Hi -- 

I'm trying to find a working example of live trading with a custom data feed. I've found two examples using Bitcoin on Github. These seem to be the only examples I can find that employ custom data with live trading. Backtesting works as expected. 

But when these examples are used in live trading, no trades are executed. No errors. 

I've scoured this forum, and I haven't found any other examples using custom data with live trading. Are there any I missed? 

And since neither example is working, I have to wonder: is the problem with the examples, or is custom data not working on live at all?

Thanks!

https://github.com/QuantConnect/Lean/blob/master/Algorithm.Python/CustomDataBitcoinAlgorithm.py

https://github.com/QuantConnect/Lean/blob/master/Algorithm.Python/CustomDataRegressionAlgorithm.py

# QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
# Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from clr import AddReference
AddReference("System")
AddReference("QuantConnect.Algorithm")
AddReference("QuantConnect.Common")

from System import *
from QuantConnect import *
from QuantConnect.Algorithm import *
from QuantConnect.Data import SubscriptionDataSource
from QuantConnect.Python import PythonData

from datetime import date, timedelta, datetime
import numpy as np
import json

### <summary>
### Demonstration of using an external custom datasource. LEAN Engine is incredibly flexible and allows you to define your own data source.
### This includes any data source which has a TIME and VALUE. These are the *only* requirements. To demonstrate this we're loading in "Bitcoin" data.
### </summary>
### <meta name="tag" content="using data" />
### <meta name="tag" content="custom data" />
### <meta name="tag" content="crypto" />
class CustomDataBitcoinAlgorithm(QCAlgorithm):

def Initialize(self):
self.SetStartDate(2011, 9, 13)
self.SetEndDate(datetime.now().date() - timedelta(1))
self.SetCash(100000)

# Define the symbol and "type" of our generic data:
self.AddData(Bitcoin, "BTC")


def OnData(self, data):
if not data.ContainsKey("BTC"): return

close = data["BTC"].Close

# If we don't have any weather "SHARES" -- invest"
if not self.Portfolio.Invested:
# Weather used as a tradable asset, like stocks, futures etc.
self.SetHoldings("BTC", 1)
self.Debug("Buying BTC 'Shares': BTC: {0}".format(close))

self.Debug("Time: {0} {1}".format(datetime.now(), close))


class Bitcoin(PythonData):
'''Custom Data Type: Bitcoin data from Quandl - http://www.quandl.com/help/api-for-bitcoin-data'''

def GetSource(self, config, date, isLiveMode):
if isLiveMode:
return SubscriptionDataSource("https://www.bitstamp.net/api/ticker/", SubscriptionTransportMedium.Rest);

#return "http://my-ftp-server.com/futures-data-" + date.ToString("Ymd") + ".zip";
# OR simply return a fixed small data file. Large files will slow down your backtest
return SubscriptionDataSource("https://www.quandl.com/api/v3/datasets/BCHARTS/BITSTAMPUSD.csv?order=asc", SubscriptionTransportMedium.RemoteFile);


def Reader(self, config, line, date, isLiveMode):
coin = Bitcoin()
coin.Symbol = config.Symbol

if isLiveMode:
# Example Line Format:
# {"high": "441.00", "last": "421.86", "timestamp": "1411606877", "bid": "421.96", "vwap": "428.58", "volume": "14120.40683975", "low": "418.83", "ask": "421.99"}
try:
liveBTC = json.loads(line)

# If value is zero, return None
value = liveBTC["last"]
if value == 0: return None

coin.Time = datetime.now()
coin.Value = value
coin["Open"] = float(liveBTC["open"])
coin["High"] = float(liveBTC["high"])
coin["Low"] = float(liveBTC["low"])
coin["Close"] = float(liveBTC["last"])
coin["Ask"] = float(liveBTC["ask"])
coin["Bid"] = float(liveBTC["bid"])
coin["VolumeBTC"] = float(liveBTC["volume"])
coin["WeightedPrice"] = float(liveBTC["vwap"])
return coin
except ValueError:
# Do nothing, possible error in json decoding
return None

# Example Line Format:
# Date Open High Low Close Volume (BTC) Volume (Currency) Weighted Price
# 2011-09-13 5.8 6.0 5.65 5.97 58.37138238, 346.0973893944 5.929230648356
if not (line.strip() and line[0].isdigit()): return None

try:
data = line.split(',')

# If value is zero, return None
value = data[4]
if value == 0: return None

coin.Time = datetime.strptime(data[0], "%Y-%m-%d")
coin.Value = value
coin["Open"] = float(data[1])
coin["High"] = float(data[2])
coin["Low"] = float(data[3])
coin["Close"] = float(data[4])
coin["VolumeBTC"] = float(data[5])
coin["VolumeUSD"] = float(data[6])
coin["WeightedPrice"] = float(data[7])
return coin;

except ValueError:
# Do nothing, possible error in json decoding
return None

 

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