def training_k_data():
df = ts.get_hs300s()
for code in df['code'].values:
try:
logger.debug('begin training mode, code:%s' % code)
data, features = k_data_60m_dao.get_k_data_with_features(
code, '2015-01-01',
datetime.now().strftime("%Y-%m-%d"))
pca = PCAModel(MODULE_NAME)
lr = LogisticRegressionClassifier()
svc = SupportVectorClassifier()
rf = RandomForestClassifierModel()
xgb = XGBoostClassier()
ann = SequantialNeuralClassifier()
pca.training_model(code, data, features)
lr.training_model(code, data, features)
svc.training_model(code, data, features)
rf.training_model(code, data, features)
xgb.training_model(code, data, features)
ann.training_model(code, data, features)
logger.debug('training mode end, code:%s' % code)
except Exception as e:
logger.error("training k data error, code:%s, error:%s" %
(code, repr(e)))
def training_k_data(start, end):
df = stock_pool_dao.get_list()
codes = df['code'].values[start:end]
for code in codes:
try:
logger.debug('begin training mode, code:%s' % code)
data, features = k_data_dao.get_k_data_with_features(
code, '2015-01-01',
datetime.now().strftime("%Y-%m-%d"))
pca = PCAModel('k_data')
lr = LogisticRegressionClassifier()
svc = SupportVectorClassifier()
rf = RandomForestClassifierModel()
xgb = XGBoostClassier()
#ann = SequantialNeuralClassifier()
pca.training_model(code, data, features)
lr.training_model(code, data, features)
svc.training_model(code, data, features)
rf.training_model(code, data, features)
xgb.training_model(code, data, features)
#ann.training_model(code, data, features)
logger.debug('training mode end, code:%s' % code)
except Exception as e:
logger.error("training k data error, code:%s, error:%s" %
(code, repr(e)))
def test_training(self):
data, features = k_data_dao.get_k_data_with_features(
'600196', '2015-01-01',
datetime.now().strftime("%Y-%m-%d"))
pca_model = PCAModel('k_data')
pca_model.training_model(code='600196', data=data, features=features)
def test_training(self):
code = '600276'
data, features = k_data_dao.get_k_data_with_features(
code, '2015-01-01', datetime_utils.get_current_date())
pac = PCAModel('k_data')
pac.training_model(code=code, data=data, features=features)
model = SequantialNeuralClassifier()
model.training_model(code, data, features)
def test_training(self):
code = '600196'
data, features = k_data_dao.get_k_data_with_features(
code, '2015-01-01',
datetime.now().strftime("%Y-%m-%d"))
pac = PCAModel('k_data')
pac.training_model(code=code, data=data, features=features)
model = RandomForestClassifierModel()
model.training_model(code, data, features)
def test_training(self):
code = '600196'
data, features = k_data_60m_dao.get_k_data_with_features(code, '2015-01-01',
datetime.now().strftime("%Y-%m-%d"))
logger.debug("features:%s, length:%s" % (features, len(features)))
pac = PCAModel('k_data')
pac.training_model(code=code, data=data, features=features)
model = RidgeRegressionModel()
model.training_model(code, data, features)
def test_training(self):
code = '600276'
# 从数据库中获取2015-01-01到今天的所有数据
data, features = k_data_dao.get_k_data_with_features(code, '2015-01-01', datetime.now().strftime("%Y-%m-%d"))
logger.debug("features:%s" % features)
pac = PCAModel('k_data')
pac.training_model(code=code, data=data,features=features)
model = SupportVectorClassifier()
model.training_model(code, data, features)
def test_training(self):
code = '600276'
# 从数据库中获取2015-01-01到今天的所有数据
data, features = k_data_60m_dao.get_k_data_with_features(
code, '2015-01-01',
datetime.now().strftime("%Y-%m-%d"))
logger.debug("features:%s" % features)
pac = PCAModel(MODULE_NAME)
pac.training_model(code=code, data=data, features=features)
model = XGBoostClassier()
model.training_model(code, data, features)
def test_training(self):
code = '600196'
# 从数据库中获取2015-01-01到今天的所有数据
data, features = k_data_60m_dao.get_k_data_with_features(
code, '2015-01-01',
datetime.now().strftime("%Y-%m-%d"))
logger.debug("features:%s, length:%s" % (features, len(features)))
data.to_csv("result.csv")
pac = PCAModel(MODULE_NAME)
pac.training_model(code=code, data=data, features=features)
model = LogisticRegressionClassifier()
model.training_model(code, data, features)
def test_training(self):
code = '600196'
# 从数据库中获取2015-01-01到今天的所有数据
data, features = k_data_dao.get_k_training_data(
code, '2012-01-01',
datetime.now().strftime("%Y-%m-%d"), self.futu_quote_ctx)
data.to_csv("result.csv")
logger.debug("features:%s, length:%s" % (features, len(features)))
pac = PCAModel('k_data')
pac.training_model(code=code, data=data, features=features)
model = LogisticRegressionClassifier()
model.training_model(code, data, features)
