Overall Statistics 
Total Trades
5
Average Win
2.10%
Average Loss
0%
Compounding Annual Return
12.807%
Drawdown
3.000%
Expectancy
0
Net Profit
11.683%
Sharpe Ratio
1.976
Loss Rate
0%
Win Rate
100%
ProfitLoss Ratio
0
Alpha
0.003
Beta
5.966
Annual Standard Deviation
0.061
Annual Variance
0.004
Information Ratio
1.655
Tracking Error
0.061
Treynor Ratio
0.02
Total Fees
$10.77

# # QuantConnect Basic Template: # Fundamentals to using a QuantConnect algorithm. # # You can view the QCAlgorithm base class on Github: # https://github.com/QuantConnect/Lean/tree/master/Algorithm # import numpy as np import decimal as d class BasicTemplateAlgorithm(QCAlgorithm): def Initialize(self): #Initialise the data and resolution required, as well as the cash and startend dates for your algorithm. All algorithms must initialized. self.SetStartDate(2017, 01, 01) #Set Start Date self.SetEndDate(2017, 12, 01) #Set End Date self.SetCash(100000) #Set Strategy Cash # Find more symbols here: http://quantconnect.com/data self.AddEquity("SPY") # 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, slice): #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 if not self.slow.IsReady: 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.Log("BUY >> {0}".format(self.Securities["SPY"].Price)) 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") self.previous = self.Time