Overall Statistics
Total Trades
268
Average Win
0.20%
Average Loss
-0.03%
Compounding Annual Return
-61.892%
Drawdown
2.400%
Expectancy
-0.340
Net Profit
-1.052%
Sharpe Ratio
-5.779
Loss Rate
90%
Win Rate
10%
Profit-Loss Ratio
5.89
Alpha
0
Beta
-47.909
Annual Standard Deviation
0.092
Annual Variance
0.008
Information Ratio
-5.895
Tracking Error
0.092
Treynor Ratio
0.011
Total Fees
$894.78
 
### <summary>
### Demonstration algorthm for the Warm Up feature with basic indicators.
### </summary>
### <meta name="tag" content="indicators" />
### <meta name="tag" content="warm up" />
### <meta name="tag" content="history and warm up" />
### <meta name="tag" content="using data" />
class WarmupAlgorithm(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(2013,10,8)   #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
        self.AddEquity("SPY", Resolution.Second)

        fast_period = 60
        slow_period = 3600

        self.fast = self.EMA("SPY", fast_period)
        self.slow = self.EMA("SPY", slow_period)

        self.SetWarmup(slow_period)
        self.first = True


    def OnData(self, data):
        '''OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here.'''
        if self.first and not self.IsWarmingUp:
            self.first = False
            self.Log("Fast: {0}".format(self.fast.Samples))
            self.Log("Slow: {0}".format(self.slow.Samples))

        if self.fast.Current.Value > self.slow.Current.Value:
            self.SetHoldings("SPY", 1)
        else:
            self.SetHoldings("SPY", -1)