Overall Statistics Total Trades 1 Average Win 0% Average Loss 0% Compounding Annual Return 264.583% Drawdown 2.200% Expectancy 0 Net Profit 1.668% Sharpe Ratio 4.41 Loss Rate 0% Win Rate 0% Profit-Loss Ratio 0 Alpha 0.007 Beta 76.354 Annual Standard Deviation 0.193 Annual Variance 0.037 Information Ratio 4.354 Tracking Error 0.193 Treynor Ratio 0.011 Total Fees \$3.27
```import numpy as np

### <summary>
### Basic template algorithm simply initializes the date range and cash. This is a skeleton
### framework you can use for designing an algorithm.
### </summary>
class BasicTemplateAlgorithm(QCAlgorithm):
'''Basic template algorithm simply initializes the date range and cash'''

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, 7)  #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.Debug("numpy test >>> print numpy.pi: " + str(np.pi))

curr = Currencies.CurrencyPairs
for i in range(len(curr)):
self.Debug(curr[i])

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

Arguments:
data: Slice object keyed by symbol containing the stock data
'''
if not self.Portfolio.Invested:
self.SetHoldings("SPY", 1)                        ```