Overall Statistics Total Trades 11 Average Win 5.27% Average Loss -2.77% Compounding Annual Return 14.401% Drawdown 7.300% Expectancy 1.322 Net Profit 30.907% Sharpe Ratio 1.392 Loss Rate 20% Win Rate 80% Profit-Loss Ratio 1.90 Alpha -0.015 Beta 0.702 Annual Standard Deviation 0.1 Annual Variance 0.01 Information Ratio -1.233 Tracking Error 0.065 Treynor Ratio 0.198 Total Fees \$44.17
```# QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
#
# you may not use this file except in compliance with the License.
#
# Unless required by applicable law or agreed to in writing, software
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and

import clr

from System import *
from QuantConnect import *
from QuantConnect.Algorithm import *
from QuantConnect.Indicators import *
import decimal as d

class MovingAverageCrossAlgorithm(QCAlgorithm):
'''In this example we look at the canonical 15/30 day moving average cross. This algorithm
will go long when the 15 crosses above the 30 and will liquidate when the 15 crosses
back below the 30.'''

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(2012, 01, 01)  #Set Start Date
self.SetEndDate(2014, 01, 1)    #Set End Date
self.SetCash(100000)             #Set Strategy Cash
# Find more symbols here: http://quantconnect.com/data

# create a 15 day exponential moving average
self.fast = self.EMA("SPY", 15, Resolution.Daily);

# create a 30 day exponential moving average
self.slow = self.EMA("SPY", 30, Resolution.Daily);

self.previous = None

def OnData(self, data):
'''OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here.'''
# a couple things to notice in this method:
#  1. We never need to 'update' our indicators with the data, the engine takes care of this for us
#  2. We can use indicators directly in math expressions
#  3. We can easily plot many indicators at the same time

# wait for our slow ema to fully initialize
return

# only once per day
if self.previous is not None and self.previous.date() == self.Time.date():
return

# define a small tolerance on our checks to avoid bouncing
tolerance = 0.00015;

holdings = self.Portfolio["SPY"].Quantity

# we only want to go long if we're currently short or flat
if holdings <= 0:
# if the fast is greater than the slow, we'll go long
if self.fast.Current.Value > self.slow.Current.Value * d.Decimal(1 + tolerance):
self.SetHoldings("SPY", 1.0)

# we only want to liquidate if we're currently long
# if the fast is less than the slow we'll liquidate our long
if holdings > 0 and self.fast.Current.Value < self.slow.Current.Value:
self.Log("SELL >> {0}".format(self.Securities["SPY"].Price))
self.Liquidate("SPY")

# Rich, call OnEndAlgo.. code myself
#self.Log("here")
#self.OnEndOfAlgorithm()  # Try to call myself as not firing itself..

self.previous = self.Time

def OnEndOfAlgorithm(self):
self.Log("****** End of algo code reached")