chore: import upstream snapshot with attribution
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wehub-resource-sync
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# QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
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# Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from AlgorithmImports import *
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### <summary>
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### Live Trading Functionality Demonstration algorithm including SMS, Email and Web hook notifications.
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### </summary>
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### <meta name="tag" content="live trading" />
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### <meta name="tag" content="alerts" />
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### <meta name="tag" content="sms alerts" />
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### <meta name="tag" content="web hooks" />
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### <meta name="tag" content="email alerts" />
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### <meta name="tag" content="runtime statistics" />
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class LiveTradingFeaturesAlgorithm(QCAlgorithm):
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### Initialize the Algorithm and Prepare Required Data
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def initialize(self):
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self.set_start_date(2013, 10, 7)
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self.set_end_date(2013, 10, 11)
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self.set_cash(25000)
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##Equity Data for US Markets
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self.add_security(SecurityType.EQUITY, 'IBM', Resolution.SECOND)
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##FOREX Data for Weekends: 24/6
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self.add_security(SecurityType.FOREX, 'EURUSD', Resolution.MINUTE)
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##Custom/Bitcoin Live Data: 24/7
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self.add_data(Bitcoin, 'BTC', Resolution.SECOND, TimeZones.UTC)
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##if the algorithm is connected to the brokerage
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self.is_connected = True
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### Raises the data event
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def on_data(self, data):
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if (not self.portfolio['IBM'].hold_stock) and data.contains_key('IBM'):
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quantity = int(np.floor(self.portfolio.margin_remaining / data['IBM'].close))
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self.market_order('IBM',quantity)
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self.debug('Purchased IBM on ' + str(self.time.strftime("%m/%d/%Y")))
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self.notify.email("myemail@gmail.com", "Test", "Test Body", "test attachment")
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if "BTC" in data:
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btcData = data['BTC']
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if self.live_mode:
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self.set_runtime_statistic('BTC', str(btcData.close))
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if not self.portfolio.hold_stock:
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self.market_order('BTC', 100)
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##Send a notification email/SMS/web request on events:
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self.notify.email("myemail@gmail.com", "Test", "Test Body", "test attachment")
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self.notify.sms("+11233456789", str(btcData.time) + ">> Test message from live BTC server.")
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self.notify.web("http://api.quantconnect.com", str(btcData.time) + ">> Test data packet posted from live BTC server.")
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self.notify.ftp("ftp.quantconnect.com", "username", "password", "path/to/file.txt",
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str(btcData.time) + ">> Test file from live BTC server.")
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self.notify.sftp("ftp.quantconnect.com", "username", "password", "path/to/file.txt",
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str(btcData.time) + ">> Test file from live BTC server.")
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self.notify.sftp("ftp.quantconnect.com", "username", "privatekey", "optionalprivatekeypassphrase", "path/to/file.txt",
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str(btcData.time) + ">> Test file from live BTC server.")
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# Brokerage message event handler. This method is called for all types of brokerage messages.
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def on_brokerage_message(self, message_event):
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self.debug(f"Brokerage meesage received - {message_event.to_string()}")
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# Brokerage disconnected event handler. This method is called when the brokerage connection is lost.
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def on_brokerage_disconnect(self):
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self.is_connected = False
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self.debug(f"Brokerage disconnected!")
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# Brokerage reconnected event handler. This method is called when the brokerage connection is restored after a disconnection.
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def on_brokerage_reconnect(self):
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self.is_connected = True
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self.debug(f"Brokerage reconnected!")
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###Custom Data Type: Bitcoin data from Quandl - http://www.quandl.com/help/api-for-bitcoin-data
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class Bitcoin(PythonData):
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def get_source(self, config, date, is_live_mode):
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if is_live_mode:
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return SubscriptionDataSource("https://www.bitstamp.net/api/ticker/", SubscriptionTransportMedium.REST)
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return SubscriptionDataSource("https://www.quandl.com/api/v3/datasets/BCHARTS/BITSTAMPUSD.csv?order=asc", SubscriptionTransportMedium.REMOTE_FILE)
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def reader(self, config, line, date, is_live_mode):
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coin = Bitcoin()
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coin.symbol = config.symbol
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if is_live_mode:
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# Example Line Format:
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# {"high": "441.00", "last": "421.86", "timestamp": "1411606877", "bid": "421.96", "vwap": "428.58", "volume": "14120.40683975", "low": "418.83", "ask": "421.99"}
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try:
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live_btc = json.loads(line)
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# If value is zero, return None
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value = live_btc["last"]
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if value == 0: return None
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coin.time = datetime.now()
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coin.value = value
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coin["Open"] = float(live_btc["open"])
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coin["High"] = float(live_btc["high"])
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coin["Low"] = float(live_btc["low"])
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coin["Close"] = float(live_btc["last"])
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coin["Ask"] = float(live_btc["ask"])
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coin["Bid"] = float(live_btc["bid"])
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coin["VolumeBTC"] = float(live_btc["volume"])
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coin["WeightedPrice"] = float(live_btc["vwap"])
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return coin
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except ValueError:
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# Do nothing, possible error in json decoding
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return None
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# Example Line Format:
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# Date Open High Low Close Volume (BTC) Volume (Currency) Weighted Price
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# 2011-09-13 5.8 6.0 5.65 5.97 58.37138238, 346.0973893944 5.929230648356
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if not (line.strip() and line[0].isdigit()): return None
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try:
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data = line.split(',')
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coin.time = datetime.strptime(data[0], "%Y-%m-%d")
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coin.value = float(data[4])
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coin["Open"] = float(data[1])
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coin["High"] = float(data[2])
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coin["Low"] = float(data[3])
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coin["Close"] = float(data[4])
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coin["VolumeBTC"] = float(data[5])
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coin["VolumeUSD"] = float(data[6])
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coin["WeightedPrice"] = float(data[7])
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return coin
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except ValueError:
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# Do nothing, possible error in json decoding
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return None
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