def add_one_stock_daily_data_wrapper(filepath, type, exchange_place, index,
date):
pid = os.getpid()
code = os.path.split(filepath)[-1].split(".csv")[0]
arc = Arctic(ARCTIC_SRV)
try:
if index % 100 == 0:
print("index = {}, filepath = {}".format(index, filepath))
error_index_list = add_one_stock_daily_data(filepath, type,
exchange_place, arc, date)
if error_index_list is not None and len(error_index_list) > 0:
f = open(
os.path.join(
LOG_FILE_PATH,
"temp_timestamp_error_{0}_{1}_{2}.txt".format(
pid, date, type)), "a+")
f.write("{}, {}, {}\n".format(filepath, error_index_list,
exchange_place + "_" + code))
f.close()
except Exception as e:
info = traceback.format_exc()
print("error:" + str(e))
f = open(
os.path.join(LOG_FILE_PATH,
"temp_fail_{0}_{1}_{2}.txt".format(pid, date, type)),
"a+")
f.write("fail:" + str(filepath) + "\n" + str(e) + "\n" + str(info) +
"\n")
f.close()
finally:
arc.reset()
def __init__(self, save_to_file: bool = True, host: str = 'localhost',
library: str = 'tick_blotter',
collection: str = 'test_blotter') -> None:
self.db = Arctic(host)
self.db.initialize_library(library, lib_type=TICK_STORE)
self.store = self.db[library]
self.collection = collection
class Store:
"""
freq in ['min', 'hour', 'day']
"""
chunk_size = {'min': 'D', 'hour': 'D', 'day': 'M'}
def __init__(self, library=default_library, db='localhost', **kwargs):
self.conn = Arctic(db)
def write(self, symbol, data, freq='min', what='TRADES'):
lib_name = f'{what}_{freq}'
if lib_name in self.conn.list_libraries():
lib = self.conn[lib_name]
lib.append(symbol, data)
else:
self.conn.initialize_library(lib_name)
lib = self.conn[lib_name]
lib.write(symbol,
data,
lib_type=CHUNK_STORE,
chunk_size=self.chunk_size['freq'])
def read(self, symbol, freq='min', what='TRADES'):
lib_name = f'{what}_{freq}'
lib = self.conn[lib_name]
return lib.read(symbol, **kwargs)
def check_data_availability(
self,
symbol,
):
pass
def __init__(self, use_arctic=False, z_score=True, alpha=None):
"""
Simulator constructor
:param use_arctic: If TRUE, Simulator creates a connection to Arctic,
Otherwise, no connection is attempted
:param z_score: If TRUE, normalize data with z-score,
ELSE use min-max scaler
"""
self._scaler = StandardScaler() if z_score else MinMaxScaler()
self.cwd = os.path.dirname(os.path.realpath(__file__))
self.ema = load_ema(alpha=alpha)
try:
if use_arctic:
print('Attempting to connect to Arctic...')
self.arctic = Arctic(MONGO_ENDPOINT)
self.arctic.initialize_library(ARCTIC_NAME,
lib_type=TICK_STORE)
self.library = self.arctic[ARCTIC_NAME]
print('Connected to Arctic.')
else:
self.arctic = self.library = None
except Exception as ex:
self.arctic = self.library = None
print('Unable to connect to Arctic database')
print(ex)
def __init__(self):
try:
print('Attempting to connect to Arctic...')
self.scaler = MinMaxScaler()
self.arctic = Arctic(MONGO_ENDPOINT)
self.arctic.initialize_library(ARCTIC_NAME, lib_type=TICK_STORE)
self.library = self.arctic[ARCTIC_NAME]
self.reference_data_old_names = [
'system_time', 'day', 'coinbase_midpoint'
]
self.reference_data_new_names = ['t', 'd', 'm']
self.number_of_workers = cpu_count()
self.queue = Queue(maxsize=self.number_of_workers)
self.return_queue = Queue(maxsize=self.number_of_workers)
self.workers = [
Process(name='Process-%i' % num,
args=(self.queue, self.return_queue),
target=self._do_work)
for num in range(self.number_of_workers)
]
print('Connected to Arctic.')
except Exception as ex:
self.arctic, self.library, self.workers = None, None, None
print('Unable to connect to Arctic database')
print(ex)
def __init__(self, stocks=['QQQ', 'TQQQ', 'SQQQ'], db='sqlite', storage_dir=None, db_file='stock_data.sqlite'):
self.db = db
if storage_dir is None:
home_dir = os.path.expanduser("~")
self.storage_dir = os.path.join(home_dir, '.yfinance_data')
if not os.path.exists(self.storage_dir):
os.makedirs(self.storage_dir)
else:
self.storage_dir = storage_dir
if db == 'sqlite':
self.db_file = db_file
# 4 slashes for absolute path: https://docs.sqlalchemy.org/en/13/core/engines.html#sqlite
self.e = create_engine('sqlite:///{}/{}'.format(self.storage_dir, self.db_file))
self.con = self.e.connect()
else:
self.e = None
self.con = None
if db == 'arctic':
self.store = Arctic('localhost')
self.store.initialize_library('yfinance_stockdata')
self.library = self.store['yfinance_stockdata']
self.stocks = stocks
class TickBlotter(AbstractBaseBlotter):
def __init__(self, save_to_file: bool = True, host: str = 'localhost',
library: str = 'tick_blotter',
collection: str = 'test_blotter') -> None:
self.db = Arctic(host)
self.db.initialize_library(library, lib_type=TICK_STORE)
self.store = self.db[library]
self.collection = collection
def write_to_file(self, data: Dict[str, Any]) -> None:
data['index'] = pd.to_datetime(data['time'], utc=True)
self.store.write(self.collection, [data])
def save(self) -> None:
data = []
for d in self.blotter:
d.update({'index': pd.to_datetime(d['time'], utc=True)})
data.append(d)
self.store.write(self.collection, data)
def delete(self, querry: Dict) -> str:
raise NotImplementedError
def clear(self) -> str:
raise NotImplementedError
class Database(object):
def __init__(self, sym, exchange):
self.counter = 0
self.data = list()
self.sym = sym
self.tz = TIMEZONE
self.exchange = exchange
if RECORD_DATA:
self.db = Arctic(MONGO_ENDPOINT)
self.db.initialize_library(ARCTIC_NAME, lib_type=TICK_STORE)
self.collection = self.db[ARCTIC_NAME]
print('\n%s is recording %s\n' % (self.exchange, self.sym))
else:
self.db = None
self.collection = None
def new_tick(self, msg):
"""
If RECORD_DATA is TRUE, add streaming ticks to a list
After the list has accumulated BATCH_SIZE ticks, insert batch into
the Arctic Tick Store
:param msg: incoming tick
:return: void
"""
if self.db is not None:
self.counter += 1
msg['index'] = dt.now(tz=self.tz)
msg['system_time'] = str(msg['index'])
self.data.append(msg)
if self.counter % BATCH_SIZE == 0:
print('%s added %i msgs to Arctic' % (self.sym, self.counter))
self.collection.write(self.sym, self.data)
self.counter = 0
self.data.clear()
def getArcticLibraries():
print(">>> Function called getArcticLibraries()")
# Connect to local MONGODB
store = Arctic('localhost')
print "+ Arctic connected to MongoDB at localhost"
print("+ Requesting libraries from Arctic store")
return store.list_libraries()
def sync():
store = Arctic('localhost')
store.initialize_library('Bitmex')
library = store['Bitmex']
df = get_candle_pandas()
print (df)
library.write('XBTUSD', df, metadata={'source': 'Bitmex'})
def show():
# Connect to Local MONGODB
store = Arctic('localhost')
# Create the library - defaults to VersionStore
store.initialize_library('Bitmex')
# Access the library
library = store['Bitmex']
#library.write('XBTUSD', df, metadata={'source': 'Bitmex'})
# Reading the data
item = library.read('XBTUSD')
xbtusd = item.data
metadata = item.metadata
print (xbtusd)
print (metadata)
xbtusd['ret'] = -1+xbtusd['close']/xbtusd['close'].shift(1)
from math import sqrt
xbtusd['vol10'] = sqrt(260)*xbtusd['ret'].rolling(10).std(ddof=0)
xbtusd['vol30'] = sqrt(260)*xbtusd['ret'].rolling(30).std(ddof=0)
#print (volList)
#plt.plot(df.index, df['close'], label='price')
plt.plot(xbtusd.index, xbtusd['vol10'], label='vol10')
plt.plot(xbtusd.index, xbtusd['vol30'], label='vol30')
#plt.plot(xbtusd['ret'])
plt.ylabel('vol')
plt.xlabel('Date')
plt.legend(loc=0)
plt.show()
def __init__(self, TYPE):
"""
:param TYPE: 'BS', 'IS', 'CF'
"""
############ SETTING #############
self.config = GetConfig()
self.TYPE = TYPE # 'BS', 'IS', 'CF'
self.MONGO = self.config.MONGO
self.CSV = self.config.CSV
self.RAW = False
self.outdir = self.config.fund_dir
self.encode = self.config.encode
self.proxypool = self.config.proxypool
############ CHANGE ABOVE SETTING #############
if self.MONGO:
from arctic import Arctic
# mongod --dbpath D:/idwzx/project/arctic
a = Arctic(self.config.ahost)
a.initialize_library('ashare_{}'.format(self.TYPE))
self.lib = a['ashare_{}'.format(self.TYPE)]
self.result_dict = {}
class ArcticBinary:
def __init__(self,
lib_name: str = _ARCTIC_BINARY_LIBRARY,
mongo_db: str = "auto"):
"""假定一个 instance 只操作一个 library
mongo_db :
"auto" 根据环境是否为 colab 自动选择 google 还是 local
"google" 选择 google 的 mongo
"intranet" 选择机房中的Mongo
"""
# 这里 暂时先 hardcode arctic 所使用的 mongo 地址
mongo_db_conn_str = get_mongo_admin_conn_str()
if mongo_db == "google":
mongo_db_conn_str = get_google_mongo_conn_str()
elif mongo_db == "intranet":
mongo_db_conn_str = get_intranet_mongo_conn_str()
self._store = Arctic(mongo_db_conn_str)
if not self._store.library_exists(lib_name):
self._store.initialize_library(lib_name, VERSION_STORE)
self._lib = self._store[lib_name]
def write_bin_object(self, bin_data: bytes, symbol: str):
self._lib.write(symbol, bin_data)
def read_bin_object(self, symbol: str) -> bytes:
return self._lib.read(symbol).data
def has_symbol(self, symbol: str) -> bytes:
return self._lib.has_symbol(symbol)
class ArcticSaver(AbstractBaseSaver):
"""
Serializer for Arctic VersionStore.
"""
def __init__(self,
host: str = 'localhost',
library: str = 'test_log',
note: str = '') -> None:
"""
Library given at init, collection determined by self.name_str.
"""
self.host = host
self.library = library
self.db = Arctic(host)
self.db.initialize_library(library)
self.store = self.db[library]
super().__init__(note)
def save(self,
df: pd.DataFrame,
what: str,
contract_str: str,
note: str = '') -> None:
self.store.write(self.name_str(what, contract_str), df)
def keys(self) -> List[str]:
return self.store.list_symbols()
def read(self, key: str) -> pd.DataFrame:
return self.store.read(key)
def __str__(self):
return (f'ArcticSaver(host={self.host}, library={self.library}, '
f'note={self.note})')
def test_multiprocessing_safety(mongo_host, library_name):
# Create/initialize library at the parent process, then spawn children, and start them aligned in time
total_processes = 64
total_writes_per_child = 100
register_get_auth_hook(my_auth_hook)
global MY_ARCTIC
MY_ARCTIC = Arctic(mongo_host=mongo_host)
MY_ARCTIC.initialize_library(library_name, VERSION_STORE)
assert isinstance(MY_ARCTIC.get_library(library_name), VersionStore)
processes = [Process(target=f, args=(library_name, total_writes_per_child, True)) for _ in range(total_processes)]
for p in processes:
p.start()
for p in processes:
p.join()
for p in processes:
assert p.exitcode == 0
assert isinstance(MY_ARCTIC.get_library(library_name), VersionStore)
def __init__(self,
collection_name,
database_name=None,
host=None,
port=None):
database_name, host, _port_not_used = mongo_defaults(db=database_name,
host=host,
port=port)
# Arctic doesn't accept a port
store = Arctic(host)
library_name = database_name + "." + collection_name
store.initialize_library(
library_name) # will this fail if already exists??
library = store[library_name]
self.database_name = database_name
self.collection_name = collection_name
self.host = host
self.store = store
self.library_name = library_name
self.library = library
class Database(object):
def __init__(self, sym, exchange):
self.counter = 0
self.data = list()
self.sym = sym
self.tz = tz.utc
self.exchange = exchange
if configs.RECORD_DATA:
self.db = Arctic(configs.MONGO_ENDPOINT)
self.db.initialize_library(configs.ARCTIC_NAME,
lib_type=TICK_STORE)
self.collection = self.db[configs.ARCTIC_NAME]
print('%s is recording %s\n' % (self.exchange, self.sym))
else:
self.db = None
self.collection = None
def new_tick(self, msg):
if self.db is not None:
self.counter += 1
msg['index'] = dt.now(tz=self.tz)
msg['system_time'] = str(msg['index'])
self.data.append(msg)
if self.counter % configs.BATCH_SIZE == 0:
print('%s added %i msgs to Arctic' % (self.sym, self.counter))
self.collection.write(self.sym, self.data)
self.counter = 0
self.data.clear()
def __init__(self, use_arctic=False):
"""
:param use_arctic: If True, Simulator creates a connection to Arctic,
Otherwise, no connection is attempted
"""
self._avg = None
self._std = None
self.cwd = os.path.dirname(os.path.realpath(__file__))
self.z_score = lambda x: (x - self._avg) / self._std
try:
if use_arctic:
print('Attempting to connect to Arctic...')
self.arctic = Arctic(MONGO_ENDPOINT)
self.arctic.initialize_library(ARCTIC_NAME,
lib_type=TICK_STORE)
self.library = self.arctic[ARCTIC_NAME]
print('Connected to Arctic.')
else:
print('Not connecting to Arctic')
self.arctic, self.library = None, None
except Exception as ex:
self.arctic, self.library = None, None
print('Unable to connect to Arctic database')
print(ex)
def import_data(self, doc_type_l=["Tick", "Transaction", "Order"]):
# clear all the old files
for fp in LOG_FILE_PATH, DATA_INFO_PATH, DATA_FINISH_INFO_PATH, DATA_PATH:
self._get_empty_folder(fp)
arc = Arctic(ARCTIC_SRV)
for doc_type in DOC_TYPE:
# arc.initialize_library(get_library_name(doc_type), lib_type=CHUNK_STORE)
arc.set_quota(get_library_name(doc_type), MAX_SIZE)
arc.reset()
# doc_type = 'Day'
for doc_type in doc_type_l:
date_list = list(
set([
int(path.split("_")[0])
for path in os.listdir(DATABASE_PATH) if doc_type in path
]))
date_list.sort()
date_list = [str(date) for date in date_list]
f = open(ALL_STOCK_PATH, "r")
stock_name_list = [lines.split("\t")[0] for lines in f.readlines()]
f.close()
stock_name_dict = {
"SH": [
stock_name[2:] for stock_name in stock_name_list
if "SH" in stock_name
],
"SZ": [
stock_name[2:] for stock_name in stock_name_list
if "SZ" in stock_name
],
}
lib_name = get_library_name(doc_type)
a = Arctic(ARCTIC_SRV)
# a.initialize_library(lib_name, lib_type=CHUNK_STORE)
stock_name_exist = a[lib_name].list_symbols()
lib = a[lib_name]
initialize_count = 0
for stock_name in stock_name_list:
if stock_name not in stock_name_exist:
initialize_count += 1
# A placeholder for stocks
pdf = pd.DataFrame(index=[pd.Timestamp("1900-01-01")])
pdf.index.name = "date" # an col named date is necessary
lib.write(stock_name, pdf)
print("initialize count: {}".format(initialize_count))
print("tasks: {}".format(date_list))
a.reset()
# date_list = [files.split("_")[0] for files in os.listdir("./raw_data_price") if "tar" in files]
# print(len(date_list))
date_list = ["20201231"] # for test
Parallel(n_jobs=min(2, len(date_list)))(
delayed(add_data)(date, doc_type, stock_name_dict)
for date in date_list)
def open(self, store='chunkstore'):
self.db = Arctic('localhost')
try:
self.store = self.db[store]
except:
self.db.initialize_library(store, lib_type=CHUNK_STORE)
self.store = self.db[store]
self.store._arctic_lib.set_quota(maxDBStorage * 1024 * 1024 * 1024)
def __init__(self, host):
#self.MONGOHOST = 'localhost'
"""Initializes the store here if it hasn't already z"""
try:
register_library_type(FunStore._LIBRARY_TYPE, FunStore)
except Exception:
pass
self.store = Arctic(host)
def initialize_db(self, lib):
if lib:
db = Arctic('localhost')
if lib not in db.list_libraries():
self.logger.info(
'Data library \'%s\' does not exist -- creating it', lib)
db.initialize_library(lib, lib_type=TICK_STORE)
self.dlib = lib
self.dbconn = db[lib]
def ingest_trades(filename: str, library: str, symbol: str) -> None:
store = Arctic('localhost')
logger.info(f"Saving to library: {library}, symbol: {symbol}")
# Defaults to VersionStore
store.initialize_library(library)
library = store[library]
df = pd.read_csv(filename, names=COLUMN_NAMES)
df.time = pd.to_datetime(df.time, unit='s')
library.write(symbol, df, metadata={'source': 'csv'})
class ArcticStoreDatabase(Database):
def __init__(self):
super().__init__()
self.db = None
self.store = None
def open(self, store='chunkstore'):
self.db = Arctic('localhost')
try:
self.store = self.db[store]
except:
self.db.initialize_library(store, lib_type=CHUNK_STORE)
self.store = self.db[store]
self.store._arctic_lib.set_quota(maxDBStorage * 1024 * 1024 * 1024)
def close(self):
pass #no need to close arctic connection
def remove(self, key):
self.store.delete(key) #used for debugging
def getMeatdata(self, key):
return self.store.read_metadata(key)
def setMetadata(self, key, metadata):
self.store.write_metadata(key, metadata)
def _save(self, key, data):
if self.has_key(key):
self.store.append(key, data)
else:
self.store.write(key, data, chunk_size=chunkSizes.get(key, 'M'))
def get(self, key, start=None, end=None, iterator=False):
if not iterator:
return self.store.read(key,
chunk_range=DateRange(
start, end, CLOSED_CLOSED))
else:
return self.store.iterator(key,
chunk_range=DateRange(
start, end, CLOSED_CLOSED))
def getLatestRow(self, key):
latestDate = self.store.read_metadata(key)['end']
return self.get(key, start=latestDate, end=None)
def getFirstRow(self, key):
firstDate = self.store.read_metadata(key)['start']
return self.get(key, start=None, end=firstDate)
def has_key(self, key):
return self.store.has_symbol(key)
def list_keys(self):
return self.store.list_symbols()
def insert_random_data(config, args, n_rows):
store = Arctic(args.mongodb, app_name="benchmark")
lib_name = lib_name_from_args(config)
store.delete_library(lib_name)
store.initialize_library(lib_name, segment='month')
lib = store[lib_name]
for sym in range(args.symbols):
df = gen_oneminute_dataset(n_row=n_rows, n_col=n_rows, dense=args.dense)
lib.write('sym' + str(sym), df)
def __init__(self, lib: str, host: str = 'localhost') -> None:
"""
Library name is whatToShow + barSize, eg.
TRADES_1_min
BID_ASK_1_hour
MIDPOINT_30_secs
"""
lib = lib.replace(' ', '_')
self.db = Arctic(host)
self.db.initialize_library(lib)
self.store = self.db[lib]
def _arctic_loader():
host = Arctic(arctic_opts['host'])
lib = host.get_library(arctic_opts['library'])
read_kwargs = {}
start, end = map(arctic_opts.get, ['start', 'end'])
if start and end:
read_kwargs['chunk_range'] = pd.date_range(start, end)
data = lib.read(arctic_opts['node'], **read_kwargs)
if isinstance(data, VersionedItem):
data = data.data
return data
def setUp(self):
self.robot_user = "******"
self.import_comment = "importcomment"
self.arctic = Arctic('localhost')
self.library_name = marc.random_library(self.arctic)
lu.logger.info("Created test library {}".format(self.library_name))
self.arctic.initialize_library(self.library_name)
simple_pd =pu.create_simple_series(['a','b','c'],5)
lib = self.arctic[self.library_name]
import_pandas(lib,simple_pd,"symbol",RevisionInfo(who=self.robot_user,what=self.import_comment, when = datetime.datetime.now()))
import_pandas(lib,simple_pd,"ES.SETL.EOD",RevisionInfo(who=self.robot_user,what="import something else", when = datetime.datetime.now()))
def _get_lib(lib_name: "lib name(str)" = "default",
lib_type: "lib type" = VERSION_STORE):
client = MongoClient(host=config.MONGO_HOST,
port=27017,
username=config.MONGO_USER,
password=config.MONGO_PWD,
authSource=config.MONGO_AUTHDB)
a = Arctic(client)
if not a.library_exists(lib_name):
a.initialize_library(lib_name, lib_type=lib_type)
return a[lib_name]
def init_db_connection(self):
"""
Initiate database connection
:return: (void)
"""
print("init_db_connection for {}...".format(self.sym))
try:
self.db = Arctic(MONGO_ENDPOINT)
self.db.initialize_library(ARCTIC_NAME, lib_type=TICK_STORE)
self.collection = self.db[ARCTIC_NAME]
except PyMongoError as e:
print("Database.PyMongoError() --> {}".format(e))
def startDB(self, storename='fx'):
self.con = subprocess.Popen("%s %s %s" %
(self.mongod, "--dbpath", self.dbPath),
shell=True)
self.store = Arctic(self.address)
if not self.store.library_exists('fx'):
self.store.initialize_library(storename)
self.library = self.store[storename]
self.rc = self.con.returncode
def read_from_db(target_building, start_time=None, end_time=None):
'''
load the data from for tgt_bldg
return:
{
point name: data
}
data is in pandas.DataFrame format with two columns ['date', 'data']
'''
if isinstance(start_time, arrow.Arrow):
start_time = start_time.datetime
elif isinstance(start_time, (dt, date)):
pass
elif start_time == None:
pass
else:
raise ValueError('the type of time value is unknown: {0}'.format(
type(start_time)))
if isinstance(end_time, arrow.Arrow):
end_time = end_time.datetime
elif isinstance(end_time, (dt, date)):
pass
elif end_time == None:
pass
else:
raise ValueError('the type of time value is unknown: {0}'.format(
type(end_time)))
if start_time and end_time:
date_range = DateRange(start=start_time, end=end_time)
else:
date_range = None
print('loading timeseries data from db for %s...' % target_building)
conn = Arctic('localhost')
if target_building not in conn.list_libraries():
raise ValueError('%s not found in the DB!' % target_building)
else:
lib = conn[target_building]
srcids = lib.list_symbols()
res = {}
for srcid in srcids:
data = lib.read(srcid, chunk_range=date_range)
if len(data) == 0:
print('WARNING: {0} has empty data.'.format(srcid))
#pdb.set_trace()
continue
res[srcid] = data
print('correctly done')
return res
def main():
parser = argparse.ArgumentParser(prog="store", description='Store data to DB')
parser.add_argument('--host', help="MongoDB host", default=MONGO_HOST_DEFAULT, type=str)
parser.add_argument('--updater', help="Updater", default=UPDATER_DEFAULT, type=str)
parser.add_argument('-s', '--source', help="Source", default=SOURCE_DEFAULT, type=str)
parser.add_argument('--symbols', help="Symbol", default=SYMBOLS_DEFAULT, type=str)
parser.add_argument('--start', help="Start date", default='', type=str)
parser.add_argument('--end', help="End date", default='', type=str)
parser.add_argument('--freq', help="Freq", default='', type=str)
parser.add_argument('--max_rows', help="max_rows", default=10, type=int)
parser.add_argument('--max_columns', help="max_columns", default=6, type=int)
parser.add_argument('--api_key', help="API key", default='', type=str)
parser.add_argument('--expire_after', help="Cache expiration ('0': no cache, '-1': no expiration, 'HH:MM:SS.X': expiration duration)", default='24:00:00.0', type=str)
args = parser.parse_args()
pd.set_option('max_rows', args.max_rows)
pd.set_option('expand_frame_repr', False)
pd.set_option('max_columns', args.max_columns)
if args.start != '':
start = pd.to_datetime(args.start)
else:
start = None
if args.end != '':
end = pd.to_datetime(args.end)
else:
end = None
if args.freq != '':
freq = args.freq
else:
freq = None
symbols = args.symbols.split(',')
session = get_session(args.expire_after, 'cache')
my_updater = updater(args.updater, session=session)
if args.api_key != '':
my_updater.set_credentials(api_key=args.api_key)
store = Arctic(args.host)
library_name = my_updater.library_name(args.source, freq)
print(library_name)
store.initialize_library(library_name)
library = store[library_name]
for symbol in symbols:
update(library, my_updater, symbol, start, end, freq, args.source.lower())
def __init__(self, database_name, collection_name, host = DEFAULT_MONGO_HOST):
if database_name is None:
database_name = DEFAULT_DB
store = Arctic(host)
library_name = database_name+"."+collection_name
store.initialize_library(library_name) # will this fail if already exists??
library = store[library_name]
self.database_name = database_name
self.collection_name = collection_name
self.host = host
self.store = store
self.library_name = library_name
self.library = library
def test_multiprocessing_safety_parent_children_race(mongo_host, library_name):
# Create Arctic and directly fork/start children (no wait)
total_iterations = 12
total_processes = 6
total_writes_per_child = 20
global MY_ARCTIC
for i in range(total_iterations):
processes = list()
MY_ARCTIC = Arctic(mongo_host=mongo_host)
for j in range(total_processes):
p = Process(target=f, args=(library_name, total_writes_per_child, False))
p.start() # start directly, don't wait to create first all children procs
processes.append(p)
MY_ARCTIC.initialize_library(library_name, VERSION_STORE) # this will unblock spinning children
for p in processes:
p.join()
for p in processes:
assert p.exitcode == 0
MY_ARCTIC.reset()
assert isinstance(MY_ARCTIC.get_library(library_name), VersionStore)
class TimeSuiteWrite(object):
params = list(range(len(TEST_SIZES)))
param_names = ['5K * 10^']
def setup(self, arg):
self.store = Arctic("127.0.0.1")
self.store.delete_library('test.lib')
self.store.initialize_library('test.lib')
self.lib = self.store['test.lib']
def teardown(self, arg):
self.store.delete_library('test.lib')
self.lib = None
def time_write_dataframe_random(self, idx):
self.lib.write('df_bench_random', df_random[idx])
def time_write_series_random(self, idx):
self.lib.write('series_bench_random', s_random[idx])
def time_write_dataframe_compressible(self, idx):
self.lib.write('df_bench_compressible', df_compress[idx])
def time_write_series_compressible(self, idx):
self.lib.write('series_bench_compressible', s_compress[idx])
class TimeSuiteAppend(object):
params = list(range(len(TEST_SIZES)))
param_names = ['5K * 10^']
def __init__(self):
self.store = Arctic("127.0.0.1")
def setup(self, idx):
self.store.delete_library('test.lib')
self.store.initialize_library('test.lib')
self.lib = self.store['test.lib']
self.lib.write('test_df', df_random[idx])
def teardown(self, arg):
self.store.delete_library('test.lib')
self.lib = None
def time_append_dataframe(self, idx):
self.lib.append('test_df', df_random[idx])
def test1(self):
self.assertEqual(Arctic.get_min_elec_dist([[0, 0], [1, 0], [1, 1], [1, 2], [0, 2]]), 1.00)
def test2(self):
self.assertEqual(
Arctic.get_min_elec_dist([[1.0, 1.0], [30.91, 8], [4.0, 7.64], [21.12, 6.0], [11.39, 3.0], [5.31, 11.0]]),
10.18,
)
def get_stock_history(ticker, start_date, end_date):
data = ystockquote.get_historical_prices(ticker, start_date, end_date)
df = pd.DataFrame(collections.OrderedDict(sorted(data.items()))).T
df = df.convert_objects(convert_numeric=True)
return df
################################################
# VersionStore: Storing and updating stock data
################################################
arctic = Arctic('localhost')
# Create a VersionStore library
arctic.delete_library('jblackburn.stocks')
arctic.initialize_library('jblackburn.stocks')
arctic.list_libraries()
stocks = arctic['jblackburn.stocks']
# get some prices
aapl = get_stock_history('aapl', '2015-01-01', '2015-02-01')
aapl
# store them in the library
stocks.write('aapl', aapl, metadata={'source': 'YAHOO'})
def setup(self, arg):
self.store = Arctic("127.0.0.1")
self.store.delete_library('test.lib')
self.store.initialize_library('test.lib')
self.lib = self.store['test.lib']
def __init__(self):
self.store = Arctic("127.0.0.1")
params["start"] += 200
return price_list
from arctic import Arctic
def download_daily_bars(instrument, start, end):
bars = get_historical_price(instrument,start,end)
dump = json.dumps(bars, ensure_ascii=False).encode('utf-8')
df = pd.read_json(dump)
print(df.head())
if df.empty:
return df
df = df.set_index('Date')
return df
store = Arctic('localhost')
if 'KRX_G' not in store.list_libraries():
store.initialize_library('KRX_G')
lib = store['KRX_G']
krx = pd.read_csv('krx_market_symbols.csv', dtype=object)
stocks = [x for x in krx['code_google']]
print(len(stocks), " symbols")
begin = datetime.date(2000,1,1)
end = datetime.date(2016,5,30)
missing = ['KRX:152550']
#for i, stock in enumerate(stocks[160:769]):
for i, stock in enumerate(missing):
print("%d th code=%s" % (i, stock))
pd.set_option('max_rows', 10)
pd.set_option('expand_frame_repr', False)
pd.set_option('max_columns', 6)
from arctic_updater.updaters.truefx import TrueFXUpdater
logging.basicConfig(level=logging.DEBUG)
logger = logging.getLogger(__name__)
my_updater = TrueFXUpdater()
symbol = 'EURUSD'
year, month = 2015, 11
%time df = my_updater._read_one_month(symbol, year, month)
# Arctic (MongoDB)
from arctic import Arctic
store = Arctic('localhost')
library_name = 'test'
store.initialize_library(library_name)
library = store[library_name]
%time library.write(symbol, df)
%time df_retrieved = library.read(symbol).data
# HDF5
filename = my_updater._filename(symbol, year, month, '.h5')
%time df.to_hdf(filename, "data", mode='w', complevel=0, complib='zlib', format='table')
%time df_retrieved = pd.read_hdf(filename)
# Make unique index
# http://stackoverflow.com/questions/34575126/create-a-dataframe-with-datetimeindex-with-unique-values-by-adding-a-timedelta/34576154#34576154
df = df.reset_index()
from arctic import Arctic
# Connect to Local MONGODB
store = Arctic('localhost')
# Create the library - defaults to VersionStore
store.initialize_library('NASDAQ')
# Access the library
library = store['NASDAQ']
# Load some data - maybe from Quandl
import quandl
quandl.ApiConfig.api_key = "Cxzxjy2eHKXgwMjVFhbH"
aapl = quandl.get("GOOG/NASDAQ_AAPL")
# Store the data in the library
library.write('AAPL', aapl, metadata={'source': 'Quandl'})
# Reading the data
item = library.read('AAPL')
aapl = item.data
metadata = item.metadata
class MongoDB(object):
# to start server - cd to "C:\Program Files\MongoDB\Server\4.0\bin"
# then use mongod --dbpath "path to db" when starting database server to point to the correct save location
# use dataframe.to_dict('records') to convert to dicts to insert to DB. The DB is stored in mongoDB software
# use Arctic from AHL to store time series data. It uses multiIndex pd.Dataframe to store, and other data goes
# into meta data as a dict. It will open its own server, so no need to run
# https://www.mongodb.com/json-and-bson
# https://stackoverflow.com/questions/20796714/how-do-i-start-mongo-db-from-windows
# To back up database, navigate to bin folder of mongodb, then run "mongodump --out ..." where ... is the output
# folder to dump
# To load into another base, navigate to bin folder again, and use "mongorestore --drop ..." where ... is the
# folder where database backup folders where dumped
# MUST RUN CMD AS ADMINISTRATOR!!!!!!!
def __init__(self):
self.conn = Arctic("127.0.0.1")
def verify_library(self, library):
""" Method to test if library exists in database"""
libs = self.conn.list_libraries()
if library in libs:
return
# if database doesnt exist, alert and ask if we should create
else:
print('"%s" library does not exist in server data path' % library)
create = input('create "%s"? (y, n)' % library)
if create.lower() == 'y':
self.conn.initialize_library(library)
else:
return
@staticmethod
def getMultiIndex(data, index, columns=None):
"""
Method to convert dataframe to MultiIndex
data: pd.DataFrame
data to be MultiInexed
index: list
list of strings containg names of columns to be used as MultiIndex (in order left to right)
columns:
"""
data = data.set_index(index)
return data
@timeMe
def save(self, data, library, meta_data={}, append=True):
"""
Method to save to mongodb library using arctic database.
Parameters
----------
data: pd.DataFrame (MultiIndex)
DataFrame containing data for database in MultiIndex structure.
Outer index must be the index used for the arctic "symbol" in the "library"
library: str
which library to write to
meta_data: dict
Dictionary of meta data values to inlcude in save.
If not provided will maintain existing metadata in database or set to None.
Keys match "symbol" from data outer index.
append: bool
True will append data to existing data for each "symbol" in database
False will replace data entirely
"""
self.verify_library(library)
if isinstance(data, pd.DataFrame):
# get unique symbols to write
indicies = list(data.index.unique())
# take first index as "symbol" for database
symbols = indicies if isinstance(indicies[0], str) else np.unique([x[0] for x in indicies])
symbol_list = self.conn[library].list_symbols()
# fill in blank metadata for symbols not in metadata
noMeta = np.array(symbols)[~np.in1d(symbols, list(meta_data.keys()))]
meta_data.update({x: None for x in noMeta})
for sym in symbols:
data_cut = data.loc[sym]
data_cut = pd.DataFrame(data_cut).T if isinstance(data_cut, pd.Series) else data_cut
if sym in symbol_list:
# get current data for symbol
db = self.conn[library].read(sym)
db_data = db.data
# if meta data is present in database but is None in metadata fed in then take from database
meta_data[sym] = db.metadata
# if the symbol is already in the database and we want to "update" the series rather than simply replace
if sym in symbol_list and append:
data_post = pd.concat([db_data, data_cut], axis=0)
# remove duplicates and keep latest data
if len(data_post.index.unique()) == 1:
data_post = data_post.drop_duplicates(keep='last')
else:
data_post = data_post.groupby(data_post.index).last()
else:
data_post = data_cut
# sort the data by the indicies
data_post = data_post.sort_index()
self.conn[library].write(sym, data_post, metadata=meta_data[sym])
@timeMe
def read(self, library, symbols=None):
"""
Method to read from mongodb library using arctic database.
Parameters
----------
library: str
which library to write to
symbols:
list of "symbols" in "library" to read
"""
if symbols is None:
symbols = self.conn[library].list_symbols()
data = {}
for sym in symbols:
db = self.conn[library].read(sym)
data[sym] = {}
data[sym]['data'] = db.data
if db.metadata is not None:
data[sym].update(db.metadata)
return data
def __init__(self):
self.conn = Arctic("127.0.0.1")
# Loading data
################################################
def get_stock_history(ticker, start_date, end_date):
data = ystockquote.get_historical_prices(ticker, start_date, end_date)
df = pandas.DataFrame(collections.OrderedDict(sorted(data.items()))).T
df = df.convert_objects(convert_numeric=True)
return df
################################################
# VersionStore: Storing and updating stock data
################################################
arctic = Arctic('localhost')
lst = arctic.list_libraries()
if 'KRX' not in lst:
arctic.initialize_library('KRX')
#################################
# Dealing with lots of data
#################################
#NSYE library
lib = arctic['KRX']
def load_all_stock_history_KRX():
#
#
# Arctic Key-Value store
#
from datetime import datetime as dt
import pandas as pd
from arctic import Arctic
# Connect to the mongo-host / cluster
store = Arctic(mongo_host)
# Data is grouped into 'libraries'.
# Users may have one or more named libraries:
store.list_libraries()
# Create a library
store.initialize_library('username.scratch')
# Get a library
# library = m['username.<library>']
library = store['username.scratch']
# Store some data in the library
df = pd.DataFrame({'prices': [1, 2, 3]},
[dt(2014, 1, 1), dt(2014, 1, 2), dt(2014, 1, 3)])
library.write('SYMBOL', df)
# Read some data from the library
# (Note the returned object has an associated version number and metadata.)
self._arctic_lib.check_quota()
self._collection.insert_one(to_store)
@mongo_retry
def delete(self, query):
"""
Simple delete method
"""
self._collection.delete_one(query)
# Hook the class in for the type string 'CustomArcticLibType'
register_library_type(CustomArcticLibType._LIBRARY_TYPE, CustomArcticLibType)
# Create a Arctic instance pointed at a mongo host
store = Arctic(mongo_host)
### Initialize the library
# Map username.custom_lib -> CustomArcticLibType
store.initialize_library("username.custom_lib", CustomArcticLibType._LIBRARY_TYPE)
# Now pull our username.custom_lib ; note that it has the:
# - query(...)
# - store(...)
# - delete(...)
# API we defined above
lib = store["username.custom_lib"]
# Store some items in the custom library type
lib.store(Stuff("thing", dt(2012, 1, 1), object()))
