Skip to content
QuantReplay

The 5-minute opening range breakout on the S&P 500

Micro E-mini S&P 500 (MES) · 5-minute bars · 2016 to 2026 · Breakout · Built on Toby Crabel's opening-range work

No edge after costs: profit factor 0.94 out of sample.

Profit factor, out of sample
0.94
Average trade, out of sample
−0.03R
Win rate, out of sample
36%
Trades, 10 years
2,738
Max drawdown
$63,134
Pass odds, median plan
9.7%

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.

SharePost on XLinkedInReddit

Abstract

We backtested the 5-minute opening range breakout rules on Micro E-mini S&P 500 (MES) 5-minute bars from 2016-01-11 to 2026-10-30: 2,738 trades, with commission and a tick of slippage on every market and stop fill. In sample (to 2023-08-03) it made −0.10R per trade with a profit factor of 0.83; out of sample (from 2023-08-04) −0.03R per trade and 0.94 over 820 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 3% to 12% of the time across 36 tracked plans.

1Hypothesis

Mark the first 5-minute bar's high and low. Trade the first close outside it, with the stop at the other side and a target of twice the risk.

If the first five minutes already show the day's direction, the first close beyond them should run to 2R often enough to beat costs, even with the narrowest opening range.

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. The opening range is the high and low of the first range_minutes (5) after 09:30 ET.
  2. 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; if that first close outside comes after last_entry_hour (12:00 ET), there is no trade.
  3. Stop: the other side of the range.
  4. Target: target_r (2) times the distance from the fill to the stop.
  5. Exit: the stop, the target, or flat at 15:55 ET.
  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
"""5-minute opening range breakout on Micro E-mini S&P 500 (MES) 5-minute bars, regular session, New York time. Our reading of the commonly taught rules; traders who teach it may trade it differently. Rules:  1. The opening range is the high and low of the first `range_minutes` (5) after 09:30 ET.  2. 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; if that first close     outside comes after `last_entry_hour` (12:00 ET), there is no trade.  3. Stop: the other side of the range.  4. Target: `target_r` (2) times the distance from the fill to the stop.  5. Exit: the stop, the target, or flat at 15:55 ET.  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 5-minute close outside the 5-minute opening range."""     symbol = "MES"    timeframe = "5m"    session = "rth"    params = {"range_minutes": 5, "target_r": 2.0, "last_entry_hour": 12, "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.range_high, self.range_low = self.opening_range(minutes=self.p.range_minutes)        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]        if not ready(high, low) or self.done_day == bar.date or not self.position.is_flat:            return        close = self.close[0]        if low <= close <= high:            return        self.done_day = bar.date        if bar.dt.hour * 60 + bar.dt.minute + 5 > self.p.last_entry_hour * 60:            return        width_pct = (high - low) / ((high + low) / 2) * 100        if close > high:            self.buy(self._size(close - low), stop_price=low, target_r=self.p.target_r, tag=f"ORB long | f={width_pct:.4f}")        else:            self.sell(self._size(high - close), stop_price=high, target_r=self.p.target_r, 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.
StatisticIn sampleOut of sampleAll
Trades1,9188202,738
Net P&L−$52,687−$7,066−$59,753
Profit factor0.830.940.86
Average trade−0.10R−0.03R−0.08R
95% interval of the average−0.16 to −0.04−0.13 to 0.06−0.13 to −0.03
t-statistic of trade R−3.35−0.72−3.19
Win rate36%36%36%
Average win / average loss1.481.651.52
Max drawdown$62,707$12,755$63,134
Sharpe ratio (daily, annualized)−1.34−0.45−1.09
Longest losing streak121414
Long / short profit factor0.90 / 0.760.91 / 0.970.90 / 0.82
Average holding time132 min109 min125 min
Commission paid$23,789$4,992$28,781
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 (1,918 trades)Out of sample (820)Averages

Averages: −0.10R in sample, −0.03R 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 2,737 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 = 0.04, p = 0.04, n = 2,737; Spearman ρ = 0.34). Out of sample alone: r = 0.03, p = 0.40. 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.

14
0.89
0.92
0.94
0.96
12
0.89
0.92
0.94
0.95
11
0.89
0.92
0.94
0.95
10
0.90
0.91
0.92
0.94
1
1.5
2
3

Target (R) across, last entry (hour ET) up. Solid outline: the defaults we tested; dashed: the in-sample best.

Hover a cell for both sides of the split.

Target (R) 1.00 to 3.00 across · Last entry (hour ET) 10.0 to 14.0 in depth · Profit factor, out of sample 0.89 to 0.96 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 target (R) and last entry (hour ET), 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, target (R) 3 and last entry (hour ET) 14, had profit factor 0.89 in sample but 0.96 out of sample, against 0.94 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 91.8% [90.5%, 92.9%]; 14% of years ended up; median ending P&L −$5,304, 5th to 95th percentile −$13,996 to +$2,717.

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 50K11.8%86%2%25
AquaFutures Beginner 50K Evaluation10.1%69%21%35
Bulenox 50K Qualification Account (Legacy, Option 2, EOD drawdown)10.1%69%21%35
Bulenox 50K Qualification Account (Legacy, Option 1, trailing drawdown)10.0%73%17%35
Alpha Futures Standard 50K Evaluation9.7%80%11%34
Alpha Futures Zero 50K Evaluation9.7%80%11%34
Apex Trader Funding 50K EOD Evaluation9.7%80%11%34
Earn2Trade Gauntlet Mini $50K9.7%80%11%34
Earn2Trade Trader Career Path TCP509.7%80%11%34
FTMO Futures Growth 50K Evaluation9.7%80%11%34
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

  • A 5-minute range makes small stops, so commission and slippage are a large share of each trade's risk.
  • One market (MES) and a fixed 2R target.
  • 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 0.94 out of sample. Nothing on the grid helps. Its in-sample best, target (R) 3 and last entry (hour ET) 14, had profit factor 0.89 in sample but 0.96 out of sample, against 0.94 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 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.