The turn-of-the-month effect in Dow futures

Micro E-mini Dow (MYM) · 5-minute bars · 2016 to 2026 · Seasonality

Too few trades to judge: 34 out of sample.

Profit factor, out of sample: 1.30
Average trade, out of sample: +0.03R
Win rate, out of sample: 56%
Trades, 10 years: 122
Max drawdown: $2,442
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 turn of the month rules on Micro E-mini Dow (MYM) 5-minute bars from 2016-01-11 to 2026-10-30: 122 trades, with commission and a tick of slippage on every market and stop fill. In sample (to 2023-08-03) it made +0.11R per trade with a profit factor of 1.89; out of sample (from 2023-08-04) +0.03R per trade and 1.30 over 34 trades. 14 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

Stock indices have been found to earn much of their return around the turn of the month. Hold the Dow future from the month's last session to the third of the next.

If month-end flows lift prices at the turn of the month, these four sessions should earn more than an average four sessions.

2Data

Instrument: Micro E-mini Dow (MYM), traded on the YM price series (YM.v.0: the most traded contract, rolled on volume). CME Globex via Databento.
Period: 2016-01-11 to 2026-10-30: 2,787 sessions, 3,754,577 one-minute bars, built into 214,002 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:

Entry: buy at 15:55 ET on the month's entry_offset (-1: the last) trading session.
Exit: at 15:55 ET on the new month's exit_day (3: third) trading session.
Stop: 3 times the 10-day average true range below the entry, for a defined risk.
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"""The turn of the month on Micro E-mini Dow (MYM), long only.2 3Each session's 15:55 ET price stands in for its daily close; positions are held overnight. The trading4calendar is the published exchange calendar (US market holidays), so the last session of a month is5known in advance.6 7Rules:8  1. Entry: buy at 15:55 ET on the month's `entry_offset` (-1: the last) trading session.9  2. Exit: at 15:55 ET on the new month's `exit_day` (3: third) trading session.10  3. Stop: 3 times the 10-day average true range below the entry, for a defined risk.11  4. Size: about `risk_usd` dollars of risk a trade (micro contracts, rounded down, at least 1 and at12     most `max_contracts`).13"""14import qy15 16 17class Bot(qy.Strategy):18    """Own the last session of the month through the first few of the next."""19 20    symbol = "MYM"21    timeframe = "5m"22    session = "rth"23    params = {"entry_offset": -1, "exit_day": 3, "risk_usd": 250, "max_contracts": 50}24    max_position = 5025    overnight = True26 27    def setup(self):28        cal = qy.montecarlo.trading_sessions("2010-01-01", "2030-12-31")29        self.from_end, self.from_start = {}, {}30        by_month = {}31        for d in cal:32            by_month.setdefault(d[:7], []).append(d)33        for days in by_month.values():34            for k, d in enumerate(days):35                self.from_start[d] = k + 136                self.from_end[d] = k - len(days)37        self.day = None38        self.prev = None39        self.trs = []40 41    def _size(self, risk_points):42        per_contract = risk_points * self.point_value43        n = int(self.p.risk_usd // per_contract) if per_contract > 0 else 144        return max(1, min(self.p.max_contracts, n))45 46    def on_bar(self, bar):47        if bar.date != self.day:48            self.day, self.dh, self.dl = bar.date, bar.high, bar.low49        else:50            self.dh, self.dl = max(self.dh, bar.high), min(self.dl, bar.low)51        if bar.dt.hour * 60 + bar.dt.minute != 950:52            return53        c = bar.close54        if self.prev is not None:55            self.trs = (self.trs + [max(self.dh, self.prev) - min(self.dl, self.prev)])[-10:]56        self.prev = c57        d = bar.date.isoformat()58        if self.position.is_long:59            if self.from_start.get(d) == self.p.exit_day:60                self.close_position("turn of the month over")61            return62        if self.from_end.get(d) == self.p.entry_offset and len(self.trs) == 10 and not self.orders:63            atr = sum(self.trs) / 1064            self.buy(self._size(3 * atr), stop_price=c - 3 * atr, tag="Turn of month long")

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	88	34	122
Net P&L	+$4,757	+$1,115	+$5,872
Profit factor	1.89	1.30	1.65
Average trade	+0.11R	+0.03R	+0.08R
95% interval of the average	0.01 to 0.21	−0.14 to 0.20	0.00 to 0.17
t-statistic of trade R	2.13	0.34	1.98
Win rate	61%	56%	60%
Average win / average loss	1.19	1.02	1.11
Max drawdown	$863	$1,804	$2,442
Sharpe ratio (daily, annualized)	3.44	1.59	2.81
Longest losing streak	4	3	4
Long / short profit factor	1.89 / n/a	1.30 / n/a	1.65 / n/a
Average holding time	7223 min	9787 min	7938 min
Commission paid	$134	$51	$185

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 (88 trades)Out of sample (34)Averages

Averages: +0.11R 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.

Figure 3. Average close-to-close return of the Dow future by trading session of the month, the last five and the first five, 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 14 out of sample.

1.09

1.37

1.13

0.98

1.58

1.85

1.71

1.30

1.14

1.33

1.14

1.06

1.00

1.22

1.01

0.74

−4

−3

−2

−1

Entry (sessions before the month ends) across, exit (session of the new month) up. Solid outline: the defaults we tested; dashed: the in-sample best.

Hover a cell for both sides of the split.

Entry (sessions before the month ends) -4.00 to -1.00 across · Exit (session of the new month) 1.00 to 5.00 in depth · Profit factor, out of sample 0.74 to 1.85 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 entry (sessions before the month ends) and exit (session of the new month), 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, entry (sessions before the month ends) -1 and exit (session of the new month) 5, had profit factor 2.28 in sample but 0.98 out of sample, against 1.30 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 0.5% [0.3%, 1.0%]; 70% of years ended up; median ending P&L +$442, 5th to 95th percentile −$873 to +$2,115.

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	0.1%	0.3%	100%	47
Alpha Futures Advanced 50K Evaluation	0.0%	0.1%	100%	n/a
Alpha Futures Standard 50K Evaluation	0.0%	0.1%	100%	n/a
Alpha Futures Zero 50K Evaluation	0.0%	0.1%	100%	n/a
Apex Trader Funding 50K EOD Evaluation	0.0%	0.1%	100%	n/a
Apex Trader Funding 50K Intraday Trailing Drawdown Evaluation	0.0%	0.3%	100%	n/a
AquaFutures Beginner 50K Evaluation	0.0%	0%	100%	n/a
Bulenox 50K Qualification Account (Legacy, Option 1, trailing drawdown)	0.0%	0.1%	100%	n/a
Bulenox 50K Qualification Account (Legacy, Option 2, EOD drawdown)	0.0%	0%	100%	n/a
Earn2Trade Gauntlet Mini $50K	0.0%	0.1%	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

Long only, in a rising market: compare the result with simply holding.
Twelve trades a year.
A backtest on the past, with fills simulated from one-minute bars; live fills, queues and outages differ.

8Conclusion

Too few trades to judge: 34 out of sample. Nothing on the grid helps. Its in-sample best, entry (sessions before the month ends) -1 and exit (session of the new month) 5, had profit factor 2.28 in sample but 0.98 out of sample, against 1.30 for the defaults. The result is too close to break-even to tell apart from noise after costs.

9References

Ariel, R. A. (1987). A Monthly Effect in Stock Returns. Journal of Financial Economics, 18(1), 161-174.
Lakonishok, J., and Smidt, S. (1988). Are Seasonal Anomalies Real? A Ninety-Year Perspective. Review of Financial Studies, 1(4), 403-425.