use crate::indicators::metadata::{IndicatorMetadata, ParamDef};
use crate::traits::Next;
use std::collections::VecDeque;
#[derive(Debug, Clone)]
pub struct ALMA {
period: usize,
_offset: f64,
_sigma: f64,
window: VecDeque<f64>,
weights: Vec<f64>,
}
impl ALMA {
pub fn new(period: usize, offset: f64, sigma: f64) -> Self {
let m = offset * (period as f64 - 1.0);
let s = period as f64 / sigma;
let mut weights = Vec::with_capacity(period);
let mut sum_w = 0.0;
for i in 0..period {
let weight = (-((i as f64 - m).powi(2) / (2.0 * s.powi(2)))).exp();
weights.push(weight);
sum_w += weight;
}
for w in weights.iter_mut() {
*w /= sum_w;
}
Self {
period,
_offset: offset,
_sigma: sigma,
window: VecDeque::with_capacity(period),
weights,
}
}
}
impl Next<f64> for ALMA {
type Output = f64;
fn next(&mut self, input: f64) -> Self::Output {
self.window.push_back(input);
if self.window.len() > self.period {
self.window.pop_front();
}
if self.window.len() < self.period {
let mut sum_w = 0.0;
let mut weighted_val_sum = 0.0;
for (i, &val) in self.window.iter().enumerate() {
let weight = self.weights[i + self.period - self.window.len()];
weighted_val_sum += val * weight;
sum_w += weight;
}
if sum_w == 0.0 {
0.0
} else {
weighted_val_sum / sum_w
}
} else {
let mut weighted_val_sum = 0.0;
for (i, &val) in self.window.iter().enumerate() {
weighted_val_sum += val * self.weights[i];
}
weighted_val_sum
}
}
}
#[cfg(test)]
mod tests {
use super::*;
use crate::test_utils::{
assert_indicator_parity, check_batch_streaming_parity, load_gold_standard,
};
use proptest::prelude::*;
#[test]
fn test_alma_gold_standard() {
let case = load_gold_standard("alma_9_085_6");
let alma = ALMA::new(9, 0.85, 6.0);
assert_indicator_parity(alma, &case.input, &case.expected);
}
fn alma_batch(data: Vec<f64>, period: usize, offset: f64, sigma: f64) -> Vec<f64> {
let mut alma = ALMA::new(period, offset, sigma);
data.into_iter().map(|x| alma.next(x)).collect()
}
proptest! {
#[test]
fn test_alma_parity(input in prop::collection::vec(0.0..1000.0, 1..100)) {
let period = 9;
let offset = 0.85;
let sigma = 6.0;
let indicator = ALMA::new(period, offset, sigma);
check_batch_streaming_parity(input, indicator, |data| alma_batch(data, period, offset, sigma));
}
}
#[test]
fn test_alma_basic() {
let mut alma = ALMA::new(9, 0.85, 6.0);
for i in 1..20 {
let val = alma.next(i as f64);
if i >= 9 {
assert!(val > 0.0);
}
}
}
}
pub const ALMA_METADATA: IndicatorMetadata = IndicatorMetadata {
name: "Arnaud Legoux Moving Average",
description: "ALMA is designed to reduce lag while providing high smoothness.",
usage: "Use as a low-latency moving average that reduces lag compared to EMA while controlling overshoot through the Gaussian offset parameter. Well-suited for momentum systems.",
keywords: &["moving-average", "smoothing", "low-latency", "adaptive"],
ehlers_summary: "The Arnaud Legoux Moving Average applies a Gaussian-shaped weight distribution offset toward the recent end of the lookback window. The sigma parameter controls weight spread and the offset parameter controls how far the Gaussian peak is positioned from the current bar, enabling a lag-accuracy trade-off unavailable in standard MAs.",
params: &[
ParamDef {
name: "period",
default: "9",
description: "Period",
},
ParamDef {
name: "offset",
default: "0.85",
description: "Offset",
},
ParamDef {
name: "sigma",
default: "6.0",
description: "Sigma",
},
],
formula_source: "https://www.prorealcode.com/prorealtime-indicators/arnaud-legoux-moving-average-alma/",
formula_latex: r#"
\[
ALMA = \sum (W_i \times P_i) / \sum W_i
\]
"#,
gold_standard_file: "alma.json",
category: "Classic",
};