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Triangular Moving Average (TRIMA)

Classic moving-average smoothing classic

A double-smoothed simple moving average that gives more weight to the middle of the lookback period.

Visual Example

Triangular Moving Average (TRIMA) — annotated preview mapping to core implementation

Synthetic ideal per library logic. Generated 2026-06-25 IST via docs/generate_all_previews.py (reproducible; maps to core Next<T> implementation).

Description

The Triangular Moving Average (TRIMA) indicator is a technical analysis tool that a double-smoothed simple moving average that gives more weight to the middle of the lookback period.

This indicator is primarily used for identifying key market conditions. It provides a robust signal that can be easily integrated into both simple strategies and more complex machine learning feature pipelines. Compared to its alternatives, it offers a distinct balance of responsiveness and stability.

Traders often combine this with other metrics to confirm signals and avoid false positives during sideways market regimes. It remains a standard tool for systematic trading models.

Use for extremely smooth trend identification. TRIMA is significantly smoother than a standard SMA but introduces more lag; it is ideal for identifying long-term cycles.

The Triangular Moving Average is an SMA of an SMA. For a period N, it averages the values over N/2 periods twice. This results in a weight distribution that is triangular, peaking at the center of the window, making it very effective at filtering out high-frequency noise. — StockCharts ChartSchool

QuantWave implements this indicator via the universal Next<T> trait, guaranteeing bit-identical results between Rust streaming, Python streaming, and Polars batch (.ta() / map_batches) surfaces.

Formula / Specification

Implementation (quantwave-core/src/indicators/overlap.rs):

\[ TRIMA = SMA(SMA(Price, n/2), n/2) \]

Gold-standard parity vectors: quantwave-core/tests/gold_standard/trima.json.

Parameters

Parameter Default Description
timeperiod 30 Smoothing period

Usage Examples

Streaming (Rust)

use quantwave_core::indicators::TRIMA;
use quantwave_core::traits::Next;

let mut ind = TRIMA::new(30);
for price in &prices {
    let value = ind.next(price);
}

Streaming (Python)

from quantwave import TRIMA

ind = TRIMA(30)
for price in prices:
    value = ind.next(price)

Polars Batch (Python)

import polars as pl

df = (
    pl.read_csv('ohlcv.csv')
    .lazy()
    .with_columns(
        pl.col("close").ta.trima(30).alias("triangular_moving_average_trima")
    )
    .collect()
)

All surfaces are bit-identical via the single Next<T> implementation and proptests.

Edge Cases & Limitations

  • Warm-up: first 30 bars may return NaN or partial state per implementation.
  • Parameter sensitivity: smaller periods increase noise; larger periods increase lag.
  • Sudden gaps or bad ticks can distort rolling windows — consider pre-filtering.
  • Single-series indicators ignore volume unless otherwise documented.
  • Validated via proptests against gold-standard vectors where available.
  • No look-ahead bias; streaming and Polars batch paths are bit-identical.

Boundary Behavior

Condition Behavior
Warm-up Leading bars return NaN until warmup_bars is satisfied.
period > len When period exceeds series length, output is all NaN.
NaN inputs NaN in input propagates to output (NaN out).
Invalid params Non-positive period or missing required params raise ValueError.
Empty data Empty input returns an empty result series.

Sources & References

Primary Source: https://www.tradingview.com/support/solutions/43000591273-triangular-moving-average-tma/

Implementation: quantwave-core/src/indicators/overlap.rs (TRIMA / TRIMA_METADATA). Parity: quantwave-core/tests/gold_standard/trima.json

Provenance: Standards bulk upgrade 2026-06-25 IST — see docs/DOCUMENTATION_STANDARDS.md.