| Overall Statistics |
|
Total Trades 52 Average Win 0.00% Average Loss 0.00% Compounding Annual Return 0.096% Drawdown 0.100% Expectancy 3.353 Net Profit 0.090% Sharpe Ratio 0.87 Probabilistic Sharpe Ratio 44.551% Loss Rate 24% Win Rate 76% Profit-Loss Ratio 4.71 Alpha 0.001 Beta 0 Annual Standard Deviation 0.001 Annual Variance 0 Information Ratio -2.148 Tracking Error 0.101 Treynor Ratio -4.174 Total Fees $52.00 |
# 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, 1, 7)
self.SetEndDate(2013, 12, 11)
self.EnableAutomaticIndicatorWarmUp = True
self.AddEquity("SPY", Resolution.Daily)
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