Overall Statistics |
Total Trades 0 Average Win 0% Average Loss 0% Compounding Annual Return 0% Drawdown 0% Expectancy 0 Net Profit 0% Sharpe Ratio 0 Loss Rate 0% Win Rate 0% Profit-Loss Ratio 0 Alpha 0 Beta 0 Annual Standard Deviation 0 Annual Variance 0 Information Ratio 0 Tracking Error 0 Treynor Ratio 0 Total Fees $0.00 |
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(2013, 05, 1) # Set Start Date self.SetEndDate(2013, 06, 01) self.SetCash(100000) # Set Strategy Cash self.symbolData = dict() for ticker in ["SPY", "FB", "TWTR"]: symbol = self.AddEquity(ticker, Resolution.Second).Symbol consolidator_daily = TradeBarConsolidator(timedelta(1)) consolidator_daily.DataConsolidated += self.OnDailyData self.SubscriptionManager.AddConsolidator(symbol, consolidator_daily) consolidator_minute = TradeBarConsolidator(60) consolidator_minute.DataConsolidated += self.OnMinuteData self.SubscriptionManager.AddConsolidator(symbol, consolidator_minute) self.symbolData[symbol] = SymbolData() self.Schedule.On(self.DateRules.EveryDay(), self.TimeRules.AfterMarketOpen('SPY', 2), 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.symbolData[bar.Symbol].daily_rw.Add(bar) def OnMinuteData(self, sender, bar): self.symbolData[bar.Symbol].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. """ for k in self.symbolData: if not (self.symbolData[k].window.IsReady and self.symbolData[k].daily_rw.IsReady and self.symbolData[k].minute_rw.IsReady): return last_close = self.symbolData[k].window[0].Close yesterday_daily_close = self.symbolData[k].daily_rw[1].Close first_minute_close = self.symbolData[k].minute_rw[1].Close first_minute_open = self.symbolData[k].minute_rw[1].Open gap = last_close - yesterday_daily_close first_minute_bar = first_minute_close - first_minute_open if not self.Portfolio[k].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(k, 0.333) 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(k, -0.333) self.Log('GOING SHORT') def before_close_market(self): """ At the end of the day, if there is a short position, close it. """ for k in self.symbolData: if self.Portfolio[k].Invested: self.Liquidate(k) self.Log('LIQUIDATE SHORT End of Day') def OnData(self, data): if data["SPY"] is None: return for k in self.symbolData: if not self.symbolData[k].window.IsReady: return if self.Portfolio[k].Invested: factor = d.Decimal(1.01) currBar = self.symbolData[stock].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[k].AveragePrice * factor < currBar: self.Liquidate(k) 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.') class SymbolData(object): def __init__(self): self.daily_rw = RollingWindow[TradeBar](2) self.minute_rw = RollingWindow[TradeBar](2) self.window = RollingWindow[TradeBar](2)