Overall Statistics |
Total Trades
36
Average Win
0.29%
Average Loss
-0.41%
Compounding Annual Return
-26.125%
Drawdown
2.500%
Expectancy
-0.341
Net Profit
-2.512%
Sharpe Ratio
-4.298
Loss Rate
61%
Win Rate
39%
Profit-Loss Ratio
0.70
Alpha
-0.262
Beta
-0.072
Annual Standard Deviation
0.067
Annual Variance
0.005
Information Ratio
-5.211
Tracking Error
0.127
Treynor Ratio
3.999
Total Fees
$143.39
|
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 symbol in self.Securities.Keys: window = self.symbolData[symbol].window daily = self.symbolData[symbol].daily_rw minute = self.symbolData[symbol].minute_rw if not (window.IsReady and daily.IsReady and minute.IsReady): continue last_close = window[0].Close yesterday_daily_close = daily[1].Close first_minute_close = minute[1].Close first_minute_open = minute[1].Open gap = last_close - yesterday_daily_close first_minute_bar = first_minute_close - first_minute_open if not self.Portfolio[symbol].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(symbol, 1.0/3.0) 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(symbol, -1.0/3.0) self.Log('GOING SHORT') def before_close_market(self): """ At the end of the day, if there is a short position, close it. """ self.Liquidate() self.Log('LIQUIDATE SHORT End of Day') def OnData(self, data): for symbol in data.Keys: if data[symbol] is None: continue # Create local variable to readability window = self.symbolData[symbol].window # Update the window. If not ready, continue window.Add(data[symbol]) if not window.IsReady: continue if self.Portfolio[symbol].Invested: # 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[symbol].AveragePrice * d.Decimal(1.01) < window[0].Close: self.Liquidate(symbol) 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)