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quantwave_core/indicators/
continuation_index.rs

1use crate::indicators::generalized_laguerre::GeneralizedLaguerre;
2use crate::indicators::metadata::{IndicatorMetadata, ParamDef};
3use crate::indicators::smoothing::SMA;
4use crate::indicators::ultimate_smoother::UltimateSmoother;
5use crate::traits::Next;
6
7/// Continuation Index
8///
9/// Based on John Ehlers' "The Continuation Index" (TASC September 2025).
10/// The indicator is designed to signal both the early onset and potential exhaustion of a trend.
11/// It uses a Laguerre filter and UltimateSmoother to reduce lag, then compresses the result
12/// using an Inverse Fisher Transform (tanh).
13#[derive(Debug, Clone)]
14pub struct ContinuationIndex {
15    us: UltimateSmoother,
16    lg: GeneralizedLaguerre,
17    variance_sma: SMA,
18}
19
20impl ContinuationIndex {
21    pub fn new(gamma: f64, order: usize, length: usize) -> Self {
22        Self {
23            us: UltimateSmoother::new(length / 2),
24            lg: GeneralizedLaguerre::new(length, gamma, order),
25            variance_sma: SMA::new(length),
26        }
27    }
28}
29
30impl Next<f64> for ContinuationIndex {
31    type Output = f64;
32
33    fn next(&mut self, input: f64) -> Self::Output {
34        let us_val = self.us.next(input);
35        let lg_val = self.lg.next(input);
36
37        let diff = us_val - lg_val;
38        let variance = self.variance_sma.next(diff.abs());
39
40        let ref_val = if variance != 0.0 {
41            2.0 * diff / variance
42        } else {
43            0.0
44        };
45
46        // CI = (exp(2 * ref) - 1) / (exp(2 * ref) + 1) which is tanh(ref)
47        ref_val.tanh()
48    }
49}
50
51pub const CONTINUATION_INDEX_METADATA: IndicatorMetadata = IndicatorMetadata {
52    name: "Continuation Index",
53    description: "An oscillator that identifies trend onset and exhaustion by comparing a fast UltimateSmoother with a Generalized Laguerre filter.",
54    usage: "Use to measure whether a price move is likely to continue or reverse based on cycle analysis. High index values suggest trend continuation; low values suggest an impending cycle turn.",
55    keywords: &["trend", "momentum", "ehlers", "cycle"],
56    ehlers_summary: "The Continuation Index measures the persistence of directional price movement relative to the dominant cycle. Ehlers derives it from the cycle phase velocity — when phase advances quickly in one direction, momentum is strong and continuation is likely; slow or reversing phase suggests the move is exhausting.",
57    params: &[
58        ParamDef {
59            name: "gamma",
60            default: "0.8",
61            description: "Laguerre gamma parameter",
62        },
63        ParamDef {
64            name: "order",
65            default: "8",
66            description: "Laguerre filter order",
67        },
68        ParamDef {
69            name: "length",
70            default: "40",
71            description: "Base smoothing length",
72        },
73    ],
74    formula_source: "https://github.com/lavs9/quantwave/blob/main/references/traderstipsreference/TRADERS%E2%80%99%20TIPS%20-%20SEPTEMBER%202025.html",
75    formula_latex: r#"
76\[
77US = UltimateSmoother(Close, Length/2)
78\]
79\[
80LG = Laguerre(Close, \gamma, Order, Length)
81\]
82\[
83Variance = SMA(|US - LG|, Length)
84\]
85\[
86Ref = 2 \times (US - LG) / Variance
87\]
88\[
89CI = \tanh(Ref)
90\]
91"#,
92    gold_standard_file: "continuation_index.json",
93    category: "Ehlers DSP",
94};
95
96#[cfg(test)]
97mod tests {
98    use super::*;
99    use crate::traits::Next;
100    use proptest::prelude::*;
101
102    #[test]
103    fn test_continuation_index_basic() {
104        let mut ci = ContinuationIndex::new(0.8, 8, 40);
105        let inputs = vec![10.0, 11.0, 12.0, 13.0, 14.0];
106        for input in inputs {
107            let res = ci.next(input);
108            assert!(!res.is_nan());
109            assert!(res >= -1.0 && res <= 1.0);
110        }
111    }
112
113    proptest! {
114        #[test]
115        fn test_continuation_index_parity(
116            inputs in prop::collection::vec(1.0..100.0, 50..100),
117        ) {
118            let gamma = 0.8;
119            let order = 8;
120            let length = 40;
121            let mut ci = ContinuationIndex::new(gamma, order, length);
122            let streaming_results: Vec<f64> = inputs.iter().map(|&x| ci.next(x)).collect();
123
124            // Reference implementation
125            let mut us = UltimateSmoother::new(length / 2);
126            let mut lg = GeneralizedLaguerre::new(length, gamma, order);
127            let mut diffs = Vec::new();
128            let mut batch_results = Vec::with_capacity(inputs.len());
129
130            for &input in &inputs {
131                let u = us.next(input);
132                let l = lg.next(input);
133                let d = u - l;
134                diffs.push(d.abs());
135
136                let start = if diffs.len() > length { diffs.len() - length } else { 0 };
137                let window = &diffs[start..];
138                let variance = window.iter().sum::<f64>() / window.len() as f64;
139
140                let ref_val = if variance != 0.0 { 2.0 * d / variance } else { 0.0 };
141                batch_results.push(ref_val.tanh());
142            }
143
144            for (s, b) in streaming_results.iter().zip(batch_results.iter()) {
145                approx::assert_relative_eq!(s, b, epsilon = 1e-10);
146            }
147        }
148    }
149}