| Overall Statistics |
|
Total Trades 35 Average Win 7.56% Average Loss -3.91% Compounding Annual Return 9.987% Drawdown 16.700% Expectancy 0.899 Net Profit 77.057% Sharpe Ratio 0.856 Probabilistic Sharpe Ratio 32.421% Loss Rate 35% Win Rate 65% Profit-Loss Ratio 1.93 Alpha 0.015 Beta 0.491 Annual Standard Deviation 0.127 Annual Variance 0.016 Information Ratio -0.629 Tracking Error 0.13 Treynor Ratio 0.222 Total Fees $205.18 Estimated Strategy Capacity $230000000.00 Lowest Capacity Asset SPY R735QTJ8XC9X |
# EMA Cross Algorithm
# -------------------------------------------------------
STOCK = 'SPY'; MA_F = 15; MA_S = 30; TOLERANCE = 0.00015;
# -------------------------------------------------------
class EMA_CrossAlgorithm(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2009, 1, 1)
self.SetEndDate(2015, 1, 1)
self.SetCash(100000)
res = Resolution.Hour
self.symbol = self.AddEquity(STOCK, res).Symbol
self.SetWarmUp(5*MA_S, Resolution.Daily)
self.ema_fast = self.EMA(self.symbol, MA_F, Resolution.Daily);
self.ema_slow = self.EMA(self.symbol, MA_S, Resolution.Daily);
self.Schedule.On(self.DateRules.EveryDay(self.symbol), self.TimeRules.AfterMarketOpen(self.symbol, 31),
self.Rebalance)
def Rebalance(self):
if self.IsWarmingUp: return
if not self.ema_fast.IsReady: return
if not self.ema_slow: return
self.Plot("EMAS", "ema_fast", self.ema_fast.Current.Value)
self.Plot("EMAS", "ema_slow", self.ema_slow.Current.Value)
if not self.Portfolio[self.symbol].IsLong:
if self.ema_fast.Current.Value > self.ema_slow.Current.Value * (1 + TOLERANCE):
# self.Log("BUY >> {0}".format(self.Securities[self.symbol].Price))
self.SetHoldings(self.symbol, 1.0)
elif self.Portfolio[self.symbol].IsLong:
if self.ema_fast.Current.Value < self.ema_slow.Current.Value:
# self.Log("SELL >> {0}".format(self.Securities[self.symbol].Price))
self.Liquidate(self.symbol)