VOSC — Volume Oscillator¶
The percentage difference between two volume moving averages. Expanding volume oscillator supports the price trend.
Inputs: [volume] | Options: [short_period, long_period] | Outputs: [vosc]
Basic¶
use tulip_rs::indicators::vosc::{Vosc, Indicator, TIndicatorState};
let volume = vec![1200.0, 1400.0, 1100.0, 1600.0, 1300.0,
900.0, 1500.0, 1800.0, 1000.0, 1700.0_f64];
// options: [short_period, long_period]
let (outputs, mut state) = Vosc::indicator(&[volume.as_slice()], &[5.0, 10.0], None).unwrap();
println!("{:?}", outputs[0]); // VOSC values
// State continuation — feed new bars without reprocessing history
let new_volume = vec![1600.0, 1250.0_f64];
let continued = state.batch_indicator(&[new_volume.as_slice()], None).unwrap();
println!("{:?}", continued[0]);
#include "tulip_rs_ffi.h"
double volume[] = {1200.0, 1400.0, 1100.0, 1600.0, 1300.0,
900.0, 1500.0, 1800.0, 1000.0, 1700.0};
double options[VOSC_OPTIONS] = {5.0, 10.0}; // short_period, long_period
const double *inputs[VOSC_INPUTS] = {volume};
/* Full computation (check r.error == C_INDICATOR_ERROR_OK in real code) */
CIndicatorResult r = vosc_indicator(inputs, 10, options, NULL, 0);
/* r.outputs[0] -> the VOSC series, length r.output_lens[0] */
tulip_ffi_result_free(r);
vosc_state_free(r.state);
/* Partial computation + state continuation */
CIndicatorResult p = vosc_indicator(inputs, 8, options, NULL, 0);
double new_volume[] = {1600.0, 1250.0};
const double *new_inputs[VOSC_INPUTS] = {new_volume};
CBatchResult b = vosc_batch(p.state, new_inputs, 2, NULL, 0);
/* b.outputs[0] -> VOSC values for just the two new bars */
tulip_ffi_batch_result_free(b);
tulip_ffi_result_free(p);
vosc_state_free(p.state);
import "github.com/me60732/tulip_rs_go/indicators"
volume := []float64{1200.0, 1400.0, 1100.0, 1600.0, 1300.0,
900.0, 1500.0, 1800.0, 1000.0, 1700.0}
options := []float64{5.0, 10.0} // short_period, long_period
// Full computation — Rows are zero-copy views, valid until Close.
res, st, _ := indicators.Vosc.Indicator(volume, options, nil)
fmt.Println(res.Rows[0]) // VOSC values
res.Close()
st.Close()
// Partial computation + state continuation.
res2, st2, _ := indicators.Vosc.Indicator(volume[:8], options, nil)
res2.Close() // outputs consumed or closed; state stays live
batch, _ := st2.Batch(volume[8:], nil)
fmt.Println(batch.Rows[0]) // continued VOSC values
batch.Close()
st2.Close()
import org.tuliprs.*;
import org.tuliprs.indicators.Vosc;
double[] volume = {1200.0, 1400.0, 1100.0, 1600.0, 1300.0,
900.0, 1500.0, 1800.0, 1000.0, 1700.0};
double[] options = {5.0, 10.0}; // short_period, long_period
// Full computation — output rows are zero-copy views, valid until close().
Outcome oc = Vosc.indicator(new double[][] {volume}, options);
try (Result res = oc.result(); State st = oc.state()) {
System.out.println(java.util.Arrays.toString(res.toDoubleArray(0))); // VOSC values
}
// Partial computation + state continuation.
Outcome p = Vosc.indicator(
new double[][] {java.util.Arrays.copyOfRange(volume, 0, 8)}, options);
try (Result pr = p.result(); State st = p.state()) {
Result br = st.batch(new double[][] {
java.util.Arrays.copyOfRange(volume, 8, 10)});
try (br) {
System.out.println(java.util.Arrays.toString(br.toDoubleArray(0))); // continued VOSC values
}
}
import numpy as np
import tulip_rs
volume = np.array([1200.0, 1400.0, 1100.0, 1600.0, 1300.0,
900.0, 1500.0, 1800.0, 1000.0, 1700.0], dtype=np.float64)
# options: [short_period, long_period]
outputs, state = tulip_rs.indicators.vosc.indicator([volume], [5.0, 10.0])
print(outputs[0]) # VOSC values
# State continuation
new_volume = np.array([1600.0, 1250.0], dtype=np.float64)
continued = state.batch_indicator([new_volume])
print(continued[0])
import * as ti from 'tulip-rs-node';
const volume = Float64Array.from([5653100, 6447400, 7690900, 3831400, 4455100, 3798000, 3936200, 4732000, 4841300, 3915300, 6830800, 6694100, 5293600, 7985800, 4807900]);
const [outputs, state] = ti.vosc.indicator([volume], [5, 10]);
console.log('VOSC:', outputs[0]);
// State continuation
const n = volume.length - 5;
const [, state2] = ti.vosc.indicator([volume.slice(0, n)], [5, 10]);
const continued = state2.batchIndicator([volume.slice(n)]);
console.log('Continued VOSC:', continued[0]);
import { init } from 'tulip-rs-wasm';
import * as ti from 'tulip-rs-wasm';
await init(); // bundler resolves the WASM asset automatically
const volume = [5653100, 6447400, 7690900, 3831400, 4455100, 3798000, 3936200, 4732000, 4841300, 3915300, 6830800, 6694100, 5293600, 7985800, 4807900];
const [outputs, state] = ti.vosc.indicator([volume], [5, 10]);
console.log('VOSC:', outputs[0]);
// State continuation
const n = volume.length - 5;
const [, state2] = ti.vosc.indicator([volume.slice(0, n)], [5, 10]);
const continued = state2.batchIndicator([volume.slice(n)]);
console.log('Continued VOSC:', continued[0]);
Optional Outputs¶
vosc exposes 2 optional outputs: short_sma, long_sma. Pass a boolean mask as the third argument — one bool per optional output, in order.
use tulip_rs::indicators::vosc::{Vosc, Indicator, TIndicatorState};
let volume = vec![10000.0, 12000.0, 9500.0, 11000.0, 13000.0, 9800.0, 10500.0, 12500.0, 11800.0, 10200.0_f64];
let mask = [true, true];
let (outputs, _state) = Vosc::indicator(&[volume.as_slice()], &[5.0, 20.0], Some(&mask)).unwrap();
let vosc = &outputs[0]; // vosc (primary)
let short_sma = &outputs[1]; // short_sma (optional — requested)
let long_sma = &outputs[2]; // long_sma (optional — requested)
#include "tulip_rs_ffi.h"
double volume[] = {10000.0, 12000.0, 9500.0, 11000.0, 13000.0,
9800.0, 10500.0, 12500.0, 11800.0, 10200.0};
double options[VOSC_OPTIONS] = {5.0, 20.0};
const double *inputs[VOSC_INPUTS] = {volume};
bool optional_outputs[2] = {true, true}; // short_sma, long_sma
CIndicatorResult r = vosc_indicator(inputs, 10, options, optional_outputs, 2);
/* r.outputs[0] -> vosc, r.outputs[1] -> short_sma, r.outputs[2] -> long_sma */
tulip_ffi_result_free(r);
vosc_state_free(r.state);
import "github.com/me60732/tulip_rs_go/indicators"
volume := []float64{10000.0, 12000.0, 9500.0, 11000.0, 13000.0, 9800.0, 10500.0, 12500.0, 11800.0, 10200.0}
options := []float64{5.0, 20.0} // short_period, long_period
mask := []bool{true, true} // short_sma, long_sma
res, st, _ := indicators.Vosc.Indicator(volume, options, mask)
fmt.Println(res.Rows[0]) // vosc (primary)
fmt.Println(res.Rows[1]) // short_sma (optional — requested)
fmt.Println(res.Rows[2]) // long_sma (optional — requested)
res.Close()
st.Close()
import org.tuliprs.*;
import org.tuliprs.indicators.Vosc;
double[] volume = {10000.0, 12000.0, 9500.0, 11000.0, 13000.0, 9800.0, 10500.0, 12500.0, 11800.0, 10200.0};
double[] options = {5.0, 20.0}; // short_period, long_period
boolean[] mask = {true, true}; // short_sma, long_sma
Outcome oc = Vosc.indicator(new double[][] {volume}, options, mask);
try (Result res = oc.result()) {
System.out.println(java.util.Arrays.toString(res.toDoubleArray(0))); // vosc (primary)
System.out.println(java.util.Arrays.toString(res.toDoubleArray(1))); // short_sma (optional — requested)
System.out.println(java.util.Arrays.toString(res.toDoubleArray(2))); // long_sma (optional — requested)
}
oc.state().close();
import numpy as np
import tulip_rs
volume = np.array([10000.0, 12000.0, 9500.0, 11000.0, 13000.0, 9800.0, 10500.0, 12500.0, 11800.0, 10200.0], dtype=np.float64)
outputs, state = tulip_rs.indicators.vosc.indicator(
[volume], [5.0, 20.0],
optional_outputs=[True, True],
)
vosc = outputs[0] # vosc (primary)
short_sma = outputs[1] # short_sma (optional — requested)
long_sma = outputs[2] # long_sma (optional — requested)
vosc exposes 2 optional outputs: short_sma, long_sma.
SIMD¶
use tulip_rs::indicators::vosc::{Vosc, Indicator};
let inputs: [&[&[f64]; 1]; 4] = [
&[v1.as_slice()],
&[v2.as_slice()],
&[v3.as_slice()],
&[v4.as_slice()],
];
let results = Vosc::indicator_by_assets::<4>(&inputs, &[5.0, 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:
use tulip_rs::indicators::vosc::{Vosc, IndicatorByOptions};
let opts: [&[f64; 2]; 4] = [&[3.0, 6.0], &[5.0, 10.0], &[8.0, 16.0], &[12.0, 24.0]];
let results = Vosc::indicator_by_options::<4>(&[volume.as_slice()], &opts, None).unwrap();
for (i, out) in results.iter().enumerate() {
println!("Option set {}: {:?}", i + 1, out[0]);
}
By assets — same options applied to 4 assets in one call (N must be 2/4/8/16):
double v1[] = {1200.0, 1400.0, 1100.0, 1600.0, 1300.0, 900.0, 1500.0, 1800.0, 1000.0, 1700.0};
double v2[] = {1100.0, 1300.0, 1000.0, 1500.0, 1200.0, 800.0, 1400.0, 1700.0, 900.0, 1600.0};
double v3[] = {1300.0, 1500.0, 1200.0, 1700.0, 1400.0, 1000.0, 1600.0, 1900.0, 1100.0, 1800.0};
double v4[] = {1400.0, 1600.0, 1300.0, 1800.0, 1500.0, 1100.0, 1700.0, 2000.0, 1200.0, 1900.0};
/* one [INPUTS]-long pointer array per asset */
const double *asset1[VOSC_INPUTS] = {v1};
const double *asset2[VOSC_INPUTS] = {v2};
const double *asset3[VOSC_INPUTS] = {v3};
const double *asset4[VOSC_INPUTS] = {v4};
const double *const *const simd_inputs[4] = {asset1, asset2, asset3, asset4};
CSimdResult r = vosc_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 series */
vosc_state_free(r.states[i]);
}
tulip_ffi_simd_result_free(r);
By options — same asset, N different option sets in parallel:
static const double volume[] = {1200.0, 1400.0, 1100.0, 1600.0, 1300.0,
900.0, 1500.0, 1800.0, 1000.0, 1700.0};
#define EXPANDED_LEN (sizeof(volume) / sizeof(double) * 20)
static double volume_expanded[EXPANDED_LEN];
for (size_t i = 0; i < 20; i++) {
for (size_t j = 0; j < sizeof(volume) / sizeof(double); j++) {
volume_expanded[i * (sizeof(volume) / sizeof(double)) + j] = volume[j];
}
}
const double *inputs[VOSC_INPUTS] = {volume_expanded};
static const double o1[VOSC_OPTIONS] = {3.0, 6.0};
static const double o2[VOSC_OPTIONS] = {5.0, 10.0};
static const double o3[VOSC_OPTIONS] = {8.0, 16.0};
static const double o4[VOSC_OPTIONS] = {12.0, 24.0};
const double *const simd_opts[4] = {o1, o2, o3, o4};
CSimdResult r = vosc_simd_by_options(inputs, EXPANDED_LEN, simd_opts, 4, NULL, 0);
for (uintptr_t i = 0; i < r.num_results; i++) {
vosc_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):
v1 := []float64{1200.0, 1400.0, 1100.0, 1600.0, 1300.0, 900.0, 1500.0, 1800.0, 1000.0, 1700.0}
// Reuse the same data for assets 2–4 in this example
v2 := v1
v3 := v1
v4 := v1
assets := [][indicators.VoscInputs][]float64{
{v1},
{v2},
{v3},
{v4},
}
sim, _ := indicators.Vosc.SimdByAssets(assets, []float64{5.0, 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, 4 different option sets in parallel:
volume := []float64{1200.0, 1400.0, 1100.0, 1600.0, 1300.0,
900.0, 1500.0, 1800.0, 1000.0, 1700.0}
sim2, _ := indicators.Vosc.SimdByOptions(volume,
[][]float64{{3.0, 6.0}, {5.0, 10.0}, {8.0, 16.0}, {12.0, 24.0}},
nil)
for i := range sim2.Results {
fmt.Printf("Option set %d: %v\n", i+1, sim2.Results[i][0])
}
sim2.Close() // frees every lane state, then the SIMD buffers
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.Vosc;
double[] v1 = {1200.0, 1400.0, 1100.0, 1600.0, 1300.0, 900.0, 1500.0, 1800.0, 1000.0, 1700.0};
double[] v2 = {1100.0, 1300.0, 1000.0, 1500.0, 1200.0, 800.0, 1400.0, 1700.0, 900.0, 1600.0};
double[] v3 = {1300.0, 1500.0, 1200.0, 1700.0, 1400.0, 1000.0, 1600.0, 1900.0, 1100.0, 1800.0};
double[] v4 = {1400.0, 1600.0, 1300.0, 1800.0, 1500.0, 1100.0, 1700.0, 2000.0, 1200.0, 1900.0};
// One entry per asset; each asset lists its INPUTS series.
double[][][] assets = {{v1}, {v2}, {v3}, {v4}};
try (SimdResult sim = Vosc.simdByAssets(assets, new double[] {5.0, 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, 4 different option sets in parallel:
import org.tuliprs.*;
import org.tuliprs.indicators.Vosc;
double[] volume = {1200.0, 1400.0, 1100.0, 1600.0, 1300.0,
900.0, 1500.0, 1800.0, 1000.0, 1700.0};
try (SimdResult sim = Vosc.simdByOptions(new double[][] {volume},
new double[][] {{3.0, 6.0}, {5.0, 10.0}, {8.0, 16.0}, {12.0, 24.0}}, null)) {
for (int i = 0; i < sim.numResults(); i++) {
System.out.printf("Option set %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 = [
[v1],
[v2],
[v3],
[v4],
]
outputs_list, states = tulip_rs.indicators.vosc.simd_by_assets(simd_inputs, [5.0, 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 = [
[volume.slice()],
[volume.map(v => v * 1.1)],
[volume.map(v => v * 0.9)],
[volume.map(v => v * 1.02)],
];
const [results] = ti.vosc.simdByAssets(simdInputs, [5, 10]);
results.forEach((out, i) => console.log(`Asset ${i + 1}:`, out[0]));
By options — same asset, 4 different option sets in parallel: