| Overall Statistics |
|
Total Trades 1378 Average Win 1.77% Average Loss -0.61% Compounding Annual Return -100% Drawdown 98.300% Expectancy -0.955 Net Profit -98.319% Sharpe Ratio -0.373 Probabilistic Sharpe Ratio 0.000% Loss Rate 99% Win Rate 1% Profit-Loss Ratio 2.88 Alpha -13.639 Beta 0.189 Annual Standard Deviation 2.679 Annual Variance 7.178 Information Ratio -23.561 Tracking Error 2.883 Treynor Ratio -5.295 Total Fees $85672.46 Estimated Strategy Capacity $0 Lowest Capacity Asset BTCUSD XJ |
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 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(2015, 1, 1) #Set Start Date
self.SetEndDate(2015, 2, 1) #Set End Date
self.SetCash(100_000) #Set Strategy Cash
#self.SetCash("BTC", 1)
self.SetBrokerageModel(BrokerageName.GDAX, AccountType.Cash)
# Find more symbols here: http://quantconnect.com/data
self.symbol = self.AddCrypto("BTCUSD", Resolution.Minute).Symbol
self.fast = self.EMA(self.symbol, 3, Resolution.Minute);
self.slow = self.EMA(self.symbol, 7, Resolution.Minute);
self.superslow = self.EMA(self.symbol, 15, Resolution.Minute);
self.timeToBuy = True
def OnData(self, data):
if not self.fast.IsReady or not self.slow.IsReady:
return
tolerance = 0.00015;
if self.Portfolio[self.symbol].Quantity <= 0 and self.fast.Current.Value > self.slow.Current.Value and self.timeToBuy:
self.SetHoldings(self.symbol, 1.0)
self.timeToBuy = False
if self.Portfolio[self.symbol].Quantity > 0 and self.Securities[self.symbol].Open != self.Securities[self.symbol].Close:
self.Liquidate()
if self.fast.Current.Value < self.slow.Current.Value:
self.timeToBuy = True