Williams %R¶
Momentum indicator measuring the current close relative to the highest high over period bars, scaled to a range of -100 to 0. Values near 0 indicate overbought conditions; values near -100 indicate oversold conditions.
Inputs: [high, low, close] | Options: [period] | Outputs: [willr] | Optional: [min, max]
Basic¶
use tulip_rs::indicators::willr::{WillR, TIndicatorState, Indicator};
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 inputs = [high.as_slice(), low.as_slice(), close.as_slice()];
let (outputs, _state) = WillR::indicator(&inputs, &[14.0], None).unwrap();
println!("Williams %R(14): {:?}", outputs[0]);
// State continuation
let inputs2 = [&high[..8], &low[..8], &close[..8]];
let (outputs2, mut state) = WillR::indicator(&inputs2, &[14.0], None).unwrap();
println!("Partial Williams %R: {:?}", outputs2[0]);
let new_inputs = [&high[8..], &low[8..], &close[8..]];
let continued = state.batch_indicator(&new_inputs, None).unwrap();
println!("Continued Williams %R: {:?}", 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 options[WILLR_OPTIONS] = {14.0}; // period
const double *inputs[WILLR_INPUTS] = {high, low, close};
/* Full computation */
CIndicatorResult r = willr_indicator(inputs, 10, options, NULL, 0);
/* r.outputs[0] -> the Williams %R(14) series, length r.output_lens[0] */
tulip_ffi_result_free(r);
willr_state_free(r.state);
/* Partial computation + state continuation */
const double *partial_inputs[WILLR_INPUTS] = {high, low, close};
CIndicatorResult p = willr_indicator(partial_inputs, 8, options, NULL, 0);
double new_high[] = {84.84, 85.00};
double new_low[] = {84.15, 84.11};
double new_close[] = {84.55, 84.36};
const double *new_inputs[WILLR_INPUTS] = {new_high, new_low, new_close};
CBatchResult b = willr_batch(p.state, new_inputs, 2, NULL, 0);
/* b.outputs[0] -> Williams %R values for just the two new bars */
tulip_ffi_batch_result_free(b);
tulip_ffi_result_free(p);
willr_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}
options := []float64{14.0} // period
// Full computation — Rows are zero-copy views, valid until Close.
res, st, _ := indicators.Willr.Indicator(high, low, close, options, nil)
fmt.Println(res.Rows[0]) // Williams %R(14) values
res.Close()
st.Close()
// Partial computation + state continuation.
res2, st2, _ := indicators.Willr.Indicator(high[:8], low[:8], close[:8], options, nil)
res2.Close() // outputs consumed or closed; state stays live
batch, _ := st2.Batch(high[8:], low[8:], close[8:], nil)
fmt.Println(batch.Rows[0]) // continued Williams %R values
batch.Close()
st2.Close()
import org.tuliprs.*;
import org.tuliprs.indicators.Willr;
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[] options = {14.0}; // period
// Full computation — output rows are zero-copy views, valid until close().
Outcome oc = Willr.indicator(new double[][] {high, low, close}, options);
try (Result res = oc.result(); State st = oc.state()) {
System.out.println(java.util.Arrays.toString(res.toDoubleArray(0))); // Williams %R(14) values
}
// Partial computation + state continuation.
int n = 8;
Outcome p = Willr.indicator(new double[][] {
java.util.Arrays.copyOfRange(high, 0, n),
java.util.Arrays.copyOfRange(low, 0, n),
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(high, n, 10),
java.util.Arrays.copyOfRange(low, n, 10),
java.util.Arrays.copyOfRange(close, n, 10)});
try (br) {
System.out.println(java.util.Arrays.toString(br.toDoubleArray(0))); // continued Williams %R
}
}
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)
outputs, state = tulip_rs.indicators.willr.indicator([high, low, close], [14.0])
print("Williams %R(14):", outputs[0])
# State continuation
outputs2, state = tulip_rs.indicators.willr.indicator([high[:8], low[:8], close[:8]], [14.0])
continued = state.batch_indicator([high[8:], low[8:], close[8:]])
print("Continued Williams %R:", 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 [outputs, state] = ti.willr.indicator([high, low, close], [14]);
console.log('Williams %R(14):', outputs[0]);
// State continuation
const n = high.length - 5;
const [, state2] = ti.willr.indicator([high.slice(0, n), low.slice(0, n), close.slice(0, n)], [14]);
const continued = state2.batchIndicator([high.slice(n), low.slice(n), close.slice(n)]);
console.log('Continued %R:', 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 [outputs, state] = ti.willr.indicator([high, low, close], [14]);
console.log('Williams %R(14):', outputs[0]);
// State continuation
const n = high.length - 5;
const [, state2] = ti.willr.indicator([high.slice(0, n), low.slice(0, n), close.slice(0, n)], [14]);
const continued = state2.batchIndicator([high.slice(n), low.slice(n), close.slice(n)]);
console.log('Continued %R:', continued[0]);
Optional Outputs¶
willr exposes 2 optional outputs: min, max. Pass a boolean mask as the third argument — one bool per optional output, in order.
use tulip_rs::indicators::willr::{WillR, TIndicatorState, Indicator};
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 mask = [true, true];
let (outputs, _state) = WillR::indicator(
&[high.as_slice(), low.as_slice(), close.as_slice()],
&[14.0],
Some(&mask),
).unwrap();
let willr = &outputs[0]; // willr (primary)
let min = &outputs[1]; // min (optional — requested)
let max = &outputs[2]; // max (optional — requested)
#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 options[WILLR_OPTIONS] = {14.0}; // period
const double *inputs[WILLR_INPUTS] = {high, low, close};
/* Request all optional outputs: min, max (2 optional) */
bool optional_outputs[2] = {true, true};
CIndicatorResult r = willr_indicator(inputs, 10, options, optional_outputs, 2);
/* r.outputs[0] -> willr (primary), length r.output_lens[0] */
/* r.outputs[1] -> min (optional — requested) */
/* r.outputs[2] -> max (optional — requested) */
tulip_ffi_result_free(r);
willr_state_free(r.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}
options := []float64{14.0} // period
mask := []bool{true, true} // one per optional output (min, max)
// Full computation with optional outputs.
res, st, _ := indicators.Willr.Indicator(high, low, close, options, mask)
fmt.Println(res.Rows[0]) // willr (primary)
fmt.Println(res.Rows[1]) // min (optional — requested)
fmt.Println(res.Rows[2]) // max (optional — requested)
res.Close()
st.Close()
willr exposes 2 optional outputs: min, max. Pass a boolean mask as the third argument — one boolean per optional output, in order.
import org.tuliprs.*;
import org.tuliprs.indicators.Willr;
// (high/low/close series as in the Basic tab)
boolean[] mask = {true, true}; // min, max
Outcome oc = Willr.indicator(new double[][] {high, low, close}, new double[] {14.0}, mask);
try (Result res = oc.result()) {
// row 0 = willr (primary), row 1 = min, row 2 = max
System.out.println(java.util.Arrays.toString(res.toDoubleArray(1))); // min
System.out.println(java.util.Arrays.toString(res.toDoubleArray(2))); // max
}
oc.state().close();
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)
outputs, state = tulip_rs.indicators.willr.indicator(
[high, low, close], [14.0],
optional_outputs=[True, True],
)
willr = outputs[0] # willr (primary)
min_ = outputs[1] # min (optional — requested)
max_ = outputs[2] # max (optional — requested)
willr exposes 2 optional outputs: min, max.
SIMD¶
By assets — same period applied to 4 assets in parallel:
use tulip_rs::indicators::willr::{WillR, Indicator};
let h1 = vec![82.15, 81.89, 83.03, 83.30, 83.85, 83.90, 83.33, 84.30, 84.84, 85.00_f64];
let l1 = vec![81.29, 80.64, 81.31, 82.65, 83.07, 83.11, 82.49, 82.30, 84.15, 84.11_f64];
let c1 = vec![81.59, 81.06, 82.87, 83.00, 83.61, 83.15, 82.84, 83.99, 84.55, 84.36_f64];
let h2 = h1.clone(); let l2 = l1.clone(); let c2 = c1.clone();
let h3 = h1.clone(); let l3 = l1.clone(); let c3 = c1.clone();
let h4 = h1.clone(); let l4 = l1.clone(); let c4 = c1.clone();
let inputs: [&[&[f64]; 3]; 4] = [
&[h1.as_slice(), l1.as_slice(), c1.as_slice()],
&[h2.as_slice(), l2.as_slice(), c2.as_slice()],
&[h3.as_slice(), l3.as_slice(), c3.as_slice()],
&[h4.as_slice(), l4.as_slice(), c4.as_slice()],
];
let results = WillR::indicator_by_assets::<4>(&inputs, &[14.0], None).unwrap();
for (i, asset_outputs) in results.iter().enumerate() {
println!("Asset {}: {:?}", i + 1, asset_outputs[0]);
}
By options — same asset, 4 different periods in parallel:
use tulip_rs::indicators::willr::{WillR, IndicatorByOptions};
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 opts: [&[f64; 1]; 4] = [&[7.0], &[14.0], &[21.0], &[28.0]];
let inputs = [high.as_slice(), low.as_slice(), close.as_slice()];
let results = WillR::indicator_by_options::<4>(&inputs, &opts, None).unwrap();
for (i, opt_outputs) in results.iter().enumerate() {
println!("Period set {}: {:?}", i + 1, opt_outputs[0]);
}
By assets — same period applied to 4 assets in one call (N must be 2/4/8/16):
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 h2[] = {83.15, 82.89, 84.03, 84.30, 84.85, 84.90, 84.33, 85.30, 85.84, 86.00};
double l2[] = {82.29, 81.64, 82.31, 83.65, 84.07, 84.11, 83.49, 83.30, 85.15, 85.11};
double c2[] = {82.59, 82.06, 83.87, 84.00, 84.61, 84.15, 83.84, 84.99, 85.55, 85.36};
double h3[] = {84.15, 83.89, 85.03, 85.30, 85.85, 85.90, 85.33, 86.30, 86.84, 87.00};
double l3[] = {83.29, 82.64, 83.31, 84.65, 85.07, 85.11, 84.49, 84.30, 86.15, 86.11};
double c3[] = {83.59, 83.06, 84.87, 85.00, 85.61, 85.15, 84.84, 85.99, 86.55, 86.36};
double h4[] = {85.15, 84.89, 86.03, 86.30, 86.85, 86.90, 86.33, 87.30, 87.84, 88.00};
double l4[] = {84.29, 83.64, 84.31, 85.65, 86.07, 86.11, 85.49, 85.30, 87.15, 87.11};
double c4[] = {84.59, 84.06, 85.87, 86.00, 86.61, 86.15, 85.84, 86.99, 87.55, 87.36};
/* one [INPUTS]-long pointer array per asset */
const double *asset1[WILLR_INPUTS] = {h1, l1, c1};
const double *asset2[WILLR_INPUTS] = {h2, l2, c2};
const double *asset3[WILLR_INPUTS] = {h3, l3, c3};
const double *asset4[WILLR_INPUTS] = {h4, l4, c4};
const double *const *const simd_inputs[4] = {asset1, asset2, asset3, asset4};
double options[WILLR_OPTIONS] = {14.0}; // same period for all assets
CSimdResult r = willr_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 Williams %R series, length r.output_lens[i][0] */
willr_state_free(r.states[i]);
}
tulip_ffi_simd_result_free(r);
By options — same asset, 4 different periods in one call:
double h[] = {82.15, 81.89, 83.03, 83.30, 83.85,
83.90, 83.33, 84.30, 84.84, 85.00};
double l[] = {81.29, 80.64, 81.31, 82.65, 83.07,
83.11, 82.49, 82.30, 84.15, 84.11};
double c[] = {81.59, 81.06, 82.87, 83.00, 83.61,
83.15, 82.84, 83.99, 84.55, 84.36};
const double *inputs[WILLR_INPUTS] = {h, l, c};
double o7[] = {7.0}, o14[] = {14.0}, o21[] = {21.0}, o28[] = {28.0};
const double *const simd_opts[4] = {o7, o14, o21, o28};
CSimdResult r = willr_simd_by_options(inputs, 10, simd_opts, 4, NULL, 0);
/* r.outputs[i] -> results for period set i (periods 7/14/21/28) */
for (uintptr_t i = 0; i < r.num_results; i++) willr_state_free(r.states[i]);
tulip_ffi_simd_result_free(r);
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}
// Reuse the same data for assets 2–4 in this example
h2, l2, c2 := h1, l1, c1
h3, l3, c3 := h1, l1, c1
h4, l4, c4 := h1, l1, c1
assets := [][indicators.WillrInputs][]float64{{h1, l1, c1}, {h2, l2, c2}, {h3, l3, c3}, {h4, l4, c4}}
sim, _ := indicators.Willr.SimdByAssets(assets, []float64{14.0}, 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 periods in parallel:
h := []float64{82.15, 81.89, 83.03, 83.30, 83.85,
83.90, 83.33, 84.30, 84.84, 85.00}
l := []float64{81.29, 80.64, 81.31, 82.65, 83.07,
83.11, 82.49, 82.30, 84.15, 84.11}
c := []float64{81.59, 81.06, 82.87, 83.00, 83.61,
83.15, 82.84, 83.99, 84.55, 84.36}
sim2, _ := indicators.Willr.SimdByOptions(h, l, c, [][]float64{{7}, {14}, {21}, {28}}, nil)
for i, lanes := range sim2.Results {
fmt.Printf("Period set %d: %v\n", i+1, lanes[0])
}
sim2.Close()
By assets — same period applied to 4 assets in parallel (N must be 2, 4, 8, or 16):
import org.tuliprs.*;
import org.tuliprs.indicators.Willr;
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[] h2 = {83.15, 82.89, 84.03, 84.30, 84.85, 84.90, 84.33, 85.30, 85.84, 86.00};
double[] l2 = {82.29, 81.64, 82.31, 83.65, 84.07, 84.11, 83.49, 83.30, 85.15, 85.11};
double[] c2 = {82.59, 82.06, 83.87, 84.00, 84.61, 84.15, 83.84, 84.99, 85.55, 85.36};
double[] h3 = {84.15, 83.89, 85.03, 85.30, 85.85, 85.90, 85.33, 86.30, 86.84, 87.00};
double[] l3 = {83.29, 82.64, 83.31, 84.65, 85.07, 85.11, 84.49, 84.30, 86.15, 86.11};
double[] c3 = {83.59, 83.06, 84.87, 85.00, 85.61, 85.15, 84.84, 85.99, 86.55, 86.36};
double[] h4 = {85.15, 84.89, 86.03, 86.30, 86.85, 86.90, 86.33, 87.30, 87.84, 88.00};
double[] l4 = {84.29, 83.64, 84.31, 85.65, 86.07, 86.11, 85.49, 85.30, 87.15, 87.11};
double[] c4 = {84.59, 84.06, 85.87, 86.00, 86.61, 86.15, 85.84, 86.99, 87.55, 87.36};
// One entry per asset; each asset lists its INPUTS series.
double[][][] assets = {{h1, l1, c1}, {h2, l2, c2}, {h3, l3, c3}, {h4, l4, c4}};
try (SimdResult sim = Willr.simdByAssets(assets, new double[] {14.0}, 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 periods in parallel:
import org.tuliprs.*;
import org.tuliprs.indicators.Willr;
double[] h = {82.15, 81.89, 83.03, 83.30, 83.85,
83.90, 83.33, 84.30, 84.84, 85.00};
double[] l = {81.29, 80.64, 81.31, 82.65, 83.07,
83.11, 82.49, 82.30, 84.15, 84.11};
double[] c = {81.59, 81.06, 82.87, 83.00, 83.61,
83.15, 82.84, 83.99, 84.55, 84.36};
try (SimdResult sim = Willr.simdByOptions(new double[][] {h, l, c},
new double[][] {{7}, {14}, {21}, {28}}, null)) {
for (int i = 0; i < sim.numResults(); i++) {
System.out.printf("Period set %d: %s%n", i + 1,
java.util.Arrays.toString(sim.toDoubleArray(i, 0)));
}
}
By assets — same period applied to N assets in parallel (must be 2, 4, 8, or 16):
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)
simd_inputs = [
[high, low, close],
[high + 0.5, low + 0.5, close + 0.5],
[high - 0.5, low - 0.5, close - 0.5],
[high * 1.01, low * 1.01, close * 1.01],
]
outputs_list, states = tulip_rs.indicators.willr.simd_by_assets(simd_inputs, [14.0])
for i, out in enumerate(outputs_list):
print(f"Asset {i + 1}: {out[0]}")
By options — same asset, N different periods in parallel:
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)
simd_options = [[7.0], [14.0], [21.0], [28.0]]
outputs_list, states = tulip_rs.indicators.willr.simd_by_options([high, low, close], simd_options)
for i, out in enumerate(outputs_list):
print(f"Period set {i + 1}: {out[0]}")
By assets — same period applied to 4 assets in parallel:
const simdInputs = [
[high.slice(), low.slice(), close.slice()],
[high.map(v => v * 1.1), low.map(v => v * 1.1), close.map(v => v * 1.1)],
[high.map(v => v * 0.9), low.map(v => v * 0.9), close.map(v => v * 0.9)],
[high.map(v => v * 1.02), low.map(v => v * 1.02), close.map(v => v * 1.02)],
];
const [results] = ti.willr.simdByAssets(simdInputs, [14]);
results.forEach((out, i) => console.log(`Asset ${i + 1}:`, out[0]));
By options — same asset, 4 different periods in parallel: