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Initial balance breakouts on the Dow

Micro E-mini Dow (MYM) · 5-minute bars · 2016 to 2026 · Breakout · The initial balance is a Market Profile idea

No edge after costs: profit factor 0.93 out of sample.

Profit factor, out of sample
0.93
Average trade, out of sample
−0.03R
Win rate, out of sample
44%
Trades, 10 years
2,239
Max drawdown
$37,439
Pass odds, median plan
3.4%

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 initial balance breakout rules on Micro E-mini Dow (MYM) 5-minute bars from 2016-01-11 to 2026-10-30: 2,239 trades, with commission and a tick of slippage on every market and stop fill. In sample (to 2023-08-03) it made −0.08R per trade with a profit factor of 0.84; out of sample (from 2023-08-04) −0.03R per trade and 0.93 over 652 trades. 1 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 6% of the time across 36 tracked plans.

1Hypothesis

The initial balance is the first hour's range. Trade the first close outside it, stop at its midpoint, target one more range-width.

If the first hour sets the day's value area, a close outside it should extend by a full range-width more often than it returns to the middle.

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:

  1. The initial balance (IB) is the high and low of the first ib_minutes (60) after 09:30 ET.
  2. Entry: the first 5-minute bar after the IB that CLOSES above it buys at the next bar's open; the first that closes below it sells. Only that first close outside counts, and only until 13:00 ET. A breakout bar that already closed beyond the target is skipped.
  3. Stop: the IB's midpoint.
  4. Target: extension (1) IB widths beyond the edge that broke.
  5. Exit: the stop, the target, or flat at 15:55 ET. One trade a day.
  6. 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
"""Initial balance breakout on Micro E-mini Dow (MYM) 5-minute bars, regular session, New York time. Rules:  1. The initial balance (IB) is the high and low of the first `ib_minutes` (60) after 09:30 ET.  2. Entry: the first 5-minute bar after the IB that CLOSES above it buys at the next bar's open; the     first that closes below it sells. Only that first close outside counts, and only until 13:00 ET.     A breakout bar that already closed beyond the target is skipped.  3. Stop: the IB's midpoint.  4. Target: `extension` (1) IB widths beyond the edge that broke.  5. Exit: the stop, the target, or flat at 15:55 ET. One trade a day.  6. Size: about `risk_usd` dollars of risk a trade (micro contracts, rounded down, at least 1 and at     most `max_contracts`)."""import qy  def ready(*values):    """True when every value is a number (indicators have no value until they have enough data)."""    return all(v is not None and v == v for v in values)  class Bot(qy.Strategy):    """Trade the first close outside the first hour's range, stop at its midpoint."""     symbol = "MYM"    timeframe = "5m"    session = "rth"    params = {"ib_minutes": 60, "extension": 1.0, "risk_usd": 250, "max_contracts": 50}    max_position = 50    entry_window = ("10:00", "13:00")    flatten_time = "15:55"    overnight = False     def setup(self):        self.ib_high, self.ib_low = self.opening_range(minutes=self.p.ib_minutes)        self.done_day = None     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):        high, low = self.ib_high[0], self.ib_low[0]        if not ready(high, low) or self.done_day == bar.date or not self.position.is_flat:            return        close = self.close[0]        if low <= close <= high:            return        self.done_day = bar.date        t = bar.dt.hour * 60 + bar.dt.minute + 5        if not 600 <= t < 780:            return        width, mid = high - low, (high + low) / 2        f = width / close * 100        if close > high and close < high + self.p.extension * width:            self.buy(self._size(close - mid), stop_price=mid, target_price=high + self.p.extension * width, tag=f"IB long | f={f:.4f}")        elif close < low and close > low - self.p.extension * width:            self.sell(self._size(mid - close), stop_price=mid, target_price=low - self.p.extension * width, tag=f"IB 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.
StatisticIn sampleOut of sampleAll
Trades1,5876522,239
Net P&L−$31,602−$4,632−$36,234
Profit factor0.840.930.86
Average trade−0.08R−0.03R−0.06R
95% interval of the average−0.13 to −0.03−0.11 to 0.05−0.11 to −0.02
t-statistic of trade R−2.98−0.69−2.88
Win rate43%44%43%
Average win / average loss1.111.201.13
Max drawdown$33,346$6,581$37,439
Sharpe ratio (daily, annualized)−1.26−0.48−1.05
Longest losing streak999
Long / short profit factor0.94 / 0.750.99 / 0.880.95 / 0.78
Average holding time157 min176 min162 min
Commission paid$19,235$3,960$23,195
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 (1,587 trades)Out of sample (652)Averages

Averages: −0.08R 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.
In sampleOut of sampleQuintile mean, 95% intervalOLS fitDensity contours

1,600 of 2,239 points drawn; the fit and the means use all of them. Drag a box to zoom, double-click to reset.

Figure 3. Trade R against the initial balance's width, log x-axis; the dashed line is the OLS fit (r = 0.06, p = 0.007, n = 2,239; Spearman ρ = 0.22). Out of sample alone: r = −0.01, p = 0.78. 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 1 out of sample.

2
0.95
0.97
0.94
0.94
1.5
0.99
0.97
0.95
0.94
1
0.95
0.94
0.93
0.99
0.50
0.88
0.84
0.84
1.01
30
45
60
90

Initial balance (minutes) across, target (range widths) up. Solid outline: the defaults we tested; dashed: the in-sample best.

Hover a cell for both sides of the split.

Initial balance (minutes) 30.0 to 90.0 across · Target (range widths) 0.50 to 2.00 in depth · Profit factor, out of sample 0.84 to 1.01 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 initial balance (minutes) and target (range widths), 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, initial balance (minutes) 60 and target (range widths) 2, had profit factor 0.89 in sample but 0.94 out of sample, against 0.93 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 82.8% [81.0%, 84.3%]; 18% of years ended up; median ending P&L −$3,167, 5th to 95th percentile −$9,017 to +$2,307.

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 50K6.0%75%19%38
Alpha Futures Standard 50K Evaluation3.4%57%40%41
Alpha Futures Zero 50K Evaluation3.4%57%40%41
Apex Trader Funding 50K EOD Evaluation3.4%57%40%41
Apex Trader Funding 50K Intraday Trailing Drawdown Evaluation3.4%62%35%41
AquaFutures Beginner 50K Evaluation3.4%38%58%41
Bulenox 50K Qualification Account (Legacy, Option 1, trailing drawdown)3.4%43%53%41
Bulenox 50K Qualification Account (Legacy, Option 2, EOD drawdown)3.4%38%58%41
Earn2Trade Gauntlet Mini $50K3.4%57%40%41
Earn2Trade Trader Career Path TCP503.4%57%40%41
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

  • No filter on the balance's width.
  • 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 0.93 out of sample. Nothing on the grid helps. Its in-sample best, initial balance (minutes) 60 and target (range widths) 2, had profit factor 0.89 in sample but 0.94 out of sample, against 0.93 for the defaults. On this reading of the rules, with these costs, the historical record gives no reason to expect a profit.

9References

  1. Dalton, J. F., Jones, E. T., and Dalton, R. B. (2013). Mind Over Markets (updated edition). Wiley.

Every number on this page comes from the MYM 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.