Breaking yesterday's high and low on the Dow

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

No edge after costs: profit factor 0.89 out of sample.

Profit factor, out of sample: 0.89
Average trade, out of sample: −0.08R
Win rate, out of sample: 36%
Trades, 10 years: 1,094
Max drawdown: $32,470
Pass odds, median plan: 1.6%

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 previous-day high/low breakout rules on Micro E-mini Dow (MYM) 5-minute bars from 2016-01-11 to 2026-10-30: 1,094 trades, with commission and a tick of slippage on every market and stop fill. In sample (to 2023-08-03) it made −0.14R per trade with a profit factor of 0.78; out of sample (from 2023-08-04) −0.08R per trade and 0.89 over 381 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 0% to 3% of the time across 36 tracked plans.

1Hypothesis

If the day opens inside yesterday's range, trade the first 5-minute close through yesterday's high or low with the move, stop two ATRs away.

If yesterday's extremes hold resting orders, a close through them should run with the breakout often enough to reach 2R.

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, 1,072,672 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:

Yesterday's high and low are its regular-session (09:30-16:00 ET) extremes. The day must open inside them.
Entry: the first 5-minute bar that CLOSES above yesterday's high buys at the next bar's open; the first that closes below yesterday's low sells. One trade a day, from 09:35 to 14:00 ET.
Stop: stop_atr (2) times ATR(14) of the 5-minute bars 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).

bot.py, the file the engine ran

1"""Previous-day high and low breakout on Micro E-mini Dow (MYM) 5-minute bars, regular session.2 3Rules:4  1. Yesterday's high and low are its regular-session (09:30-16:00 ET) extremes. The day must open5     inside them.6  2. Entry: the first 5-minute bar that CLOSES above yesterday's high buys at the next bar's open; the7     first that closes below yesterday's low sells. One trade a day, from 09:35 to 14:00 ET.8  3. Stop: `stop_atr` (2) times ATR(14) of the 5-minute bars from the fill.9  4. Target: `target_r` (2) times the risk.10  5. Exit: the stop, the target, or flat at 15:55 ET.11  6. 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 17def ready(*values):18    """True when every value is a number (indicators have no value until they have enough data)."""19    return all(v is not None and v == v for v in values)20 21 22class Bot(qy.Strategy):23    """Trade a close through yesterday's high or low with the move."""24 25    symbol = "MYM"26    timeframe = "5m"27    session = "rth"28    params = {"stop_atr": 2.0, "target_r": 2.0, "risk_usd": 250, "max_contracts": 50}29    max_position = 5030    entry_window = ("09:35", "14:00")31    flatten_time = "15:55"32    overnight = False33 34    def setup(self):35        self.atr_line = self.atr(14)36        self.day = None37        self.h = self.l = None38        self.yh = self.yl = None39        self.ok = False40        self.done = False41 42    def _size(self, risk_points):43        per_contract = risk_points * self.point_value44        n = int(self.p.risk_usd // per_contract) if per_contract > 0 else 145        return max(1, min(self.p.max_contracts, n))46 47    def on_bar(self, bar):48        if bar.date != self.day:49            if self.h is not None:50                self.yh, self.yl = self.h, self.l51            self.day, self.h, self.l = bar.date, bar.high, bar.low52            self.ok = self.yh is not None and self.yl < bar.open < self.yh53            self.done = False54        else:55            self.h, self.l = max(self.h, bar.high), min(self.l, bar.low)56        atr = self.atr_line[0]57        t = bar.dt.hour * 60 + bar.dt.minute + 558        if not self.ok or self.done or not ready(atr) or not self.position.is_flat or not 575 <= t < 840:59            return60        close = bar.close61        f = (self.yh - self.yl) / close * 10062        if close > self.yh:63            self.done = True64            self.buy(self._size(self.p.stop_atr * atr), stop_atr=self.p.stop_atr, target_r=self.p.target_r, tag=f"PDH break long | f={f:.4f}")65        elif close < self.yl:66            self.done = True67            self.sell(self._size(self.p.stop_atr * atr), stop_atr=self.p.stop_atr, target_r=self.p.target_r, tag=f"PDL break 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	713	381	1,094
Net P&L	−$24,424	−$6,053	−$30,477
Profit factor	0.78	0.89	0.82
Average trade	−0.14R	−0.08R	−0.12R
95% interval of the average	−0.24 to −0.05	−0.21 to 0.06	−0.20 to −0.04
t-statistic of trade R	−2.98	−1.12	−3.05
Win rate	34%	36%	35%
Average win / average loss	1.51	1.61	1.55
Max drawdown	$27,169	$7,556	$32,470
Sharpe ratio (daily, annualized)	−1.81	−0.84	−1.47
Longest losing streak	13	9	13
Long / short profit factor	0.81 / 0.76	0.96 / 0.82	0.86 / 0.78
Average holding time	133 min	110 min	125 min
Commission paid	$10,073	$2,970	$13,043

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 (713 trades)Out of sample (381)Averages

Averages: −0.14R in sample, −0.08R 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 yesterday's range, log x-axis; the dashed line is the OLS fit (r = 0.01, p = 0.77, n = 1,094; Spearman ρ = 0.31). Out of sample alone: r = −0.01, p = 0.81. 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.

0.90

0.84

0.87

0.92

0.84

0.89

1.5

0.80

0.79

0.89

0.92

0.82

0.78

0.80

0.91

1.5

Stop (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.

Stop (ATRs) 1.00 to 3.00 across · Target (R) 1.00 to 3.00 in depth · Profit factor, out of sample 0.78 to 0.92 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 stop (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, stop (ATRs) 2 and target (R) 1, had profit factor 0.83 in sample but 0.80 out of sample, against 0.89 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 79.4% [77.6%, 81.1%]; 17% of years ended up; median ending P&L −$2,766, 5th to 95th percentile −$7,563 to +$2,422.

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%	63%	34%	41
Alpha Futures Standard 50K Evaluation	1.6%	40%	58%	43
Alpha Futures Zero 50K Evaluation	1.6%	40%	58%	43
Apex Trader Funding 50K EOD Evaluation	1.6%	40%	58%	43
Apex Trader Funding 50K Intraday Trailing Drawdown Evaluation	1.6%	49%	50%	43
AquaFutures Beginner 50K Evaluation	1.6%	23%	76%	43
Bulenox 50K Qualification Account (Legacy, Option 1, trailing drawdown)	1.6%	28%	71%	43
Bulenox 50K Qualification Account (Legacy, Option 2, EOD drawdown)	1.6%	23%	76%	43
Earn2Trade Gauntlet Mini $50K	1.6%	40%	58%	43
Earn2Trade Trader Career Path TCP50	1.6%	40%	58%	43

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 stop is volatility-based, not at a structure level.
One market (MYM).
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 0.89 out of sample. Nothing on the grid helps. Its in-sample best, stop (ATRs) 2 and target (R) 1, had profit factor 0.83 in sample but 0.80 out of sample, against 0.89 for the defaults. On this reading of the rules, with these costs, the historical record gives no reason to expect a profit.