# 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 'ConfidenceWeightedPortfolioConstructionModel' and 'ConstantAlphaModel'
### generating a constant 'Insight' with a 0.25 confidence
### </summary>
class ConfidenceWeightedFrameworkAlgorithm(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.'''

 # Set requested data resolution
 self.universe_settings.resolution = Resolution.MINUTE

 # Order margin value has to have a minimum of 0.5% of Portfolio value, allows filtering out small trades and reduce fees.
 # Commented so regression algorithm is more sensitive
 #self.settings.minimum_order_margin_portfolio_percentage = 0.005

 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

 symbols = [ Symbol.create("SPY", SecurityType.EQUITY, Market.USA) ]

 # set algorithm framework models
 self.set_universe_selection(ManualUniverseSelectionModel(symbols))
 self.set_alpha(ConstantAlphaModel(InsightType.PRICE, InsightDirection.UP, timedelta(minutes = 20), 0.025, 0.25))
 self.set_portfolio_construction(ConfidenceWeightedPortfolioConstructionModel())
 self.set_execution(ImmediateExecutionModel())

 def on_end_of_algorithm(self):
 # holdings value should be 0.25 - to avoid price fluctuation issue we compare with 0.28 and 0.23
 if (self.portfolio.total_holdings_value > self.portfolio.total_portfolio_value * 0.28
 or self.portfolio.total_holdings_value < self.portfolio.total_portfolio_value * 0.23):
 raise ValueError("Unexpected Total Holdings Value: " + str(self.portfolio.total_holdings_value))