def create_data_portal(asset_finder, tempdir, sim_params, sids,
trading_calendar, adjustment_reader=None):
if sim_params.data_frequency == "daily":
daily_path = write_daily_data(tempdir, sim_params, sids,
trading_calendar)
equity_daily_reader = BcolzDailyBarReader(daily_path)
return DataPortal(
asset_finder, trading_calendar,
first_trading_day=equity_daily_reader.first_trading_day,
equity_daily_reader=equity_daily_reader,
adjustment_reader=adjustment_reader
)
else:
minutes = trading_calendar.minutes_in_range(
sim_params.first_open,
sim_params.last_close
)
minute_path = write_minute_data(trading_calendar, tempdir, minutes,
sids)
equity_minute_reader = BcolzMinuteBarReader(minute_path)
return DataPortal(
asset_finder, trading_calendar,
first_trading_day=equity_minute_reader.first_trading_day,
equity_minute_reader=equity_minute_reader,
adjustment_reader=adjustment_reader
)
def test_unadjusted_minutes_early_close(self):
"""
Test unadjusted minute window, ensuring that early closes are filtered
out.
"""
day_before_thanksgiving = Timestamp('2015-11-25', tz='UTC')
xmas_eve = Timestamp('2015-12-24', tz='UTC')
market_day_after_xmas = Timestamp('2015-12-28', tz='UTC')
minutes = [self.market_closes[day_before_thanksgiving] -
Timedelta('2 min'),
self.market_closes[xmas_eve] - Timedelta('1 min'),
self.market_opens[market_day_after_xmas] +
Timedelta('1 min')]
sids = [1, 2]
data_1 = DataFrame(
data={
'open': [
15.0, 15.1, 15.2],
'high': [17.0, 17.1, 17.2],
'low': [11.0, 11.1, 11.3],
'close': [14.0, 14.1, 14.2],
'volume': [1000, 1001, 1002],
},
index=minutes)
self.writer.write_sid(sids[0], data_1)
data_2 = DataFrame(
data={
'open': [25.0, 25.1, 25.2],
'high': [27.0, 27.1, 27.2],
'low': [21.0, 21.1, 21.2],
'close': [24.0, 24.1, 24.2],
'volume': [2000, 2001, 2002],
},
index=minutes)
self.writer.write_sid(sids[1], data_2)
reader = BcolzMinuteBarReader(self.dest)
columns = ['open', 'high', 'low', 'close', 'volume']
sids = [sids[0], sids[1]]
arrays = list(map(transpose, reader.load_raw_arrays(
columns, minutes[0], minutes[-1], sids,
)))
data = {sids[0]: data_1, sids[1]: data_2}
start_minute_loc = \
self.trading_calendar.all_minutes.get_loc(minutes[0])
minute_locs = [
self.trading_calendar.all_minutes.get_loc(minute)
- start_minute_loc
for minute in minutes
]
for i, col in enumerate(columns):
for j, sid in enumerate(sids):
assert_almost_equal(data[sid].loc[minutes, col],
arrays[i][j][minute_locs])
def test_truncate_between_data_points(self):
tds = self.market_opens.index
days = tds[tds.slice_indexer(
start=self.test_calendar_start + 1,
end=self.test_calendar_start + 3
)]
minutes = DatetimeIndex([
self.market_opens[days[0]] + timedelta(minutes=60),
self.market_opens[days[1]] + timedelta(minutes=120),
])
sid = 1
data = DataFrame(
data={
'open': [10.0, 11.0],
'high': [20.0, 21.0],
'low': [30.0, 31.0],
'close': [40.0, 41.0],
'volume': [50.0, 51.0]
},
index=minutes)
self.writer.write_sid(sid, data)
# Open a new writer to cover `open` method, also truncating only
# applies to an existing directory.
writer = BcolzMinuteBarWriter.open(self.dest)
# Truncate to first day with data.
writer.truncate(days[0])
# Refresh the reader since truncate update the metadata.
self.reader = BcolzMinuteBarReader(self.dest)
self.assertEqual(self.writer.last_date_in_output_for_sid(sid), days[0])
cal = self.trading_calendar
_, last_close = cal.open_and_close_for_session(days[0])
self.assertEqual(self.reader.last_available_dt, last_close)
minute = minutes[0]
open_price = self.reader.get_value(sid, minute, 'open')
self.assertEquals(10.0, open_price)
high_price = self.reader.get_value(sid, minute, 'high')
self.assertEquals(20.0, high_price)
low_price = self.reader.get_value(sid, minute, 'low')
self.assertEquals(30.0, low_price)
close_price = self.reader.get_value(sid, minute, 'close')
self.assertEquals(40.0, close_price)
volume_price = self.reader.get_value(sid, minute, 'volume')
self.assertEquals(50.0, volume_price)
def init_instance_fixtures(self):
super(BcolzMinuteBarTestCase, self).init_instance_fixtures()
self.dest = self.instance_tmpdir.getpath('minute_bars')
os.makedirs(self.dest)
self.writer = BcolzMinuteBarWriter(
self.dest,
self.trading_calendar,
TEST_CALENDAR_START,
TEST_CALENDAR_STOP,
US_EQUITIES_MINUTES_PER_DAY,
)
self.reader = BcolzMinuteBarReader(self.dest)
def test_minute_updates(self):
"""
Test minute updates.
"""
start_minute = self.market_opens[TEST_CALENDAR_START]
minutes = [start_minute,
start_minute + Timedelta('1 min'),
start_minute + Timedelta('2 min')]
sids = [1, 2]
data_1 = DataFrame(
data={
'open': [15.0, nan, 15.1],
'high': [17.0, nan, 17.1],
'low': [11.0, nan, 11.1],
'close': [14.0, nan, 14.1],
'volume': [1000, 0, 1001]
},
index=minutes)
data_2 = DataFrame(
data={
'open': [25.0, nan, 25.1],
'high': [27.0, nan, 27.1],
'low': [21.0, nan, 21.1],
'close': [24.0, nan, 24.1],
'volume': [2000, 0, 2001]
},
index=minutes)
frames = {1: data_1, 2: data_2}
update_path = self.instance_tmpdir.getpath('updates.h5')
update_writer = H5MinuteBarUpdateWriter(update_path)
update_writer.write(frames)
update_reader = H5MinuteBarUpdateReader(update_path)
self.writer.write(update_reader.read(minutes, sids))
# Refresh the reader since truncate update the metadata.
reader = BcolzMinuteBarReader(self.dest)
columns = ['open', 'high', 'low', 'close', 'volume']
sids = [sids[0], sids[1]]
arrays = list(map(transpose, reader.load_raw_arrays(
columns, minutes[0], minutes[-1], sids,
)))
data = {sids[0]: data_1, sids[1]: data_2}
for i, col in enumerate(columns):
for j, sid in enumerate(sids):
assert_almost_equal(data[sid][col], arrays[i][j])
def test_append_on_new_day(self):
sid = 1
ohlcv = {
'open': [2.0],
'high': [3.0],
'low': [1.0],
'close': [2.0],
'volume': [10.0]
}
dt = self.market_opens[TEST_CALENDAR_STOP]
data = DataFrame(
data=ohlcv,
index=[dt])
self.writer.write_sid(sid, data)
# Open a new writer to cover `open` method, also a common usage
# of appending new days will be writing to an existing directory.
cday = self.trading_calendar.schedule.index.freq
new_end_session = TEST_CALENDAR_STOP + cday
writer = BcolzMinuteBarWriter.open(self.dest, new_end_session)
next_day_minute = dt + cday
new_data = DataFrame(
data=ohlcv,
index=[next_day_minute])
writer.write_sid(sid, new_data)
# Get a new reader to test updated calendar.
reader = BcolzMinuteBarReader(self.dest)
second_minute = dt + Timedelta(minutes=1)
# The second minute should have been padded with zeros
for col in ('open', 'high', 'low', 'close'):
assert_almost_equal(
nan, reader.get_value(sid, second_minute, col)
)
self.assertEqual(
0, reader.get_value(sid, second_minute, 'volume')
)
# The next day minute should have data.
for col in ('open', 'high', 'low', 'close', 'volume'):
assert_almost_equal(
ohlcv[col], reader.get_value(sid, next_day_minute, col)
)
def test_unadjusted_minutes(self):
"""
Test unadjusted minutes.
"""
start_minute = self.market_opens[TEST_CALENDAR_START]
minutes = [start_minute,
start_minute + Timedelta('1 min'),
start_minute + Timedelta('2 min')]
sids = [1, 2]
data_1 = DataFrame(
data={
'open': [15.0, nan, 15.1],
'high': [17.0, nan, 17.1],
'low': [11.0, nan, 11.1],
'close': [14.0, nan, 14.1],
'volume': [1000, 0, 1001]
},
index=minutes)
self.writer.write_sid(sids[0], data_1)
data_2 = DataFrame(
data={
'open': [25.0, nan, 25.1],
'high': [27.0, nan, 27.1],
'low': [21.0, nan, 21.1],
'close': [24.0, nan, 24.1],
'volume': [2000, 0, 2001]
},
index=minutes)
self.writer.write_sid(sids[1], data_2)
reader = BcolzMinuteBarReader(self.dest)
columns = ['open', 'high', 'low', 'close', 'volume']
sids = [sids[0], sids[1]]
arrays = list(map(transpose, reader.load_raw_arrays(
columns, minutes[0], minutes[-1], sids,
)))
data = {sids[0]: data_1, sids[1]: data_2}
for i, col in enumerate(columns):
for j, sid in enumerate(sids):
assert_almost_equal(data[sid][col], arrays[i][j])
def test_write_one_ohlcv_with_ratios(self):
minute = self.market_opens[self.test_calendar_start]
sid = 1
data = DataFrame(
data={
'open': [10.0],
'high': [20.0],
'low': [30.0],
'close': [40.0],
'volume': [50.0],
},
index=[minute],
)
# Create a new writer with `ohlc_ratios_per_sid` defined.
writer_with_ratios = BcolzMinuteBarWriter(
self.dest,
self.trading_calendar,
TEST_CALENDAR_START,
TEST_CALENDAR_STOP,
US_EQUITIES_MINUTES_PER_DAY,
ohlc_ratios_per_sid={sid: 25},
)
writer_with_ratios.write_sid(sid, data)
reader = BcolzMinuteBarReader(self.dest)
open_price = reader.get_value(sid, minute, 'open')
self.assertEquals(10.0, open_price)
high_price = reader.get_value(sid, minute, 'high')
self.assertEquals(20.0, high_price)
low_price = reader.get_value(sid, minute, 'low')
self.assertEquals(30.0, low_price)
close_price = reader.get_value(sid, minute, 'close')
self.assertEquals(40.0, close_price)
volume_price = reader.get_value(sid, minute, 'volume')
self.assertEquals(50.0, volume_price)
def test_truncate_all_data_points(self):
tds = self.market_opens.index
days = tds[tds.slice_indexer(
start=self.test_calendar_start + 1,
end=self.test_calendar_start + 3
)]
minutes = DatetimeIndex([
self.market_opens[days[0]] + timedelta(minutes=60),
self.market_opens[days[1]] + timedelta(minutes=120),
])
sid = 1
data = DataFrame(
data={
'open': [10.0, 11.0],
'high': [20.0, 21.0],
'low': [30.0, 31.0],
'close': [40.0, 41.0],
'volume': [50.0, 51.0]
},
index=minutes)
self.writer.write_sid(sid, data)
# Truncate to first day in the calendar, a day before the first
# day with minute data.
self.writer.truncate(self.test_calendar_start)
# Refresh the reader since truncate update the metadata.
self.reader = BcolzMinuteBarReader(self.dest)
self.assertEqual(
self.writer.last_date_in_output_for_sid(sid),
self.test_calendar_start,
)
cal = self.trading_calendar
_, last_close = cal.open_and_close_for_session(
self.test_calendar_start)
self.assertEqual(self.reader.last_available_dt, last_close)
def create_data_portal_from_trade_history(asset_finder, trading_calendar,
tempdir, sim_params, trades_by_sid):
if sim_params.data_frequency == "daily":
path = os.path.join(tempdir.path, "testdaily.bcolz")
writer = BcolzDailyBarWriter(
path, trading_calendar,
sim_params.start_session,
sim_params.end_session
)
writer.write(
trades_by_sid_to_dfs(trades_by_sid, sim_params.sessions),
)
equity_daily_reader = BcolzDailyBarReader(path)
return DataPortal(
asset_finder, trading_calendar,
first_trading_day=equity_daily_reader.first_trading_day,
equity_daily_reader=equity_daily_reader,
)
else:
minutes = trading_calendar.minutes_in_range(
sim_params.first_open,
sim_params.last_close
)
length = len(minutes)
assets = {}
for sidint, trades in iteritems(trades_by_sid):
opens = np.zeros(length)
highs = np.zeros(length)
lows = np.zeros(length)
closes = np.zeros(length)
volumes = np.zeros(length)
for trade in trades:
# put them in the right place
idx = minutes.searchsorted(trade.dt)
opens[idx] = trade.open_price * 1000
highs[idx] = trade.high * 1000
lows[idx] = trade.low * 1000
closes[idx] = trade.close_price * 1000
volumes[idx] = trade.volume
assets[sidint] = pd.DataFrame({
"open": opens,
"high": highs,
"low": lows,
"close": closes,
"volume": volumes,
"dt": minutes
}).set_index("dt")
write_bcolz_minute_data(
trading_calendar,
sim_params.sessions,
tempdir.path,
assets
)
equity_minute_reader = BcolzMinuteBarReader(tempdir.path)
return DataPortal(
asset_finder, trading_calendar,
first_trading_day=equity_minute_reader.first_trading_day,
equity_minute_reader=equity_minute_reader,
)
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')
asset1 = self.asset_finder.retrieve_asset(1)
metadata = make_simple_equity_info([asset1.sid], self.start, self.end)
with TempDirectory() as tempdir, \
tmp_trading_env(equities=metadata,
load=self.make_load_function()) as env:
if trade_interval < timedelta(days=1):
sim_params = factory.create_simulation_parameters(
start=self.start, end=self.end, data_frequency="minute")
minutes = self.trading_calendar.minutes_window(
sim_params.first_open,
int((trade_interval.total_seconds() / 60) * trade_count) +
100)
price_data = np.array([10.1] * len(minutes))
assets = {
asset1.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(
self.trading_calendar,
self.trading_calendar.sessions_in_range(
self.trading_calendar.minute_to_session_label(
minutes[0]),
self.trading_calendar.minute_to_session_label(
minutes[-1])),
tempdir.path,
iteritems(assets),
)
equity_minute_reader = BcolzMinuteBarReader(tempdir.path)
data_portal = DataPortal(
env.asset_finder,
self.trading_calendar,
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.sessions
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, self.trading_calendar, days[0],
days[-1]).write(assets.items())
equity_daily_reader = BcolzDailyBarReader(path)
data_portal = DataPortal(
env.asset_finder,
self.trading_calendar,
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, slippage_func)
start_date = sim_params.first_open
if alternate:
alternator = -1
else:
alternator = 1
tracker = PerformanceTracker(sim_params, self.trading_calendar,
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(asset1, 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=data_portal,
simulation_dt_func=lambda: tick,
data_frequency=sim_params.data_frequency,
trading_calendar=self.trading_calendar,
restrictions=NoRestrictions(),
)
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.asset, asset1)
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[asset1]
if total_volume == 0:
self.assertIsNone(cumulative_pos)
else:
self.assertEqual(total_volume, cumulative_pos.amount)
# the open orders should not contain the asset.
oo = blotter.open_orders
self.assertNotIn(asset1, oo,
"Entry is removed when no open orders")