| Overall Statistics |
|
Total Trades 598 Average Win 2.57% Average Loss -2.01% Compounding Annual Return 26.464% Drawdown 63.700% Expectancy 0.463 Net Profit 128.516% Sharpe Ratio 0.668 Probabilistic Sharpe Ratio 15.969% Loss Rate 36% Win Rate 64% Profit-Loss Ratio 1.28 Alpha 0.428 Beta -0.444 Annual Standard Deviation 0.578 Annual Variance 0.334 Information Ratio 0.455 Tracking Error 0.638 Treynor Ratio -0.869 Total Fees $0.00 Estimated Strategy Capacity $6000000.00 Lowest Capacity Asset BTCUSD XJ Portfolio Turnover 6.99% |
from clr import AddReference
AddReference("System")
AddReference("QuantConnect.Algorithm")
AddReference("QuantConnect.Common")
from System import *
from QuantConnect import *
from QuantConnect.Algorithm import *
from QuantConnect.Indicators import *
class MovingAverageCrossover(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2020, 1, 1) # set start date
self.SetCash(10000) # set strategy cash
self.AddCrypto("BTCUSD", Resolution.Daily) # Bitcoin data
self.fast = self.EMA("BTCUSD", 14, Resolution.Daily)
self.slow = self.EMA("BTCUSD", 28, Resolution.Daily)
self.long_term = self.EMA("BTCUSD", 50, Resolution.Daily)
self.previous = None
def OnData(self, data):
if not self.fast.IsReady or not self.slow.IsReady or not self.long_term.IsReady:
return
if self.previous is not None and self.previous.date() == self.Time.date():
return
if self.fast.Current.Value > self.slow.Current.Value and self.Securities["BTCUSD"].Close > self.long_term.Current.Value:
self.SetHoldings("BTCUSD", 1.0)
elif self.fast.Current.Value < self.slow.Current.Value and self.Securities["BTCUSD"].Close < self.long_term.Current.Value:
self.SetHoldings("BTCUSD", -1.0)
self.previous = self.Time