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Donchian channel breakouts on crude oil, intraday

Micro Crude Oil (MCL) · 15-minute bars · 2016 to 2026 · Breakout · The turtle traders' channel rule, run intraday

No edge after costs: profit factor 0.79 out of sample.

Profit factor, out of sample
0.79
Average trade, out of sample
−0.12R
Win rate, out of sample
38%
Trades, 10 years
2,789
Max drawdown
$43,273
Pass odds, median plan
10.8%

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 donchian channel breakout rules on Micro Crude Oil (MCL) 15-minute bars from 2016-01-11 to 2026-10-30: 2,789 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 0.93; out of sample (from 2023-08-04) −0.12R per trade and 0.79 over 861 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 4% to 12% of the time across 36 tracked plans.

1Hypothesis

Buy a close above the highest high of the last 20 fifteen-minute bars, sell a close below the lowest low, and exit on the opposite 10-bar channel.

If crude's intraday moves trend, closes outside a 20-bar channel should catch moves long enough to pay for the false breaks.

2Data

Instrument
Micro Crude Oil (MCL), traded on the CL price series (CL.v.0: the most traded contract, rolled on volume). CME Globex via Databento.
Period
2016-01-11 to 2026-10-30: 2,791 sessions, 3,787,721 one-minute bars, built into 255,261 15-minute bars around the clock.
Costs
$0.75 commission a contract each way and 1 tick ($1.00) 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 entry channel is the highest high and lowest low of the previous entry_len (20) 15-minute bars; the exit channel the same over exit_len (10) bars. Bars run around the clock.
  2. Entry: a bar that closes above the entry channel's high buys at the next bar's open; one that closes below its low sells. New entries from 09:30 to 13:30 ET.
  3. Stop: stop_atr (2) times ATR(20) of the 15-minute bars from the fill.
  4. Exit: a close beyond the opposite side of the exit channel (at the next bar's open), the stop, or flat at 14:30 ET, the close of crude's day session.
  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
"""Donchian channel breakout on Micro Crude Oil (MCL) 15-minute bars, New York time. A turtle-style channel breakout, run intraday: no position is held overnight. Rules:  1. The entry channel is the highest high and lowest low of the previous `entry_len` (20) 15-minute     bars; the exit channel the same over `exit_len` (10) bars. Bars run around the clock.  2. Entry: a bar that closes above the entry channel's high buys at the next bar's open; one that     closes below its low sells. New entries from 09:30 to 13:30 ET.  3. Stop: `stop_atr` (2) times ATR(20) of the 15-minute bars from the fill.  4. Exit: a close beyond the opposite side of the exit channel (at the next bar's open), the stop, or     flat at 14:30 ET, the close of crude's day session.  5. 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):    """Close beyond the 20-bar channel, exit on the 10-bar channel."""     symbol = "MCL"    timeframe = "15m"    session = None    params = {"entry_len": 20, "exit_len": 10, "stop_atr": 2.0, "risk_usd": 250, "max_contracts": 50}    max_position = 50    entry_window = ("09:30", "13:30")    flatten_time = "14:30"    overnight = False    atr_period = 20     def setup(self):        self.hi = self.highest(self.high, self.p.entry_len)        self.lo = self.lowest(self.low, self.p.entry_len)        self.xhi = self.highest(self.high, self.p.exit_len)        self.xlo = self.lowest(self.low, self.p.exit_len)        self.atr_line = self.atr(20)     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 not ready(self.hi[1], self.lo[1], self.xhi[1], self.xlo[1], self.atr_line[0]):            return        close = self.close[0]        if self.position.is_long and close < self.xlo[1]:            self.close_position("exit channel")            return        if self.position.is_short and close > self.xhi[1]:            self.close_position("exit channel")            return        t = bar.dt.hour * 60 + bar.dt.minute + 15        if not self.position.is_flat or self.orders or not 570 <= t < 810:            return        atr = self.atr_line[0]        f = atr / close * 100        if close > self.hi[1]:            self.buy(self._size(self.p.stop_atr * atr), stop_atr=self.p.stop_atr, tag=f"Channel long | f={f:.4f}")        elif close < self.lo[1]:            self.sell(self._size(self.p.stop_atr * atr), stop_atr=self.p.stop_atr, tag=f"Channel 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
Trades1,9288612,789
Net P&L−$16,364−$21,887−$38,251
Profit factor0.930.790.89
Average trade−0.04R−0.12R−0.06R
95% interval of the average−0.10 to 0.02−0.20 to −0.04−0.11 to −0.01
t-statistic of trade R−1.20−2.96−2.49
Win rate39%38%38%
Average win / average loss1.491.291.43
Max drawdown$26,690$23,793$43,273
Sharpe ratio (daily, annualized)−0.45−1.70−0.78
Longest losing streak151215
Long / short profit factor0.97 / 0.900.75 / 0.840.90 / 0.88
Average holding time153 min143 min150 min
Commission paid$17,321$6,060$23,381
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 (1,928 trades)Out of sample (861)Averages

Averages: −0.04R in sample, −0.12R 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 2,789 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 volatility at entry, log x-axis; the dashed line is the OLS fit (r = 0.00, p = 0.95, n = 2,789; Spearman ρ = 0.24). Out of sample alone: r = −0.01, p = 0.88. 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.

20
0.81
0.80
0.82
0.81
15
0.80
0.80
0.82
0.81
10
0.80
0.79
0.82
0.80
5
0.78
0.77
0.79
0.79
10
20
30
55

Entry channel (bars) across, exit channel (bars) up. Solid outline: the defaults we tested; dashed: the in-sample best.

Hover a cell for both sides of the split.

Entry channel (bars) 10.0 to 55.0 across · Exit channel (bars) 5.00 to 20.0 in depth · Profit factor, out of sample 0.77 to 0.82 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 channel (bars) and exit channel (bars), 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 channel (bars) 55 and exit channel (bars) 10, had profit factor 0.96 in sample but 0.80 out of sample, against 0.79 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 (full 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 85.8% [84.1%, 87.2%]; 24% of years ended up; median ending P&L −$3,571, 5th to 95th percentile −$11,416 to +$4,343.

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
AquaFutures Beginner 50K Evaluation11.8%58%30%36
Bulenox 50K Qualification Account (Legacy, Option 2, EOD drawdown)11.8%58%30%36
Bulenox 50K Qualification Account (Legacy, Option 1, trailing drawdown)11.8%66%23%36
Alpha Futures Zero 50K Evaluation11.5%73%16%36
Apex Trader Funding 50K EOD Evaluation11.5%73%16%36
Funded Futures Family 50K Evaluation (standard plan)11.5%73%16%36
Top One Futures Elite Challenge 50K11.5%73%16%36
Elite Trader Funding 50K End of Day Drawdown Evaluation11.5%73%16%36
FundedNext Futures Rapid Pro Challenge 50K11.5%73%16%36
Lucid Trading LucidPro 50K Evaluation11.5%73%16%36
Scale 0 to 25%.
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

  • Turtle rules were daily and held for weeks; this version is flat by 14:30 ET.
  • One market (MCL).
  • 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 0.79 out of sample. Nothing on the grid helps. Its in-sample best, entry channel (bars) 55 and exit channel (bars) 10, had profit factor 0.96 in sample but 0.80 out of sample, against 0.79 for the defaults. On this reading of the rules, with these costs, the historical record gives no reason to expect a profit.

9References

  1. Faith, C. (2007). Way of the Turtle. McGraw-Hill.

Every number on this page comes from the MCL 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.