Skip to content
QuantReplay

Keltner channel breakouts on gold

Micro Gold (MGC) · 15-minute bars · 2016 to 2026 · Breakout · Channels after Chester Keltner

No edge after costs: profit factor 0.87 out of sample.

Profit factor, out of sample
0.87
Average trade, out of sample
−0.07R
Win rate, out of sample
40%
Trades, 10 years
2,121
Max drawdown
$16,602
Pass odds, median plan
3.6%

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.

SharePost on XLinkedInReddit

Abstract

We backtested the keltner channel breakout rules on Micro Gold (MGC) 15-minute bars from 2016-01-11 to 2026-10-30: 2,121 trades, with commission and a tick of slippage on every market and stop fill. In sample (to 2023-08-03) it made −0.02R per trade with a profit factor of 0.96; out of sample (from 2023-08-04) −0.07R per trade and 0.87 over 651 trades. 4 of the 16 parameter settings we tried were profitable out of sample. Sized at $250 of risk a trade, simulated 50K evaluations passed 1% to 6% of the time across 36 tracked plans.

1Hypothesis

Buy a 15-minute close above the 20-bar EMA plus two ATRs, sell a close below the EMA minus two ATRs, with the stop at the EMA.

If a close two ATRs from the average marks a real move in gold, it should reach twice its risk before falling back to the average more often than one time in three.

2Data

Instrument
Micro Gold (MGC), traded on the GC price series (GC.v.0: the most traded contract, rolled on volume). CME Globex via Databento.
Period
2016-01-11 to 2026-10-30: 2,791 sessions, 3,771,561 one-minute bars, built into 254,816 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 channel is an EMA of the closes (length, 20 bars) plus and minus mult (2) times ATR(10) of the 15-minute bars. Bars run around the clock.
  2. Entry: a bar that closes above the upper band buys at the next bar's open; one that closes below the lower band sells. New entries from 08:30 to 13:00 ET, gold's busiest hours.
  3. Stop: the EMA at the signal, the channel's middle.
  4. Target: target_r (2) times the risk.
  5. Exit: the stop, the target, or flat at 13:30 ET, the close of the gold pit session.
  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
"""Keltner channel breakout on Micro Gold (MGC) 15-minute bars, New York time. Rules:  1. The channel is an EMA of the closes (`length`, 20 bars) plus and minus `mult` (2) times ATR(10)     of the 15-minute bars. Bars run around the clock.  2. Entry: a bar that closes above the upper band buys at the next bar's open; one that closes below     the lower band sells. New entries from 08:30 to 13:00 ET, gold's busiest hours.  3. Stop: the EMA at the signal, the channel's middle.  4. Target: `target_r` (2) times the risk.  5. Exit: the stop, the target, or flat at 13:30 ET, the close of the gold pit session.  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):    """Close outside the Keltner channel, stop at its middle."""     symbol = "MGC"    timeframe = "15m"    session = None    params = {"length": 20, "mult": 2.0, "target_r": 2.0, "risk_usd": 250, "max_contracts": 50}    max_position = 50    entry_window = ("08:30", "13:00")    flatten_time = "13:30"    overnight = False     def setup(self):        self.mid = self.ema(self.close, self.p.length)        self.atr_line = self.atr(10)     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):        mid, atr = self.mid[0], self.atr_line[0]        if not ready(mid, atr) or not self.position.is_flat or self.orders:            return        t = bar.dt.hour * 60 + bar.dt.minute + 15        if not 510 <= t < 780:            return        close = self.close[0]        f = atr / close * 100        if close > mid + self.p.mult * atr:            self.buy(self._size(close - mid), stop_price=mid, target_r=self.p.target_r, tag=f"Keltner long | f={f:.4f}")        elif close < mid - self.p.mult * atr:            self.sell(self._size(mid - close), stop_price=mid, target_r=self.p.target_r, tag=f"Keltner 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
Trades1,4706512,121
Net P&L−$5,423−$8,005−$13,428
Profit factor0.960.870.93
Average trade−0.02R−0.07R−0.03R
95% interval of the average−0.07 to 0.03−0.14 to 0.00−0.07 to 0.01
t-statistic of trade R−0.68−1.88−1.57
Win rate44%40%43%
Average win / average loss1.221.301.24
Max drawdown$14,461$8,663$16,602
Sharpe ratio (daily, annualized)−0.28−0.89−0.48
Longest losing streak91010
Long / short profit factor0.95 / 0.980.83 / 0.930.91 / 0.96
Average holding time152 min153 min152 min
Commission paid$9,294$1,802$11,096
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 (1,470 trades)Out of sample (651)Averages

Averages: −0.02R in sample, −0.07R 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 2,121 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 = 0.90, n = 2,121; Spearman ρ = 0.13). Out of sample alone: r = 0.03, p = 0.45. 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. 8 were profitable in sample and 4 out of sample.

50
1.03
1.00
0.95
0.98
30
0.90
0.90
0.92
1.02
20
0.92
0.87
0.94
1.02
10
0.88
0.84
0.83
1.21
1.5
2
2.5
3

Band width (ATRs) across, EMA length (bars) up. Solid outline: the defaults we tested; dashed: the in-sample best.

Hover a cell for both sides of the split.

Band width (ATRs) 1.50 to 3.00 across · EMA length (bars) 10.0 to 50.0 in depth · Profit factor, out of sample 0.83 to 1.21 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 band width (ATRs) and EMA length (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, band width (ATRs) 3 and EMA length (bars) 20, had profit factor 1.36 in sample but 1.02 out of sample, against 0.87 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 65.8% [63.6%, 67.8%]; 34% of years ended up; median ending P&L −$1,261, 5th to 95th percentile −$6,184 to +$4,019.

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 50K6.3%58%36%44
AquaFutures Beginner 50K Evaluation3.9%21%75%44.5
Bulenox 50K Qualification Account (Legacy, Option 1, trailing drawdown)3.9%25%71%44.5
Bulenox 50K Qualification Account (Legacy, Option 2, EOD drawdown)3.9%20%76%44.5
Alpha Futures Zero 50K Evaluation3.9%36%60%44
Apex Trader Funding 50K EOD Evaluation3.9%36%60%44
Apex Trader Funding 50K Intraday Trailing Drawdown Evaluation3.9%43%53%44
Elite Trader Funding 50K 1-Step Evaluation3.9%43%53%44
Elite Trader Funding 50K End of Day Drawdown Evaluation3.9%36%60%44
Funded Futures Family 50K Evaluation (standard plan)3.9%36%61%44
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

  • Gold's busiest hours only (08:30 to 13:00 ET entries).
  • One market (MGC).
  • 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 0.87 out of sample. Nothing on the grid helps. Its in-sample best, band width (ATRs) 3 and EMA length (bars) 20, had profit factor 1.36 in sample but 1.02 out of sample, against 0.87 for the defaults. On this reading of the rules, with these costs, the historical record gives no reason to expect a profit.

9References

  1. Keltner, C. W. (1960). How to Make Money in Commodities. Keltner Statistical Service.

Every number on this page comes from the MGC 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.