def main(_):
pp.pprint(FLAGS.__flags)
run_config = tf.ConfigProto()
run_config.gpu_options.allow_growth = True
with tf.Session(config=run_config) as sess:
rnn_model = LstmRNN(
sess,
FLAGS.stock_count,
lstm_size=FLAGS.lstm_size,
num_layers=FLAGS.num_layers,
num_steps=FLAGS.num_steps,
input_size=FLAGS.input_size,
keep_prob=FLAGS.keep_prob,
)
stock_data_list = load_sp500(
FLAGS.input_size,
FLAGS.num_steps,
k=FLAGS.stock_count,
target_symbol=FLAGS.stock_symbol,
)
if FLAGS.train:
rnn_model.train(stock_data_list, FLAGS)
else:
if not rnn_model.load()[0]:
raise Exception("[!] Train a model first, then run test mode")
def main(_):
pp.pprint(flags.FLAGS.__flags)
# gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.333)
run_config = tf.ConfigProto()
run_config.gpu_options.allow_growth = True
with tf.Session(config=run_config) as sess:
rnn_model = LstmRNN(
sess,
FLAGS.stock_count,
lstm_size=FLAGS.lstm_size,
num_layers=FLAGS.num_layers,
num_steps=FLAGS.num_steps,
input_size=FLAGS.input_size,
embed_size=FLAGS.embed_size,
)
show_all_variables()
stock_data_list = load_sp500(
FLAGS.input_size,
FLAGS.num_steps,
k=FLAGS.stock_count,
target_symbol=FLAGS.stock_symbol,
)
if FLAGS.train:
rnn_model.train(stock_data_list, FLAGS)
else:
if not rnn_model.load()[0]:
raise Exception("[!] Train a model first, then run test mode")
def main(_):
pp.pprint(flags.FLAGS.__flags)
# gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.333)
run_config = tf.ConfigProto()
run_config.gpu_options.allow_growth = True
with tf.Session(config=run_config) as sess:
rnn_model = LstmRNN(
sess,
FLAGS.stock_count,
lstm_size=FLAGS.lstm_size,
num_layers=FLAGS.num_layers,
num_steps=FLAGS.num_steps,
input_size=FLAGS.input_size,
embed_size=FLAGS.embed_size,
)
show_all_variables()
stock_data_list = load_sp500(
FLAGS.input_size,
FLAGS.num_steps,
k=FLAGS.stock_count,
target_symbol=FLAGS.stock_symbol,
)
if FLAGS.train:
rnn_model.train(stock_data_list, FLAGS)
else:
if not rnn_model.load()[0]:
raise Exception("[!] Train a model first, then run test mode")
def main(_):
pp.pprint(flags.FLAGS.__flags)
# gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.333)
run_config = tf.ConfigProto()
run_config.gpu_options.allow_growth = True ## use powerful gpu
with tf.Session(config=run_config) as sess:
rnn_model = LstmRNN(
sess,
FLAGS.stock_count,
lstm_size=FLAGS.lstm_size,
num_layers=FLAGS.num_layers,
num_steps=FLAGS.num_steps,
input_size=FLAGS.input_size,
embed_size=FLAGS.embed_size,
stock=FLAGS.stock_symbol,
)
show_all_variables()
if FLAGS.train:
print('--------------load dataset begin--------------------------')
stock_data_list = load_stock( ## load_sp500
FLAGS.input_size,
FLAGS.num_steps,
k=FLAGS.stock_count,
target_symbol=FLAGS.stock_symbol,
)
print('----------------end------------------------')
rnn_model.train(stock_data_list, FLAGS)
if FLAGS.prediction:
print('--------------load dataset begin--------------------------')
stock_data_list = load_stock( ## load_sp500
FLAGS.input_size,
FLAGS.num_steps,
k=FLAGS.stock_count,
target_symbol=FLAGS.stock_symbol,
TushareFlag=True,
)
print('----------------end------------------------')
## stock_data_list = ts.get_k_data(FLAGS.stock_symbol, autype='hfq', start='2016-01-01') ##time.strftime('%Y-%m-%d',time.localtime(time.time())))
rnn_model.prediction(stock_data_list, FLAGS)
else:
if not rnn_model.load()[0]:
raise Exception("[!] Train a model first, then run test mode")
def main(_):
pp.pprint(flags.FLAGS.__flags)
# gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.333)
run_config = tf.ConfigProto()
run_config.gpu_options.allow_growth = True
with tf.Session(config=run_config) as sess:
rnn_model = LstmRNN(
sess,
FLAGS.stock_count,
lstm_size=FLAGS.lstm_size,
num_layers=FLAGS.num_layers,
num_steps=FLAGS.num_steps,
input_size=FLAGS.input_size,
keep_prob=FLAGS.keep_prob,
embed_size=FLAGS.embed_size,
)
show_all_variables()
stock_data_list = load_sp500(
FLAGS.input_size,
FLAGS.num_steps,
k=FLAGS.stock_count,
target_symbol=FLAGS.stock_symbol,
)
if FLAGS.train:
rnn_model.train(stock_data_list, FLAGS)
else:
test_prediction, test_loss = rnn_model.predict(
stock_data_list, 50, FLAGS)
if FLAGS.write:
with open('api_log/' + FLAGS.stock_symbol + ".pkl", 'wb') as f:
pickle.dump(test_prediction, f)
#rnn_model.plot_samples(test_prediction, test_prediction, 'check.png', 'GOOG')
#plt.show(block=True)
print '-' * 33
if not rnn_model.load()[0]:
raise Exception("[!] Train a model first, then run test mode")
# In[]
pp.pprint(flags.FLAGS.__flags)
# In[]
# gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.333)
run_config = tf.ConfigProto()
run_config.gpu_options.allow_growth = True
# In[]
with tf.Session(config=run_config) as sess:
rnn_model = LstmRNN(
sess,
FLAGS.stock_count,
lstm_size=FLAGS.lstm_size,
num_layers=FLAGS.num_layers,
num_steps=FLAGS.num_steps,
input_size=FLAGS.input_size,
embed_size=FLAGS.embed_size,
)
# In[]
show_all_variables()
# In[]
stock_data_list = load_sp500(
FLAGS.input_size,
FLAGS.num_steps,
k=FLAGS.stock_count,
target_symbol=FLAGS.stock_symbol,
)
# In[]
def main(_):
pp.pprint(FLAGS.__flags)
run_config = tf.ConfigProto() #控制GPU资源的使用
run_config.gpu_options.allow_growth = True # 使用allow_growth option，刚一开始分配少量的GPU容量，然后按需慢慢的增加，由于不会释放内存，所以会导致碎片
tf.reset_default_graph()
result = []
with tf.Session(config=run_config) as sess:
rnn_model = LstmRNN(
sess,
FLAGS.stock_count,
lstm_size=FLAGS.lstm_size,
num_layers=FLAGS.num_layers,
num_steps=FLAGS.num_steps,
input_size=FLAGS.input_size,
keep_prob=FLAGS.keep_prob,
)
stock_data_list = load_sp500(
FLAGS.input_size,
FLAGS.num_steps,
k=FLAGS.stock_count,
target_symbol=FLAGS.stock_symbol,
)
result.append(stock_data_list[0].final_price)
if FLAGS.train:
rnn_model.load()
# rnn_model.train(stock_data_list, FLAGS) #LSTM的训练过程
# rnn_model.test(stock_data_list, FLAGS) #LSTM的测试过程
Lstm_predict = rnn_model.test2(stock_data_list) #LSTM的预测过程
tf.reset_default_graph()
with tf.Session(config=run_config) as sess:
cnn_model = CNN(
sess,
embedding_dim=FLAGS.embedding_dim,
num_steps=FLAGS.num_steps2,
num_filters=FLAGS.num_filters, # 卷积核数目
kernel_size=FLAGS.kernel_size, # 卷积核尺寸
hidden_dim=FLAGS.hidden_dim, # 全连接层神经元
keep_prob=FLAGS.keep_prob,
)
cnn_data_list = load_szzs2(
FLAGS.embedding_dim,
FLAGS.num_steps2,
target_symbol=FLAGS.stock_symbol2,
)
if FLAGS.train:
cnn_model.load()
# cnn_model.train(cnn_data_list, FLAGS) #CNN的训练过程
CNN_predict = cnn_model.test(cnn_data_list) #CNN的预测过程
for i in range(len(CNN_predict)):
Lstm_predict[i] = CNN_predict[i]
print(Lstm_predict)
for i in range(len(Lstm_predict)):
result.append(result[i] * (1 + Lstm_predict[i]))
print(result[1:])
# 将预测结果写入.CSV文件内
dataframe = pd.DataFrame({'pred': np.array(result[1:]).tolist()})
dataframe.to_csv(os.path.join("predictions", "pred.csv"),
index=False,
sep=',')
