# 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 *
from Alphas.RsiAlphaModel import RsiAlphaModel
from Portfolio.EqualWeightingPortfolioConstructionModel import EqualWeightingPortfolioConstructionModel
from Execution.SpreadExecutionModel import SpreadExecutionModel

### <summary>
### Regression algorithm for the SpreadExecutionModel.
### This algorithm shows how the execution model works to 
### submit orders only when the price is on desirably tight spread.
### </summary>
### <meta name="tag" content="using data" />
### <meta name="tag" content="using quantconnect" />
### <meta name="tag" content="trading and orders" />
class SpreadExecutionModelRegressionAlgorithm(QCAlgorithm):
    '''Regression algorithm for the SpreadExecutionModel.
    This algorithm shows how the execution model works to 
    submit orders only when the price is on desirably tight spread.'''

    def Initialize(self):
        self.SetStartDate(2013,10,7)
        self.SetEndDate(2013,10,11)

        self.SetUniverseSelection(ManualUniverseSelectionModel([
            Symbol.Create('AIG', SecurityType.Equity, Market.USA),
            Symbol.Create('BAC', SecurityType.Equity, Market.USA),
            Symbol.Create('IBM', SecurityType.Equity, Market.USA),
            Symbol.Create('SPY', SecurityType.Equity, Market.USA)
        ]))

        # using hourly rsi to generate more insights
        self.SetAlpha(RsiAlphaModel(14, Resolution.Hour))
        self.SetPortfolioConstruction(EqualWeightingPortfolioConstructionModel())
        self.SetExecution(SpreadExecutionModel())

        self.InsightsGenerated += self.OnInsightsGenerated

    def OnInsightsGenerated(self, algorithm, data):
        self.Log(f"{self.Time}: {', '.join(str(x) for x in data.Insights)}")

    def OnOrderEvent(self, orderEvent):
        self.Log(f"{self.Time}: {orderEvent}")