quantwave_core/indicators/
precision_trend.rs

1use crate::indicators::high_pass::HighPass;
2use crate::indicators::metadata::{IndicatorMetadata, ParamDef};
3use crate::traits::Next;
4
5/// Precision Trend Analysis
6///
7/// Based on John Ehlers' "Precision Trend Analysis" (TASC September 2024).
8/// Uses the difference between two HighPass filters to identify the trend
9/// and its Rate of Change (ROC) to pinpoint reversals.
10/// Returns (Trend, ROC).
11#[derive(Debug, Clone)]
12pub struct PrecisionTrendAnalysis {
13    hp1: HighPass,
14    hp2: HighPass,
15    prev_trend: f64,
16    length2: f64,
17    count: usize,
18}
19
20impl PrecisionTrendAnalysis {
21    pub fn new(length1: usize, length2: usize) -> Self {
22        Self {
23            hp1: HighPass::new(length1),
24            hp2: HighPass::new(length2),
25            prev_trend: 0.0,
26            length2: length2 as f64,
27            count: 0,
28        }
29    }
30}
31
32impl Default for PrecisionTrendAnalysis {
33    fn default() -> Self {
34        Self::new(250, 40)
35    }
36}
37
38impl Next<f64> for PrecisionTrendAnalysis {
39    type Output = (f64, f64);
40
41    fn next(&mut self, input: f64) -> Self::Output {
42        self.count += 1;
43        let v_hp1 = self.hp1.next(input);
44        let v_hp2 = self.hp2.next(input);
45        let trend = v_hp1 - v_hp2;
46        
47        let roc = (self.length2 / std::f64::consts::TAU) * (trend - self.prev_trend);
48        
49        self.prev_trend = trend;
50        (trend, roc)
51    }
52}
53
54pub const PRECISION_TREND_ANALYSIS_METADATA: IndicatorMetadata = IndicatorMetadata {
55    name: "Precision Trend Analysis",
56    description: "Trend identification using the difference between two high-pass filters.",
57    usage: "Use as a high-precision trend indicator that applies DSP filtering to remove cycle noise before measuring trend direction, giving fewer but more reliable trend signals.",
58    keywords: &["trend", "ehlers", "dsp", "filter"],
59    ehlers_summary: "Ehlers Precision Trend analysis applies a roofing-filter style preprocessing to price before computing the trend indicator, removing the cyclical component that causes premature trend reversals in standard indicators. The result is a trend signal that changes state only when the genuine trend direction changes.",
60    params: &[
61        ParamDef {
62            name: "length1",
63            default: "250",
64            description: "First HighPass filter period",
65        },
66        ParamDef {
67            name: "length2",
68            default: "40",
69            description: "Second HighPass filter period",
70        },
71    ],
72    formula_source: "https://github.com/lavs9/quantwave/blob/main/references/traderstipsreference/TRADERS’%20TIPS%20-%20SEPTEMBER%202024.html",
73    formula_latex: r#"
74\[
75HP1 = HighPass(Price, Length1)
76\]
77\[
78HP2 = HighPass(Price, Length2)
79\]
80\[
81Trend = HP1 - HP2
82\]
83\[
84ROC = \frac{Length2}{6.28} \cdot (Trend - Trend_{t-1})
85\]
86"#,
87    gold_standard_file: "precision_trend.json",
88    category: "Ehlers DSP",
89};
90
91#[cfg(test)]
92mod tests {
93    use super::*;
94    use crate::traits::Next;
95    use proptest::prelude::*;
96
97    #[test]
98    fn test_precision_trend_basic() {
99        let mut pt = PrecisionTrendAnalysis::new(250, 40);
100        let inputs = vec![10.0; 10];
101        for input in inputs {
102            let (trend, roc) = pt.next(input);
103            assert!(!trend.is_nan());
104            assert!(!roc.is_nan());
105        }
106    }
107
108    proptest! {
109        #[test]
110        fn test_precision_trend_parity(
111            inputs in prop::collection::vec(1.0..100.0, 10..100),
112        ) {
113            let l1 = 250;
114            let l2 = 40;
115            let mut pt = PrecisionTrendAnalysis::new(l1, l2);
116            let streaming_results: Vec<(f64, f64)> = inputs.iter().map(|&x| pt.next(x)).collect();
117
118            // Batch implementation
119            let mut hp1 = HighPass::new(l1);
120            let mut hp2 = HighPass::new(l2);
121            let mut prev_trend = 0.0;
122            let mut batch_results = Vec::with_capacity(inputs.len());
123
124            for input in inputs {
125                let v_hp1 = hp1.next(input);
126                let v_hp2 = hp2.next(input);
127                let trend = v_hp1 - v_hp2;
128                let roc = (l2 as f64 / std::f64::consts::TAU) * (trend - prev_trend);
129                prev_trend = trend;
130                batch_results.push((trend, roc));
131            }
132
133            for (s, b) in streaming_results.iter().zip(batch_results.iter()) {
134                approx::assert_relative_eq!(s.0, b.0, epsilon = 1e-10);
135                approx::assert_relative_eq!(s.1, b.1, epsilon = 1e-10);
136            }
137        }
138    }
139}
quantwave_core/indicators/precision_trend.rs

quantwave_core/indicators/
precision_trend.rs
