Overall Statistics
Total Trades
1
Average Win
0%
Average Loss
0%
Compounding Annual Return
13.221%
Drawdown
6.500%
Expectancy
0
Net Profit
0%
Sharpe Ratio
1.139
Loss Rate
0%
Win Rate
0%
Profit-Loss Ratio
0
Alpha
0.06
Beta
0.479
Annual Standard Deviation
0.093
Annual Variance
0.009
Information Ratio
0.114
Tracking Error
0.096
Treynor Ratio
0.22
Total Fees
$1.94
 
#
#   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

class BasicTemplateAlgorithm(QCAlgorithm):

	def Initialize(self):
		self.SetCash(100000)
		
		self.SetStartDate(2016,1,5)
		self.SetEndDate(2017,1,1)
		
		self.AddSecurity(SecurityType.Equity, "IBM", Resolution.Daily)
		self.AddSecurity(SecurityType.Equity, "GOOG", Resolution.Daily)
		self._count = 0


	def OnData(self, slice):
		if not self._count:
			self.SetHoldings(self.Securities["IBM"].Symbol, 0.5)
			self.SetHoldings(self.Securities["IBM"].Symbol, 0,liquidateExistingHoldings = True)
			self._count += 1