Overall Statistics |
Total Trades 5234 Average Win 0.15% Average Loss -0.11% Compounding Annual Return 33.121% Drawdown 25.800% Expectancy 0.320 Net Profit 172.498% Sharpe Ratio 1.498 Probabilistic Sharpe Ratio 74.038% Loss Rate 44% Win Rate 56% Profit-Loss Ratio 1.34 Alpha 0.297 Beta -0.052 Annual Standard Deviation 0.193 Annual Variance 0.037 Information Ratio 0.473 Tracking Error 0.283 Treynor Ratio -5.53 Total Fees $28992.95 Estimated Strategy Capacity $1700000.00 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) # self.Debug(stocks) 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.Debug (self.stocks_weight[stock]) self.SetHoldings(stock, self.stocks_weight[stock]* 0.8) 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 (stock, self.stocks_weight[stock] * 0.2) self.SetHoldings("GOLD", 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()