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_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 test_with_multiple_bar(self):
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]
rank = Rank(inputs=barlist, input_keys='close')
avg = Average(inputs=barlist, input_keys='close')
gssum = GSSum(inputs=barlist, input_keys='close')
absv = Abs(inputs=barlist, input_keys='close')
tail = Tail(inputs=barlist,
input_keys='close',
lb=94,
ub=103,
newval=101)
signvec = Sign(inputs=barlist, input_keys='close')
decaylinear = DecayLinear(inputs=barlist, input_keys='close', length=3)
scale = Scale(inputs=barlist, input_keys='close')
rank.start(self.app_context)
avg.start(self.app_context)
gssum.start(self.app_context)
absv.start(self.app_context)
tail.start(self.app_context)
signvec.start(self.app_context)
decaylinear.start(self.app_context)
scale.start(self.app_context)
t1 = 1
bar_t1_array = np.array([80, 95, 102, 105])
bar0.add(data={"timestamp": t1, "close": bar_t1_array[0], "open": 0})
bar1.add(data={"timestamp": t1, "close": bar_t1_array[1], "open": 0})
bar2.add(data={"timestamp": t1, "close": bar_t1_array[2], "open": 0})
bar3.add(data={"timestamp": t1, "close": bar_t1_array[3], "open": 0})
# self.assertEquals([{"timestamp": t1,
# "name": "'Rank('bar0','bar1','bar2','bar3',close)'",
# "value": np.arange(4)/3.0}],
# rank.get_data())
rank_target = np.arange(4) / 3.0
rank_target = rank_target.reshape((1, 4))
avg_target = np.array([[95.5]])
sum_target = np.array([[382.0]])
abs_target = np.array([[80.0, 95.0, 102.0, 105.0]])
tail_target = np.array([[80.0, 101.0, 101.0, 105.0]])
signvec_target = np.array([[1.0, 1.0, 1.0, 1.0]])
scale_target = bar_t1_array / np.sum(bar_t1_array)
scale_target = scale_target.reshape(1, 4)
self.__np_assert_almost_equal(abs_target, absv.now()["value"])
self.__np_assert_almost_equal(rank_target,
rank.get_data()[0]["value"], 5)
self.__np_assert_almost_equal(avg_target,
avg.get_data()[0]["value"], 5)
self.__np_assert_almost_equal(sum_target,
gssum.get_data()[0]["value"], 5)
self.__np_assert_almost_equal(tail_target,
tail.get_data()[0]["value"], 5)
self.__np_assert_almost_equal(signvec_target,
signvec.get_data()[0]["value"], 5)
self.__np_assert_almost_equal(scale_target,
scale.get_data()[0]["value"], 5)
t2 = t1 + 3
bar_t2_array = np.array([85, 98, 101.5, 103])
bar0.add(data={"timestamp": t2, "close": bar_t2_array[0], "open": 0})
bar1.add(data={"timestamp": t2, "close": bar_t2_array[1], "open": 0})
bar2.add(data={"timestamp": t2, "close": bar_t2_array[2], "open": 0})
bar3.add(data={"timestamp": t2, "close": bar_t2_array[3], "open": 0})
t3 = t2 + 3
bar_t3_array = np.array([87, 91, 107.0, 115])
bar0.add(data={"timestamp": t3, "close": bar_t3_array[0], "open": 0})
bar1.add(data={"timestamp": t3, "close": bar_t3_array[1], "open": 0})
bar2.add(data={"timestamp": t3, "close": bar_t3_array[2], "open": 0})
bar3.add(data={"timestamp": t3, "close": bar_t3_array[3], "open": 0})
stack = np.vstack([bar_t1_array, bar_t2_array, bar_t3_array])
decaylinear_target = np.dot(np.arange(3, 0, -1), stack) / np.sum(
np.arange(3, 0, -1))
scale_target = bar_t3_array / np.sum(bar_t3_array)
scale_target = scale_target.reshape(1, 4)
self.__np_assert_almost_equal(decaylinear_target,
decaylinear.now(keys=PipeLine.VALUE))
self.__np_assert_almost_equal(scale_target,
scale.now(keys=PipeLine.VALUE))
def test_with_multiple_bar(self):
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]
rank = Rank(barlist, input_key='close')
avg = Average(barlist, input_key='close')
gssum = GSSum(barlist, input_key='close')
absv = Abs(barlist, input_key='close')
tail = Tail(barlist, lb=94, ub=103, newval=101, input_key='close')
signvec = Sign(barlist, input_key='close')
decaylinear = DecayLinear(barlist, length=3, input_key='close')
scale = Scale(barlist, input_key='close')
rank.start(self.app_context)
avg.start(self.app_context)
gssum.start(self.app_context)
absv.start(self.app_context)
tail.start(self.app_context)
signvec.start(self.app_context)
decaylinear.start(self.app_context)
scale.start(self.app_context)
t1 = datetime.datetime.now()
bar_t1_array = np.array([80, 95, 102, 105])
bar0.add({"timestamp": t1, "close": bar_t1_array[0], "open": 0})
bar1.add({"timestamp": t1, "close": bar_t1_array[1], "open": 0})
bar2.add({"timestamp": t1, "close": bar_t1_array[2], "open": 0})
bar3.add({"timestamp": t1, "close": bar_t1_array[3], "open": 0})
# self.assertEquals([{"timestamp": t1,
# "name": "'Rank('bar0','bar1','bar2','bar3',close)'",
# "value": np.arange(4)/3.0}],
# rank.get_data())
rank_target = np.arange(4)/3.0
rank_target = rank_target.reshape((1,4))
avg_target = 95.5 # for those return scala, it is a scala
sum_target = 382.0
abs_target = np.array([[80.0, 95.0, 102.0, 105.0]])
tail_target = np.array([[80.0, 101.0, 101.0, 105.0]])
signvec_target = np.array([[1.0, 1.0, 1.0, 1.0]])
scale_target = bar_t1_array / np.sum(bar_t1_array)
scale_target = scale_target.reshape(1, 4)
# self.__np_assert_almost_equal(abs_target, absv.now()["value"])
# self.__np_assert_almost_equal(rank_target, rank.get_data()[0]["value"], 5)
# self.__np_assert_almost_equal(avg_target, avg.get_data()[0]["value"], 5)
# self.__np_assert_almost_equal(sum_target, gssum.get_data()[0]["value"], 5)
# self.__np_assert_almost_equal(tail_target, tail.get_data()[0]["value"], 5)
# self.__np_assert_almost_equal(signvec_target, signvec.get_data()[0]["value"], 5)
# self.__np_assert_almost_equal(scale_target, scale.get_data()[0]["value"], 5)
self.assertAlmostEqual(abs_target.tolist(), absv.now()["value"])
self.assertAlmostEqual(abs_target.tolist(), absv.get_data()[0]["value"], 5)
self.assertAlmostEqual(rank_target.tolist(), rank.get_data()[0]["value"], 5)
self.assertAlmostEqual(avg_target, avg.get_data()[0]["value"], 5)
self.assertAlmostEqual(sum_target, gssum.get_data()[0]["value"], 5)
self.assertAlmostEqual(tail_target.tolist(), tail.get_data()[0]["value"], 5)
self.assertAlmostEqual(signvec_target.tolist(), signvec.get_data()[0]["value"], 5)
self.assertAlmostEqual(scale_target.tolist(), scale.get_data()[0]["value"], 5)
t2 = t1 + datetime.timedelta(0, 3)
bar_t2_array = np.array([85, 98, 101.5, 103])
bar0.add({"timestamp": t2, "close": bar_t2_array[0], "open": 0})
bar1.add({"timestamp": t2, "close": bar_t2_array[1], "open": 0})
bar2.add({"timestamp": t2, "close": bar_t2_array[2], "open": 0})
bar3.add({"timestamp": t2, "close": bar_t2_array[3], "open": 0})
t3 = t2 + datetime.timedelta(0, 3)
bar_t3_array = np.array([87, 91, 107.0, 115])
bar0.add({"timestamp": t3, "close": bar_t3_array[0], "open": 0})
bar1.add({"timestamp": t3, "close": bar_t3_array[1], "open": 0})
bar2.add({"timestamp": t3, "close": bar_t3_array[2], "open": 0})
bar3.add({"timestamp": t3, "close": bar_t3_array[3], "open": 0})
stack = np.vstack([bar_t1_array, bar_t2_array, bar_t3_array])
decaylinear_target = np.dot(np.arange(3, 0, -1), stack)/np.sum(np.arange(3, 0, -1))
scale_target = bar_t3_array / np.sum(bar_t3_array)
scale_target = scale_target.reshape(1, 4)
self.assertAlmostEqual(decaylinear_target.tolist(), decaylinear.now(keys=PipeLine.VALUE))
self.assertAlmostEqual(scale_target.tolist(), scale.now(keys=PipeLine.VALUE))
