# 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 *

### <summary>
### This algorithm shows how to grab symbols from an external api each day
### and load data using the universe selection feature. In this example we
### define a custom data type for the NYSE top gainers and then short the
### top 2 gainers each day
### </summary>
### <meta name="tag" content="using data" />
### <meta name="tag" content="universes" />
### <meta name="tag" content="custom universes" />
class CustomDataUniverseAlgorithm(QCAlgorithm):

 def initialize(self):

 # Data ADDED via universe selection is added with Daily resolution.
 self.universe_settings.resolution = Resolution.DAILY

 self.set_start_date(2015,1,5)
 self.set_end_date(2015,7,1)
 self.set_cash(100000)

 self.add_equity("SPY", Resolution.DAILY)
 self.set_benchmark("SPY")

 # add a custom universe data source (defaults to usa-equity)
 self.add_universe(NyseTopGainers, "universe-nyse-top-gainers", Resolution.DAILY, self.nyse_top_gainers)
 
 def nyse_top_gainers(self, data):
 return [ x.symbol for x in data if x["TopGainersRank"] <= 2 ]


 def on_data(self, slice):
 pass
 
 def on_securities_changed(self, changes):
 self._changes = changes

 for security in changes.removed_securities:
 # liquidate securities that have been removed
 if security.invested:
 self.liquidate(security.symbol)
 self.log("Exit {0} at {1}".format(security.symbol, security.close))

 for security in changes.added_securities:
 # enter short positions on new securities
 if not security.invested and security.close != 0:
 qty = self.calculate_order_quantity(security.symbol, -0.25)
 self.market_on_open_order(security.symbol, qty)
 self.log("Enter {0} at {1}".format(security.symbol, security.close))

 
class NyseTopGainers(PythonData):
 def __init__(self):
 self.count = 0
 self.last_date = datetime.min

 def get_source(self, config, date, is_live_mode):
 url = "http://www.wsj.com/mdc/public/page/2_3021-gainnyse-gainer.html" if is_live_mode else \
 "https://www.dropbox.com/s/vrn3p38qberw3df/nyse-gainers.csv?dl=1"

 return SubscriptionDataSource(url, SubscriptionTransportMedium.REMOTE_FILE)
 
 def reader(self, config, line, date, is_live_mode):
 
 if not is_live_mode:
 # backtest gets data from csv file in dropbox
 if not (line.strip() and line[0].isdigit()): return None
 csv = line.split(',')
 nyse = NyseTopGainers()
 nyse.time = datetime.strptime(csv[0], "%Y%m%d")
 nyse.end_time = nyse.time + timedelta(1)
 nyse.symbol = Symbol.create(csv[1], SecurityType.EQUITY, Market.USA)
 nyse["TopGainersRank"] = int(csv[2])
 return nyse

 if self.last_date != date:
 # reset our counter for the new day
 self.last_date = date
 self.count = 0
 
 # parse the html into a symbol
 if not line.startswith('<a href=\"/public/quotes/main.html?symbol='):
 # we're only looking for lines that contain the symbols
 return None
 
 last_close_paren = line.rfind(')')
 last_open_paren = line.rfind('(')
 if last_open_paren == -1 or last_close_paren == -1:
 return None

 symbol_string = line[last_open_paren + 1:last_close_paren]
 nyse = NyseTopGainers()
 nyse.time = date
 nyse.end_time = nyse.time + timedelta(1)
 nyse.symbol = Symbol.create(symbol_string, SecurityType.EQUITY, Market.USA)
 nyse["TopGainersRank"] = self.count
 self.count = self.count + 1
 return nyse