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Crude oil's opening range after the 09:00 open

Micro Crude Oil (MCL) · 5-minute bars · 2016 to 2026 · Breakout · Built on Toby Crabel's opening-range work

No edge after costs: profit factor 0.90 out of sample.

Profit factor, out of sample
0.90
Average trade, out of sample
−0.06R
Win rate, out of sample
38%
Trades, 10 years
2,754
Max drawdown
$32,512
Pass odds, median plan
10.8%

Hypothetical results. These results are based on simulated or hypothetical performance results that have certain inherent limitations. Unlike the results shown in an actual performance record, these results do not represent actual trading. Also, because these trades have not actually been executed, these results may have under- or over-compensated for the impact, if any, of certain market factors, such as lack of liquidity. Simulated or hypothetical trading programs in general are also subject to the fact that they are designed with the benefit of hindsight. No representation is being made that any account will or is likely to achieve profits or losses similar to these being shown.

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Abstract

We backtested the crude oil opening range breakout rules on Micro Crude Oil (MCL) 5-minute bars from 2016-01-11 to 2026-10-30: 2,754 trades, with commission and a tick of slippage on every market and stop fill. In sample (to 2023-08-03) it made −0.04R per trade with a profit factor of 0.93; out of sample (from 2023-08-04) −0.06R per trade and 0.90 over 827 trades. 0 of the 16 parameter settings we tried were profitable out of sample. Sized at $250 of risk a trade, simulated 50K evaluations passed 4% to 14% of the time across 36 tracked plans.

1Hypothesis

Crude's day session opens at 09:00 ET. Mark the first 15 minutes' range and trade the first close outside it, stop at the other side, target twice the risk.

If crude's open concentrates the day's order flow like the stock index open does, the first break of its opening range should follow through.

2Data

Instrument
Micro Crude Oil (MCL), traded on the CL price series (CL.v.0: the most traded contract, rolled on volume). CME Globex via Databento.
Period
2016-01-11 to 2026-10-30: 2,791 sessions, 3,787,721 one-minute bars, built into 765,177 5-minute bars around the clock.
Costs
$0.75 commission a contract each way and 1 tick ($1.00) of slippage on every market and stop fill. Limit orders fill when price touches them; market orders fill at the next bar's open and stops at their price, each with one tick of slippage; when a bar touches both the stop and the target, the stop counts.
Position size
About $250 of risk a trade: micro contracts, rounded down, at least one. R is a trade's net P&L divided by its risk at entry.
Out of sample
The last 30% of weekdays: 2023-08-04 on (846 days), after 1,974 in-sample days. The rules were fixed before either side was run.

3Method

Our reading of the publicly described rules; the creator may trade it differently. The rules, exactly as the code runs them:

  1. The opening range is the high and low of the first range_minutes (15) after 09:00 ET, crude's regular open.
  2. Entry: the first 5-minute bar after the range that CLOSES above it buys at the next bar's open; the first that closes below it sells. One trade a day, until 11:30 ET.
  3. Stop: the other side of the range.
  4. Target: target_r (2) times the risk.
  5. Exit: the stop, the target, or flat at 14:30 ET, the close of crude's day session.
  6. Size: about risk_usd dollars of risk a trade (micro contracts, rounded down, at least 1 and at most max_contracts).
bot.py, the file the engine ran
"""Crude oil's opening range breakout from the 09:00 ET pit open, on Micro Crude Oil (MCL) 5-minute bars. Rules:  1. The opening range is the high and low of the first `range_minutes` (15) after 09:00 ET, crude's     regular open.  2. Entry: the first 5-minute bar after the range that CLOSES above it buys at the next bar's open;     the first that closes below it sells. One trade a day, until 11:30 ET.  3. Stop: the other side of the range.  4. Target: `target_r` (2) times the risk.  5. Exit: the stop, the target, or flat at 14:30 ET, the close of crude's day session.  6. Size: about `risk_usd` dollars of risk a trade (micro contracts, rounded down, at least 1 and at     most `max_contracts`)."""import qy  def ready(*values):    """True when every value is a number (indicators have no value until they have enough data)."""    return all(v is not None and v == v for v in values)  class Bot(qy.Strategy):    """Trade the first close outside crude's opening range."""     symbol = "MCL"    timeframe = "5m"    session = None    params = {"range_minutes": 15, "target_r": 2.0, "risk_usd": 250, "max_contracts": 50}    max_position = 50    entry_window = ("09:05", "11:30")    flatten_time = "14:30"    overnight = False     def setup(self):        self.range_high, self.range_low = self.opening_range(minutes=self.p.range_minutes, start="09:00")        self.done_day = None     def _size(self, risk_points):        per_contract = risk_points * self.point_value        n = int(self.p.risk_usd // per_contract) if per_contract > 0 else 1        return max(1, min(self.p.max_contracts, n))     def on_bar(self, bar):        high, low = self.range_high[0], self.range_low[0]        t = bar.dt.hour * 60 + bar.dt.minute + 5        if not ready(high, low) or self.done_day == bar.date or not self.position.is_flat or not 545 <= t <= 690:            return        close = self.close[0]        if low <= close <= high:            return        self.done_day = bar.date        if t >= 690:            return        f = (high - low) / close * 100        if close > high:            self.buy(self._size(close - low), stop_price=low, target_r=self.p.target_r, tag=f"CL ORB long | f={f:.4f}")        else:            self.sell(self._size(high - close), stop_price=high, target_r=self.p.target_r, tag=f"CL ORB short | f={f:.4f}")

4Results

Table 1. In sample (2016-01-11 to 2023-08-03) against out of sample (2023-08-04 to 2026-10-30), after costs.
StatisticIn sampleOut of sampleAll
Trades1,9278272,754
Net P&L−$18,647−$12,130−$30,777
Profit factor0.930.900.92
Average trade−0.04R−0.06R−0.05R
95% interval of the average−0.09 to 0.02−0.15 to 0.03−0.09 to 0.00
t-statistic of trade R−1.36−1.36−1.89
Win rate39%38%39%
Average win / average loss1.431.491.45
Max drawdown$22,191$14,023$32,512
Sharpe ratio (daily, annualized)−0.52−0.79−0.60
Longest losing streak111212
Long / short profit factor0.86 / 1.010.86 / 0.930.86 / 0.98
Average holding time141 min137 min140 min
Commission paid$18,209$6,402$24,611
Cumulative P&LDrawdown from the highOut of sample from 2023-08-04

Drag across the chart to zoom into a period, double-click to reset.

Figure 1. Cumulative P&L after costs and its drawdown from the running high, 2016-01-11 to 2026-10-30, at about $250 of risk a trade. The shaded part is out of sample.
In sample (1,927 trades)Out of sample (827)Averages

Averages: −0.04R in sample, −0.06R out of sample.

Figure 2. The distribution of trade results in R, as a share of each side's trades. Bars are in sample; the line is out of sample.
In sampleOut of sampleQuintile mean, 95% intervalOLS fitDensity contours

1,600 of 2,754 points drawn; the fit and the means use all of them. Drag a box to zoom, double-click to reset.

Figure 3. Trade R against the opening range's width, log x-axis; the dashed line is the OLS fit (r = 0.03, p = 0.14, n = 2,754; Spearman ρ = 0.31). Out of sample alone: r = −0.02, p = 0.66. Squares are the means of five equal-count bins, with 95% intervals.

5Robustness

We reran the strategy on a grid of 16 settings of its two main parameters, each on the full history with the same split. 0 were profitable in sample and 0 out of sample.

3
0.89
0.88
0.84
0.85
2
0.81
0.90
0.83
0.86
1.5
0.81
0.84
0.84
0.86
1
0.81
0.78
0.87
0.86
5
15
30
60

Range length (minutes) across, target (R) up. Solid outline: the defaults we tested; dashed: the in-sample best.

Hover a cell for both sides of the split.

Range length (minutes) 5.00 to 60.0 across · Target (R) 1.00 to 3.00 in depth · Profit factor, out of sample 0.78 to 0.90 as height and shade. Drag or use the arrow keys to turn it.

Drag to rotate; ctrl or ⌘ and scroll to zoom.

Figure 4. Profit factor across range length (minutes) and target (R), as a map and as a surface of the same grid. Values above 4 are drawn at 4.

What would change it

Nothing on the grid helps. Its in-sample best, range length (minutes) 60 and target (R) 3, had profit factor 0.97 in sample but 0.85 out of sample, against 0.90 for the defaults.

In-sample yearOut-of-sample year95% interval
Figure 5. Average trade by calendar year with its 95% interval: a year-by-year walk through the history. Lighter bars are in-sample years.
Figure 6. Average trade in three volatility regimes: terciles of the 20-session average of the daily range (% of the open), up to the day before the trade (full session). The cut-offs use the whole period, so this describes, it doesn't forecast.
Figure 7. Average trade by weekday and by the hour of entry (New York time), with 95% intervals.
Median25th to 75th percentile5th and 95thRuin: down $2,000
Figure 8. 2,000 simulated years of 252 sessions, drawn session by session from the whole backtest (days without trades included), with 48 paths drawn; red paths lost $2,000 at some point. Risk of ruin 83.0% [81.3%, 84.6%]; 27% of years ended up; median ending P&L −$2,925, 5th to 95th percentile −$10,581 to +$4,788.

6Prop-firm implications

The backtest's sessions run through the rules of 36 tracked 50K evaluations: 2,000 runs of up to 60 sessions each, with the drawdown and daily limits checked against each trade's intraday low. Intervals are Wilson 95% intervals of the simulation. A simulation of the past, not the chance that anyone passes.

PlanPass odds, 95% intervalPassFailOut of timeMedian sessions
FundedNext Futures Flex Challenge 50K13.8%81%5%28
AquaFutures Beginner 50K Evaluation11.3%55%34%36.5
Bulenox 50K Qualification Account (Legacy, Option 1, trailing drawdown)11.3%60%29%36.5
Bulenox 50K Qualification Account (Legacy, Option 2, EOD drawdown)11.3%55%34%36.5
Alpha Futures Standard 50K Evaluation10.8%70%19%36
Alpha Futures Zero 50K Evaluation10.8%70%19%36
Apex Trader Funding 50K EOD Evaluation10.8%70%19%36
Earn2Trade Gauntlet Mini $50K10.8%70%19%36
Earn2Trade Trader Career Path TCP5010.8%70%19%36
Elite Trader Funding 50K End of Day Drawdown Evaluation10.8%70%19%36
Scale 0 to 25%.
Figure 9. Simulated pass odds by plan, best first, with each plan's fail and out-of-time shares and the median sessions to a pass.

7Limitations

  • Crude's news (EIA on Wednesdays, OPEC) is not filtered.
  • One market (MCL).
  • A backtest on the past, with fills simulated from one-minute bars; live fills, queues and outages differ.

8Conclusion

No edge after costs: profit factor 0.90 out of sample. Nothing on the grid helps. Its in-sample best, range length (minutes) 60 and target (R) 3, had profit factor 0.97 in sample but 0.85 out of sample, against 0.90 for the defaults. On this reading of the rules, with these costs, the historical record gives no reason to expect a profit.

9References

  1. Crabel, T. (1990). Day Trading with Short Term Price Patterns and Opening Range Breakout. Traders Press.

Every number on this page comes from the MCL bot above, run by our backtest engine on our own data. We publish what doesn't work as well as what does, and we don't tune rules to look good.

Hypothetical results. These results are based on simulated or hypothetical performance results that have certain inherent limitations. Unlike the results shown in an actual performance record, these results do not represent actual trading. Also, because these trades have not actually been executed, these results may have under- or over-compensated for the impact, if any, of certain market factors, such as lack of liquidity. Simulated or hypothetical trading programs in general are also subject to the fact that they are designed with the benefit of hindsight. No representation is being made that any account will or is likely to achieve profits or losses similar to these being shown.