Overall Statistics
# 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,
# See the License for the specific language governing permissions and
# limitations under the License.

from clr import AddReference

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))

        # 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
        # Uncommment this line for the error
        #volumeBTC = data["BTC"].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))
        #self.Debug("volumeBTC: {0} {1}".format(datetime.now(), volumeBTC))

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"}
                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

            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