def update_market(cid, crypto, pair, market, market_name, platform):
account = get_settings(cid)
account.current_crypto = crypto
account.current_pair = pair
account.current_market = market
account.current_market_name = market_name
account.current_platform = platform
system('sql').session.commit()
def get_settings(cid):
try:
system('sql').session.expire_all()
return system('sql').session.query(Account).filter(
Account.id == cid).one()
except Exception as e:
system('sql').session.rollback()
print("Error al consultar datos de la cuenta")
print(e)
return None
def get_groups():
try:
system('sql').session.expire_all()
return system('sql').session.query(Account).filter(
Account.is_group == 1, Account.is_verified == 1).all()
except Exception as e:
system('sql').session.rollback()
print("Error al consultar grupos activos")
print(e)
return None
def get_binance_symbol_data(symbol,
kline_size,
save=False,
sma=None,
auto_increment=True):
if sma is None:
form = 'all'
data_df = get_data_frame(symbol, kline_size, form='all')
klines = get_klines_times(symbol, kline_size, data_df)
else:
form = 'sma-%s' % sma
data_df = get_data_frame(symbol, kline_size, form=form)
if data_df.shape[0] < sma or auto_increment is False:
oldest_point = (datetime.now() -
timedelta(days=sma)).strftime("%d %b %Y %H:%M:%S")
newest_point = pd.to_datetime(
system('algorithms').client.get_klines(
symbol=symbol, interval=kline_size)[-1][0],
unit='ms').strftime("%d %b %Y %H:%M:%S")
# print(oldest_point, newest_point)
klines = system('algorithms').client.get_historical_klines(
symbol=symbol,
interval=kline_size,
start_str=oldest_point,
end_str=newest_point)
else:
klines = get_klines_times(symbol, kline_size, data_df)
data = pd.DataFrame(klines,
columns=[
'timestamp', 'open', 'high', 'low', 'close',
'volume', 'close_time', 'quote_av', 'trades',
'tb_base_av', 'tb_quote_av', 'ignore'
])
# print(len(data.index))
data.drop(
['close_time', 'ignore', 'tb_base_av', 'quote_av', 'tb_quote_av'],
axis='columns',
inplace=True)
data['timestamp'] = pd.to_datetime(data['timestamp'], unit='ms')
data['time'] = data['timestamp']
data.set_index('timestamp', inplace=True, drop=False)
convert_columns_to_float(
data, ['open', 'high', 'low', 'close', 'volume', 'trades'])
data_df = merge_df(data_df, data, auto_increment)
if save:
save_extracted_data(symbol, kline_size, data_df, form=form)
# print(data_df.shape)
return data_df
def symbol_info(crypto, ref, exchange='BINANCE'):
data = {}
if exchange == 'BINANCE':
info = system('algorithms').client.get_symbol_info(crypto + ref)
data['min_quantity'] = float(info['filters'][2]['minQty'])
data['max_quantity'] = float(info['filters'][2]['maxQty'])
data['min_notional'] = float(info['filters'][3]['minNotional'])
data['crypto_quantity'] = system(
'algorithms').client.get_asset_balance(asset=crypto).get('free')
data['ref_quantity'] = system('algorithms').client.get_asset_balance(
asset=ref).get('free')
return data
def get_klines(symbol, kline_size, data_df):
oldest_point, newest_point = minutes_of_new_data(symbol,
kline_size,
data_df,
source="algorithms")
delta_min = (newest_point - oldest_point).total_seconds() / 60
available_data = math.ceil(delta_min /
system('algorithms').sizes[kline_size])
print(oldest_point, newest_point, available_data)
return system('algorithms').client.get_historical_klines(
symbol=symbol,
interval=kline_size,
start_str=oldest_point.strftime("%d %b %Y %H:%M:%S"),
end_str=newest_point.strftime("%d %b %Y %H:%M:%S"))
def minutes_of_new_data(symbol, kline_size, data, source):
global old, new
if len(data) > 0:
old = parser.parse(data["timestamp"].iloc[-1])
elif source == "algorithms":
old = datetime.strptime('1 Jan 2021', '%d %b %Y')
if source == "algorithms":
new = pd.to_datetime(system('algorithms').client.get_klines(
symbol=symbol, interval=kline_size)[-1][0],
unit='ms')
return old, new
def get_binance_klines_times(symbol,
kline_size,
data,
old_date=None,
new_date=None):
try:
if old_date is None or new_date is None:
old_date = (pd.to_datetime(data.iloc[-1:].timestamp.item()) +
timedelta(minutes=1)).strftime("%d %b %Y %H:%M:%S")
new_date = pd.to_datetime(system('algorithms').client.get_klines(
symbol=symbol, interval=kline_size)[-1][0],
unit='ms').strftime("%d %b %Y %H:%M:%S")
# print(old_date, new_date)
return system('algorithms').client.get_historical_klines(
symbol=symbol,
interval=kline_size,
start_str=old_date,
end_str=new_date)
except:
print('Error de extracción, reintentando')
return None
def update_settings(cid, name, verified, group):
try:
account = Account(cid, name)
account.is_verified = verified
account.is_group = group
system('sql').session.merge(account)
system('sql').session.commit()
except Exception as e:
system('sql').session.rollback()
print("Error al guardar datos del usuario")
print(e)
def create_order(symbol, quantity, price, operation='BUY'):
order = None
side = None
if operation == 'BUY':
side = SIDE_BUY
if operation == 'SELL':
side = SIDE_SELL
try:
order = system('binance').client.create_order(
symbol=symbol,
side=side,
type=ORDER_TYPE_LIMIT,
timeInForce=TIME_IN_FORCE_GTC,
quantity=quantity,
price=price)
except BinanceAPIException as e:
print(e)
return order, order.get('orderId')
def __init__(self,
crypto,
ref,
configuration,
temporalities,
indicators,
trade,
effectivity,
result_indicators,
exchange='BINANCE'):
self.client = system('algorithms').client
self.symbol = crypto + ref
self.crypto = crypto
self.ref = ref
self.exchange = exchange
# Default Values
self.max_min = None
self.order_status = None
self.order = None
self.process_is_started = False
self.first_iteration = False
self.trade_type = 'micro'
self.operative = False
# Settings
self.strategy = strategy_selector[os.environ["strategy"]]
self.configuration = configuration
self.indicators = indicators
self.result_indicators = result_indicators
self.trade = trade
self.effectivity = effectivity
self.temporalities = temporalities
self.testing = []
self.chat_ids = []
def cancel_order(symbol, order):
order_id = order.get('orderId')
cancel = system('binance').client.cancel_order(symbol=symbol,
orderId=order_id)
return cancel