def init_class_fixtures(cls):
super(SecurityListTestCase, cls).init_class_fixtures()
# this is ugly, but we need to create two different
# TradingEnvironment/DataPortal pairs
cls.start = pd.Timestamp(list(LEVERAGED_ETFS.keys())[0])
end = pd.Timestamp('2015-02-17', tz='utc')
cls.extra_knowledge_date = pd.Timestamp('2015-01-27', tz='utc')
cls.trading_day_before_first_kd = pd.Timestamp('2015-01-23', tz='utc')
symbols = ['AAPL', 'GOOG', 'BZQ', 'URTY', 'JFT']
cls.env = cls.enter_class_context(tmp_trading_env(
equities=pd.DataFrame.from_records([{
'start_date': cls.start,
'end_date': end,
'symbol': symbol,
'exchange': "TEST",
} for symbol in symbols]),
load=cls.make_load_function(),
))
cls.sim_params = factory.create_simulation_parameters(
start=cls.start,
num_days=4,
trading_calendar=cls.trading_calendar
)
cls.sim_params2 = sp2 = factory.create_simulation_parameters(
start=cls.trading_day_before_first_kd, num_days=4
)
cls.env2 = cls.enter_class_context(tmp_trading_env(
equities=pd.DataFrame.from_records([{
'start_date': sp2.start_session,
'end_date': sp2.end_session,
'symbol': symbol,
'exchange': "TEST",
} for symbol in symbols]),
load=cls.make_load_function(),
))
cls.tempdir = cls.enter_class_context(tmp_dir())
cls.tempdir2 = cls.enter_class_context(tmp_dir())
cls.data_portal = create_data_portal(
asset_finder=cls.env.asset_finder,
tempdir=cls.tempdir,
sim_params=cls.sim_params,
sids=range(0, 5),
trading_calendar=cls.trading_calendar,
)
cls.data_portal2 = create_data_portal(
asset_finder=cls.env2.asset_finder,
tempdir=cls.tempdir2,
sim_params=cls.sim_params2,
sids=range(0, 5),
trading_calendar=cls.trading_calendar,
)
def init_class_fixtures(cls):
super(SecurityListTestCase, cls).init_class_fixtures()
# this is ugly, but we need to create two different
# TradingEnvironment/DataPortal pairs
cls.start = pd.Timestamp(list(LEVERAGED_ETFS.keys())[0])
end = pd.Timestamp('2015-02-17', tz='utc')
cls.extra_knowledge_date = pd.Timestamp('2015-01-27', tz='utc')
cls.trading_day_before_first_kd = pd.Timestamp('2015-01-23', tz='utc')
symbols = ['AAPL', 'GOOG', 'BZQ', 'URTY', 'JFT']
cls.env = cls.enter_class_context(
tmp_trading_env(equities=pd.DataFrame.from_records([{
'start_date':
cls.start,
'end_date':
end,
'symbol':
symbol,
'exchange':
"TEST",
} for symbol in symbols]), ))
cls.sim_params = factory.create_simulation_parameters(
start=cls.start, num_days=4, trading_calendar=cls.trading_calendar)
cls.sim_params2 = sp2 = factory.create_simulation_parameters(
start=cls.trading_day_before_first_kd, num_days=4)
cls.env2 = cls.enter_class_context(
tmp_trading_env(equities=pd.DataFrame.from_records([{
'start_date':
sp2.start_session,
'end_date':
sp2.end_session,
'symbol':
symbol,
'exchange':
"TEST",
} for symbol in symbols]), ))
cls.tempdir = cls.enter_class_context(tmp_dir())
cls.tempdir2 = cls.enter_class_context(tmp_dir())
cls.data_portal = create_data_portal(
asset_finder=cls.env.asset_finder,
tempdir=cls.tempdir,
sim_params=cls.sim_params,
sids=range(0, 5),
trading_calendar=cls.trading_calendar,
)
cls.data_portal2 = create_data_portal(
asset_finder=cls.env2.asset_finder,
tempdir=cls.tempdir2,
sim_params=cls.sim_params2,
sids=range(0, 5),
trading_calendar=cls.trading_calendar,
)
def test_algo_without_rl_violation_after_delete(self):
sim_params = factory.create_simulation_parameters(
start=self.extra_knowledge_date,
num_days=4,
)
equities = pd.DataFrame.from_records([{
'symbol': 'BZQ',
'start_date': sim_params.start_session,
'end_date': sim_params.end_session,
'exchange': "TEST",
}])
with TempDirectory() as new_tempdir, \
security_list_copy(), \
tmp_trading_env(equities=equities) as env:
# add a delete statement removing bzq
# write a new delete statement file to disk
add_security_data([], ['BZQ'])
data_portal = create_data_portal(
env.asset_finder,
new_tempdir,
sim_params,
range(0, 5),
trading_calendar=self.trading_calendar,
)
algo = RestrictedAlgoWithoutCheck(
symbol='BZQ', sim_params=sim_params, env=env
)
algo.run(data_portal)
def test_algo_without_rl_violation_after_delete(self):
sim_params = factory.create_simulation_parameters(
start=self.extra_knowledge_date,
num_days=4,
)
equities = pd.DataFrame.from_records([{
'symbol':
'BZQ',
'start_date':
sim_params.period_start,
'end_date':
sim_params.period_end,
}])
with TempDirectory() as new_tempdir, \
security_list_copy(), \
tmp_trading_env(equities=equities) as env:
# add a delete statement removing bzq
# write a new delete statement file to disk
add_security_data([], ['BZQ'])
data_portal = create_data_portal(
env.asset_finder,
new_tempdir,
sim_params,
range(0, 5),
trading_schedule=self.trading_schedule,
)
algo = RestrictedAlgoWithoutCheck(symbol='BZQ',
sim_params=sim_params,
env=env)
algo.run(data_portal)
def transaction_sim(self, **params):
"""This is a utility method that asserts expected
results for conversion of orders to transactions given a
trade history
"""
trade_count = params['trade_count']
trade_interval = params['trade_interval']
order_count = params['order_count']
order_amount = params['order_amount']
order_interval = params['order_interval']
expected_txn_count = params['expected_txn_count']
expected_txn_volume = params['expected_txn_volume']
# optional parameters
# ---------------------
# if present, alternate between long and short sales
alternate = params.get('alternate')
# if present, expect transaction amounts to match orders exactly.
complete_fill = params.get('complete_fill')
sid = 1
metadata = make_simple_equity_info([sid], self.start, self.end)
with TempDirectory() as tempdir, \
tmp_trading_env(equities=metadata) as env:
if trade_interval < timedelta(days=1):
sim_params = factory.create_simulation_parameters(
start=self.start, end=self.end, data_frequency="minute")
minutes = env.market_minute_window(
sim_params.first_open,
int((trade_interval.total_seconds() / 60) * trade_count) +
100)
price_data = np.array([10.1] * len(minutes))
assets = {
sid:
pd.DataFrame({
"open": price_data,
"high": price_data,
"low": price_data,
"close": price_data,
"volume": np.array([100] * len(minutes)),
"dt": minutes
}).set_index("dt")
}
write_bcolz_minute_data(
env, env.days_in_range(minutes[0], minutes[-1]),
tempdir.path, assets)
equity_minute_reader = BcolzMinuteBarReader(tempdir.path)
data_portal = DataPortal(
env,
equity_minute_reader=equity_minute_reader,
)
else:
sim_params = factory.create_simulation_parameters(
data_frequency="daily")
days = sim_params.trading_days
assets = {
1:
pd.DataFrame(
{
"open": [10.1] * len(days),
"high": [10.1] * len(days),
"low": [10.1] * len(days),
"close": [10.1] * len(days),
"volume": [100] * len(days),
"day": [day.value for day in days]
},
index=days)
}
path = os.path.join(tempdir.path, "testdata.bcolz")
BcolzDailyBarWriter(path, days).write(assets.items())
equity_daily_reader = BcolzDailyBarReader(path)
data_portal = DataPortal(
env,
equity_daily_reader=equity_daily_reader,
)
if "default_slippage" not in params or \
not params["default_slippage"]:
slippage_func = FixedSlippage()
else:
slippage_func = None
blotter = Blotter(sim_params.data_frequency, self.env.asset_finder,
slippage_func)
start_date = sim_params.first_open
if alternate:
alternator = -1
else:
alternator = 1
tracker = PerformanceTracker(sim_params, self.env)
# replicate what tradesim does by going through every minute or day
# of the simulation and processing open orders each time
if sim_params.data_frequency == "minute":
ticks = minutes
else:
ticks = days
transactions = []
order_list = []
order_date = start_date
for tick in ticks:
blotter.current_dt = tick
if tick >= order_date and len(order_list) < order_count:
# place an order
direction = alternator**len(order_list)
order_id = blotter.order(
blotter.asset_finder.retrieve_asset(sid),
order_amount * direction, MarketOrder())
order_list.append(blotter.orders[order_id])
order_date = order_date + order_interval
# move after market orders to just after market next
# market open.
if order_date.hour >= 21:
if order_date.minute >= 00:
order_date = order_date + timedelta(days=1)
order_date = order_date.replace(hour=14, minute=30)
else:
bar_data = BarData(data_portal, lambda: tick,
sim_params.data_frequency)
txns, _ = blotter.get_transactions(bar_data)
for txn in txns:
tracker.process_transaction(txn)
transactions.append(txn)
for i in range(order_count):
order = order_list[i]
self.assertEqual(order.sid, sid)
self.assertEqual(order.amount, order_amount * alternator**i)
if complete_fill:
self.assertEqual(len(transactions), len(order_list))
total_volume = 0
for i in range(len(transactions)):
txn = transactions[i]
total_volume += txn.amount
if complete_fill:
order = order_list[i]
self.assertEqual(order.amount, txn.amount)
self.assertEqual(total_volume, expected_txn_volume)
self.assertEqual(len(transactions), expected_txn_count)
cumulative_pos = tracker.position_tracker.positions[sid]
if total_volume == 0:
self.assertIsNone(cumulative_pos)
else:
self.assertEqual(total_volume, cumulative_pos.amount)
# the open orders should not contain sid.
oo = blotter.open_orders
self.assertNotIn(sid, oo, "Entry is removed when no open orders")
def transaction_sim(self, **params):
"""This is a utility method that asserts expected
results for conversion of orders to transactions given a
trade history
"""
trade_count = params["trade_count"]
trade_interval = params["trade_interval"]
order_count = params["order_count"]
order_amount = params["order_amount"]
order_interval = params["order_interval"]
expected_txn_count = params["expected_txn_count"]
expected_txn_volume = params["expected_txn_volume"]
# optional parameters
# ---------------------
# if present, alternate between long and short sales
alternate = params.get("alternate")
# if present, expect transaction amounts to match orders exactly.
complete_fill = params.get("complete_fill")
sid = 1
metadata = make_simple_equity_info([sid], self.start, self.end)
with TempDirectory() as tempdir, tmp_trading_env(equities=metadata) as env:
if trade_interval < timedelta(days=1):
sim_params = factory.create_simulation_parameters(
start=self.start, end=self.end, data_frequency="minute"
)
minutes = env.market_minute_window(
sim_params.first_open, int((trade_interval.total_seconds() / 60) * trade_count) + 100
)
price_data = np.array([10.1] * len(minutes))
assets = {
sid: pd.DataFrame(
{
"open": price_data,
"high": price_data,
"low": price_data,
"close": price_data,
"volume": np.array([100] * len(minutes)),
"dt": minutes,
}
).set_index("dt")
}
write_bcolz_minute_data(
env, env.days_in_range(minutes[0], minutes[-1]), tempdir.path, iteritems(assets)
)
equity_minute_reader = BcolzMinuteBarReader(tempdir.path)
data_portal = DataPortal(
env,
first_trading_day=equity_minute_reader.first_trading_day,
equity_minute_reader=equity_minute_reader,
)
else:
sim_params = factory.create_simulation_parameters(data_frequency="daily")
days = sim_params.trading_days
assets = {
1: pd.DataFrame(
{
"open": [10.1] * len(days),
"high": [10.1] * len(days),
"low": [10.1] * len(days),
"close": [10.1] * len(days),
"volume": [100] * len(days),
"day": [day.value for day in days],
},
index=days,
)
}
path = os.path.join(tempdir.path, "testdata.bcolz")
BcolzDailyBarWriter(path, days).write(assets.items())
equity_daily_reader = BcolzDailyBarReader(path)
data_portal = DataPortal(
env,
first_trading_day=equity_daily_reader.first_trading_day,
equity_daily_reader=equity_daily_reader,
)
if "default_slippage" not in params or not params["default_slippage"]:
slippage_func = FixedSlippage()
else:
slippage_func = None
blotter = Blotter(sim_params.data_frequency, self.env.asset_finder, slippage_func)
start_date = sim_params.first_open
if alternate:
alternator = -1
else:
alternator = 1
tracker = PerformanceTracker(sim_params, self.env)
# replicate what tradesim does by going through every minute or day
# of the simulation and processing open orders each time
if sim_params.data_frequency == "minute":
ticks = minutes
else:
ticks = days
transactions = []
order_list = []
order_date = start_date
for tick in ticks:
blotter.current_dt = tick
if tick >= order_date and len(order_list) < order_count:
# place an order
direction = alternator ** len(order_list)
order_id = blotter.order(
blotter.asset_finder.retrieve_asset(sid), order_amount * direction, MarketOrder()
)
order_list.append(blotter.orders[order_id])
order_date = order_date + order_interval
# move after market orders to just after market next
# market open.
if order_date.hour >= 21:
if order_date.minute >= 00:
order_date = order_date + timedelta(days=1)
order_date = order_date.replace(hour=14, minute=30)
else:
bar_data = BarData(data_portal, lambda: tick, sim_params.data_frequency)
txns, _, closed_orders = blotter.get_transactions(bar_data)
for txn in txns:
tracker.process_transaction(txn)
transactions.append(txn)
blotter.prune_orders(closed_orders)
for i in range(order_count):
order = order_list[i]
self.assertEqual(order.sid, sid)
self.assertEqual(order.amount, order_amount * alternator ** i)
if complete_fill:
self.assertEqual(len(transactions), len(order_list))
total_volume = 0
for i in range(len(transactions)):
txn = transactions[i]
total_volume += txn.amount
if complete_fill:
order = order_list[i]
self.assertEqual(order.amount, txn.amount)
self.assertEqual(total_volume, expected_txn_volume)
self.assertEqual(len(transactions), expected_txn_count)
cumulative_pos = tracker.position_tracker.positions[sid]
if total_volume == 0:
self.assertIsNone(cumulative_pos)
else:
self.assertEqual(total_volume, cumulative_pos.amount)
# the open orders should not contain sid.
oo = blotter.open_orders
self.assertNotIn(sid, oo, "Entry is removed when no open orders")
def test_ingest(self):
zipline_root = self.enter_instance_context(tmp_dir()).path
env = self.enter_instance_context(tmp_trading_env())
start = pd.Timestamp('2014-01-06', tz='utc')
end = pd.Timestamp('2014-01-10', tz='utc')
calendar = trading_days[trading_days.slice_indexer(start, end)]
minutes = env.minutes_for_days_in_range(calendar[0], calendar[-1])
outer_environ = {
'ZIPLINE_ROOT': zipline_root,
}
sids = tuple(range(3))
equities = make_simple_equity_info(
sids,
calendar[0],
calendar[-1],
)
daily_bar_data = make_bar_data(equities, calendar)
minute_bar_data = make_bar_data(equities, minutes)
first_split_ratio = 0.5
second_split_ratio = 0.1
splits = pd.DataFrame.from_records([
{
'effective_date': str_to_seconds('2014-01-08'),
'ratio': first_split_ratio,
'sid': 0,
},
{
'effective_date': str_to_seconds('2014-01-09'),
'ratio': second_split_ratio,
'sid': 1,
},
])
@self.register('bundle',
calendar=calendar,
opens=env.opens_in_range(calendar[0], calendar[-1]),
closes=env.closes_in_range(calendar[0], calendar[-1]))
def bundle_ingest(environ,
asset_db_writer,
minute_bar_writer,
daily_bar_writer,
adjustment_writer,
calendar,
cache,
show_progress):
assert_is(environ, outer_environ)
asset_db_writer.write(equities=equities)
minute_bar_writer.write(minute_bar_data)
daily_bar_writer.write(daily_bar_data)
adjustment_writer.write(splits=splits)
assert_is_instance(calendar, pd.DatetimeIndex)
assert_is_instance(cache, dataframe_cache)
assert_is_instance(show_progress, bool)
self.ingest('bundle', environ=outer_environ)
bundle = load('bundle', environ=outer_environ)
assert_equal(set(bundle.asset_finder.sids), set(sids))
columns = 'open', 'high', 'low', 'close', 'volume'
actual = bundle.minute_bar_reader.load_raw_arrays(
columns,
minutes[0],
minutes[-1],
sids,
)
for actual_column, colname in zip(actual, columns):
assert_equal(
actual_column,
expected_bar_values_2d(minutes, equities, colname),
msg=colname,
)
actual = bundle.daily_bar_reader.load_raw_arrays(
columns,
calendar[0],
calendar[-1],
sids,
)
for actual_column, colname in zip(actual, columns):
assert_equal(
actual_column,
expected_bar_values_2d(calendar, equities, colname),
msg=colname,
)
adjustments_for_cols = bundle.adjustment_reader.load_adjustments(
columns,
calendar,
pd.Index(sids),
)
for column, adjustments in zip(columns, adjustments_for_cols[:-1]):
# iterate over all the adjustments but `volume`
assert_equal(
adjustments,
{
2: [Float64Multiply(
first_row=0,
last_row=2,
first_col=0,
last_col=0,
value=first_split_ratio,
)],
3: [Float64Multiply(
first_row=0,
last_row=3,
first_col=1,
last_col=1,
value=second_split_ratio,
)],
},
msg=column,
)
# check the volume, the value should be 1/ratio
assert_equal(
adjustments_for_cols[-1],
{
2: [Float64Multiply(
first_row=0,
last_row=2,
first_col=0,
last_col=0,
value=1 / first_split_ratio,
)],
3: [Float64Multiply(
first_row=0,
last_row=3,
first_col=1,
last_col=1,
value=1 / second_split_ratio,
)],
},
msg='volume',
)
def test_ingest(self):
env = self.enter_instance_context(tmp_trading_env())
start = pd.Timestamp('2014-01-06', tz='utc')
end = pd.Timestamp('2014-01-10', tz='utc')
calendar = trading_days[trading_days.slice_indexer(start, end)]
minutes = env.minutes_for_days_in_range(calendar[0], calendar[-1])
sids = tuple(range(3))
equities = make_simple_equity_info(
sids,
calendar[0],
calendar[-1],
)
daily_bar_data = make_bar_data(equities, calendar)
minute_bar_data = make_bar_data(equities, minutes)
first_split_ratio = 0.5
second_split_ratio = 0.1
splits = pd.DataFrame.from_records([
{
'effective_date': str_to_seconds('2014-01-08'),
'ratio': first_split_ratio,
'sid': 0,
},
{
'effective_date': str_to_seconds('2014-01-09'),
'ratio': second_split_ratio,
'sid': 1,
},
])
@self.register('bundle',
calendar=calendar,
opens=env.opens_in_range(calendar[0], calendar[-1]),
closes=env.closes_in_range(calendar[0], calendar[-1]))
def bundle_ingest(environ,
asset_db_writer,
minute_bar_writer,
daily_bar_writer,
adjustment_writer,
calendar,
cache,
show_progress,
output_dir):
assert_is(environ, self.environ)
asset_db_writer.write(equities=equities)
minute_bar_writer.write(minute_bar_data)
daily_bar_writer.write(daily_bar_data)
adjustment_writer.write(splits=splits)
assert_is_instance(calendar, pd.DatetimeIndex)
assert_is_instance(cache, dataframe_cache)
assert_is_instance(show_progress, bool)
self.ingest('bundle', environ=self.environ)
bundle = self.load('bundle', environ=self.environ)
assert_equal(set(bundle.asset_finder.sids), set(sids))
columns = 'open', 'high', 'low', 'close', 'volume'
actual = bundle.minute_bar_reader.load_raw_arrays(
columns,
minutes[0],
minutes[-1],
sids,
)
for actual_column, colname in zip(actual, columns):
assert_equal(
actual_column,
expected_bar_values_2d(minutes, equities, colname),
msg=colname,
)
actual = bundle.daily_bar_reader.load_raw_arrays(
columns,
calendar[0],
calendar[-1],
sids,
)
for actual_column, colname in zip(actual, columns):
assert_equal(
actual_column,
expected_bar_values_2d(calendar, equities, colname),
msg=colname,
)
adjustments_for_cols = bundle.adjustment_reader.load_adjustments(
columns,
calendar,
pd.Index(sids),
)
for column, adjustments in zip(columns, adjustments_for_cols[:-1]):
# iterate over all the adjustments but `volume`
assert_equal(
adjustments,
{
2: [Float64Multiply(
first_row=0,
last_row=2,
first_col=0,
last_col=0,
value=first_split_ratio,
)],
3: [Float64Multiply(
first_row=0,
last_row=3,
first_col=1,
last_col=1,
value=second_split_ratio,
)],
},
msg=column,
)
# check the volume, the value should be 1/ratio
assert_equal(
adjustments_for_cols[-1],
{
2: [Float64Multiply(
first_row=0,
last_row=2,
first_col=0,
last_col=0,
value=1 / first_split_ratio,
)],
3: [Float64Multiply(
first_row=0,
last_row=3,
first_col=1,
last_col=1,
value=1 / second_split_ratio,
)],
},
msg='volume',
)