quantwave-core 0.1.1

A high-performance, Polars-native technical analysis library for Rust.
Documentation 
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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;
        }

        // Normalize weights
        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.",
    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",
};