Overall Statistics |
Total Trades 1 Average Win 0% Average Loss 0% Compounding Annual Return 263.209% Drawdown 2.200% Expectancy 0 Net Profit 1.663% Sharpe Ratio 4.41 Loss Rate 0% Win Rate 0% Profit-Loss Ratio 0 Alpha 0.007 Beta 76.134 Annual Standard Deviation 0.192 Annual Variance 0.037 Information Ratio 4.354 Tracking Error 0.192 Treynor Ratio 0.011 Total Fees $3.29 |
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.AddEquity("SPY") self.spy_std_volume = self.STD("SPY", 30, Resolution.Minute, Field.Volume) 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) self.Plot('SPY','Volume', self.spy_std_volume.Current.Value)