Overall Statistics
Total Trades
20
Average Win
0.10%
Average Loss
-0.09%
Compounding Annual Return
-4.557%
Drawdown
0.400%
Expectancy
-0.167
Net Profit
-0.150%
Sharpe Ratio
0.934
Probabilistic Sharpe Ratio
49.438%
Loss Rate
60%
Win Rate
40%
Profit-Loss Ratio
1.08
Alpha
-0.03
Beta
0.024
Annual Standard Deviation
0.015
Annual Variance
0
Information Ratio
-12
Tracking Error
0.15
Treynor Ratio
0.574
Total Fees
$37.00
# 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 System import *
from QuantConnect import *
from QuantConnect.Orders import *
from QuantConnect.Securities import *
from QuantConnect.Algorithm import *
from QuantConnect.Algorithm.Framework import *
from QuantConnect.Algorithm.Framework.Alphas import *
from QuantConnect.Algorithm.Framework.Portfolio import *
from QuantConnect.Algorithm.Framework.Selection import *
from Alphas.ConstantAlphaModel import ConstantAlphaModel
from Selection.FutureUniverseSelectionModel import FutureUniverseSelectionModel
from QuantConnect.Algorithm.Framework.Execution import *
from QuantConnect.Algorithm.Framework.Risk import *
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(ConstantFutureContractAlphaModel(InsightType.Price, InsightDirection.Up, timedelta(minutes=59)))
        
        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 ConstantFutureContractAlphaModel(ConstantAlphaModel):
    '''Implementation of a constant alpha model that only emits insights for future symbols'''
    def __init__(self, type, direction, period):
        super().__init__(type, direction, period)

    def ShouldEmitInsight(self, utcTime, symbol):
        # only emit alpha for future symbols and not underlying equity symbols
        if symbol.SecurityType != SecurityType.Future:
            return False

        if not (utcTime.hour == 16 and utcTime.minute == 0):
            return False

        return super().ShouldEmitInsight(utcTime, symbol)


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