# Stochastic Momentum Index

A Momentum & Oscillators concept (Stochastics) in the LuxAlgo Library, with 1 indicator implementation.

## What is the Stochastic Momentum Index?

The Stochastic Momentum Index (SMI), introduced by William Blau in 1993, relocates the stochastic's anchor point. A standard [Stochastic Oscillator](https://www.luxalgo.com/library/concept/stochastic-oscillator/) asks where the close sits between the period's high and low; the SMI asks how far the close sits from the midpoint of that high-low range, then double-smooths both that distance and the range itself with successive EMAs before dividing. Scaling by half the smoothed range bounds the result near plus and minus 100, with zero meaning the close sat exactly at the range midpoint.

The double EMA smoothing is the point: raw %K can jump between extremes on every bar, while the SMI's numerator and denominator are each smoothed twice, producing a line that drifts rather than snaps. Readings are conventionally judged against plus and minus 40 for [overbought and oversold](https://www.luxalgo.com/library/concept/overbought-oversold/), with a signal-line EMA layered on for crossovers. It shares its double-smoothing DNA with Blau's True Strength Index: one smooths the close's distance from the range midpoint, the other smooths bar-to-bar price change.

Blau introduced the SMI in the January 1993 issue of Technical Analysis of Stocks & Commodities and expanded it in his 1995 book Momentum, Direction, and Divergence. His project was noise reduction without the heavy lag of long lookbacks: two short EMAs in series filter more bar-to-bar noise for a given lag than a single longer average, and smoothing numerator and denominator identically keeps the ratio's scale honest. The SMI is that idea applied to George Lane's stochastic.

In practice the SMI behaves like a slower, centered stochastic. Zero-line position carries trend information the 0-100 stochastic lacks, and the smoothed line makes swing comparison cleaner for [divergence](https://www.luxalgo.com/library/concept/regular-bullish-bearish-divergence/) work, including [hidden divergence](https://www.luxalgo.com/library/concept/hidden-divergence/) in pullbacks. The cost is lag: turns register later than on a raw stochastic or [momentum](https://www.luxalgo.com/library/concept/momentum/) line, which suits swing timeframes better than fast scalping. Traders wanting sharper extremes sometimes pass the SMI through an [Inverse Fisher Transform](https://www.luxalgo.com/library/concept/inverse-fisher-transform/), as in the library's KivancOzbilgic study.

## How to calculate the Stochastic Momentum Index

The calculation applies the same double smoothing to a distance series and a range series.

1. Over the lookback window, find the highest high and lowest low; their average is the range midpoint.
2. Compute the distance: close minus that midpoint. Unlike %K's close-minus-low, this is negative whenever the close is in the lower half of the range.
3. Smooth the distance with an EMA, then smooth the result with a second EMA; apply the identical double smoothing to the high-low range.
4. Divide the double-smoothed distance by half the double-smoothed range and multiply by 100, giving a reading near the plus/minus 100 bounds; an EMA of the SMI serves as the signal line.

## How it's calculated

Locates the close relative to the midpoint of the recent high/low range, double smoothed and scaled to -100 to +100.

```
HH_t = max(H) over the last n bars
LL_t = min(L) over the last n bars
d_t = C_t - (HH_t + LL_t) / 2
r_t = HH_t - LL_t
SMI_t = 100 × EMA(EMA(d_t, s1), s2) / (EMA(EMA(r_t, s1), s2) / 2)

  t: current bar index
  H: bar high
  L: bar low
  C_t: current close
  n: range lookback length (commonly 10)
  HH_t: highest high of the last n bars
  LL_t: lowest low of the last n bars
  d_t: distance of the close from the range midpoint
  r_t: full height of the n bar range
  EMA(x, len): exponential moving average of series x over len bars
  s1: first smoothing length (commonly 3)
  s2: second smoothing length (commonly 3)
  SMI_t: Stochastic Momentum Index value
```

A value of 0 puts the close exactly at the midpoint of the n bar range; +40 and -40 are common overbought and oversold marks.

A signal line, typically a 3 period EMA of the SMI, is plotted alongside.

William Blau's indicator, introduced in 1993; platform defaults vary but 10, 3, 3 is the most common.

## How traders use it

- For overbought/oversold reversion: readings beyond plus or minus 40 mark stretch, with entries commonly waiting for the SMI to hook back toward zero rather than fading the first touch.
- For signal-line crossovers, which fire less often than standard stochastic crosses because both lines are already double-smoothed.
- For zero-line context: above zero, closes are persistently in the upper half of their recent range, a trend-quality read as much as a momentum one.
- For divergence at swing extremes, where the smoothing makes peaks easier to compare, at the cost of signals arriving later than the raw stochastic's.
- As a base for composite studies: the formula ports to other inputs, as in the library's SMI of the Money Flow Index, and pairs well with a trend filter such as [MACD](https://www.luxalgo.com/library/concept/macd/) slope for timing entries.

## Stochastic Momentum Index vs. related oscillators

- **Stochastic Oscillator** (https://www.luxalgo.com/library/concept/stochastic-oscillator/): The stochastic anchors to the period low and lives on a 0-100 scale; the SMI anchors to the range midpoint, centers at zero, and double-smooths everything, so it turns later but whipsaws less.
- **Stochastic RSI** (https://www.luxalgo.com/library/concept/stochastic-rsi/): Stochastic RSI applies the stochastic formula to RSI values, making a bounded series twitchier; the SMI goes the opposite way, re-anchoring and double-smoothing to slow the stochastic down. They sit at opposite ends of the responsiveness spectrum.
- **RSI** (https://www.luxalgo.com/library/concept/rsi/): RSI measures the balance of up and down closes with no reference to the high-low range; the SMI measures where the close sits within that range. RSI centers at 50 on a 0-100 scale, the SMI at zero on a plus/minus 100 scale.

## FAQ

### What is the difference between the SMI and the Stochastic Oscillator?

Two things: the anchor and the smoothing. %K measures the close relative to the period low and ranges 0 to 100 with no natural center. The SMI measures the close relative to the range midpoint, so it is centered at zero, and it double-smooths numerator and denominator with EMAs, trading %K's speed for fewer whipsaws.

### What are the overbought and oversold levels for the SMI?

Plus 40 and minus 40 are the conventional bands, on a scale bounded near plus and minus 100. As with any oscillator threshold, they mark stretch, not reversal: in strong trends the SMI can hold beyond 40 for long stretches, so many traders require a hook back through the band or a signal-line cross before acting.

### Who created the Stochastic Momentum Index?

William Blau, who introduced it in 1993 in Technical Analysis of Stocks & Commodities. It belongs to his double-smoothing family alongside the True Strength Index: in both, a raw momentum quantity and its normalizing denominator are each smoothed twice with EMAs before dividing, which suppresses noise while keeping the ratio's scale stable.

### What are good SMI settings?

Platform defaults vary. Common forms use a 10- to 14-bar window with two rounds of short EMA smoothing and a short signal EMA; Blau's own examples often ran a much longer first-stage smoothing over a shorter window. Shortening everything revives the whipsaws the design exists to remove.

### Can the SMI stay overbought in a strong trend?

Yes: the double smoothing lets the line plateau above +40 for extended stretches while price grinds higher. Persistent upper-half closes are a trend signature, which is why many traders read the SMI's zero side for direction and reserve the 40 bands for timing rather than fading every extreme.

### Is the SMI better than the stochastic oscillator?

Better for some jobs. For fewer, cleaner signals in swing timing or divergence comparison, the SMI's smoothing helps; for fast reaction at turns, %K's immediacy wins and the SMI's lag costs entries. They compute different quantities (distance from the midpoint versus distance from the low), so some traders keep both and treat agreement as conviction. Williams %R, the stochastic's unsmoothed mirror measured from the period high, marks the fastest, noisiest end of the family, the SMI the opposite extreme.

## Implementations in the Library

- SMI Ergodic (LuxAlgo, the standard build of the classic formula): https://www.luxalgo.com/library/indicator/smi-ergodic/

## Related concepts

- Stochastic Oscillator: https://www.luxalgo.com/library/concept/stochastic-oscillator/
- Double Stochastic: https://www.luxalgo.com/library/concept/double-stochastic/
- DSS Bressert: https://www.luxalgo.com/library/concept/dss-bressert/
- Adaptive Stochastic: https://www.luxalgo.com/library/concept/adaptive-stochastic/
- Premier Stochastic: https://www.luxalgo.com/library/concept/premier-stochastic/
- Stochastic Pop: https://www.luxalgo.com/library/concept/stochastic-pop/

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Source: https://www.luxalgo.com/library/concept/stochastic-momentum-index/ (LuxAlgo Library, the encyclopedia of trading & technical analysis). Free to use with attribution: https://www.luxalgo.com/library/license/