| Overall Statistics |
|
Total Trades 1 Average Win 0% Average Loss 0% Compounding Annual Return 264.548% Drawdown 2.200% Expectancy 0 Net Profit 1.668% Sharpe Ratio 4.41 Loss Rate 0% Win Rate 0% Profit-Loss Ratio 0 Alpha 0.007 Beta 76.342 Annual Standard Deviation 0.193 Annual Variance 0.037 Information Ratio 4.355 Tracking Error 0.193 Treynor Ratio 0.011 Total Fees $3.24 |
# 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.Orders import *
from QuantConnect.Algorithm import *
from QuantConnect.Algorithm.Framework import *
from QuantConnect.Algorithm.Framework.Execution import *
from QuantConnect.Algorithm.Framework.Portfolio import *
from QuantConnect.Algorithm.Framework.Risk import *
from QuantConnect.Algorithm.Framework.Selection import *
from QuantConnect.Algorithm.Framework.Alphas import *
import numpy as np
### <summary>
### Basic template framework algorithm uses framework components to define the algorithm.
### </summary>
### <meta name="tag" content="using data" />
### <meta name="tag" content="using quantconnect" />
### <meta name="tag" content="trading and orders" />
class BasicTemplateFrameworkAlgorithm(QCAlgorithmFramework):
'''Basic template framework algorithm uses framework components to define the algorithm.'''
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.'''
# Set requested data resolution
self.UniverseSettings.Resolution = Resolution.Minute
self.SetStartDate(2013,10,07) #Set Start Date
self.SetEndDate(2013,10,11) #Set End Date
self.SetCash(100000) #Set Strategy Cash
# Find more symbols here: http://quantconnect.com/data
# Forex, CFD, Equities Resolutions: Tick, Second, Minute, Hour, Daily.
# Futures Resolution: Tick, Second, Minute
# Options Resolution: Minute Only.
symbols = [ Symbol.Create("SPY", SecurityType.Equity, Market.USA) ]
# set algorithm framework models
self.PortfolioSelection = ManualPortfolioSelectionModel(symbols)
self.Alpha = ConstantAlphaModel(AlphaType.Price, AlphaDirection.Up, TimeSpan.FromMinutes(20), 0.025, None)
self.PortfolioConstruction = SimplePortfolioConstructionModel()
# these are the default values for Execution and RiskManagement models
#self.Execution = ImmediateExecutionModel()
#self.RiskManagement = NullRiskManagementModel()
self.Debug("numpy test >>> print numpy.pi: " + str(np.pi))
def OnOrderEvent(self, orderEvent):
if orderEvent.Status == OrderStatus.Filled:
self.Debug("Purchased Stock: {0}".format(orderEvent.Symbol))