def vote(self, code, day):
features = []
values = db_helper.select(query_kdj_fn, (code, day, 30))
if len(values) < 30:
return features
values = np.array(values)[::-1]
k_values = values[:, 1].astype(float)
d_values = values[:, 2].astype(float)
j_values = values[:, 3].astype(float)
if k_values[-1] <= 20 and d_values[-1] <= 20 and j_values[-1] <= 20:
features.append(self.OVER_SOLD)
if ins.match(k_values[-3:], d_values[-3:]) == 1 and ins.match(
j_values[-3:], d_values[-3:]):
features.append(self.GOOD_CROSS)
s = 0
if trend.is_uptrend(k_values[-10:]):
s = s + 1
if trend.is_uptrend(d_values[-10:]):
s = s + 1
if trend.is_uptrend(j_values[-10:]):
s = s + 1
if s >= 2:
k_values = db_helper.select(query_k_fn, (code, day, 10))
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)
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]
rsi_6_lines = values[:, 1].astype(float)
rsi_12_lines = values[:, 2].astype(float)
rsi_24_lines = values[:, 3].astype(float)
if rsi_6_lines[-1] <= 20:
features.append(self.OVER_SOLD)
if rsi_6_lines[-1] < 50 and rsi_12_lines[-1] < 50 and rsi_24_lines[
-1] < 50:
if ins.match(rsi_6_lines[-3:],
rsi_12_lines[-3:]) == 1 and ins.match(
rsi_6_lines[-3:], rsi_24_lines[-3:]):
features.append(self.GOOD_CROSS)
if trend.is_uptrend(rsi_6_lines[-10:]):
k_values = db_helper.select(query_k_fn, (code, day, 10))
if len(k_values) == 10:
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)
return features
def vote(self, code, day):
features = []
arbr_values = db_helper.select(query_arbr_fn, (code, day, 14))
if len(arbr_values) < 14:
return features
arbr_values = np.array(arbr_values)[::-1]
ar_values = arbr_values[:, 1].astype(float)
br_values = arbr_values[:, 2].astype(float)
if ins.match(br_values[-3:], ar_values[-3:]) == 1:
features.append(self.GOOD_CROSS)
if trend.is_uptrend(ar_values) and trend.is_uptrend(br_values):
k_values = db_helper.select(query_k_fn, (code, day, 14))
if len(k_values) == 14:
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)
return features
def positive(data):
size = len(data)
if size < 4:
print('数据必须多余4个')
return False
min_idx = 0
min_v = np.max(data)
for i in range(size):
if data[i] <= min_v:
min_idx = i
min_v = data[i]
if min_idx == 0:
return False, 0
if min_idx == size - 1:
return False, size - 1
left_data = data[0:min_idx + 1]
right_data = data[min_idx:]
return trend.is_downtrend(left_data) and trend.is_uptrend(
right_data), min_idx
def vote(self, code, day):
features = []
values = db_helper.select(query_wr_fn, (code, day, 30))
if len(values) < 30:
return features
values = np.array(values)[::-1]
wr_6_values = values[:, 1].astype(float)
if trend.is_uptrend(wr_6_values[-10:]):
k_values = db_helper.select(query_k_fn, (code, day, 10))
if len(k_values) == 10:
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)
if wr_6_values[-1] > 80:
features.append(self.OVER_SOLD)
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
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]
b6_lines = values[:, 1].astype(float)
b12_lines = values[:, 1].astype(float)
b24_lines = values[:, 1].astype(float)
if b6_lines[-1] <= -4 and b12_lines[-1] <= -5.5 and b24_lines[-1] <= -8:
features.append(self.OVER_SOLD)
if ins.match(b6_lines[-3:], b12_lines[-3:]) == 1 and ins.match(
b6_lines[-3:], b24_lines[-3:]):
features.append(self.GOOD_CROSS)
if trend.is_uptrend(b6_lines[-10:]):
k_values = db_helper.select(query_k_fn, (code, day, 10))
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)
return features
