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