The volatility squeeze on the Nasdaq-100

Micro E-mini Nasdaq-100 (MNQ) · 15-minute bars · 2016 to 2026 · Volatility · Popularized by John Carter as the TTM Squeeze

Depends on the period: it lost in sample (profit factor 0.91) and made money out of sample (1.21).

Profit factor, out of sample: 1.21
Average trade, out of sample: +0.07R
Win rate, out of sample: 46%
Trades, 10 years: 804
Max drawdown: $11,927
Pass odds, median plan: 0.4%

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 bollinger-Keltner squeeze rules on Micro E-mini Nasdaq-100 (MNQ) 15-minute bars from 2016-01-11 to 2026-10-30: 804 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 0.91; out of sample (from 2023-08-04) +0.07R per trade and 1.21 over 252 trades. 12 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 1% of the time across 36 tracked plans.

1Hypothesis

When Bollinger Bands contract inside Keltner Channels, volatility is compressed. Trade the release in the direction of the 20-bar average.

If compressed volatility tends to expand with a directional move, the first bar after a squeeze ends should start a trade worth twice its risk.

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 70,275 15-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:

On 15-minute regular-session bars: Bollinger Bands are the 20-bar SMA plus and minus 2 standard deviations; Keltner Channels the 20-bar EMA plus and minus kc_mult (1.5) times ATR(20).
The squeeze is on while both Bollinger Bands sit inside the Keltner Channels.
Entry: on the first bar after a squeeze ends (the bands move back outside the channels), buy at the next bar's open if the close is above the 20-bar SMA, sell if it is below. New entries from 10:00 to 15:00 ET.
Stop: 1.5 times ATR(20) from the fill.
Target: target_r (2) times the risk.
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).

The commonly taught version reads the direction from a momentum histogram; we use the side of the

20-bar average, the simplest reading.

bot.py, the file the engine ran

1"""The volatility squeeze (Bollinger Bands inside Keltner Channels), on Micro E-mini Nasdaq-100 (MNQ) 15-minute bars.2 3Our reading of the commonly taught rules; traders who teach it may trade it differently.4 5Rules:6  1. On 15-minute regular-session bars: Bollinger Bands are the 20-bar SMA plus and minus 2 standard7     deviations; Keltner Channels the 20-bar EMA plus and minus `kc_mult` (1.5) times ATR(20).8  2. The squeeze is on while both Bollinger Bands sit inside the Keltner Channels.9  3. Entry: on the first bar after a squeeze ends (the bands move back outside the channels), buy at10     the next bar's open if the close is above the 20-bar SMA, sell if it is below. New entries from11     10:00 to 15:00 ET.12  4. Stop: 1.5 times ATR(20) from the fill.13  5. Target: `target_r` (2) times the risk.14  6. Exit: the stop, the target, or flat at 15:55 ET.15  7. Size: about `risk_usd` dollars of risk a trade (micro contracts, rounded down, at least 1 and at16     most `max_contracts`).17  The commonly taught version reads the direction from a momentum histogram; we use the side of the18  20-bar average, the simplest reading.19"""20import qy21 22 23def ready(*values):24    """True when every value is a number (indicators have no value until they have enough data)."""25    return all(v is not None and v == v for v in values)26 27 28class Bot(qy.Strategy):29    """Trade the release of a Bollinger-inside-Keltner squeeze."""30 31    symbol = "MNQ"32    timeframe = "15m"33    session = "rth"34    params = {"kc_mult": 1.5, "target_r": 2.0, "risk_usd": 250, "max_contracts": 50}35    max_position = 5036    entry_window = ("10:00", "15:00")37    flatten_time = "15:55"38    overnight = False39    atr_period = 2040 41    def setup(self):42        self.mid, self.bb_up, self.bb_lo = self.bbands(self.close, 20, 2.0)43        self.kc_mid = self.ema(self.close, 20)44        self.atr_line = self.atr(20)45        self.was_on = False46 47    def _size(self, risk_points):48        per_contract = risk_points * self.point_value49        n = int(self.p.risk_usd // per_contract) if per_contract > 0 else 150        return max(1, min(self.p.max_contracts, n))51 52    def on_bar(self, bar):53        mid, up, lo, kc, atr = self.mid[0], self.bb_up[0], self.bb_lo[0], self.kc_mid[0], self.atr_line[0]54        if not ready(mid, up, lo, kc, atr):55            return56        on = up < kc + self.p.kc_mult * atr and lo > kc - self.p.kc_mult * atr57        fired = self.was_on and not on58        self.was_on = on59        t = bar.dt.hour * 60 + bar.dt.minute + 1560        if not fired or not self.position.is_flat or self.orders or not 600 <= t < 900:61            return62        f = atr / bar.close * 10063        qty = self._size(1.5 * atr)64        if bar.close > mid:65            self.buy(qty, stop_atr=1.5, target_r=self.p.target_r, tag=f"Squeeze long | f={f:.4f}")66        elif bar.close < mid:67            self.sell(qty, stop_atr=1.5, target_r=self.p.target_r, tag=f"Squeeze 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.
Statistic	In sample	Out of sample	All
Trades	552	252	804
Net P&L	−$5,745	+$4,958	−$787
Profit factor	0.91	1.21	0.99
Average trade	−0.04R	+0.07R	0.00R
95% interval of the average	−0.13 to 0.05	−0.07 to 0.22	−0.08 to 0.07
t-statistic of trade R	−0.84	1.04	−0.11
Win rate	42%	46%	44%
Average win / average loss	1.23	1.40	1.28
Max drawdown	$11,927	$2,891	$11,927
Sharpe ratio (daily, annualized)	−0.70	1.32	−0.07
Longest losing streak	9	7	9
Long / short profit factor	0.92 / 0.89	1.12 / 1.31	0.97 / 1.01
Average holding time	157 min	166 min	160 min
Commission paid	$4,205	$522	$4,727

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 (552 trades)Out of sample (252)Averages

Averages: −0.04R in sample, +0.07R 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 volatility when the squeeze fired, log x-axis; the dashed line is the OLS fit (r = 0.10, p = 0.003, n = 804; Spearman ρ = 0.24). Out of sample alone: r = 0.02, p = 0.74. 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. 1 were profitable in sample and 12 out of sample.

1.33

1.13

1.22

1.00

1.30

1.13

1.21

0.95

1.5

1.37

1.21

1.27

0.90

1.40

1.09

1.05

0.98

1.25

1.5

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

Hover a cell for both sides of the split.

Keltner width (ATRs) 1.00 to 2.00 across · Target (R) 1.00 to 3.00 in depth · Profit factor, out of sample 0.90 to 1.40 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 keltner width (ATRs) 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, keltner width (ATRs) 2 and target (R) 3, had profit factor 1.00 in sample but 1.00 out of sample, against 1.21 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 33.3% [31.3%, 35.4%]; 48% of years ended up; median ending P&L −$103, 5th to 95th percentile −$3,553 to +$3,560.

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	1.3%	19%	80%	47
Alpha Futures Standard 50K Evaluation	0.4%	7%	93%	55
Alpha Futures Zero 50K Evaluation	0.4%	7%	93%	55
Apex Trader Funding 50K EOD Evaluation	0.4%	7%	93%	55
Apex Trader Funding 50K Intraday Trailing Drawdown Evaluation	0.4%	9%	91%	55
AquaFutures Beginner 50K Evaluation	0.4%	2%	98%	55
Bulenox 50K Qualification Account (Legacy, Option 1, trailing drawdown)	0.4%	3%	97%	55
Bulenox 50K Qualification Account (Legacy, Option 2, EOD drawdown)	0.4%	2%	98%	55
Elite Trader Funding 50K 1-Step Evaluation	0.4%	9%	91%	55
Elite Trader Funding 50K End of Day Drawdown Evaluation	0.4%	7%	93%	55

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

The commonly taught version reads direction from a momentum histogram; we use the side of the 20-bar average.
One market (MNQ).
A backtest on the past, with fills simulated from one-minute bars; live fills, queues and outages differ.

8Conclusion

Depends on the period: it lost in sample (profit factor 0.91) and made money out of sample (1.21). Nothing on the grid helps. Its in-sample best, keltner width (ATRs) 2 and target (R) 3, had profit factor 1.00 in sample but 1.00 out of sample, against 1.21 for the defaults. A rule that loses for years and then wins says more about the market changing than about the rule: the out-of-sample gain needs data after 2026-10-30 before it means anything.

9References

Carter, J. F. (2005). Mastering the Trade. McGraw-Hill.