def _start(self, app_context, **kwargs):
self.length = self.get_stg_config_value("length", 10)
self.bars = [
self.app_context.inst_data_mgr.get_series("Bar.%s.Time.300" % i)
for i in self.app_context.app_config.instrument_ids
]
for bar in self.bars:
bar.start(app_context)
self.opens = MakeVector(self.bars, input_key='Open')
self.volumes = MakeVector(self.bars, input_key="Volume")
self.rank_opens = Rank(self.bars, input_key='open')
self.rank_opens.start(app_context)
self.rank_volumes = Rank(self.bars, input_key='Volume')
self.rank_volumes.start(app_context)
#
self.pair_correlation = PairCorrelation(self.rank_opens,
self.rank_volumes,
length=self.length)
self.pair_correlation.start(app_context)
super(AlphaFormula3, self)._start(app_context, **kwargs)
class AlphaFormula3(Strategy):
def __init__(self, stg_id=None, stg_configs=None):
super(AlphaFormula3, self).__init__(stg_id=stg_id,
stg_configs=stg_configs)
self.day_count = 0
self.order = None
def _start(self, app_context, **kwargs):
self.length = self.get_stg_config_value("length", 10)
self.bars = [
self.app_context.inst_data_mgr.get_series("Bar.%s.Time.300" % i)
for i in self.app_context.app_config.instrument_ids
]
for bar in self.bars:
bar.start(app_context)
self.opens = MakeVector(self.bars, input_key='Open')
self.volumes = MakeVector(self.bars, input_key="Volume")
self.rank_opens = Rank(self.bars, input_key='open')
self.rank_opens.start(app_context)
self.rank_volumes = Rank(self.bars, input_key='Volume')
self.rank_volumes.start(app_context)
#
self.pair_correlation = PairCorrelation(self.rank_opens,
self.rank_volumes,
length=self.length)
self.pair_correlation.start(app_context)
super(AlphaFormula3, self)._start(app_context, **kwargs)
def _stop(self):
super(AlphaFormula3, self)._stop()
def on_bar(self, bar):
# rank = self.rank_opens.now('value')
# logger.info("[%s] %s" % (self.__class__.__name__, rank))
# if np.all(np.isnan(rank)):
# return
corr = self.pair_correlation.now('value')
if np.any(np.isnan(corr)):
return
weight = [corr[i, i + 2] for i in range(len(self.bars))]
# weight = rank
weight = -1 * weight[0]
portfolio = self.get_portfolio()
allocation = portfolio.total_equity * weight
delta = allocation - portfolio.stock_value
index = self.app_context.app_config.instrument_ids.index(bar.inst_id)
qty = delta[index]
# logger.info("%s,B,%.2f" % (bar.timestamp, bar.close))
self.order = self.market_order(inst_id=bar.inst_id, action=OrdAction.BUY, qty=qty) if qty > 0 else \
self.market_order(inst_id=bar.inst_id, action=OrdAction.SELL, qty=-qty)
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)
class AlphaFormula3(Strategy):
def __init__(self, stg_id=None, stg_configs=None):
super(AlphaFormula3, self).__init__(stg_id=stg_id, stg_configs=stg_configs)
self.day_count = 0
self.order = None
def _start(self, app_context, **kwargs):
self.length = self.get_stg_config_value("length", 10)
self.bars = [self.app_context.inst_data_mgr.get_series(
"Bar.%s.Time.300" % i) for i in self.app_context.app_config.instrument_ids]
for bar in self.bars:
bar.start(app_context)
self.opens = MakeVector(self.bars, input_key='Open')
self.volumes = MakeVector(self.bars, input_key="Volume")
self.rank_opens = Rank(self.bars, input_key='open')
self.rank_opens.start(app_context)
self.rank_volumes = Rank(self.bars, input_key='Volume')
self.rank_volumes.start(app_context)
#
self.pair_correlation = PairCorrelation(self.rank_opens, self.rank_volumes, length=self.length)
self.pair_correlation.start(app_context)
super(AlphaFormula3, self)._start(app_context, **kwargs)
def _stop(self):
super(AlphaFormula3, self)._stop()
def on_bar(self, bar):
# rank = self.rank_opens.now('value')
# logger.info("[%s] %s" % (self.__class__.__name__, rank))
# if np.all(np.isnan(rank)):
# return
corr = self.pair_correlation.now('value')
if np.any(np.isnan(corr)):
return
weight = [corr[i, i+2] for i in range(len(self.bars))]
# weight = rank
weight = -1*weight[0]
portfolio = self.get_portfolio()
allocation = portfolio.total_equity * weight
delta = allocation - portfolio.stock_value
index = self.app_context.app_config.instrument_ids.index(bar.inst_id)
qty = delta[index]
# logger.info("%s,B,%.2f" % (bar.timestamp, bar.close))
self.order = self.market_order(inst_id=bar.inst_id, action=OrdAction.BUY, qty=qty) if qty > 0 else \
self.market_order(inst_id=bar.inst_id, action=OrdAction.SELL, qty=-qty)
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_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_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)
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(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_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_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_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 _start(self, app_context, **kwargs):
self.length = self.get_stg_config_value("length", 10)
self.bars = [self.app_context.inst_data_mgr.get_series(
"Bar.%s.Time.300" % i) for i in self.app_context.app_config.instrument_ids]
for bar in self.bars:
bar.start(app_context)
self.opens = MakeVector(self.bars, input_key='Open')
self.volumes = MakeVector(self.bars, input_key="Volume")
self.rank_opens = Rank(self.bars, input_key='open')
self.rank_opens.start(app_context)
self.rank_volumes = Rank(self.bars, input_key='Volume')
self.rank_volumes.start(app_context)
#
self.pair_correlation = PairCorrelation(self.rank_opens, self.rank_volumes, length=self.length)
self.pair_correlation.start(app_context)
super(AlphaFormula3, self)._start(app_context, **kwargs)
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_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_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] = 0.0
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, 0.0]])
self.__np_assert_almost_equal(target, rank.now()["value"])
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))
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))
