quantwave_core/indicators/
volume.rs

1use crate::indicators::metadata::{IndicatorMetadata, ParamDef};
2use crate::traits::Next;
3
4talib_4_in_1_out!(AD, talib_rs::volume::ad);
5impl Default for AD {
6    fn default() -> Self {
7        Self::new()
8    }
9}
10talib_4_in_1_out!(ADOSC, talib_rs::volume::adosc, fastperiod: usize, slowperiod: usize);
11talib_2_in_1_out!(OBV, talib_rs::volume::obv);
12impl Default for OBV {
13    fn default() -> Self {
14        Self::new()
15    }
16}
17
18pub const AD_METADATA: IndicatorMetadata = IndicatorMetadata {
19    name: "Accumulation/Distribution Line (AD)",
20    description: "A volume-based indicator designed to measure the cumulative flow of money into and out of a security.",
21    usage: "Use to confirm price trends or identify potential reversals through divergences. Rising AD confirms an uptrend; falling AD confirms a downtrend.",
22    keywords: &["volume", "momentum", "classic", "accumulation", "distribution"],
23    ehlers_summary: "Developed by Marc Chaikin, the AD line uses the relationship between price and volume to determine whether a security is being accumulated or distributed. It is calculated by multiplying the Money Flow Multiplier by the period's volume and adding it to a cumulative total. — StockCharts ChartSchool",
24    params: &[],
25    formula_source: "https://www.investopedia.com/terms/a/accumulationdistributioncurve.asp",
26    formula_latex: r#"
27\[
28\text{MFM} = \frac{(Close - Low) - (High - Close)}{High - Low} \\ \text{MFV} = \text{MFM} \times Volume \\ AD_t = AD_{t-1} + \text{MFV}
29\]
30"#,
31    gold_standard_file: "ad.json",
32    category: "Classic",
33};
34
35pub const ADOSC_METADATA: IndicatorMetadata = IndicatorMetadata {
36    name: "Chaikin Oscillator (ADOSC)",
37    description: "An indicator that measures the momentum of the Accumulation/Distribution Line using the difference between two exponential moving averages.",
38    usage: "Use to anticipate changes in the AD Line. Positive values indicate increasing buying pressure, while negative values indicate increasing selling pressure.",
39    keywords: &["volume", "oscillator", "momentum", "classic"],
40    ehlers_summary: "Marc Chaikin developed this oscillator to identify momentum shifts in the AD Line. By applying EMAs of different lengths to the AD Line, it highlights changes in money flow before they become apparent in the cumulative total, providing an early warning system for trend exhaustion. — StockCharts ChartSchool",
41    params: &[
42        ParamDef { name: "fastperiod", default: "3", description: "Fast EMA period" },
43        ParamDef { name: "slowperiod", default: "10", description: "Slow EMA period" },
44    ],
45    formula_source: "https://www.investopedia.com/terms/c/chaikinoscillator.asp",
46    formula_latex: r#"
47\[
48ADOSC = EMA(AD, 3) - EMA(AD, 10)
49\]
50"#,
51    gold_standard_file: "adosc.json",
52    category: "Classic",
53};
54
55pub const OBV_METADATA: IndicatorMetadata = IndicatorMetadata {
56    name: "On-Balance Volume (OBV)",
57    description: "A momentum indicator that uses volume flow to predict changes in stock price.",
58    usage: "Use to identify accumulation by institutions. When price is flat but OBV is rising, a breakout to the upside is likely. Conversely, when price is flat but OBV is falling, a breakdown is likely.",
59    keywords: &["volume", "momentum", "classic", "accumulation", "distribution"],
60    ehlers_summary: "Introduced by Joe Granville in his 1963 book 'Granville's New Key to Stock Market Profits', OBV is one of the oldest and most respected volume indicators. It operates on the principle that volume precedes price, and that institutional money flow leaves a detectable trail in the volume data before the price move occurs. — StockCharts ChartSchool",
61    params: &[],
62    formula_source: "https://www.investopedia.com/terms/o/onbalancevolume.asp",
63    formula_latex: r#"
64\[
65OBV_t = OBV_{t-1} + \begin{cases} Volume & \text{if } Close_t > Close_{t-1} \\ 0 & \text{if } Close_t = Close_{t-1} \\ -Volume & \text{if } Close_t < Close_{t-1} \end{cases}
66\]
67"#,
68    gold_standard_file: "obv.json",
69    category: "Classic",
70};
71
72#[cfg(test)]
73mod tests {
74    use super::*;
75    use crate::traits::Next;
76    use proptest::prelude::*;
77
78    proptest! {
79        #[test]
80        fn test_ad_parity(
81            h in prop::collection::vec(10.0..100.0, 1..100),
82            l in prop::collection::vec(10.0..100.0, 1..100),
83            c in prop::collection::vec(10.0..100.0, 1..100),
84            v in prop::collection::vec(1.0..1000.0, 1..100)
85        ) {
86            let len = h.len().min(l.len()).min(c.len()).min(v.len());
87            if len == 0 { return Ok(()); }
88            let mut high = Vec::with_capacity(len);
89            let mut low = Vec::with_capacity(len);
90            let mut close = Vec::with_capacity(len);
91            let mut volume = Vec::with_capacity(len);
92            for i in 0..len {
93                let v_h: f64 = h[i];
94                let v_l: f64 = l[i];
95                let v_c: f64 = c[i];
96                let v_v: f64 = v[i];
97                high.push(v_h.max(v_l).max(v_c));
98                low.push(v_h.min(v_l).min(v_c));
99                close.push(v_c);
100                volume.push(v_v);
101            }
102
103            let mut ad = AD::new();
104            let streaming_results: Vec<f64> = (0..len).map(|i| ad.next((high[i], low[i], close[i], volume[i]))).collect();
105            let batch_results = talib_rs::volume::ad(&high, &low, &close, &volume).unwrap_or_else(|_| vec![f64::NAN; len]);
106
107            for (s, b) in streaming_results.iter().zip(batch_results.iter()) {
108                if s.is_nan() {
109                    assert!(b.is_nan());
110                } else {
111                    approx::assert_relative_eq!(s, b, epsilon = 1e-6);
112                }
113            }
114        }
115
116        #[test]
117        fn test_obv_parity(
118            c in prop::collection::vec(10.0..100.0, 1..100),
119            v in prop::collection::vec(1.0..1000.0, 1..100)
120        ) {
121            let len = c.len().min(v.len());
122            if len == 0 { return Ok(()); }
123            let close = c[..len].to_vec();
124            let volume = v[..len].to_vec();
125
126            let mut obv = OBV::new();
127            let streaming_results: Vec<f64> = (0..len).map(|i| obv.next((close[i], volume[i]))).collect();
128            let batch_results = talib_rs::volume::obv(&close, &volume).unwrap_or_else(|_| vec![f64::NAN; len]);
129
130            for (s, b) in streaming_results.iter().zip(batch_results.iter()) {
131                if s.is_nan() {
132                    assert!(b.is_nan());
133                } else {
134                    approx::assert_relative_eq!(s, b, epsilon = 1e-6);
135                }
136            }
137        }
138    }
139}
quantwave_core/indicators/volume.rs

quantwave_core/indicators/
volume.rs
