| Overall Statistics |
|
Total Trades 18 Average Win 2.83% Average Loss -1.83% Compounding Annual Return -48.062% Drawdown 13.300% Expectancy -0.434 Net Profit -7.095% Sharpe Ratio -1.56 Probabilistic Sharpe Ratio 18.621% Loss Rate 78% Win Rate 22% Profit-Loss Ratio 1.55 Alpha -0.45 Beta -0.17 Annual Standard Deviation 0.296 Annual Variance 0.088 Information Ratio -1.522 Tracking Error 0.351 Treynor Ratio 2.719 Total Fees $0.00 |
class BounceBack(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2015, 1, 1)
self.SetEndDate(2015, 2, 10)
self.SetCash(100000)
# Volatile ETFs
tickers = 'UVXY,XIV,NUGT,DUST,JNUG,JDUST,LABU,LABD,GUSH,DRIP,TVIX,GASL,GASX,DWTI,UWTI,DGAZ,UGAZ,UBIO,ZBIO,BRZU,RUSS,SCO,UCO,RUSL,ERY,ERX,BIOL,SVXY,VXX,SILJ,BIB,BIS,VIXY,SOXL,VIIX,SOXS,BZQ,USLV,SLVP,DSLV,GDXJ,GLDX'
resolution = Resolution.Hour
security = self.AddEquity('TQQQ', resolution)
security.FeeModel = ConstantFeeModel(0)
self.sym = security.Symbol
self.ma = self.SMA(self.sym, 50, resolution)
self.over = None
self.SetWarmUp(50)
def OnData(self, data):
if self.IsWarmingUp or not self.ma.IsReady:
return
if self.sym not in data or data[self.sym] is None:
return
price = data[self.sym].Price
ma = self.ma.Current.Value
if price > 0:
over = price > ma
if self.over is not None and self.over != over:
self.Liquidate()
limit = 0.03
stop = 0.01
quant = self.CalculateOrderQuantity(self.sym, 1)
if over:
self.MarketOrder(self.sym, quant)
self.LimitOrder(self.sym, -quant, price * (1 + limit))
self.StopMarketOrder(self.sym, -quant, price * (1 - stop))
# elif not over:
# stop = price * 1.02
# self.MarketOrder(self.sym, -quant)
# self.LimitOrder(self.sym, quant, price * (1 - limit))
# self.StopMarketOrder(self.sym, quant, price * (1 + stop))
self.over = over
self.Plot('Chart', 'MA', ma)
self.Plot('Chart', 'Price', price)