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NR7: breaking out of the narrowest day in seven

Micro E-mini S&P 500 (MES) · 5-minute bars · 2016 to 2026 · Breakout · Narrow-range days are from Toby Crabel's work

Marginal: profit factor 1.71 out of sample, not clearly above break-even, t = 1.7.

Profit factor, out of sample
1.71
Average trade, out of sample
+0.15R
Win rate, out of sample
59%
Trades, 10 years
156
Max drawdown
$2,117
Pass odds, median plan
0.0%

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 NR7 breakout rules on Micro E-mini S&P 500 (MES) 5-minute bars from 2016-01-11 to 2026-10-30: 156 trades, with commission and a tick of slippage on every market and stop fill. In sample (to 2023-08-03) it made +0.03R per trade with a profit factor of 1.16; out of sample (from 2023-08-04) +0.15R per trade and 1.71 over 56 trades. 16 of the 16 parameter settings we tried were profitable out of sample. Sized at $250 of risk a trade, simulated 50K evaluations passed 0% to 0% of the time across 36 tracked plans.

1Hypothesis

When yesterday was the quietest day of the last seven, bracket its range today and trade whichever side breaks, with the stop at the other side.

If volatility contracts before it expands, the day after the narrowest range in seven should break that range and keep going more often than it reverses.

2Data

Instrument
Micro E-mini S&P 500 (MES), traded on the ES price series (ES.c.0: front month, rolled on the calendar). CME Globex via Databento.
Period
2016-01-11 to 2026-10-30: 2,746 sessions, 1,056,820 one-minute bars, built into 210,816 5-minute bars of the regular session (09:30 to 16:00 ET).
Costs
$0.75 commission a contract each way and 1 tick ($1.25) 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. Each day's range is its regular-session (09:30-16:00 ET) high minus low. An NR7 day has the smallest range of the last lookback (7) days, itself included.
  2. The day after an NR7 day, if the first 5-minute bar opens and closes inside the NR7 day's range, a buy stop goes in buffer_ticks (1) tick above its high and a sell stop 1 tick below its low. When one fills, the other is cancelled. Orders work from 09:35 to 15:30 ET.
  3. Stop: the other side of the NR7 day's range.
  4. Target: target_r (1) times the risk.
  5. Exit: the stop, the target, or flat at 15:55 ET. One trade a day.
  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
"""NR7 breakout on Micro E-mini S&P 500 (MES) 5-minute bars, regular session, New York time. After the narrowest regular-session range of the last seven days, trade the break of that day's range. Rules:  1. Each day's range is its regular-session (09:30-16:00 ET) high minus low. An NR7 day has the     smallest range of the last `lookback` (7) days, itself included.  2. The day after an NR7 day, if the first 5-minute bar opens and closes inside the NR7 day's range,     a buy stop goes in `buffer_ticks` (1) tick above its high and a sell stop 1 tick below its low.     When one fills, the other is cancelled. Orders work from 09:35 to 15:30 ET.  3. Stop: the other side of the NR7 day's range.  4. Target: `target_r` (1) times the risk.  5. Exit: the stop, the target, or flat at 15:55 ET. One trade a day.  6. Size: about `risk_usd` dollars of risk a trade (micro contracts, rounded down, at least 1 and at     most `max_contracts`)."""import qy  class Bot(qy.Strategy):    """Break of the range of the narrowest day of the last seven."""     symbol = "MES"    timeframe = "5m"    session = "rth"    params = {"lookback": 7, "target_r": 1.0, "buffer_ticks": 1, "risk_usd": 250, "max_contracts": 50}    max_position = 50    entry_window = ("09:35", "15:30")    flatten_time = "15:55"    overnight = False     def setup(self):        self.days = []          # (high, low) of completed sessions        self.day = None        self.h = self.l = 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_fill(self, fill):        if fill.kind == "entry":            self.cancel_all()     def on_bar(self, bar):        if bar.date != self.day:            if self.h is not None:                self.days = (self.days + [(self.h, self.l)])[-60:]            self.day, self.h, self.l = bar.date, bar.high, bar.low            self._first_bar(bar)            return        self.h, self.l = max(self.h, bar.high), min(self.l, bar.low)     def _first_bar(self, bar):        n = self.p.lookback        if len(self.days) < n:            return        ranges = [h - l for h, l in self.days[-n:]]        yh, yl = self.days[-1]        if ranges[-1] > min(ranges) or not (yl < bar.open < yh and yl < bar.close < yh):            return        buf = self.p.buffer_ticks * self.tick_size        risk = yh - yl + 2 * buf        qty = self._size(risk)        f = (yh - yl) / bar.close * 100        self.buy(qty, stop=yh + buf, stop_price=yl - buf, target_r=self.p.target_r, tag=f"NR7 long | f={f:.4f}")        self.sell(qty, stop=yl - buf, stop_price=yh + buf, target_r=self.p.target_r, tag=f"NR7 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
Trades10056156
Net P&L+$992+$1,796+$2,788
Profit factor1.161.711.32
Average trade+0.03R+0.15R+0.07R
95% interval of the average−0.11 to 0.17−0.03 to 0.33−0.04 to 0.18
t-statistic of trade R0.381.661.26
Win rate53%59%55%
Average win / average loss1.031.191.07
Max drawdown$2,117$875$2,117
Sharpe ratio (daily, annualized)1.003.551.85
Longest losing streak747
Long / short profit factor1.50 / 0.841.58 / 1.851.52 / 1.12
Average holding time326 min252 min299 min
Commission paid$431$120$551
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 (100 trades)Out of sample (56)Averages

Averages: +0.03R in sample, +0.15R 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

Drag a box to zoom, double-click to reset.

Figure 3. Trade R against the narrow day's range, log x-axis; the dashed line is the OLS fit (r = −0.01, p = 0.87, n = 156; Spearman ρ = 0.03). Out of sample alone: r = −0.14, p = 0.29. 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. 5 were profitable in sample and 16 out of sample.

2
1.44
1.83
3.15
3.63
1.5
1.46
1.85
3.03
3.59
1
1.43
1.71
2.98
3.78
0.50
1.34
1.55
2.19
2.44
4
7
10
15

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

Hover a cell for both sides of the split.

Lookback (days) 4.00 to 15.0 across · Target (R) 0.50 to 2.00 in depth · Profit factor, out of sample 1.34 to 3.78 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 lookback (days) 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

The defaults were already the in-sample best of the grid, so there is no tweak to test: profit factor 1.16 in sample and 1.71 out of sample.

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 0.0% [0.0%, 0.2%]; 68% of years ended up; median ending P&L +$266, 5th to 95th percentile −$661 to +$1,242.

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
Alpha Futures Advanced 50K Evaluation0.0%0%100%n/a
Alpha Futures Standard 50K Evaluation0.0%0%100%n/a
Alpha Futures Zero 50K Evaluation0.0%0%100%n/a
Apex Trader Funding 50K EOD Evaluation0.0%0%100%n/a
Apex Trader Funding 50K Intraday Trailing Drawdown Evaluation0.0%0%100%n/a
AquaFutures Beginner 50K Evaluation0.0%0%100%n/a
Bulenox 50K Qualification Account (Legacy, Option 1, trailing drawdown)0.0%0%100%n/a
Bulenox 50K Qualification Account (Legacy, Option 2, EOD drawdown)0.0%0%100%n/a
Earn2Trade Gauntlet Mini $50K0.0%0%100%n/a
Earn2Trade Trader Career Path TCP500.0%0%100%n/a
Scale 0 to 10%.
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

  • Ranges are the regular session's only; overnight trading isn't counted.
  • Stop entries fill with a tick of slippage; fast breaks can slip more.
  • One market (MES).
  • A backtest on the past, with fills simulated from one-minute bars; live fills, queues and outages differ.

8Conclusion

Marginal: profit factor 1.71 out of sample, not clearly above break-even, t = 1.7. The defaults were already the in-sample best of the grid, so there is no tweak to test: profit factor 1.16 in sample and 1.71 out of sample. The result is too close to break-even to tell apart from noise after costs.

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 MES 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.