| Overall Statistics |
|
Total Trades 28 Average Win 5.18% Average Loss -4.45% Compounding Annual Return 0.592% Drawdown 17.500% Expectancy 0.083 Net Profit 2.750% Sharpe Ratio 0.091 Probabilistic Sharpe Ratio 0.959% Loss Rate 50% Win Rate 50% Profit-Loss Ratio 1.17 Alpha -0.025 Beta 0.251 Annual Standard Deviation 0.081 Annual Variance 0.007 Information Ratio -0.849 Tracking Error 0.141 Treynor Ratio 0.03 Total Fees $28.00 Estimated Strategy Capacity $1100000000.00 Lowest Capacity Asset SPY R735QTJ8XC9X |
from AlgorithmImports import *
class MACDTrendAlgorithm(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2017, 7, 1)
self.SetEndDate(2022, 2, 1)
self.SetCash(10000)
self.AddEquity("SPY", Resolution.Daily)
# define our daily macd(12,26) with a 9 day signal
self.__macd = self.MACD("SPY", 12, 26, 9, MovingAverageType.Exponential, Resolution.Daily)
self.__previous = datetime.min
self.PlotIndicator("MACD", True, self.__macd, self.__macd.Signal)
self.PlotIndicator("SPY", self.__macd.Fast, self.__macd.Slow)
def OnData(self, data):
'''OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here.'''
# wait for our macd to fully initialize
if not self.__macd.IsReady: return
# only once per day
if self.__previous.date() == self.Time.date(): return
# define a small tolerance on our checks to avoid bouncing
tolerance = 0.0025
holdings = self.Portfolio["SPY"].Quantity
signalDeltaPercent = (self.__macd.Current.Value - self.__macd.Signal.Current.Value)/self.__macd.Fast.Current.Value
# if our macd is greater than our signal, then let's go long
if holdings <= 0 and signalDeltaPercent > tolerance: # 0.01%
# longterm says buy as well
self.SetHoldings("SPY", 1.0)
# of our macd is less than our signal, then let's go short
elif holdings >= 0 and signalDeltaPercent < -tolerance:
self.Liquidate("SPY")
self.__previous = self.Time