We followed a popular ORB strategy guide to the letter on 10 years of NIFTY 50 data

The opening range breakout (ORB) is probably the most taught intraday strategy. One widely shared guide adds three filters to the basic idea, each meant to cut out false breakouts, and quotes win rates of 42–65%. We followed it exactly on every NIFTY 50 stock from January 2016 to August 2026.

The rules, as written:

1. Range: the high and low of the first 30 minutes (09:15–09:45).
2. Trend: long only when the 50 EMA is above the 200 EMA and the candle closes above both (15-minute chart); short only in the mirror case.
3. Entry: a 15-minute candle closes beyond the range, with a full body (at least 60% of its range) in the breakout direction and volume at least 1.5× the average of the previous 20 candles.
4. Exit: stop at the far end of the breakout candle, target 2× the risk, anything open is closed at 15:15.

One trade per stock per day, ₹5 lakh per trade, entered at the next bar’s open after the candle closes.

It isn’t one bad year

Ten of the eleven years lost money, mostly ₹2–4 lakh each. The one exception was 2020, a year of unusually large daily moves, which made ₹3.5 lakh (about ₹140 per trade). Every year since has lost money again.

The filters help, just not enough

The guide’s filters do what they claim: each one improves the average trade. Remove all three and the gross edge falls from 4.5 to 2.3 bps per trade while the number of trades grows 4.6 times, and the loss grows almost tenfold, to −₹2.27 crore. The breakouts it picks are also better than a random NIFTY 50 stock traded at the same minute in the same direction (+4.5 vs +3.0 bps).

The problem is size. A ₹5 lakh round trip costs about 6.5 bps (brokerage, STT, exchange fees, stamp duty, GST and 1 bp of slippage on each fill; see how we count costs). An edge of 4.5 bps before costs is a loss of about 2 bps after them, on every trade.

Eight variants, none profitable

We changed one thing at a time: dropped each filter, used a 15-minute range, moved the target to 1R or 3R, and used 30-minute candles. These were decided before running anything. Here is every result:

₹5 lakh per trade, 5-minute bars, v2026.08 snapshot (Jan 2016 – Aug 2026), NIFTY 50 point-in-time members, discount-broker (₹20/order) and statutory charges + 1 bp slippage per fill. Generated 2026-10-07.

The closest to break-even was the 30-minute candle version: −₹74,000 over 13,961 trades, about −₹5 per trade. It made ₹0.8 lakh in 2021–23 and lost in 2016–20 and 2024–26. It’s tempting to call that “the version that works”. It isn’t. We’d be choosing it after seeing eight results, it didn’t make money over the full period, and three basis points of extra slippage per fill turn it into a ₹43 lakh loss.

def orb_pdf(or_min=30, chart=15, trend=True, full_body=True, vol=1.5, r=2.0):
    """Range = first `or_min` minutes. Signal = a `chart`-minute candle CLOSES beyond the range; optional EMA50/200
    trend filter (on chart candles), full-body candle (body >= 60% of range), volume >= vol x 20-candle average.
    Stop at the far end of the breakout candle, target r x risk, one trade per stock per day."""
    def strategy(p):
        c = p.candles(chart)
        cl = p.candle_series(c, 'close')
        cv = p.candle_series(c, 'volume')
        o, h, l, cc, v = c['open'], c['high'], c['low'], c['close'], c['volume']
        orh, orl = p.opening_range(or_min)
        long, short = cc > orh, cc < orl
        if full_body:
            body, rng = (cc - o).abs(), (h - l)
            long &= (cc > o) & (body >= 0.6 * rng)
            short &= (cc < o) & (body >= 0.6 * rng)
        if vol:
            vavg = cv.rolling(20).mean().shift(1).reindex(p.index)
            long &= v >= vol * vavg
            short &= v >= vol * vavg
        if trend:
            e50 = cl.ewm(span=50, adjust=False).mean().reindex(p.index)
            e200 = cl.ewm(span=200, adjust=False).mean().reindex(p.index)
            long &= (e50 > e200) & (cc > e50) & (cc > e200)
            short &= (e50 < e200) & (cc < e50) & (cc < e200)
        first = p.once_per_day(long | short)
        risk = ((cc - l) / cc).where(long, (h - cc) / cc).clip(lower=0.001)
        return bt.Signals(long=long & first, short=short & first, stop=risk, target=r * risk)
    return strategy