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Simple Moving Average

Classic moving-average smoothing classic ema

The Simple Moving Average calculates the unweighted mean of the previous N data points.

Visual Example

Simple Moving Average — 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 Simple Moving Average indicator is a technical analysis tool that the simple moving average calculates the unweighted mean of the previous n data points.

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 as the foundational smoothing module providing SMA, EMA, WMA, and SMMA implementations that power higher-level indicators across the library.

The core smoothing algorithms — SMA, EMA, WMA — are the building blocks of nearly all technical indicators. EMA applies exponential decay weighting (alpha = 2/(n+1)), SMA applies uniform weighting over N bars, and WMA applies linearly increasing weights emphasizing more recent bars.

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/smoothing.rs):

\[ SMA = \frac{1}{n} \sum_{i=1}^{n} P_i \]

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

Parameters

Parameter Default Description
period 14 Smoothing period

Usage Examples

Streaming (Rust)

use quantwave_core::indicators::SMA;
use quantwave_core::traits::Next;

let mut ind = SMA::new(14);
for price in &prices {
    let value = ind.next(price);
}

Streaming (Python)

from quantwave import SMA

ind = SMA(14)
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.sma(14).alias("simple_moving_average")
    )
    .collect()
)

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

Edge Cases & Limitations

  • Warm-up: first 14 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.investopedia.com/terms/s/sma.asp

Implementation: quantwave-core/src/indicators/smoothing.rs (SMA / SMA_METADATA). Parity: quantwave-core/tests/gold_standard/sma.json

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