def vote(self, code, day):
features = []
values = db_helper.select(self.query_kpi_fn, (code, day, 30, DMA))
if len(values) < 30:
return features
values = np.array(values)[::-1]
dma_values = np.array(list(map(lambda x:np.float(x.dma), values)))
ama_values = np.array(list(map(lambda x:np.float(x.ama), values)))
bt, l = zero_line.breakthrough(dma_values[-10:])
if bt and l <= 3:
features.append(self.BREAK_ZERO)
if ins.match(dma_values[-3:], ama_values[-3:]) == 1:
features.append(self.GOOD_CROSS)
return features
def vote(self, code, day):
features = []
values = db_helper.select(query_kpi_fn, (code, day, 30))
if len(values) < 30:
return features
values = np.array(values)[::-1]
pdi_lines = values[:, 1].astype(float)
mdi_lines = values[:, 2].astype(float)
adx_lines = values[:, 3].astype(float)
adxr_lines = values[:, 4].astype(float)
bt, l = zero_line.breakthrough(adx_lines[-10:], 30)
if bt and l <= 3:
features.append(self.BREAK_ZERO)
if ins.match(pdi_lines[-6:], mdi_lines[-6:]) == 1 and ins.match(adx_lines[-3:], adxr_lines[-3:]) == 1:
features.append(self.GOOD_CROSS)
return features
def vote(self, code, day):
features = []
values = db_helper.select(self.query_kpi_fn, (code, day, 30, BOLL))
k_values = db_helper.select(self.query_kpi_fn,
(code, day, 30, TradingDataDaily))
if len(values) < 30 or len(k_values) < 30 or len(values) != len(
k_values):
return features
values = np.array(values)[::-1]
k_values = np.array(k_values)[::-1]
md_values = np.array(list(map(lambda x: np.float(x.md), values)))
close_values = np.array(
list(map(lambda x: np.float(x.close), k_values)))
bt, l = zero_line.breakthrough(close_values, md_values)
if bt and l <= 3:
features.append(self.BREAK_ZERO)
return features
def vote(self, code, day):
features = []
values = db_helper.select(self.query_kpi_fn, (code, day, 30, EMV))
if len(values) < 30:
return features
values = np.array(values)[::-1]
emv_values = np.array(list(map(lambda x: np.float(x.emv), values)))
if trend.is_uptrend(emv_values[-10:]):
k_values = db_helper.select(self.query_kpi_fn,
(code, day, 10, TradingDataDaily))
if len(k_values) == 10:
k_values = np.array(k_values)[::-1]
close_values = np.array(
list(map(lambda x: np.float(x.close), k_values)))
if trend.is_downtrend(close_values):
features.append(self.BOTTOM_DEVIATION)
bt, l = zero_line.breakthrough(emv_values[-10:])
if bt and l <= 3:
features.append(self.BREAK_ZERO)
return features
def vote(self, code, day):
features = []
values = db_helper.select(query_roc_fn, (code, day, 30))
if len(values) < 30:
return features
values = np.array(values)[::-1]
roc_values = values[:, 1].astype(float)
rocma_values = values[:, 2].astype(float)
if ins.match(roc_values[-3:], rocma_values[-3:]) == 1:
features.append(self.GOOD_CROSS)
if trend.is_uptrend(roc_values):
k_values = db_helper.select(query_k_fn, (code, day, 30))
if len(k_values) == 30:
k_values = np.array(k_values)[::-1]
close_values = k_values[:, 0].astype(float)
if trend.is_downtrend(close_values):
features.append(self.BOTTOM_DEVIATION)
bt, l = zero_line.breakthrough(roc_values, 0)
if bt and l <= 3:
features.append(self.BREAK_ZERO)
return features