.asset_finder,
slippage_func=VolumeShareSlippage(),
commission=PerShare(),
# Default to NeverCancel in zipline
cancel_policy=self.cancel_policy
)
# 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
# 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._in_before_trading_start = False
self.event_manager = EventManager(
create_context=kwargs.pop('create_event_context', None),
)
self._handle_data = None
def noop(*args, **kwargs):
pass
if self.algoscript is not None:
api_methods = {
'initialize',
'handle_data',
'before_trading_start',
'analyze',
}
unexpected_api_methods = viewkeys(kwargs) & api_methods
if unexpected_api_methods:
raise ValueError(
"TradingAlgorithm received a script and the following API"
" methods as functions:\n{funcs}".format(
funcs=unexpected_api_methods,
)
)
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', noop)
self._handle_data = self.namespace.get('handle_data', noop)
self._before_trading_start = self.namespace.get(
'before_trading_start',
)
# Optional analyze function, gets called after run
self._analyze = self.namespace.get('analyze')
else:
self._initialize = kwargs.pop('initialize', noop)
self._handle_data = kwargs.pop('handle_data', noop)
self._before_trading_start = kwargs.pop(
'before_trading_start',
None,
)
self._analyze = kwargs.pop('analyze', 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,
)
# Alternative way of setting data_frequency for backwards
# compatibility.
if 'data_frequency' in kwargs:
self.data_frequency = kwargs.pop('data_frequency')
# Prepare the algo for initialization
self.initialized = False
self.initialize_args = args
self.initialize_kwargs = kwargs
self.benchmark_sid = kwargs.pop('benchmark_sid', None)
# A dictionary of capital changes, keyed by timestamp, indicating the
# target/delta of the capital changes, along with values
self.capital_changes = kwargs.pop('capital_changes', {})
# A dictionary of the actual capital change deltas, keyed by timestamp
self.capital_change_deltas = {}
self.restrictions = NoRestrictions()
def init_engine(self, get_loader):
"""
Construct and store a PipelineEngine from loader.
If get_loader is None, constructs an ExplodingPipelineEngine
"""
if get_loader is not None:
self.engine = SimplePipelineEngine(
get_loader,
self.trading_calendar.all_sessions,
self.asset_finder,
)
else:
self.engine = ExplodingPipelineEngine()
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._in_before_trading_start = True
with handle_non_market_minutes(data) if \
self.data_frequency == "minute" else ExitStack():
self._before_trading_start(self, data)
self._in_before_trading_start = False
def handle_data(self, data):
if self._handle_data:
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.blotter.slippage_func),
commission=repr(self.blotter.commission),
blotter=repr(self.blotter),
recorded_vars=repr(self.recorded_vars))
def _create_clock(self):
"""
If the clock property is not set, then create one based on frequency.
"""
trading_o_and_c = self.trading_calendar.schedule.ix[
self.sim_params.sessions]
market_closes = trading_o_and_c['market_close']
minutely_emission = False
if self.sim_params.data_frequency == 'minute':
market_opens = trading_o_and_c['market_open']
minutely_emission = self.sim_params.emission_rate == "minute"
else:
# in daily mode, we want to have one bar per session, timestamped
# as the last minute of the session.
market_opens = market_closes
# The calendar's execution times are the minutes over which we actually
# want to run the clock. Typically the execution times simply adhere to
# the market open and close times. In the case of the futures calendar,
# for example, we only want to simulate over a subset of the full 24
# hour calendar, so the execution times dictate a market open time of
# 6:31am US/Eastern and a close of 5:00pm US/Eastern.
execution_opens = \
self.trading_calendar.execution_time_from_open(market_opens)
execution_closes = \
self.trading_calendar.execution_time_from_close(market_closes)
# FIXME generalize these values
before_trading_start_minutes = days_at_time(
self.sim_params.sessions,
time(8, 45),
"US/Eastern"
)
return MinuteSimulationClock(
self.sim_params.sessions,
execution_opens,
execution_closes,
before_trading_start_minutes,
minute_emission=minutely_emission,
)
def _create_benchmark_source(self):
return BenchmarkSource(
benchmark_sid=self.benchmark_sid,
env=self.trading_environment,
trading_calendar=self.trading_calendar,
sessions=self.sim_params.sessions,
data_portal=self.data_portal,
emission_rate=self.sim_params.emission_rate,
)
def _create_generator(self, sim_params):
if sim_params is not None:
self.sim_params = sim_params
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.sim_params,
trading_calendar=self.trading_calendar,
env=self.trading_environment,
)
# Set the dt initially to the period start by forcing it to change.
self.on_dt_changed(self.sim_params.start_session)
if not self.initialized:
self.initialize(*self.initialize_args, **self.initialize_kwargs)
self.initialized = True
self.trading_client = AlgorithmSimulator(
self,
sim_params,
self.data_portal,
self._create_clock(),
self._create_benchmark_source(),
self.restrictions,
universe_func=self._calculate_universe
)
return self.trading_client.transform()
def _calculate_universe(self):
# this exists to provide backwards compatibility for older,
# deprecated APIs, particularly around the iterability of
# BarData (ie, 'for sid in data`).
# our universe is all the assets passed into `run`.
return self._assets_from_source
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 run(self, data=None, overwrite_sim_params=True):
"""Run the algorithm.
:Arguments:
source : DataPortal
:Returns:
daily_stats : pandas.DataFrame
Daily performance metrics such as returns, alpha etc.
"""
self._assets_from_source = []
if isinstance(data, DataPortal):
self.data_portal = data
# define the universe as all the assets in the assetfinder
# This is not great, because multiple runs can accumulate assets
# in the assetfinder, but it's better than spending time adding
# functionality in the dataportal to report all the assets it
# knows about.
self._assets_from_source = \
self.trading_environment.asset_finder.retrieve_all(
self.trading_environment.asset_finder.sids
)
else:
if isinstance(data, pd.DataFrame):
# If a DataFrame is passed. Promote it to a Panel.
# The reader will fake volume values.
data = pd.Panel({'close': data.copy()})
data = data.swapaxes(0, 2)
if isinstance(data, pd.Panel):
# Guard against tz-naive index.
if data.major_axis.tz is None:
data.major_axis = data.major_axis.tz_localize('UTC')
# For compatibility with existing examples allow start/end
# to be inferred.
if overwrite_sim_params:
self.sim_params = self.sim_params.create_new(
self.trading_calendar.minute_to_session_label(
data.major_axis[0]
),
self.trading_calendar.minute_to_session_label(
data.major_axis[-1]
),
)
# Assume data is daily if timestamp times are
# standardized, otherwise assume minute bars.
times = data.major_axis.time
if np.all(times == times[0]):
self.sim_params.data_frequency = 'daily'
else:
self.sim_params.data_frequency = 'minute'
copy_panel = data.rename(
# These were the old names for the close/open columns. We
# need to make a copy anyway, so swap these for backwards
# compat while we're here.
minor_axis={'close_price': 'close', 'open_price': 'open'},
copy=True,
)
copy_panel.items = self._write_and_map_id_index_to_sids(
copy_panel.items, copy_panel.major_axis[0],
)
self._assets_from_source = (
self.asset_finder.retrieve_all(
copy_panel.items
)
)
if self.sim_params.data_frequency == 'daily':
equity_reader_arg = 'equity_daily_reader'
elif self.sim_params.data_frequency == 'minute':
equity_reader_arg = 'equity_minute_reader'
equity_reader = PanelBarReader(
self.trading_calendar,
copy_panel,
self.sim_params.data_frequency,
)
self.data_portal = DataPortal(
self.asset_finder,
self.trading_calendar,
first_trading_day=equity_reader.first_trading_day,
**{equity_reader_arg: equity_reader}
)
# Force a reset of the performance tracker, in case
# this is a repeat run of the algorithm.
self.perf_tracker = None
# Create zipline and loop through simulated_trading.
# Each iteration returns a perf dictionary
try:
perfs = []
for perf in self.get_generator():
perfs.append(perf)
# convert perf dict to pandas dataframe
daily_stats = self._create_daily_stats(perfs)
self.analyze(daily_stats)
finally:
self.data_portal = None
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
def is_unknown(asset_or_sid):
sid = op.index(asset_or_sid)
return self.asset_finder.retrieve_asset(
sid=sid,
default_none=True
) is None
new_assets = set()
new_sids = set()
new_symbols = set()
for identifier in identifiers:
if isinstance(identifier, Asset) and is_unknown(identifier):
new_assets.add(identifier)
elif isinstance(identifier, Integral) and is_unknown(identifier):
new_sids.add(identifier)
elif isinstance(identifier, (string_types)):
new_symbols.add(identifier)
else:
try:
new_sids.add(op.index(identifier))
except TypeError:
raise TypeError(
"Can't convert %s to an asset." % identifier
)
new_assets = tuple(new_assets)
new_sids = tuple(new_sids)
new_symbols = tuple(new_symbols)
number_of_kinds_of_new_things = (
sum((bool(new_assets), bool(new_sids), bool(new_symbols)))
)
# Nothing to insert, bail early.
if not number_of_kinds_of_new_things:
return self.asset_finder.map_identifier_index_to_sids(
identifiers, as_of_date,
)
elif number_of_kinds_of_new_things == 1:
warnings.warn(
'writing unknown identifiers into the assets db of the trading'
' environment is deprecated; please write this information'
' to the assets db before constructing the environment',
DeprecationWarning,
stacklevel=2,
)
else:
raise ValueError(
"Mixed types in DataFrame or Panel index.\n"
"Asset Count: %d, Sid Count: %d, Symbol Count: %d.\n"
"Choose one type and stick with it." % (
len(new_assets),
len(new_sids),
len(new_symbols),
)
)
def map_getattr(iterable, attr):
return [getattr(i, attr) for i in iterable]
if new_assets:
frame_to_write = pd.DataFrame(
data=dict(
symbol=map_getattr(new_assets, 'symbol'),
start_date=map_getattr(new_assets, 'start_date'),
end_date=map_getattr(new_assets, 'end_date'),
exchange=map_getattr(new_assets, 'exchange'),
),
index=map_getattr(new_assets, 'sid'),
)
elif new_sids:
frame_to_write = make_simple_equity_info(
new_sids,
start_date=self.sim_params.start_session,
end_date=self.sim_params.end_session,
symbols=map(str, new_sids),
)
elif new_symbols:
existing_sids = self.asset_finder.sids
first_sid = max(existing_sids) + 1 if existing_sids else 0
fake_sids = range(first_sid, first_sid + len(new_symbols))
frame_to_write = make_simple_equity_info(
sids=fake_sids,
start_date=as_of_date,
end_date=self.sim_params.end_session,
symbols=new_symbols,
)
else:
raise AssertionError("This should never happen.")
self.trading_environment.write_data(equities=frame_to_write)
# We need to clear out any cache misses that were stored while trying
# to do lookups. The real fix for this problem is to not construct an
# AssetFinder until we `run()` when we actually have all the data we
# need to so.
self.asset_finder._reset_caches()
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 = pd.DatetimeIndex(
[p['period_close'] for p in daily_perfs], tz='UTC'
)
daily_stats = pd.DataFrame(daily_perfs, index=daily_dts)
return daily_stats
def calculate_capital_changes(self, dt, emission_rate, is_interday,
portfolio_value_adjustment=0.0):
"""
If there is a capital change for a given dt, this means the the change
occurs before `handle_data` on the given dt. In the case of the
change being a target value, the change will be computed on the
portfolio value according to prices at the given dt
`portfolio_value_adjustment`, if specified, will be removed from the
portfolio_value of the cumulative performance when calculating deltas
from target capital changes.
"""
try:
capital_change = self.capital_changes[dt]
except KeyError:
return
if emission_rate == 'daily':
# If we are running daily emission, prices won't
# necessarily be synced at the end of every minute, and we
# need the up-to-date prices for capital change
# calculations. We want to sync the prices as of the
# last market minute, and this is okay from a data portal
# perspective as we have technically not "advanced" to the
# current dt yet.
self.perf_tracker.position_tracker.sync_last_sale_prices(
self.trading_calendar.previous_minute(
dt
),
False,
self.data_portal
)
self.perf_tracker.prepare_capital_change(is_interday)
if capital_change['type'] == 'target':
target = capital_change['value']
capital_change_amount = target - \
(self.updated_portfolio().portfolio_value -
portfolio_value_adjustment)
self.portfolio_needs_update = True
log.info('Processing capital change to target %s at %s. Capital '
'change delta is %s' % (target, dt,
capital_change_amount))
elif capital_change['type'] == 'delta':
target = None
capital_change_amount = capital_change['value']
log.info('Processing capital change of delta %s at %s'
% (capital_change_amount, dt))
else:
log.error("Capital change %s does not indicate a valid type "
"('target' or 'delta')" % capital_change)
return
self.capital_change_deltas.update({dt: capital_change_amount})
self.perf_tracker.process_capital_change(capital_change_amount,
is_interday)
yield {
'capital_change':
{'date': dt,
'type': 'cash',
'target': target,
'delta': capital_change_amount}
}
@api_method
def get_environment(self, field='platform'):
"""Query the execution environment.
Parameters
----------
field : {'platform', 'arena', 'data_frequency',
'start', 'end', 'capital_base', 'platform', '*'}
The field to query. The options have the following meanings:
arena : str
The arena from the simulation parameters. This will normally
be ``'backtest'`` but some systems may use this distinguish
live trading from backtesting.
data_frequency : {'daily', 'minute'}
data_frequency tells the algorithm if it is running with
daily data or minute data.
start : datetime
The start date for the simulation.
end : datetime
The end date for the simulation.
capital_base : float
The starting capital for the simulation.
platform : str
The platform that the code is running on. By default this
will be the string 'zipline'. This can allow algorithms to
know if they are running on the Quantopian platform instead.
* : dict[str -> any]
Returns all of the fields in a dictionary.
Returns
-------
val : any
The value for the field queried. See above for more information.
Raises
------
ValueError
Raised when ``field`` is not a valid option.
"""
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:
try:
return env[field]
except KeyError:
raise ValueError(
'%r is not a valid field for get_environment' % field,
)
@api_method
def fetch_csv(self,
url,
pre_func=None,
post_func=None,
date_column='date',
date_format=None,
timezone=pytz.utc.zone,
symbol=None,
mask=True,
symbol_column=None,
special_params_checker=None,
**kwargs):
"""Fetch a csv from a remote url and register the data so that it is
queryable from the ``data`` object.
Parameters
----------
url : str
The url of the csv file to load.
pre_func : callable[pd.DataFrame -> pd.DataFrame], optional
A callback to allow preprocessing the raw data returned from
fetch_csv before dates are paresed or symbols are mapped.
post_func : callable[pd.DataFrame -> pd.DataFrame], optional
A callback to allow postprocessing of the data after dates and
symbols have been mapped.
date_column : str, optional
The name of the column in the preprocessed dataframe containing
datetime information to map the data.
date_format : str, optional
The format of the dates in the ``date_column``. If not provided
``fetch_csv`` will attempt to infer the format. For information
about the format of this string, see :func:`pandas.read_csv`.
timezone : tzinfo or str, optional
The timezone for the datetime in the ``date_column``.
symbol : str, optional
If the data is about a new asset or index then this string will
be the name used to identify the values in ``data``. For example,
one may use ``fetch_csv`` to load data for VIX, then this field
could be the string ``'VIX'``.
mask : bool, optional
Drop any rows which cannot be symbol mapped.
symbol_column : str
If the data is attaching some new attribute to each asset then this
argument is the name of the column in the preprocessed dataframe
containing the symbols. This will be used along with the date
information to map the sids in the asset finder.
**kwargs
Forwarded to :func:`pandas.read_csv`.
Returns
-------
csv_data_source : zipline.sources.requests_csv.PandasRequestsCSV
A requests source that will pull data from the url specified.
"""
# Show all the logs every time fetcher is used.
csv_data_source = PandasRequestsCSV(
url,
pre_func,
post_func,
self.asset_finder,
self.trading_calendar.day,
self.sim_params.start_session,
self.sim_params.end_session,
date_column,
date_format,
timezone,
symbol,
mask,
symbol_column,
data_frequency=self.data_frequency,
special_params_checker=special_params_checker,
**kwargs
)
# ingest this into dataportal
self.data_portal.handle_extra_source(csv_data_source.df,
self.sim_params)
return csv_data_source
def add_event(self, rule=None, callback=None):
"""Adds an event to the algorithm's EventManager.
Parameters
----------
rule : EventRule
The rule for when the callback should be triggered.
callback : callable[(context, data) -> None]
The function to execute when the rule is triggered.
"""
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,
calendar=None):
"""Schedules a function to be called according to some timed rules.
Parameters
----------
func : callable[(context, data) -> None]
The function to execute when the rule is triggered.
date_rule : EventRule, optional
The rule for the dates to execute this function.
time_rule : EventRule, optional
The rule for the times to execute this function.
half_days : bool, optional
Should this rule fire on half days?
See Also
--------
:class:`zipline.api.date_rules`
:class:`zipline.api.time_rules`
"""
# When the user calls schedule_function(func, <time_rule>), assume that
# the user meant to specify a time rule but no date rule, instead of
# a date rule and no time rule as the signature suggests
if isinstance(date_rule, (AfterOpen, BeforeClose)) and not time_rule:
warnings.warn('Got a time rule for the second positional argument '
'date_rule. You should use keyword argument '
'time_rule= when calling schedule_function without '
'specifying a date_rule', stacklevel=3)
date_rule = date_rule or date_rules.every_day()
time_rule = ((time_rule or time_rules.every_minute())
if self.sim_params.data_frequency == 'minute' else
# If we are in daily mode the time_rule is ignored.
time_rules.every_minute())
# Check the type of the algorithm's schedule before pulling calendar
# Note that the ExchangeTradingSchedule is currently the only
# TradingSchedule class, so this is unlikely to be hit
cal = calendar or self.trading_calendar
self.add_event(
make_eventrule(date_rule, time_rule, cal, half_days),
func,
)
@api_method
def record(self, *args, **kwargs):
"""Track and record values each day.
Parameters
----------
**kwargs
The names and values to record.
Notes
-----
These values will appear in the performance packets and the performance
dataframe passed to ``analyze`` and returned from
:func:`~zipline.run_algorithm`.
"""
# 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 set_benchmark(self, benchmark):
"""Set the benchmark asset.
Parameters
----------
benchmark : Asset
The asset to set as the new benchmark.
Notes
-----
Any dividends payed out for that new benchmark asset will be
automatically reinvested.
"""
if self.initialized:
raise SetBenchmarkOutsideInitialize()
self.benchmark_sid = benchmark
@api_method
@preprocess(symbol_str=ensure_upper_case)
def symbol(self, symbol_str):
"""Lookup an Equity by its ticker symbol.
Parameters
----------
symbol_str : str
The ticker symbol for the equity to lookup.
Returns
-------
equity : Equity
The equity that held the ticker symbol on the current
symbol lookup date.
Raises
------
SymbolNotFound
Raised when the symbols was not held on the current lookup date.
See Also
--------
:func:`zipline.api.set_symbol_lookup_date`
"""
# If the user has not set the symbol lookup date,
# use the end_session 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.end_session
return self.asset_finder.lookup_symbol(
symbol_str,
as_of_date=_lookup_date,
)
@api_method
@preprocess(root_symbol_str=ensure_upper_case)
def continuous_future(self, root_symbol_str, offset, roll):
"""Create a specifier for a continuous contract.
Parameters
----------
root_symbol_str : str
The root symbol for the future chain.
offset : int
The distance from the primary contract.
roll_style : str
How rolls are determined.
Returns
-------
continuous_future : ContinuousFuture
The continuous future specifier.
"""
return self.asset_finder.create_continuous_future(
root_symbol_str,
offset,
roll,
)
@api_method
def symbols(self, *args):
"""Lookup multuple Equities as a list.
Parameters
----------
*args : iterable[str]
The ticker symbols to lookup.
Returns
-------
equities : list[Equity]
The equities that held the given ticker symbols on the current
symbol lookup date.
Raises
------
SymbolNotFound
Raised when one of the symbols was not held on the current
lookup date.
See Also
--------
:func:`zipline.api.set_symbol_lookup_date`
"""
return [self.symbol(identifier) for identifier in args]
@api_method
def sid(self, sid):
"""Lookup an Asset by its unique asset identifier.
Parameters
----------
sid : int
The unique integer that identifies an asset.
Returns
-------
asset : Asset
The asset with the given ``sid``.
Raises
------
SidsNotFound
When a requested ``sid`` does not map to any asset.
"""
return self.asset_finder.retrieve_asset(sid)
@api_method
@preprocess(symbol=ensure_upper_case)
def future_symbol(self, symbol):
"""Lookup a futures contract with a given symbol.
Parameters
----------
symbol : str
The symbol of the desired contract.
Returns
-------
future : Future
The future that trades with the name ``symbol``.
Raises
------
SymbolNotFound
Raised when no contract named 'symbol' is found.
"""
return self.asset_finder.lookup_future_symbol(symbol)
def _calculate_order_value_amount(self, asset, value):
"""
Calculates how many shares/contracts to order based on the type of
asset being ordered.
"""
# Make sure the asset exists, and that there is a last price for it.
# FIXME: we should use BarData's can_trade logic here, but I haven't
# yet found a good way to do that.
normalized_date = normalize_date(self.datetime)
if normalized_date < asset.start_date:
raise CannotOrderDelistedAsset(
msg="Cannot order {0}, as it started trading on"
" {1}.".format(asset.symbol, asset.start_date)
)
elif normalized_date > asset.end_date:
raise CannotOrderDelistedAsset(
msg="Cannot order {0}, as it stopped trading on"
" {1}.".format(asset.symbol, asset.end_date)
)
else:
last_price = \
self.trading_client.current_data.current(asset, "price")
if np.isnan(last_price):
raise CannotOrderDelistedAsset(
msg="Cannot order {0} on {1} as there is no last "
"price for the security.".format(asset.symbol,
self.datetime)
)
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.multiplier
else:
value_multiplier = 1
return value / (last_price * value_multiplier)
def _can_order_asset(self, asset):
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."
)
if asset.auto_close_date:
day = normalize_date(self.get_datetime())
if day > min(asset.end_date, asset.auto_close_date):
# If we are after the asset's end date or auto close date, warn
# the user that they can't place an order for this asset, and
# return None.
log.warn("Cannot place order for {0}, as it has de-listed. "
"Any existing positions for this asset will be "
"liquidated on "
"{1}.".format(asset.symbol, asset.auto_close_date))
return False
return True
@api_method
@disallowed_in_before_trading_start(OrderInBeforeTradingStart())
def order(self,
asset,
amount,
limit_price=None,
stop_price=None,
style=None):
"""Place an order.
Parameters
----------
asset : Asset
The asset that this order is for.
amount : int
The amount of shares to order. If ``amount`` is positive, this is
the number of shares to buy or cover. If ``amount`` is negative,
this is the number of shares to sell or short.
limit_price : float, optional
The limit price for the order.
stop_price : float, optional
The stop price for the order.
style : ExecutionStyle, optional
The execution style for the order.
Returns
-------
order_id : str or None
The unique identifier for this order, or None if no order was
placed.
Notes
-----
The ``limit_price`` and ``stop_price`` arguments provide shorthands for
passing common execution styles. Passing ``limit_price=N`` is
equivalent to ``style=LimitOrder(N)``. Similarly, passing
``stop_price=M`` is equivalent to ``style=StopOrder(M)``, and passing
``limit_price=N`` and ``stop_price=M`` is equivalent to
``style=StopLimitOrder(N, M)``. It is an error to pass both a ``style``
and ``limit_price`` or ``stop_price``.
See Also
--------
:class:`zipline.finance.execution.ExecutionStyle`
:func:`zipline.api.order_value`
:func:`zipline.api.order_percent`
"""
if not self._can_order_asset(asset):
return None
amount, style = self._calculate_order(asset, amount,
limit_price, stop_price, style)
return self.blotter.order(asset, amount, style)
def _calculate_order(self, asset, amount,
limit_price=None, stop_price=None, style=None):
# 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(asset,
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 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."
)
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).
"""
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
@disallowed_in_before_trading_start(OrderInBeforeTradingStart())
def order_value(self,
asset,
value,
limit_price=None,
stop_price=None,
style=None):
"""Place an order by desired value rather than desired number of
shares.
Parameters
----------
asset : Asset
The asset that this order is for.
value : float
If the requested asset exists, 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
limit_price : float, optional
The limit price for the order.
stop_price : float, optional
The stop price for the order.
style : ExecutionStyle
The execution style for the order.
Returns
-------
order_id : str
The unique identifier for this order.
Notes
-----
See :func:`zipline.api.order` for more information about
``limit_price``, ``stop_price``, and ``style``
See Also
--------
:class:`zipline.finance.execution.ExecutionStyle`
:func:`zipline.api.order`
:func:`zipline.api.order_percent`
"""
if not self._can_order_asset(asset):
return None
amount = self._calculate_order_value_amount(asset, value)
return self.order(asset, 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.perf_tracker.position_tracker.sync_last_sale_prices(
self.datetime, self._in_before_trading_start, self.data_portal)
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.perf_tracker.position_tracker.sync_last_sale_prices(
self.datetime, self._in_before_trading_start, self.data_portal)
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.
"""
self.datetime = dt
self.perf_tracker.set_date(dt)
self.blotter.set_date(dt)
self.portfolio_needs_update = True
self.account_needs_update = True
self.performance_needs_update = True
@api_method
@preprocess(tz=coerce_string(pytz.timezone))
@expect_types(tz=optional(tzinfo))
def get_datetime(self, tz=None):
"""
Returns the current simulation datetime.
Parameters
----------
tz : tzinfo or str, optional
The timezone to return the datetime in. This defaults to utc.
Returns
-------
dt : datetime
The current simulation datetime converted to ``tz``.
"""
dt = self.datetime
assert dt.tzinfo == pytz.utc, "Algorithm should have a utc datetime"
if tz is not None:
dt = dt.astimezone(tz)
return dt
@api_method
def set_slippage(self, slippage):
"""Set the slippage model for the simulation.
Parameters
----------
slippage : SlippageModel
The slippage model to use.
See Also
--------
:class:`zipline.finance.slippage.SlippageModel`
"""
if not isinstance(slippage, SlippageModel):
raise UnsupportedSlippageModel()
if self.initialized:
raise SetSlippagePostInit()
self.blotter.slippage_func = slippage
@api_method
def set_commission(self, commission):
"""Sets the commission model for the simulation.
Parameters
----------
commission : CommissionModel
The commission model to use.
See Also
--------
:class:`zipline.finance.commission.PerShare`
:class:`zipline.finance.commission.PerTrade`
:class:`zipline.finance.commission.PerDollar`
"""
if not isinstance(commission, CommissionModel):
raise UnsupportedCommissionModel()
if self.initialized:
raise SetCommissionPostInit()
self.blotter.commission = commission
@api_method
def set_cancel_policy(self, cancel_policy):
"""Sets the order cancellation policy for the simulation.
Parameters
----------
cancel_policy : CancelPolicy
The cancellation policy to use.
See Also
--------
:class:`zipline.api.EODCancel`
:class:`zipline.api.NeverCancel`
"""
if not isinstance(cancel_policy, CancelPolicy):
raise UnsupportedCancelPolicy()
if self.initialized:
raise SetCancelPolicyPostInit()
self.blotter.cancel_policy = cancel_policy
@api_method
def set_symbol_lookup_date(self, dt):
"""Set the date for which symbols will be resolved to their assets
(symbols may map to different firms or underlying assets at
different times)
Parameters
----------
dt : datetime
The new symbol lookup date.
"""
try:
self._symbol_lookup_date = pd.Timestamp(dt, tz='UTC')
except ValueError:
raise UnsupportedDatetimeFormat(input=dt,
method='set_symbol_lookup_date')
# 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
@disallowed_in_before_trading_start(OrderInBeforeTradingStart())
def order_percent(self,
asset,
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.
Parameters
----------
asset : Asset
The asset that this order is for.
percent : float
The percentage of the porfolio value to allocate to ``asset``.
This is specified as a decimal, for example: 0.50 means 50%.
limit_price : float, optional
The limit price for the order.
stop_price : float, optional
The stop price for the order.
style : ExecutionStyle
The execution style for the order.
Returns
-------
order_id : str
The unique identifier for this order.
Notes
-----
See :func:`zipline.api.order` for more information about
``limit_price``, ``stop_price``, and ``style``
See Also
--------
:class:`zipline.finance.execution.ExecutionStyle`
:func:`zipline.api.order`
:func:`zipline.api.order_value`
"""
if not self._can_order_asset(asset):
return None
amount = self._calculate_order_percent_amount(asset, percent)
return self.order(asset, amount,
limit_price=limit_price,
stop_price=stop_price,
style=style)
def _calculate_order_percent_amount(self, asset, percent):
value = self.portfolio.portfolio_value * percent
return self._calculate_order_value_amount(asset, value)
@api_method
@disallowed_in_before_trading_start(OrderInBeforeTradingStart())
def order_target(self,
asset,
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.
Parameters
----------
asset : Asset
The asset that this order is for.
target : int
The desired number of shares of ``asset``.
limit_price : float, optional
The limit price for the order.
stop_price : float, optional
The stop price for the order.
style : ExecutionStyle
The execution style for the order.
Returns
-------
order_id : str
The unique identifier for this order.
Notes
-----
``order_target`` does not take into account any open orders. For
example:
.. code-block:: python
order_target(sid(0), 10)
order_target(sid(0), 10)
This code will result in 20 shares of ``sid(0)`` because the first
call to ``order_target`` will not have been filled when the second
``order_target`` call is made.
See :func:`zipline.api.order` for more information about
``limit_price``, ``stop_price``, and ``style``
See Also
--------
:class:`zipline.finance.execution.ExecutionStyle`
:func:`zipline.api.order`
:func:`zipline.api.order_target_percent`
:func:`zipline.api.order_target_value`
"""
if not self._can_order_asset(asset):
return None
amount = self._calculate_order_target_amount(asset, target)
return self.order(asset, amount,
limit_price=limit_price,
stop_price=stop_price,
style=style)
def _calculate_order_target_amount(self, asset, target):
if asset in self.portfolio.positions:
current_position = self.portfolio.positions[asset].amount
target -= current_position
return target
@api_method
@disallowed_in_before_trading_start(OrderInBeforeTradingStart())
def order_target_value(self,
asset,
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'.
Parameters
----------
asset : Asset
The asset that this order is for.
target : float
The desired total value of ``asset``.
limit_price : float, optional
The limit price for the order.
stop_price : float, optional
The stop price for the order.
style : ExecutionStyle
The execution style for the order.
Returns
-------
order_id : str
The unique identifier for this order.
Notes
-----
``order_target_value`` does not take into account any open orders. For
example:
.. code-block:: python
order_target_value(sid(0), 10)
order_target_value(sid(0), 10)
This code will result in 20 dollars of ``sid(0)`` because the first
call to ``order_target_value`` will not have been filled when the
second ``order_target_value`` call is made.
See :func:`zipline.api.order` for more information about
``limit_price``, ``stop_price``, and ``style``
See Also
--------
:class:`zipline.finance.execution.ExecutionStyle`
:func:`zipline.api.order`
:func:`zipline.api.order_target`
:func:`zipline.api.order_target_percent`
"""
if not self._can_order_asset(asset):
return None
target_amount = self._calculate_order_value_amount(asset, target)
amount = self._calculate_order_target_amount(asset, target_amount)
return self.order(asset, amount,
limit_price=limit_price,
stop_price=stop_price,
style=style)
@api_method
@disallowed_in_before_trading_start(OrderInBeforeTradingStart())
def order_target_percent(self, asset, 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.
Parameters
----------
asset : Asset
The asset that this order is for.
target : float
The desired percentage of the porfolio value to allocate to
``asset``. This is specified as a decimal, for example:
0.50 means 50%.
limit_price : float, optional
The limit price for the order.
stop_price : float, optional
The stop price for the order.
style : ExecutionStyle
The execution style for the order.
Returns
-------
order_id : str
The unique identifier for this order.
Notes
-----
``order_target_value`` does not take into account any open orders. For
example:
.. code-block:: python
order_target_percent(sid(0), 10)
order_target_percent(sid(0), 10)
This code will result in 20% of the portfolio being allocated to sid(0)
because the first call to ``order_target_percent`` will not have been
filled when the second ``order_target_percent`` call is made.
See :func:`zipline.api.order` for more information about
``limit_price``, ``stop_price``, and ``style``
See Also
--------
:class:`zipline.finance.execution.ExecutionStyle`
:func:`zipline.api.order`
:func:`zipline.api.order_target`
:func:`zipline.api.order_target_value`
"""
if not self._can_order_asset(asset):
return None
amount = self._calculate_order_target_percent_amount(asset, target)
return self.order(asset, amount,
limit_price=limit_price,
stop_price=stop_price,
style=style)
def _calculate_order_target_percent_amount(self, asset, target):
target_amount = self._calculate_order_percent_amount(asset, target)
return self._calculate_order_target_amount(asset, target_amount)
@api_method
@disallowed_in_before_trading_start(OrderInBeforeTradingStart())
def batch_order_target_percent(self, weights):
"""Place orders towards a given portfolio of weights.
Parameters
----------
weights : collections.Mapping[Asset -> float]
Returns
-------
order_ids : pd.Series[Asset -> str]
The unique identifiers for the orders that were placed.
See Also
--------
:func:`zipline.api.order_target_percent`
"""
order_args = OrderedDict()
for asset, target in iteritems(weights):
if self._can_order_asset(asset):
amount = self._calculate_order_target_percent_amount(
asset, target,
)
amount, style = self._calculate_order(asset, amount)
order_args[asset] = (asset, amount, style)
order_ids = self.blotter.batch_order(viewvalues(order_args))
order_ids = pd.Series(data=order_ids, index=order_args)
return order_ids[~order_ids.isnull()]
@error_keywords(sid='Keyword argument `sid` is no longer supported for '
'get_open_orders. Use `asset` instead.')
@api_method
def get_open_orders(self, asset=None):
"""Retrieve all of the current open orders.
Parameters
----------
asset : Asset
If passed and not None, return only the open orders for the given
asset instead of all open orders.
Returns
-------
open_orders : dict[list[Order]] or list[Order]
If no asset is passed this will return a dict mapping Assets
to a list containing all the open orders for the asset.
If an asset is passed then this will return a list of the open
orders for this asset.
"""
if asset is None:
return {
key: [order.to_api_obj() for order in orders]
for key, orders in iteritems(self.blotter.open_orders)
if orders
}
if asset in self.blotter.open_orders:
orders = self.blotter.open_orders[asset]
return [order.to_api_obj() for order in orders]
return []
@api_method
def get_order(self, order_id):
"""Lookup an order based on the order id returned from one of the
order functions.
Parameters
----------
order_id : str
The unique identifier for the order.
Returns
-------
order : Order
The order object.
"""
if order_id in self.blotter.orders:
return self.blotter.orders[order_id].to_api_obj()
@api_method
def cancel_order(self, order_param):
"""Cancel an open order.
Parameters
----------
order_param : str or Order
The order_id or order object to cancel.
"""
order_id = order_param
if isinstance(order_param, zipline.protocol.Order):
order_id = order_param.id
self.blotter.cancel(order_id)
@api_method
@require_initialized(HistoryInInitialize())
def history(self, bar_count, frequency, field, ffill=True):
"""DEPRECATED: use ``data.history`` instead.
"""
warnings.warn(
"The `history` method is deprecated. Use `data.history` instead.",
category=ZiplineDeprecationWarning,
stacklevel=4
)
return self.get_history_window(
bar_count,
frequency,
self._calculate_universe(),
field,
ffill
)
def get_history_window(self, bar_count, frequency, assets, field, ffill):
if not self._in_before_trading_start:
return self.data_portal.get_history_window(
assets,
self.datetime,
bar_count,
frequency,
field,
ffill,
)
else:
# If we are in before_trading_start, we need to get the window
# as of the previous market minute
adjusted_dt = \
self.trading_calendar.previous_minute(
self.datetime
)
window = self.data_portal.get_history_window(
assets,
adjusted_dt,
bar_count,
frequency,
field,
ffill,
)
# Get the adjustments between the last market minute and the
# current before_trading_start dt and apply to the window
adjs = self.data_portal.get_adjustments(
assets,
field,
adjusted_dt,
self.datetime
)
window = window * adjs
return window
####################
# 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):
"""Set a limit on the maximum leverage of the algorithm.
Parameters
----------
max_leverage : float
The maximum leverage for the algorithm. If not provided there will
be no maximum.
"""
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,
asset=None,
max_shares=None,
max_notional=None,
on_error='fail'):
"""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.
Parameters
----------
asset : Asset, optional
If provided, this sets the guard only on positions in the given
asset.
max_shares : int, optional
The maximum number of shares to hold for an asset.
max_notional : float, optional
The maximum value to hold for an asset.
"""
control = MaxPositionSize(asset=asset,
max_shares=max_shares,
max_notional=max_notional,
on_error=on_error)
self.register_trading_control(control)
@api_method
def set_max_order_size(self,
asset=None,
max_shares=None,
max_notional=None,
on_error='fail'):
"""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.
Parameters
----------
asset : Asset, optional
If provided, this sets the guard only on positions in the given
asset.
max_shares : int, optional
The maximum number of shares that can be ordered at one time.
max_notional : float, optional
The maximum value that can be ordered at one time.
"""
control = MaxOrderSize(asset=asset,
max_shares=max_shares,
max_notional=max_notional,
on_error=on_error)
self.register_trading_control(control)
@api_method
def set_max_order_count(self, max_count, on_error='fail'):
"""Set a limit on the number of orders that can be placed in a single
day.
Parameters
----------
max_count : int
The maximum number of orders that can be placed on any single day.
"""
control = MaxOrderCount(on_error, max_count)
self.register_trading_control(control)
@api_method
def set_do_not_order_list(self, restricted_list, on_error='fail'):
"""Set a restriction on which assets can be ordered.
Parameters
----------
restricted_list : container[Asset], SecurityList
The assets that cannot be ordered.
"""
if isinstance(restricted_list, SecurityList):
warnings.warn(
"`set_do_not_order_list(security_lists.leveraged_etf_list)` "
"is deprecated. Use `set_asset_restrictions("
"security_lists.restrict_leveraged_etfs)` instead.",
category=ZiplineDeprecationWarning,
stacklevel=2
)
restrictions = SecurityListRestrictions(restricted_list)
else:
warnings.warn(
"`set_do_not_order_list(container_of_assets)` is deprecated. "
"Create a zipline.finance.asset_restrictions."
"StaticRestrictions object with a container of assets and use "
"`set_asset_restrictions(StaticRestrictions("
"container_of_assets))` instead.",
category=ZiplineDeprecationWarning,
stacklevel=2
)
restrictions = StaticRestrictions(restricted_list)
self.set_asset_restrictions(restrictions, on_error)
@api_method
@expect_types(
restrictions=Restrictions,
on_error=str,
)
def set_asset_restrictions(self, restrictions, on_error='fail'):
"""Set a restriction on which assets can be ordered.
Parameters
----------
restricted_list : Restrictions
An object providing information about restricted assets.
See Also
--------
zipline.finance.asset_restrictions.Restrictions
"""
control = RestrictedListOrder(on_error, restrictions)
self.register_trading_control(control)
self.restrictions |= restrictions
@api_method
def set_long_only(self, on_error='fail'):
"""Set a rule specifying that this algorithm cannot take short
positions.
"""
self.register_trading_control(LongOnly(on_error))
##############
# Pipeline API
##############
@api_method
@require_not_initialized(AttachPipelineAfterInitialize())
@expect_types(
pipeline=Pipeline,
name=string_types,
chunks=(int, Iterable, type(None)),
)
def attach_pipeline(self, pipeline, name, chunks=None):
"""Register a pipeline to be computed at the start of each day.
Parameters
----------
pipeline : Pipeline
The pipeline to have computed.
name : str
The name of the pipeline.
chunks : int or iterator, optional
The number of days to compute pipeline results for. Increasing
this number will make it longer to get the first results but
may improve the total runtime of the simulation. If an iterator
is passed, we will run in chunks based on values of the itereator.
Returns
-------
pipeline : Pipeline
Returns the pipeline that was attached unchanged.
See Also
--------
:func:`zipline.api.pipeline_output`
"""
if self._pipelines:
raise NotImplementedError("Multiple pipelines are not supported.")
if chunks is None:
# Make the first chunk smaller to get more immediate results:
# (one week, then every half year)
chunks = chain([5], repeat(126))
elif isinstance(chunks, int):
chunks = repeat(chunks)
self._pipelines[name] = pipeline, iter(chunks)
# Return the pipeline to allow expressions like
# p = attach_pipeline(Pipeline(), 'name')
return pipeline
@api_method
@require_initialized(PipelineOutputDuringInitialize())
def pipeline_output(self, name):
"""Get the results of the pipeline that was attached with the name:
``name``.
Parameters
----------
name : str
Name of the pipeline for which results are requested.
Returns
-------
results : pd.DataFrame
DataFrame containing the results of the requested pipeline for
the current simulation date.
Raises
------
NoSuchPipeline
Raised when no pipeline with the name `name` has been registered.
See Also
--------
:func:`zipline.api.attach_pipeline`
:meth:`zipline.pipeline.engine.PipelineEngine.run_pipeline`
"""
# NOTE: We don't currently support multiple pipelines, but we plan to
# in the future.
try:
p, chunks = self._pipelines[name]
except KeyError:
raise NoSuchPipeline(
name=name,
valid=list(self._pipelines.keys()),
)
return self._pipeline_output(p, chunks)
def _pipeline_output(self, pipeline, chunks):
"""
Internal implementation of `pipeline_output`.
"""
today = normalize_date(self.get_datetime())
data = NO_DATA = object()
try:
data = self._pipeline_cache.unwrap(today)
except Expired:
# We can't handle the exception in this block because in Python 3
# sys.exc_info isn't cleared until we leave the block. See note
# below for why we need to clear exc_info.
pass
if data is NO_DATA:
# Try to deterministically garbage collect the previous result by
# removing any references to it. There are at least three sources
# of references:
# 1. self._pipeline_cache holds a reference.
# 2. The dataframe itself holds a reference via cached .iloc/.loc
# accessors.
# 3. The traceback held in sys.exc_info includes stack frames in
# which self._pipeline_cache is a local variable.
# We remove the above sources of references in reverse order:
# 3. Clear the traceback. This is no-op in Python 3.
exc_clear()
# 2. Clear the .loc/.iloc caches.
clear_dataframe_indexer_caches(
self._pipeline_cache._unsafe_get_value()
)
# 1. Clear the reference to self._pipeline_cache.
self._pipeline_cache = None
# Calculate the next block.
data, valid_until = self._run_pipeline(
pipeline, today, next(chunks),
)
self._pipeline_cache = CachedObject(data, valid_until)
# Now that we have a cached result, try to return the data for today.
try:
return data.loc[today]
except KeyError:
# This happens if no assets passed the pipeline screen on a given
# day.
return pd.DataFrame(index=[], columns=data.columns)
def _run_pipeline(self, pipeline, start_session, chunksize):
"""
Compute `pipeline`, providing values for at least `start_date`.
Produces a DataFrame containing data for days between `start_date` and
`end_date`, where `end_date` is defined by:
`end_date = min(start_date + chunksize trading days,
simulation_end)`
Returns
-------
(data, valid_until) : tuple (pd.DataFrame, pd.Timestamp)
See Also
--------
PipelineEngine.run_pipeline
"""
sessions = self.trading_calendar.all_sessions
# Load data starting from the previous trading day...
start_date_loc = sessions.get_loc(start_session)
# ...continuing until either the day before the simulation end, or
# until chunksize days of data have been loaded.
sim_end_session = self.sim_params.end_session
end_loc = min(
start_date_loc + chunksize,
sessions.get_loc(sim_end_session)
)
end_session = sessions[end_loc]
return \
self.engine.run_pipeline(pipeline, start_session, end_session), \
end_session
##################
# End Pipeline API
##################
@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)
]
Reference in New Issue View Git Blame Copy Permalink