Overall Statistics |
Total Trades 12 Average Win 0% Average Loss 0.00% Compounding Annual Return -0.200% Drawdown 0.000% Expectancy -1 Net Profit -0.013% Sharpe Ratio -4.275 Probabilistic Sharpe Ratio 6.162% Loss Rate 100% Win Rate 0% Profit-Loss Ratio 0 Alpha -0.003 Beta 0.003 Annual Standard Deviation 0 Annual Variance 0 Information Ratio -3.41 Tracking Error 0.155 Treynor Ratio -0.736 Total Fees $12.00 |
# 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.Common") from System import * from QuantConnect import * from QuantConnect.Algorithm import * # <summary> # Regression algorithm to test the behaviour of ARMA versus AR models at the same order of differencing. # In particular, an ARIMA(1,1,1) and ARIMA(1,1,0) are instantiated while orders are placed if their difference # is sufficiently large (which would be due to the inclusion of the MA(1) term). # </summary> class AutoRegressiveIntegratedMovingAverageRegressionAlgorithm(QCAlgorithm): def Initialize(self): '''Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.''' self.SetStartDate(2013, 4, 7) self.SetEndDate(2013, 12, 11) self.EnableAutomaticIndicatorWarmUp = True self.AddEquity("SPY", Resolution.Minute) self.arima = self.ARIMA("SPY", 1, 1, 1, 50) self.ar = self.ARIMA("SPY", 1, 1, 0, 50) def OnData(self, data): '''OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here. Arguments: data: Slice object keyed by symbol containing the stock data ''' if self.arima.IsReady: if abs(self.arima.Current.Value - self.ar.Current.Value) > 1: if self.arima.Current.Value > self.last: self.MarketOrder("SPY", 1) else: self.MarketOrder("SPY", -1) self.last = self.arima.Current.Value