Hilbert Transform — Hilbert Transform¶
Decomposes the roofing-filtered price series into in-phase and quadrature components using Ehlers' Hilbert Transform; the two outputs represent the cycle's cosine and sine components respectively.
Inputs: [real] | Options: [ss_period, hp_period] | Outputs: [in_phase, quadrature] | Optional: [roofing, highpass]
Basic¶
use tulip_rs::indicators::hilberttransform::{HilbertTransform, Indicator, TIndicatorState};
let close = vec![
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, 87.50, 88.10, 88.50, 87.90, 88.20,
88.80, 89.10, 88.70, 89.30, 89.70, 90.10, 89.50, 90.20, 90.80, 91.10,
90.50, 91.20, 91.80, 92.10, 91.50, 92.20, 92.80, 93.10, 92.50, 93.20_f64,
];
// Options: [ss_period, hp_period]
let (outputs, _state) = HilbertTransform::indicator(&[close.as_slice()], &[10.0, 20.0], None).unwrap();
println!("In-Phase: {:?}", outputs[0]);
println!("Quadrature: {:?}", outputs[1]);
// State continuation
let n = close.len() - 5;
let partial = close[..n].to_vec();
let (outputs2, mut state) = HilbertTransform::indicator(&[partial.as_slice()], &[10.0, 20.0], None).unwrap();
println!("Partial In-Phase: {:?}", outputs2[0]);
let rest = close[n..].to_vec();
let continued = state.batch_indicator(&[rest.as_slice()], None).unwrap();
println!("Continued In-Phase: {:?}", continued[0]);
println!("Continued Quadrature: {:?}", continued[1]);
#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,
85.53, 86.54, 86.89, 87.77, 87.29, 87.50, 88.10, 88.50, 87.90, 88.20,
88.80, 89.10, 88.70, 89.30, 89.70, 90.10, 89.50, 90.20, 90.80, 91.10,
90.50, 91.20, 91.80, 92.10, 91.50, 92.20, 92.80, 93.10, 92.50, 93.20};
double options[HILBERTTRANSFORM_OPTIONS] = {10.0, 20.0}; // ss_period, hp_period
const double *inputs[HILBERTTRANSFORM_INPUTS] = {close};
/* Full computation (check r.error == C_INDICATOR_ERROR_OK in real code) */
bool optional_outputs[2] = {true, true}; // roofing, highpass
CIndicatorResult r = hilberttransform_indicator(inputs, 40, options, optional_outputs, 2);
/* r.outputs[0] -> in_phase, r.outputs[1] -> quadrature */
/* r.outputs[2] -> roofing (optional), r.outputs[3] -> highpass (optional) */
tulip_ffi_result_free(r);
hilberttransform_state_free(r.state);
/* Partial computation + state continuation */
CIndicatorResult p = hilberttransform_indicator(inputs, 35, options, NULL, 0);
double new_close[] = {89.70, 90.10, 89.50, 90.20, 90.80};
const double *new_inputs[HILBERTTRANSFORM_INPUTS] = {new_close};
CBatchResult b = hilberttransform_batch(p.state, new_inputs, 5, NULL, 0);
/* b.outputs[0] -> in_phase for new bars, b.outputs[1] -> quadrature */
tulip_ffi_batch_result_free(b);
tulip_ffi_result_free(p);
hilberttransform_state_free(p.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,
85.53, 86.54, 86.89, 87.77, 87.29, 87.50, 88.10, 88.50, 87.90, 88.20,
88.80, 89.10, 88.70, 89.30, 89.70, 90.10, 89.50, 90.20, 90.80, 91.10,
90.50, 91.20, 91.80, 92.10, 91.50, 92.20, 92.80, 93.10, 92.50, 93.20}
options := []float64{10.0, 20.0} // ss_period, hp_period
// Full computation — Rows are zero-copy views, valid until Close.
res, st, _ := indicators.Hilberttransform.Indicator(close, options, nil)
fmt.Println(res.Rows[0]) // in_phase values
fmt.Println(res.Rows[1]) // quadrature values
res.Close();
st.Close();
// Partial computation + state continuation.
res2, st2, _ := indicators.Hilberttransform.Indicator(close[:35], options, nil);
res2.Close(); // outputs consumed or closed; state stays live
batch, _ := st2.Batch(close[35:], nil);
fmt.Println(batch.Rows[0]); // continued in_phase values
fmt.Println(batch.Rows[1]); // continued quadrature values
batch.Close();
st2.Close();
import org.tuliprs.*;
import org.tuliprs.indicators.Hilberttransform;
double[] 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, 87.50, 88.10, 88.50, 87.90, 88.20,
88.80, 89.10, 88.70, 89.30, 89.70, 90.10, 89.50, 90.20, 90.80, 91.10,
90.50, 91.20, 91.80, 92.10, 91.50, 92.20, 92.80, 93.10, 92.50, 93.20};
double[] options = {10.0, 20.0}; // ss_period, hp_period
// Full computation — output rows are zero-copy views, valid until close().
Outcome oc = Hilberttransform.indicator(new double[][] {close}, options);
try (Result res = oc.result(); State st = oc.state()) {
System.out.println(java.util.Arrays.toString(res.toDoubleArray(0))); // in_phase values
System.out.println(java.util.Arrays.toString(res.toDoubleArray(1))); // quadrature values
}
// Partial computation + state continuation.
int n = 35;
Outcome p = Hilberttransform.indicator(
new double[][] {java.util.Arrays.copyOfRange(close, 0, n)}, options);
try (Result pr = p.result(); State st = p.state()) {
Result br = st.batch(new double[][] {java.util.Arrays.copyOfRange(close, n, 40)});
try (br) {
System.out.println(java.util.Arrays.toString(br.toDoubleArray(0))); // continued in_phase values
System.out.println(java.util.Arrays.toString(br.toDoubleArray(1))); // continued quadrature values
}
}
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,
85.53, 86.54, 86.89, 87.77, 87.29, 87.50, 88.10, 88.50, 87.90, 88.20,
88.80, 89.10, 88.70, 89.30, 89.70, 90.10, 89.50, 90.20, 90.80, 91.10,
90.50, 91.20, 91.80, 92.10, 91.50, 92.20, 92.80, 93.10, 92.50, 93.20,
], dtype=np.float64)
# Options: [ss_period, hp_period]
outputs, state = tulip_rs.indicators.hilberttransform.indicator([close], [10.0, 20.0])
print("In-Phase: ", outputs[0])
print("Quadrature: ", outputs[1])
// State continuation
partial = close[:-5]
outputs2, state = tulip_rs.indicators.hilberttransform.indicator([partial], [10.0, 20.0])
rest = close[-5:]
continued = state.batch_indicator([rest])
print("Continued In-Phase: ", continued[0])
print("Continued Quadrature: ", continued[1])
import * as ti from 'tulip-rs-node';
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, 87.50, 88.10, 88.50, 87.90, 88.20,
88.80, 89.10, 88.70, 89.30, 89.70, 90.10, 89.50, 90.20, 90.80, 91.10,
90.50, 91.20, 91.80, 92.10, 91.50, 92.20, 92.80, 93.10, 92.50, 93.20,
]);
const [outputs, state] = ti.hilberttransform.indicator([close], [10, 20]);
console.log('In-Phase: ', outputs[0]);
console.log('Quadrature: ', outputs[1]);
// State continuation
const [, state2] = ti.hilberttransform.indicator([close.slice(0, -5)], [10, 20]);
const continued = state2.batchIndicator([close.slice(-5)]);
console.log('Continued In-Phase: ', continued[0]);
console.log('Continued Quadrature: ', continued[1]);
import { init } from 'tulip-rs-wasm';
import * as ti from 'tulip-rs-wasm';
await init(); // bundler resolves the WASM asset automatically
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, 87.50, 88.10, 88.50, 87.90, 88.20,
88.80, 89.10, 88.70, 89.30, 89.70, 90.10, 89.50, 90.20, 90.80, 91.10,
90.50, 91.20, 91.80, 92.10, 91.50, 92.20, 92.80, 93.10, 92.50, 93.20,
];
const [outputs, state] = ti.hilberttransform.indicator([close], [10, 20]);
console.log('In-Phase: ', outputs[0]);
console.log('Quadrature: ', outputs[1]);
// State continuation
const [, state2] = ti.hilberttransform.indicator([close.slice(0, -5)], [10, 20]);
const continued = state2.batchIndicator([close.slice(-5)]);
console.log('Continued In-Phase: ', continued[0]);
console.log('Continued Quadrature: ', continued[1]);
Optional Outputs¶
hilberttransform exposes 2 optional outputs: roofing, highpass. Pass a boolean mask as the third argument — one bool per optional output, in order.
use tulip_rs::indicators::hilberttransform::{HilbertTransform, Indicator, TIndicatorState};
let close = vec![
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, 87.50, 88.10, 88.50, 87.90, 88.20,
88.80, 89.10, 88.70, 89.30, 89.70, 90.10, 89.50, 90.20, 90.80, 91.10,
90.50, 91.20, 91.80, 92.10, 91.50, 92.20, 92.80, 93.10, 92.50, 93.20_f64,
];
let mask = [true, true]; // one per optional output
let (outputs, _state) = HilbertTransform::indicator(&[close.as_slice()], &[10.0, 20.0], Some(&mask)).unwrap();
let in_phase = &outputs[0]; // in_phase (primary)
let quadrature = &outputs[1]; // quadrature (primary)
let roofing = &outputs[2]; // roofing (optional — requested)
let highpass = &outputs[3]; // highpass (optional — requested)
hilberttransform exposes 2 optional outputs: roofing, highpass. Pass a boolean mask as the third argument — one bool per optional output, in order.
#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,
85.53, 86.54, 86.89, 87.77, 87.29, 87.50, 88.10, 88.50, 87.90, 88.20,
88.80, 89.10, 88.70, 89.30, 89.70, 90.10, 89.50, 90.20, 90.80, 91.10,
90.50, 91.20, 91.80, 92.10, 91.50, 92.20, 92.80, 93.10, 92.50, 93.20};
double options[HILBERTTRANSFORM_OPTIONS] = {10.0, 20.0}; // ss_period, hp_period
const double *inputs[HILBERTTRANSFORM_INPUTS] = {close};
bool mask[2] = {true, true}; // one per optional output
CIndicatorResult r = hilberttransform_indicator(inputs, 40, options, mask, 2);
/* r.outputs[0] -> in_phase (primary) */
/* r.outputs[1] -> quadrature (primary) */
/* r.outputs[2] -> roofing (optional — requested) */
/* r.outputs[3] -> highpass (optional — requested) */
tulip_ffi_result_free(r);
hilberttransform_state_free(r.state);
hilberttransform exposes 2 optional outputs: roofing, highpass. Pass a boolean mask as the third argument.
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,
85.53, 86.54, 86.89, 87.77, 87.29, 87.50, 88.10, 88.50, 87.90, 88.20,
88.80, 89.10, 88.70, 89.30, 89.70, 90.10, 89.50, 90.20, 90.80, 91.10,
90.50, 91.20, 91.80, 92.10, 91.50, 92.20, 92.80, 93.10, 92.50, 93.20}
options := []float64{10.0, 20.0} // ss_period, hp_period
mask := []bool{true, true} // roofing, highpass
res, st, _ := indicators.Hilberttransform.Indicator(close, options, mask);
in_phase := res.Rows[0] // in_phase (primary)
quadrature := res.Rows[1] // quadrature (primary)
roofing := res.Rows[2] // roofing (optional — requested)
highpass := res.Rows[3] // highpass (optional — requested)
fmt.Println(in_phase);
fmt.Println(quadrature);
fmt.Println(roofing);
fmt.Println(highpass);
res.Close();
st.Close();
hilberttransform exposes 2 optional outputs: roofing, highpass. Pass a boolean mask as the third argument — one boolean per optional output, in order.
import org.tuliprs.*;
import org.tuliprs.indicators.Hilberttransform;
// (close series as in the Basic tab)
boolean[] mask = {true, true}; // roofing, highpass
Outcome oc = Hilberttransform.indicator(new double[][] {close}, new double[] {10.0, 20.0}, mask);
try (Result res = oc.result()) {
// row 0 = in_phase (primary), row 1 = quadrature (primary)
// rows 2.. = requested optionals in order
System.out.println(java.util.Arrays.toString(res.toDoubleArray(2))); // roofing
}
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,
85.53, 86.54, 86.89, 87.77, 87.29, 87.50, 88.10, 88.50, 87.90, 88.20,
88.80, 89.10, 88.70, 89.30, 89.70, 90.10, 89.50, 90.20, 90.80, 91.10,
90.50, 91.20, 91.80, 92.10, 91.50, 92.20, 92.80, 93.10, 92.50, 93.20,
], dtype=np.float64)
outputs, state = tulip_rs.indicators.hilberttransform.indicator(
[close], [10.0, 20.0],
optional_outputs=[True, True],
)
in_phase = outputs[0] // in_phase (primary)
quadrature = outputs[1] // quadrature (primary)
roofing = outputs[2] // roofing (optional — requested)
highpass = outputs[3] // highpass (optional — requested)
hilberttransform exposes 2 optional outputs: roofing, highpass.
The WASM API is identical to Node.js — pass the boolean mask as the third argument.
SIMD¶
By assets — same options applied to 4 assets in parallel:
use tulip_rs::indicators::hilberttransform::{HilbertTransform, Indicator, TIndicatorState};
let a1 = vec![81.59, 81.06, 82.87, 83.00, 83.61, 83.15, 82.84, 83.99, 84.55, 84.36_f64];
let a2 = vec![86.59, 86.06, 87.87, 88.00, 88.61, 88.15, 87.84, 88.99, 89.55, 89.36_f64];
let a3 = vec![78.59, 78.06, 79.87, 80.00, 80.61, 80.15, 79.84, 80.99, 81.55, 81.36_f64];
let a4 = vec![83.22, 82.68, 84.53, 84.66, 85.28, 84.81, 84.50, 85.67, 86.24, 86.05_f64];
let inputs: [&[&[f64]; 1]; 4] = [
&[a1.as_slice()],
&[a2.as_slice()],
&[a3.as_slice()],
&[a4.as_slice()],
];
let results = HilbertTransform::indicator_by_assets::<4>(&inputs, &[10.0, 20.0], None).unwrap();
for (i, asset_outputs) in results.iter().enumerate() {
println!("Asset {} In-Phase: {:?}", i + 1, asset_outputs[0]);
println!("Asset {} Quadrature: {:?}", i + 1, asset_outputs[1]);
}
By options — same asset, 4 different option sets in parallel:
use tulip_rs::indicators::hilberttransform::{HilbertTransform, IndicatorByOptions};
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 opts: [&[f64; 2]; 4] = [
&[5.0, 10.0],
&[10.0, 20.0],
&[14.0, 30.0],
&[20.0, 40.0],
];
let results = HilbertTransform::indicator_by_options::<4>(&[close.as_slice()], &opts, None).unwrap();
for (i, opt_outputs) in results.iter().enumerate() {
println!("Option set {} In-Phase: {:?}", i + 1, opt_outputs[0]);
println!("Option set {} Quadrature: {:?}", i + 1, opt_outputs[1]);
}
By assets — same options applied to 4 assets in one call (N must be 2/4/8/16):
double a1[] = {81.59, 81.06, 82.87, 83.00, 83.61, 83.15, 82.84, 83.99, 84.55, 84.36};
double a2[] = {86.59, 86.06, 87.87, 88.00, 88.61, 88.15, 87.84, 88.99, 89.55, 89.36};
double a3[] = {78.59, 78.06, 79.87, 80.00, 80.61, 80.15, 79.84, 80.99, 81.55, 81.36};
double a4[] = {83.22, 82.68, 84.53, 84.66, 85.28, 84.81, 84.50, 85.67, 86.24, 86.05};
/* one [INPUTS]-long pointer array per asset */
const double *asset1[HILBERTTRANSFORM_INPUTS] = {a1};
const double *asset2[HILBERTTRANSFORM_INPUTS] = {a2};
const double *asset3[HILBERTTRANSFORM_INPUTS] = {a3};
const double *asset4[HILBERTTRANSFORM_INPUTS] = {a4};
const double *const *const simd_inputs[4] = {asset1, asset2, asset3, asset4};
bool optional_outputs[2] = {true, true}; // roofing, highpass
CSimdResult r = hilberttransform_simd_by_assets(simd_inputs, 4, 10, options, optional_outputs, 2);
for (uintptr_t i = 0; i < r.num_results; i++) {
/* r.outputs[i][0] -> asset i's in_phase, length r.output_lens[i][0] */
/* r.outputs[i][1] -> asset i's quadrature */
hilberttransform_state_free(r.states[i]);
}
tulip_ffi_simd_result_free(r);
By options — same asset, 4 different option sets in one call:
double o5[] = {5.0}, o10[] = {10.0};
double o10b[] = {10.0}, o20b[] = {20.0};
double o14[] = {14.0}, o30[] = {30.0};
double o20c[] = {20.0}, o40[] = {40.0};
const double *const simd_opts[4] = {o5, o10, o10b, o20b, o14, o30, o20c, o40};
/* Actually: each option set has 2 values */
const double *const simd_opts[4] = {
(double[]){5.0, 10.0},
(double[]){10.0, 20.0},
(double[]){14.0, 30.0},
(double[]){20.0, 40.0}
};
CSimdResult r = hilberttransform_simd_by_options(inputs, 10, simd_opts, 4, NULL, 0);
/* r.outputs[i][0] -> in_phase for option set i */
for (uintptr_t i = 0; i < r.num_results; i++) hilberttransform_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):
import "github.com/me60732/tulip_rs_go/indicators"
a1 := []float64{81.59, 81.06, 82.87, 83.00, 83.61, 83.15, 82.84, 83.99, 84.55, 84.36}
// Reuse the same data for assets 2–4 in this example
a2, a3, a4 := a1, a1, a1
options := []float64{10.0, 20.0} // ss_period, hp_period
assets := [][indicators.HilberttransformInputs][]float64{{a1}, {a2}, {a3}, {a4}}
sim, _ := indicators.Hilberttransform.SimdByAssets(assets, options, nil);
for i, lanes := range sim.Results {
fmt.Printf("Asset %d In-Phase: %v\n", i+1, lanes[0]);
fmt.Printf("Asset %d Quadrature: %v\n", i+1, lanes[1]);
}
sim.Close();
By options — same asset, 4 different option sets in parallel:
import "github.com/me60732/tulip_rs_go/indicators"
close := []float64{81.59, 81.06, 82.87, 83.00, 83.61};
options := [][]float64{{5, 10}, {10, 20}, {14, 30}, {20, 40}};
sim, _ := indicators.Hilberttransform.SimdByOptions(close, options, nil);
for i, lanes := range sim.Results {
fmt.Printf("Option set %d In-Phase: %v\n", i+1, lanes[0]);
fmt.Printf("Option set %d Quadrature: %v\n", i+1, lanes[1]);
}
sim.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.Hilberttransform;
double[] a1 = {81.59, 81.06, 82.87, 83.00, 83.61, 83.15, 82.84, 83.99, 84.55, 84.36};
double[] a2 = {86.59, 86.06, 87.87, 88.00, 88.61, 88.15, 87.84, 88.99, 89.55, 89.36};
double[] a3 = {78.59, 78.06, 79.87, 80.00, 80.61, 80.15, 79.84, 80.99, 81.55, 81.36};
double[] a4 = {83.22, 82.68, 84.53, 84.66, 85.28, 84.81, 84.50, 85.67, 86.24, 86.05};
// One entry per asset; each asset lists its INPUTS series.
double[][][] assets = {{a1}, {a2}, {a3}, {a4}};
try (SimdResult sim = Hilberttransform.simdByAssets(assets, new double[] {10.0, 20.0}, null)) {
for (int i = 0; i < sim.numResults(); i++) {
System.out.printf("Asset %d In-Phase: %s%n", i + 1,
java.util.Arrays.toString(sim.toDoubleArray(i, 0)));
System.out.printf("Asset %d Quadrature: %s%n", i + 1,
java.util.Arrays.toString(sim.toDoubleArray(i, 1)));}
} // 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.Hilberttransform;
double[] close = {81.59, 81.06, 82.87, 83.00, 83.61,
83.15, 82.84, 83.99, 84.55, 84.36};
try (SimdResult sim = Hilberttransform.simdByOptions(new double[][] {close},
new double[][] {{5, 10}, {10, 20}, {14, 30}, {20, 40}}, null)) {
for (int i = 0; i < sim.numResults(); i++) {
System.out.printf("Option set %d In-Phase: %s%n", i + 1,
java.util.Arrays.toString(sim.toDoubleArray(i, 0)));
System.out.printf("Option set %d Quadrature: %s%n", i + 1,
java.util.Arrays.toString(sim.toDoubleArray(i, 1)));}
}
By assets — same options applied to N assets in parallel (must be 2, 4, 8, or 16):
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,
85.53, 86.54, 86.89, 87.77, 87.29, 87.50, 88.10, 88.50, 87.90, 88.20,
88.80, 89.10, 88.70, 89.30, 89.70, 90.10, 89.50, 90.20, 90.80, 91.10,
90.50, 91.20, 91.80, 92.10, 91.50, 92.20, 92.80, 93.10, 92.50, 93.20,
], dtype=np.float64)
simd_inputs = [[close], [close + 5.0], [close - 3.0], [close * 1.02]];
outputs_list, states = tulip_rs.indicators.hilberttransform.simd_by_assets(simd_inputs, [10.0, 20.0]);
for i, out in enumerate(outputs_list):
print(f"Asset {i + 1} In-Phase: {out[0]}");
print(f"Asset {i + 1} Quadrature: {out[1]}");
By options — same asset, N different option sets in parallel:
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,
85.53, 86.54, 86.89, 87.77, 87.29, 87.50, 88.10, 88.50, 87.90, 88.20,
88.80, 89.10, 88.70, 89.30, 89.70, 90.10, 89.50, 90.20, 90.80, 91.10,
90.50, 91.20, 91.80, 92.10, 91.50, 92.20, 92.80, 93.10, 92.50, 93.20,
], dtype=np.float64)
simd_options = [
[5.0, 10.0],
[10.0, 20.0],
[14.0, 30.0],
[20.0, 40.0],
]
outputs_list, states = tulip_rs.indicators.hilberttransform.simd_by_options([close], simd_options);
for i, out in enumerate(outputs_list):
print(f"Option set {i + 1} In-Phase: {out[0]}");
print(f"Option set {i + 1} Quadrature: {out[1]}");
By assets — same options applied to 4 assets in parallel:
const simdInputs = [
[close.slice()],
[close.map(v => v + 5.0)],
[close.map(v => v - 3.0)],
[close.map(v => v * 1.02)],
];
const [results] = ti.hilberttransform.simdByAssets(simdInputs, [10, 20]);
results.forEach((out, i) => console.log(`Asset ${i + 1} In-Phase:`, out[0], 'Quadrature:', out[1]));
By options — same asset, 4 different option sets in parallel: