# 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 clr import AddReference
AddReference("System")
AddReference("QuantConnect.Algorithm")
AddReference("QuantConnect.Algorithm.Framework")
AddReference("QuantConnect.Common")

from Selection.FutureUniverseSelectionModel import FutureUniverseSelectionModel
from datetime import date, timedelta

### <summary>
### Basic template futures framework algorithm uses framework components
### to define an algorithm that trades futures.
### </summary>
class BasicTemplateFuturesFrameworkAlgorithm(QCAlgorithm):

    def Initialize(self):

        self.UniverseSettings.Resolution = Resolution.Minute

        self.SetStartDate(2013, 10, 7)
        self.SetEndDate(2013, 10, 18)
        self.SetCash(100000)

        # set framework models
        self.SetUniverseSelection(FrontMonthFutureUniverseSelectionModel(self.SelectFutureChainSymbols))
        
        self.SetAlpha(MyAlphaModel(self))
        
        self.SetPortfolioConstruction(SingleSharePortfolioConstructionModel())
        self.SetExecution(ImmediateExecutionModel())
        self.SetRiskManagement(NullRiskManagementModel())


    def SelectFutureChainSymbols(self, utcTime):
        return [ Symbol.Create(Futures.Indices.SP500EMini, SecurityType.Future, Market.CME) ]

class FrontMonthFutureUniverseSelectionModel(FutureUniverseSelectionModel):
    '''Creates futures chain universes that select the front month contract and runs a user
    defined futureChainSymbolSelector every day to enable choosing different futures chains'''
    def __init__(self, select_future_chain_symbols):
        super().__init__(timedelta(1), select_future_chain_symbols)

    def Filter(self, filter):
        '''Defines the futures chain universe filter'''
        return (filter.FrontMonth()
                      .OnlyApplyFilterAtMarketOpen())
                      
class MyAlphaModel(AlphaModel):
    def __init__(self, algorithm):
    
        self.indicator_value = None
        
    def Update(self, algorithm, slice):
        if not (algorithm.UtcTime.hour == 16 and algorithm.UtcTime.minute == 0 and algorithm.UtcTime.second == 0):
            return []
            
        symbol = slice.Keys[0]
        bar = slice.Bars[symbol]
        
        self.UpdateIndicatorValue(bar.High, bar.Low, bar.Close)
            
        if self.indicator_value is not None:
            algorithm.Plot("Custom", "Indicator", self.indicator_value)
            
        insights = []
    
        for symbol in slice.Keys:
            if symbol.SecurityType != SecurityType.Future:
                continue
            insights.append(Insight.Price(symbol, timedelta(minutes=59), InsightDirection.Up))
        
        return insights

    def UpdateIndicatorValue(self, high, low, close):
        if high != low:
            self.indicator_value = (close - low) / (high - low)
        else:
            self.indicator_value = None
        

class SingleSharePortfolioConstructionModel(PortfolioConstructionModel):
    all_insights = []
    
    def CreateTargets(self, algorithm, insights):
        targets = []
        active_symbols = []
        expired_symbols = []
        active_insights = []

        while len(self.all_insights) > 0:
            insight = self.all_insights.pop()
            symbol = insight.Symbol
            
            if insight.IsActive(algorithm.UtcTime):
                active_insights.append(insight)
                if symbol not in active_symbols:
                    active_symbols.append(symbol)
            else:
                if symbol not in expired_symbols:
                    expired_symbols.append(symbol)

        for insight in insights:
            active_insights.append(insight)
            if insight.Symbol not in active_symbols:
                active_symbols.append(insight.Symbol)
        self.all_insights = active_insights
        
        liquidate_symbols = [ symbol for symbol in expired_symbols if symbol not in active_symbols ]
        
        for symbol in active_symbols:
            targets.append(PortfolioTarget(symbol, 1))
        for symbol in liquidate_symbols:
            targets.append(PortfolioTarget(symbol, 0))
            
        return targets
        
class QuandlFutures(PythonQuandl):
    def __init__(self):
        self.ValueColumnName = 'Settle'