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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]

Basic

use tulip_rs::indicators::willr::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) = 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) = 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]);

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  = [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]);

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]);

SIMD

By assets — same period applied to 4 assets in parallel:

use tulip_rs::indicators::willr::indicator_by_assets;

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 = indicator_by_assets::<4>(&inputs, &[14.0], None).unwrap();
for (i, asset_outputs) in results.0.iter().enumerate() {
    println!("Asset {}: {:?}", i + 1, asset_outputs[0]);
}

By options — same asset, 4 different periods in parallel:

use tulip_rs::indicators::willr::indicator_by_options;

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 = indicator_by_options::<4>(&inputs, &opts, None).unwrap();
for (i, opt_outputs) in results.0.iter().enumerate() {
    println!("Period set {}: {:?}", i + 1, opt_outputs[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], [...low], [...close]],
    [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:

const simdOptions = [[7], [14], [21], [28]];
const [results] = ti.willr.simdByOptions([high, low, close], simdOptions);
results.forEach((out, i) => console.log(`Period ${simdOptions[i][0]}:`, out[0]));