CVI — Chaikin Volatility¶
Measures the rate of change of the trading range (high minus low) EMA. Rising values indicate increasing volatility.
Inputs: [high, low] | Options: [period] | Outputs: [cvi]
Basic¶
use tulip_rs::indicators::cvi::{Cvi, Indicator, TIndicatorState};
let high = vec![82.15, 81.89, 83.03, 83.30, 83.85,
83.90, 83.33, 84.30, 84.84, 85.00_f64];
let low = vec![81.29, 80.64, 81.31, 82.65, 83.07,
83.11, 82.49, 82.30, 84.15, 84.11_f64];
let inputs = [high.as_slice(), low.as_slice()];
let (outputs, mut state) = Cvi::indicator(&inputs, &[10.0], None).unwrap();
println!("{:?}", outputs[0]); // CVI values
// State continuation — feed new bars without reprocessing history
let partial_high = high[..8].to_vec();
let partial_low = low[..8].to_vec();
let (outputs2, mut state) = Cvi::indicator(&[partial_high.as_slice(), partial_low.as_slice()], &[10.0], None).unwrap();
println!("{:?}", outputs2[0]);
let new_high = vec![85.90_f64];
let new_low = vec![84.03_f64];
let continued = state.batch_indicator(
&[new_high.as_slice(), new_low.as_slice()],
None,
).unwrap();
println!("{:?}", continued[0]);
#include "tulip_rs_ffi.h"
double high[] = {82.15, 81.89, 83.03, 83.30, 83.85,
83.90, 83.33, 84.30, 84.84, 85.00};
double low[] = {81.29, 80.64, 81.31, 82.65, 83.07,
83.11, 82.49, 82.30, 84.15, 84.11};
double options[CVI_OPTIONS] = {10.0}; // period
const double *inputs[CVI_INPUTS] = {high, low};
/* Full computation (check r.error == C_INDICATOR_ERROR_OK in real code) */
CIndicatorResult r = cvi_indicator(inputs, 10, options, NULL, 0);
/* r.outputs[0] -> cvi_line */
tulip_ffi_result_free(r);
cvi_state_free(r.state);
/* Partial computation + state continuation */
CIndicatorResult p = cvi_indicator(inputs, 8, options, NULL, 0);
double new_high[] = {85.90};
double new_low[] = {84.03};
const double *new_inputs[CVI_INPUTS] = {new_high, new_low};
CBatchResult b = cvi_batch(p.state, new_inputs, 1, NULL, 0);
/* b.outputs[0] -> cvi_line for the one new bar */
tulip_ffi_batch_result_free(b);
tulip_ffi_result_free(p);
cvi_state_free(p.state);
import "github.com/me60732/tulip_rs_go/indicators"
high := []float64{82.15, 81.89, 83.03, 83.30, 83.85,
83.90, 83.33, 84.30, 84.84, 85.00}
low := []float64{81.29, 80.64, 81.31, 82.65, 83.07,
83.11, 82.49, 82.30, 84.15, 84.11}
options := []float64{10.0} // period
// Full computation — Rows are zero-copy views, valid until Close.
res, st, _ := indicators.Cvi.Indicator(high, low, options, nil)
fmt.Println(res.Rows[0]) // CVI values
res.Close()
st.Close()
// Partial computation + state continuation.
res2, st2, _ := indicators.Cvi.Indicator(high[:8], low[:8], options, nil)
res2.Close() // outputs consumed or closed; state stays live
batch, _ := st2.Batch(high[8:], low[8:], nil)
fmt.Println(batch.Rows[0]) // continued CVI
batch.Close()
st2.Close()
import org.tuliprs.*;
import org.tuliprs.indicators.Cvi;
double[] high = {82.15, 81.89, 83.03, 83.30, 83.85,
83.90, 83.33, 84.30, 84.84, 85.00};
double[] low = {81.29, 80.64, 81.31, 82.65, 83.07,
83.11, 82.49, 82.30, 84.15, 84.11};
double[] options = {10.0}; // period
// Full computation — output rows are zero-copy views, valid until close().
Outcome oc = Cvi.indicator(new double[][] {high, low}, options);
try (Result res = oc.result(); State st = oc.state()) {
System.out.println(java.util.Arrays.toString(res.toDoubleArray(0))); // CVI values
}
// Partial computation + state continuation.
Outcome p = Cvi.indicator(
new double[][] {java.util.Arrays.copyOfRange(high, 0, 8),
java.util.Arrays.copyOfRange(low, 0, 8)}, options);
try (Result pr = p.result(); State st = p.state()) {
Result br = st.batch(new double[][] {
java.util.Arrays.copyOfRange(high, 8, 10),
java.util.Arrays.copyOfRange(low, 8, 10)});
try (br) {
System.out.println(java.util.Arrays.toString(br.toDoubleArray(0))); // continued CVI
}
}
import numpy as np
import tulip_rs
high = np.array([82.15, 81.89, 83.03, 83.30, 83.85,
83.90, 83.33, 84.30, 84.84, 85.00], dtype=np.float64)
low = np.array([81.29, 80.64, 81.31, 82.65, 83.07,
83.11, 82.49, 82.30, 84.15, 84.11], dtype=np.float64)
outputs, state = tulip_rs.indicators.cvi.indicator([high, low], [10.0])
print(outputs[0]) # CVI values
# State continuation
new_high = np.array([85.30], dtype=np.float64)
new_low = np.array([84.60], dtype=np.float64)
continued = state.batch_indicator([new_high, low])
print(continued[0])
import * as ti from 'tulip-rs-node';
const high = Float64Array.from([82.15, 81.89, 83.03, 83.30, 83.85, 83.90, 83.33, 84.30, 84.84, 85.00, 85.90, 86.58, 86.98, 88.00, 87.87]);
const low = Float64Array.from([81.29, 80.64, 81.31, 82.65, 83.07, 83.11, 82.49, 82.30, 84.15, 84.11, 84.03, 85.39, 85.76, 87.17, 87.01]);
const [outputs, state] = ti.cvi.indicator([high, low], [10]);
console.log('CVI(10):', outputs[0]);
// State continuation
const n = high.length - 5;
const [, state2] = ti.cvi.indicator([high.slice(0, n), low.slice(0, n)], [10]);
const continued = state2.batchIndicator([high.slice(n), low.slice(n)]);
console.log('Continued CVI:', continued[0]);
import { init } from 'tulip-rs-wasm';
import * as ti from 'tulip-rs-wasm';
await init(); // bundler resolves the WASM asset automatically
const high = [82.15, 81.89, 83.03, 83.30, 83.85, 83.90, 83.33, 84.30, 84.84, 85.00, 85.90, 86.58, 86.98, 88.00, 87.87];
const low = [81.29, 80.64, 81.31, 82.65, 83.07, 83.11, 82.49, 82.30, 84.15, 84.11, 84.03, 85.39, 85.76, 87.17, 87.01];
const [outputs, state] = ti.cvi.indicator([high, low], [10]);
console.log('CVI(10):', outputs[0]);
// State continuation
const n = high.length - 5;
const [, state2] = ti.cvi.indicator([high.slice(0, n), low.slice(0, n)], [10]);
const continued = state2.batchIndicator([high.slice(n), low.slice(n)]);
console.log('Continued CVI:', continued[0]);
SIMD¶
use tulip_rs::indicators::cvi::{Cvi, Indicator};
let inputs: [&[&[f64]; 2]; 4] = [
&[h1.as_slice(), l1.as_slice()],
&[h2.as_slice(), l2.as_slice()],
&[h3.as_slice(), l3.as_slice()],
&[h4.as_slice(), l4.as_slice()],
];
let results = Cvi::indicator_by_assets::<4>(&inputs, &[10.0], None).unwrap();
for (i, asset_outputs) in results.iter().enumerate() {
println!("Asset {}: {:?}", i + 1, asset_outputs[0]);
}
By options — same asset, N option sets in parallel:
By assets — same options applied to 4 assets in one call (N must be 2/4/8/16):
double a1_high[] = {82.15, 81.89, 83.03, 83.30, 83.85, 83.90, 83.33, 84.30, 84.84, 85.00};
double a1_low[] = {81.29, 80.64, 81.31, 82.65, 83.07, 83.11, 82.49, 82.30, 84.15, 84.11};
const double *const asset1[CVI_INPUTS] = {a1_high, a1_low};
double a2_high[] = {90.0, 89.5, 91.0, 91.3, 91.9, 92.0, 91.5, 92.0, 92.5, 92.8};
double a2_low[] = {89.0, 88.5, 90.0, 90.3, 90.9, 91.0, 90.5, 91.0, 91.5, 91.8};
const double *const asset2[CVI_INPUTS] = {a2_high, a2_low};
double a3_high[] = {75.0 + (double)i * 0.2 for i in 0..10}; /* simplified */
double a3_low[] = {74.0 + (double)i * 0.2 for i in 0..10};
const double *const asset3[CVI_INPUTS] = {a3_high, a3_low};
double a4_high[] = {85.0 - (double)i * 0.1 for i in 0..10};
double a4_low[] = {84.0 - (double)i * 0.1 for i in 0..10};
const double *const asset4[CVI_INPUTS] = {a4_high, a4_low};
/* simd_inputs is indexed by asset (the N=4 SIMD lanes) */
const double *const *const simd_inputs[4] = {asset1, asset2, asset3, asset4};
CSimdResult r = cvi_simd_by_assets(simd_inputs, 4, 10, options, NULL, 0);
for (uintptr_t i = 0; i < r.num_results; i++) {
/* r.outputs[i][0] -> asset i's cvi_line */
cvi_state_free(r.states[i]);
}
tulip_ffi_simd_result_free(r);
By options — same asset, 4 different periods in one call:
double o5[] = {5.0}, o10[] = {10.0}, o14[] = {14.0}, o20[] = {20.0};
const double *const simd_opts[4] = {o5, o10, o14, o20};
CSimdResult r = cvi_simd_by_options(inputs, 10, simd_opts, 4, NULL, 0);
/* r.outputs[i] -> results for option set i */
for (uintptr_t i = 0; i < r.num_results; i++) cvi_state_free(r.states[i]);
tulip_ffi_simd_result_free(r);
By assets — same options applied to 4 assets in parallel (lane counts 2/4/8/16):
h1 := []float64{82.15, 81.89, 83.03, 83.30, 83.85, 83.90, 83.33, 84.30, 84.84, 85.00}
l1 := []float64{81.29, 80.64, 81.31, 82.65, 83.07, 83.11, 82.49, 82.30, 84.15, 84.11}
// Reuse the same data for assets 2–4 in this example
h2, l2 := h1, l1
h3, l3 := h1, l1
h4, l4 := h1, l1
assets := [][indicators.CviInputs][]float64{{h1, l1}, {h2, l2}, {h3, l3}, {h4, l4}}
sim, _ := indicators.Cvi.SimdByAssets(assets, []float64{10.0}, nil)
for i, lanes := range sim.Results {
fmt.Printf("Asset %d: %v\n", i+1, lanes[0])
}
sim.Close() // frees every lane state, then the SIMD buffers
By options — same asset, N option sets in parallel:
high := []float64{82.15, 81.89, 83.03, 83.30, 83.85, 83.90, 83.33, 84.30, 84.84, 85.00}
low := []float64{81.29, 80.64, 81.31, 82.65, 83.07, 83.11, 82.49, 82.30, 84.15, 84.11}
assets2 := [][indicators.CviInputs][]float64{{high, low}}
sim2, _ := indicators.Cvi.SimdByOptions(high, low, [][]float64{{5.0}, {10.0}, {14.0}, {20.0}}, nil)
for i, lanes := range sim2.Results {
fmt.Printf("Period %d: %v\n", i+1, lanes[0])
}
sim2.Close()
By assets — same options applied to 4 assets in parallel (N must be 2, 4, 8, or 16):
import org.tuliprs.*;
import org.tuliprs.indicators.Cvi;
double[] a1_high = {82.15, 81.89, 83.03, 83.30, 83.85, 83.90, 83.33, 84.30, 84.84, 85.00};
double[] a1_low = {81.29, 80.64, 81.31, 82.65, 83.07, 83.11, 82.49, 82.30, 84.15, 84.11};
double[] a2_high = {90.0, 89.5, 91.0, 91.3, 91.9, 92.0, 91.5, 92.0, 92.5, 92.8};
double[] a2_low = {89.0, 88.5, 90.0, 90.3, 90.9, 91.0, 90.5, 91.0, 91.5, 91.8};
double[] a3_high = {75.2, 75.4, 75.6, 75.8, 76.0, 76.2, 76.4, 76.6, 76.8, 77.0};
double[] a3_low = {74.2, 74.4, 74.6, 74.8, 75.0, 75.2, 75.4, 75.6, 75.8, 76.0};
double[] a4_high = {85.9, 85.8, 85.7, 85.6, 85.5, 85.4, 85.3, 85.2, 85.1, 85.0};
double[] a4_low = {84.9, 84.8, 84.7, 84.6, 84.5, 84.4, 84.3, 84.2, 84.1, 84.0};
// One entry per asset; each asset lists its INPUTS series.
double[][][] assets = {{a1_high, a1_low}, {a2_high, a2_low}, {a3_high, a3_low}, {a4_high, a4_low}};
try (SimdResult sim = Cvi.simdByAssets(assets, new double[] {10.0}, null)) {
for (int i = 0; i < sim.numResults(); i++) {
System.out.printf("Asset %d: %s%n", i + 1,
java.util.Arrays.toString(sim.toDoubleArray(i, 0)));
}
} // frees every lane state, then the SIMD buffers (contractual order)
By options — same asset, N option sets in parallel:
import org.tuliprs.*;
import org.tuliprs.indicators.Cvi;
double[] high = {82.15, 81.89, 83.03, 83.30, 83.85, 83.90, 83.33, 84.30, 84.84, 85.00};
double[] low = {81.29, 80.64, 81.31, 82.65, 83.07, 83.11, 82.49, 82.30, 84.15, 84.11};
try (SimdResult sim = Cvi.simdByOptions(new double[][] {high, low},
new double[][] {{5.0}, {10.0}, {14.0}, {20.0}}, null)) {
for (int i = 0; i < sim.numResults(); i++) {
System.out.printf("Period %d: %s%n", i + 1,
java.util.Arrays.toString(sim.toDoubleArray(i, 0)));
}
}
By assets — same options, N assets in parallel (must be 2, 4, 8, or 16):
simd_inputs = [[h1, l1], [h2, l2], [h3, l3], [h4, l4]]
outputs_list, states = tulip_rs.indicators.cvi.simd_by_assets(simd_inputs, [10.0])
for i, asset_outputs in enumerate(outputs_list):
print(f"Asset {i+1}: {asset_outputs[0]}")
By options — same asset, N option sets in parallel:
By assets — same options applied to 4 assets in parallel:
const simdInputs = [
[high.slice(), low.slice()],
[high.map(v => v * 1.1), low.map(v => v * 1.1)],
[high.map(v => v * 0.9), low.map(v => v * 0.9)],
[high.map(v => v * 1.02), low.map(v => v * 1.02)],
];
const [results] = ti.cvi.simdByAssets(simdInputs, [10]);
results.forEach((out, i) => console.log(`Asset ${i + 1}:`, out[0]));
By options — same asset, 4 different periods in parallel: