Overall Statistics |
Total Trades 12961 Average Win 0.14% Average Loss -0.12% Compounding Annual Return 29.053% Drawdown 26.800% Expectancy 0.128 Net Profit 147.161% Sharpe Ratio 1.282 Probabilistic Sharpe Ratio 61.393% Loss Rate 49% Win Rate 51% Profit-Loss Ratio 1.22 Alpha 0.265 Beta -0.038 Annual Standard Deviation 0.202 Annual Variance 0.041 Information Ratio 0.361 Tracking Error 0.286 Treynor Ratio -6.895 Total Fees $41676.59 Estimated Strategy Capacity $96000000.00 Lowest Capacity Asset ABX 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 self.AddEquity("GOLD", Resolution.Daily) self.AddEquity("WMP", Resolution.Daily) self.SetWarmUp(50) # 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]* 0.8) self.SetHoldings([PortfolioTarget("GOOG", 0.13*0.8), PortfolioTarget("TSLA", 0.22*0.8), PortfolioTarget("JPM", 0.08*0.8),PortfolioTarget("QCOM", 0.13*0.8), PortfolioTarget("AMD",0.2*0.8), PortfolioTarget("FB", 0.11*0.8), PortfolioTarget("QQQ", 0.13*0.8), PortfolioTarget("GOLD", 0.05), PortfolioTarget("WMP", 0.05)]) # self.SetHoldings("GOLD", 0.1) # self.SetHoldings("WMP", 0.1) # When slow sma > fast sma, sell the stock if MA_Slow_Pre > MA_Fast_Pre: self.SetHoldings([PortfolioTarget("GOOG", 0.13*0.2), PortfolioTarget("TSLA", 0.22*0.2), PortfolioTarget("JPM", 0.08*0.2),PortfolioTarget("QCOM", 0.13*0.2), PortfolioTarget("AMD",0.2*0.2), PortfolioTarget("FB", 0.11*0.2), PortfolioTarget("QQQ", 0.13*0.2), PortfolioTarget("GOLD", 0.4), PortfolioTarget("WMP", 0.4)]) # self.SetHoldings("WMP", 0.4) # Part 4 Step 4: Make Drawdown stop # if self.Portfolio.Cash < 0.85*1000: # self.stop = True # self.Liquidate()