def test_compare_against_oneoff_calculation(self):
rw = np.cumsum(np.random.normal(0, 2, 1000)) + 100
close = DataSeries("close")
close.start(self.app_context)
t = datetime.datetime.now()
sma = SMA(close, input_key='close', length=50)
sma.start(self.app_context)
result = []
for x in rw:
close.add({"timestamp": t, "close": x})
result.append(sma.now('value'))
t = t + datetime.timedelta(0, 3)
result = np.array(result)
# either apply or direct call is equivalent
target = close.apply('close', start=None, end=None, func=talib.SMA, timeperiod=50)
# target = talib.SMA(np.array(close.get_series('close')), timeperiod=50)
result[np.isnan(result)] = 0
target[np.isnan(target)] = 0
try:
np.testing.assert_almost_equal(target, result, 5)
except AssertionError as e:
self.fail(e.message)
def test_compare_against_oneoff_calculation(self):
rw = np.cumsum(np.random.normal(0, 2, 1000)) + 100
close = DataSeries(time_series=ModelFactory.build_time_series(
series_id="close"))
close.start(self.app_context)
t = 1
sma = SMA(inputs=close, input_keys='close', length=50)
sma.start(self.app_context)
result = []
for x in rw:
close.add(timestamp=t, data={"close": x})
result.append(sma.now('value'))
t = t + 3
result = np.array(result)
# either apply or direct call is equivalent
target = close.apply('close',
start=None,
end=None,
func=talib.SMA,
timeperiod=50)
# target = talib.SMA(np.array(close.get_series('close')), timeperiod=50)
result[np.isnan(result)] = 0
target[np.isnan(target)] = 0
try:
np.testing.assert_almost_equal(target, result, 5)
except AssertionError as e:
self.fail(e.message)
def test_empty_at_initialize(self):
close = self.app_context.inst_data_mgr.get_series("bar")
close.start(self.app_context)
sma = SMA(inputs=close, input_keys='close', length=3)
sma.start(self.app_context)
self.assertEquals(0, len(sma.get_data()))
def test_empty_at_initialize(self):
close = self.app_context.inst_data_mgr.get_series("bar")
close.start(self.app_context)
sma = SMA(close, 'close', 3)
sma.start(self.app_context)
self.assertEquals(0, len(sma.get_data()))
def test_name(self):
bar = self.app_context.inst_data_mgr.get_series("bar")
bar.start(self.app_context)
sma = SMA(bar, input_key='close', length=3)
sma.start(self.app_context)
self.assertEquals("SMA('bar',close,3)", sma.name)
sma2 = SMA(sma, input_key='value', length=10)
self.assertEquals("SMA(SMA('bar',close,3),value,10)", sma2.name)
def test_name(self):
bar = self.app_context.inst_data_mgr.get_series("bar")
bar.start(self.app_context)
sma = SMA(inputs=bar, input_keys='close', length=3)
sma.start(self.app_context)
self.assertEquals("SMA(bar[close],length=3)", sma.name)
sma2 = SMA(inputs=sma, input_keys='value', length=10)
self.assertEquals("SMA(SMA(bar[close],length=3)[value],length=10)",
sma2.name)
def test_with_multi_bar_multi_indicator_with_tail_start(self):
bar0 = self.app_context.inst_data_mgr.get_series("bar0")
bar1 = self.app_context.inst_data_mgr.get_series("bar1")
bar0.start(self.app_context)
bar1.start(self.app_context)
# sma_2_bar0 = SMA(bar0, "close", 2)
# sma_4_bar0 = SMA(bar0, "close", 4)
# sma_3_bar1 = SMA(bar1, "close", 3)
#
# sma_2_bar0.start(self.app_context)
# sma_4_bar0.start(self.app_context)
# sma_3_bar1.start(self.app_context)
# rank = Rank([sma_2_bar0, sma_3_bar1, sma_4_bar0], input_key=Indicator.VALUE)
rank = Rank([
SMA(bar0, "close", 2),
SMA(bar1, "close", 3),
SMA(bar0, "close", 4)
],
input_key=Indicator.VALUE)
rank.start(self.app_context) # only start at the tail is enough
t = datetime.datetime.now()
bar0.add({"timestamp": t, "close": 80.0, "open": 0})
bar1.add({"timestamp": t, "close": 95.0, "open": 0})
nan_arr = np.empty([1, 3])
nan_arr[:] = np.nan
# self.assertListEqual(nan_arr.tolist(), rank.now()["value"])
self.__np_assert_almost_equal(nan_arr, np.array(rank.now()["value"]))
t = t + datetime.timedelta(0, 3)
bar0.add({"timestamp": t, "close": 85.0, "open": 0})
bar1.add({"timestamp": t, "close": 93.0, "open": 0})
target = nan_arr
target[0, 0] = 0.
self.__np_assert_almost_equal(target, np.array(rank.now()["value"]))
t = t + datetime.timedelta(0, 3)
bar0.add({"timestamp": t, "close": 86.0, "open": 0})
bar1.add({"timestamp": t, "close": 91.0, "open": 0})
target[0, 1] = 0.5
self.__np_assert_almost_equal(target, np.array(rank.now()["value"]))
t = t + datetime.timedelta(0, 3)
bar0.add({"timestamp": t, "close": 90.0, "open": 0})
bar1.add({"timestamp": t, "close": 95.0, "open": 0})
target = np.array([[0.5, 1.0, 0.]])
# self.assertAlmostEqual(target.tolist(), rank.now()["value"])
self.__np_assert_almost_equal(target, np.array(rank.now()["value"]))
def test_empty_at_initialize(self):
close = self.app_context.inst_data_mgr.get_series("bar")
close.start(self.app_context)
sma3 = SMA(inputs=close, input_keys='close', length=3)
sma20 = SMA(inputs=close, input_keys='close', length=20)
sma50 = SMA(inputs=close, input_keys='close', length=50)
sma3.start(self.app_context)
sma20.start(self.app_context)
rank = Rank(inputs=[sma3, sma20, sma50], input_keys='close')
rank.start(self.app_context)
self.assertEquals(0, len(rank.get_data()))
def test_nan_before_size(self):
bar = self.app_context.inst_data_mgr.get_series("bar")
bar.start(self.app_context)
sma = SMA(inputs=bar, input_keys='close', length=3)
sma.start(self.app_context)
t1 = 1
t2 = t1 + 3
t3 = t2 + 3
bar.add(timestamp=t1, data={"close": 2.0, "open": 0})
self.assertEquals([{'value': np.nan}], sma.get_data())
bar.add(timestamp=t2, data={"close": 2.4, "open": 1.4})
self.assertEquals([{
'value': np.nan
}, {
'value': np.nan
}], sma.get_data())
bar.add(timestamp=t3, data={"close": 2.8, "open": 1.8})
self.assertEquals([{
'value': np.nan
}, {
'value': np.nan
}, {
'value': 2.4
}], sma.get_data())
def test_nan_before_size(self):
bar = self.app_context.inst_data_mgr.get_series("bar")
bar.start(self.app_context)
sma = SMA(bar, 'close', 3)
sma.start(self.app_context)
t1 = datetime.datetime.now()
t2 = t1 + datetime.timedelta(0, 3)
t3 = t2 + datetime.timedelta(0, 3)
bar.add({"timestamp": t1, "close": 2.0, "open": 0})
self.assertEquals([{"timestamp": t1, 'value': np.nan, "name": "'SMA('bar',close,3)'"}],
sma.get_data())
bar.add({"timestamp": t2, "close": 2.4, "open": 1.4})
self.assertEquals([{"timestamp": t1, 'value':np.nan, "name": "'SMA('bar',close,3)'"},
{"timestamp": t2, 'value':np.nan, "name": "'SMA('bar',close,3)'"}],
sma.get_data())
bar.add({"timestamp": t3, "close": 2.8, "open": 1.8})
self.assertEquals([{"timestamp": t1, 'value':np.nan, "name": "'SMA('bar',close,3)'"},
{"timestamp": t2, 'value':np.nan, "name": "'SMA('bar',close,3)'"},
{"timestamp": t3, 'value': 2.4, "name": "'SMA('bar',close,3)'"}],
sma.get_data())
def test_with_multi_bar_multi_indicator(self):
bar0 = self.app_context.inst_data_mgr.get_series("bar0")
bar1 = self.app_context.inst_data_mgr.get_series("bar1")
bar0.start(self.app_context)
bar1.start(self.app_context)
sma_2_bar0 = SMA(bar0, "close", 2)
sma_4_bar0 = SMA(bar0, "close", 4)
sma_3_bar1 = SMA(bar1, "close", 3)
sma_2_bar0.start(self.app_context)
sma_4_bar0.start(self.app_context)
sma_3_bar1.start(self.app_context)
rank = Rank([sma_2_bar0, sma_3_bar1, sma_4_bar0], input_key=Indicator.VALUE)
rank.start(self.app_context)
t = datetime.datetime.now()
bar0.add({"timestamp": t, "close": 80.0, "open": 0})
bar1.add({"timestamp": t, "close": 95.0, "open": 0})
nan_arr = np.empty([1,3])
nan_arr[:] = np.nan
# self.assertListEqual(nan_arr.tolist(), rank.now()["value"])
self.__np_assert_almost_equal(nan_arr, np.array(rank.now()["value"]))
t = t + datetime.timedelta(0, 3)
bar0.add({"timestamp": t, "close": 85.0, "open": 0})
bar1.add({"timestamp": t, "close": 93.0, "open": 0})
target = nan_arr
target[0, 0] = 0.
self.__np_assert_almost_equal(target, np.array(rank.now()["value"]))
t = t + datetime.timedelta(0, 3)
bar0.add({"timestamp": t, "close": 86.0, "open": 0})
bar1.add({"timestamp": t, "close": 91.0, "open": 0})
target[0, 1] = 0.5
self.__np_assert_almost_equal(target.tolist(), rank.now()["value"])
# self.__np_assert_almost_equal(target, rank.now()["value"])
t = t + datetime.timedelta(0, 3)
bar0.add({"timestamp": t, "close": 90.0, "open": 0})
bar1.add({"timestamp": t, "close": 95.0, "open": 0})
target = np.array([[0.5, 1.0, 0.]])
# self.assertAlmostEqual(target.tolist(), rank.now()["value"])
self.__np_assert_almost_equal(target, np.array(rank.now()["value"]))
def test_shape(self):
close = self.app_context.inst_data_mgr.get_series("bar")
close.start(self.app_context)
sma3 = SMA(close, input_key='close', length=3)
sma20 = SMA(close, input_key='close', length=20)
sma50 = SMA(close, input_key='close', length=50)
sma3.start(self.app_context)
sma20.start(self.app_context)
sma50.start(self.app_context)
rank = Rank([sma3, sma20, sma50], input_key='close')
rank.start(self.app_context)
self.assertAlmostEqual([1, 3], rank.shape(), 5)
def test_shape(self):
close = self.app_context.inst_data_mgr.get_series("bar")
close.start(self.app_context)
sma3 = SMA(close, input_key="close", length=3)
sma20 = SMA(close, input_key="close", length=20)
sma50 = SMA(close, input_key="close", length=50)
sma3.start(self.app_context)
sma20.start(self.app_context)
sma50.start(self.app_context)
rank = Rank([sma3, sma20, sma50], input_key="close")
rank.start(self.app_context)
try:
np.testing.assert_almost_equal(np.array([1, 3]), rank.shape(), 5)
except AssertionError as e:
self.fail(e.message)
def test_name(self):
bar = self.app_context.inst_data_mgr.get_series("bar")
bar.start(self.app_context)
sma3 = SMA(bar, input_key='close', length=3)
sma20 = SMA(bar, input_key='close', length=20)
sma50 = SMA(bar, input_key='close', length=50)
sma3.start(self.app_context)
sma20.start(self.app_context)
sma50.start(self.app_context)
rank = Rank([sma3, sma20, sma50], input_key='close')
rank.start(self.app_context)
self.assertEquals("Rank(SMA('bar',close,3),SMA('bar',close,20),SMA('bar',close,50),close)",
rank.name)
bar0 = self.app_context.inst_data_mgr.get_series("bar0")
bar1 = self.app_context.inst_data_mgr.get_series("bar1")
bar2 = self.app_context.inst_data_mgr.get_series("bar2")
bar3 = self.app_context.inst_data_mgr.get_series("bar3")
bar0.start(self.app_context)
bar1.start(self.app_context)
bar2.start(self.app_context)
bar3.start(self.app_context)
barlist = [bar0, bar1, bar2, bar3]
avg = Average([bar0, bar1, bar2, bar3], input_key='close')
gssum = GSSum([bar0, bar1, bar2, bar3], input_key='close')
basket = MakeVector([bar0, bar1, bar2, bar3], input_key='close')
# TODO: the name printed by pipeline now break the "parse" machnism so we have to review it
self.assertEquals("Average('bar0','bar1','bar2','bar3',close)", avg.name)
self.assertEquals("Sum('bar0','bar1','bar2','bar3',close)", gssum.name)
self.assertEquals("MakeVector('bar0','bar1','bar2','bar3',close)", basket.name)
bar4 = self.app_context.inst_data_mgr.get_series("bar4")
bar5 = self.app_context.inst_data_mgr.get_series("bar5")
bar6 = self.app_context.inst_data_mgr.get_series("bar6")
bar7 = self.app_context.inst_data_mgr.get_series("bar7")
basket2 = MakeVector([bar4, bar5, bar6, bar7], input_key='close')
cross_basket_spread = Minus(basket2, basket)
def test_empty_at_initialize(self):
close = self.app_context.inst_data_mgr.get_series("bar")
close.start(self.app_context)
sma3 = SMA(close, input_key='close', length=3)
sma20 = SMA(close, input_key='close', length=20)
sma50 = SMA(close, input_key='close', length=50)
sma3.start(self.app_context)
sma20.start(self.app_context)
rank = Rank([sma3, sma20, sma50], input_key='close')
rank.start(self.app_context)
self.assertEquals(0, len(rank.get_data()))
def _start(self, app_context: Context) -> None:
self.qty = self._get_stg_config("qty", 1)
self.threshold = self._get_stg_config("threshold", 1)
self.xiv = app_context.ref_data_mgr.get_inst('XIV', 'SMART')
self.vxx = app_context.ref_data_mgr.get_inst('VXX', 'SMART')
self.vxv = app_context.ref_data_mgr.get_inst('VXV', 'SMART')
self.vxmt = app_context.ref_data_mgr.get_inst('VXMT', 'SMART')
instruments = [self.vxx, self.xiv, self.vxmt]
self.vix_close = app_context.inst_data_mgr.get_series(
"Bar.%s.Time.86400" % self.vix.get_symbol())
self.xiv_close = app_context.inst_data_mgr.get_series(
"Bar.%s.Time.86400" % self.xiv.get_symbol())
self.vxv_close = app_context.inst_data_mgr.get_series(
"Bar.%s.Time.86400" % self.vxv.get_symbol())
self.vxmt_close = app_context.inst_data_mgr.get_series(
"Bar.%s.Time.86400" % self.vxmt.get_symbol())
self.ema_60 = EMA()
self.sma_fast = SMA(self.bar, 'close', 10)
super(VxvVxmtRatio, self)._start(app_context)
def test_moving_average_calculation(self):
bar = self.app_context.inst_data_mgr.get_series("bar")
bar.start(self.app_context)
sma = SMA(inputs=bar, input_keys='close', length=3)
sma.start(self.app_context)
t1 = 1
t2 = t1 + 3
t3 = t2 + 3
t4 = t3 + 3
t5 = t4 + 3
bar.add(timestamp=t1, data={"close": 2.0, "open": 0})
self.assertTrue(math.isnan(sma.now('value')))
bar.add(timestamp=t2, data={"close": 2.4, "open": 1.4})
self.assertTrue(math.isnan(sma.now('value')))
bar.add(timestamp=t3, data={"close": 2.8, "open": 1.8})
self.assertEquals(2.4, sma.now('value'))
bar.add(timestamp=t4, data={"close": 3.2, "open": 2.2})
# self.assertEquals(2.8, sma.now('value'))
self.assertAlmostEqual(2.8, sma.now('value'), places=3)
bar.add(timestamp=t5, data={"close": 3.6, "open": 2.6})
self.assertAlmostEqual(3.2, sma.now('value'), places=3)
# self.assertEquals(3.2, sma.now('value'))
self.assertTrue(math.isnan(sma.get_by_idx(0, 'value')))
self.assertTrue(math.isnan(sma.get_by_idx(1, 'value')))
self.assertAlmostEqual(2.4, sma.get_by_idx(2, 'value'), places=3)
self.assertAlmostEquals(2.8, sma.get_by_idx(3, 'value'), places=3)
self.assertAlmostEquals(3.2, sma.get_by_idx(4, 'value'), places=3)
self.assertTrue(math.isnan(sma.get_by_time(t1, 'value')))
self.assertTrue(math.isnan(sma.get_by_time(t2, 'value')))
self.assertAlmostEquals(2.4, sma.get_by_time(t3, 'value'), places=3)
self.assertAlmostEquals(2.8, sma.get_by_time(t4, 'value'), places=3)
self.assertAlmostEquals(3.2, sma.get_by_time(t5, 'value'), places=3)
def test_shape(self):
close = self.app_context.inst_data_mgr.get_series("bar")
close.start(self.app_context)
sma3 = SMA(close, input_key='close', length=3)
sma20 = SMA(close, input_key='close', length=20)
sma50 = SMA(close, input_key='close', length=50)
sma3.start(self.app_context)
sma20.start(self.app_context)
sma50.start(self.app_context)
rank = Rank([sma3, sma20, sma50], input_key='close')
rank.start(self.app_context)
self.assertAlmostEqual([1, 3], rank.shape(), 5)
def test_moving_average_calculation(self):
bar = self.app_context.inst_data_mgr.get_series("bar")
bar.start(self.app_context)
sma = SMA(bar, input_key='close', length=3)
sma.start(self.app_context)
t1 = datetime.datetime.now()
t2 = t1 + datetime.timedelta(0, 3)
t3 = t2 + datetime.timedelta(0, 3)
t4 = t3 + datetime.timedelta(0, 3)
t5 = t4 + datetime.timedelta(0, 3)
bar.add({"timestamp": t1, "close": 2.0, "open": 0})
self.assertTrue(math.isnan(sma.now('value')))
bar.add({"timestamp": t2, "close": 2.4, "open": 1.4})
self.assertTrue(math.isnan(sma.now('value')))
bar.add({"timestamp": t3, "close": 2.8, "open": 1.8})
self.assertEquals(2.4, sma.now('value'))
bar.add({"timestamp": t4, "close": 3.2, "open": 2.2})
# self.assertEquals(2.8, sma.now('value'))
self.assertAlmostEqual(2.8, sma.now('value'), places=3)
bar.add({"timestamp": t5, "close": 3.6, "open": 2.6})
self.assertAlmostEqual(3.2, sma.now('value'), places=3)
# self.assertEquals(3.2, sma.now('value'))
self.assertTrue(math.isnan(sma.get_by_idx(0, 'value')))
self.assertTrue(math.isnan(sma.get_by_idx(1, 'value')))
self.assertAlmostEqual(2.4, sma.get_by_idx(2, 'value'), places=3)
self.assertAlmostEquals(2.8, sma.get_by_idx(3, 'value'), places=3)
self.assertAlmostEquals(3.2, sma.get_by_idx(4, 'value'), places=3)
self.assertTrue(math.isnan(sma.get_by_time(t1, 'value')))
self.assertTrue(math.isnan(sma.get_by_time(t2, 'value')))
self.assertAlmostEquals(2.4, sma.get_by_time(t3, 'value'), places=3)
self.assertAlmostEquals(2.8, sma.get_by_time(t4, 'value'), places=3)
self.assertAlmostEquals(3.2, sma.get_by_time(t5, 'value'), places=3)
def test_name(self):
bar = self.app_context.inst_data_mgr.get_series("bar")
bar.start(self.app_context)
sma3 = SMA(bar, input_key='close', length=3)
sma20 = SMA(bar, input_key='close', length=20)
sma50 = SMA(bar, input_key='close', length=50)
sma3.start(self.app_context)
sma20.start(self.app_context)
sma50.start(self.app_context)
rank = Rank([sma3, sma20, sma50], input_key='close')
rank.start(self.app_context)
self.assertEquals("Rank(SMA('bar',close,3),SMA('bar',close,20),SMA('bar',close,50),close)",
rank.name)
bar0 = self.app_context.inst_data_mgr.get_series("bar0")
bar1 = self.app_context.inst_data_mgr.get_series("bar1")
bar2 = self.app_context.inst_data_mgr.get_series("bar2")
bar3 = self.app_context.inst_data_mgr.get_series("bar3")
bar0.start(self.app_context)
bar1.start(self.app_context)
bar2.start(self.app_context)
bar3.start(self.app_context)
barlist = [bar0, bar1, bar2, bar3]
avg = Average([bar0, bar1, bar2, bar3], input_key='close')
gssum = GSSum([bar0, bar1, bar2, bar3], input_key='close')
basket = MakeVector([bar0, bar1, bar2, bar3], input_key='close')
# TODO: the name printed by pipeline now break the "parse" machnism so we have to review it
self.assertEquals("Average('bar0','bar1','bar2','bar3',close)", avg.name)
self.assertEquals("Sum('bar0','bar1','bar2','bar3',close)", gssum.name)
self.assertEquals("MakeVector('bar0','bar1','bar2','bar3',close)", basket.name)
bar4 = self.app_context.inst_data_mgr.get_series("bar4")
bar5 = self.app_context.inst_data_mgr.get_series("bar5")
bar6 = self.app_context.inst_data_mgr.get_series("bar6")
bar7 = self.app_context.inst_data_mgr.get_series("bar7")
basket2 = MakeVector([bar4, bar5, bar6, bar7], input_key='close')
cross_basket_spread = Minus(basket2, basket)
def test_with_multi_bar_multi_indicator(self):
bar0 = self.app_context.inst_data_mgr.get_series("bar0")
bar1 = self.app_context.inst_data_mgr.get_series("bar1")
bar0.start(self.app_context)
bar1.start(self.app_context)
sma_2_bar0 = SMA(bar0, "close", 2)
sma_4_bar0 = SMA(bar0, "close", 4)
sma_3_bar1 = SMA(bar1, "close", 3)
sma_2_bar0.start(self.app_context)
sma_4_bar0.start(self.app_context)
sma_3_bar1.start(self.app_context)
rank = Rank([sma_2_bar0, sma_3_bar1, sma_4_bar0], input_key=Indicator.VALUE)
rank.start(self.app_context)
t = datetime.datetime.now()
bar0.add({"timestamp": t, "close": 80.0, "open": 0})
bar1.add({"timestamp": t, "close": 95.0, "open": 0})
nan_arr = np.empty([1,3])
nan_arr[:] = np.nan
self.__np_assert_almost_equal(nan_arr, rank.now()["value"])
t = t + datetime.timedelta(0, 3)
bar0.add({"timestamp": t, "close": 85.0, "open": 0})
bar1.add({"timestamp": t, "close": 93.0, "open": 0})
target = nan_arr
target[0, 0] = 0.
self.__np_assert_almost_equal(target, rank.now()["value"])
t = t + datetime.timedelta(0, 3)
bar0.add({"timestamp": t, "close": 86.0, "open": 0})
bar1.add({"timestamp": t, "close": 91.0, "open": 0})
target[0, 1] = 0.5
self.__np_assert_almost_equal(target, rank.now()["value"])
t = t + datetime.timedelta(0, 3)
bar0.add({"timestamp": t, "close": 90.0, "open": 0})
bar1.add({"timestamp": t, "close": 95.0, "open": 0})
target = np.array([[0.5, 1.0, 0.]])
self.__np_assert_almost_equal(target, rank.now()["value"])
def test_shape(self):
close = self.app_context.inst_data_mgr.get_series("bar")
close.start(self.app_context)
sma3 = SMA(inputs=close, input_keys='close', length=3)
sma20 = SMA(inputs=close, input_keys='close', length=20)
sma50 = SMA(inputs=close, input_keys='close', length=50)
sma3.start(self.app_context)
sma20.start(self.app_context)
sma50.start(self.app_context)
rank = Rank(inputs=[sma3, sma20, sma50], input_keys='close')
rank.start(self.app_context)
try:
np.testing.assert_almost_equal(np.array([1, 3]), rank.shape(), 5)
except AssertionError as e:
self.fail(e.message)
def test_name(self):
bar = self.app_context.inst_data_mgr.get_series("bar")
bar.start(self.app_context)
sma3 = SMA(inputs=bar, input_keys='close', length=3)
sma20 = SMA(inputs=bar, input_keys='close', length=20)
sma50 = SMA(inputs=bar, input_keys='close', length=50)
sma3.start(self.app_context)
sma20.start(self.app_context)
sma50.start(self.app_context)
rank = Rank(inputs=[sma3, sma20, sma50], input_keys='close')
rank.start(self.app_context)
self.assertEquals(
"Rank(SMA(bar[close],length=3)[close],SMA(bar[close],length=20)[close],SMA(bar[close],length=50)[close],ascending=True)",
rank.name)
bar0 = self.app_context.inst_data_mgr.get_series("bar0")
bar1 = self.app_context.inst_data_mgr.get_series("bar1")
bar2 = self.app_context.inst_data_mgr.get_series("bar2")
bar3 = self.app_context.inst_data_mgr.get_series("bar3")
bar0.start(self.app_context)
bar1.start(self.app_context)
bar2.start(self.app_context)
bar3.start(self.app_context)
barlist = [bar0, bar1, bar2, bar3]
avg = Average(inputs=barlist, input_keys='close')
gssum = GSSum(inputs=barlist, input_keys='close')
basket = MakeVector(inputs=barlist, input_keys='close')
# TODO: the name printed by pipeline now break the "parse" machnism so we have to review it
self.assertEquals(
"Average(bar0[close],bar1[close],bar2[close],bar3[close],length=1)",
avg.name)
self.assertEquals(
"Sum(bar0[close],bar1[close],bar2[close],bar3[close],length=1)",
gssum.name)
self.assertEquals(
"MakeVector(bar0[close],bar1[close],bar2[close],bar3[close],length=1)",
basket.name)
bar4 = self.app_context.inst_data_mgr.get_series("bar4")
bar5 = self.app_context.inst_data_mgr.get_series("bar5")
bar6 = self.app_context.inst_data_mgr.get_series("bar6")
bar7 = self.app_context.inst_data_mgr.get_series("bar7")
basket2 = MakeVector(inputs=[bar4, bar5, bar6, bar7],
input_keys='close')
self.assertEquals(
"MakeVector(bar4[close],bar5[close],bar6[close],bar7[close],length=1)",
basket2.name)
cross_basket_spread = Minus(inputs=[basket2, basket])
self.assertEquals(
"Minus(MakeVector(bar4[close],bar5[close],bar6[close],bar7[close],length=1)[close],MakeVector(bar0[close],bar1[close],bar2[close],bar3[close],length=1)[close],length=1)",
cross_basket_spread.name)
def test_with_multi_bar_multi_indicator(self):
bar0 = self.app_context.inst_data_mgr.get_series("bar0")
bar1 = self.app_context.inst_data_mgr.get_series("bar1")
bar0.start(self.app_context)
bar1.start(self.app_context)
sma_2_bar0 = SMA(inputs=bar0, input_keys="close", length=2)
sma_4_bar0 = SMA(inputs=bar0, input_keys="close", length=4)
sma_3_bar1 = SMA(inputs=bar1, input_keys="close", length=3)
sma_2_bar0.start(self.app_context)
sma_4_bar0.start(self.app_context)
sma_3_bar1.start(self.app_context)
rank = Rank(inputs=[sma_2_bar0, sma_3_bar1, sma_4_bar0],
input_keys=Indicator.VALUE)
rank.start(self.app_context)
t = 1
bar0.add(data={"timestamp": t, "close": 80.0, "open": 0})
bar1.add(data={"timestamp": t, "close": 95.0, "open": 0})
print(rank.now(keys=PipeLine.VALUE))
t = t + 3
bar0.add(data={"timestamp": t, "close": 85.0, "open": 0})
bar1.add(data={"timestamp": t, "close": 93.0, "open": 0})
print(rank.now(keys=PipeLine.VALUE))
t = t + 3
bar0.add(data={"timestamp": t, "close": 86.0, "open": 0})
bar1.add(data={"timestamp": t, "close": 91.0, "open": 0})
print(rank.now(keys=PipeLine.VALUE))
t = t + 3
bar0.add(data={"timestamp": t, "close": 90.0, "open": 0})
bar1.add(data={"timestamp": t, "close": 95.0, "open": 0})
print(rank.now(keys=PipeLine.VALUE))
