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Turnaround Tuesday on the Nasdaq-100

Micro E-mini Nasdaq-100 (MNQ) · 5-minute bars · 2016 to 2026 · Seasonality

Marginal: profit factor 1.09 out of sample, not clearly above break-even, t = 0.4.

Profit factor, out of sample
1.09
Average trade, out of sample
+0.02R
Win rate, out of sample
59%
Trades, 10 years
202
Max drawdown
$4,350
Pass odds, median plan
0.1%

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 turnaround Tuesday rules on Micro E-mini Nasdaq-100 (MNQ) 5-minute bars from 2016-01-11 to 2026-10-30: 202 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 1.27; out of sample (from 2023-08-04) +0.02R per trade and 1.09 over 59 trades. 10 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

Buy Monday's close after a down Monday and sell Tuesday's close: a folk pattern that weak Mondays bounce.

If Monday weakness tends to reverse the next day, a down Monday's close should be a better-than-average entry for one day.

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, 3,739,235 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:

  1. A down Monday: Monday's 15:55 ET price is at least min_drop_pct (0%) below Monday's 09:30 open. A Monday holiday means no trade that week.
  2. Entry: buy at Monday's 15:55 ET price (the next bar's open).
  3. Exit: at 15:55 ET, hold_days (1) sessions later (Tuesday, by default).
  4. Stop: 2 times the 10-day average true range below the entry, for a defined risk.
  5. 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
"""Turnaround Tuesday on Micro E-mini Nasdaq-100 (MNQ): buy a down Monday's close, long only. Each session's 15:55 ET price stands in for its daily close; positions are held overnight. Rules:  1. A down Monday: Monday's 15:55 ET price is at least `min_drop_pct` (0%) below Monday's 09:30 open.     A Monday holiday means no trade that week.  2. Entry: buy at Monday's 15:55 ET price (the next bar's open).  3. Exit: at 15:55 ET, `hold_days` (1) sessions later (Tuesday, by default).  4. Stop: 2 times the 10-day average true range below the entry, for a defined risk.  5. 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):    """After a down Monday, own Monday's close into Tuesday's."""     symbol = "MNQ"    timeframe = "5m"    session = "rth"    params = {"min_drop_pct": 0.0, "hold_days": 1, "risk_usd": 250, "max_contracts": 50}    max_position = 50    overnight = True     def setup(self):        self.day = None        self.prev = None        self.trs = []        self.held = 0     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):        if bar.date != self.day:            self.day, self.do, self.dh, self.dl = bar.date, bar.open, bar.high, bar.low        else:            self.dh, self.dl = max(self.dh, bar.high), min(self.dl, bar.low)        if bar.dt.hour * 60 + bar.dt.minute != 950:            return        c = bar.close        if self.prev is not None:            self.trs = (self.trs + [max(self.dh, self.prev) - min(self.dl, self.prev)])[-10:]        self.prev = c        if self.position.is_long:            self.held += 1            if self.held >= self.p.hold_days:                self.close_position("holding period")            return        if bar.date.weekday() != 0 or len(self.trs) < 10 or self.orders:            return        drop = (self.do - c) / self.do * 100        if drop > 0 and drop >= self.p.min_drop_pct:            atr = sum(self.trs) / 10            self.held = 0            self.buy(self._size(2 * atr), stop_price=c - 2 * atr, tag=f"Turnaround long | f={drop:.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
Trades14359202
Net P&L+$3,670+$1,140+$4,810
Profit factor1.271.091.18
Average trade+0.04R+0.02R+0.03R
95% interval of the average−0.02 to 0.11−0.08 to 0.12−0.02 to 0.09
t-statistic of trade R1.240.371.25
Win rate60%59%60%
Average win / average loss0.840.750.79
Max drawdown$1,932$4,350$4,350
Sharpe ratio (daily, annualized)1.360.490.90
Longest losing streak636
Long / short profit factor1.27 / n/a1.09 / n/a1.18 / n/a
Average holding time1458 min1436 min1452 min
Commission paid$215$89$303
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 (143 trades)Out of sample (59)Averages

Averages: +0.04R in sample, +0.02R 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.
Figure 3. Average return of the Nasdaq-100 future by weekday, open to close and close to close, with 95% intervals
In sampleOut of sampleQuintile mean, 95% intervalOLS fitDensity contours

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

Figure 4. Trade R against the size of Monday's drop, log x-axis; the dashed line is the OLS fit (r = 0.08, p = 0.26, n = 202; Spearman ρ = 0.07). Out of sample alone: r = 0.07, p = 0.61. 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. 16 were profitable in sample and 10 out of sample.

4
0.99
0.82
0.72
1.11
3
1.09
0.93
0.82
1.41
2
1.42
1.28
1.19
1.44
1
1.09
0.97
1.42
3.11
0
0.25
0.50
1

Smallest Monday drop (%) across, holding period (sessions) up. Solid outline: the defaults we tested; dashed: the in-sample best.

Hover a cell for both sides of the split.

Smallest Monday drop (%) 0.00 to 1.00 across · Holding period (sessions) 1.00 to 4.00 in depth · Profit factor, out of sample 0.72 to 3.11 as height and shade. Drag or use the arrow keys to turn it.

Drag to rotate; ctrl or ⌘ and scroll to zoom.

Figure 5. Profit factor across smallest Monday drop (%) and holding period (sessions), 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, smallest Monday drop (%) 0.5 and holding period (sessions) 2, had profit factor 2.28 in sample but 1.19 out of sample, against 1.09 for the defaults.

In-sample yearOut-of-sample year95% interval
Figure 6. Average trade by calendar year with its 95% interval: a year-by-year walk through the history. Lighter bars are in-sample years.
Figure 7. 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 8. 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 9. 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 18.6% [17.0%, 20.4%]; 62% of years ended up; median ending P&L +$478, 5th to 95th percentile −$2,663 to +$3,416.

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 50K0.4%9%91%49
Alpha Futures Standard 50K Evaluation0.1%5%94%58
Alpha Futures Zero 50K Evaluation0.1%6%94%58
Apex Trader Funding 50K EOD Evaluation0.1%6%94%58
Apex Trader Funding 50K Intraday Trailing Drawdown Evaluation0.1%6%94%58
Bulenox 50K Qualification Account (Legacy, Option 1, trailing drawdown)0.1%3%96%58
Bulenox 50K Qualification Account (Legacy, Option 2, EOD drawdown)0.1%1%98%58
FTMO Futures Pro 50K Evaluation0.1%11%89%58
Funded Futures Family 50K Evaluation (standard plan)0.1%5%94%58
Funded Futures Network Express MAX 50K Evaluation0.1%6%94%58
Scale 0 to 10%.
Figure 10. 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

  • About one trade a fortnight: few trades for a weekday effect.
  • Long only, in a rising market.
  • A backtest on the past, with fills simulated from one-minute bars; live fills, queues and outages differ.

8Conclusion

Marginal: profit factor 1.09 out of sample, not clearly above break-even, t = 0.4. Nothing on the grid helps. Its in-sample best, smallest Monday drop (%) 0.5 and holding period (sessions) 2, had profit factor 2.28 in sample but 1.19 out of sample, against 1.09 for the defaults. The result is too close to break-even to tell apart from noise after costs.

9References

  1. French, K. R. (1980). Stock Returns and the Weekend Effect. Journal of Financial Economics, 8(1), 55-69.

Every number on this page comes from the MNQ 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.