# # Copyright 2014 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 copy import copy import warnings import pytz import pandas as pd from pandas.tseries.tools import normalize_date import numpy as np from datetime import datetime from itertools import groupby, chain, repeat from numbers import Integral from operator import attrgetter from six.moves import filter from six import ( exec_, iteritems, itervalues, string_types, ) from zipline.errors import ( AttachPipelineAfterInitialize, HistoryInInitialize, NoSuchPipeline, OrderDuringInitialize, PipelineOutputDuringInitialize, RegisterAccountControlPostInit, RegisterTradingControlPostInit, SetCommissionPostInit, SetSlippagePostInit, UnsupportedCommissionModel, UnsupportedDatetimeFormat, UnsupportedOrderParameters, UnsupportedSlippageModel, ) from zipline.finance.trading import TradingEnvironment from zipline.finance.blotter import Blotter from zipline.finance.commission import PerShare, PerTrade, PerDollar 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.slippage import ( VolumeShareSlippage, SlippageModel, transact_partial ) from zipline.assets import Asset, Future from zipline.assets.futures import FutureChain from zipline.gens.composites import date_sorted_sources from zipline.gens.tradesimulation import AlgorithmSimulator from zipline.pipeline.engine import ( NoOpPipelineEngine, SimplePipelineEngine, ) from zipline.sources import DataFrameSource, DataPanelSource from zipline.utils.api_support import ( api_method, require_initialized, require_not_initialized, ZiplineAPI, ) from zipline.utils.input_validation import ensure_upper_case from zipline.utils.cache import CachedObject, Expired import zipline.utils.events from zipline.utils.events import ( EventManager, make_eventrule, DateRuleFactory, TimeRuleFactory, ) from zipline.utils.factory import create_simulation_parameters from zipline.utils.math_utils import tolerant_equals from zipline.utils.preprocess import preprocess import zipline.protocol from zipline.protocol import Event from zipline.history import HistorySpec from zipline.history.history_container import HistoryContainer DEFAULT_CAPITAL_BASE = float("1.0e5") 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: ''. 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): 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 # 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) 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( create_context=kwargs.pop('create_event_context', None), ) if self.algoscript is not None: filename = kwargs.pop('algo_filename', None) if filename is None: filename = '' 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._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, ) # 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, get_loader): """ Construct and store a PipelineEngine from loader. If get_loader is None, constructs a NoOpPipelineEngine. """ if get_loader is not None: self.engine = SimplePipelineEngine( get_loader, self.trading_environment.trading_days, self.asset_finder, ) else: self.engine = NoOpPipelineEngine() 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 sids = {sid for source in self.sources for sid in source.sids} # 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. self._current_universe = set(self.asset_finder.retrieve_all(sids)) # 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 isinstance(identifier, Integral): 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) # 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 = [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 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 @preprocess(symbol_str=ensure_upper_case) 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 @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 A Future object. Raises ------ SymbolNotFound Raised when no contract named 'symbol' is found. """ return self.asset_finder.lookup_future_symbol(symbol) @api_method @preprocess(root_symbol=ensure_upper_case) 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, 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.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 SetSlippagePostInit() self.slippage = slippage @api_method def set_commission(self, commission): if not isinstance(commission, (PerShare, PerTrade, PerDollar)): raise UnsupportedCommissionModel() if self.initialized: raise SetCommissionPostInit() 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 @require_initialized(HistoryInInitialize()) 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()) ############## # Pipeline API ############## @api_method @require_not_initialized(AttachPipelineAfterInitialize()) def attach_pipeline(self, pipeline, name, chunksize=None): """ Register a pipeline to be computed at the start of each day. """ if self._pipelines: raise NotImplementedError("Multiple pipelines are not supported.") if chunksize is None: # Make the first chunk smaller to get more immediate results: # (one week, then every half year) chunks = iter(chain([5], repeat(126))) else: chunks = iter(repeat(int(chunksize))) self._pipelines[name] = pipeline, 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 pipeline with 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 -------- :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()) try: data = self._pipeline_cache.unwrap(today) except Expired: 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_date, 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 """ 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 chunksize days of data have been loaded. sim_end = self.sim_params.last_close.normalize() end_loc = min(start_date_loc + chunksize, days.get_loc(sim_end)) end_date = days[end_loc] return \ self.engine.run_pipeline(pipeline, start_date, end_date), end_date ################## # End Pipeline API ################## 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) ]