Moving Average Convergence Divergence (MACD)
A trend-following momentum indicator that shows the relationship between two moving averages.
Visual Example

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 Moving Average Convergence Divergence (MACD) indicator is a technical analysis tool that a trend-following momentum indicator that shows the relationship between two moving averages.
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 to identify trend direction and momentum. Crossovers of the MACD line and signal line provide entry and exit signals, while the histogram shows the strength of the trend.
Gerald Appel developed the MACD in the late 1970s. It is calculated by subtracting the 26-period EMA from the 12-period EMA. A nine-day EMA of the MACD, called the 'signal line,' is then plotted on top of the MACD line, which can function as a trigger for buy and sell signals. — Investopedia
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/momentum.rs):
Gold-standard parity vectors: quantwave-core/tests/gold_standard/macd.json.
Parameters
| Parameter | Default | Description |
|---|---|---|
fastperiod |
12 | Fast EMA period |
slowperiod |
26 | Slow EMA period |
signalperiod |
9 | Signal EMA period |
Usage Examples
Streaming (Rust)
use quantwave_core::indicators::MACD;
use quantwave_core::traits::Next;
let mut ind = MACD::new(12);
for price in &prices {
let value = ind.next(price);
}
Streaming (Python)
Polars Batch (Python)
import polars as pl
import quantwave as qw
def apply_moving_average_convergence_divergence_macd(series: pl.Series) -> pl.Series:
ind = qw.MACD(12)
return pl.Series([ind.next(float(v)) for v in series.to_list()])
df = (
pl.read_csv('ohlcv.csv')
.lazy()
.with_columns(
pl.col("close").map_batches(apply_moving_average_convergence_divergence_macd, return_dtype=pl.Float64).alias("moving_average_convergence_divergence_macd")
)
.collect()
)
All surfaces are bit-identical via the single Next<T> implementation and proptests.
Edge Cases & Limitations
- Warm-up: first
12bars 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. |
Related Indicators & See Also
Sources & References
Primary Source: https://www.investopedia.com/terms/m/macd.asp
Implementation: quantwave-core/src/indicators/momentum.rs (MACD / MACD_METADATA).
Parity: quantwave-core/tests/gold_standard/macd.json
Provenance: Standards bulk upgrade 2026-06-25 IST — see docs/DOCUMENTATION_STANDARDS.md.