KVO — Klinger Volume Oscillator¶
Identifies long-term money flow trends while remaining sensitive enough to detect short-term fluctuations.
Inputs: [high, low, close, volume] | Options: [short_period, long_period] | Outputs: [kvo]
Basic¶
use tulip_rs::indicators::kvo::{Kvo, 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 close = vec![81.59, 81.06, 82.87, 83.00, 83.61,
83.15, 82.84, 83.99, 84.55, 84.36_f64];
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 inputs = [high.as_slice(), low.as_slice(), close.as_slice(), volume.as_slice()];
let (outputs, mut state) = Kvo::indicator(&inputs, &[34.0, 55.0], None).unwrap();
println!("{:?}", outputs[0]); // KVO values
// State continuation — feed new bars without reprocessing history
let new_high = vec![85.20_f64];
let new_low = vec![84.50_f64];
let new_close = vec![85.00_f64];
let new_volume = vec![1550.0_f64];
let continued = state.batch_indicator(
&[new_high.as_slice(), new_low.as_slice(),
new_close.as_slice(), new_volume.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 close[] = {81.59, 81.06, 82.87, 83.00, 83.61,
83.15, 82.84, 83.99, 84.55, 84.36};
double volume[] = {1200.0, 1400.0, 1100.0, 1600.0, 1300.0,
900.0, 1500.0, 1800.0, 1000.0, 1700.0};
const double options[KVO_OPTIONS] = {34.0, 55.0}; // short_period, long_period
/* Full computation (check r.error == C_INDICATOR_ERROR_OK in real code) */
CIndicatorResult r = kvo_indicator(inputs, 10, options, NULL, 0);
/* r.outputs[0] -> the KVO series, length r.output_lens[0] */
tulip_ffi_result_free(r);
kvo_state_free(r.state);
/* Partial computation + state continuation */
CIndicatorResult p = kvo_indicator(inputs, 8, options, NULL, 0);
double new_high[] = {85.20};
double new_low[] = {84.50};
double new_close[] = {85.00};
double new_volume[] = {1550.0};
const double *new_inputs[KVO_INPUTS] = {new_high, new_low, new_close, new_volume};
CBatchResult b = kvo_batch(p.state, new_inputs, 1, NULL, 0);
/* b.outputs[0] -> KVO value for the single new bar */
tulip_ffi_batch_result_free(b);
tulip_ffi_result_free(p);
kvo_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}
close := []float64{81.59, 81.06, 82.87, 83.00, 83.61,
83.15, 82.84, 83.99, 84.55, 84.36}
volume := []float64{1200.0, 1400.0, 1100.0, 1600.0, 1300.0,
900.0, 1500.0, 1800.0, 1000.0, 1700.0}
options := []float64{34.0, 55.0} // short_period, long_period
// Full computation — Rows are zero-copy views, valid until Close.
res, st, _ := indicators.Kvo.Indicator(high, low, close, volume, options, nil)
fmt.Println(res.Rows[0]) // KVO values
res.Close()
st.Close()
// Partial computation + state continuation.
res2, st2, _ := indicators.Kvo.Indicator(high[:8], low[:8], close[:8], volume[:8], options, nil)
res2.Close() // outputs consumed or closed; state stays live
batch, _ := st2.Batch(high[8:], low[8:], close[8:], volume[8:], nil)
fmt.Println(batch.Rows[0]) // continued KVO values
batch.Close()
st2.Close()
import org.tuliprs.*;
import org.tuliprs.indicators.Kvo;
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[] close = {81.59, 81.06, 82.87, 83.00, 83.61,
83.15, 82.84, 83.99, 84.55, 84.36};
double[] volume = {1200.0, 1400.0, 1100.0, 1600.0, 1300.0,
900.0, 1500.0, 1800.0, 1000.0, 1700.0};
double[] options = {34.0, 55.0}; // short_period, long_period
// Full computation — output rows are zero-copy views, valid until close().
Outcome oc = Kvo.indicator(new double[][] {high, low, close, volume}, options);
try (Result res = oc.result(); State st = oc.state()) {
System.out.println(java.util.Arrays.toString(res.toDoubleArray(0))); // KVO values
}
// Partial computation + state continuation.
int n = 8;
Outcome p = Kvo.indicator(new double[][] {
java.util.Arrays.copyOfRange(high, 0, n),
java.util.Arrays.copyOfRange(low, 0, n),
java.util.Arrays.copyOfRange(close, 0, n),
java.util.Arrays.copyOfRange(volume, 0, n)}, options);
try (Result pr = p.result(); State st = p.state()) {
Result br = st.batch(new double[][] {
java.util.Arrays.copyOfRange(high, n, 10),
java.util.Arrays.copyOfRange(low, n, 10),
java.util.Arrays.copyOfRange(close, n, 10),
java.util.Arrays.copyOfRange(volume, n, 10)});
try (br) {
System.out.println(java.util.Arrays.toString(br.toDoubleArray(0))); // continued KVO
}
}
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)
close = np.array([81.59, 81.06, 82.87, 83.00, 83.61,
83.15, 82.84, 83.99, 84.55, 84.36], dtype=np.float64)
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.kvo.indicator(
[high, low, close, volume], [34.0, 55.0]
)
print(outputs[0]) # KVO values
# State continuation
new_high = np.array([85.20], dtype=np.float64)
new_low = np.array([84.50], dtype=np.float64)
new_close = np.array([85.00], dtype=np.float64)
new_volume = np.array([1550.0], dtype=np.float64)
continued = state.batch_indicator([new_high, new_low, new_close, new_volume])
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 close = Float64Array.from([81.59, 81.06, 82.87, 83.00, 83.61, 83.15, 82.84, 83.99, 84.55, 84.36, 85.53, 86.54, 86.89, 87.77, 87.29]);
const volume = Float64Array.from([5653100, 6447400, 7690900, 3831400, 4455100, 3798000, 3936200, 4732000, 4841300, 3915300, 6830800, 6694100, 5293600, 7985800, 4807900]);
const [outputs, state] = ti.kvo.indicator([high, low, close, volume], [34, 55]);
console.log('KVO:', outputs[0]);
// State continuation
const n = high.length - 5;
const [, state2] = ti.kvo.indicator([high.slice(0, n), low.slice(0, n), close.slice(0, n), volume.slice(0, n)], [34, 55]);
const continued = state2.batchIndicator([high.slice(n), low.slice(n), close.slice(n), volume.slice(n)]);
console.log('Continued KVO:', 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 close = [81.59, 81.06, 82.87, 83.00, 83.61, 83.15, 82.84, 83.99, 84.55, 84.36, 85.53, 86.54, 86.89, 87.77, 87.29];
const volume = [5653100, 6447400, 7690900, 3831400, 4455100, 3798000, 3936200, 4732000, 4841300, 3915300, 6830800, 6694100, 5293600, 7985800, 4807900];
const [outputs, state] = ti.kvo.indicator([high, low, close, volume], [34, 55]);
console.log('KVO:', outputs[0]);
// State continuation
const n = high.length - 5;
const [, state2] = ti.kvo.indicator([high.slice(0, n), low.slice(0, n), close.slice(0, n), volume.slice(0, n)], [34, 55]);
const continued = state2.batchIndicator([high.slice(n), low.slice(n), close.slice(n), volume.slice(n)]);
console.log('Continued KVO:', continued[0]);
Optional Outputs¶
kvo exposes 2 optional outputs: short_ema, long_ema. Pass a boolean mask as the third argument — one bool per optional output, in order.
The mask is an array of booleans (one per optional output) passed to kvo_indicator():
#include "tulip_rs_ffi.h"
double close[] = {81.59, 81.06, 82.87, 83.00, 83.61,
83.15, 82.84, 83.99, 84.55, 84.36};
double high[] = {82.59, 82.06, 83.87, 84.00, 84.61,
84.15, 83.84, 84.99, 85.55, 85.36};
double low[] = {80.59, 80.06, 81.87, 82.00, 82.61,
82.15, 81.84, 82.99, 83.55, 83.36};
double volume[] = {10000.0, 12000.0, 9500.0, 11000.0, 13000.0,
9800.0, 10500.0, 12500.0, 11800.0, 10200.0};
const double options[KVO_OPTIONS] = {9.0, 26.0};
bool optional_outputs[2] = {true, false}; // short_ema, long_ema
CIndicatorResult r = kvo_indicator(inputs, 10, options, optional_outputs, 2);
/* r.outputs[0] -> kvo (primary) */
/* r.outputs[1] -> short_ema (requested) */
/* r.outputs[2] -> long_ema (not requested) */
tulip_ffi_result_free(r);
kvo_state_free(r.state);
import "github.com/me60732/tulip_rs_go/indicators"
close := []float64{81.59, 81.06, 82.87, 83.00, 83.61, 83.15, 82.84, 83.99, 84.55, 84.36}
high := make([]float64, len(close))
low := make([]float64, len(close))
for i, v := range close {
high[i] = v + 1.0
low[i] = v - 1.0
}
volume := []float64{10000.0, 12000.0, 9500.0, 11000.0, 13000.0, 9800.0, 10500.0, 12500.0, 11800.0, 10200.0}
options := []float64{9.0, 26.0} // short_period, long_period
mask := []bool{true, false} // short_ema, long_ema
// Full computation — Rows are zero-copy views, valid until Close.
res, st, _ := indicators.Kvo.Indicator(high, low, close, volume, options, mask)
fmt.Println(res.Rows[0]) // kvo (primary)
fmt.Println(res.Rows[1]) // short_ema (optional — requested)
fmt.Println(res.Rows[2]) // long_ema (optional — not requested)
res.Close()
st.Close()
import org.tuliprs.*;
import org.tuliprs.indicators.Kvo;
double[] close = {81.59, 81.06, 82.87, 83.00, 83.61, 83.15, 82.84, 83.99, 84.55, 84.36};
double[] high = new double[close.length];
double[] low = new double[close.length];
for (int i = 0; i < close.length; i++) {
high[i] = close[i] + 1.0;
low[i] = close[i] - 1.0;
}
double[] volume = {10000.0, 12000.0, 9500.0, 11000.0, 13000.0, 9800.0, 10500.0, 12500.0, 11800.0, 10200.0};
double[] options = {9.0, 26.0}; // short_period, long_period
boolean[] mask = {true, false}; // short_ema, long_ema
// Full computation — output rows are zero-copy views, valid until close().
Outcome oc = Kvo.indicator(new double[][] {high, low, close, volume}, options, mask);
try (Result res = oc.result()) {
System.out.println(java.util.Arrays.toString(res.toDoubleArray(0))); // kvo (primary)
System.out.println(java.util.Arrays.toString(res.toDoubleArray(1))); // short_ema (requested)
System.out.println(java.util.Arrays.toString(res.toDoubleArray(2))); // long_ema (not requested)
}
oc.state().close();
import numpy as np
import tulip_rs
close = np.array([81.59, 81.06, 82.87, 83.00, 83.61, 83.15, 82.84, 83.99, 84.55, 84.36], dtype=np.float64)
high = close + 1.0
low = close - 1.0
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.kvo.indicator(
[high, low, close, volume], [9.0, 26.0],
optional_outputs=[True, False],
)
kvo = outputs[0] # kvo (primary)
short_ema = outputs[1] # short_ema (optional — requested)
long_ema = outputs[2] # long_ema (optional — not requested)
kvo exposes 2 optional outputs: short_ema, long_ema.
The WASM API is identical to Node.js — pass the boolean mask as the third argument.
SIMD¶
By assets — same options, N assets in parallel:
use tulip_rs::indicators::kvo::{Kvo, Indicator};
let inputs: [&[&[f64]; 4]; 4] = [
&[h1.as_slice(), l1.as_slice(), c1.as_slice(), v1.as_slice()],
&[h2.as_slice(), l2.as_slice(), c2.as_slice(), v2.as_slice()],
&[h3.as_slice(), l3.as_slice(), c3.as_slice(), v3.as_slice()],
&[h4.as_slice(), l4.as_slice(), c4.as_slice(), v4.as_slice()],
];
let results = Kvo::indicator_by_assets::<4>(&inputs, &[34.0, 55.0], 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::kvo::{Kvo, IndicatorByOptions};
let opts: [&[f64; 2]; 4] = [&[13.0, 21.0], &[21.0, 34.0], &[34.0, 55.0], &[55.0, 89.0]];
let results = Kvo::indicator_by_options::<4>(&inputs, &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:
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 a1_close[] = {81.59, 81.06, 82.87, 83.00, 83.61, 83.15, 82.84, 83.99, 84.55, 84.36};
double a1_volume[] = {1200.0, 1400.0, 1100.0, 1600.0, 1300.0, 900.0, 1500.0, 1800.0, 1000.0, 1700.0};
double a2_high[] = {98.58, 96.47, 99.63, 99.96, 100.62, 100.68, 99.99, 101.16, 102.31, 103.50};
double a2_low[] = {97.95, 95.81, 98.94, 99.27, 100.01, 100.06, 99.33, 100.50, 101.65, 102.80};
double a2_close[] = {83.18, 82.12, 85.74, 86.00, 87.22, 86.30, 85.68, 87.98, 89.10, 88.72};
double a2_volume[] = {2400.0, 2800.0, 2200.0, 3200.0, 2600.0, 1800.0, 3000.0, 3600.0, 2000.0, 3400.0};
double a3_high[] = {75.00, 74.50, 76.00, 76.30, 76.85, 76.90, 76.33, 77.30, 77.84, 78.00};
double a3_low[] = {74.29, 73.64, 75.31, 75.65, 76.07, 76.11, 75.49, 75.30, 77.15, 77.11};
double a3_close[] = {74.59, 74.06, 76.87, 76.00, 76.61, 76.15, 75.84, 76.99, 77.55, 77.36};
double a3_volume[] = {600.0, 700.0, 550.0, 800.0, 650.0, 450.0, 750.0, 900.0, 500.0, 850.0};
double a4_high[] = {102.00, 101.25, 103.50, 103.80, 104.30, 104.35, 103.75, 104.75, 105.25, 105.40};
double a4_low[] = {100.65, 99.80, 102.00, 102.20, 103.00, 103.05, 102.40, 103.30, 104.10, 104.25};
const double *asset1[KVO_INPUTS] = {a1_high, a1_low, a1_close, a1_volume};
const double *asset2[KVO_INPUTS] = {a2_high, a2_low, a2_close, a2_volume};
const double *asset3[KVO_INPUTS] = {a3_high, a3_low, a3_close, a3_volume};
const double *asset4[KVO_INPUTS] = {a4_high, a4_low, a4_close, a4_volume};
const double *const *const simd_inputs[4] = {asset1, asset2, asset3, asset4};
CSimdResult r = kvo_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, length r.output_lens[i][0] */
kvo_state_free(r.states[i]);
}
tulip_ffi_simd_result_free(r);
By options — same asset, 4 different option sets in parallel:
const double h[] = {82.15, 81.89, 83.03, 83.30, 83.85, 83.90, 83.33, 84.30, 84.84, 85.00};
const double l[] = {81.29, 80.64, 81.31, 82.65, 83.07, 83.11, 82.49, 82.30, 84.15, 84.11};
const double c[] = {81.59, 81.06, 82.87, 83.00, 83.61, 83.15, 82.84, 83.99, 84.55, 84.36};
const double v[] = {1200.0, 1400.0, 1100.0, 1600.0, 1300.0, 900.0, 1500.0, 1800.0, 1000.0, 1700.0};
/* Tile the series 20x so longer-period option sets have enough data */
#define EXPANDED_LEN (10 * 20)
static double high_expanded[EXPANDED_LEN];
static double low_expanded[EXPANDED_LEN];
static double close_expanded[EXPANDED_LEN];
static double volume_expanded[EXPANDED_LEN];
for (size_t i = 0; i < 20; i++) {
for (size_t j = 0; j < 10; j++) {
high_expanded[i * 10 + j] = h[j];
low_expanded[i * 10 + j] = l[j];
close_expanded[i * 10 + j] = c[j];
volume_expanded[i * 10 + j] = v[j];
}
}
const double *inputs[KVO_INPUTS] = {high_expanded, low_expanded, close_expanded, volume_expanded};
static const double o2_5[KVO_OPTIONS] = {2.0, 5.0};
static const double o3_7[KVO_OPTIONS] = {3.0, 7.0};
static const double o4_10[KVO_OPTIONS] = {4.0, 10.0};
static const double o5_13[KVO_OPTIONS] = {5.0, 13.0};
const double *const simd_opts[4] = {o2_5, o3_7, o4_10, o5_13};
CSimdResult r = kvo_simd_by_options(inputs, EXPANDED_LEN, simd_opts, 4, NULL, 0);
for (uintptr_t i = 0; i < r.num_results; i++) {
/* r.outputs[i][0] -> asset i's KVO series, length r.output_lens[i][0] */
kvo_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}
c1 := []float64{81.59, 81.06, 82.87, 83.00, 83.61, 83.15, 82.84, 83.99, 84.55, 84.36}
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
h2, l2, c2, v2 := h1, l1, c1, v1
h3, l3, c3, v3 := h1, l1, c1, v1
h4, l4, c4, v4 := h1, l1, c1, v1
assets := [][indicators.KvoInputs][]float64{{h1, l1, c1, v1}, {h2, l2, c2, v2}, {h3, l3, c3, v3}, {h4, l4, c4, v4}}
sim, _ := indicators.Kvo.SimdByAssets(assets, []float64{34.0, 55.0}, 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 assets — same options applied to 4 assets in parallel (N must be 2, 4, 8, or 16):
import org.tuliprs.*;
import org.tuliprs.indicators.Kvo;
double[] h1 = {82.15, 81.89, 83.03, 83.30, 83.85, 83.90, 83.33, 84.30, 84.84, 85.00};
double[] l1 = {81.29, 80.64, 81.31, 82.65, 83.07, 83.11, 82.49, 82.30, 84.15, 84.11};
double[] c1 = {81.59, 81.06, 82.87, 83.00, 83.61, 83.15, 82.84, 83.99, 84.55, 84.36};
double[] v1 = {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
double[] h2 = h1;
double[] l2 = l1;
double[] c2 = c1;
double[] v2 = v1;
double[] h3 = h1;
double[] l3 = l1;
double[] c3 = c1;
double[] v3 = v1;
double[] h4 = h1;
double[] l4 = l1;
double[] c4 = c1;
double[] v4 = v1;
// One entry per asset; each asset lists its INPUTS series.
double[][][] assets = {{h1, l1, c1, v1}, {h2, l2, c2, v2}, {h3, l3, c3, v3}, {h4, l4, c4, v4}};
try (SimdResult sim = Kvo.simdByAssets(assets, new double[] {34.0, 55.0}, 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.Kvo;
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[] close = {81.59, 81.06, 82.87, 83.00, 83.61, 83.15, 82.84, 83.99, 84.55, 84.36};
double[] volume = {1200.0, 1400.0, 1100.0, 1600.0, 1300.0, 900.0, 1500.0, 1800.0, 1000.0, 1700.0};
// Tile the series 20x so longer-period option sets have enough data
int tiledLen = high.length * 20;
double[] highTiled = new double[tiledLen];
double[] lowTiled = new double[tiledLen];
double[] closeTiled = new double[tiledLen];
double[] volumeTiled = new double[tiledLen];
for (int i = 0; i < 20; i++) {
System.arraycopy(high, i * high.length, highTiled, i * high.length, high.length);
System.arraycopy(low, i * low.length, lowTiled, i * low.length, low.length);
System.arraycopy(close, i * close.length, closeTiled, i * close.length, close.length);
System.arraycopy(volume, i * volume.length, volumeTiled, i * volume.length, volume.length);
}
try (SimdResult sim = Kvo.simdByOptions(new double[][] {highTiled, lowTiled, closeTiled, volumeTiled},
new double[][] {{3.0, 7.0}, {5.0, 10.0}, {8.0, 15.0}, {10.0, 20.0}}, 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 = [
[h1, l1, c1, v1],
[h2, l2, c2, v2],
[h3, l3, c3, v3],
[h4, l4, c4, v4],
]
outputs_list, states = tulip_rs.indicators.kvo.simd_by_assets(simd_inputs, [34.0, 55.0])
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(), close.slice(), volume.slice()],
[high.map(v => v * 1.1), low.map(v => v * 1.1), close.map(v => v * 1.1), volume.map(v => v * 1.1)],
[high.map(v => v * 0.9), low.map(v => v * 0.9), close.map(v => v * 0.9), volume.map(v => v * 0.9)],
[high.map(v => v * 1.02), low.map(v => v * 1.02), close.map(v => v * 1.02), volume.map(v => v * 1.02)],
];
const [results] = ti.kvo.simdByAssets(simdInputs, [34, 55]);
results.forEach((out, i) => console.log(`Asset ${i + 1}:`, out[0]));
By options — same asset, 4 different option sets in parallel: