| Overall Statistics |
|
Total Trades 116 Average Win 1.11% Average Loss -0.66% Compounding Annual Return 15.459% Drawdown 5.500% Expectancy 0.387 Net Profit 15.414% Sharpe Ratio 1.578 Probabilistic Sharpe Ratio 69.228% Loss Rate 48% Win Rate 52% Profit-Loss Ratio 1.68 Alpha -0.022 Beta 0.589 Annual Standard Deviation 0.085 Annual Variance 0.007 Information Ratio -1.882 Tracking Error 0.07 Treynor Ratio 0.229 Total Fees $0.00 |
class RSIAlgorithm(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2019, 1, 1)
self.SetEndDate(2019, 12, 30)
self.SetCash(10000)
EMA_Period = 100
RSI_Period = 14
self.RSI_OB = 70
self.RSI_OS = 30
#self.Allocate = 0.25
self.spx = self.AddCfd("SPX500USD", Resolution.Hour, Market.Oanda)
self.SetBrokerageModel(BrokerageName.OandaBrokerage)
self.ema_spy = self.EMA("SPX500USD", EMA_Period)
self.PSAR_NAS = self.PSAR("SPX500USD")
self.rsi_spy = self.RSI("SPX500USD", RSI_Period)
self.Ichi = self.ICHIMOKU("SPX500USD", 9, 26, 26, 52, 26, 26, Resolution.Daily)
self.Ichi_Tenkan = self.Ichi.Tenkan.Current.Value
self.Schedule.On(self.DateRules.EveryDay("SPX500USD"), self.TimeRules.BeforeMarketClose("SPX500USD", 5), self.Rebalance)
self.SetWarmUp(RSI_Period)
self.SetWarmUp(EMA_Period)
def Rebalance(self):
if self.IsWarmingUp:
return
price = round(self.Securities["SPX500USD"].Close, 1)
if not self.Portfolio.Invested:
# if self.rsi_spy.Current.Value < self.RSI_OS and price > self.ema_spy.Current.Value:
self.SetHoldings("SPX500USD", 1)
elif self.Portfolio.Invested:
if price < self.ema_spy.Current.Value:
self.Liquidate("SPX500USD")