Pivot points: 85,701 trades show they are coin flips

Floor pivots are on almost every Indian trading chart. From yesterday’s high (H), low (L) and close (C):

• P = (H + L + C) / 3
• R1 = 2P − L, S1 = 2P − H
• R2 = P + (H − L), S2 = P − (H − L)

Textbooks give two ways to trade them, and we tested both exactly:

• Breakout: buy when price breaks above R1 (stop at P, target R2); sell when it breaks below S1 (stop at P, target S2).
• Bounce: buy the first touch of S1 (stop at S2, target P); sell the first touch of R1 (stop at R2, target P).

The order rests at the level and fills there (or at the open if price gaps through). Only the first touch counts, and the day must have opened on the other side of the level. One trade per stock per day, ₹5 lakh per trade, every NIFTY 50 stock, January 2016 to August 2026.

The breakout and the bounce are the same trade, reversed

Look at what each strategy does at R1. The breakout buys when price touches R1 from below. The bounce sells when price touches R1 from below. Same level, same moment, opposite direction. The same holds at S1. So both strategies trade exactly the same 85,701 touches, and their gross results are almost exact mirror images: +1.32 bps for the breakout, −1.35 bps for the bounce. (They aren’t exact opposites because the stops and targets sit at different levels.)

That mirror is the finding. If pivot levels carried real information, one side would earn a meaningful edge and the other would lose it. Instead the price after a touch is close to a coin flip: a basis point or so either way, against a round-trip cost of about 6.5 bps.

Every year, both ways

Neither strategy had a single profitable year, and neither turned profitable in 2024–26.

₹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.

Why pivots feel like they work

Pivots are drawn from yesterday’s range, and most days trade inside a range of a similar size, so prices really do reach R1 and S1 often and frequently turn near them. On a chart that looks like the levels “hold”. But a level that price often turns at is not the same as a level where turning is more likely than continuing. Across 85,701 touches, it isn’t.

Win rates sit near 50% for the breakout (49.7%) and the bounce (47.1%), close to what you’d expect from noise once the stop and target distances are taken into account.

def pivot(setup='breakout'):
    """breakout: resting buy stop at R1 (sell stop at S1), stop at P, target R2 (S2).
    bounce:   resting buy limit at S1 (sell limit at R1), stop S2 (R2), target P.
    Only the first touch counts and the day must have opened on the other side of the level."""
    def strategy(p):
        lv = _pivots(p)
        o = p.day_open
        if setup == 'breakout':
            up, dn = lv['R1'], lv['S1']
            long = (p.high >= up) & (o < up)
            short = (p.low <= dn) & (o > dn)
            ep_l, ep_s = np.maximum(up, p.open), np.minimum(dn, p.open)
            stop_l, stop_s = (ep_l - lv['P']) / ep_l, (lv['P'] - ep_s) / ep_s
            tgt_l, tgt_s = (lv['R2'] - ep_l) / ep_l, (ep_s - lv['S2']) / ep_s
        else:
            up, dn = lv['R1'], lv['S1']
            long = (p.low <= dn) & (o > dn)
            short = (p.high >= up) & (o < up)
            ep_l, ep_s = np.minimum(dn, p.open), np.maximum(up, p.open)
            stop_l, stop_s = (ep_l - lv['S2']) / ep_l, (lv['R2'] - ep_s) / ep_s
            tgt_l, tgt_s = (lv['P'] - ep_l) / ep_l, (ep_s - lv['P']) / ep_s
        first = p.once_per_day(long | short)
        long, short = long & first, short & first
        ep = ep_l.where(long, ep_s)
        stop = stop_l.where(long, stop_s).clip(lower=0.0005)
        tgt = tgt_l.where(long, tgt_s).clip(lower=0.0005)
        return bt.Signals(long=long, short=short, entry_price=ep, stop=stop, target=tgt)
    return strategy