Fading the opening gap on the Russell 2000

Micro E-mini Russell 2000 (M2K) · 5-minute bars · 2017 to 2026 · Mean reversion

Marginal: profit factor 1.00 out of sample, not clearly above break-even, t = 0.3.

Profit factor, out of sample: 1.00
Average trade, out of sample: +0.01R
Win rate, out of sample: 55%
Trades, 10 years: 1,578
Max drawdown: $22,279
Pass odds, median plan: 1.5%

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 opening gap fade rules on Micro E-mini Russell 2000 (M2K) 5-minute bars from 2017-07-17 to 2026-10-30: 1,578 trades, with commission and a tick of slippage on every market and stop fill. In sample (to 2024-01-17) it made −0.07R per trade with a profit factor of 0.84; out of sample (from 2024-01-18) +0.01R per trade and 1.00 over 497 trades. 9 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 3% of the time across 36 tracked plans.

1Hypothesis

When the Russell 2000 future opens away from yesterday's close, bet on the gap filling: trade back toward the close, with the stop one gap-size away.

If most opening gaps are overnight noise, they should fill back to the prior close more often than they extend by their own size.

2Data

Instrument: Micro E-mini Russell 2000 (M2K), traded on the RTY price series (RTY.v.0: the most traded contract, rolled on volume). CME Globex via Databento.
Period: 2017-07-17 to 2026-10-30: 2,396 sessions, 922,339 one-minute bars, built into 184,092 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: 2024-01-18 on (727 days), after 1,698 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 gap is the 09:30 ET open against the previous session's 16:00 close, in percent.
Entry: if the gap is at least min_gap_pct (0.15%) and at most 2%, and the first 5-minute bar has not already traded back to the previous close, fade it at 09:35 ET: sell a gap up, buy a gap down.
Stop: stop_mult (1) gap's size beyond the entry.
Target: the previous session's close (the gap filled).
Exit: the stop, the target, or flat at 15:55 ET. One trade a day.
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

1"""Opening gap fade on Micro E-mini Russell 2000 (M2K) 5-minute bars, regular session, New York time.2 3Rules:4  1. The gap is the 09:30 ET open against the previous session's 16:00 close, in percent.5  2. Entry: if the gap is at least `min_gap_pct` (0.15%) and at most 2%, and the first 5-minute bar has6     not already traded back to the previous close, fade it at 09:35 ET: sell a gap up, buy a gap down.7  3. Stop: `stop_mult` (1) gap's size beyond the entry.8  4. Target: the previous session's close (the gap filled).9  5. Exit: the stop, the target, or flat at 15:55 ET. One trade a day.10  6. Size: about `risk_usd` dollars of risk a trade (micro contracts, rounded down, at least 1 and at11     most `max_contracts`).12"""13import qy14 15 16class Bot(qy.Strategy):17    """Fade the opening gap toward yesterday's close."""18 19    symbol = "M2K"20    timeframe = "5m"21    session = "rth"22    params = {"min_gap_pct": 0.15, "stop_mult": 1.0, "risk_usd": 250, "max_contracts": 50}23    max_position = 5024    entry_window = ("09:35", "09:40")25    flatten_time = "15:55"26    overnight = False27 28    def setup(self):29        self.day = None30        self.prev_close = None31        self.last_close = None32 33    def _size(self, risk_points):34        per_contract = risk_points * self.point_value35        n = int(self.p.risk_usd // per_contract) if per_contract > 0 else 136        return max(1, min(self.p.max_contracts, n))37 38    def on_bar(self, bar):39        first = bar.date != self.day40        if first:41            self.day = bar.date42            self.prev_close = self.last_close43        self.last_close = bar.close44        if not first or self.prev_close is None:45            return46        pc = self.prev_close47        gap = bar.open - pc48        gap_pct = abs(gap) / pc * 10049        if not self.p.min_gap_pct <= gap_pct <= 2.0:50            return51        size = abs(gap)52        if gap > 0 and bar.low > pc:53            stop = bar.close + self.p.stop_mult * size54            self.sell(self._size(stop - bar.close), stop_price=stop, target_price=pc, tag=f"Gap fade short | f={gap_pct:.4f}")55        elif gap < 0 and bar.high < pc:56            stop = bar.close - self.p.stop_mult * size57            self.buy(self._size(bar.close - stop), stop_price=stop, target_price=pc, tag=f"Gap fade long | f={gap_pct:.4f}")

4Results

Table 1. In sample (2017-07-17 to 2024-01-17) against out of sample (2024-01-18 to 2026-10-30), after costs.
Statistic	In sample	Out of sample	All
Trades	1,081	497	1,578
Net P&L	−$18,326	+$89	−$18,237
Profit factor	0.84	1.00	0.89
Average trade	−0.07R	+0.01R	−0.04R
95% interval of the average	−0.13 to −0.01	−0.07 to 0.09	−0.09 to 0.00
t-statistic of trade R	−2.43	0.33	−1.87
Win rate	49%	55%	51%
Average win / average loss	0.88	0.82	0.86
Max drawdown	$22,279	$5,405	$22,279
Sharpe ratio (daily, annualized)	−1.21	0.01	−0.85
Longest losing streak	10	8	10
Long / short profit factor	0.83 / 0.85	1.04 / 0.97	0.89 / 0.89
Average holding time	123 min	159 min	134 min
Commission paid	$9,585	$2,894	$12,479

Cumulative P&LDrawdown from the highOut of sample from 2024-01-18

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, 2017-07-17 to 2026-10-30, at about $250 of risk a trade. The shaded part is out of sample.

In sample (1,081 trades)Out of sample (497)Averages

Averages: −0.07R in sample, +0.01R 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 size of the gap, log x-axis; the dashed line is the OLS fit (r = 0.06, p = 0.02, n = 1,578; Spearman ρ = 0.20). Out of sample alone: r = 0.14, p = 0.002. 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 9 out of sample.

1.01

1.00

1.17

1.21

1.5

0.98

1.15

1.22

1.00

1.18

1.25

0.50

0.81

0.82

1.00

0.95

0.10

0.15

0.30

0.50

Smallest gap (%) across, stop (gap sizes) up. Solid outline: the defaults we tested; dashed: the in-sample best.

Hover a cell for both sides of the split.

Smallest gap (%) 0.10 to 0.50 across · Stop (gap sizes) 0.50 to 2.00 in depth · Profit factor, out of sample 0.81 to 1.25 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 smallest gap (%) and stop (gap sizes), 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 clearly helps. Its in-sample best, smallest gap (%) 0.5 and stop (gap sizes) 1, did better out of sample, profit factor 1.25 against 1.00 for the defaults, but over only 299 trades, t = 1.8: too few, or too noisy, to call it a fix.

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 70.7% [68.7%, 72.7%]; 25% of years ended up; median ending P&L −$1,990, 5th to 95th percentile −$6,277 to +$2,658.

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	3.2%	56%	40%	44
Alpha Futures Standard 50K Evaluation	1.5%	34%	65%	48
Alpha Futures Zero 50K Evaluation	1.5%	34%	65%	48
Apex Trader Funding 50K EOD Evaluation	1.5%	34%	65%	48
Apex Trader Funding 50K Intraday Trailing Drawdown Evaluation	1.5%	38%	60%	48
AquaFutures Beginner 50K Evaluation	1.5%	17%	82%	48
Bulenox 50K Qualification Account (Legacy, Option 1, trailing drawdown)	1.5%	20%	79%	48
Bulenox 50K Qualification Account (Legacy, Option 2, EOD drawdown)	1.5%	17%	82%	48
Earn2Trade Gauntlet Mini $50K	1.5%	34%	65%	48
Earn2Trade Trader Career Path TCP50	1.5%	34%	65%	48

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

Gaps over 2% are skipped.
The entry is the 09:35 price, after the first bar.
One market (M2K).
A backtest on the past, with fills simulated from one-minute bars; live fills, queues and outages differ.

8Conclusion

Marginal: profit factor 1.00 out of sample, not clearly above break-even, t = 0.3. Nothing on the grid clearly helps. Its in-sample best, smallest gap (%) 0.5 and stop (gap sizes) 1, did better out of sample, profit factor 1.25 against 1.00 for the defaults, but over only 299 trades, t = 1.8: too few, or too noisy, to call it a fix. The result is too close to break-even to tell apart from noise after costs.