George C. Lane's Stochastic Oscillator (late 1950s) was originally this: the raw, unsmoothed position-in-range calculation with a simple SMA signal line. The "Fast" label was applied retroactively when traders began smoothing %K with an additional SMA to create the "Slow" variant. What Lane invented is what we now call Fast Stochastic.
TA-Lib codified the distinction by providing separate functions: `STOCH` (slow, with configurable smoothing on %K) and `STOCHF` (fast, raw %K). QuanTAlib follows this convention. The `Stoch` class defaults to `kLength=14`; the `Stochf` class defaults to `kLength=5` for faster response. Both produce raw %K internally; the difference is the default parameterization and the explicit naming that signals intent.
The shorter default period makes Stochf more reactive to short-term price action. That reactivity is simultaneously its strength (early signals) and its weakness (more false signals in choppy markets). Traders who want the responsiveness of a 5-period lookback but less noise typically apply additional smoothing externally rather than switching to the Slow variant.
Stochf measures where the current close sits within the highest-high to lowest-low range over the past `kLength` bars, expressed as a percentage from $0$ to $100$. The %D line is the SMA of %K over `dPeriod` bars.
The indicator prioritizes speed over smoothness. Because %K is unsmoothed and the default lookback is only 5 bars, Stochf reacts to price changes faster than its Slow Stochastic counterpart. This makes it useful for short-term trading where early detection of momentum shifts matters more than filtering noise.
The trade-off is straightforward: faster response means more false signals. In trending markets, Stochf whipsaws through overbought/oversold zones rapidly. In range-bound markets, the quick response helps identify turning points before slower indicators confirm. Knowing which regime you are trading determines whether Stochf helps or hurts.
The indicator requires $n$ bars to fill the sliding window. The %D SMA pre-fills its buffer with the first %K value (PineScript convention), producing output from bar 0.
Identical architecture to `Stoch`: two `MonotonicDeque` instances (max for highs, min for lows) provide O(1) amortized sliding min/max. Circular buffers (`_hBuf`, `_lBuf`) store raw H/L values for deque rebuild on bar correction.
A circular buffer (`_dBuf`) with running sum computes the SMA of %K in O(1). First bar pre-fills the entire buffer with the initial %K value; subsequent bars replace the oldest entry.
`Batch(ReadOnlySpan, ..., Span, Span, int, int)` delegates to `Highest.Batch()` and `Lowest.Batch()`. Intermediate buffers use `stackalloc` for $\leq 256$ elements and `ArrayPool<double>` beyond that threshold.
1.**Fast vs Slow confusion**: StochF is the unsmoothed variant. TA-Lib's `STOCH` applies %K smoothing; `STOCHF` does not. Comparing outputs without matching smoothing parameters produces mismatches.
2.**Default period difference**: StochF defaults to `kLength=5`, not 14. Comparing directly to `Stoch(14, 3)` produces different results even though the formula is identical.
3.**More whipsaws**: The shorter lookback and lack of smoothing generate more %K/%D crossovers. Most are noise in trending markets.
4.**Zero range returns 0**: When all bars in the window have identical H/L, %K returns $0$. Williams %R returns $-50$ for the same condition.
5.**%D warmup convention**: First %D value pre-fills the SMA buffer with the initial %K, matching PineScript behavior. Other implementations may emit NaN until `dPeriod` bars of %K are available.
6.**Overbought persistence**: In strong trends, %K stays near extremes. The fast response makes this more pronounced than with Slow Stochastic.
## FAQ
**Q: What is the difference between Stochf and Stoch?**
A: Identical formula, different defaults. Stochf defaults to `kLength=5` for faster response. Stoch defaults to `kLength=14`. Neither applies additional smoothing to %K in this implementation. The "Slow Stochastic" convention requires smoothing %K with an SMA, which is a separate operation.
**Q: Why does TA-Lib separate STOCH and STOCHF?**
A: TA-Lib's `STOCH` function includes a `smoothK` parameter that applies SMA smoothing to %K before computing %D (Slow Stochastic). `STOCHF` omits that smoothing step entirely. QuanTAlib's `Stoch` and `Stochf` both output raw %K; the distinction is in default parameters and naming convention.
**Q: When should I prefer Stochf over Stoch?**
A: When you need faster signal detection and can tolerate more false positives. Typical use cases: scalping, intraday mean reversion, or as a timing tool within a larger trend-following system.
