Overall Statistics |
Total Trades 13 Average Win 0.67% Average Loss -0.37% Compounding Annual Return 11.538% Drawdown 3.800% Expectancy 0.882 Net Profit 2.351% Sharpe Ratio 1.098 Loss Rate 33% Win Rate 67% Profit-Loss Ratio 1.82 Alpha 0.06 Beta 0.791 Annual Standard Deviation 0.104 Annual Variance 0.011 Information Ratio 0.769 Tracking Error 0.06 Treynor Ratio 0.144 Total Fees $32.72 |
from QuantConnect.Data.Market import TradeBar from datetime import timedelta from System import * from QuantConnect import * from QuantConnect.Algorithm import * from QuantConnect.Indicators import * import decimal as d class MyAlgorithm(QCAlgorithm): def Initialize(self): self.SetStartDate(2015, 05, 1) # Set Start Date self.SetEndDate(2015, 07, 19) self.SetCash(100000) # Set Strategy Cash self.AddEquity("SPY", Resolution.Second) consolidator_daily = TradeBarConsolidator(timedelta(1)) consolidator_daily.DataConsolidated += self.OnDailyData self.SubscriptionManager.AddConsolidator("SPY", consolidator_daily) consolidator_minute = TradeBarConsolidator(60) consolidator_minute.DataConsolidated += self.OnMinuteData self.SubscriptionManager.AddConsolidator("SPY", consolidator_minute) self.daily_rw = RollingWindow[TradeBar](2) self.minute_rw = RollingWindow[TradeBar](2) self.window = RollingWindow[TradeBar](2) self.Schedule.On(self.DateRules.EveryDay(), self.TimeRules.AfterMarketOpen('SPY', 1), Action(self.one_minute_after_open_market)) self.Schedule.On(self.DateRules.EveryDay(), self.TimeRules.BeforeMarketClose('SPY', 1), Action(self.before_close_market)) # Add daily bar to daily rolling window def OnDailyData(self, sender, bar): self.daily_rw.Add(bar) def OnMinuteData(self, sender, bar): self.minute_rw.Add(bar) def one_minute_after_open_market(self): """ At 9:31 check if there has been a gap at the market open from the previous day. If so and the stock is gapping up and the first minute bar is negative, create a short selling signal. If the stock is gapping down and the first minute bar is positive, create a buying signal. """ if not (self.window.IsReady and self.daily_rw.IsReady and self.minute_rw.IsReady): return last_close = self.window[0].Close yesterday_daily_close = self.daily_rw[1].Close first_minute_close = self.minute_rw[1].Close first_minute_open = self.minute_rw[1].Open gap = last_close - yesterday_daily_close first_minute_bar = first_minute_close - first_minute_open if not self.Portfolio["SPY"].Invested: # If the stock is gapping down and the first minute bar is positive, create a buying signal. if gap < 0 and first_minute_bar > 0: self.SetHoldings("SPY", 1) self.Log('GOING LONG') # If the stock is gapping up and the first minute bar is negative, create a short selling signal elif gap > 0 and first_minute_bar < 0: self.SetHoldings("SPY", -1) self.Log('GOING SHORT') def before_close_market(self): """ At the end of the day, if there is a short position, close it. """ if self.Portfolio["SPY"].IsShort: self.Liquidate("SPY") self.Log('LIQUIDATE SHORT End of Day') # Add second bar to window rolling window def OnData(self, data): if data["SPY"] is None: return self.window.Add(data["SPY"]) if not (self.window.IsReady): return # self.Log("haha") factor = d.Decimal(1.01) currBar = self.window[0].Close # Every second, check the price and if it's higher than the price the stock was bought for times 1.01, close the position. if self.Portfolio["SPY"].Invested and self.Portfolio["SPY"].AveragePrice * factor < currBar: self.Liquidate("SPY") self.Log('LIQUIDATE AT THRESHOLD REACHED.') def OnEndOfDay(self): self.Plot("Portfolio", "MarginRemaining", self.Portfolio.MarginRemaining) def OnEndOfAlgorithm(self): self.Liquidate() self.Log('LIQUIDATE AT End Of Algorithm.')