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TRIMA

Triangular Moving Average — a simple moving average applied twice, which triangular-weights the window so the middle bars carry the most weight.

Quick reference

FieldValue
FamilyMoving Averages
Input typef64 (single close)
Output typef64
Output rangeunbounded; tracks the input price scale
Default parametersperiod is required (no default in either binding)
Warmup periodperiod
InterpretationVery smooth price level; the triangular weighting suppresses edge bars.

Formula

TRIMA(n) is SMA stacked on SMA. For period n the two lengths are:

odd  n:  n1 = n2 = (n + 1) / 2
even n:  n1 = n / 2,  n2 = n / 2 + 1
TRIMA_t = SMA_{n2}( SMA_{n1}(price) )_t

Composing two equal-weight means convolves two rectangular windows, which yields a triangular weight profile over the original n closes — the centre bar gets the largest weight, the two edges the smallest. Both stacked SMAs are O(1), so update is O(1) regardless of period.

Parameters

NameTypeDefaultValid rangeDescription
periodusizenone>= 1Window length. period = 0 errors with Error::PeriodZero. period = 1 and period = 2 degenerate to short SMAs.

There is no Python #[pyo3(signature = …)] default for TRIMA, so wickra.TRIMA(period) requires the period explicitly.

Inputs / Outputs

From crates/wickra-core/src/indicators/trima.rs:

rust
use wickra::{Indicator, Trima};
// Trima: Input = f64, Output = f64
const _: fn(&mut Trima, f64) -> Option<f64> = <Trima as Indicator>::update;

A single f64 close in, an Option<f64> out. Python maps this to float | None / numpy.ndarray (NaN warmup); Node to number | null / Array<number> (NaN warmup).

Warmup

Trima::new(period).warmup_period() == period. The inner SMA emits after n1 inputs; the outer SMA then needs n2 − 1 more, and n1 + n2 − 1 = n for both the odd and even splits. So the first non-None output lands on exactly the period-th update().

Edge cases

  • Constant series. [42.0; n] returns Some(42.0) from input period onward — both SMAs are exact for constants (constant_series_yields_the_constant pins this).
  • NaN / infinity inputs. update returns self.outer.value() for a non-finite input without feeding either SMA, so the inner SMA's stale value is never double-counted into the outer SMA. State is left untouched.
  • Reset. trima.reset() resets both inner and outer SMAs, restarting the warmup countdown.

Examples

Rust

rust
use wickra::{BatchExt, Indicator, Trima};

fn main() -> Result<(), Box<dyn std::error::Error>> {
    let mut trima = Trima::new(5)?;
    let out: Vec<Option<f64>> = trima.batch(&[1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0]);
    println!("{:?}", out);
    println!("warmup_period = {}", trima.warmup_period());
    Ok(())
}

Output:

[None, None, None, None, Some(3.0), Some(4.0), Some(5.0)]
warmup_period = 5

TRIMA(5) is SMA(3) of SMA(3). SMA(3) of 1..=7 is [_, _, 2, 3, 4, 5, 6]; SMA(3) of that is [_, _, _, _, 3, 4, 5]. This matches the odd_period_reference_values test in crates/wickra-core/src/indicators/trima.rs.

Python

python
import numpy as np
import wickra as ta

trima = ta.TRIMA(5)
print(trima.batch(np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0])))
print("warmup_period =", trima.warmup_period())

Output:

[nan nan nan nan  3.  4.  5.]
warmup_period = 5

Node

javascript
const ta = require('wickra');
const trima = new ta.TRIMA(5);
console.log(trima.batch([1, 2, 3, 4, 5, 6, 7]));
console.log('warmupPeriod:', trima.warmupPeriod());

Output:

[ NaN, NaN, NaN, NaN, 3, 4, 5 ]
warmupPeriod: 5

Interpretation

Trima is one of the smoothest single-line averages in the library: the triangular weight profile damps the most recent bar far more than a plain Sma does, so whipsaws are rare. The cost is lag — a Trima(n) lags roughly like an Sma(n/2) doubled. Use it as a slow trend filter where a clean, low-noise line matters more than fast reaction; prefer Ema or Hma when responsiveness matters.

Common pitfalls

  • Expecting Sma-like lag. Stacking two means roughly doubles the effective lag; size the period accordingly.
  • Treating period = 0 as "use a default". Trima::new(0) returns Err(Error::PeriodZero) in Rust and a ValueError in Python.

References

The triangular moving average is a standard double-smoothed SMA; the odd/even split used here (n1, n2) matches TA-Lib's TRIMA.

See also