I've been having problems getting custom data into my own algo so I went back to the docuemented example (CustomDataBitcoinAlgorithm.py) and made a small change that illustrates the problem. I'm curious if this is a bug in QC or if I'm doing something wrong...

Using the CustomDataBitcoinAlgorithm.py example, all i'm trying to do is access one of the other fields from the custom data (VolumeBTC instead of Close). When I do, I get this runtime error:

Runtime Error: AttributeError : 'Data' object has no attribute 'VolumeBTC' at OnData in main.py:line 52 AttributeError : 'Data' object has no attribute 'VolumeBTC' (Open Stacktrace)

Instead of attempting to access:


I'm attempting to access:


Any idea what's going on?


BTW, here's the entire algo (with the only real change on line 52:

# 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 decimal 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 volumeBTC = data["BTC"].VolumeBTC self.Debug("volumeBTC: {0} {1}".format(datetime.now(), volumeBTC)) # 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("http://www.quandl.com/api/v1/datasets/BCHARTS/BITSTAMPUSD.csv?sort_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 = decimal.Decimal(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 = decimal.Decimal(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