The 15-minute opening range breakout on the Nasdaq-100

Micro E-mini Nasdaq-100 (MNQ) · 5-minute bars · 2016 to 2026 · Breakout · Built on Toby Crabel's opening-range work; the 15-minute version is widely taught

Marginal: profit factor 1.07 out of sample, not clearly above break-even, t = 1.1.

Profit factor, out of sample: 1.07
Average trade, out of sample: +0.04R
Win rate, out of sample: 46%
Trades, 10 years: 2,686
Max drawdown: $14,585
Pass odds, median plan: 13.1%

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 15-minute opening range breakout rules on Micro E-mini Nasdaq-100 (MNQ) 5-minute bars from 2016-01-11 to 2026-10-30: 2,686 trades, with commission and a tick of slippage on every market and stop fill. In sample (to 2023-08-03) it made +0.02R per trade with a profit factor of 1.01; out of sample (from 2023-08-04) +0.04R per trade and 1.07 over 810 trades. 24 of the 25 parameter settings we tried were profitable out of sample. Sized at $250 of risk a trade, simulated 50K evaluations passed 5% to 18% of the time across 36 tracked plans.

1Hypothesis

Mark the high and low of the first 15 minutes after the 09:30 open. Trade the first 5-minute close outside that range, with the stop at the other side and a target at twice the risk.

If the first 15 minutes set the day's balance and a close outside it starts a directional move, the first breakout should reach 2R more often than one in three times, enough to pay for costs.

2Data

Instrument: Micro E-mini Nasdaq-100 (MNQ), traded on the NQ price series (NQ.c.0: front month, rolled on the calendar). CME Globex via Databento.
Period: 2016-01-11 to 2026-10-30: 2,745 sessions, 1,056,797 one-minute bars, built into 210,814 5-minute bars of the regular session (09:30 to 16:00 ET).
Costs: $0.75 commission a contract each way and 1 tick ($0.50) 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:

The opening range is the high and low of the first range_minutes (15) after 09:30 ET.
Entry: the first 5-minute bar after the range that CLOSES above the range high buys at the next bar's open; the first that closes below the range low sells. One trade a day, from 09:45 to 12:00 ET.
Stop: the other side of the range.
Target: target_r (2) times the distance from the fill to the stop.
Exit: the stop, the target, or flat at 15:55 ET.
Size: about risk_usd dollars of risk a trade (micro contracts, rounded down, at least 1 and at most max_contracts).

No filter on the range's width, the gap or the trend: the plain rule.

bot.py, the file the engine ran

1"""15-minute opening range breakout on MNQ 5-minute bars, regular session, New York time.2 3Our reading of the publicly described rules; traders who teach it may trade it differently.4 5Rules:6  1. The opening range is the high and low of the first `range_minutes` (15) after 09:30 ET.7  2. Entry: the first 5-minute bar after the range that CLOSES above the range high buys at the next8     bar's open; the first that closes below the range low sells. One trade a day, from 09:45 to9     12:00 ET.10  3. Stop: the other side of the range.11  4. Target: `target_r` (2) times the distance from the fill to the stop.12  5. Exit: the stop, the target, or flat at 15:55 ET.13  6. Size: about `risk_usd` dollars of risk a trade (micro contracts, rounded down, at least 1 and at14     most `max_contracts`).15  No filter on the range's width, the gap or the trend: the plain rule.16"""17import qy18 19 20def ready(*values):21    """True when every value is a number (indicators have no value until they have enough data)."""22    return all(v is not None and v == v for v in values)23 24 25class Bot(qy.Strategy):26    """Trade the first 5-minute close outside the 15-minute opening range."""27 28    symbol = "MNQ"29    timeframe = "5m"30    session = "rth"31    params = {"range_minutes": 15, "target_r": 2.0, "risk_usd": 250, "max_contracts": 50}32    max_position = 5033    entry_window = ("09:35", "12:00")34    flatten_time = "15:55"35    overnight = False36 37    def setup(self):38        self.range_high, self.range_low = self.opening_range(minutes=self.p.range_minutes)39        self.done_day = None40 41    def _size(self, risk_points):42        per_contract = risk_points * self.point_value43        n = int(self.p.risk_usd // per_contract) if per_contract > 0 else 144        return max(1, min(self.p.max_contracts, n))45 46    def on_bar(self, bar):47        high, low = self.range_high[0], self.range_low[0]48        if not ready(high, low) or self.done_day == bar.date or not self.position.is_flat:49            return50        close = self.close[0]51        width_pct = (high - low) / ((high + low) / 2) * 10052        if close > high:53            self.done_day = bar.date54            self.buy(self._size(close - low), stop_price=low, target_r=self.p.target_r,55                     tag=f"ORB long | f={width_pct:.4f}")56        elif close < low:57            self.done_day = bar.date58            self.sell(self._size(high - close), stop_price=high, target_r=self.p.target_r,59                      tag=f"ORB short | f={width_pct:.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.
Statistic	In sample	Out of sample	All
Trades	1,876	810	2,686
Net P&L	+$1,316	+$6,294	+$7,610
Profit factor	1.01	1.07	1.03
Average trade	+0.02R	+0.04R	+0.03R
95% interval of the average	−0.03 to 0.07	−0.03 to 0.12	−0.02 to 0.07
t-statistic of trade R	0.68	1.13	1.18
Win rate	44%	46%	44%
Average win / average loss	1.31	1.28	1.30
Max drawdown	$14,585	$10,071	$14,585
Sharpe ratio (daily, annualized)	0.04	0.43	0.17
Longest losing streak	16	7	16
Long / short profit factor	1.10 / 0.92	1.12 / 1.02	1.11 / 0.95
Average holding time	233 min	242 min	236 min
Commission paid	$10,140	$1,472	$11,612

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 (1,876 trades)Out of sample (810)Averages

Averages: +0.02R in sample, +0.04R 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 2,685 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 = 0.00, p = 0.84, n = 2,685; Spearman ρ = 0.17). Out of sample alone: r = 0.00, p = 0.99. Squares are the means of five equal-count bins, with 95% intervals.

5Robustness

We reran the strategy on a grid of 25 settings of its two main parameters, each on the full history with the same split. 12 were profitable in sample and 24 out of sample.

1.07

1.03

1.05

1.07

1.08

1.06

1.05

1.06

1.07

1.02

1.07

1.04

1.06

1.5

1.06

0.99

1.09

1.03

1.07

1.04

1.03

1.10

1.07

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 4.00 in depth · Profit factor, out of sample 0.99 to 1.10 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) 10 and target (R) 3, had profit factor 1.05 in sample but 1.05 out of sample, against 1.07 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 (regular 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 55.4% [53.2%, 57.5%]; 58% of years ended up; median ending P&L +$875, 5th to 95th percentile −$6,047 to +$7,657.

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.

Plan	Pass	Fail	Out of time	Median sessions
FundedNext Futures Flex Challenge 50K	17.6%	67%	15%	35
AquaFutures Beginner 50K Evaluation	13.7%	33%	54%	40
Bulenox 50K Qualification Account (Legacy, Option 2, EOD drawdown)	13.7%	33%	54%	40
Bulenox 50K Qualification Account (Legacy, Option 1, trailing drawdown)	13.5%	38%	48%	40
Alpha Futures Zero 50K Evaluation	13.3%	49%	37%	40
Apex Trader Funding 50K EOD Evaluation	13.3%	49%	37%	40
Funded Futures Family 50K Evaluation (standard plan)	13.3%	49%	37%	40
Top One Futures Elite Challenge 50K	13.3%	49%	37%	40
FundedNext Futures Rapid Pro Challenge 50K	13.3%	50%	37%	40
Lucid Trading LucidPro 50K Evaluation	13.3%	50%	37%	40

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

One market (MNQ) and one exit rule; many traders manage the trade by hand.
No filter for news days, gaps or the overnight trend.
Wide opening ranges make wide stops, so position size swings a lot from day to day.
A backtest on the past, with fills simulated from one-minute bars; live fills, queues and outages differ.

8Conclusion

Marginal: profit factor 1.07 out of sample, not clearly above break-even, t = 1.1. Nothing on the grid helps. Its in-sample best, range length (minutes) 10 and target (R) 3, had profit factor 1.05 in sample but 1.05 out of sample, against 1.07 for the defaults. The result is too close to break-even to tell apart from noise after costs.

9References

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