Overall Statistics |

Total Trades 33 Average Win 7.54% Average Loss -3.11% Compounding Annual Return 11.438% Drawdown 18.700% Expectancy 1.353 Net Profit 91.567% Sharpe Ratio 0.926 Loss Rate 31% Win Rate 69% Profit-Loss Ratio 2.42 Alpha 0.119 Beta -0.02 Annual Standard Deviation 0.125 Annual Variance 0.016 Information Ratio -0.245 Tracking Error 0.222 Treynor Ratio -5.815 Total Fees $190.79 |

class MovingAverageCrossAlgorithm(QCAlgorithm): '''In this example we look at the canonical 15/30 day moving average cross. This algorithm will go long when the 15 crosses above the 30 and will liquidate when the 15 crosses back below the 30.''' def __init__(self): self.symbol = "SPY" self.previous = None self.fast = None self.slow = None 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(2009, 01, 01) #Set Start Date self.SetEndDate(2015, 01, 01) #Set End Date self.SetCash(100000) #Set Strategy Cash # Find more symbols here: http://quantconnect.com/data self.AddSecurity(SecurityType.Equity, self.symbol, Resolution.Minute) # create a 15 day exponential moving average self.fast = self.EMA(self.symbol, 15, Resolution.Daily); # create a 30 day exponential moving average self.slow = self.EMA(self.symbol, 30, Resolution.Daily); 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: TradeBars IDictionary object with your stock data ''' # 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[self.symbol].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 * Decimal(1 + tolerance): self.Log("BUY >> {0}".format(self.Securities[self.symbol].Price)) self.SetHoldings(self.symbol, 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[self.symbol].Price)) self.Liquidate(self.symbol) self.previous = self.Time