Overall Statistics Total Trades81Average Win5.29%Average Loss-2.17%Compounding Annual Return8.551%Drawdown18.700%Expectancy1.060Net Profit155.077%Sharpe Ratio0.829Probabilistic Sharpe Ratio24.489%Loss Rate40%Win Rate60%Profit-Loss Ratio2.43Alpha0.035Beta0.371Annual Standard Deviation0.112Annual Variance0.012Information Ratio-0.424Tracking Error0.145Treynor Ratio0.249Total Fees\$429.53
class MovingAverageCrossAlgorithm(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(2009, 1, 1)    #Set Start Date
self.SetEndDate(2020, 5, 28)     #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 *(1 + tolerance):
self.previous = self.Time