def test_add_generic_data_adds_to_container(self):
# Arrange
engine = BacktestEngine()
data_type = DataType(MyData, metadata={"news_wire": "hacks"})
generic_data1 = [
GenericData(data_type, MyData("AAPL hacked")),
GenericData(
data_type,
MyData("AMZN hacked", 1000, 1000),
),
GenericData(
data_type,
MyData("NFLX hacked", 3000, 3000),
),
GenericData(
data_type,
MyData("MSFT hacked", 2000, 2000),
),
]
generic_data2 = [
GenericData(
data_type,
MyData("FB hacked", 1500, 1500),
),
]
# Act
engine.add_generic_data(ClientId("NEWS_CLIENT"), generic_data1)
engine.add_generic_data(ClientId("NEWS_CLIENT"), generic_data2)
def test_with_mix_of_stream_data_produces_correct_stream_of_data(self):
# Assert
data = BacktestDataContainer()
data.add_instrument(ETHUSDT_BINANCE)
snapshot1 = OrderBookSnapshot(
instrument_id=ETHUSDT_BINANCE.id,
level=OrderBookLevel.L2,
bids=[[1550.15, 0.51], [1580.00, 1.20]],
asks=[[1552.15, 1.51], [1582.00, 2.20]],
timestamp_ns=0,
)
data_type = DataType(str, metadata={"news_wire": "hacks"})
generic_data1 = [
GenericData(data_type, data="AAPL hacked", timestamp_ns=0),
GenericData(data_type, data="AMZN hacked", timestamp_ns=500_000),
GenericData(data_type, data="NFLX hacked", timestamp_ns=1_000_000),
GenericData(data_type, data="MSFT hacked", timestamp_ns=2_000_000),
]
snapshot2 = OrderBookSnapshot(
instrument_id=ETHUSDT_BINANCE.id,
level=OrderBookLevel.L2,
bids=[[1551.15, 0.51], [1581.00, 1.20]],
asks=[[1553.15, 1.51], [1583.00, 2.20]],
timestamp_ns=1_000_000,
)
data.add_generic_data(ClientId("NEWS_CLIENT"), generic_data1)
data.add_order_book_data([snapshot1, snapshot2])
producer = BacktestDataProducer(data=data, logger=self.logger)
producer.setup(producer.min_timestamp_ns, producer.max_timestamp_ns)
# Act
streamed_data = []
while producer.has_data:
streamed_data.append(producer.next())
# Assert
timestamps = [x.timestamp_ns for x in streamed_data]
assert timestamps == [0, 0, 500000, 1000000, 1000000, 2000000]
assert producer.min_timestamp_ns == 0
assert producer.max_timestamp_ns == 2_000_000
assert producer.min_timestamp == pd.Timestamp(
"1970-01-01 00:00:00.000000+0000", tz="UTC")
assert producer.max_timestamp == pd.Timestamp(
"1970-01-01 00:00:00.002000+0000", tz="UTC")
def test_handle_data_sends_to_data_engine(self):
# Arrange
data_type = DataType(str, {"Type": "NEWS_WIRE"})
data = GenericData(data_type, "Some news headline", UNIX_EPOCH)
# Act
self.client._handle_data_py(data)
# Assert
self.assertEqual(1, self.data_engine.data_count)
def test_data_instantiation(self):
# Arrange
# Act
data_type = DataType(str, {"type": "NEWS_WIRE"})
data = GenericData(data_type, "Some News Headline", UNIX_EPOCH)
# Assert
self.assertEqual(data_type, data.data_type)
self.assertEqual("Some News Headline", data.data)
self.assertEqual(UNIX_EPOCH, data.timestamp)
def test_add_generic_data_adds_to_container(self):
# Arrange
engine = BacktestEngine()
data_type = DataType(str, metadata={"news_wire": "hacks"})
generic_data1 = [
GenericData(
data_type, data="AAPL hacked", timestamp_origin_ns=0, timestamp_ns=0
),
GenericData(
data_type,
data="AMZN hacked",
timestamp_origin_ns=1000,
timestamp_ns=1000,
),
GenericData(
data_type,
data="NFLX hacked",
timestamp_origin_ns=3000,
timestamp_ns=3000,
),
GenericData(
data_type,
data="MSFT hacked",
timestamp_origin_ns=2000,
timestamp_ns=2000,
),
]
generic_data2 = [
GenericData(
data_type, data="FB hacked", timestamp_origin_ns=1500, timestamp_ns=1500
),
]
# Act
engine.add_generic_data(ClientId("NEWS_CLIENT"), generic_data1)
engine.add_generic_data(ClientId("NEWS_CLIENT"), generic_data2)
def test_handle_data_response_sends_to_data_engine(self):
# Arrange
data_type = DataType(str, {
"Type": "ECONOMIC_DATA",
"topic": "unemployment"
})
data = GenericData(data_type, "may 2020, 6.9%", UNIX_EPOCH)
# Act
self.client._handle_data_response_py(data,
self.uuid_factory.generate())
# Assert
self.assertEqual(1, self.data_engine.response_count)
def test_add_generic_data_adds_to_container(self):
# Arrange
data = BacktestDataContainer()
data_type = DataType(str, metadata={"news_wire": "hacks"})
generic_data1 = [
GenericData(data_type, data="AAPL hacked", timestamp_ns=0),
GenericData(data_type, data="AMZN hacked", timestamp_ns=1000),
GenericData(data_type, data="NFLX hacked", timestamp_ns=3000),
GenericData(data_type, data="MSFT hacked", timestamp_ns=2000),
]
generic_data2 = [
GenericData(data_type, data="FB hacked", timestamp_ns=1500),
]
# Act
data.add_generic_data(ClientId("NEWS_CLIENT"), generic_data1)
data.add_generic_data(ClientId("NEWS_CLIENT"), generic_data2)
# Assert
assert ClientId("NEWS_CLIENT") in data.clients
assert len(data.generic_data) == 5
assert data.generic_data[-1].timestamp_ns == 3000 # sorted
def test_data_instantiation(self):
# Arrange
# Act
data_type = DataType(NewsEvent, {"publisher": "NEWS_WIRE"})
data = NewsEvent(
impact=NewsImpact.HIGH,
name="Unemployment Rate",
currency=USD,
ts_event_ns=0,
ts_recv_ns=0,
)
generic_data = GenericData(data_type, data)
# Assert
self.assertEqual(data_type, generic_data.data_type)
self.assertEqual(data, generic_data.data)
def test_handle_data_sends_to_data_engine(self):
# Arrange
data_type = DataType(NewsEvent, {"Type": "NEWS_WIRE"})
data = NewsEvent(
impact=NewsImpact.HIGH,
name="Unemployment Rate",
currency=USD,
ts_event_ns=0,
ts_recv_ns=0,
)
generic_data = GenericData(data_type, data)
# Act
self.client._handle_data_py(generic_data)
# Assert
self.assertEqual(1, self.data_engine.data_count)
def test_with_mix_of_stream_data_produces_correct_stream_of_data(self):
# Assert
snapshot1 = OrderBookSnapshot(
instrument_id=ETHUSDT_BINANCE.id,
level=BookLevel.L2,
bids=[[1550.15, 0.51], [1580.00, 1.20]],
asks=[[1552.15, 1.51], [1582.00, 2.20]],
ts_event_ns=0,
ts_recv_ns=0,
)
data_type = DataType(MyData, metadata={"news_wire": "hacks"})
generic_data1 = [
GenericData(
data_type,
data=MyData("AAPL hacked"),
),
GenericData(
data_type,
data=MyData("AMZN hacked", 500_000, 500_000),
),
GenericData(
data_type,
data=MyData("NFLX hacked", 1_000_000, 1_000_000),
),
GenericData(
data_type,
data=MyData("MSFT hacked", 2_000_000, 2_000_000),
),
]
snapshot2 = OrderBookSnapshot(
instrument_id=ETHUSDT_BINANCE.id,
level=BookLevel.L2,
bids=[[1551.15, 0.51], [1581.00, 1.20]],
asks=[[1553.15, 1.51], [1583.00, 2.20]],
ts_event_ns=1_000_000,
ts_recv_ns=1_000_000,
)
producer = BacktestDataProducer(
logger=self.logger,
instruments=[ETHUSDT_BINANCE],
generic_data=generic_data1,
order_book_data=[snapshot1, snapshot2],
)
producer.setup(producer.min_timestamp_ns, producer.max_timestamp_ns)
# Act
streamed_data = []
while producer.has_data:
streamed_data.append(producer.next())
# Assert
timestamps = [x.ts_recv_ns for x in streamed_data]
assert timestamps == [0, 0, 500000, 1000000, 1000000, 2000000]
assert producer.min_timestamp_ns == 0
assert producer.max_timestamp_ns == 2_000_000
assert producer.min_timestamp == pd.Timestamp(
"1970-01-01 00:00:00.000000+0000", tz="UTC")
assert producer.max_timestamp == pd.Timestamp(
"1970-01-01 00:00:00.002000+0000", tz="UTC")
