Commodity · United States · futures · Intraday · United States Free

Opening Range Breakout within Crude Oil

Academic paper

Paper linkhttps://www.researchgate.net/publication/254420457_Assessing_the_profitability_of_intraday_opening_range_breakout_strategies

Strategy in a nutshell

The strategy trades US crude oil futures using daily open-range breakout thresholds. Long positions are opened when prices rise above 1.6524% of the open, and short positions when prices fall below -1.5784%, with all positions closed at day’s end.

Economic rationale

The strategy exploits momentum and the Contraction-Expansion principle, capitalizing on days with large price movements. High volatility between 2001–2011 amplified profitability, allowing trends to continue within favorable market conditions.

Backtest performance

Annualised return9.77%

Beta-0.03

Sortino ratio-0.108

Win rate45%

Full Python code

1import numpy as np
2from AlgorithmImports import *
3class OpeningRangeBreakoutCrudeOil(QCAlgorithm):
4def Initialize(self):
5self.SetStartDate(2010, 1, 1)
6self.SetCash(100000)
7self.period:int = 21
8self.treshhold_value:int = 2
9self.future:Future = self.AddFuture(Futures.Energies.CrudeOilWTI, \
10                    Resolution.Minute, \
11                    dataNormalizationMode=DataNormalizationMode.BackwardsRatio, \
12                    contractDepthOffset=0)
13self.symbol:Symbol = self.future.Symbol
14self.daily_ret:RollingWindow = RollingWindow[float](self.period)
15
16self.recent_open:float = 0
17
18self.day_close_flag:bool = False
19self.day_open_flag:bool = False
20self.market:Symbol = self.AddEquity('SPY', Resolution.Daily).Symbol
21self.Schedule.On(self.DateRules.EveryDay(self.market), self.TimeRules.BeforeMarketClose(self.market, 1), self.DayClose)
22self.Schedule.On(self.DateRules.EveryDay(self.market), self.TimeRules.AfterMarketOpen(self.market, 1), self.DayOpen)
23
24def OnData(self, data: Slice) -> None:
25# close
26if self.day_close_flag:
27    self.day_close_flag = False
28    self.Liquidate()
29    if self.symbol in data and data[self.symbol]:
30        close:float = data[self.symbol].Close
31        if close != 0 and self.recent_open != 0 and close != self.recent_open:
32            todays_ret:float = close / self.recent_open - 1
33            self.daily_ret.Add(todays_ret)
34            if self.daily_ret.IsReady:
35                daily_returns:List[float] = list(self.daily_ret)
36                mean:float = np.mean(daily_returns)
37                std:float = np.std(daily_returns)
38        
39                high_threshhold:float = mean + self.treshhold_value * std
40                low_threshhold:float = mean - self.treshhold_value * std
41                
42                if todays_ret > high_threshhold:
43                    if not self.Portfolio.Invested:
44                        self.MarketOrder(self.future.Mapped, 1)
45                elif todays_ret < low_threshhold:
46                    if not self.Portfolio.Invested:
47                        self.MarketOrder(self.future.Mapped, -1)
48    
49    self.recent_open = 0
50
51# open
52if self.day_open_flag:
53    self.day_open_flag = False
54    if self.symbol in data and data[self.symbol]:
55        self.recent_open = data[self.symbol].Open
56def DayClose(self) -> None:
57self.day_close_flag = True
58
59def DayOpen(self) -> None:
60self.day_open_flag = True