def get_predicts(self, comp_code, tp):
learning = Learning(self.params)
investX = tp.data_params.investX
#print(investX)
graph_params = learning.get_train_model()
X = graph_params.X
Y_pred = graph_params.Y_pred
training = graph_params.training
output_keep_prob = graph_params.output_keep_prob
saver = tf.train.Saver()
session_path = learning.get_session_path(comp_code)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
saver.restore(sess, session_path)
invest_predicts = None
if len(investX) > 0:
invest_predicts = sess.run(Y_pred,
feed_dict={
X: investX,
output_keep_prob: 1.0,
training: False
})
last_predict = sess.run(Y_pred,
feed_dict={
X: tp.dataX_last,
output_keep_prob: 1.0,
training: False
})
return invest_predicts, last_predict[0]
def restore(self) -> bool:
learning = Learning(self._params)
if learning.exist_learning_image(self._corp_code, True, self._name):
saver = tf.train.Saver()
saved_session_path = learning.get_session_path(
self._corp_code, True, self._name)
saver.restore(self.sess, saved_session_path)
return True
return False
def let_invest(self, comp_code, dataX_last, data_params):
"""학습 후 모의 주식 거래를 한다."""
stacked_rnn = StackedRnn(self.params)
learning = Learning(self.params)
invest_count = self.params.invest_count
invest_money = self.params.invest_money
investCloses = data_params['investCloses']
investRealCloses = data_params['investRealCloses']
investX = data_params['investX']
graph_params = stacked_rnn.get_stacted_rnn_model()
X = graph_params['X']
Y_pred = graph_params['Y_pred']
output_keep_prob = graph_params['output_keep_prob']
session_file_path = learning.get_session_path(comp_code)
now_stock_cnt = 0
all_invest_money = invest_money
all_stock_count = now_stock_cnt
predicts = []
now_close = 0
saver = tf.train.Saver()
with tf.Session() as sess:
init = tf.global_variables_initializer()
sess.run(init)
saver.restore(sess, session_file_path)
for i in range(invest_count):
invest_predicts = sess.run(Y_pred,
feed_dict={
X: investX[i:i + 1],
output_keep_prob: 1.0
})
predicts.append(invest_predicts[0])
invest_predict = invest_predicts[0][0]
now_scaled_close = investCloses[i][0]
now_close = investRealCloses[i]
#print(invest_predict, now_scaled_close, now_close)
invest_money, now_stock_cnt = self.let_invest_money(
invest_predict, now_scaled_close, now_close, invest_money,
now_stock_cnt)
if i == 0:
all_invest_money, all_stock_count = self.let_invest_money(
10.0, now_scaled_close, now_close, all_invest_money,
all_stock_count)
invest_money += self.to_money(now_stock_cnt, now_close)
all_invest_money += self.to_money(all_stock_count, now_close)
last_predict = sess.run(Y_pred,
feed_dict={
X: dataX_last,
output_keep_prob: 1.0
})
# print(now_money)
return invest_money, last_predict, predicts, all_invest_money
def _get_predict(self, params, comp_code, investX):
stacked_rnn = StackedRnn(params)
graph_params = stacked_rnn.get_stacted_rnn_model()
Y_pred = graph_params['Y_pred']
output_keep_prob = graph_params['output_keep_prob']
X = graph_params['X']
learning = Learning(params)
file_path = learning.get_session_path(comp_code)
saver = tf.train.Saver()
with tf.Session() as sess:
init = tf.global_variables_initializer()
sess.run(init)
saver.restore(sess, file_path)
last_predict = sess.run(Y_pred,
feed_dict={
X: investX,
output_keep_prob: 1.0
})
return last_predict