quantwave-core 0.1.0

use crate::indicators::metadata::{IndicatorMetadata, ParamDef};
use crate::traits::Next;
use std::collections::VecDeque;

/// MESA Adaptive Moving Average (MAMA)
/// Adapts to price movement based on the rate change of phase as measured by the Hilbert Transform Discriminator.
/// Returns (MAMA, FAMA).
#[derive(Debug, Clone)]
pub struct MAMA {
    fast_limit: f64,
    slow_limit: f64,
    price_history: VecDeque<f64>,
    smooth_history: VecDeque<f64>,
    detrender_history: VecDeque<f64>,
    i1_history: VecDeque<f64>,
    q1_history: VecDeque<f64>,
    i2_prev: f64,
    q2_prev: f64,
    re_prev: f64,
    im_prev: f64,
    period_prev: f64,
    smooth_period_prev: f64,
    phase_prev: f64,
    mama_prev: f64,
    fama_prev: f64,
    count: usize,
}

impl MAMA {
    pub fn new(fast_limit: f64, slow_limit: f64) -> Self {
        Self {
            fast_limit,
            slow_limit,
            price_history: VecDeque::from(vec![0.0; 4]),
            smooth_history: VecDeque::from(vec![0.0; 7]),
            detrender_history: VecDeque::from(vec![0.0; 7]),
            i1_history: VecDeque::from(vec![0.0; 7]),
            q1_history: VecDeque::from(vec![0.0; 7]),
            i2_prev: 0.0,
            q2_prev: 0.0,
            re_prev: 0.0,
            im_prev: 0.0,
            period_prev: 0.0,
            smooth_period_prev: 0.0,
            phase_prev: 0.0,
            mama_prev: 0.0,
            fama_prev: 0.0,
            count: 0,
        }
    }
}

impl Default for MAMA {
    fn default() -> Self {
        Self::new(0.5, 0.05)
    }
}

impl Next<f64> for MAMA {
    type Output = (f64, f64);

    fn next(&mut self, price: f64) -> Self::Output {
        self.count += 1;

        self.price_history.pop_back();
        self.price_history.push_front(price);

        if self.count < 6 {
            self.mama_prev = price;
            self.fama_prev = price;
            return (price, price);
        }

        // Smooth = (4*Price + 3*Price[1] + 2*Price[2] + Price[3]) / 10;
        let smooth = (4.0 * self.price_history[0]
            + 3.0 * self.price_history[1]
            + 2.0 * self.price_history[2]
            + self.price_history[3])
            / 10.0;

        self.smooth_history.pop_back();
        self.smooth_history.push_front(smooth);

        // Detrender = (.0962*Smooth + .5769*Smooth[2] - .5769*Smooth[4] - .0962*Smooth[6])*(.075*Period[1] + .54);
        let detrender = (0.0962 * self.smooth_history[0] + 0.5769 * self.smooth_history[2]
            - 0.5769 * self.smooth_history[4]
            - 0.0962 * self.smooth_history[6])
            * (0.075 * self.period_prev + 0.54);

        self.detrender_history.pop_back();
        self.detrender_history.push_front(detrender);

        // Q1 = (.0962*Detrender + .5769*Detrender[2] - .5769*Detrender[4] - .0962*Detrender[6])*(.075*Period[1] + .54);
        let q1 = (0.0962 * self.detrender_history[0] + 0.5769 * self.detrender_history[2]
            - 0.5769 * self.detrender_history[4]
            - 0.0962 * self.detrender_history[6])
            * (0.075 * self.period_prev + 0.54);

        // I1 = Detrender[3];
        let i1 = self.detrender_history[3];

        self.i1_history.pop_back();
        self.i1_history.push_front(i1);
        self.q1_history.pop_back();
        self.q1_history.push_front(q1);

        // jI = (.0962*I1 + .5769*I1[2] - .5769*I1[4] - .0962*I1[6])*(.075*Period[1] + .54);
        let ji = (0.0962 * self.i1_history[0] + 0.5769 * self.i1_history[2]
            - 0.5769 * self.i1_history[4]
            - 0.0962 * self.i1_history[6])
            * (0.075 * self.period_prev + 0.54);

        // jQ = (.0962*Q1 + .5769*Q1[2] - .5769*Q1[4] - .0962*Q1[6])*(.075*Period[1] + .54);
        let jq = (0.0962 * self.q1_history[0] + 0.5769 * self.q1_history[2]
            - 0.5769 * self.q1_history[4]
            - 0.0962 * self.q1_history[6])
            * (0.075 * self.period_prev + 0.54);

        // I2 = I1 - jQ;
        // Q2 = Q1 + jI;
        let mut i2 = i1 - jq;
        let mut q2 = q1 + ji;

        // I2 = .2*I2 + .8*I2[1];
        // Q2 = .2*Q2 + .8*Q2[1];
        i2 = 0.2 * i2 + 0.8 * self.i2_prev;
        q2 = 0.2 * q2 + 0.8 * self.q2_prev;
        self.i2_prev = i2;
        self.q2_prev = q2;

        // Homodyne Discriminator
        // Re = I2*I2[1] + Q2*Q2[1];
        // Im = I2*Q2[1] - Q2*I2[1];
        let mut re = i2 * self.i2_prev + q2 * self.q2_prev;
        let mut im = i2 * self.q2_prev - q2 * self.i2_prev;

        // Note: The EL code uses I2[1] and Q2[1] which are the values BEFORE the current i2/q2 update.
        // Wait, in EL, Vars are updated at the end of the bar or immediately.
        // I2 = .2*I2 + .8*I2[1] update i2. Then Re = I2*I2[1].
        // This means I2[1] is indeed the PREVIOUS value.

        re = 0.2 * re + 0.8 * self.re_prev;
        im = 0.2 * im + 0.8 * self.im_prev;
        self.re_prev = re;
        self.im_prev = im;

        let mut period = self.period_prev;
        if im != 0.0 && re != 0.0 {
            period = 360.0 / (im / re).atan().to_degrees();
        }
        if period > 1.5 * self.period_prev {
            period = 1.5 * self.period_prev;
        }
        if period < 0.67 * self.period_prev {
            period = 0.67 * self.period_prev;
        }
        if period < 6.0 {
            period = 6.0;
        }
        if period > 50.0 {
            period = 50.0;
        }
        period = 0.2 * period + 0.8 * self.period_prev;
        self.period_prev = period;

        let _smooth_period = 0.33 * period + 0.67 * self.smooth_period_prev;
        self.smooth_period_prev = _smooth_period;

        let mut phase = 0.0;
        if i1 != 0.0 {
            phase = (q1 / i1).atan().to_degrees();
        }

        let mut delta_phase = self.phase_prev - phase;
        self.phase_prev = phase;

        if delta_phase < 1.0 {
            delta_phase = 1.0;
        }

        let mut alpha = self.fast_limit / delta_phase;
        if alpha < self.slow_limit {
            alpha = self.slow_limit;
        }
        if alpha > self.fast_limit {
            alpha = self.fast_limit;
        }

        let mama = alpha * price + (1.0 - alpha) * self.mama_prev;
        let fama = 0.5 * alpha * mama + (1.0 - 0.5 * alpha) * self.fama_prev;

        self.mama_prev = mama;
        self.fama_prev = fama;

        (mama, fama)
    }
}

#[cfg(test)]
mod tests {
    use super::*;
    use proptest::prelude::*;

    #[test]
    fn test_mama_basic() {
        let mut mama = MAMA::new(0.5, 0.05);
        let prices = vec![10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0];
        for p in prices {
            let (m, f) = mama.next(p);
            assert!(!m.is_nan());
            assert!(!f.is_nan());
        }
    }

    fn mama_batch(data: Vec<f64>, fast: f64, slow: f64) -> Vec<(f64, f64)> {
        let mut mama = MAMA::new(fast, slow);
        data.into_iter().map(|x| mama.next(x)).collect()
    }

    proptest! {
        #[test]
        fn test_mama_parity(input in prop::collection::vec(1.0..100.0, 10..100)) {
            let fast = 0.5;
            let slow = 0.05;
            let mut mama = MAMA::new(fast, slow);
            let mut streaming_results = Vec::with_capacity(input.len());
            for &val in &input {
                streaming_results.push(mama.next(val));
            }

            let batch_results = mama_batch(input, fast, slow);

            for (s, b) in streaming_results.iter().zip(batch_results.iter()) {
                approx::assert_relative_eq!(s.0, b.0, epsilon = 1e-6);
                approx::assert_relative_eq!(s.1, b.1, epsilon = 1e-6);
            }
        }
    }
}

pub const MAMA_METADATA: IndicatorMetadata = IndicatorMetadata {
    name: "MESA Adaptive Moving Average",
    description: "MAMA adapts to price movement in an entirely new and unique way based on the rate change of phase.",
    params: &[
        ParamDef {
            name: "fast_limit",
            default: "0.5",
            description: "Fast limit for alpha",
        },
        ParamDef {
            name: "slow_limit",
            default: "0.05",
            description: "Slow limit for alpha",
        },
    ],
    formula_source: "https://github.com/lavs9/quantwave/blob/main/references/Ehlers%20Papers/implemented/MAMA.pdf",
    formula_latex: r#"
\[
\text{MAMA} = \alpha \cdot \text{Price} + (1 - \alpha) \cdot \text{MAMA}_{1}
\]
\[
\text{FAMA} = 0.5\alpha \cdot \text{MAMA} + (1 - 0.5\alpha) \cdot \text{FAMA}_{1}
\]
"#,
    gold_standard_file: "mama.json",
    category: "Ehlers DSP",
};