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(load=noop_load)
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
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
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))
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))
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))
