quantwave_core/indicators/
cycle_trend_analytics.rs

1use crate::indicators::metadata::{IndicatorMetadata, ParamDef};
2use crate::indicators::smoothing::SMA;
3use crate::traits::Next;
4
5/// Cycle/Trend Analytics Indicator
6///
7/// Based on John Ehlers' "Cycle/Trend Analytics And The MAD Indicator" (2021).
8/// It computes a series of oscillators: Price - SMA(Price, Length) for Length 5 to 30.
9#[derive(Debug, Clone)]
10pub struct CycleTrendAnalytics {
11    smas: Vec<SMA>,
12}
13
14impl CycleTrendAnalytics {
15    pub fn new(min_length: usize, max_length: usize) -> Self {
16        let smas = (min_length..=max_length).map(SMA::new).collect();
17        Self { smas }
18    }
19}
20
21impl Next<f64> for CycleTrendAnalytics {
22    type Output = Vec<f64>; // Price - SMA for each length from min to max
23
24    fn next(&mut self, input: f64) -> Self::Output {
25        self.smas
26            .iter_mut()
27            .map(|sma| input - sma.next(input))
28            .collect()
29    }
30}
31
32pub const CYCLE_TREND_ANALYTICS_METADATA: IndicatorMetadata = IndicatorMetadata {
33    name: "Cycle/Trend Analytics",
34    description: "A set of oscillators (Price - SMA) with lengths from 5 to 30 used to visualize cycles and trends.",
35    usage: "Use to classify the current market mode as trending or cycling before selecting your strategy. Apply trend-following systems in trend mode and mean-reversion systems in cycle mode.",
36    keywords: &["cycle", "trend", "ehlers", "classification", "adaptive"],
37    ehlers_summary: "Ehlers presents Cycle/Trend Analytics in Cycle Analytics for Traders as a framework for determining the dominant market mode. By measuring the correlation between price and the best-fit dominant cycle, the indicator classifies market behavior, enabling traders to switch between trend and cycle trading strategies dynamically.",
38    params: &[
39        ParamDef {
40            name: "min_length",
41            default: "5",
42            description: "Minimum SMA length",
43        },
44        ParamDef {
45            name: "max_length",
46            default: "30",
47            description: "Maximum SMA length",
48        },
49    ],
50    formula_source: "https://github.com/lavs9/quantwave/blob/main/references/traderstipsreference/TRADERS’ TIPS - OCTOBER 2021.html",
51    formula_latex: r#"
52\[
53Osc(L) = Price - SMA(Price, L) \quad \text{for } L \in [min, max]
54\]
55"#,
56    gold_standard_file: "cycle_trend_analytics.json",
57    category: "Ehlers DSP",
58};
59
60#[cfg(test)]
61mod tests {
62    use super::*;
63    use crate::test_utils::{assert_indicator_parity_vec, load_gold_standard_vec};
64    use crate::traits::Next;
65    use proptest::prelude::*;
66
67    #[test]
68    fn test_cycle_trend_analytics_gold_standard() {
69        let case = load_gold_standard_vec("cycle_trend_analytics");
70        let cta = CycleTrendAnalytics::new(5, 15);
71        assert_indicator_parity_vec(cta, &case.input, &case.expected);
72    }
73
74    #[test]
75    fn test_cycle_trend_analytics_basic() {
76        let mut cta = CycleTrendAnalytics::new(5, 10);
77        let inputs = vec![10.0, 11.0, 12.0, 13.0, 14.0, 15.0];
78        for input in inputs {
79            let res = cta.next(input);
80            assert_eq!(res.len(), 6);
81        }
82    }
83
84    proptest! {
85        #[test]
86        fn test_cycle_trend_analytics_parity(
87            inputs in prop::collection::vec(1.0..100.0, 30..100),
88        ) {
89            let min = 5;
90            let max = 15;
91            let mut cta = CycleTrendAnalytics::new(min, max);
92            let streaming_results: Vec<Vec<f64>> = inputs.iter().map(|&x| cta.next(x)).collect();
93
94            // Batch implementation
95            let mut batch_results = Vec::with_capacity(inputs.len());
96            for i in 0..inputs.len() {
97                let mut bar_results = Vec::with_capacity(max - min + 1);
98                for length in min..=max {
99                    let sum: f64 = inputs[(i.saturating_sub(length - 1))..=i].iter().sum();
100                    let count = (i + 1).min(length);
101                    let sma = sum / count as f64;
102                    bar_results.push(inputs[i] - sma);
103                }
104                batch_results.push(bar_results);
105            }
106
107            for (s, b) in streaming_results.iter().zip(batch_results.iter()) {
108                for (sv, bv) in s.iter().zip(b.iter()) {
109                    approx::assert_relative_eq!(sv, bv, epsilon = 1e-10);
110                }
111            }
112        }
113    }
114}
quantwave_core/indicators/cycle_trend_analytics.rs

quantwave_core/indicators/
cycle_trend_analytics.rs
