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)
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_with_single_bar_multi_time(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)
plus = Plus(bar0, bar1, input_key='close')
minus = Minus(bar0, bar1, input_key='close')
times = Times(bar0, bar1, input_key='close')
divides = Divides(bar0, bar1, input_key='close')
pcorr = PairCorrelation(bar0, bar1, length=4, input_key='close')
plus.start(self.app_context)
minus.start(self.app_context)
times.start(self.app_context)
divides.start(self.app_context)
pcorr.start(self.app_context)
now = datetime.datetime.now()
x = np.array([80.0, 102.0, 101.0, 99.0])
y = np.array([95.0, 98.0, 105.2, 103.3])
ts = [now + datetime.timedelta(0, i * 3) for i in range(4)]
x_p_y = x + y
x_m_y = x - y
x_t_y = x * y
x_d_y = x / y
bar0.add({"timestamp": ts[0], "close": x[0], "open": 0})
bar1.add({"timestamp": ts[0], "close": y[0], "open": 0})
self.assertEqual(plus.now('value'), 175.0)
self.assertEqual(minus.now('value'), -15.0)
self.assertEqual(times.now('value'), 7600.0)
self.assertEqual(divides.now('value'), 80.0 / 95.0)
bar0.add({"timestamp": ts[1], "close": x[1], "open": 0})
bar1.add({"timestamp": ts[1], "close": y[1], "open": 0})
self.assertEqual(plus.now('value'), 200.0)
self.assertEqual(minus.now('value'), 4.0)
self.assertEqual(times.now('value'), 102.0 * 98.0)
self.assertEqual(divides.now('value'), 102.0 / 98.0)
bar0.add({"timestamp": ts[2], "close": x[2], "open": 0})
bar1.add({"timestamp": ts[2], "close": y[2], "open": 0})
bar0.add({"timestamp": ts[3], "close": x[3], "open": 0})
bar1.add({"timestamp": ts[3], "close": y[3], "open": 0})
# self.assertEqual(pcorr.now('value'), np.corrcoef(x, y))
self.__np_assert_almost_equal(pcorr.now('value'), np.corrcoef(x, y))
def test_with_single_bar_multi_time(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)
plus = Plus(bar0, bar1, input_key='close')
minus = Minus(bar0, bar1, input_key='close')
times = Times(bar0, bar1, input_key='close')
divides = Divides(bar0, bar1, input_key='close')
pcorr = PairCorrelation(bar0, bar1, length=4, input_key='close')
plus.start(self.app_context)
minus.start(self.app_context)
times.start(self.app_context)
divides.start(self.app_context)
pcorr.start(self.app_context)
now = datetime.datetime.now()
x = np.array([80.0, 102.0, 101.0, 99.0])
y = np.array([95.0, 98.0, 105.2, 103.3])
ts = [now + datetime.timedelta(0, i*3) for i in range(4)]
x_p_y = x + y
x_m_y = x - y
x_t_y = x * y
x_d_y = x / y
bar0.add({"timestamp": ts[0], "close": x[0], "open": 0})
bar1.add({"timestamp": ts[0], "close": y[0], "open": 0})
self.assertEqual(plus.now('value'), 175.0)
self.assertEqual(minus.now('value'), -15.0)
self.assertEqual(times.now('value'), 7600.0)
self.assertEqual(divides.now('value'), 80.0/95.0)
bar0.add({"timestamp": ts[1], "close": x[1], "open": 0})
bar1.add({"timestamp": ts[1], "close": y[1], "open": 0})
self.assertEqual(plus.now('value'), 200.0)
self.assertEqual(minus.now('value'), 4.0)
self.assertEqual(times.now('value'), 102.0*98.0)
self.assertEqual(divides.now('value'), 102.0/98.0)
bar0.add({"timestamp": ts[2], "close": x[2], "open": 0})
bar1.add({"timestamp": ts[2], "close": y[2], "open": 0})
bar0.add({"timestamp": ts[3], "close": x[3], "open": 0})
bar1.add({"timestamp": ts[3], "close": y[3], "open": 0})
# self.assertEqual(pcorr.now('value'), np.corrcoef(x, y))
self.__np_assert_almost_equal(pcorr.now('value'), np.corrcoef(x, y))
def test_with_pair_corr_with_vec(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)
vec0 = MakeVector([bar0, bar1], input_key='close')
vec1 = MakeVector([bar2, bar3], input_key='close')
vec0.start(self.app_context)
vec1.start(self.app_context)
pcorr = PairCorrelation(vec0, vec1, length=4, input_key=PipeLine.VALUE)
pcorr.start(self.app_context)
now = datetime.datetime.now()
x0 = np.array([80.0, 102.0, 101.0, 99.0 ])
x1 = np.array([102.0, 101.5, 99.0, 97.0])
x2 = np.array([94.0, 98.5, 91.0, 87.0])
x3 = np.array([104.5, 107.5, 97.0, 91.0])
ts = [now + datetime.timedelta(0, i*3) for i in range(4)]
for i in range(4):
bar0.add({"timestamp": ts[i], "close": x0[i], "open": 0})
bar1.add({"timestamp": ts[i], "close": x1[i], "open": 0})
bar2.add({"timestamp": ts[i], "close": x2[i], "open": 0})
bar3.add({"timestamp": ts[i], "close": x3[i], "open": 0})
x = np.vstack([x0, x1])
y = np.vstack([x2, x3])
self.__np_assert_almost_equal(pcorr.now('value'), np.corrcoef(x, y))
def test_with_pair_corr_with_vec(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)
vec0 = MakeVector([bar0, bar1], input_key='close')
vec1 = MakeVector([bar2, bar3], input_key='close')
vec0.start(self.app_context)
vec1.start(self.app_context)
pcorr = PairCorrelation(vec0, vec1, length=4, input_key=PipeLine.VALUE)
pcorr.start(self.app_context)
now = datetime.datetime.now()
x0 = np.array([80.0, 102.0, 101.0, 99.0])
x1 = np.array([102.0, 101.5, 99.0, 97.0])
x2 = np.array([94.0, 98.5, 91.0, 87.0])
x3 = np.array([104.5, 107.5, 97.0, 91.0])
ts = [now + datetime.timedelta(0, i * 3) for i in range(4)]
for i in range(4):
bar0.add({"timestamp": ts[i], "close": x0[i], "open": 0})
bar1.add({"timestamp": ts[i], "close": x1[i], "open": 0})
bar2.add({"timestamp": ts[i], "close": x2[i], "open": 0})
bar3.add({"timestamp": ts[i], "close": x3[i], "open": 0})
x = np.vstack([x0, x1])
y = np.vstack([x2, x3])
self.__np_assert_almost_equal(pcorr.now('value'), np.corrcoef(x, y))
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)
