本文整理汇总了Python中zipline.finance.trading.TradingEnvironment.market_minutes_for_day方法的典型用法代码示例。如果您正苦于以下问题：Python TradingEnvironment.market_minutes_for_day方法的具体用法？Python TradingEnvironment.market_minutes_for_day怎么用？Python TradingEnvironment.market_minutes_for_day使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类zipline.finance.trading.TradingEnvironment的用法示例。
在下文中一共展示了TradingEnvironment.market_minutes_for_day方法的3个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: create_test_df_source
# 需要导入模块: from zipline.finance.trading import TradingEnvironment [as 别名]
# 或者: from zipline.finance.trading.TradingEnvironment import market_minutes_for_day [as 别名]
def create_test_df_source(sim_params=None, env=None, bars='daily'):
if bars == 'daily':
freq = pd.datetools.BDay()
elif bars == 'minute':
freq = pd.datetools.Minute()
else:
raise ValueError('%s bars not understood.' % bars)
if sim_params and bars == 'daily':
index = sim_params.trading_days
else:
if env is None:
env = TradingEnvironment()
start = pd.datetime(1990, 1, 3, 0, 0, 0, 0, pytz.utc)
end = pd.datetime(1990, 1, 8, 0, 0, 0, 0, pytz.utc)
days = env.days_in_range(start, end)
if bars == 'daily':
index = days
if bars == 'minute':
index = pd.DatetimeIndex([], freq=freq)
for day in days:
day_index = env.market_minutes_for_day(day)
index = index.append(day_index)
x = np.arange(1, len(index) + 1)
df = pd.DataFrame(x, index=index, columns=[0])
return DataFrameSource(df), df示例2: minutes_for_days
# 需要导入模块: from zipline.finance.trading import TradingEnvironment [as 别名]
# 或者: from zipline.finance.trading.TradingEnvironment import market_minutes_for_day [as 别名]
def minutes_for_days():
"""
500 randomly selected days.
This is used to make sure our test coverage is unbaised towards any rules.
We use a random sample because testing on all the trading days took
around 180 seconds on my laptop, which is far too much for normal unit
testing.
We manually set the seed so that this will be deterministic.
Results of multiple runs were compared to make sure that this is actually
true.
This returns a generator of tuples each wrapping a single generator.
Iterating over this yeilds a single day, iterating over the day yields
the minutes for that day.
"""
env = TradingEnvironment()
random.seed('deterministic')
return ((env.market_minutes_for_day(random.choice(env.trading_days)),)
for _ in range(500))示例3: minutes_for_days
# 需要导入模块: from zipline.finance.trading import TradingEnvironment [as 别名]
# 或者: from zipline.finance.trading.TradingEnvironment import market_minutes_for_day [as 别名]
def minutes_for_days(ordered_days=False):
"""
500 randomly selected days.
This is used to make sure our test coverage is unbaised towards any rules.
We use a random sample because testing on all the trading days took
around 180 seconds on my laptop, which is far too much for normal unit
testing.
We manually set the seed so that this will be deterministic.
Results of multiple runs were compared to make sure that this is actually
true.
This returns a generator of tuples each wrapping a single generator.
Iterating over this yields a single day, iterating over the day yields
the minutes for that day.
"""
env = TradingEnvironment()
random.seed('deterministic')
if ordered_days:
# Get a list of 500 trading days, in order. As a performance
# optimization in AfterOpen and BeforeClose, we rely on the fact that
# the clock only ever moves forward in a simulation. For those cases,
# we guarantee that the list of trading days we test is ordered.
ordered_day_list = random.sample(list(env.trading_days), 500)
ordered_day_list.sort()
def day_picker(day):
return ordered_day_list[day]
else:
# Other than AfterOpen and BeforeClose, we don't rely on the the nature
# of the clock, so we don't care.
def day_picker(day):
return random.choice(env.trading_days[:-1])
return ((env.market_minutes_for_day(day_picker(cnt)),)
for cnt in range(500))