Overall Statistics Total Trades33Average Win7.54%Average Loss-3.11%Compounding Annual Return11.438%Drawdown18.700%Expectancy1.353Net Profit91.567%Sharpe Ratio0.926Loss Rate31%Win Rate69%Profit-Loss Ratio2.42Alpha0.119Beta-0.02Annual Standard Deviation0.125Annual Variance0.016Information Ratio-0.245Tracking Error0.222Treynor Ratio-5.815Total Fees\$190.79
```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 __init__(self):
self.symbol = "SPY"
self.previous = None
self.fast = None
self.slow = None

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(2009, 01, 01)  #Set Start Date
self.SetEndDate(2015, 01, 01)    #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(self.symbol, 15, Resolution.Daily);

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

def OnData(self, data):
'''OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here.

Arguments:
'''
# 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[self.symbol].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 * Decimal(1 + tolerance):