WE TESTED A COMMUNITY STRATEGY · USATECHIDXUSD 15min

15-Min ORB with Noise Band and VWAP Filter (Nasdaq)

A 15-minute opening-range breakout on the Nasdaq 100, filtered by the time-of-day noise band and VWAP. As posted it made money after spread (+44% over five years, £1.25 won per £1 lost), and tuned settings held up on six months they never saw. The VWAP filter changed nothing.

From r/pinescript. The idea, in our words: Opening range 9:30-9:45 NY. At each 15-minute close from 10:00 to 2:00pm, go long when the close is above the OR high + 0.3 ATR, above the upper Noise Band and above VWAP (shorts mirrored). One trade a day, a wide 10 x 5-min ATR stop, flat at 3:45pm.

held up

As posted · the rules as described (see what we assumed below)

+44.0%total return
1.25profit factor
57.1%win rate
6.7%max drawdown
669trades
4.9years tested

Optimized: best settings Quant Core found · last months held back as unseen data

+209.6%total return
1.50profit factor
41.3%win rate
8.5%max drawdown
334trades
1.90profit factor on unseen months

Improved: skip the quietest markets (volatility in its bottom 30%)

We then tried 8 changes a quant would test first (640 settings in all) and kept the best on past data only. The held-back months played no part in choosing it. It did not hold up on the unseen months.

+259.2%total return
1.65profit factor
42.7%win rate
7.6%max drawdown
314trades
0.99profit factor on unseen months

What we had to assume

  • Nasdaq 100 CFD instead of MNQ futures
  • 15-minute bars, so the post's 5-minute ATR is scaled: 0.3 x ATR5 ~ 0.17 x ATR15 buffer, 10 x ATR5 ~ 5.8 x ATR15 stop
  • The 1R target is left out: at a 10 x ATR stop it is almost never reached, and the post says the trade is really held to 3:45pm
  • Noise Band: 20-day lookback, 1.25 multiplier, gap-adjusted, measured per 15-minute slot from the 9:30 open

A different reading of the rules could give a different result.

Backtested with the StratOS optimizer on historical data, with spread and costs charged, and the most recent months held back as unseen data. A backtest is not a forecast, and nothing here is investment advice.

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