QQE Settings Compared: 200 Settings Tested

Search for QQE settings and you land on the same three numbers almost every time: RSI period 14, smoothing 5, factor 4.236. Most tools ship with them.

So are they the right ones? I backtested 200 parameter combinations on USD/JPY across the whole of 2025, ran the same 200 on three timeframes, and then ran all of it again on 2024 to see whether anything survived the change of year.

The headline answer: the largest 2025 annual net came from RSI period 17, smoothing 3, factor 4.236: 465 trades for +3,341.0 pips. A different combination made +4,027.1 pips in 2024, then returned +1,186.1 pips in 2025, below that year's +1,822.3-pip median. Choosing from one year's annual net can leave the next year below the middle of the same 200-setting grid.

best of 2024best of 2025default 14/5/4.236one dot per parameter set · rank correlation 0.25

What QQE is measuring

QQE (Quantitative Qualitative Estimation) does not read RSI directly. It smooths RSI, then compares that smoothed line against a band built from the line's own volatility.

1. Calculate RSI (RSI period)
2. Smooth it with an EMA (smoothing, or SF)
3. Take the bar-to-bar change of that smoothed line and smooth it too
4. Multiply by a coefficient to set the band width (factor, 4.236 by default)
5. Read trend direction from which side of the band the smoothed RSI sits on

QQE has three parameters: RSI period, smoothing, and factor.

The measured version: on hourly bars in 2025, smoothing of 2 averaged 689 trades a year and smoothing of 12 averaged 369. Raising factor from 2 to 5.236 took the average from 656 trades to 391. Raising RSI period, smoothing, or factor reduced trade count and therefore reduced the total spread paid.

Adding QQE to a chart

QQE is not built into every platform, so the route differs.

One caveat worth stating up front: QQE implementations differ. Whether the smoothing uses an EMA or Wilder's method, and how the band's trailing rule is written, changes where the line sits. The numbers here come from Formiq's implementation, and another script fed the same three parameters may not produce the same trades.

How this was measured

Holding to the opposite crossover with no stop keeps the test about QQE and nothing else. Stops and targets are measured separately further down.

Are the defaults good enough?

The 14 / 5 / 4.236 defaults cleared the 200-setting median in both years. Across the 200 hourly settings, RSI period 17, smoothing 3 and factor 4.236 produced the largest 2025 annual net: 465 trades, +3,341.0 pips, a 42.37% win rate and a 1.348 PF. Its 2024 annual net was +2,553.6 pips.

The defaults, 14 / 5 / 4.236, returned +2,182.3 pips in 2025 and +2,309.7 pips in 2024. The medians across the 200 settings were +1,822.3 and +2,088 pips. Showing the maximum beside the defaults and the median keeps all three comparisons on the same metric.

The losing end is worth the same attention. The worst 2025 combination was RSI 6 / smoothing 2 / factor 4.236 at −1,516.0 pips, and it lost 580.9 pips in 2024 as well: the only one of the 200 hourly combinations to lose in both years. It is the most sensitive setting on the grid: the shortest RSI period paired with the least smoothing.

What are the best QQE settings?

The largest result of one year finished 636.2 pips below the next year's median.

RSI 10, smoothing 8 and factor 5.236 made +4,027.1 pips in 2024, then +1,186.1 pips in 2025. The 2025 median across all 200 settings was +1,822.3 pips, so selecting that setting from its 2024 annual net left it 636.2 pips below the next year's median.

The figure at the top plots one dot per setting, with 2024 annual net pips on the horizontal axis and 2025 annual net pips on the vertical axis. If the same settings produced similar annual net pips in both years, the dots would cluster along a rising line. Instead, they are widely scattered.

None of which means QQE failed. On hourly bars, 194 of the 200 combinations were profitable in 2025 and 188 in 2024, with 183 profitable in both years. The indicator worked across this two-year window on this pair. What did not work was the search for the single best number.

Which timeframe suits QQE?

183 of 200 hourly settings were profitable in both years, against 3 on the 15-minute chart. The same 200 combinations, changing only the timeframe:

20252024box = middle 50%, line = min to max, white tick = median

On 15-minute bars in 2024, four combinations of 200 finished positive. All four sit at the blunt end of the grid (RSI period 20 or 25, smoothing 8 or 12) and three of them were profitable in 2025 as well.

Four-hour bars beat hourly on the 2025 count, then fell to 138 in 2024. Counting only the settings that came through both years, hourly leads with 183 of 200 against 136.

Holding the parameters at the defaults and moving nothing but the timeframe:

Was the 15-minute chart losing before costs?

Not because the indicator degrades. Because of how many times the cost is paid.

M15 · 1,981 tradesH1 · 477 tradesH4 · 124 tradessame year, same pair — only the spread changes; trade counts are medians

Half of it was, and the other half was not: the two years answer differently.

Set the spread to zero and the result is the gross edge before costs. Medians are across all 200 combinations.

In 2025 the 15-minute chart was positive before costs. The gross median was +181 pips with 116 of 200 combinations in profit. Charging 0.3 pips drops the median to −305 and the count to 78. The median crosses zero at about 0.12 pips of spread: below any spread that actually exists.

In 2024 it was already at −1,251 pips gross. That year it lost on the trades themselves, and the spread only deepened it. "The 15-minute chart loses to costs" is true of 2025 here and false of 2024.

The cost itself behaves predictably. Taking the spread from 0 to 2.0 pips moves the medians from +181 to −3,381 on 15-minute bars, +1,976 to +969 on hourly, and +1,428 to +1,140 on four-hour. At 1.5 pips every one of the 200 fifteen-minute combinations is negative, while 196 of the four-hour ones are still positive. What you give up is the trade count times the spread, exactly.

This is not specific to QQE. RVI trades 1,094 times a year on the same terms and gives up 2,188 pips going from 0 to 2.0; Supertrend trades 152 times and gives up 304. Both match trade count × spread without error, which is why checking it against your own account's spread is worth more than another pass over the parameters.

Which parameters held across both years?

Smoothing and RSI period, and nothing else. Each RSI-period, smoothing, and factor value was averaged over every combination of the other two parameters. Smoothing and RSI period were the two parameters whose annual-net ordering was consistent in 2024 and 2025.

Smoothing of 2 came last in both years, at under half the return of the better values.

A short RSI period came last in both years too: monotonically improving out to 25 in 2024, and flattening out from 17 onward in 2025. Both findings say the same thing: do not set these short.

Factor swapped optima: 2.618 averaged best in 2025 (+1,840 pips), 4.236 in 2024 (+2,255 pips). It does have one consistent effect across both years: raising it raises the win rate, from 39.8% to 43.5% in 2025 and 38.8% to 43.2% in 2024, while trade count falls from 656 to 391. Factor trades frequency for win rate. It does not maximise profit.

Does a higher win rate make more money?

It did not.

Across the 200 hourly combinations in 2025, win rates ranged from 37.09% to 47.32%. The filter comparison below shows what happened to trade count and annual net when the win rate changed.

Adding the filters people usually reach for, on top of the defaults:

Not one of the six beat the unfiltered version in both years. Tightening ADX cuts the trade count and cuts the total with it; at ADX ≥ 25 the 2024 result crosses into negative territory at −257.4 pips.

The filters increased win rate but reduced annual net. Restricting to Tokyo hours lifts the win rate from 42.89% to 47.90%, and ADX ≥ 25 lifts it to 43.43%. Both filters remove trades, and neither increases annual net in both years.

Stops and targets

Same pattern again. Adding exits to RSI 17 / smoothing 3 / factor 4.236, with the crossover exit still active:

All three stop-and-target pairs came in below the plain crossover exit in both years. Stop 50 / target 50 pushes the 2025 win rate to 48.24% and still gives back a thousand pips; in 2024 it finishes at +85.7, which is almost nothing.

The 24-bar time exit is the exception: it beat the baseline in both years, +3,341.0 to +3,519.5 and +2,553.6 to +2,609.5. It is the only addition in this article that improved both.

Nothing in this test rewarded narrowing QQE down to one triple of numbers. In the plain RSI test, the fade had the most settings profitable in both years at period 30, while the level-cross rule had the most at period 9. For QQE, timeframe, avoiding extreme parameter values, and cost per trade mattered more than selecting one exact triple. If you want to check the same thing on your own pair and dates, building the rules without writing code takes about as long as reading this article.

Notes

• The sample covers USD/JPY in 2024 and 2025; it does not include other pairs or periods
• QQE implementations vary between scripts; these figures come from Formiq's and may not match another script fed the same parameters
• Four-hour settings range from 67 to 257 trades a year, so win rates and profit factors from the smaller samples vary more
• Entries and exits both fill at bar closes. Real fills differ
• The spread is modelled as a flat 0.3 pips for the whole period. Real spreads move with the session and around data releases