Overall Statistics |
Total Trades 0 Average Win 0% Average Loss 0% Compounding Annual Return 0% Drawdown 0% Expectancy 0 Net Profit 0% Sharpe Ratio 0 Loss Rate 0% Win Rate 0% Profit-Loss Ratio 0 Alpha 0 Beta 0 Annual Standard Deviation 0 Annual Variance 0 Information Ratio 0 Tracking Error 0 Treynor Ratio 0 Total Fees $0.00 |
from Alphas.EmaCrossAlphaModel import EmaCrossAlphaModel from Execution.ImmediateExecutionModel import ImmediateExecutionModel from Risk.NullRiskManagementModel import NullRiskManagementModel from Selection.QC500UniverseSelectionModel import QC500UniverseSelectionModel from SlopeBasedEquityMomentumAlphaModel import SlopeBasedEquityMomentumAlphaModel class BasicTemplateFrameworkAlgorithm(QCAlgorithmFramework): def Initialize(self): # Set requested data resolution self.UniverseSettings.Resolution = Resolution.Daily self.SetStartDate(2018, 9, 4) #Set Start Date self.SetEndDate(2019, 3, 7) #Set End Date self.SetCash(100000) #Set Strategy Cash # selection will run on mon/tues/thurs at 00:00/06:00/12:00/18:00 self.SetUniverseSelection(QC500UniverseSelectionModel()) self.SetAlpha(SlopeBasedEquityMomentumAlphaModel()) self.SetPortfolioConstruction(NullPortfolioConstructionModel()) self.SetExecution(ImmediateExecutionModel()) self.SetRiskManagement(NullRiskManagementModel()) def OnOrderEvent(self, orderEvent): if orderEvent.Status == OrderStatus.Filled: # self.Debug("Purchased Stock: {0}".format(orderEvent.Symbol)) pass
# 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("QuantConnect.Common") AddReference("QuantConnect.Algorithm") AddReference("QuantConnect.Algorithm.Framework") AddReference("QuantConnect.Indicators") from QuantConnect import * from QuantConnect.Indicators import * from QuantConnect.Algorithm import * from QuantConnect.Algorithm.Framework import * from QuantConnect.Algorithm.Framework.Alphas import * class SlopeBasedEquityMomentumAlphaModel(AlphaModel): def __init__(self, shortTermMomentumWindow = 60, longTermMomentumWindow = 90, minimumMomentum = 60, indexAverageWindow = 100, resolution = Resolution.Daily): self.shortTermMomentumWindow = shortTermMomentumWindow self.longTermMomentumWindow = longTermMomentumWindow self.minimumMomentum = minimumMomentum self.indexAverageWindow = indexAverageWindow self.resolution = resolution self.symbolData = {} resolutionString = Extensions.GetEnumString(resolution, Resolution) self.symbolDataBySymbol = {} self.month = None def slope(ts): x = np.arange(len(ts)) log_ts = np.log(ts) slope, intercept, r_value, p_value, std_err = stats.linregress(x, log_ts) annualized_slope = (np.power(np.exp(slope), 250) - 1) * 100 return annualized_slope * (r_value ** 2) def Update(self, algorithm, data): insights = [] ## If it has already run Update this month, then return nothing ## You can replace month with week if you want weekly if algorithm.Time.month == self.month: return [] algorithm.Log('Update() called: ' + str(algorithm.Time)) ## Update self.month so that it won't do anything in Update until a month has gone by self.month = algorithm.Time.month ## Local dictionary of rolling windows shortTermBars = {} longTermBars = {} ## Add current bar to rolling window for symbol, symbolData in self.symbolDataBySymbol.items(): if not data.Bars.ContainsKey(symbol): ## If there is no new bar, i.e. dividend being paid, then forward fill rolling window if symbolData.LongTermBars.Samples > 0: symbolData.UpdateRollingWindows(symbolData.LongTermBars[0]) elif symbolData.ShortTermBars.Samples > 0: symbolData.UpdateRollingWindows(symbolData.ShortTermBars[0]) else: continue else: symbolData.UpdateRollingWindows(data.Bars[symbol]) shortTermBars[symbol] = symbolData.ShortTermBars longTermBars[symbol] = symbolData.LongTermBars ## Insight code here return insights def OnSecuritiesChanged(self, algorithm, changes): for security in changes.AddedSecurities: if security.Symbol not in self.symbolDataBySymbol.keys(): symbolData = SymbolData(security.Symbol, self.shortTermMomentumWindow, self.longTermMomentumWindow) self.symbolDataBySymbol[security.Symbol] = symbolData for security in changes.RemovedSecurities: self.symbolDataBySymbol.pop(security.Symbol) class SymbolData: def __init__(self, symbol, shortTermMomentumWindow, longTermMomentumWindow): self.Symbol = symbol self.shortTermRollingWindow = RollingWindow[TradeBar](shortTermMomentumWindow) self.longTermRollingWindow = RollingWindow[TradeBar](longTermMomentumWindow) def UpdateRollingWindows(self, bar): self.shortTermRollingWindow.Add(bar) self.longTermRollingWindow.Add(bar) @property def ShortTermBars(self): return self.shortTermRollingWindow @property def LongTermBars(self): return self.longTermRollingWindow