| Overall Statistics |
|
Total Trades 29 Average Win 0.23% Average Loss -0.05% Compounding Annual Return 0.685% Drawdown 0.900% Expectancy 3.859 Net Profit 2.416% Sharpe Ratio 0.593 Probabilistic Sharpe Ratio 17.834% Loss Rate 15% Win Rate 85% Profit-Loss Ratio 4.74 Alpha 0.005 Beta 0.001 Annual Standard Deviation 0.01 Annual Variance 0 Information Ratio -0.761 Tracking Error 0.197 Treynor Ratio 4.333 Total Fees $46.03 Estimated Strategy Capacity $65000000.00 Lowest Capacity Asset AMD R735QTJ8XC9X |
class MACrossover(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2018, 1, 1) # Set Start Date
# self.SetEndDate(2020, 1, 1) # Set End Date
self.SetCash(100000) # Set Strategy Cash
# self.SetBrokerageModel(BrokerageName.InteractiveBrokersBrokerage)
self.stop = False
self.stocks = ["GOOG","TSLA", "JPM", "QCOM", "AMD", "FB","QQQ"]
self.stocks_weight = {"GOOG":0.13, "TSLA":0.22, "JPM":0.08, "QCOM":0.13, "AMD":0.20, "FB":0.11, "QQQ":0.13 }
self.AddEquity("GOOG", Resolution.Daily) # Google
self.AddEquity("TSLA", Resolution.Daily) # Electric Cars, Solar & Clean Energy
self.AddEquity("JPM", Resolution.Daily) # Banking / Finance
self.AddEquity("QCOM", Resolution.Daily) # Semiconductor stock
self.AddEquity("AMD", Resolution.Daily) # Semiconductor stock
self.AddEquity("FB", Resolution.Daily) # FaceBook
self.AddEquity("QQQ", Resolution.Daily) # QQQ is an etf that tracks Nasdaq index
# Part 2 Step 2: Calculate Moving Averages
def OnData(self, data):
if self.stop:
return
stocks = self.stocks
for stock in stocks:
# self.Debug(stock)
stock_data = self.History ([stock], 30, Resolution.Daily)
MA_Fast_Pre = stock_data.close[25:30].mean()
MA_Slow_Pre = stock_data.close [9:30].mean()
#
# Part 3 Strategy: Make Crossover rule
#
# When slow sma < fast sma, buy the stock
if MA_Slow_Pre < MA_Fast_Pre:
self.Debug (self.stocks_weight[stock])
self.SetHoldings (stock, self.stocks_weight[stock])
# When slow sma > fast sma, sell the stock
if MA_Slow_Pre > MA_Fast_Pre:
self.SetHoldings (stock, 0)
# Part 4 Step 4: Make Drawdown stop
if self.Portfolio.Cash < 0.85*1000:
self.stop = True
self.Liquidate()