# 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 AlgorithmImports import *
import pytz

### <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.set_start_date(2011, 9, 13)
 self.set_end_date(datetime.now().date() - timedelta(1))
 self.set_cash(100000)

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


 def on_data(self, data):
 if not data.contains_key("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.
 # It's only OK to use SetHoldings with crypto when using custom data. When trading with built-in crypto data, 
 # use the cashbook. Reference https://github.com/QuantConnect/Lean/blob/master/Algorithm.python/BasicTemplateCryptoAlgorithm.py 
 self.set_holdings("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 get_source(self, config, date, is_live_mode):
 if is_live_mode:
 return SubscriptionDataSource("https://www.bitstamp.net/api/ticker/", SubscriptionTransportMedium.REST)

 #return "http://my-ftp-server.com/futures-data-" + date.to_string("Ymd") + ".zip"
 # OR simply return a fixed small data file. Large files will slow down your backtest
 return SubscriptionDataSource("https://www.quantconnect.com/api/v2/proxy/quandl/api/v3/datasets/BCHARTS/BITSTAMPUSD.csv?order=asc&api_key=WyAazVXnq7ATy_fefTqm", SubscriptionTransportMedium.REMOTE_FILE)


 def reader(self, config, line, date, is_live_mode):
 coin = Bitcoin()
 coin.symbol = config.symbol

 if is_live_mode:
 # 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:
 live_btc = json.loads(line)

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

 coin.end_time = datetime.now(pytz.timezone(str(config.exchange_time_zone)))
 coin.value = value
 coin["Open"] = float(live_btc["open"])
 coin["High"] = float(live_btc["high"])
 coin["Low"] = float(live_btc["low"])
 coin["Close"] = float(live_btc["last"])
 coin["Ask"] = float(live_btc["ask"])
 coin["Bid"] = float(live_btc["bid"])
 coin["VolumeBTC"] = float(live_btc["volume"])
 coin["WeightedPrice"] = float(live_btc["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年09月13日 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.end_time = coin.time + timedelta(days=1)
 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