def test_ibgw_port(host):
account = os.environ['IB_ACCOUNT']
password = os.environ['IB_PASSWORD']
trade_mode = os.environ['TRADE_MODE']
# build local ./Dockerfile
subprocess.check_call(['docker', 'build', '-t', IMAGE_NAME, '.'])
# run a container
docker_id = subprocess.check_output(
['docker', 'run',
'--env', 'IB_ACCOUNT={}'.format(account),
'--env', 'IB_PASSWORD={}'.format(password),
'--env', 'TRADE_MODE={}'.format(trade_mode),
'-p', '4002:4002',
'-d', IMAGE_NAME,
"tail", "-f", "/dev/null"]).decode().strip()
time.sleep(60)
ib = IB()
ib.connect('localhost', 4002, clientId=1)
contract = Forex('EURUSD')
bars = ib.reqHistoricalData(
contract, endDateTime='', durationStr='30 D',
barSizeSetting='1 hour', whatToShow='MIDPOINT', useRTH=True)
# convert to pandas dataframe:
df = util.df(bars)
print(df)
# at the end of the test suite, destroy the container
subprocess.check_call(['docker', 'rm', '-f', docker_id])
def test_ibgw_restart(ib_docker):
subprocess.check_output(
['docker', 'container', 'stop', ib_docker]).decode().strip()
subprocess.check_output(
['docker', 'container', 'start', ib_docker]).decode().strip()
ib = IB()
wait = 60
while not ib.isConnected():
try:
IB.sleep(1)
ib.connect('localhost', 4002, clientId=999)
except:
pass
wait -= 1
if wait <= 0:
break
contract = Forex('EURUSD')
bars = ib.reqHistoricalData(
contract, endDateTime='', durationStr='30 D',
barSizeSetting='1 hour', whatToShow='MIDPOINT', useRTH=True)
# convert to pandas dataframe:
df = util.df(bars)
print(df)
def plot_contracts_bars(ib: IB,
contracts: List[Contract],
end: Optional[datetime.datetime] = None,
bars_settings: Optional[List[Tuple[str, str]]] = None,
bar_type: Optional[str] = "TRADES"):
if end is None:
end = datetime.datetime.now().replace(tzinfo=datetime.timezone.utc)
if bars_settings is None or len(bars_settings) < 1:
bars_settings = [
("2 Y", "1 day"),
("6 M", "4 hours"),
("1 W", "10 mins"),
("1 D", "1 min"),
]
for durationStr, barSizeSetting in bars_settings:
fig, ax = plt.subplots(figsize=(24, 14))
for contract in contracts:
bars = util.df(
ib.reqHistoricalData(
contract,
endDateTime=end,
durationStr=durationStr,
barSizeSetting=barSizeSetting,
whatToShow=bar_type,
useRTH=True,
formatDate=2 # UTC
))
ys = np.cumprod(np.insert(1 + np.diff(np.log(bars["close"])), 0,
1))
xs = bars["date"].values
ax.plot(xs, ys, label=contract.symbol)
ax.set_title = f"{durationStr} - {barSizeSetting} bars"
plt.xticks(rotation=45)
plt.legend()
def spx_bar_history(update_bars=True):
file_name = 'sp500_5min_bars'
df_hist = read_feather(UpdateSP500Data.DATA_BASE_PATH / file_name)
# Download latest
if update_bars:
ib = IB()
ib.connect('127.0.0.1', port=4001, clientId=40)
contract = Index('SPX', 'CBOE', 'USD')
bars = ib.reqHistoricalData(
contract,
endDateTime='',
durationStr='1 M',
barSizeSetting='5 mins',
whatToShow='TRADES',
useRTH=True,
formatDate=1)
ib.disconnect()
df = util.df(bars)
df = df.set_index('date')
full_hist = df.combine_first(df_hist)
write_feather(full_hist, UpdateSP500Data.DATA_BASE_PATH / file_name)
else:
full_hist = df_hist.copy()
return full_hist
def test_request_historical_data(ib: IB, contract: Contract,
barSizeSetting: str):
contract = Forex('EURUSD')
bars = ib.reqHistoricalData(
contract,
endDateTime='',
durationStr='1 D',
barSizeSetting='10 secs',
# whatToShow='ADJUSTED_LAST',
whatToShow='MIDPOINT',
useRTH=True,
formatDate=1,
keepUpToDate=True,
)
bars.updateEvent += on_bar_update
return bars
class Window(QWidget):
def __init__(self, host, port, clientId):
QWidget.__init__(self)
self.edit = QLineEdit('', self)
self.edit.editingFinished.connect(self.add)
self.connectButton = QPushButton('Connect')
self.connectButton.clicked.connect(self.onConnectButtonClicked)
layout = QVBoxLayout(self)
layout.addWidget(self.edit)
layout.addWidget(self.connectButton)
self.graph = TickerGraph(self, width=5, height=4)
self.graph.move(0, 0)
layout.addWidget(self.graph)
self.connectInfo = (host, port, clientId)
self.ib = IB()
def add(self, text=''):
text = text #or self.edit.text()
if text:
contract = eval(text)
if (contract and self.ib.qualifyContracts(contract)):
data = self.ib.reqHistoricalData(contract,
endDateTime='',
durationStr='30 D',
barSizeSetting='4 hours',
whatToShow='MIDPOINT',
useRTH=True)
df = util.df(data)
print(df["close"])
self.graph.plot(df["close"])
self.edit.setText(text)
def onConnectButtonClicked(self, _):
if self.ib.isConnected():
self.ib.disconnect()
self.connectButton.setText('Connect')
else:
self.ib.connect(*self.connectInfo)
self.connectButton.setText('Disconnect')
self.add(f"Forex('" + str(self.edit.text()) + "')")
def closeEvent(self, ev):
asyncio.get_event_loop().stop()
def request_historical_data(ib: IB, contract: Contract, barSizeSetting: str):
"""
订阅历史 K 线
:param ib: ib_insync.IB 实例
:param contract: 标的
:param barSizeSetting: K 线类型, 参见 https://interactivebrokers.github.io/tws-api/historical_bars.html 的 Valid Bar Sizes
:return: BarDataList
"""
bars = ib.reqHistoricalData(
contract,
endDateTime='',
durationStr='1 D',
barSizeSetting=barSizeSetting,
# whatToShow='ADJUSTED_LAST',
whatToShow='MIDPOINT',
useRTH=True,
formatDate=1,
keepUpToDate=True,
)
return bars
def test_ibgw_interactive(ib_docker):
ib = IB()
wait = 120
while not ib.isConnected():
try:
IB.sleep(1)
ib.connect('localhost', 4002, clientId=999)
except:
pass
wait -= 1
if wait <= 0:
break
contract = Forex('EURUSD')
bars = ib.reqHistoricalData(
contract, endDateTime='', durationStr='30 D',
barSizeSetting='1 hour', whatToShow='MIDPOINT', useRTH=True)
# convert to pandas dataframe:
df = util.df(bars)
print(df)
class GatherData:
def __init__(self):
self.analyze = AnalyzeData()
access_type = 'tws'
client_id = 0
if access_type == 'gateway':
__port = 4001
elif access_type == 'tws':
__port = 7497
__host = '127.0.0.1'
self.ib = IB()
self.ib.connect(__host, __port, clientId=client_id)
self.db = Database()
self.conn = self.db.connect_()
self.limit = 50
def load_scanner(self, scanner):
ss = ScannerSubscription()
ss.instrument = "STK" # STOCK.EU
ss.locationCode = "STK.NASDAQ" # US.MAJOR
ss.scanCode = scanner
scan = self.ib.reqScannerData(ss)
# ss.cancelScannerSubscription(ss)
# self.ib.cancelScannerSubscription(ss)
# arr = np.array(scan)
return scan
@classmethod
def fields_scanner(cls, unix_ts, dt_ts, scanner, data_):
fields = []
for line in data_:
_symbol = line.contractDetails.contract.symbol
_rank = line.rank
fields.append([unix_ts, dt_ts, scanner, _symbol, _rank, BAR_SIZE])
return fields
@classmethod
def insert_scanner(cls, unix_ts, dt_ts, scanner, data_, cursor):
# if isinstance(data_, np.ndarray) or isinstance(data_, np.ndarray):
if not isinstance(data_, Exception):
insert_data = cls.fields_scanner(unix_ts, dt_ts, scanner, data_)
query = "insert into hist_scanner values(?,?,?,?,?,?);".format(
insert_data)
cursor.executemany(query, insert_data)
else:
cursor.execute(
"insert into hist_scanner values({},{},{},{},{},{});".format(
unix_ts, dt_ts, scanner, np.nan, np.nan, BAR_SIZE))
@classmethod
def save_scanner(cls, unix_ts, dt_ts, scanner, data_):
path_ = str(os.path.dirname(
os.path.realpath(__file__))) + '\\hist_files\\scanners\\'
saving_data = cls.fields_scanner(unix_ts, dt_ts, scanner, data_)
df = pd.DataFrame(saving_data)
df.to_csv(path_ + str(unix_ts) + '_' + scanner + '.csv',
',',
index=False)
def load_hist_data(self, dt_ts_str, symbol, duration_secs):
contract = Stock(symbol, 'SMART', 'USD')
data_ = self.ib.reqHistoricalData(
contract,
endDateTime=''.format(dt_ts_str),
durationStr='{} S'.format(duration_secs),
barSizeSetting='{} secs'.format(BAR_SIZE),
whatToShow='ADJUSTED_LAST',
useRTH=True,
formatDate=1)
# ss.cancelScannerSubscription(ss)
# self.ib.cancelScannerSubscription(ss)
# arr = np.array(scan)
return data_
@classmethod
def fields_hist_data(cls, data_, symbol):
fields = []
for i in data_:
unix_ts = time.mktime(i.date.timetuple())
fields.append([
int(unix_ts), i.date, symbol,
float(i.open),
float(i.high),
float(i.low),
float(i.close),
float(i.volume)
])
return fields
@classmethod
def insert_hist_data(cls, data_, symbol, cursor):
insert_data = cls.fields_hist_data(data_, symbol)
query = "insert into hist_ohlcv values(?,?,?,?,?,?,?,?);".format(
insert_data)
cursor.executemany(query, insert_data)
def run_scanner(self, on_this_tz, off_this_tz, save_mode='db'):
done = False
while True:
now = datetime.datetime.now().time()
while now > on_this_tz and now < off_this_tz:
# while schedule_agent(on_this_tz, off_this_tz):
previous_unix_ts = int(time.time())
previous_dt_ts = datetime.datetime.now()
with self.conn.cursor() as cursor:
try:
# if isinstance(self.conn, self.db.Connection):
for scanner in SCANNER_SCOPE:
data_ = self.load_scanner(scanner)
if save_mode == 'db':
self.insert_scanner(previous_unix_ts,
previous_dt_ts, scanner,
data_, cursor)
print(
'requested and inserted {} scanners, bar size is {}'
.format(str(len(SCANNER_SCOPE)),
str(BAR_SIZE)))
elif save_mode == 'files':
self.save_scanner(previous_unix_ts,
previous_dt_ts, scanner,
data_)
print(
'requested and saved {} scanners, bar size is {}'
.format(str(len(SCANNER_SCOPE)),
str(BAR_SIZE)))
except Exception as e:
print('error occured and inserted:', e)
self.insert_scanner(previous_unix_ts, previous_dt_ts,
np.nan, e, cursor)
current_unix_ts = int(time.time())
print('waiting for next bar:', BAR_SIZE)
while current_unix_ts - previous_unix_ts < BAR_SIZE:
current_unix_ts = int(time.time())
done = True
now = datetime.datetime.now().time()
else:
time.sleep(1)
if done:
dt_ts_str, scanners = self.analyze.get_todays_insertions(
self.conn)
matches = self.analyze.query_matches(dt_ts_str, scanners,
self.limit, self.conn)
insertion_li = []
# dt_ts_str = '20181026 09:40:00'
for match in matches:
try:
hist_data = self.load_hist_data(
dt_ts_str, match, 10 * 60)
insertion_li.append(hist_data)
with self.conn.cursor() as cursor:
self.insert_hist_data(hist_data, match, cursor)
except IndexError:
print('stop')
done = False
def currency_exchange(request, currencies='EURUSD'):
# print(currencies)
ss1 = 'ss1'
ss2 = 'ss2'
ss3 = 'ss3'
ss4 = 'ss4'
ss5 = 'ss5'
ss6 = 'ss6'
ss7 = 'ss7'
ss8 = 'ss8'
ss9 = 'ss9'
df = None
is_error = 0
try:
loop = asyncio.get_event_loop_policy().new_event_loop()
asyncio.set_event_loop(loop)
ib_server = 'twoprojectsib1_tws_1'
ib_port = 4003
if settings.DEBUG:
ib_server = '127.0.0.1'
ib_port = 4002
ib_ = IB()
ci = randint(0, 100000)
ib_.connect(ib_server, ib_port, clientId=ci)
# print('ib_')
# print(ib_)
# print('ib_')
ss1 = str(ib_)
except Exception as e:
ss1 = "Error connecting to: " + ib_server + ":" + str(ib_port)
ss2 = e
is_error = 1
try:
c = Forex(currencies)
bars = ib_.reqHistoricalData(c,
endDateTime='',
durationStr='1 D',
barSizeSetting='1 min',
whatToShow='MIDPOINT',
useRTH=True)
# print(bars)
ss3 = 'good 3'
df = util.df(bars)
# print(df[['date', 'open', 'high', 'low', 'close']])
ss4 = 'good 4'
df = df.sort_values(by=['date'], ascending=False)
ss5 = 'good 5'
ib_.disconnect()
del ib_
ss6 = 'good 6'
except Exception as e2:
ss7 = e2
is_error = 1
context = {
'df':
df,
'ss1':
ss1,
'ss2':
ss2,
'ss3':
ss3,
'ss4':
ss4,
'ss5':
ss5,
'ss6':
ss6,
'ss7':
ss7,
'ss8':
ss8,
'ss9':
ss9,
'is_error':
is_error,
'currencies':
currencies,
'title':
'Currency Exchange',
'cur_list': [
'GBPUSD', 'GBPZAR', 'HKDJPY', 'KRWAUD', 'KRWCAD', 'KRWCHF',
'KRWEUR', 'KRWGBP', 'KRWHKD', 'KRWJPY', 'KRWUSD', 'MXNJPY',
'NOKJPY', 'NOKSEK', 'NZDCAD', 'NZDCHF', 'NZDJPY', 'NZDUSD',
'SEKJPY', 'SGDCNH', 'SGDJPY', 'TRYJPY', 'USDCAD', 'USDCHF',
'USDCNH', 'USDCZK', 'USDDKK', 'USDHKD', 'USDHUF', 'USDILS',
'USDJPY', 'USDKRW', 'USDMXN', 'USDNOK', 'USDPLN', 'USDRUB',
'USDSEK', 'USDSGD', 'USDTRY', 'USDZAR', 'ZARJPY', 'EURPLN',
'EURRUB', 'EURSEK', 'EURSGD', 'EURTRY', 'EURUSD', 'EURZAR',
'GBPAUD', 'GBPCAD', 'GBPCHF', 'GBPCNH', 'GBPCZK', 'GBPDKK',
'GBPHKD', 'GBPHUF', 'GBPJPY', 'GBPMXN', 'GBPNOK', 'GBPNZD',
'GBPPLN', 'GBPSEK', 'GBPSGD', 'GBPTRY', 'GBPUSD', 'GBPZAR',
'HKDJPY', 'KRWAUD', 'KRWCAD', 'KRWCHF', 'KRWEUR', 'KRWGBP',
'KRWHKD', 'KRWJPY', 'KRWUSD', 'MXNJPY', 'NOKJPY', 'NOKSEK',
'NZDCAD', 'NZDCHF', 'NZDJPY', 'NZDUSD', 'SEKJPY', 'SGDCNH',
'SGDJPY', 'TRYJPY', 'USDCAD', 'USDCHF', 'USDCNH', 'USDCZK',
'USDDKK', 'USDHKD', 'USDHUF', 'USDILS', 'USDJPY', 'USDKRW',
'USDMXN', 'USDNOK', 'USDPLN', 'USDRUB', 'USDSEK', 'USDSGD',
'USDTRY', 'USDZAR', 'ZARJPY'
]
}
return render(request, 'trades/currency_exchange.html', context)
class Live(IB):
def __init__(self,
symbol,
temp,
client,
verbose=False,
notification=False):
self.symbol = symbol
instruments = pd.read_csv('instruments.csv').set_index('symbol')
params = instruments.loc[self.symbol]
self.market = str(params.market)
self.exchange = str(params.exchange)
self.temp = temp
self.tick_size = float(params.tick_size)
self.digits = int(params.digits)
self.leverage = int(params.leverage)
self.client = client
self.verbose = verbose
self.notification = notification
self.ib = IB()
print(self.ib.connect('127.0.0.1', 7497, client))
self.get_contract()
self.data = self.download_data(tempo=self.temp, duration='1 D')
self.current_date()
self.pool = pd.DataFrame(columns=[
'date', 'id', 'type', 'lots', 'price', 'S/L', 'T/P', 'commission',
'comment', 'profit'
])
self.history = pd.DataFrame(columns=[
'date', 'id', 'type', 'lots', 'price', 'S/L', 'T/P', 'commission',
'comment', 'profit'
])
self.pending = pd.DataFrame(columns=[
'date', 'id', 'type', 'lots', 'price', 'S/L', 'T/P', 'commission',
'comment', 'profit'
])
self.position = 0
self.number = 0
def get_contract(self):
if self.market == 'futures':
expiration = self.ib.reqContractDetails(
Future(self.symbol,
self.exchange))[0].contract.lastTradeDateOrContractMonth
self.contract = Future(symbol=self.symbol,
exchange=self.exchange,
lastTradeDateOrContractMonth=expiration)
elif self.market == 'forex':
self.contract = Forex(self.symbol)
elif self.market == 'stocks':
self.contract = Stock(symbol=self.symbol,
exchange=self.exchange,
currency='USD')
def download_data(self, tempo, duration):
pr = (lambda mark: 'TRADES' if mark == 'futures' else
('TRADES' if mark == 'stocks' else 'MIDPOINT'))(self.market)
historical = self.ib.reqHistoricalData(self.contract,
endDateTime='',
durationStr=duration,
barSizeSetting=tempo,
whatToShow=pr,
useRTH=True,
keepUpToDate=True)
return historical
def data_to_df(self, data):
df = util.df(data)[['date', 'open', 'high', 'low', 'close',
'volume']].set_index('date')
df.index = pd.to_datetime(df.index)
return df
def send_telegram_message(self, msg):
'''Sends a telegram message
'''
requests.post(
'https://api.telegram.org/bot804823606:AAFq-YMKr4hIjQ4N5M8GYCGa5w9JJ1kIunk/sendMessage',
data={
'chat_id': '@ranguito_channel',
'text': msg
})
def current_date(self):
self.date = datetime.now().strftime('%Y-%m-%d')
self.weekday = datetime.now().weekday()
self.hour = datetime.now().strftime('%H:%M:%S')
def pool_check(self):
'''Check pool trades'''
if self.position == 0:
self.pool = pd.DataFrame(columns=[
'date', 'type', 'lots', 'price', 'S/L', 'T/P', 'commission',
'comment', 'profit'
])
def calculate_profit(self, type, price, lots):
'''Calculates profit'''
if type == 'BUY':
profit = (lambda pos: 0
if pos >= 0 else (self.pool[self.pool.type == 'SELL'])[
'price'].iloc[0] - price)(self.position)
else:
profit = (lambda pos: 0 if pos <= 0 else price -
(self.pool[self.pool.type == 'BUY'])['price'].iloc[0])(
self.position)
return profit * self.leverage * lots
def order_values(self, order_id):
price = 0
commission = 0
if len(self.ib.fills()) > 0:
for trade in util.tree(self.ib.fills()):
if ('OrderId' and 'clientId') in trade[1]['Execution']:
if ((nested_lookup('orderId', trade)[0] == order_id) and
(nested_lookup('clientId', trade)[0] == self.client)):
commission = nested_lookup('commission', trade)[0]
price = nested_lookup('price', trade)[0]
return (price, commission)
def order_send(self, type, lots, sl=0, tp=0, comment=''):
market_order = MarketOrder(type, lots)
#initial_margin, maintenance_margin = self.get_margins(market_order)
self.ib.placeOrder(self.contract, market_order)
id = market_order.orderId
self.number += 1
price = 0
while price == 0:
self.ib.sleep(1)
price, commission = self.order_values(id)
profit = self.calculate_profit(type, price, lots)
trade = {
'date': str(self.date) + ' ' + str(self.hour),
'id': id,
'type': type,
'lots': lots,
'price': price,
'S/L': sl,
'T/P': tp,
'commission': commission,
'comment': comment,
'profit': profit
}
self.save_trade(trade)
self.pool = pd.concat(
[self.pool, pd.DataFrame(trade, index=[self.number])], sort=False)
self.history = pd.concat(
[self.history,
pd.DataFrame(trade, index=[self.number])],
sort=False)
mult = (lambda dir: 1 if dir == 'BUY' else -1)(type)
self.position += (mult * lots)
self.pool_check()
if self.verbose:
print('%s %s | %sING %d units at %5.2f in %s' % (str(
self.date), str(self.hour), type, lots, price, self.symbol))
if self.notification:
if self.position != 0:
self.send_message_in(type, price, sl, tp, lots)
else:
self.send_message_out(type, price, lots, profit, commission,
commission)
def bracket_stop_order(self,
type,
lots,
entry_price,
sl=0,
tp=0,
comment=''):
bracket_order = self.ib.bracketStopOrder(type, lots, entry_price, tp,
sl)
#initial_margin, maintenance_margin = self.get_margins(bracket_order[0])
for order in bracket_order:
self.ib.placeOrder(self.contract, order)
id_entry = bracket_order[0].orderId
id_tp = bracket_order[1].orderId
id_sl = bracket_order[2].orderId
trade = {
'date': str(self.date) + ' ' + str(self.hour),
'id': id_entry,
'type': bracket_order[0].action,
'lots': lots,
'price': entry_price,
'S/L': sl,
'T/P': tp,
'commission': 0,
'comment': comment,
'profit': 0
}
self.pending = pd.concat(
[self.pending, pd.DataFrame(trade, index=[id_entry])], sort=False)
trade = {
'date': str(self.date) + ' ' + str(self.hour),
'id': id_tp,
'type': bracket_order[1].action,
'lots': lots,
'price': tp,
'S/L': 0,
'T/P': 0,
'commission': 0,
'comment': comment,
'profit': 0
}
self.pending = pd.concat(
[self.pending, pd.DataFrame(trade, index=[id_entry])], sort=False)
trade = {
'date': str(self.date) + ' ' + str(self.hour),
'id': id_sl,
'type': bracket_order[2].action,
'lots': lots,
'price': sl,
'S/L': 0,
'T/P': 0,
'commission': 0,
'comment': comment,
'profit': 0
}
self.pending = pd.concat(
[self.pending, pd.DataFrame(trade, index=[id_entry])], sort=False)
return (bracket_order[0], bracket_order[1], bracket_order[2])
def pending_check(self, order):
id = order.orderId
if len(self.pending) > 0:
price, commission = self.order_values(id)
if price > 0:
self.number += 1
order_select = self.pending[self.pending.id == id]
profit = self.calculate_profit(order_select.type.iloc[0],
price,
order_select.lots.iloc[0])
trade = {
'date': str(self.date) + ' ' + str(self.hour),
'id': id,
'type': order_select.type.iloc[0],
'lots': order_select.lots.iloc[0],
'price': price,
'S/L': order_select['S/L'].iloc[0],
'T/P': order_select['T/P'].iloc[0],
'commission': commission,
'comment': '',
'profit': profit
}
self.save_trade(trade)
self.pool = pd.concat(
[self.pool,
pd.DataFrame(trade, index=[self.number])],
sort=False)
self.history = pd.concat(
[self.history,
pd.DataFrame(trade, index=[self.number])],
sort=False)
mult = (lambda dir: 1
if dir == 'BUY' else -1)(order_select.type.iloc[0])
self.position += (mult * order_select.lots.iloc[0])
self.pool_check()
if self.verbose:
print('%s %s | %sING %d units at %5.2f in %s' %
(str(self.date), str(
self.hour), order_select.type.iloc[0],
order_select.lots.iloc[0], price, self.symbol))
if self.notification:
if self.position != 0:
self.send_message_in(order_select.type.iloc[0], price,
order_select['S/L'].iloc[0],
order_select['T/P'].iloc[0],
order_select.lots.iloc[0])
else:
self.send_message_out(order_select.type.iloc[0], price,
order_select.lots.iloc[0],
profit, commission, commission)
return True
else:
return False
def get_margins(self, order):
init_margin = float(
self.ib.whatIfOrder(self.contract, order).initMarginChange)
maint_margin = float(
self.ib.whatIfOrder(self.contract, order).maintMarginChange)
return (init_margin, maint_margin)
def send_message_in(self, type, price_in, sl, tp, lots):
msg_in = '%s Opened in %s \nPrice: %5.2f \nS/L: %5.2f \nT/P: %5.2f \nLots: %d \nAt: %s' % (
type, self.symbol, price_in, sl, tp, lots, self.hour)
self.send_telegram_message(msg_in)
def send_message_out(self, type, price_out, lots, profit, comm_in,
comm_out):
msg_out = '%s Closed in %s \nPrice: %5.2f \nProfit(USD): %5.2f \nCommissions(USD): %5.2f \nAt: %s' % \
(type, self.symbol, price_out, profit, (comm_in+comm_out),self.hour)
self.send_telegram_message(msg_out)
def save_trade(self, trade):
if not path.exists('history_trades_%s.csv' % self.symbol):
initial = pd.DataFrame(columns=[
'date', 'id', 'type', 'lots', 'price', 'S/L', 'T/P',
'commission', 'comment', 'profit'
]).set_index('date')
initial.to_csv('history_trades_%s.csv' % self.symbol)
history = pd.read_csv('history_trades_%s.csv' % self.symbol)
trade = pd.DataFrame(trade, index=[0])
history = pd.concat([history, trade], sort=False)
history['net profit'] = history['profit'] - history['commission']
history['accumulated profit'] = history['net profit'].cumsum()
history['max profit'] = history['accumulated profit'].cummax()
history.set_index('date').to_csv('history_trades_%s.csv' % self.symbol)
class Basem:
''' 导入分钟级别股票信息类
'''
def __init__(self):
self.log = log(__name__, 'logs/basem.log')
self.db = Basedb()
self.empty = []
self.total = 0
self.i = 0
self.ib = IB()
self.ib.connect(Config.ib_host, Config.ib_port, Config.ib_client_id)
def __del__(self):
self.ib.disconnect()
def deal_data(self, future, symbol):
''' 回调函数，处理接口返回的股票数据
'''
self.i += 1
print('(%d/%d) 正在导入 %s HK' % (self.i, self.total, symbol), flush=True)
data = future.result()
if not data:
self.empty.append((symbol,))
return
open_sql = 'insert into `open_5m` (`code`, `code_type`, `date`, `value`) values '
high_sql = 'insert into `high_5m` (`code`, `code_type`, `date`, `value`) values '
low_sql = 'insert into `low_5m` (`code`, `code_type`, `date`, `value`) values '
close_sql = 'insert into `close_5m` (`code`, `code_type`, `date`, `value`) values '
volume_sql = 'insert into `volume_5m` (`code`, `code_type`, `date`, `value`) values '
average_sql = 'insert into `average_5m` (`code`, `code_type`, `date`, `value`) values '
for bar_data in data:
date = bar_data.date
open_price = bar_data.open
high = bar_data.high
low = bar_data.low
close = bar_data.close
average = bar_data.average
# volume 有不存在的情况, 16:00 收市，交易量不存在
try:
volume = bar_data.volume
except AttributeError:
volume = 0
open_sql += "('{code}', '{code_type}', '{date}', {value:.4f}),".format(code=symbol, code_type='hk', date=date, value=open_price)
high_sql += "('{code}', '{code_type}', '{date}', {value:.4f}),".format(code=symbol, code_type='hk', date=date, value=high)
low_sql += "('{code}', '{code_type}', '{date}', {value:.4f}),".format(code=symbol, code_type='hk', date=date, value=low)
close_sql += "('{code}', '{code_type}', '{date}', {value:.4f}),".format(code=symbol, code_type='hk', date=date, value=close)
volume_sql += "('{code}', '{code_type}', '{date}', {value}),".format(code=symbol, code_type='hk', date=date, value=volume)
average_sql += "('{code}', '{code_type}', '{date}', {value:.4f}),".format(code=symbol, code_type='hk', date=date, value=average)
open_rows = self.db.query(open_sql.rstrip(','))
high_rows = self.db.query(high_sql.rstrip(','))
low_rows = self.db.query(low_sql.rstrip(','))
close_rows = self.db.query(close_sql.rstrip(','))
volume_rows = self.db.query(volume_sql.rstrip(','))
average_rows = self.db.query(average_sql.rstrip(','))
if open_rows.rowcount == 0:
raise RuntimeError('open_sql 语句执行失败：%s' % open_sql)
elif high_rows.rowcount == 0:
raise RuntimeError('high_sql 语句执行失败：%s' % high_sql)
elif low_rows.rowcount == 0:
raise RuntimeError('low_sql 语句执行失败：%s' % low_sql)
elif close_rows.rowcount == 0:
raise RuntimeError('close_sql 语句执行失败：%s' % close_sql)
elif volume_rows.rowcount == 0:
raise RuntimeError('volume_sql 语句执行失败：%s' % volume_sql)
elif average_rows.rowcount == 0:
raise RuntimeError('average_sql 语句执行失败：%s' % average_sql)
else:
pass
def crawl_data(self, codes):
''' 爬取 IB 接口股票的交易信息
'''
futures = []
i = 0
for code in codes:
i += 1
symbol, _ = code
stock = Stock(symbol, Config.hk_exchange, Config.hk_currency)
future = self.ib.reqHistoricalDataAsync(stock, endDateTime='', durationStr='900 S',
barSizeSetting='5 mins', whatToShow='TRADES', useRTH=True)
self.ib.sleep(0.02)
future.add_done_callback(functools.partial(self.deal_data, symbol=symbol))
futures.append(future)
return futures
def get_codes_data(self, codes=None):
''' 爬取股票信息
1个月的5分钟交易信息
'''
t1 = time.time()
# codes => None 则从数据库获取股票列表
# 否则，使用传递进来的codes list，目的是再次爬取那些空数据的股票
# 以确保股票数据为空而不会遗漏有数据的股
# 因为有时连接超时，接口会返回空列表，但此股是有数据的
if codes is None:
codes = self.db.get_codes()
if not codes.rowcount:
raise RuntimeError('获取股票失败，stock 表返回空.')
codes = list(codes)
self.total = len(codes)
self.i = 0
futures = self.crawl_data(codes)
self.ib.run(*futures)
# 爬取完成，记录爬取的endDateTime时间，供下次增量爬取使用
end_date_time = '2017-12-31 23:59:59'
res = self.db.set_record(end_date_time)
if not res.rowcount:
raise RuntimeError('记录一个月5分钟的end_date_time失败.')
t2 = time.time()
t3 = t2 - t1
print('HK 股票交易信息全部导入完成，耗时：%.2fs' % t3)
self.log.info('导入股票信息完成，数据为空的股票有：{}'.format(self.empty))
def get_hsi_data(self):
''' 获取 HSI 一个月5分钟的信息
'''
symbol = 'HSI'
exchange = 'HKFE'
currency = 'HKD'
index = Index(symbol, exchange, currency)
data = self.ib.reqHistoricalData(index, endDateTime='20180119 15:00:00', durationStr='900 S',
barSizeSetting='5 mins', whatToShow='TRADES', useRTH=True)
if not data:
raise RuntimeError('HSI 数据接口返回空.')
sql = 'insert into `hsi_5m` (`date`, `open`, `high`, `low`, `close`) values '
for bar_data in data:
date = bar_data.date
open_price = bar_data.open
high = bar_data.high
low = bar_data.low
close = bar_data.close
sql += "('{date}', {open:.4f}, {high:.4f}, {low:.4f}, {close:.4f}),".format(date=date, open=open_price, high=high, low=low, close=close)
res = self.db.query(sql.rstrip(','))
if res.rowcount == 0:
raise RuntimeError('SQL 语句执行异常， 插入数据库失败：%s' % sql)
else:
print('HSI Index 1个月5分钟数据导入完成.', flush=True)
class AsyncIBDataProvider(GenericDataProvider):
logger = logging.getLogger(__name__)
def __init__(self,
verbose: int,
host: str,
port: int,
timeout: int,
chunk_size: int,
id=0,
tz='America/New_York',
**kwargs):
super(AsyncIBDataProvider, self).__init__(self.logger,
verbose,
tz,
chunk_size=chunk_size,
**kwargs)
self.port = port
self.host = host
self.timeout = timeout
self.keep_alive = False
if 'keep_alive' in kwargs:
self.keep_alive = kwargs['keep_alive']
self.ib = IB()
self.id = id
def disconnect(self):
self.ib.disconnect()
def connect(self):
if self.id == 0:
id = int(random.uniform(1, 1000))
else:
id = self.id
self.logger.info(
f"IBAsync: {self.host}:{self.port}, timeout={self.timeout}, id={id}"
)
self.ib.connect(self.host,
self.port,
clientId=id,
timeout=self.timeout,
readonly=True)
def _initialize(self):
if not self.ib.isConnected():
self.connect()
def _finish(self):
if not self.keep_alive:
self.disconnect()
async def _get_data_internal_async(self, symbol_data: SymbolData,
**kwargs) -> pd.DataFrame:
return self._get_data_internal(symbol_data)
def _get_data_internal(self, symbol_data: SymbolData) -> pd.DataFrame:
self.logger.info(f"Getting symbol data: {symbol_data}")
if symbol_data.timeframe == 'day':
symbol, bars = self._get_daily(symbol_data.start,
symbol_data.symbol, symbol_data.end)
symbol = symbol_data.symbol.split('-')[0]
dataframe = self._to_dataframe(bars)
elif symbol_data.timeframe == '60min':
now = f"{(datetime.now()):%Y-%m-%d %H:%M}"
duration = '365 D'
if symbol_data.start:
diff = datetime.strptime(
now, '%Y-%m-%d %H:%M') - datetime.strptime(
symbol_data.start, '%Y-%m-%d %H:%M')
if diff.days < 365:
duration = f"{diff.days} D"
symbol, bars = self._get_intraday(symbol_data.symbol, now,
duration, '1 hour',
symbol_data.rth_only)
symbol = symbol_data.symbol.split('-')[0]
dataframe = self._to_dataframe(bars, tz_fix=True)
elif symbol_data.timeframe == '5min':
now = f"{(datetime.now()):%Y-%m-%d %H:%M}"
duration = '30 D'
if symbol_data.start:
diff = datetime.strptime(
now, '%Y-%m-%d %H:%M') - datetime.strptime(
symbol_data.start, '%Y-%m-%d %H:%M')
if diff.days < 30:
duration = f"{diff.days} D"
symbol, bars = self._get_intraday(symbol_data.symbol, now,
duration, '5 mins',
symbol_data.rth_only)
symbol = symbol_data.symbol.split('-')[0]
dataframe = self._to_dataframe(bars, tz_fix=True)
elif symbol_data.timeframe == '15min':
now = f"{(datetime.now()):%Y-%m-%d %H:%M}"
duration = '60 D'
if symbol_data.start:
diff = datetime.strptime(
now, '%Y-%m-%d %H:%M') - datetime.strptime(
symbol_data.start, '%Y-%m-%d %H:%M')
if diff.days < 60:
duration = f"{diff.days} D"
symbol, bars = self._get_intraday(symbol_data.symbol, now,
duration, '15 mins',
symbol_data.rth_only)
symbol = symbol_data.symbol.split('-')[0]
dataframe = self._to_dataframe(bars, tz_fix=True)
else:
raise Exception(f"{symbol_data.timeframe} not implemented!")
df = dataframe
if dataframe.empty:
self.logger.warning(f"Got empty df for {symbol_data}")
else:
df = self._post_process(dataframe, symbol, symbol_data.start,
symbol_data.end, symbol_data.timeframe,
symbol_data.transform)
return df
@staticmethod
def exctract_symbol(ticker: str,
type: str = 'STK',
exchange: str = 'ARCA',
currency: str = 'USD',
expire='',
multiplier='') -> tuple:
if ticker.count('-') == 4:
symbol, type, exchange, currency, multiplier = ticker.split('-')
if type.isdigit():
expire = type
type = "FUT"
elif ticker.count('-') == 3:
symbol, type, exchange, currency = ticker.split('-')
if type.isdigit():
expire = type
type = "FUT"
elif ticker.count('-') == 2:
if ticker.find('CASH') > -1:
symbol, currency, exchange = ticker.split('-')
symbol = symbol.replace('.', '')
type = 'FX'
else:
a, b, c = ticker.split('-')
if b.isdigit():
type = 'FUT'
symbol = a
exchange = c
expire = b
else:
symbol = a
exchange = b
currency = c
elif ticker.count('-') == 1:
symbol, exchange = ticker.split('-')
else:
symbol = ticker
return type, symbol, exchange, currency, expire, multiplier
@staticmethod
def parse_contract(ticker):
"""
Backtrader contract specification (https://www.backtrader.com/docu/live/ib/ib.html):
TICKER # Stock type and SMART exchange
TICKER-STK # Stock and SMART exchange
TICKER-STK-EXCHANGE # Stock
TICKER-STK-EXCHANGE-CURRENCY # Stock
TICKER-CFD # CFD and SMART exchange
TICKER-CFD-EXCHANGE # CFD
TICKER-CDF-EXCHANGE-CURRENCY # Stock
TICKER-IND-EXCHANGE # Index
TICKER-IND-EXCHANGE-CURRENCY # Index
TICKER-YYYYMM-EXCHANGE # Future
TICKER-YYYYMM-EXCHANGE-CURRENCY # Future
TICKER-YYYYMM-EXCHANGE-CURRENCY-MULT # Future
TICKER-FUT-EXCHANGE-CURRENCY-YYYYMM-MULT # Future
TICKER-YYYYMM-EXCHANGE-CURRENCY-STRIKE-RIGHT # FOP
TICKER-YYYYMM-EXCHANGE-CURRENCY-STRIKE-RIGHT-MULT # FOP
TICKER-FOP-EXCHANGE-CURRENCY-YYYYMM-STRIKE-RIGHT # FOP
TICKER-FOP-EXCHANGE-CURRENCY-YYYYMM-STRIKE-RIGHT-MULT # FOP
CUR1.CUR2-CASH-IDEALPRO # Forex
TICKER-YYYYMMDD-EXCHANGE-CURRENCY-STRIKE-RIGHT # OPT
TICKER-YYYYMMDD-EXCHANGE-CURRENCY-STRIKE-RIGHT-MULT # OPT
TICKER-OPT-EXCHANGE-CURRENCY-YYYYMMDD-STRIKE-RIGHT # OPT
TICKER-OPT-EXCHANGE-CURRENCY-YYYYMMDD-STRIKE-RIGHT-MULT # OPT
:return:
"""
contract_type, symbol, exchange, currency, expire, multiplier = \
AsyncIBDataProvider.exctract_symbol(ticker)
if contract_type == 'FX':
return Forex(pair=symbol)
if contract_type == 'IND':
return Index(symbol, exchange, currency)
if contract_type == 'FUT':
return Future(symbol,
expire,
exchange,
currency=currency,
multiplier=multiplier)
else:
return Stock(symbol, exchange, currency)
def _get_intraday(self, ticker: str, to_date: str, duration: str,
barsize: str, rth_only: bool) -> (str, [BarData]):
to_dt = datetime.strptime(f"{to_date}", '%Y-%m-%d %H:%M')
contract = AsyncIBDataProvider.parse_contract(ticker)
whatToShow = 'MIDPOINT' if isinstance(contract,
(Forex, CFD,
Commodity)) else 'TRADES'
bars = self.ib.reqHistoricalData(contract,
endDateTime=to_dt,
durationStr=duration,
barSizeSetting=barsize,
whatToShow=whatToShow,
useRTH=rth_only,
formatDate=2)
return contract.symbol, bars
def _get_daily(self, from_date: str, ticker: str,
to_date: str) -> (str, [BarData]):
#TODO: strip HH:MM from start/end dates?
from_dt = datetime.strptime(from_date, "%Y-%m-%d")
today = datetime.strptime(to_date, "%Y-%m-%d")
to_dt = datetime(today.year, today.month, today.day, 23, 59, 59)
days = (to_dt - from_dt).days
if days > 365:
self.logger.warning(f"Historical data is limited to 365 Days. "
f"Only requesting for year '{from_dt.year}'")
days = 365
to_dt = datetime(from_dt.year, 12, 31, 23, 59, 59)
if to_dt > datetime.today():
to_dt = None
contract = AsyncIBDataProvider.parse_contract(ticker)
whatToShow = 'MIDPOINT' if isinstance(contract,
(Forex, CFD,
Commodity)) else 'TRADES'
# bars = self.ib.reqDailyBars(contract, 2016)
bars = self.ib.reqHistoricalData(contract,
endDateTime=to_dt,
durationStr=F"{days} D",
barSizeSetting='1 day',
whatToShow=whatToShow,
useRTH=True,
formatDate=1)
return contract.symbol, bars
def _to_dataframe(self, bars, tz_fix=False):
if tz_fix:
data = [{
'Date':
pd.to_datetime(
b.date.astimezone(self.tz).replace(tzinfo=None)),
'Open':
b.open,
'High':
b.high,
'Low':
b.low,
'Close':
b.close,
'Volume':
b.volume
} for b in bars]
else:
data = [{
'Date': pd.to_datetime(b.date),
'Open': b.open,
'High': b.high,
'Low': b.low,
'Close': b.close,
'Volume': b.volume
} for b in bars]
if len(data) > 0:
return pd.DataFrame(data).set_index('Date')
else:
return pd.DataFrame()
def add_quotes(self, data, ticker):
return data
class IBDataService:
ip = "127.0.0.1"
port = 4002 # 4001 for real trading
def __init__(self):
self.uid = random.randint(1000, 10000)
print(f"init - UID: {str(self.uid)}")
self.ib = IB()
self.connect()
def connect(self, *args):
print(f"connectToIB - UID: {str(self.uid)}")
if self.ib.isConnected() is False:
print("CONNECTING ...")
self.ib.connect("127.0.0.1", 4002, clientId=self.uid)
print("CONNECTED")
def disconnect(self, *args):
print(f"connectToIB - UID: {str(self.uid)}")
if self.ib.isConnected():
print("DISCONNECTING ...")
self.ib.disconnect()
print("DISCONNECTED ...")
def getContractDetail(self, contract):
print(f"getContractDetail - UID: {str(self.uid)}")
data = self.ib.reqContractDetails(contract)
# print(data)
if len(data) > 0:
return data[0]
else:
return None
def getFuturesContractDetail(self, contract):
print(f"getFuturesContractDetail - UID: {str(self.uid)}")
data = self.ib.reqContractDetails(contract)
if len(data) > 0:
return data
else:
return None
def getHistoricalData(self,
contract,
endDate="",
duration="1 Y",
barSize="1 day",
price="MIDPOINT"):
print(f"getHistoricalData - UID: {str(self.uid)}")
data = self.ib.reqHistoricalData(contract, endDate, duration, barSize,
price, 1, 1, False, [])
return data
async def startRealtimeData(self, contract, method):
print(f"startRealtimeData - UID: {str(self.uid)}")
self.ib.reqMktData(contract, "233", False, False)
ticker = self.ib.reqTickByTickData(contract, TickDataType.LAST.value)
ticker.updateEvent += method
print(f"ENDS - startRealtimeData - UID: {str(self.uid)}")
def stopRealtimeData(self, contract):
print(f"stopRealtimeData - UID: {str(self.uid)}")
self.ib.cancelMktData(contract)
self.ib.cancelTickByTickData(contract, TickDataType.LAST.value)
print(f"ENDS - stopRealtimeData - UID: {str(self.uid)}")
class trade_ES():
def __init__(self):
self.ib = IB()
self.ib.connect('127.0.0.1',
7497,
clientId=np.random.randint(10, 1000))
self.tickers_ret = {}
self.endDateTime = ''
self.No_days = '43200 S'
self.interval = '30 secs'
self.tickers_signal = "Hold"
self.ES = Future(symbol='ES',
lastTradeDateOrContractMonth='20200619',
exchange='GLOBEX',
currency='USD')
self.ib.qualifyContracts(self.ES)
self.ES_df = self.ib.reqHistoricalData(contract=self.ES,
endDateTime=self.endDateTime,
durationStr=self.No_days,
barSizeSetting=self.interval,
whatToShow='TRADES',
useRTH=False,
keepUpToDate=True)
self.tickers_ret = []
self.options_ret = []
self.option = {'call': FuturesOption, 'put': FuturesOption}
self.options_history = {}
self.trade_options = {'call': [], 'put': []}
self.price = 0
self.i = -1
self.ES_df.updateEvent += self.make_clean_df
self.Buy = True
self.Sell = False
self.ib.positionEvent += self.order_verify
self.waitTimeInSeconds = 220
self.tradeTime = 0
self.mySemaphore = asyncio.Semaphore(1)
def run(self):
self.make_clean_df(self.ES_df)
def next_exp_weekday(self):
weekdays = {2: [6, 0], 4: [0, 1, 2], 0: [3, 4]}
today = datetime.date.today().weekday()
for exp, day in weekdays.items():
if today in day:
return exp
def next_weekday(self, d, weekday):
days_ahead = weekday - d.weekday()
if days_ahead <= 0: # Target day already happened this week
days_ahead += 7
date_to_return = d + datetime.timedelta(
days_ahead) # 0 = Monday, 1=Tuself.ESday, 2=Wednself.ESday...
return date_to_return.strftime('%Y%m%d')
def get_strikes_and_expiration(self):
expiration = self.next_weekday(datetime.date.today(),
self.next_exp_weekday())
chains = self.ib.reqSecDefOptParams(underlyingSymbol='ES',
futFopExchange='GLOBEX',
underlyingSecType='FUT',
underlyingConId=self.ES.conId)
chain = util.df(chains)
strikes = chain[chain['expirations'].astype(str).str.contains(
expiration)].loc[:, 'strikes'].values[0]
[ESValue] = self.ib.reqTickers(self.ES)
ES_price = ESValue.marketPrice()
strikes = [
strike for strike in strikes
if strike % 5 == 0 and ES_price - 10 < strike < ES_price + 10
]
return strikes, expiration
def get_contract(self, right, net_liquidation):
strikes, expiration = self.get_strikes_and_expiration()
for strike in strikes:
contract = FuturesOption(symbol='ES',
lastTradeDateOrContractMonth=expiration,
strike=strike,
right=right,
exchange='GLOBEX')
self.ib.qualifyContracts(contract)
self.price = self.ib.reqMktData(contract, "", False, False)
if float(self.price.last) * 50 >= net_liquidation:
continue
else:
return contract
def make_clean_df(self, ES_df, hashbar=None):
ES_df = util.df(ES_df)
ES_df['RSI'] = ta.RSI(ES_df['close'])
ES_df['macd'], ES_df['macdsignal'], ES_df['macdhist'] = ta.MACD(
ES_df['close'], fastperiod=12, slowperiod=26, signalperiod=9)
ES_df['MA_9'] = ta.MA(ES_df['close'], timeperiod=9)
ES_df['MA_21'] = ta.MA(ES_df['close'], timeperiod=21)
ES_df['MA_200'] = ta.MA(ES_df['close'], timeperiod=200)
ES_df['EMA_9'] = ta.EMA(ES_df['close'], timeperiod=9)
ES_df['EMA_21'] = ta.EMA(ES_df['close'], timeperiod=21)
ES_df['EMA_200'] = ta.EMA(ES_df['close'], timeperiod=200)
ES_df['ATR'] = ta.ATR(ES_df['high'], ES_df['low'], ES_df['close'])
ES_df['roll_max_cp'] = ES_df['high'].rolling(20).max()
ES_df['roll_min_cp'] = ES_df['low'].rolling(20).min()
ES_df['roll_max_vol'] = ES_df['volume'].rolling(20).max()
ES_df.dropna(inplace=True)
self.loop_function(ES_df)
def placeOrder(self, contract, order):
trade = self.ib.placeOrder(contract, order)
tradeTime = datetime.datetime.now()
return ([trade, contract, tradeTime])
def sell(self, contract, position):
self.ib.qualifyContracts(contract)
if position.position > 0:
order = 'Sell'
else:
order = 'Buy'
marketorder = MarketOrder(order, abs(position.position))
marketTrade, contract, tradeTime = self.placeOrder(
contract, marketorder)
while self.ib.position.position != 0:
self.ib.sleep(1)
self.mySemaphore.release()
async def buy(self, contract):
await self.semaphore.acquire()
self.ib.qualifyContracts(contract)
marketorder = MarketOrder('Buy', 1)
marketTrade = self.ib.placeOrder(contract, marketorder)
def order_verify(self, order):
if order.position == 0.0 or order.position < 0:
self.Buy = True
self.Sell = False
elif order.position > 0:
self.Buy = False
self.Sell = True
else:
self.Buy = False
self.Sell = False
print(f'Buy= {self.Buy}, sell = {self.Sell}')
def loop_function(self, ES_df):
df = ES_df[[
'high', 'low', 'volume', 'close', 'RSI', 'ATR', 'roll_max_cp',
'roll_min_cp', 'roll_max_vol', 'EMA_9', 'EMA_21', 'macd',
'macdsignal'
]]
if self.tickers_signal == "Hold":
print('Hold')
if df["high"].iloc[self.i] >= df["roll_max_cp"].iloc[self.i] and \
df["volume"].iloc[self.i] > df["roll_max_vol"].iloc[self.i - 1] and df['RSI'].iloc[self.i] > 30 \
and df['macd'].iloc[self.i] > df['macdsignal'].iloc[self.i] :
self.tickers_signal = "Buy"
return
elif df["low"].iloc[self.i] <= df["roll_min_cp"].iloc[self.i] and \
df["volume"].iloc[self.i] > df["roll_max_vol"].iloc[self.i - 1] and df['RSI'].iloc[self.i] < 70 \
and df['macd'].iloc[self.i] < df['macdsignal'].iloc[self.i]:
self.tickers_signal = "Sell"
return
else:
self.tickers_signal = "Hold"
return
elif self.tickers_signal == "Buy":
print('BUY SIGNAL')
if df["close"].iloc[self.i] > df["close"].iloc[self.i - 1] - (
0.75 * df["ATR"].iloc[self.i - 1]) and len(
self.ib.positions()) != 0:
print(
f'{df["close"].iloc[self.i]} > {df["close"].iloc[self.i - 1] - (0.75 * df["ATR"].iloc[self.i - 1])}'
)
print('first buy condition')
positions = self.ib.positions()
for position in positions:
if position.contract.right == 'C':
self.sell(position.contract, position)
self.tickers_signal = "Hold"
return
elif df["low"].iloc[self.i] <= df["roll_min_cp"].iloc[self.i] and \
df["volume"].iloc[self.i] > df["roll_max_vol"].iloc[self.i - 1] and df['RSI'].iloc[self.i] < 70 \
and df['macd'].iloc[self.i] < df['macdsignal'].iloc[self.i] and len(self.ib.positions())!=0:
self.tickers_signal = "Sell"
print('sell')
positions = self.ib.positions()
for position in positions:
if position.contract.right == 'C':
self.sell(position.contract, position)
self.tickers_signal == "Sell"
return
else:
if len(self.ib.positions()) == 0:
self.option['call'] = self.get_contract(
right="C", net_liquidation=2000)
self.buy(self.option['call'])
self.tickers_signal = "Hold"
else:
self.tickers_signal = "Hold"
elif self.tickers_signal == "Sell":
print('SELL SIGNAL')
if df["close"].iloc[self.i] < df["close"].iloc[self.i - 1] + (
0.75 * df["ATR"].iloc[self.i - 1]) and len(
self.ib.positions()) != 0:
print('first sell condition')
print(
f'{df["close"].iloc[self.i]} < {df["close"].iloc[self.i - 1] - (0.75 * df["ATR"].iloc[self.i - 1])}'
)
print('sell')
positions = self.ib.positions()
for position in positions:
if position.contract.right == 'P':
self.sell(position.contract, position)
self.tickers_signal = "Hold"
return
elif df["high"].iloc[self.i] >= df["roll_max_cp"].iloc[self.i] and \
df["volume"].iloc[self.i] > df["roll_max_vol"].iloc[self.i - 1] and df['RSI'].iloc[self.i] > 30 \
and df['macd'].iloc[self.i] > df['macdsignal'].iloc[self.i] and len(self.ib.positions())!=0:
self.tickers_signal = "Buy"
print('sell')
positions = self.ib.positions()
for position in positions:
if position.contract.right == 'P':
self.sell(position.contract, position)
self.tickers_signal == "Buy"
return
else:
if len(self.ib.positions()) == 0:
self.option['put'] = self.get_contract(
right="P", net_liquidation=2000)
self.buy(self.option['put'])
self.tickers_signal = "Hold"
else:
self.tickers_signal = "Hold"
def checkError(self, errCode, errString):
print('Error Callback', errCode, errString)
if errCode == 2104:
print('re-connect after 5 secs')
self.ib.sleep(5)
self.ib.disconnect()
self.ib.connect('127.0.0.1',
7497,
clientId=np.random.randint(10, 1000))
self.make_clean_df(self.ES)
class _DataLoader:
"""
Private class Responsible for all ETL related tasks. Loads data from csv, fetches data from Binance API.
Attributes
-----------
binance: Client object which is the Binance API Python wrapper
Methods
------------
_load_csv
_get_range
_get_binance_futures_candles
_timeframe_setter
Please look at each method for descriptions
"""
SECOND_TO_MILLISECOND = 1000
def __init__(self, db=False, qtrade=False, ib=False):
self.sql = SqlMapper()
self.ib = IB()
self.binance = Client()
if qtrade:
self.qtrade = Questrade(
token_yaml=
'C:/Users/haseab/Desktop/Python/PycharmProjects/FAB/local/Workers/access_token.yml',
save_yaml=True)
print('Connected to Questrade API')
if db:
self.conn = self.sql.connect_psql()
if ib:
print(self.ib.connect('127.0.0.1', 7496, 104))
def load_db_data(self, symbol, conns, chunks=3):
results = []
resp_df = self.sql.SELECT(
f"explain select * from candlesticks where symbol = '{symbol}'",
show_select=False,
cursor=conns[0].cursor())
table_row_count = int(
resp_df.loc[0, 'QUERY PLAN'].split(' ')[-2].strip("rows="))
limit = table_row_count // chunks
with ThreadPoolExecutor(max_workers=3) as executor:
for chunk, conn in zip(range(0, table_row_count, limit), conns):
print('start')
cursor = conn.cursor()
results.append(
executor.submit(
self.sql.SELECT,
f"* FROM candlesticks WHERE SYMBOL = '{symbol}' AND TF = '1' LIMIT {limit} OFFSET {chunk}",
cursor))
print('done')
executor.shutdown(wait=True)
return results
def _randomly_delete_rows(self, df, percentage_of_data=0.10):
index_list = []
for _ in range(len(df) // (1 / percentage_of_data)):
index = random.choice(df.index)
if index not in index_list:
index_list.append(index)
return df.drop(index_list)
def _clean_1m_data(self, df):
start_date = df['timestamp'].iloc[0]
end_date = df['timestamp'].iloc[-1]
full_timestamps = pd.DataFrame(
[time for time in range(start_date, end_date + 60, 60)],
columns=['timestamp'])
full_df = full_timestamps.merge(df.reset_index(),
on='timestamp',
how='left')
full_df['volume'] = full_df['volume'].fillna(0.001)
filled_df = full_df.fillna(method='ffill')
filled_df['date'] = [
datetime.fromtimestamp(timestamp)
for timestamp in filled_df['timestamp'].values
]
return filled_df
def _load_csv_v2(self, csv_url):
tf = csv_url.split(' ')[2][:-1]
symbol = csv_url.split(' ')[1]
data = pd.read_csv(csv_url)
data['timestamp'] = [
int(timestamp / 1000) for timestamp in data['timestamp']
]
data['date'] = [
datetime.fromtimestamp(timestamp)
for timestamp in data['timestamp'].values
]
data['tf'] = [tf] * len(data)
data['symbol'] = [symbol] * len(data)
data[["open", "high", "low", "close",
"volume"]] = data[["open", "high", "low", "close",
"volume"]].astype(float)
data = data[[
'symbol', 'tf', 'timestamp', 'date', 'open', 'high', 'low',
'close', 'volume'
]]
return data.set_index(["symbol", "tf", "timestamp"])
def _load_csv(self, csv_url: str) -> pd.DataFrame:
"""Function used to load 1-minute historical candlestick data with a given csv url
The important columns are the ones that create the candlestick (open, high, low, close) """
# Reading CSV File containing 1 min candlestick data
data = pd.read_csv(csv_url, index_col='timestamp')
# Converting Timestamp numbers into a new column of readable dates
data['date'] = [
datetime.fromtimestamp(timestamp) for timestamp in data.index
]
data[["open", "high", "low", "close",
"volume"]] = data[["open", "high", "low", "close",
"volume"]].astype(float)
data = data[['date', 'open', 'high', 'low', 'close', 'volume']]
return data
def _get_binance_futures_candles(self,
symbol: str,
tf: int,
start_candles_ago: int,
end_candles_ago: int = 0,
now: float = None) -> pd.DataFrame:
"""
Provides a method for getting a set of candlestick data without inputting start and end date.
Ex. _get_binance_futures_candles("BTCUSDT", 5, 3) = get candlestick data from 5 minutes ago to 3 minutes ago.
Parameters:
-----------
symbol: str Ex. "BTCUSDT", "ETHUSDT"
start_minutes_ago: int Ex. 1, 5, 1000
end_minutes_ago: int Ex. 1, 5, 1000
:return pd.DataFrame of candlestick data.
"""
if now == None:
now = time.time()
# Defining params to put in exchange API call
map_tf = {
1: "1m",
3: "3m",
5: "5m",
15: "15m",
30: "30m",
60: "1h",
120: "2h",
240: "4h",
360: "6h",
480: "8h"
}
start_minutes_ago = start_candles_ago * tf
end_minutes_ago = end_candles_ago * tf
start_time = Helper().minutes_ago_to_timestamp(
start_minutes_ago, now, adjust=self.SECOND_TO_MILLISECOND)
end_time = Helper().minutes_ago_to_timestamp(
end_minutes_ago, now, adjust=self.SECOND_TO_MILLISECOND)
num_candles = abs(start_candles_ago - end_candles_ago)
data = self.binance.futures_klines(symbol=symbol,
interval=map_tf[tf],
startTime=start_time,
endTime=end_time,
limit=num_candles)
return Helper.into_dataframe(data, symbol=symbol, tf=tf, index=False)
def load_finviz_data():
import pandas as pd
from finviz.screener import Screener
filters_nyse = ['exch_nyse'
] # Shows companies in NASDAQ which are in the S&P500
stock_list_nyse = Screener(
filters=filters_nyse, table='Ownership', order='Market Cap'
) # Get the performance table and sort it by price ascending
filters_nasdaq = ['exch_nasd']
stock_list_nasd = Screener(
filters=filters_nasdaq, table='Ownership', order='Market Cap'
) # Get the performance table and sort it by price ascending
nasdaq_df = pd.DataFrame(stock_list_nasd.data).drop('No.', axis=1)
nyse_df = pd.DataFrame(stock_list_nyse.data).drop('No.', axis=1)
df = nyse_df.append(nasdaq_df).reset_index(drop=True)
df.to_csv('finviz_stocks.csv', index=False)
return df
def _get_ibkr_stocks_candles(self, symbol: str, tf: int, start_time,
end_time):
tf_map = {
1: "1 min",
5: "5 mins",
15: "15 mins",
30: "30 mins",
60: "1 hour",
240: "4 hours",
1440: "1 day"
}
parsed_start, parsed_end = dateparser.parse(
start_time), dateparser.parse(end_time)
duration = (parsed_end - parsed_start).days + 1
bars = self.ib.reqHistoricalData(Stock(str(symbol), 'SMART', 'USD'),
endDateTime=parsed_end,
durationStr=f'{duration} D',
barSizeSetting=tf_map[tf],
whatToShow='TRADES',
useRTH=False,
formatDate=1)
return Helper.into_dataframe(bars, symbol=symbol, tf=tf)
def _get_range(self,
dataframe: pd.DataFrame,
start_date: str = None,
end_date: str = None) -> pd.DataFrame:
"""Returns the range of 1-min data within specified start & end date from the entire dataset
Parameters
----------
dataframe: pd.DataFrame object with a Timestamp as its index
start_date: date in the format of YYYY-MM-DD format
end_date: date in the format of YYYY-MM-DD format
:return dataframe
"""
if start_date == None or end_date == None:
raise Exception("No Start date given")
start_date = Helper.string_to_timestamp(start_date)
end_date = Helper.string_to_timestamp(end_date)
# Converting from timestamp index to numbered index, then adding numbered index as column
dataframe_temp = dataframe.reset_index().reset_index().set_index(
'timestamp')
start_index = dataframe_temp.loc[start_date, 'index']
try:
end_index = dataframe_temp.loc[end_date, 'index']
except KeyError:
end_index = dataframe_temp['index'].iloc[-1]
return dataframe[start_index:end_index + 1]
def _timeframe_setter(self,
dataframe: pd.DataFrame,
skip: int,
shift: int = 0,
keep_last_row=False) -> pd.DataFrame:
""" Vertical way of abstracting data
Converts minute candlestick data into the timeframe(tf) of choice.
Parameters
-----------
dataframe: the dataframe that is being passed as an argument
tf: The combination of 1-min candles into one value. Number of 1-min candles combined
is the timeframe value itself.
The raw data is in a 1-min timeframe. Dataframe contains the following
columns: ['open', 'high', 'Low, 'close']. Converting to a X minute timeframe is
handled differently for every column of the candlestick:
Close - Since all that matters is the close value every 'tf' minutes, you can skip
every 'tf' minutes.
Ex.
df['close'] = pd.Series([4.50, 4.60, 4.65, 4.44, 4.21, 4.54, 4.10])
_timeframe_setter(df['close'], 2) -> [4.50, 4.65, 4.21, 4.10]
_timeframe_setter(df['close'], 3) -> [[4.50, 4.44, 4.10]
Open - Same rules as Close
High - Get the maximum 1-min high value given the range of the timeframe
Ex.
df['close'] = pd.Series([4.50, 4.60, 4.65, 4.44, 4.21, 4.54, 4.10])
_timeframe_setter(df['high'], 2) -> [4.60, 4.65, 4.44, 4.54]
_timeframe_setter(df['high'], 3) -> [4.65, 4.54]
Low - Same rules as 'high', but instead the minimum of that range
Volume - Same rules as "High", but instead the sum of that range
If the range of tf is not even (such as having a tf=2 but only 5 elements), then the
last value will be dropped
:return dataframe
"""
if skip == 1:
return dataframe
base_tf = int(dataframe['tf'].iloc[0])
if shift == None:
# This is making sure that there it shifts so that the last tf candle includes the last 1-minute candle
shift = skip - len(dataframe) % skip - 1
dataframe[["open", "high", "low", "close", "volume"
]] = dataframe[["open", "high", "low", "close",
"volume"]].astype(float)
# Creating a new dataframe so that the size of the rows of the new dataframe will be the same as the new columns
df = dataframe.iloc[shift::skip].copy()
rolled_df = dataframe.rolling(skip)
high = rolled_df['high'].max()
low = rolled_df['low'].min()
volume = rolled_df['volume'].sum()
close = dataframe.copy()['close']
# Abstracting based on the highest, lowest and sum respectively.
df['high'] = np.append(high.iloc[shift + skip::skip].values,
high.iloc[-1])
df['low'] = np.append(low.iloc[shift + skip::skip].values,
low.iloc[-1])
df['volume'] = np.append(volume.iloc[shift + skip::skip].values,
volume.iloc[-1])
# Selecting every nth value in the list, where n is the timeframe
try:
df['close'] = close.iloc[shift + skip - 1::skip].values
except ValueError as e:
df['close'] = np.append(close.iloc[shift + skip - 1::skip].values,
close.iloc[-1])
tf = base_tf * skip
df['tf'] = [tf] * len(df['volume'])
# Dropping the last value, this gets rid of the candle that isn't complete until the end of the tf
if not keep_last_row:
df.drop(df.tail(1).index, inplace=True)
return df.reset_index().set_index(['symbol', 'tf', 'timestamp'])
def _get_fast_questrade_data(self, symbol, start_datetime, end_datetime,
tf_str, tf):
data = self.qtrade.get_historical_data(symbol, start_datetime,
end_datetime, tf_str)
return Helper.into_dataframe(data, symbol=symbol, tf=tf, qtrade=True)
def _get_fast_ibkr_data(self, symbol, duration, end_datetime, tf_str, tf):
data = self.ib.reqHistoricalData(Stock(str(symbol), 'SMART', 'USD'),
endDateTime=end_datetime,
durationStr=f'{duration} D',
barSizeSetting=tf_str,
whatToShow='TRADES',
useRTH=False,
formatDate=1)
return Helper.into_dataframe(data, symbol=symbol, tf=tf)
################################################# ASYNC FUNCTIONS ############################################################
async def _async_get_fast_questrade_data(self, symbol, start_datetime,
end_datetime, tf_str, tf):
data = self.qtrade.get_historical_data(symbol, start_datetime,
end_datetime, tf_str)
return Helper.into_dataframe(data, symbol=symbol, tf=tf, qtrade=True)
async def _async_get_fast_ibkr_data(self, symbol, duration, end_datetime,
tf_str, tf):
data = await self.ib.reqHistoricalDataAsync(
Stock(str(symbol), 'SMART', 'USD'),
endDateTime=end_datetime,
durationStr=f'{duration} D',
barSizeSetting=tf_str,
whatToShow='TRADES',
useRTH=False,
formatDate=1)
print(symbol, tf)
# return data
return Helper.into_dataframe(data, symbol=symbol, tf=tf)
##################################################################################################################################
def get_ibkr_stock_candles(self, symbol, tf, start_time, end_time):
tf_map = {
1: "1 min",
5: "5 mins",
15: "15 mins",
30: "30 mins",
60: "1 hour",
240: "4 hours",
1440: "1 day"
}
start_datetime, end_datetime = dateparser.parse(
start_time), dateparser.parse(end_time)
duration = (end_datetime - start_datetime).days + 1
data = self.ib.reqHistoricalData(Stock(str(symbol), 'SMART', 'USD'),
endDateTime=end_datetime,
durationStr=f'{duration} D',
barSizeSetting=tf_map[tf],
whatToShow='TRADES',
useRTH=False,
formatDate=1)
return util.df(data)
def get_questrade_stock_candles(self, symbol: str, tf: int, start_time,
end_time):
tf_map = {
1: "OneMinute",
5: "FiveMinutes",
15: "FifteenMinutes",
30: "HalfHour",
60: "OneHour",
240: "FourHours",
1440: "OneDay"
}
parsed_start, parsed_end = dateparser.parse(
start_time), dateparser.parse(end_time)
parsed_start, parsed_end = parsed_start.strftime(
'%Y-%m-%d %H:%M:%S.%f'), parsed_end.strftime(
'%Y-%m-%d %H:%M:%S.%f')
print('finished converting the times', parsed_start, parsed_end)
data = self.qtrade.get_historical_data(symbol, parsed_start,
parsed_end, tf_map[tf])
print('got data', len(data))
return Helper.into_dataframe(data, symbol=symbol, tf=tf, qtrade=True)
def get_binance_candles(self, symbol, tf, start_date, end_date=None):
map_tf = {
1: "1m",
3: "3m",
5: "5m",
15: "15m",
30: "30m",
60: "1h",
120: "2h",
240: "4h",
360: "6h",
480: "8h"
}
lst = self.binance.get_historical_klines(symbol=symbol,
interval=map_tf[tf],
start_str=start_date)
return Helper.into_dataframe(lst, symbol=symbol, tf=tf, index=False)
def get_all_binance_data(self, symbol, tf, start_date, end_date=None):
map_tf = {
1: "1m",
3: "3m",
5: "5m",
15: "15m",
30: "30m",
60: "1h",
120: "2h",
240: "4h",
360: "6h",
480: "8h"
}
list_symbol = self.binance.get_historical_klines(symbol=symbol,
interval=map_tf[tf],
start_str=start_date)
df_symbol = pd.DataFrame(list_symbol)
df_symbol.columns = [
"timestamp", "open", "high", "low", "close", "volume",
"timestamp_end", "", "", "", "", ""
]
##Fixing Columns
df_symbol['timestamp'] = [
int(timestamp / 1000) for timestamp in df_symbol['timestamp']
]
df_symbol['date'] = [
datetime.fromtimestamp(timestamp)
for timestamp in df_symbol['timestamp'].values
]
df_symbol['tf'] = [tf[:-1]] * len(df_symbol)
df_symbol['symbol'] = [symbol] * len(df_symbol)
df_symbol[["open", "high", "low", "close", "volume"
]] = df_symbol[["open", "high", "low", "close",
"volume"]].astype(float)
df_symbol = df_symbol[[
'symbol', 'tf', 'timestamp', 'date', 'open', 'high', 'low',
'close', 'volume'
]]
df_symbol = df_symbol.set_index(["symbol", "tf", "timestamp"])
start_date = str(df_symbol.iloc[0, 0])[:10]
string = f"Binance {symbol} {tf}m data from {start_date} to {str(datetime.now())[:10]}.csv"
print(string)
# df_symbol.to_csv(string)
return df_symbol
class Window(qt.QWidget):
def __init__(self, host, port, clientId):
qt.QWidget.__init__(self)
self.setWindowTitle("Giulio's App")
self.canvas = MplCanvas()
# self.edit = qt.QLineEdit('', self)
# self.edit.editingFinished.connect(self.add)
self.table = HistoricalTable()
self.MAList = []
self.MADict = {}
self.connectButton = qt.QPushButton('Connect')
self.connectButton.setStyleSheet(
"border: 1px solid black; background: white")
self.connectButton.resize(100, 32)
self.connectButton.setGeometry(200, 150, 100, 40)
self.connectButton.clicked.connect(self.onConnectButtonClicked)
self.displayButton = qt.QPushButton('Display values')
self.displayButton.setStyleSheet(
"border: 1px solid black; background: white")
self.displayButton.resize(100, 32)
self.displayButton.clicked.connect(self.onDisplayButtonClicked)
self.cancelAllButton = qt.QPushButton('CancelAll')
self.cancelAllButton.setStyleSheet(
"border: 1px solid black; background: white")
self.cancelAllButton.resize(100, 32)
self.cancelAllButton.setGeometry(200, 150, 100, 40)
self.cancelAllButton.clicked.connect(self.onCancelAllButtonClicked)
layout = qt.QVBoxLayout(self)
# layout.addWidget(self.edit)
layout.addWidget(self.table)
#layout.addWidget(self.canvas)
layout.addWidget(self.connectButton)
layout.addWidget(self.cancelAllButton)
# layout.addStretch(1)
# self.fig = plt.figure()
# self.ax = self.fig.add_subplot(1, 1, 1)
self.xs = []
self.ys = []
# layout.addWidget(self.fig)
self.connectInfo = (host, port, clientId)
self.ib = IB()
self.headers = [
'symbol', 'bidSize', 'bid', 'ask', 'askSize', 'last', 'lastSize',
'close'
]
self.id = 1
self.firstSignal = True
self.isConnectionBroken = False
self.firstma50 = 0
self.firstma200 = 0
self.availableCash = 0
self.ib.orderStatusEvent += self.order_status_cb
self.ib.execDetailsEvent += self.exec_details_cb
self.ib.errorEvent += self.error_cb
self.ib.accountSummaryEvent += self.accountSummary
self.ib.pendingTickersEvent += self.onPendingTickers
# self.ib.pendingTickersEvent += self.table.onPendingTickers
async def accountSummaryAsync(self, account: str = '') -> \
List[AccountValue]:
if not self.wrapper.acctSummary:
# loaded on demand since it takes ca. 250 ms
await self.reqAccountSummaryAsync()
if account:
return [
v for v in self.wrapper.acctSummary.values()
if v.account == account
]
else:
return list(self.wrapper.acctSummary.values())
def accountSummary(self, account: str = '') -> List[AccountValue]:
if (account.tag == 'BuyingPower'):
logging.info('account buying power - ' + account.value)
accVal: float = 0.0
accVal = account.value
self.availableCash = float(accVal)
self.availableCash = round(self.availableCash, 2)
logging.info('available cash - ' + str(self.availableCash))
logging.info("account summary:: " + str(account.account) + " " +
account.tag + " " + account.value)
return [] #self._run(self.accountSummaryAsync(account))
def error_cb(self, reqId, errorCode, errorString, contract):
logging.error("error: " + str(reqId) + " , " + str(errorCode) + " , " +
str(errorString))
logging.error("string - " + str(errorString))
"""if(errorCode == 1100):
logging.error("Connectivity between IB and TWS has been lost")
self.isConnectionBroken = True
if (errorCode == 1300):
logging.error("socket connection dropped")
self.isConnectionBroken = True
if(errorCode == 2105):
logging.error("HMDS data farm connection is broken")
if ((errorCode == 2104 or errorCode == 2106) and self.isConnectionBroken == True):
logging.info("HMDS data farm connection has been restored")
self.reqData()"""
def reqGlobalCancel(self):
"""
Cancel all active trades including those placed by other
clients or TWS/IB gateway.
"""
self.ib.reqGlobalCancel()
logging.info('reqGlobalCancel')
def order_status_cb(self, trade):
logging.info("order status for " + str(trade.order.orderId))
logging.info("Status filled and remaining - " +
trade.orderStatus.status + " " +
str(trade.orderStatus.filled) + " " +
str(trade.orderStatus.remaining))
def exec_details_cb(self, trade, fill):
logging.info("exec details for " + fill.contract.symbol +
" with orderid " + str(fill.execution.orderId))
if (fill.execution.side == "Sell"):
self.availableCash += fill.execution.price
def onPendingTickers(self, tickers):
for ticker in tickers:
logging.info("ticker - " + str(ticker.contract.conId) + " " +
str(ticker.contract.secType) + " " +
ticker.contract.symbol + " " +
ticker.contract.currency)
for col, header in enumerate(self.headers):
if col == 0:
continue
val = getattr(ticker, header)
symbol = ticker.contract.symbol + (ticker.contract.currency
if ticker.contract.secType
== 'CASH' else '')
ma = self.MADict[symbol]
logging.info("Values - " + str(ticker.contract.secType) + " " +
str(ticker.contract.conId) + " " + symbol + " " +
str(header) + " " + str(col) + " val- " +
str(val))
if (str(header) == 'bid'):
ma.bid = val
if (str(header) == 'ask'):
ma.ask = val
def onBarUpdate(self, bars, hasNewBar):
self.xs.append(dt.datetime.now().strftime('%H:%M:%S.%f'))
# logging.debug("bar update " + str(hasNewBar) + " for " + str(bars.reqId))
logging.info(bars[-1])
symbol = bars.contract.symbol + (
bars.contract.currency if bars.contract.secType == 'CASH' else '')
ma = self.MADict[symbol]
logging.info("update for " + ma.symbol)
df = util.df(bars)
# logging.debug(df)
ma.setMAs(df)
ma50 = ta.MA(df['close'], 50)
ma200 = ta.MA(df['close'], 200)
self.ys.append(ma50)
self.xs = self.xs[-50:]
self.ys = self.ys[-50:]
# self.ax.clear()
# self.ax.plot(self.xs, self.ys)
plt.xticks(rotation=45, ha='right')
plt.subplots_adjust(bottom=0.30)
plt.title('50MA')
plt.ylabel('MA')
"""logging.debug("ma50")
logging.debug(ma50)
logging.debug("ma200")
logging.debug(ma200)
logging.debug("last items")
logging.debug(ma50.tail(1).item())
logging.debug(ma200.tail(1).item())"""
orderList = ma.checkGCDC()
if (orderList is not None):
orderQuantity = 0
for order in orderList:
if (order.orderType == "LMT"):
if (order.action == "Buy"):
order.totalQuantity = 1000 #(self.availableCash/ma.bid) * .01
self.availableCash -= (order.totalQuantity *
order.trailStopPrice)
logging.info("Placing buy order for " + ma.symbol +
" " + str(ma.bid) + " with orderId " +
str(order.orderId))
else:
order.totalQuantity = 1000 #(self.availableCash/ma.ask) * .01
logging.info("Placing sell order for " + ma.symbol +
" at " + str(ma.ask) + " with orderId " +
str(order.orderId))
orderQuantity = order.totalQuantity
else:
if (order.orderType == "TRAIL"):
order.totalQuantity = orderQuantity
if (order.action == "Buy"):
#order.totalQuantity = (self.availableCash / ma.bid) * .01
self.availableCash -= (order.totalQuantity *
order.trailStopPrice)
logging.info("Placing buy order for " + ma.symbol +
" " + str(ma.bid) + " with orderId " +
str(order.orderId))
else:
#order.totalQuantity = (self.availableCash / ma.ask) * .01
logging.info("Placing sell order for " +
ma.symbol + " at " + str(ma.ask) +
" with orderId " + str(order.orderId))
logging.info("Placing " + order.action +
" order for " + ma.symbol + " at " +
str(order.trailStopPrice) + " " +
str(ma.ask) + " with orderId " +
str(order.orderId) + " " +
str(trade.order.orderId))
trade = self.ib.placeOrder(bars.contract, order)
if (ma.isOrderActive == False and ma.GCCheck == True):
logging.info("order is not active and gccheck is true")
self.MADict[symbol] = ma
"""if (ma.firstSignal == True):
ma.firstma50 = round(ma50.tail(1).item(), 6)
ma.firstma200 = round(ma200.tail(1).item(), 6)
ma.firstSignal = False
if (ma.firstma50 < ma.firstma200):
logging.info("checking golden cross for " + ma.symbol + " : mas - " + str(ma.firstma50) + " " + str(ma.firstma200))
else:
logging.info("checking death cross for " + ma.symbol + " : mas - " + str(ma.firstma50) + " " + str(ma.firstma200))
ma.GCCheck = False
self.MADict[symbol] = ma
else:
prevma50 = ma.getMa50()
prevma200 = ma.getMa200()
currma50 = round(ma50.tail(1).item(), 6)
currma200 = round(ma200.tail(1).item(), 6)
if(ma.isOrderActive == False):
if(ma.GCCheck == True):
logging.info("golden cross check for " + ma.symbol)
logging.info("prev mas - " + str(prevma50) + " " + str(prevma200))
logging.info("curr mas - " + str(currma50) + " " + str(currma200))
logging.info("curr bid and ask vals - " + str(ma.bid) + " " + str(ma.ask))
if((prevma50 <= prevma200) and (currma50 > currma200)):
logging.info(("golden cross occured for " + ma.symbol))
ma.GCCheck = False
if(ma.isOrderActive == False):
ma.isOrderActive = True
order = TrailOrder("Buy", 1000, ma.ask, 20)
trade = self.ib.placeOrder(bars.contract, order)
logging.info("Placing buy order for " + ma.symbol + " at " + str(order.trailStopPrice) + " " + str(ma.ask) + " with orderId " + str(order.orderId) + " " + str(trade.order.orderId))
self.MADict[symbol] = ma
else:
logging.info("death cross check for " + ma.symbol)
logging.info("prev mas - " + str(prevma50) + " " + str(prevma200))
logging.info("curr mas - " + str(currma50) + " " + str(currma200))
if ((prevma50 >= prevma200) and (currma50 < currma200)):
logging.info(("death cross occured for " + ma.symbol))
ma.GCCheck = True
if (ma.isOrderActive == False):
ma.isOrderActive = True
order = TrailOrder("Sell", 1000, ma.bid, 20)
trade = self.ib.placeOrder(bars.contract, order)
logging.info("Placing sell order for " + ma.symbol + " at " + str(ma.bid) + " with orderId " + str(trade.order.orderId))
self.MADict[symbol] = ma """
ma.setMa50(round(ma50.tail(1).item(), 6))
ma.setMa200(round(ma200.tail(1).item(), 6))
self.MADict[symbol] = ma
logging.debug("MAs for " + str(bars.contract.secType) + " " +
str(bars.contract.symbol) + " " +
bars.contract.currency + " , reqid: " + str(bars.reqId) +
" " + str(ma50.values[-1]) + " " +
str(ma200.values[-1]) + " : " +
str(ma50.tail(1).item()) + " " +
str(ma200.tail(1).item()))
self.table.updateData(bars.reqId, round(ma50.tail(1).item(), 6),
round(ma200.tail(1).item(), 6))
# logging.debug(ma50.values[-1])
# plt.close()
# plot = util.barplot(bars)
# clear_output(wait=True)
# display(plot)
def add_historical(self, text=''):
logging.debug("text - " + text)
logger.debug("logging")
text = text or self.edit.text()
if text:
logging.debug('eval text ') # + eval(text))
contract = eval(text)
logging.debug("requesting historical and mkt data for " + text)
bars = self.ib.reqHistoricalData(contract,
endDateTime='',
durationStr='2000 S',
barSizeSetting='10 secs',
whatToShow='MIDPOINT',
useRTH=True,
formatDate=1,
keepUpToDate=True)
ticker = self.ib.reqMktData(contract, '', False, False, None)
logging.info(bars[-1])
logging.debug("sectype " + str(bars.reqId) + " " +
str(bars.contract.conId) + " " +
bars.contract.secType + " " + bars.contract.symbol +
" " + bars.contract.currency)
self.table.addHistoricalData(bars.reqId, contract)
df = util.df(bars)
# with pd.option_context('display.max_rows', None, 'display.max_columns',
# None): # more options can be specified also
# logging.debug(df)
close = pd.DataFrame(df, columns=['close'])
logging.debug("close ")
logging.debug(close)
# df['pandas_SMA_3'] = df.iloc[:, 1].rolling(window=3).mean()
# df.head()
#ma50 = ta.MA(df['close'], 50)
#ma200 = ta.MA(df['close'], 200)
symbol = bars.contract.symbol + (bars.contract.currency
if bars.contract.secType == 'CASH'
else '')
logging.info("symbol - " + symbol)
ma = MovingAverages(
self.ib, symbol, bars.reqId
) #, round(ma50.tail(1).item(), 6), round(ma200.tail(1).item(), 6))
ma.setMAs(df)
self.MAList.append(ma)
self.MADict[symbol] = ma
"""logging.debug("ma50")
logging.debug(ma50)
logging.debug("ma200")
logging.debug(ma200)
logging.debug("initial ma vals for " + symbol)
logging.debug(ma50.tail(1).item())
logging.debug(ma200.tail(1).item())"""
self.table.updateData(bars.reqId, round(ma.ma50.tail(1).item(), 6),
round(ma.ma200.tail(1).item(), 6))
# sma = pd.SMA(df['close'].values, timeperiod=4)
"""portfolio = self.ib.portfolio()#.wrapper.portfolio.cash = 10000
logging.debug("portfolio")
logging.debug(portfolio)
positions = self.ib.positions()
logging.debug("positions")
for x in range(len(positions)):
logging.debug(positions[x].contract.symbol)
logging.debug(positions[x].position)"""
# logging.debug(positions)
bars.updateEvent += self.onBarUpdate
logging.debug("reqid is " + str(bars.reqId) + " for " +
bars.contract.symbol + " " + bars.contract.currency +
" , sectype - " + bars.contract.secType)
def onDisplayButtonClicked(self, _):
logging.debug("MA values")
for ma in self.MAList:
logging.debug("symbol - " + " " + ma.symbol)
logging.debug(
str(ma.firstma50) + " " + str(ma.firstma200) + " " +
str(ma.firstSignal) + " " + str(ma.ma50) + " " + str(ma.ma200))
for x in self.MADict:
logging.debug(x)
for x in self.MADict.values():
logging.debug("dict values - " + str(x.firstSignal) + " " +
x.symbol + " " + str(x.firstma50) + " " +
str(x.firstma200) + " " + str(x.ma50) + " " +
str(x.ma200))
def onConnectButtonClicked(self, _):
logging.debug("isconnected: " + str(self.ib.isConnected()))
if self.ib.isConnected():
self.ib.disconnect()
logging.debug("clearing data")
self.table.clearData()
self.connectButton.setText('Connect')
logging.debug("done")
else:
logging.debug("trying to connect")
# ib = IB()
# ib.connect('127.0.0.1', 7497, clientId=3)
self.ib.connect('127.0.0.1', 7497,
clientId=2) # *self.connectInfo)
logging.debug("connected - ") # + self.ib.isConnected())
# self.ib.reqMarketDataType(2)
self.connectButton.setText('Disconnect')
self.ib.reqAccountSummary()
self.reqData()
def onCancelAllButtonClicked(self):
logging.info("Cancelling all open orders")
#self.ib.connect('127.0.0.1', 7497, clientId=2) # *self.connectInfo)
self.reqGlobalCancel()
def reqData(self):
#self.reqGlobalCancel()
"""for symbol in ('EURUSD', 'USDJPY', 'EURGBP', 'USDCAD',
'EURCHF', 'AUDUSD', 'NZDUSD'):
logging.debug("requesting for " + symbol)
self.add_historical(f"Forex('{symbol}')")"""
#self.add_historical("Stock('TSLA', 'SMART', 'USD')")
#self.add_historical("Stock('IBM', 'SMART', 'USD')")
#self.add_historical("Stock('MSFT', 'SMART', 'USD')")
self.add_historical("Stock('FB', 'SMART', 'USD')")
def closeEvent(self, ev):
logging.debug("closing")
asyncio.get_event_loop().stop()
from ib_insync import IB, util, Forex
if __name__ == "__main__":
ib = IB()
ib.connect('localhost', 4001, clientId=1)
contract = Forex('EURUSD')
bars = ib.reqHistoricalData(contract,
endDateTime='',
durationStr='30 D',
barSizeSetting='1 hour',
whatToShow='MIDPOINT',
useRTH=True)
# convert to pandas dataframe:
df = util.df(bars)
print(df)
class request(IB):
def __init__(self, symbol, temp, client):
self.symbol = symbol
self.temp = temp
instruments = pd.read_csv('instruments.csv').set_index('symbol')
self.params = instruments.loc[self.symbol]
self.market = str(self.params.market)
self.exchange = str(self.params.exchange)
self.tick_size = float(self.params.tick_size)
self.digits = int(self.params.digits)
self.leverage = int(self.params.leverage)
self.client = client
self.current_date()
self._sundays_activation()
self.ib = IB()
print(self.ib.connect('127.0.0.1', 7497, self.client))
self.connected = self.ib.isConnected() #######
self.get_contract()
self.interrumption = False
#self.data = self.download_data(tempo=temp, duration='1 D')
#self.ib.reqMktData(self.contract, '', False, False); self.ticker = self.ib.ticker(self.contract) #########
#self.ticker = self.ib.reqTickByTickData(self.contract, 'Last', 0)
self.bars = self.ib.reqRealTimeBars(self.contract, 5, 'MIDPOINT',
False)
self.operable = True
def operable_schedule(self):
if self.weekday == 4 and pd.to_datetime(
self.hour).time() > pd.to_datetime('18:00:00').time():
print('%s %s | Today is Friday and Market has Closed!' %
(self.date, self.hour))
self.operable = False
elif self.weekday == 5:
print('%s %s | Today is Saturday and market is not Opened' %
(self.date, self.hour))
self.operable = False
else:
self.operable = True
def current_date(self):
self.date = datetime.now().strftime('%Y-%m-%d')
self.weekday = datetime.now().weekday()
self.hour = datetime.now().strftime('%H:%M:%S')
def _sundays_activation(self):
hour = '18:00:05'
if self.weekday == 6:
if pd.to_datetime(self.hour).time() < pd.to_datetime(hour).time():
print('Today is Sunday. Bot activation is at 18:00:00')
while True:
self.current_date()
if pd.to_datetime(
self.hour).time() >= pd.to_datetime(hour).time():
print('Activation Done')
self.send_telegram_message(
'%s %s | Bot Activation Done' %
(self.date, self.hour))
break
def continuous_check_message(self, message):
if datetime.now().minute == 0 and datetime.now().second == 0:
self.send_telegram_message(message, type='info')
def reconnection(self):
if self.hour == '23:44:30' or self.hour == '16:59:30':
self.interrumption = True
self.ib.disconnect()
self.connected = self.ib.isConnected()
print('%s %s | Ib disconnection' % (self.date, self.hour))
print('Connected: %s' % self.connected)
if self.hour == '23:46:00' or self.hour == '18:00:05':
self.interrumption = False
print('%s %s | Reconnecting...' % (self.date, self.hour))
while not self.connected:
try:
self.ib.connect('127.0.0.1', 7497, self.client)
self.connected = self.ib.isConnected()
if self.connected:
print('%s %s | Connection reestablished!' %
(self.date, self.hour))
print('Requesting Market Data...')
self.bars = self.ib.reqRealTimeBars(
self.contract, 5, 'MIDPOINT', False)
print('Last Close of %s: %.2f' %
(self.symbol, self.bars[-1].close))
print('%s Data has been Updated!' % self.symbol)
except:
print(
'%s %s | Connection Failed! Trying to reconnect in 10 seconds...'
% (self.date, self.hour))
self.ib.sleep(10)
print('%s %s | %s Data has been Updated!' %
(self.date, self.hour, self.symbol))
def _local_symbol_selection(self):
'''Selects local symbol according to symbol and current date'''
current_date = datetime.now().date()
# csv file selection according to symbol
if self.symbol in ['ES', 'RTY', 'NQ', 'MES', 'MNQ', 'M2K']:
contract_dates = pd.read_csv(
'D:/Archivos/futuro/Algorithmics/Codes/My_Bots/Hermes/contract_dates/indexes_globex.txt',
parse_dates=True)
elif self.symbol in ['YM', 'MYM', 'DAX']:
contract_dates = pd.read_csv(
'D:/Archivos/futuro/Algorithmics/Codes/My_Bots/Hermes/contract_dates/indexes_ecbot_dtb.txt',
parse_dates=True)
elif self.symbol in ['QO', 'MGC']:
contract_dates = pd.read_csv(
'D:/Archivos/futuro/Algorithmics/Codes/My_Bots/Hermes/contract_dates/QO_MGC.txt',
parse_dates=True)
elif self.symbol in ['CL', 'QM']:
contract_dates = pd.read_csv(
'D:/Archivos/futuro/Algorithmics/Codes/My_Bots/Hermes/contract_dates/CL_QM.txt',
parse_dates=True)
else:
contract_dates = pd.read_csv(
'D:/Archivos/futuro/Algorithmics/Codes/My_Bots/Hermes/contract_dates/%s.txt'
% symbol,
parse_dates=True)
# Current contract selection according to current date
for i in range(len(contract_dates)):
initial_date = pd.to_datetime(
contract_dates.iloc[i].initial_date).date()
final_date = pd.to_datetime(
contract_dates.iloc[i].final_date).date()
if initial_date <= current_date <= final_date:
current_contract = contract_dates.iloc[i].contract
break
# local symbol selection
local = current_contract
if self.symbol in [
'ES', 'RTY', 'NQ', 'MES', 'MNQ', 'M2K', 'QO', 'CL', 'MGC', 'QM'
]:
local = '%s%s' % (self.symbol, current_contract)
if self.symbol in ['YM', 'ZS']:
local = '%s %s' % (self.symbol, current_contract)
if self.symbol == 'MYM':
local = '%s %s' % (self.symbol, current_contract)
if self.symbol == 'DAX': local = 'FDAX %s' % current_contract
return local
def get_contract(self):
if self.market == 'futures':
local = self._local_symbol_selection()
self.contract = Future(symbol=self.symbol,
exchange=self.exchange,
localSymbol=local)
print(
self.ib.reqContractDetails(
self.contract)[0].contract.lastTradeDateOrContractMonth)
'''expiration = self.ib.reqContractDetails(Future(self.symbol,self.exchange))[0].contract.lastTradeDateOrContractMonth
self.contract = Future(symbol=self.symbol, exchange=self.exchange, lastTradeDateOrContractMonth=expiration)'''
elif self.market == 'forex':
self.contract = Forex(self.symbol)
elif self.market == 'stocks':
self.contract = Stock(symbol=self.symbol,
exchange=self.exchange,
currency='USD')
def download_data(self, tempo, duration):
pr = (lambda market: 'MIDPOINT'
if market == 'forex' else 'TRADES')(self.market)
historical = self.ib.reqHistoricalData(self.contract,
endDateTime='',
durationStr=duration,
barSizeSetting=tempo,
whatToShow=pr,
useRTH=False,
keepUpToDate=False)
return historical
def send_telegram_message(self, message, type='trades'):
bot_token = '1204313430:AAGonra1LaFhyI1gCVOHsz8yAohJUeFgplo'
bot_chatID = '-499850995' if type == 'trades' else '-252750334'
url = 'https://api.telegram.org/bot%s/sendMessage?chat_id=%s&text=%s' % (
bot_token, bot_chatID, message)
requests.get(url)
class IBStore(with_metaclass(MetaSingleton, object)):
'''Singleton class wrapping an ibpy ibConnection instance.
The parameters can also be specified in the classes which use this store,
like ``IBData`` and ``IBBroker``
Params:
- ``host`` (default:``127.0.0.1``): where IB TWS or IB Gateway are
actually running. And although this will usually be the localhost, it
must not be
- ``port`` (default: ``7496``): port to connect to. The demo system uses
``7497``
- ``clientId`` (default: ``None``): which clientId to use to connect to
TWS.
``None``: generates a random id between 1 and 65535
An ``integer``: will be passed as the value to use.
- ``notifyall`` (default: ``False``)
If ``False`` only ``error`` messages will be sent to the
``notify_store`` methods of ``Cerebro`` and ``Strategy``.
If ``True``, each and every message received from TWS will be notified
- ``_debug`` (default: ``False``)
Print all messages received from TWS to standard output
- ``reconnect`` (default: ``3``)
Number of attempts to try to reconnect after the 1st connection attempt
fails
Set it to a ``-1`` value to keep on reconnecting forever
- ``timeout`` (default: ``3.0``)
Time in seconds between reconnection attemps
- ``timeoffset`` (default: ``True``)
If True, the time obtained from ``reqCurrentTime`` (IB Server time)
will be used to calculate the offset to localtime and this offset will
be used for the price notifications (tickPrice events, for example for
CASH markets) to modify the locally calculated timestamp.
The time offset will propagate to other parts of the ``backtrader``
ecosystem like the **resampling** to align resampling timestamps using
the calculated offset.
- ``timerefresh`` (default: ``60.0``)
Time in seconds: how often the time offset has to be refreshed
- ``indcash`` (default: ``True``)
Manage IND codes as if they were cash for price retrieval
'''
# Set a base for the data requests (historical/realtime) to distinguish the
# id in the error notifications from orders, where the basis (usually
# starting at 1) is set by TWS
REQIDBASE = 0x01000000
BrokerCls = None #getattr(sys.modules["cerebro.strategies." +classname.split('.')[0]], classname.split('.')[1])IBBroker #None # broker class will autoregister
DataCls = None # data class will auto register
params = (
('host', '127.0.0.1'),
('port', 7496),
('clientId', None), # None generates a random clientid 1 -> 2^16
('notifyall', False), # NOT IMPLEMENTED
('_debug', False),
('reconnect', 3), # -1 forever, 0 No, > 0 number of retries
('timeout', 3.0), # timeout between reconnections
('timeoffset', True), # Use offset to server for timestamps if needed
('timerefresh', 60.0), # How often to refresh the timeoffset
('indcash', True), # Treat IND codes as CASH elements
('readonly', False), # Set to True when IB API is in read-only mode
('account', ''), # Main account to receive updates for
)
@classmethod
def getdata(cls, *args, **kwargs):
'''Returns ``DataCls`` with args, kwargs'''
return cls.DataCls(*args, **kwargs)
@classmethod
def getbroker(cls, *args, **kwargs):
'''Returns broker with *args, **kwargs from registered ``BrokerCls``'''
return cls.BrokerCls(*args, **kwargs)
def __init__(self):
super(IBStore, self).__init__()
self._env = None # reference to cerebro for general notifications
self.broker = None # broker instance
self.datas = list() # datas that have registered over start
# self.ccount = 0 # requests to start (from cerebro or datas)
# self._lock_tmoffset = threading.Lock()
# self.tmoffset = timedelta() # to control time difference with server
# # Structures to hold datas requests
# self.qs = collections.OrderedDict() # key: tickerId -> queues
# self.ts = collections.OrderedDict() # key: queue -> tickerId
self.iscash = dict() # tickerIds from cash products (for ex: EUR.JPY)
self.acc_cash = AutoDict() # current total cash per account
self.acc_value = AutoDict() # current total value per account
self.acc_upds = AutoDict() # current account valueinfos per account
self.positions = collections.defaultdict(Position) # actual positions
self.orderid = None # next possible orderid (will be itertools.count)
self.managed_accounts = list() # received via managedAccounts
self.notifs = queue.Queue() # store notifications for cerebro
self.orders = collections.OrderedDict() # orders by order ided
self.opending = collections.defaultdict(list) # pending transmission
self.brackets = dict() # confirmed brackets
self.last_tick = None
# Use the provided clientId or a random one
if self.p.clientId is None:
self.clientId = random.randint(1, pow(2, 16) - 1)
else:
self.clientId = self.p.clientId
if self.p.timeout is None:
self.timeout = 2
else:
self.timeout = self.p.timeout
if self.p.readonly is None:
self.readonly = False
else:
self.readonly = self.p.readonly
if self.p.account is None:
self.account = ""
else:
self.account = self.p.account
if self.p._debug:
util.logToConsole(level=logging.DEBUG)
util.patchAsyncio()
util.startLoop()
self.ib = IB()
self.ib.connect(
host=self.p.host, port=self.p.port,
clientId=self.clientId,
timeout=self.timeout,
readonly=self.readonly,
account=self.account,
)
# This utility key function transforms a barsize into a:
# (Timeframe, Compression) tuple which can be sorted
def keyfn(x):
n, t = x.split()
tf, comp = self._sizes[t]
return (tf, int(n) * comp)
# This utility key function transforms a duration into a:
# (Timeframe, Compression) tuple which can be sorted
def key2fn(x):
n, d = x.split()
tf = self._dur2tf[d]
return (tf, int(n))
# Generate a table of reverse durations
self.revdur = collections.defaultdict(list)
# The table (dict) is a ONE to MANY relation of
# duration -> barsizes
# Here it is reversed to get a ONE to MANY relation of
# barsize -> durations
for duration, barsizes in self._durations.items():
for barsize in barsizes:
self.revdur[keyfn(barsize)].append(duration)
# Once managed, sort the durations according to real duration and not
# to the text form using the utility key above
for barsize in self.revdur:
self.revdur[barsize].sort(key=key2fn)
def start(self, data=None, broker=None):
#self.reconnect(fromstart=True) # reconnect should be an invariant
# Datas require some processing to kickstart data reception
if data is not None:
self._env = data._env
# For datas simulate a queue with None to kickstart co
self.datas.append(data)
# if connection fails, get a fakeation that will force the
# datas to try to reconnect or else bail out
return self.getTickerQueue(start=True)
elif broker is not None:
self.broker = broker
def stop(self):
try:
self.ib.disconnect() # disconnect should be an invariant
except AttributeError:
pass # conn may have never been connected and lack "disconnect"
def get_notifications(self):
'''Return the pending "store" notifications'''
# The background thread could keep on adding notifications. The None
# mark allows to identify which is the last notification to deliver
self.notifs.put(None) # put a mark
notifs = list()
while True:
notif = self.notifs.get()
if notif is None: # mark is reached
break
notifs.append(notif)
return notifs
def managedAccounts(self):
# 1st message in the stream
self.managed_accounts = self.ib.managedAccounts()
# Request time to avoid synchronization issues
self.reqCurrentTime()
def currentTime(self,msg):
if not self.p.timeoffset: # only if requested ... apply timeoffset
return
curtime = datetime.fromtimestamp(float(msg.time))
with self._lock_tmoffset:
self.tmoffset = curtime - datetime.now()
threading.Timer(self.p.timerefresh, self.reqCurrentTime).start()
def timeoffset(self):
with self._lock_tmoffset:
return self.tmoffset
def reqCurrentTime(self):
self.ib.reqCurrentTime()
def nextOrderId(self):
# Get the next ticker using a new request value from TWS
self.orderid = self.ib.client.getReqId()
return self.orderid
def getTickerQueue(self, start=False):
'''Creates ticker/Queue for data delivery to a data feed'''
q = queue.Queue()
if start:
q.put(None)
return q
return q
def getContractDetails(self, contract, maxcount=None):
#cds = list()
cds = self.ib.reqContractDetails(contract)
#cds.append(cd)
if not cds or (maxcount and len(cds) > maxcount):
err = 'Ambiguous contract: none/multiple answers received'
self.notifs.put((err, cds, {}))
return None
return cds
def reqHistoricalDataEx(self, contract, enddate, begindate,
timeframe, compression,
what=None, useRTH=False, tz='',
sessionend=None,
#tickerId=None
):
'''
Extension of the raw reqHistoricalData proxy, which takes two dates
rather than a duration, barsize and date
It uses the IB published valid duration/barsizes to make a mapping and
spread a historical request over several historical requests if needed
'''
# Keep a copy for error reporting purposes
kwargs = locals().copy()
kwargs.pop('self', None) # remove self, no need to report it
if timeframe < TimeFrame.Seconds:
# Ticks are not supported
return self.getTickerQueue(start=True)
if enddate is None:
enddate = datetime.now()
if begindate is None:
duration = self.getmaxduration(timeframe, compression)
if duration is None:
err = ('No duration for historical data request for '
'timeframe/compresison')
self.notifs.put((err, (), kwargs))
return self.getTickerQueue(start=True)
barsize = self.tfcomp_to_size(timeframe, compression)
if barsize is None:
err = ('No supported barsize for historical data request for '
'timeframe/compresison')
self.notifs.put((err, (), kwargs))
return self.getTickerQueue(start=True)
return self.reqHistoricalData(contract=contract, enddate=enddate,
duration=duration, barsize=barsize,
what=what, useRTH=useRTH, tz=tz,
sessionend=sessionend)
# Check if the requested timeframe/compression is supported by IB
durations = self.getdurations(timeframe, compression)
# if not durations: # return a queue and put a None in it
# return self.getTickerQueue(start=True)
# Get or reuse a queue
# if tickerId is None:
# tickerId, q = self.getTickerQueue()
# else:
# tickerId, q = self.reuseQueue(tickerId) # reuse q for old tickerId
# Get the best possible duration to reduce number of requests
duration = None
# for dur in durations:
# intdate = self.dt_plus_duration(begindate, dur)
# if intdate >= enddate:
# intdate = enddate
# duration = dur # begin -> end fits in single request
# break
intdate = begindate
if duration is None: # no duration large enough to fit the request
duration = durations[-1]
# Store the calculated data
# self.histexreq[tickerId] = dict(
# contract=contract, enddate=enddate, begindate=intdate,
# timeframe=timeframe, compression=compression,
# what=what, useRTH=useRTH, tz=tz, sessionend=sessionend)
barsize = self.tfcomp_to_size(timeframe, compression)
if contract.secType in ['CASH', 'CFD']:
#self.iscash[tickerId] = 1 # msg.field code
if not what:
what = 'BID' # default for cash unless otherwise specified
elif contract.secType in ['IND'] and self.p.indcash:
#self.iscash[tickerId] = 4 # msg.field code
pass
what = what or 'TRADES'
q = self.getTickerQueue()
histdata = self.ib.reqHistoricalData(
contract,
intdate.strftime('%Y%m%d %H:%M:%S') + ' GMT',
duration,
barsize,
what,
useRTH,
2) # dateformat 1 for string, 2 for unix time in seconds
for msg in histdata:
q.put(msg)
return q
def reqHistoricalData(self, contract, enddate, duration, barsize,
what=None, useRTH=False, tz='', sessionend=None):
'''Proxy to reqHistorical Data'''
# get a ticker/queue for identification/data delivery
q = self.getTickerQueue()
if contract.secType in ['CASH', 'CFD']:
#self.iscash[tickerId] = True
if not what:
what = 'BID' # TRADES doesn't work
elif what == 'ASK':
#self.iscash[tickerId] = 2
pass
else:
what = what or 'TRADES'
# split barsize "x time", look in sizes for (tf, comp) get tf
#tframe = self._sizes[barsize.split()[1]][0]
# self.histfmt[tickerId] = tframe >= TimeFrame.Days
# self.histsend[tickerId] = sessionend
# self.histtz[tickerId] = tz
histdata = self.ib.reqHistoricalData(
contract,
enddate.strftime('%Y%m%d %H:%M:%S') + ' GMT',
duration,
barsize,
what,
useRTH,
2) # dateformat 1 for string, 2 for unix time in seconds
for msg in histdata:
q.put(msg)
return q
def reqRealTimeBars(self, contract, useRTH=False, duration=5):
'''Creates a request for (5 seconds) Real Time Bars
Params:
- contract: a ib.ext.Contract.Contract intance
- useRTH: (default: False) passed to TWS
- duration: (default: 5) passed to TWS
Returns:
- a Queue the client can wait on to receive a RTVolume instance
'''
# get a ticker/queue for identification/data delivery
q = self.getTickerQueue()
rtb = self.ib.reqRealTimeBars(contract, duration,
'MIDPOINT', useRTH=useRTH)
self.ib.sleep(duration)
for bar in rtb:
q.put(bar)
return q
def reqMktData(self, contract, what=None):
'''Creates a MarketData subscription
Params:
- contract: a ib.ext.Contract.Contract intance
Returns:
- a Queue the client can wait on to receive a RTVolume instance
'''
# get a ticker/queue for identification/data delivery
q = self.getTickerQueue()
ticks = '233' # request RTVOLUME tick delivered over tickString
if contract.secType in ['CASH', 'CFD']:
#self.iscash[tickerId] = True
ticks = '' # cash markets do not get RTVOLUME
if what == 'ASK':
#self.iscash[tickerId] = 2
pass
# q.put(None) # to kickstart backfilling
# Can request 233 also for cash ... nothing will arrive
md = MktData()
q_ticks = queue.Queue()
util.run(md.update_ticks(self.ib, contract, ticks, q_ticks))
while not q_ticks.empty():
ticker = q_ticks.get()
for tick in ticker.ticks:
# https://interactivebrokers.github.io/tws-api/tick_types.html
if tick != self.last_tick: #last price
#print(str(tick.time) +" >> " + str(tick.price))
self.last_tick = tick
q.put(tick)
return q
# The _durations are meant to calculate the needed historical data to
# perform backfilling at the start of a connetion or a connection is lost.
# Using a timedelta as a key allows to quickly find out which bar size
# bar size (values in the tuples int the dict) can be used.
_durations = dict([
# 60 seconds - 1 min
('60 S',
('1 secs', '5 secs', '10 secs', '15 secs', '30 secs',
'1 min')),
# 120 seconds - 2 mins
('120 S',
('1 secs', '5 secs', '10 secs', '15 secs', '30 secs',
'1 min', '2 mins')),
# 180 seconds - 3 mins
('180 S',
('1 secs', '5 secs', '10 secs', '15 secs', '30 secs',
'1 min', '2 mins', '3 mins')),
# 300 seconds - 5 mins
('300 S',
('1 secs', '5 secs', '10 secs', '15 secs', '30 secs',
'1 min', '2 mins', '3 mins', '5 mins')),
# 600 seconds - 10 mins
('600 S',
('1 secs', '5 secs', '10 secs', '15 secs', '30 secs',
'1 min', '2 mins', '3 mins', '5 mins', '10 mins')),
# 900 seconds - 15 mins
('900 S',
('1 secs', '5 secs', '10 secs', '15 secs', '30 secs',
'1 min', '2 mins', '3 mins', '5 mins', '10 mins', '15 mins')),
# 1200 seconds - 20 mins
('1200 S',
('1 secs', '5 secs', '10 secs', '15 secs', '30 secs',
'1 min', '2 mins', '3 mins', '5 mins', '10 mins', '15 mins',
'20 mins')),
# 1800 seconds - 30 mins
('1800 S',
('1 secs', '5 secs', '10 secs', '15 secs', '30 secs',
'1 min', '2 mins', '3 mins', '5 mins', '10 mins', '15 mins',
'20 mins', '30 mins')),
# 3600 seconds - 1 hour
('3600 S',
('5 secs', '10 secs', '15 secs', '30 secs',
'1 min', '2 mins', '3 mins', '5 mins', '10 mins', '15 mins',
'20 mins', '30 mins',
'1 hour')),
# 7200 seconds - 2 hours
('7200 S',
('5 secs', '10 secs', '15 secs', '30 secs',
'1 min', '2 mins', '3 mins', '5 mins', '10 mins', '15 mins',
'20 mins', '30 mins',
'1 hour', '2 hours')),
# 10800 seconds - 3 hours
('10800 S',
('10 secs', '15 secs', '30 secs',
'1 min', '2 mins', '3 mins', '5 mins', '10 mins', '15 mins',
'20 mins', '30 mins',
'1 hour', '2 hours', '3 hours')),
# 14400 seconds - 4 hours
('14400 S',
('15 secs', '30 secs',
'1 min', '2 mins', '3 mins', '5 mins', '10 mins', '15 mins',
'20 mins', '30 mins',
'1 hour', '2 hours', '3 hours', '4 hours')),
# 28800 seconds - 8 hours
('28800 S',
('30 secs',
'1 min', '2 mins', '3 mins', '5 mins', '10 mins', '15 mins',
'20 mins', '30 mins',
'1 hour', '2 hours', '3 hours', '4 hours', '8 hours')),
# 1 days
('1 D',
('1 min', '2 mins', '3 mins', '5 mins', '10 mins', '15 mins',
'20 mins', '30 mins',
'1 hour', '2 hours', '3 hours', '4 hours', '8 hours',
'1 day')),
# 2 days
('2 D',
('2 mins', '3 mins', '5 mins', '10 mins', '15 mins',
'20 mins', '30 mins',
'1 hour', '2 hours', '3 hours', '4 hours', '8 hours',
'1 day')),
# 1 weeks
('1 W',
('3 mins', '5 mins', '10 mins', '15 mins',
'20 mins', '30 mins',
'1 hour', '2 hours', '3 hours', '4 hours', '8 hours',
'1 day', '1 W')),
# 2 weeks
('2 W',
('15 mins', '20 mins', '30 mins',
'1 hour', '2 hours', '3 hours', '4 hours', '8 hours',
'1 day', '1 W')),
# 1 months
('1 M',
('30 mins',
'1 hour', '2 hours', '3 hours', '4 hours', '8 hours',
'1 day', '1 W', '1 M')),
# 2+ months
('2 M', ('1 day', '1 W', '1 M')),
('3 M', ('1 day', '1 W', '1 M')),
('4 M', ('1 day', '1 W', '1 M')),
('5 M', ('1 day', '1 W', '1 M')),
('6 M', ('1 day', '1 W', '1 M')),
('7 M', ('1 day', '1 W', '1 M')),
('8 M', ('1 day', '1 W', '1 M')),
('9 M', ('1 day', '1 W', '1 M')),
('10 M', ('1 day', '1 W', '1 M')),
('11 M', ('1 day', '1 W', '1 M')),
# 1+ years
('1 Y', ('1 day', '1 W', '1 M')),
])
# Sizes allow for quick translation from bar sizes above to actual
# timeframes to make a comparison with the actual data
_sizes = {
'secs': (TimeFrame.Seconds, 1),
'min': (TimeFrame.Minutes, 1),
'mins': (TimeFrame.Minutes, 1),
'hour': (TimeFrame.Minutes, 60),
'hours': (TimeFrame.Minutes, 60),
'day': (TimeFrame.Days, 1),
'W': (TimeFrame.Weeks, 1),
'M': (TimeFrame.Months, 1),
}
_dur2tf = {
'S': TimeFrame.Seconds,
'D': TimeFrame.Days,
'W': TimeFrame.Weeks,
'M': TimeFrame.Months,
'Y': TimeFrame.Years,
}
def getdurations(self, timeframe, compression):
key = (timeframe, compression)
if key not in self.revdur:
return []
return self.revdur[key]
def getmaxduration(self, timeframe, compression):
key = (timeframe, compression)
try:
return self.revdur[key][-1]
except (KeyError, IndexError):
pass
return None
def tfcomp_to_size(self, timeframe, compression):
if timeframe == TimeFrame.Months:
return '{} M'.format(compression)
if timeframe == TimeFrame.Weeks:
return '{} W'.format(compression)
if timeframe == TimeFrame.Days:
if not compression % 7:
return '{} W'.format(compression // 7)
return '{} day'.format(compression)
if timeframe == TimeFrame.Minutes:
if not compression % 60:
hours = compression // 60
return ('{} hour'.format(hours)) + ('s' * (hours > 1))
return ('{} min'.format(compression)) + ('s' * (compression > 1))
if timeframe == TimeFrame.Seconds:
return '{} secs'.format(compression)
# Microseconds or ticks
return None
def dt_plus_duration(self, dt, duration):
size, dim = duration.split()
size = int(size)
if dim == 'S':
return dt + timedelta(seconds=size)
if dim == 'D':
return dt + timedelta(days=size)
if dim == 'W':
return dt + timedelta(days=size * 7)
if dim == 'M':
month = dt.month - 1 + size # -1 to make it 0 based, readd below
years, month = divmod(month, 12)
return dt.replace(year=dt.year + years, month=month + 1)
if dim == 'Y':
return dt.replace(year=dt.year + size)
return dt # could do nothing with it ... return it intact
# def histduration(self, dt1, dt2):
# # Given two dates calculates the smallest possible duration according
# # to the table from the Historical Data API limitations provided by IB
# #
# # Seconds: 'x S' (x: [60, 120, 180, 300, 600, 900, 1200, 1800, 3600,
# # 7200, 10800, 14400, 28800])
# # Days: 'x D' (x: [1, 2]
# # Weeks: 'x W' (x: [1, 2])
# # Months: 'x M' (x: [1, 11])
# # Years: 'x Y' (x: [1])
# td = dt2 - dt1 # get a timedelta for calculations
# # First: array of secs
# tsecs = td.total_seconds()
# secs = [60, 120, 180, 300, 600, 900, 1200, 1800, 3600, 7200, 10800,
# 14400, 28800]
# idxsec = bisect.bisect_left(secs, tsecs)
# if idxsec < len(secs):
# return '{} S'.format(secs[idxsec])
# tdextra = bool(td.seconds or td.microseconds) # over days/weeks
# # Next: 1 or 2 days
# days = td.days + tdextra
# if td.days <= 2:
# return '{} D'.format(days)
# # Next: 1 or 2 weeks
# weeks, d = divmod(td.days, 7)
# weeks += bool(d or tdextra)
# if weeks <= 2:
# return '{} W'.format(weeks)
# # Get references to dt components
# y2, m2, d2 = dt2.year, dt2.month, dt2.day
# y1, m1, d1 = dt1.year, dt1.month, dt2.day
# H2, M2, S2, US2 = dt2.hour, dt2.minute, dt2.second, dt2.microsecond
# H1, M1, S1, US1 = dt1.hour, dt1.minute, dt1.second, dt1.microsecond
# # Next: 1 -> 11 months (11 incl)
# months = (y2 * 12 + m2) - (y1 * 12 + m1) + (
# (d2, H2, M2, S2, US2) > (d1, H1, M1, S1, US1))
# if months <= 1: # months <= 11
# return '1 M' # return '{} M'.format(months)
# elif months <= 11:
# return '2 M' # cap at 2 months to keep the table clean
# # Next: years
# # y = y2 - y1 + (m2, d2, H2, M2, S2, US2) > (m1, d1, H1, M1, S1, US1)
# # return '{} Y'.format(y)
# return '1 Y' # to keep the table clean
def makecontract(self, symbol, sectype, exch, curr,
expiry='', strike=0.0, right='', mult=1):
'''returns a contract from the parameters without check'''
contract = Contract()
contract.symbol = symbol
contract.secType = sectype
contract.exchange = exch
if curr:
contract.currency = curr
if sectype in ['FUT', 'OPT', 'FOP']:
contract.lastTradeDateOrContractMonth = expiry
if sectype in ['OPT', 'FOP']:
contract.strike = strike
contract.right = right
if mult:
contract.multiplier = mult
return contract
def cancelOrder(self, orderid):
'''Proxy to cancelOrder'''
self.ib.cancelOrder(orderid)
def placeOrder(self, orderid, contract, order):
'''Proxy to placeOrder'''
trade = self.ib.placeOrder(contract, order)
while not trade.isDone():
self.ib.waitOnUpdate()
return trade
def reqTrades(self):
'''Proxy to Trades'''
return self.ib.trades()
def reqPositions(self):
'''Proxy to reqPositions'''
return self.ib.reqPositions()
def getposition(self, contract, clone=False):
# Lock access to the position dicts. This is called from main thread
# and updates could be happening in the background
#with self._lock_pos:
position = self.positions[contract.conId]
if clone:
return copy(position)
return position
def reqAccountUpdates(self, subscribe=True, account=None):
'''Proxy to reqAccountUpdates
If ``account`` is ``None``, wait for the ``managedAccounts`` message to
set the account codes
'''
if account is None:
#self._event_managed_accounts.wait()
self.managedAccounts()
account = self.managed_accounts[0]
#self.ib.reqAccountUpdates(subscribe, bytes(account))
self.updateAccountValue()
def updateAccountValue(self):
# Lock access to the dicts where values are updated. This happens in a
# sub-thread and could kick it at anytime
#with self._lock_accupd:
#if self.connected():
ret = self.ib.accountValues()
for msg in ret:
try:
value = float(msg.value)
except ValueError:
value = msg.value
self.acc_upds[msg.account][msg.tag][msg.currency] = value
if msg.tag == 'NetLiquidation':
# NetLiquidationByCurrency and currency == 'BASE' is the same
self.acc_value[msg.account] = value
elif msg.tag == 'TotalCashBalance' and msg.currency == 'BASE':
self.acc_cash[msg.account] = value
def get_acc_values(self, account=None):
'''Returns all account value infos sent by TWS during regular updates
Waits for at least 1 successful download
If ``account`` is ``None`` then a dictionary with accounts as keys will
be returned containing all accounts
If account is specified or the system has only 1 account the dictionary
corresponding to that account is returned
'''
# Wait for at least 1 account update download to have been finished
# before the account infos can be returned to the calling client
# if self.connected():
# self._event_accdownload.wait()
# Lock access to acc_cash to avoid an event intefering
#with self._updacclock:
if account is None:
# wait for the managedAccount Messages
# if self.connected():
# self._event_managed_accounts.wait()
if not self.managed_accounts:
return self.acc_upds.copy()
elif len(self.managed_accounts) > 1:
return self.acc_upds.copy()
# Only 1 account, fall through to return only 1
account = self.managed_accounts[0]
try:
return self.acc_upds[account].copy()
except KeyError:
pass
return self.acc_upds.copy()
def get_acc_value(self, account=None):
'''Returns the net liquidation value sent by TWS during regular updates
Waits for at least 1 successful download
If ``account`` is ``None`` then a dictionary with accounts as keys will
be returned containing all accounts
If account is specified or the system has only 1 account the dictionary
corresponding to that account is returned
'''
# Wait for at least 1 account update download to have been finished
# before the value can be returned to the calling client
# if self.connected():
# self._event_accdownload.wait()
# Lock access to acc_cash to avoid an event intefering
#with self._lock_accupd:
if account is None:
# wait for the managedAccount Messages
# if self.connected():
# self._event_managed_accounts.wait()
if not self.managed_accounts:
return float()
elif len(self.managed_accounts) > 1:
return sum(self.acc_value.values())
# Only 1 account, fall through to return only 1
account = self.managed_accounts[0]
try:
return self.acc_value[account]
except KeyError:
pass
return float()
def get_acc_cash(self, account=None):
'''Returns the total cash value sent by TWS during regular updates
Waits for at least 1 successful download
If ``account`` is ``None`` then a dictionary with accounts as keys will
be returned containing all accounts
If account is specified or the system has only 1 account the dictionary
corresponding to that account is returned
'''
# Wait for at least 1 account update download to have been finished
# before the cash can be returned to the calling client'
# if self.connected():
# self._event_accdownload.wait()
# result = [v for v in self.ib.accountValues() \
# if v.tag == 'TotalCashBalance' and v.currency == 'BASE']
# Lock access to acc_cash to avoid an event intefering
#with self._lock_accupd:
if account is None:
#wait for the managedAccount Messages
# if self.connected():
# self._event_managed_accounts.wait()
if not self.managed_accounts:
return float()
elif len(self.managed_accounts) > 1:
return sum(self.acc_cash.values())
# Only 1 account, fall through to return only 1
account = self.managed_accounts[0]
try:
return self.acc_cash[account]
except KeyError:
pass
class SpectreDB:
def __init__(self,
json_path=None,
key_name=None,
table_name=None,
use_IB=False):
self.json_path = json_path
self.key_name = key_name
if table_name:
self.table_name = table_name
SpectreDB.instantiate_engine(self)
if use_IB:
from ib_insync import IB, Forex, util
# Need to have IB Gateway open to execute
# util.startLoop() # uncomment this line when in a notebook
self.ib = IB()
self.ib.connect('127.0.0.1', 7497, clientId=1)
def instantiate_engine(self):
'''
:return engine: sqlalchemy engine for colornoun database
:return conn: sqlalchemy engine connection for colornoun database
'''
# Connecting to mysql by providing a sqlachemy engine
with open(self.json_path) as secrets_file:
secrets = json.load(secrets_file)
secrets_file.close()
key_dict = secrets[self.key_name]
engine_string = 'mysql+pymysql://{MYSQL_USER}:{MYSQL_PASSWORD}@{MYSQL_HOST_IP}:{MYSQL_PORT}/{MYSQL_DATABASE}'
self.engine = sa.create_engine(engine_string.format(
MYSQL_USER=key_dict['MYSQL_USER'],
MYSQL_PASSWORD=key_dict['MYSQL_PASSWORD'],
MYSQL_HOST_IP=key_dict['MYSQL_HOST_IP'],
MYSQL_PORT=key_dict['MYSQL_PORT'],
MYSQL_DATABASE=key_dict['MYSQL_DATABASE']),
echo=False)
self.conn = self.engine.connect()
def parse_df(df, num_rows=10000):
'''
Meant to parse pandas dataframe before upload. SQL connection times out if uploading more than
25k rows.
:param df: pandas datatframe
:param num_rows: number of rows for dataframe to be split into
:return list of datafraes of size n_rows
'''
df_list = list()
num_split = len(df) // num_rows + 1
for i in range(num_split):
df_list.append(df[i * num_rows:(i + 1) * num_rows])
return df_list
def filter_duplicates(self, upload_df, table_name):
'''
:param upload_df: pandas dataframe with column 'date'
:param table_name: string
return: upload_df without columns already in table
'''
#Query dates in table of that time frame
#Filter upload df of overlaping timestamps
start = str(upload_df['date'].min())
end = str(upload_df['date'].max())
date_df = self.query_by_date(start, end, table_name, fields=['date'])
out_df = upload_df.append(date_df, ignore_index=True, sort=False)
out_df.drop_duplicates(['date'], keep=False, inplace=True)
return out_df
def upload_df_to_db(self, df, table_name=None, date_encode=False):
'''
This is to help the parsing and uploading of pandas dataframes
:param df: pandas dataframe
:param table_name: name of table on database
'''
if not table_name:
table_name = self.table_name
if date_encode:
df = self.date_encoder(df)
df.drop_duplicates(['date'], inplace=True)
if sa.inspect(self.engine).get_table_names().count(table_name) > 0:
df = self.filter_duplicates(df, table_name)
df_list = SpectreDB.parse_df(df)
for upload_df in df_list:
try:
upload_df.to_sql(table_name,
con=self.engine,
index=False,
if_exists='append')
#print('Success')
except Exception as e:
#print('Failure')
print(e)
return 1
def date_encoder(self, df):
'''
input df: pandas dataframe with column 'date' in timezone-aware datetime.datetime
example('2019-08-28 21:15:00+00:00')
output: pandas dataframe with additional columns ['weekday', 'day_of_year'
'year', 'month', 'day', 'hour', 'minute']
'''
df['date'] = pd.to_datetime(df['date'])
df['weekday'] = df['date'].apply(lambda x: x.weekday())
df['day_of_year'] = df['date'].apply(lambda x: x.dayofyear)
df['year'] = df['date'].apply(lambda x: x.year)
df['month'] = df['date'].apply(lambda x: x.month)
df['day_of_month'] = df['date'].apply(lambda x: x.day)
df['hour'] = df['date'].apply(lambda x: x.hour)
df['minute'] = df['date'].apply(lambda x: x.minute)
return df
def drop_table(self, table_name=None):
if not table_name:
table_name = self.table_name
self.conn.execute("DROP TABLE {}".format(table_name))
def query_by_date(self,
start,
end,
table_name=None,
fields=['date', 'close']):
'''
This returns a dataframe of the requested data
:param start: string in datetime format, eg. '2019-07-18 17:15:00'
:param end: same format as start
:param data: list of strings
:return: pandas dataframe
'''
if not table_name:
table_name = self.table_name
# query data and save file
fields.append('date')
#make sure no duplicate entries in data list
fields = list(set(fields))
out_df = pd.DataFrame()
stmt = 'SELECT ' + ','.join(map(str, list(fields)))
stmt += ''' FROM `{}` '''.format(table_name)
stmt += '''WHERE `date` BETWEEN '{}' AND '{}' '''.format(start, end)
return pd.read_sql(stmt, self.conn)
def parse_datetime(self, start, end, freq):
'''
:param start: string in datetime format, eg. '2019-07-18 17:15:00'
:param end: same format as start
:param freq: string, format '# timeUnit' e.g. '1 hour'
returns list of tuples (end[datetime], duration[str]) for requesting IB data
'''
#Change the string declared frequency to timedelta object
freq_unit = freq.split()[1][0]
if freq_unit == 's':
max_dur = dt.timedelta(days=1)
elif freq_unit == 'm':
if (freq.split()[1][1]) == 'i':
max_dur = dt.timedelta(weeks=1)
else:
max_dur = dt.timedelta(weeks=104)
elif freq_unit == 'h':
max_dur = dt.timedelta(weeks=4)
elif freq_unit == 'd':
max_dur = dt.timedelta(weeks=24)
elif freq_unit == 'w':
max_dur = dt.timedelta(weeks=104)
else:
raise (RuntimeError(''' Incorrect input for 'freq' argument'''))
out_list = []
start = dt.datetime.strptime(start + ' +0000', '%Y-%m-%d %H:%M:%S %z')
end = dt.datetime.strptime(end + ' +0000', '%Y-%m-%d %H:%M:%S %z')
t_diff = end - start
window_count = t_diff // max_dur
max_dur_str = '{} D'.format(max(1, max_dur.days))
while window_count > 0:
out_list.append((end, max_dur_str))
end -= max_dur
window_count -= 1
leftover_time = end - start
out_list.append((end, '{} D'.format(max(1, 1 + leftover_time.days))))
return out_list
def pull_from_IB(self, start, end, market='EURUSD', freq='1 hour'):
'''
Note, all times should be UTC (aka Zulu, Z, GMT)
:param start: string in datetime format, eg. '2019-07-18 17:15:00'
:param end: same format as start
:param freq: Must be one of: ‘1 secs’, ‘5 secs’, ‘10 secs’ 15 secs’,
‘30 secs’, ‘1 min’, ‘2 mins’, ‘3 mins’, ‘5 mins’, ‘10 mins’,
‘15 mins’, ‘20 mins’, ‘30 mins’, ‘1 hour’, ‘2 hours’,
‘3 hours’, ‘4 hours’, ‘8 hours’, ‘1 day’, ‘1 week’,
‘1 month’ (see IB Docs)
'''
#Parse the requested window so API calls are not as lengthy
time_list = self.parse_datetime(start, end, freq)
df_list = []
contract = Forex(market)
for time_pair in time_list:
'''
useRTH (bool) – If True then only show data from within Regular Trading Hours,
if False then show all data.
formatDate (int) – For an intraday request setting to 2 will cause the returned
date fields to be timezone-aware datetime.datetime with UTC timezone,
instead of local timezone as used by TWS.
'''
bars = self.ib.reqHistoricalData(contract,
endDateTime=time_pair[0],
durationStr=time_pair[1],
barSizeSetting=freq,
whatToShow='MIDPOINT',
useRTH=True)
# convert to pandas dataframe:
df = util.df(bars)
#drop columns ['barCount', 'average']
df_list.append(df[['date', 'open', 'high', 'low', 'close']].copy())
return df_list
def update_from_IB(self, start, end, market='EURUSD', freq='1 hour'):
df_list = self.pull_from_IB(start, end, market, freq)
for df in df_list:
self.upload_df_to_db(df, date_encode=True)
return 1
