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MAINT: Store values for market open and close in environment.

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Instead of creating the market open and close mid-simulation,
calculate upfront the values for market open and close in a
DataFrame, so that they values can be looked up by date, as
viewed as series while investigating data issues.

One downside of this implementation is that the entire history
has open and close values calculated, even though the simulation
may only be a subset of the trade data on record.
Should consider moving the `times` property and other methods
that care about the start and end date of a simulation to
SimulationParameters or another like object.
This commit is contained in:
Eddie Hebert
2013-10-17 17:38:25 -04:00
parent 800210fbb3
commit 17b8980fb9
1 changed files with 41 additions and 26 deletions
Download Patch File Download Diff File Expand all files Collapse all files
zipline/finance/trading.py
+41 -26
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@@ -115,6 +115,9 @@ class TradingEnvironment(object):
self.early_closes = get_early_closes(self.first_trading_day,
self.last_trading_day)
# The market open and close for the exchange.
self._times = None
def __enter__(self, *args, **kwargs):
global environment
self.prev_environment = environment
@@ -130,6 +133,39 @@ class TradingEnvironment(object):
# stack.
return False
@property
def times(self):
if self._times is not None:
return self._times
else:
self._times = pd.DataFrame(index=self.trading_days,
columns=('market_open', 'market_close'))
for day in self.trading_days:
self._times['market_open'][day] = pd.Timestamp(
datetime.datetime(
year=day.year,
month=day.month,
day=day.day,
hour=9,
minute=31),
tz=self.exchange_tz).tz_convert('UTC')
if day in self.early_closes:
close_hour = 13
else:
close_hour = 16
self._times['market_close'][day] = pd.Timestamp(
datetime.datetime(
year=day.year,
month=day.month,
day=day.day,
hour=close_hour,
minute=0),
tz=self.exchange_tz).tz_convert('UTC')
return self._times
def normalize_date(self, test_date):
test_date = pd.Timestamp(test_date, tz='UTC')
return pd.tseries.tools.normalize_date(test_date)
@@ -181,40 +217,19 @@ Last successful date: %s" % self.last_trading_day)
return self.get_open_and_close(next_open)
def get_open_and_close(self, next_open):
def get_open_and_close(self, dt):
# creating a naive datetime with the correct hour,
# minute, and date. this will allow us to use pandas to
# shift the time between EST and UTC.
next_open = next_open.replace(
hour=9,
minute=31,
second=0,
microsecond=0,
tzinfo=None
)
# create a new Timestamp with the next_open naive date and
# the correct timezone for the exchange.
open_utc = self.exchange_dt_in_utc(next_open)
day = self.normalize_date(dt)
market_open = open_utc
market_close = (market_open
+ self.get_trading_day_duration(open_utc)
- datetime.timedelta(minutes=1))
times_for_day = self.times.ix[day]
return market_open, market_close
return (times_for_day['market_open'],
times_for_day['market_close'])
def market_minutes_for_day(self, midnight):
market_open, market_close = self.get_open_and_close(midnight)
return pd.date_range(market_open, market_close, freq='T')
def get_trading_day_duration(self, trading_day):
trading_day = self.normalize_date(trading_day)
if trading_day in self.early_closes:
return self.early_close_trading_day
return self.full_trading_day
def trading_day_distance(self, first_date, second_date):
first_date = self.normalize_date(first_date)
second_date = self.normalize_date(second_date)