# 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