def dump_record():
"""
:return: 存储本次获取数据的日期信息、之后15天的开市日期
存放于DataStore中
"""
record_info_dict = {}
record_info_dict['now'] = Time.now()
pro = ts.pro_api()
record_info_dict['open'] = list(
pro.trade_cal(exchange='',
start_date=Time.now(),
end_date=Time.delta(15),
is_open='1').cal_date)
with open(RECORD_FILE, 'wb') as fw:
pickle.dump(record_info_dict, fw)
def get_ipo(start_date):
"""
:param start_date: str, 开始日期
:return: 指定日期范围内的上市股票
ts_code 股票代码
sub_code 申购代码
name 名称
ipo_date 上网发行日期
issue_date 上市日期
amount 发行总量（万股）
market_amount 上网发行总量（万股）
price 发行价格
pe 市盈率
limit_amount 个人申购上限（万股）
funds 募集资金（亿元）
ballot 中签率
"""
pro = ts.pro_api()
ipo_data = pro.new_share(start_date=start_date)
return ipo_data[ipo_data.issue_date < Time.now()]
def runTrain(sess,d,rnn,msg):
sess.run(tf.global_variables_initializer())
experiment='{}_{}_{}'.format(rnn.name,datanum,Time.now())
model_path="model/{}".format(experiment)
log_path="SAVE_Logs/{}.txt".format(experiment)
stat_path="SAVE_Logs/{}.stat".format(experiment)
logger=Logger(log_path)
stat={"tests":0}
stat_lowAbs={"dist":100}
total_number_of_batch=0
for number in trainRange:
total_number_of_batch+=d[number].numberBatch
total_number_of_batch_test=0
for number in testRange:
total_number_of_batch_test+=d[number].numberBatch
num_epoch=100
totalTime=Time()
for curr_epoch in range(0,num_epoch):
cost_sum=0
test_cost_sum=0
trainTime=Time()
for number in trainRange:
for index in range(d[number].numberBatch):
cost,_=rnn.Train(d[number]._MFCC[index],d[number]._LABEL[index],0.8)
cost_sum+=cost
avg_cost=cost_sum/total_number_of_batch
acc1=0.0
acc0=0.0
for number in trainRange:
for index in range(d[number].numberBatch):
ac1,ac0=rnn.Accuracy(d[number]._MFCC[index],d[number]._LABEL[index])
acc1+=ac1
acc0+=ac0
avg_train_accuracy= (acc1/total_number_of_batch+acc0/total_number_of_batch)/2
acc1=0.0
acc0=0.0
test_cost_sum=0
resultMatrix=np.zeros([2,2],int)
for number in testRange:
for index in range(d[number].numberBatch):
ac1,ac0=rnn.Accuracy(d[number]._MFCC[index],d[number]._LABEL[index])
test_cost_sum+=rnn.Cost(d[number]._MFCC[index],d[number]._LABEL[index])
resultMatrix+=rnn.return_ResultMatrix(d[number]._MFCC[index],d[number]._LABEL[index])
acc1+=ac1
acc0+=ac0
avg_test_accuracy= (acc1/total_number_of_batch_test+acc0/total_number_of_batch_test)/2
test_distance=np.abs(acc1/total_number_of_batch_test-acc0/total_number_of_batch_test)
avg_test_cost=test_cost_sum/total_number_of_batch_test
if(avg_test_accuracy>stat["tests"]):
stat['tests']=avg_test_accuracy
stat['trains']=avg_train_accuracy
stat['epoch']=curr_epoch
stat['cost']=avg_cost
stat['traincost']=avg_test_cost
stat['resultMatrix']=resultMatrix
stat['dist']=test_distance
rnn.Save(model_path)
if(test_distance<stat_lowAbs['dist']):
stat_lowAbs['tests']=avg_test_accuracy
stat_lowAbs['trains']=avg_train_accuracy
stat_lowAbs['epoch']=curr_epoch
stat_lowAbs['cost']=avg_cost
stat_lowAbs['traincost']=avg_test_cost
stat_lowAbs['resultMatrix']=resultMatrix
stat_lowAbs['dist']=test_distance
rnn.Save(model_path+'lowdist')
log="Epoch {}/{}, l_rate:{:.10f}, cost = {:>7.4f},train cost={:>7.4f}, accracy(train,test/best):({:.4f}, {:.4f}/{:.4f}), test_distance ={:.4f} ,time = {}/{}\n".format(
curr_epoch, num_epoch, rnn.learning_rate,avg_cost,avg_test_cost,
avg_train_accuracy,avg_test_accuracy,stat['tests'],test_distance ,trainTime.duration(), totalTime.duration())
logger.write(log)
summary ="""
{}.{}.{}
learning_rate : {} train_data_ratio : {} num_epoch : {} batch_size : {} windowsize : {} windowshift : {}
Best evaulation based on test_data : Accuracy_train : {} Accuracy_test : {} at epoch :{}
Best evaulation based on test_data at lowest distance : Accuracy_train : {} Accuracy_test : {} at epoch :{} \n
best Result Matrix : \n{}{}\n
best Reuslt Matrix at lowest distance : \n{}{}\n
""".format(
rnn.name,experiment,msg,
rnn.learning_rate, train_rate, num_epoch,a.batch_size,a.windowsize,a.windowstep,
stat["trains"],stat["tests"],stat['epoch'],stat_lowAbs['trains'],stat_lowAbs['tests'],stat_lowAbs['epoch'],
stat['resultMatrix'],matrixAccuracy(stat['resultMatrix']),stat_lowAbs['resultMatrix'],matrixAccuracy(stat_lowAbs['resultMatrix']))
print(summary)
logger.flush()
logger.close()
plot_static(log_path)
with open("SAVE_Logs/log.txt","a") as f:
f.write(summary)