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))