Stochastic vs RSI for Scalping: Same Rules, Same Data (2026)

Stochastic and RSI produce nearly identical win rates on EURUSD 5-minute data, 46.1% against 46.5% on a 30-minute hold across 1,997 signals I measured from June 3 to August 26, 2026. The difference that matters is not accuracy, it is frequency: Stochastic signals half again as often, which means half again as much spread for the same hit rate. Once a trend filter is applied and the hold stretched to four hours, Stochastic pulls ahead to 51.3%.

How were the two oscillators tested?

The point of this test was to remove every excuse. Both indicators ran over the same 16,926 EURUSD 5-minute bars, with the same entry logic, the same holding periods and no discretion on either side.

• Stochastic: 14, 3, 3. Short when %K crosses up through 80, long when it crosses down through 20.
• RSI: 14. Short when RSI crosses up through 70, long when it crosses down through 30.
• Holds: 30 minutes, 2 hours and 4 hours, measured in pips in the signal direction with no stop and no target.
• Filter version: the identical signals, but a short only counts below the 200-period moving average and a long only above it.

If you want the deeper single-indicator breakdowns, they live in the Stochastic overbought test and in RSI 7 against RSI 14 for scalping.

Which oscillator won on raw signals?

Nothing here is a decisive win. The largest gap on raw signals is the two-hour row, where Stochastic beats RSI by 4.2 percentage points and a full pip. Reverse the horizon and RSI takes the four-hour row back. That instability is the honest headline: neither oscillator has a stable standalone edge on this pair and timeframe.

Do they confirm each other?

This is the question the tutorials never test, and it has the cleanest answer in the whole study.

What happens once you add a trend filter?

Here the two separate properly. Same signals, but only in the direction the 200-period moving average allows.

Two things stand out. First, the filter turned every single variant positive on average pips, including the ones whose win rate fell. That is the signature of a filter that trims losers rather than adding winners. Second, the only row above 50% in this entire study is filtered Stochastic on a four-hour hold. It is also the smallest sample, 234 signals, so treat it as a lead worth following rather than a settled result.

Which one should a scalper use?

• If you must pick one, pick Stochastic, but only with a direction filter and a hold measured in hours, not minutes. That is the one configuration that cleared 50%.
• Do not run both. They agree 149 times out of thousands, and when they do agree they are agreeing about the same thing: price sits at the edge of its recent range.
• Count the spread. Stochastic’s extra 397 signals on the 30-minute test cost real money at typical EURUSD spreads and bought nothing in win rate.
• Fix the horizon before the settings. Changing the hold moved results more than changing any parameter on either indicator, which is the same conclusion I reached in the Bollinger squeeze test.

Frequently Asked Questions

Is Stochastic better than RSI for scalping?

On raw signals the two are effectively tied, 46.1% against 46.5% on a 30-minute hold. Stochastic becomes the better of the two only when a trend filter is applied and the trade is held for hours, where it reached 51.3% against RSI’s 47.3%.

Can I use Stochastic and RSI together for confirmation?

The data says the idea does not work as advertised. They fired together on the same bar in the same direction only 149 times across the whole dataset. A rule that waits for both would trade almost never, and the trades it did take would still be exposed to the same failure mode.

What settings did you use, and would others change the result?

Stochastic 14, 3, 3 and RSI 14, both platform defaults. I swept five Stochastic settings separately and the spread between best and worst was under two percentage points, far smaller than the effect of the holding period. Those numbers are in the MT4 settings guide.

Why measure without a stop loss?

A stop turns the test into a test of the stop. Measuring the raw move after a fixed time shows what the signal itself predicts, which is the only thing being compared here. Stop placement is a separate question I looked at with the ATR trailing stop.

Does this apply to other pairs and timeframes?

These numbers are EURUSD 5-minute over one summer window, so read them as specific. The structural point, that a direction filter beats oscillator choice, has held on every dataset I have run it against so far.