def insert_hist_trade(self):
self.set_data()
db = Db()
engine = db._get_engine()
sql_stocklist = "select code,name from stock_code"
codes = pd.read_sql_query(sql_stocklist, engine)
codes = codes.to_dict('records')
i = 1
for row in codes:
gta = td.get_hist_data(code=row['code'],
start=self.nowdate,
end=self.nowdate,
ktype='D',
retry_count=3,
pause=0.001)
gta['datain_date'] = self.nowtime
gta['code'] = row['code']
gta['name'] = row['name']
gta['c_yearmonthday'] = gta.index
gta = gta.to_dict('records')
try:
db.insertmany(
"""INSERT INTO trade_hist(c_yearmonthday,code,name,open,high,close,low,volume,price_change,p_change,ma5,ma10,ma20,v_ma5,v_ma10,v_ma20,turnover,datain_date)
VALUES (%(c_yearmonthday)s,%(code)s,%(name)s,%(open)s,%(high)s,%(close)s,%(low)s,%(volume)s,%(price_change)s,%(p_change)s,%(ma5)s,%(ma10)s,%(ma20)s,%(v_ma5)s,%(v_ma10)s,%(v_ma20)s,%(turnover)s,%(datain_date)s)""",
gta)
except Exception, e:
log.error('insert error:%s ', e)
log.info('%s stock insert finished,%s,%s', i, row['code'],
row['name'].decode('utf-8'))
i += 1
def predict_today(datatype, timesteps, data_dim=15):
# log = logger.log
nowdate = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(time.time()))
x_predict, id_predict, name_predict = trade.get_today(seg_len=timesteps,
datatype=datatype,
split=0.1,
debug=False)
network = policy.LSTMPolicy.create_network(timesteps=timesteps,
data_dim=data_dim)
USER_HOME = os.environ['HOME']
out_directory_path = USER_HOME + '/dw/'
meta_file = os.path.join(out_directory_path, 'metadata.json')
weights_path = policy_trainer.get_best_weights(meta_file)
network.load_weights(weights_path)
predicts = network.predict(x_predict, batch_size=16)
v_predicts = pd.DataFrame()
v_predicts['code'] = id_predict
v_predicts['name'] = name_predict
v_predicts['predict'] = predicts
v_predicts['datain_date'] = nowdate
db = Db()
v_predicts = v_predicts.to_dict('records')
db.insertmany(
"""INSERT INTO predicts(code,name,predict,datain_date)
VALUES (%(code)s,%(name)s,%(predict)s,%(datain_date)s)""", v_predicts)
log.info('predicts finished')
def validate(rule, result):
logid = "Validate [" + str(os.getpid()) + "] "
log.debug(logid + " multi name = " + current_process().name)
db = Db(True, 15)
resultObj = db.Results.objects(file_id=result.file_id,
rule_id=result.rule_id)[0]
log.debug(logid + "Result = " + str(resultObj))
notifier = Notifications()
source = ""
if 'Source' in rule:
source = rule['Source']
else:
source = rule['source']
result, message = read_file_and_evaluate(source, resultObj)
log.debug("Running validation process for " + rule['name'] +
" got result " + str(result) + " and message " + message)
if result:
resultObj.state = 0
else:
resultObj.state = 1
if resultObj.mandatory:
resultObj.message = "Failed"
else:
resultObj.message = "Warning"
resultObj.save()
notifier.publish('fileStatus', resultObj.to_json())
def conecta(): #print(config) db = Db(config) #print(db.config); conn = db.connect() #print(db) return db
def start(token, backend, db, dialog, filename):
bot = init_backend(token, backend)
path = db['path']
del db['path']
db_path = init_db(path, **db)
db = Db(db_path)
dialog = Dialog(db, dialog)
print('Start')
while True:
messages = bot.get_unread_messages()
if messages["count"] >= 1:
id, message_id, body, url = bot.get_message_ids_image(messages)
print('Запрос:', id, body)
try:
if url and url[-3:] == 'jpg':
urllib.request.urlretrieve(url, filename)
db.save_image(id, body, filename)
bot.send_message(id, dialog.ok)
continue
if body.lower() in dialog.common_answer:
bot.send_message(id, dialog.common_answer[body.lower()])
continue
for key in dialog.action:
if key in body.lower():
part = body.lower().split(':')[1].strip()
if '&' in body.lower():
splitted = part.split('&')
part_1, part_2 = splitted[0].strip(
), splitted[1].strip()
db.hset(id, part_1, part_2)
bot.send_message(id, dialog.ok)
break
data = db.hget(id, part)
if data and len(data) > 15000:
with open(filename, 'wb') as file:
file.write(data)
bot.upload_image(id, filename)
break
elif data:
bot.send_message(id, data)
else:
bot.send_message(id, dialog.text_does_not_exist)
break
else:
bot.send_message(id, dialog.don_t_understand)
except Exception as e:
print(str(e))
bot.send_message(id, dialog.error)
time.sleep(1)
def __init__(self, hostname, protocol, username=None, password=None):
self.db = Db()
self.connection = Connection()
self.hostname = protocol + hostname
self.username = username
self.password = password
self.is_logged_in = False
self.exploit_results = {}
self.connection.reset_session()
def setUp(self):
init()
self.parser = ConfigParser()
logging.config.fileConfig('test/db_test.cfg')
logger = logging.getLogger('basic')
self.parser.read('test/db_test.cfg')
connection_string = self.parser.get('Db', 'connection_string')
self.db = Db(connection_string, logger)
self.db.open()
def test():
infile = 'data.csv'
df = pd.read_csv(infile)
df = df.groupby('X')
db = Db(TEMP_DB)
with open('script.sql', 'w') as file:
for d in df:
(table_name, data) = d
t = Table(db, table_name, data, file, test_mode=True)
t.prepare_temp_table()
def process_item(self, item, spider):
db = Db()
if item['table_name'] == 'movie':
if item['summary']:
db.movie_update(item)
else:
db.movie_insert(item)
else:
db.comment_insert(item)
return item
def get_predict_acc1(debug=False):
db = Db()
engine = db._get_engine()
sql_tradehist = "select code,name,p_change from trade_hist where code in (select code from predict_head where c_yearmonthday in (select max(c_yearmonthday) from predict_head) ) order by c_yearmonthday desc"
sql_predicthead = "select code,predict from predict_head order by c_yearmonthday desc"
if debug:
pass
df_trade = pd.read_sql_query(sql_tradehist, engine).head(2)
df_predict = pd.read_sql_query(sql_predicthead, engine).head(2)
df = pd.merge(df_trade, df_predict, on='code')
df['acc'] = (df.p_change > 0).astype(float)
return df
def get_hist_orgindata(debug=False):
db = Db()
engine = db._get_engine()
sql_stocklist = "select * from trade_hist where code in (select code from trade_hist where high<>0.0 and low <>0.0 group by code having count(code)>100)"
if debug:
sql_stocklist += " and code in ('002717','601888','002405')"
df = pd.read_sql_query(sql_stocklist, engine)
codes = df['code'].unique()
# 增加技术指标
df = add_volatility(df)
df = get_technique(df)
return df, codes
def mockdb(mocker):
"""A test mock database."""
mock_init_conn = mocker.patch('db.db.Db.initConnection')
mock_init_conn.side_effect = mock_db
db = Db()
# Tried scoping to module - but mocker is function, so not able. Is there a bulk delete?
for r in Results.objects():
r.delete()
Rules(name="rule1", source="${file.name}!=badFile", mandatory=False).save()
Rules(name="rule2", source="${file.size}<500", state=1,
mandatory=True).save()
return db
def get_predict(debug=False):
db = Db()
engine = db._get_engine()
sql_stocklist = "select * from predicts where datain_date in(select max(datain_date) from predicts) order by predict desc"
if debug:
pass
df = pd.read_sql_query(sql_stocklist, engine)
headpredict = df.head(2)
psummery = df.describe().T
psummery.columns = [
'p_cnt', 'p_mean', 'p_std', 'p_min', 'p25', 'p50', 'p75', 'p_max'
]
return psummery, headpredict
def mockdb(mocker):
"""A test mock database."""
mock_init_conn = mocker.patch('db.db.Db.initConnection')
mock_init_conn.side_effect = mock_db
db = Db()
# Tried scoping to module - but mocker is function, so not able. Is there a bulk delete?
for r in Results.objects():
r.delete()
Results(file_id="file_1", rule_id="rule_1", state=1, message="").save()
Results(file_id="file_2", rule_id="rule_1", state=1, message="").save()
Results(file_id="file_2", rule_id="rule_1", state=1, message="").save()
return db
def run():
infile = 'data.csv'
result_file = 'results.csv'
alter_table_file = 'alter_tables.sql'
df = pd.read_csv(infile)
df = df.groupby('X')
db = Db(TEMP_DB)
total_errors = float(0)
alter_statements = []
with open('script.sql', 'w') as file:
with open(result_file, 'w') as results:
with open(alter_table_file, 'w') as alter_file:
for d in df:
(table_name, data) = d
t = Table(db, table_name, data, sql_log=file, results_log=results, test_mode=False)
t.prepare_temp_table()
errors = t.test_temp_table()
if errors == 0:
statement = t.get_alter_table_for_original_table()
alter_statements.append(statement)
alter_file.write(statement)
total_errors += float(errors)
if total_errors == 0:
print(f'All tests passed: alter table file generated in {alter_table_file}')
do_it = input(f"Do you want to apply the changes to the original database ({ORIGINAL_DB}) [yN]:")
if do_it.lower() == 'y':
print('Applying modifications to the original database')
db = Db(ORIGINAL_DB)
for statement in alter_statements:
db.execute(statement)
print('All done')
else:
print(f'Not all tests passed')
sys.exit(1)
def insert_today_trade(self):
self.set_data()
db = Db()
gta = td.get_today_all()
gta['datain_date'] = self.nowtime
gta['c_yearmonthday'] = self.nowdate
gta = gta.to_dict('records')
db.insertmany(
"""INSERT INTO trade_record(c_yearmonthday,code,name,changepercent,trade,open,high,low,settlement
,volume,turnoverratio,amount,per,pb,mktcap,nmc,datain_date)
VALUES (%(c_yearmonthday)s,%(code)s,%(name)s,%(changepercent)s,%(trade)s,%(open)s,%(high)s,%(low)s,%(settlement)s,%(volume)s,%(turnoverratio)s,%(amount)s,%(per)s,%(pb)s,%(mktcap)s,%(nmc)s,%(datain_date)s)""",
gta)
def insert_predict_statics():
db = Db()
nowdate = time.strftime("%Y-%m-%d", time.localtime(time.time()))
psummery, headpredict = trade.get_predict()
psummery['c_yearmonthday'] = nowdate
headpredict['c_yearmonthday'] = nowdate
psummery = psummery.to_dict('records')
headpredict = headpredict.to_dict('records')
db.insertmany(
"""INSERT INTO predict_head(c_yearmonthday,code,name,predict)
VALUES (%(c_yearmonthday)s,%(code)s,%(name)s,%(predict)s)""",
headpredict)
db.insertmany(
"""INSERT INTO predict_statics(c_yearmonthday,p_cnt,p_mean,p_std,p_min,p25,p50,p75,p_max)
VALUES (%(c_yearmonthday)s,%(p_cnt)s,%(p_mean)s,%(p_std)s,%(p_min)s,%(p25)s,%(p50)s,%(p75)s,%(p_max)s)""",
psummery)
def checker():
print("checker started")
db = Db(cfg)
while True:
time.sleep(cfg.SLEEP_TIME)
ipPort = db.select_proxy()
if ipPort:
proxyDict = {
"http": ipPort,
"https": ipPort,
}
r = requests.get(cfg.LINK_TO_BE_CHECKED, headers={'User-Agent': random.choice(cfg.USER_AGENT)}, proxies=proxyDict)
if not 200 <= r.status_code <= 299:
db.delete_row(ipPort)
logger.info("{} deleted".format(ipPort))
else:
logger.error("{} : {}".format(r.status_code, r.text))
def insert_predict_acc():
db = Db()
nowdate = time.strftime("%Y-%m-%d", time.localtime(time.time()))
acc1 = trade.get_predict_acc1()
acc1['c_yearmonthday'] = nowdate
acc1 = acc1.to_dict('records')
db.insertmany(
"""INSERT INTO acc1(c_yearmonthday,code,name,predict,p_change,acc)
VALUES (%(c_yearmonthday)s,%(code)s,%(name)s,%(predict)s,%(p_change)s,%(acc)s)""",
acc1)
acc2 = trade.get_predict_acc2()
acc2['c_yearmonthday'] = nowdate
acc2 = acc2.to_dict('records')
db.insertmany(
"""INSERT INTO acc2(c_yearmonthday,p_acc,p_change,h_p_acc,h_p_change)
VALUES (%(c_yearmonthday)s,%(p_acc)s,%(p_change)s,%(h_p_acc)s,%(h_p_change)s)""",
acc2)
def get_predict_acc2(debug=False):
db = Db()
engine = db._get_engine()
sql_stocklist = "select * from acc1"
if debug:
pass
df = pd.read_sql_query(sql_stocklist, engine)
acc2 = df.sort_values('c_yearmonthday', ascending=0)
acc2 = acc2.head(2)
acc2 = acc2.groupby('c_yearmonthday').sum()
acc2_final = pd.DataFrame()
acc2_final['h_p_acc'] = [df['acc'].sum() / float(df['acc'].count())]
acc2_final['h_p_change'] = [df['p_change'].sum() / 2.0]
acc2_final['p_acc'] = [acc2['acc'].sum() / 2.0]
acc2_final['p_change'] = [acc2['p_change'].sum() / 2.0]
return acc2_final
def mockdb(mocker):
"""A test mock database."""
mock_init_conn = mocker.patch('db.db.Db.initConnection')
mock_init_conn.side_effect = mock_db
db = Db()
# Tried scoping to module - but mocker is function, so not able. Is there a bulk delete?
for r in db.Rules.objects():
r.delete()
for p in db.Policies.objects():
p.delete()
Rules(name="rule1",source="${file.name}!=badFile",mandatory=False).save()
Rules(name="rule2",source="${file.size}<500",mandatory=True).save()
Policies(name="export-data",rules=['rule1','rule2']).save()
Policies(name="bad-policy",rules=['rule1','rule2','rule3']).save()
return db
def new_proxy():
print("new_proxy started")
db = Db(cfg)
while True:
time.sleep(cfg.SLEEP_TIME)
if db.tot_rows() <= cfg.MAX_IP:
try:
proxyjson = requests.get('http://gimmeproxy.com/api/getProxy?maxCheckPeriod=300?protocol=http').json()
print(proxyjson)
except requests.exceptions.RequestException as e:
logger.error(e)
except ValueError as e: # includes simplejson.decoder.JSONDecodeError
logger.error(e)
if 'ipPort' in proxyjson.keys():
db.insert_row(proxyjson['ipPort'])
logger.info("proxy added: {}".format(proxyjson['ipPort']))
else:
logger.error(proxyjson)
def start(token, backend, db, filename):
bot = init_backend(token, backend)
path = db['path']
del db['path']
db_path = init_db(path, **db)
db = Db(db_path)
print('Start')
while True:
try:
messages = bot.get_unread_messages()
if messages["count"] >= 1:
id, body = bot.get_message_and_id(messages)
bot.convert_text_to_voice(body)
uploaded_voice = bot.upload_file(filename, id)
bot.send_message(id, attach=uploaded_voice)
db.rpush(id, body)
print('Запрос:', id, body)
time.sleep(1)
except Exception as e:
print('Error:', str(e))
def get_today(split=0.2,
seg_len=3,
debug=False,
datatype='cnn',
datafile=None):
log = logger.log
db = Db()
engine = db._get_engine()
sql_stocklist = "select * from trade_hist where code in (select code from trade_hist where high<>0.0 and low <>0.0 group by code having count(code)>100)"
if debug:
sql_stocklist += " and code in ('002717','601888','002405')"
df = pd.read_sql_query(sql_stocklist, engine)
df = add_volatility(df)
stockcodes = df['code'].unique()
df = get_technique(df)
print stockcodes
X_predict = []
ID_predict = []
NAME_predict = []
log.info('begin generate train data and validate data.')
k = 0
for codes in stockcodes:
temp_df = df[df.code == codes]
temp_df1 = temp_df.copy(deep=True)
temp_df1 = temp_df1.sort_values(by='c_yearmonthday', ascending=1)
tradedaylist = temp_df1['c_yearmonthday'].values
tradedaylist.sort()
tradedaylist = tradedaylist[::-1]
temp_df1 = temp_df1.set_index('c_yearmonthday')
if len(tradedaylist) < seg_len:
log.info('not enough trade days ,code is :%s', codes)
continue
i = 0
segdays = tradedaylist[i:i + seg_len]
segbegin = segdays[len(segdays) - 1]
segend = segdays[0]
if len(segdays) < seg_len:
break
data = []
data = temp_df1.loc[segbegin:segend, [
'open', 'high', 'close', 'low', 'volume', 'price_change',
'p_change', 'ma5', 'ma10', 'ma20', 'v_ma5', 'v_ma10', 'v_ma20',
'turnover', 'deltat', 'BIAS_B', 'BIAS_S', 'BOLL_B', 'BOLL_S',
'CCI_B', 'CCI_S', 'DMI_B', 'DMI_HL', 'DMI_IF1', 'DMI_IF2',
'DMI_MAX1', 'DMI_S', 'KDJ_B', 'KDJ_S', 'KD_B', 'KD_S', 'MACD',
'MACD_B', 'MACD_DEA', 'MACD_DIFF', 'MACD_EMA_12', 'MACD_EMA_26',
'MACD_EMA_9', 'MACD_S', 'MA_B', 'MA_S', 'PSY_B', 'PSY_MYPSY1',
'PSY_S', 'ROC_B', 'ROC_S', 'RSI_B', 'RSI_S', 'VR_B', 'VR_IF1',
'VR_IF2', 'VR_IF3', 'VR_S', 'XYYH_B', 'XYYH_B1', 'XYYH_B2',
'XYYH_B3', 'XYYH_CC', 'XYYH_DD'
]]
data = data.values
if datatype == 'cnn':
data = [data]
data_tag = temp_df[temp_df.c_yearmonthday == tradedaylist[0]][[
'code', 'name', 'p_change'
]]
temp_id = data_tag['code'].values[0]
temp_name = data_tag['name'].values[0]
X_predict.append(data)
ID_predict.append(temp_id)
NAME_predict.append(temp_name)
k += 1
log.info('%s stock finished ', k)
return (np.asarray(X_predict), np.asarray(ID_predict),
np.asarray(NAME_predict))
from os import environ, getcwd, path
from dotenv import load_dotenv
from db.db import Db
from api.api import Api
env_path = path.join(getcwd(), ".env")
if path.isfile(env_path):
load_dotenv(env_path)
hostname = environ.get('SQL_HOSTNAME')
unix_socket = environ.get('SQL_CONNECTION_NAME')
database = environ.get('SQL_DATABASE_NAME')
user = environ.get('SQL_USERNAME')
password = environ.get('SQL_PASSWORD')
db = Db(hostname, database, user, password, unix_socket)
api = Api(db)
app = api.app()
if __name__ == '__main__':
app.run(host="127.0.0.1", port=8080),
def get_hist6years(split=0.2,
seg_len=3,
debug=False,
datatype='cnn',
datafile=None,
predict_days=18):
log = logger.log
db = Db()
engine = db._get_engine()
sql_stocklist = "select * from trade_hist where code in (select code from trade_hist where high<>0.0 and low <>0.0 group by code having count(code)>100)"
if debug:
sql_stocklist += " and code in ('002717','601888','002405')"
df = pd.read_sql_query(sql_stocklist, engine)
# 增加技术指标
df = add_volatility(df)
stockcodes = df['code'].unique()
df = get_technique(df, stockcodes)
X_train = []
X_valid = []
Y_train = []
Y_valid = []
ID_train = []
ID_valid = []
log.info('begin generate train data and validate data.')
begin_time = time.clock()
k = 0
predict_days = predict_days
for codes in stockcodes:
temp_df = df[df.code == codes]
temp_df1 = temp_df.copy(deep=True)
temp_df1 = temp_df1.sort_values(by='c_yearmonthday', ascending=1)
tradedaylist = temp_df1['c_yearmonthday'].values
tradedaylist.sort()
tradedaylist = tradedaylist[::-1]
temp_df1 = temp_df1.set_index('c_yearmonthday')
if len(tradedaylist) < seg_len:
log.info('not enough trade days ,code is :%s', codes)
continue
validdays = np.round(split * len(tradedaylist))
# validdays = 2
i = 0
for day in tradedaylist:
i += 1
segdays = tradedaylist[i + predict_days:i + predict_days + seg_len]
segbegin = segdays[len(segdays) - 1]
segend = segdays[0]
if len(segdays) < seg_len:
break
data = []
# for segday in segdays:
data = temp_df1.loc[segbegin:segend, [
'open', 'high', 'close', 'low', 'volume', 'price_change',
'p_change', 'ma5', 'ma10', 'ma20', 'v_ma5', 'v_ma10', 'v_ma20',
'turnover', 'deltat', 'BIAS_B', 'BIAS_S', 'BOLL_B', 'BOLL_S',
'CCI_B', 'CCI_S', 'DMI_B', 'DMI_HL', 'DMI_IF1', 'DMI_IF2',
'DMI_MAX1', 'DMI_S', 'KDJ_B', 'KDJ_S', 'KD_B', 'KD_S', 'MACD',
'MACD_B', 'MACD_DEA', 'MACD_DIFF', 'MACD_EMA_12',
'MACD_EMA_26', 'MACD_EMA_9', 'MACD_S', 'MA_B', 'MA_S', 'PSY_B',
'PSY_MYPSY1', 'PSY_S', 'ROC_B', 'ROC_S', 'RSI_B', 'RSI_S',
'VR_B', 'VR_IF1', 'VR_IF2', 'VR_IF3', 'VR_S', 'XYYH_B',
'XYYH_B1', 'XYYH_B2', 'XYYH_B3', 'XYYH_CC', 'XYYH_DD'
]]
data = data.values
if datatype == 'cnn':
data = [data]
d1 = tradedaylist[i - 1]
d3 = tradedaylist[i + predict_days - 1]
data_tag = temp_df[temp_df.c_yearmonthday == d1][[
'code', 'name', 'p_change', 'close'
]]
data_tag3 = temp_df[temp_df.c_yearmonthday == d3][[
'code', 'name', 'p_change', 'close'
]]
temp_y = data_tag['close'].values[0]
temp_y3 = data_tag3['close'].values[0]
temp_y = (temp_y - temp_y3) / temp_y3
temp_y = to_cate01(temp_y)
temp_id = data_tag['code'].values[0]
if (i > 0 and i <= validdays):
X_valid.append(data)
ID_valid.append(temp_id)
Y_valid.append(temp_y)
else:
X_train.append(data)
ID_train.append(temp_id)
Y_train.append(temp_y)
k += 1
samples = 12
if k % samples == 0:
print k
log.info('%s stock finished ', k)
yield ((np.asarray(X_train), np.asarray(Y_train),
np.asarray(ID_train)), (np.asarray(X_valid),
np.asarray(Y_valid),
np.asarray(ID_valid)))
X_train = []
X_valid = []
Y_train = []
Y_valid = []
ID_train = []
ID_valid = []
yield ((np.asarray(X_train), np.asarray(Y_train), np.asarray(ID_train)),
(np.asarray(X_valid), np.asarray(Y_valid), np.asarray(ID_valid)))
def get_histdata(split=0.15, seg_len=3, debug=False, datatype='cnn'):
db = Db()
engine = db._get_engine()
sql_stocklist = "select * from trade_record where code in (select code from trade_record where high<>0.0 and low <>0.0 group by code having count(code)=(select count(distinct c_yearmonthday) from trade_record))"
if debug:
sql_stocklist += " and code in ('300138','002372')"
df = pd.read_sql_query(sql_stocklist, engine)
stockcodes = df['code'].unique()
X_train = []
X_valid = []
Y_train = []
Y_valid = []
ID_train = []
ID_valid = []
log.info('begin generate train data and validate data.')
begin_time = time.clock()
k = 0
for codes in stockcodes:
temp_df = df[df.code == codes]
tradedaylist = temp_df.copy(deep=True)['c_yearmonthday'].values
tradedaylist.sort()
tradedaylist = tradedaylist[::-1]
if len(tradedaylist) < seg_len:
log.info('not enough trade days ,code is :%s', codes)
continue
validdays = np.round(split * len(tradedaylist))
i = 0
for day in tradedaylist:
i += 1
segdays = tradedaylist[i:i + seg_len]
if len(segdays) < seg_len:
break
SEG_X = []
data = []
for segday in segdays:
data = temp_df[temp_df.c_yearmonthday == segday][[
'changepercent', 'trade', 'open', 'high', 'low',
'settlement', 'volume', 'turnoverratio', 'amount', 'per',
'pb', 'mktcap', 'nmc', 'deltat'
]]
data = data.values
SEG_X.append(data[0])
# SEG_X=np.array(SEG_X).T
if datatype == 'cnn':
SEG_X = [SEG_X]
data_tag = temp_df[temp_df.c_yearmonthday == day][[
'code', 'name', 'changepercent'
]]
temp_y = data_tag['changepercent'].values[0]
temp_y = to_cate01(temp_y)
temp_id = data_tag['code'].values[0]
if (i > 0 and i <= validdays):
X_valid.append(SEG_X)
ID_valid.append(temp_id)
Y_valid.append(temp_y)
else:
X_train.append(SEG_X)
ID_train.append(temp_id)
Y_train.append(temp_y)
k += 1
if k % 500 == 0:
log.info('%s stock finished ', k)
log.info('generate data finished ,cost time:%s', time.clock() - begin_time)
log.info('X_train shape is :%s', np.asarray(X_train).shape)
log.info('Y_train shape is :%s', np.asarray(Y_train).shape)
log.info('X_valid shape is :%s', np.asarray(X_valid).shape)
log.info('Y_valid shape is :%s', np.asarray(Y_valid).shape)
# X_train=normalize(X_train)
# X_valid=normalize(X_valid)
if debug:
print(np.asarray(X_train), np.asarray(Y_train),
np.asarray(ID_train)), (np.asarray(X_valid), np.asarray(Y_valid),
np.asarray(ID_valid))
print(np.asarray(X_train[0][0][0]))
pickle.dump(
((np.asarray(X_train), np.asarray(Y_train), np.asarray(ID_train)),
(np.asarray(X_valid), np.asarray(Y_valid), np.asarray(ID_valid))),
open(datatype + '_seg' + str(seg_len) + '.pkl', 'wb'))
def get(self, *args, **kwargs):
exploits = Db().get_exploits()
self.render('exploits.html', exploits=exploits)
def get(self, *args, **kwargs):
exploit = Db().get_exploit_by_id(self.get_argument('id'))
self.render('exploit.html',
exploit=exploit,
type=Db().get_exploit_type_by_id(exploit.type_id))
