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320 lines
11 KiB
Python
320 lines
11 KiB
Python
#
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# Copyright 2013 Quantopian, Inc.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import bisect
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import pytz
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import logbook
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import datetime
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from delorean import Delorean
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import pandas as pd
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from zipline.data.loader import load_market_data
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from zipline.utils.tradingcalendar import get_early_closes
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log = logbook.Logger('Trading')
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# The financial simulations in zipline depend on information
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# about the benchmark index and the risk free rates of return.
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# The benchmark index defines the benchmark returns used in
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# the calculation of performance metrics such as alpha/beta. Many
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# components, including risk, performance, transforms, and
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# batch_transforms, need access to a calendar of trading days and
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# market hours. The TradingEnvironment maintains two time keeping
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# facilities:
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# - a DatetimeIndex of trading days for calendar calculations
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# - a timezone name, which should be local to the exchange
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# hosting the benchmark index. All dates are normalized to UTC
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# for serialization and storage, and the timezone is used to
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# ensure proper rollover through daylight savings and so on.
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#
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# This module maintains a global variable, environment, which is
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# subsequently referenced directly by zipline financial
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# components. To set the environment, you can set the property on
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# the module directly:
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# import zipline.finance.trading as trading
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# trading.environment = TradingEnvironment()
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#
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# or if you want to switch the environment for a limited context
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# you can use a TradingEnvironment in a with clause:
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# lse = TradingEnvironment(bm_index="^FTSE", exchange_tz="Europe/London")
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# with lse:
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# # the code here will have lse as the global trading.environment
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# algo.run(start, end)
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#
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# User code will not normally need to use TradingEnvironment
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# directly. If you are extending zipline's core financial
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# compponents and need to use the environment, you must import the module
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# NOT the variable. If you import the module, you will get a
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# reference to the environment at import time, which will prevent
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# your code from responding to user code that changes the global
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# state.
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environment = None
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class TradingEnvironment(object):
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def __init__(
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self,
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load=None,
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bm_symbol='^GSPC',
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exchange_tz="US/Eastern",
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max_date=None,
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extra_dates=None
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):
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self.prev_environment = self
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self.bm_symbol = bm_symbol
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if not load:
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load = load_market_data
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self.benchmark_returns, treasury_curves_map = \
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load(self.bm_symbol)
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self.treasury_curves = pd.Series(treasury_curves_map)
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if max_date:
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self.treasury_curves = self.treasury_curves[:max_date]
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self.full_trading_day = datetime.timedelta(hours=6, minutes=30)
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self.early_close_trading_day = datetime.timedelta(hours=3, minutes=30)
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self.exchange_tz = exchange_tz
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bm = None
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trading_days_list = []
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for bm in self.benchmark_returns:
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if max_date and bm.date > max_date:
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break
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trading_days_list.append(bm.date)
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self.trading_days = pd.DatetimeIndex(trading_days_list)
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if bm and extra_dates:
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for extra_date in extra_dates:
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extra_date = extra_date.replace(hour=0, minute=0, second=0,
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microsecond=0)
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if extra_date not in self.trading_days:
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self.trading_days = self.trading_days + \
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pd.DatetimeIndex([extra_date])
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self.first_trading_day = self.trading_days[0]
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self.last_trading_day = self.trading_days[-1]
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self.early_closes = get_early_closes(self.first_trading_day,
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self.last_trading_day)
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def __enter__(self, *args, **kwargs):
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global environment
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self.prev_environment = environment
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environment = self
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# return value here is associated with "as such_and_such" on the
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# with clause.
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return self
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def __exit__(self, exc_type, exc_val, exc_tb):
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global environment
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environment = self.prev_environment
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# signal that any exceptions need to be propagated up the
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# stack.
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return False
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def normalize_date(self, test_date):
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return datetime.datetime(
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year=test_date.year,
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month=test_date.month,
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day=test_date.day,
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tzinfo=pytz.utc
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)
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def utc_dt_in_exchange(self, dt):
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delorean = Delorean(dt, pytz.utc.zone)
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return delorean.shift(self.exchange_tz).datetime
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def exchange_dt_in_utc(self, dt):
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delorean = Delorean(dt, self.exchange_tz)
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return delorean.shift(pytz.utc.zone).datetime
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def is_market_hours(self, test_date):
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if not self.is_trading_day(test_date):
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return False
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mkt_open, mkt_close = self.get_open_and_close(test_date)
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return test_date >= mkt_open and test_date <= mkt_close
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def is_trading_day(self, test_date):
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dt = self.normalize_date(test_date)
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return (dt in self.trading_days)
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def next_trading_day(self, test_date):
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dt = self.normalize_date(test_date)
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delta = datetime.timedelta(days=1)
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while dt <= self.last_trading_day:
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dt += delta
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if dt in self.trading_days:
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return dt
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return None
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def next_open_and_close(self, start_date):
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"""
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Given the start_date, returns the next open and close of
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the market.
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"""
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next_open = self.next_trading_day(start_date)
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if next_open is None:
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raise Exception(
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"Attempt to backtest beyond available history. \
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Last successful date: %s" % self.last_trading_day)
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return self.get_open_and_close(next_open)
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def get_open_and_close(self, next_open):
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# creating a naive datetime with the correct hour,
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# minute, and date. this will allow us to use Delorean to
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# shift the time between EST and UTC.
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next_open = next_open.replace(
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hour=9,
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minute=31,
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second=0,
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microsecond=0,
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tzinfo=None
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)
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# create a new Delorean with the next_open naive date and
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# the correct timezone for the exchange.
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open_utc = self.exchange_dt_in_utc(next_open)
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market_open = open_utc
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market_close = (market_open
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+ self.get_trading_day_duration(open_utc)
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- datetime.timedelta(minutes=1))
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return market_open, market_close
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def get_trading_day_duration(self, trading_day):
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trading_day = self.normalize_date(trading_day)
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if trading_day in self.early_closes:
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return self.early_close_trading_day
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return self.full_trading_day
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def trading_day_distance(self, first_date, second_date):
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first_date = self.normalize_date(first_date)
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second_date = self.normalize_date(second_date)
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# TODO: May be able to replace the following with searchsorted.
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# Find leftmost item greater than or equal to day
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i = bisect.bisect_left(self.trading_days, first_date)
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if i == len(self.trading_days): # nothing found
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return None
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j = bisect.bisect_left(self.trading_days, second_date)
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if j == len(self.trading_days):
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return None
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return j - i
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def get_index(self, dt):
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ndt = self.normalize_date(dt)
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return self.trading_days.searchsorted(ndt)
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class SimulationParameters(object):
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def __init__(self, period_start, period_end,
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capital_base=10e3,
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emission_rate='daily',
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data_frequency='daily'):
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global environment
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if not environment:
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# This is the global environment for trading simulation.
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environment = TradingEnvironment()
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self.period_start = period_start
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self.period_end = period_end
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self.capital_base = capital_base
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self.emission_rate = emission_rate
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self.data_frequency = data_frequency
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assert self.period_start <= self.period_end, \
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"Period start falls after period end."
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assert self.period_start <= environment.last_trading_day, \
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"Period start falls after the last known trading day."
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assert self.period_end >= environment.first_trading_day, \
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"Period end falls before the first known trading day."
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self.first_open = self.calculate_first_open()
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self.last_close = self.calculate_last_close()
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start_index = \
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environment.get_index(self.first_open)
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end_index = environment.get_index(self.last_close)
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# take an inclusive slice of the environment's
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# trading_days.
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self.trading_days = \
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environment.trading_days[start_index:end_index + 1]
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def calculate_first_open(self):
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"""
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Finds the first trading day on or after self.period_start.
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"""
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first_open = self.period_start
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one_day = datetime.timedelta(days=1)
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while not environment.is_trading_day(first_open):
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first_open = first_open + one_day
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mkt_open, _ = environment.get_open_and_close(first_open)
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return mkt_open
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def calculate_last_close(self):
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"""
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Finds the last trading day on or before self.period_end
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"""
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last_close = self.period_end
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one_day = datetime.timedelta(days=1)
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while not environment.is_trading_day(last_close):
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last_close = last_close - one_day
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_, mkt_close = environment.get_open_and_close(last_close)
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return mkt_close
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@property
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def days_in_period(self):
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"""return the number of trading days within the period [start, end)"""
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return len(self.trading_days)
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def __repr__(self):
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return """
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{class_name}(
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period_start={period_start},
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period_end={period_end},
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capital_base={capital_base},
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emission_rate={emission_rate},
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first_open={first_open},
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last_close={last_close})\
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""".format(class_name=self.__class__.__name__,
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period_start=self.period_start,
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period_end=self.period_end,
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capital_base=self.capital_base,
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emission_rate=self.emission_rate,
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first_open=self.first_open,
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last_close=self.last_close)
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