# 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>
### Test algorithm using 'QCAlgorithm.add_alpha_model()'
### </summary>
class AddAlphaModelAlgorithm(QCAlgorithm):
 def initialize(self):
 ''' Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.'''

 self.set_start_date(2013,10,7) #Set Start Date
 self.set_end_date(2013,10,11) #Set End Date
 self.set_cash(100000) #Set Strategy Cash

 self.universe_settings.resolution = Resolution.DAILY

 spy = Symbol.create("SPY", SecurityType.EQUITY, Market.USA)
 fb = Symbol.create("FB", SecurityType.EQUITY, Market.USA)
 ibm = Symbol.create("IBM", SecurityType.EQUITY, Market.USA)

 # set algorithm framework models
 self.set_universe_selection(ManualUniverseSelectionModel([ spy, fb, ibm ]))
 self.set_portfolio_construction(EqualWeightingPortfolioConstructionModel())
 self.set_execution(ImmediateExecutionModel())

 self.add_alpha(OneTimeAlphaModel(spy))
 self.add_alpha(OneTimeAlphaModel(fb))
 self.add_alpha(OneTimeAlphaModel(ibm))

class OneTimeAlphaModel(AlphaModel):
 def __init__(self, symbol):
 self.symbol = symbol
 self.triggered = False

 def update(self, algorithm, data):
 insights = []
 if not self.triggered:
 self.triggered = True
 insights.append(Insight.price(
 self.symbol,
 Resolution.DAILY,
 1,
 InsightDirection.DOWN))
 return insights