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Donchian Channel

Three-band channel based on the rolling highest high and lowest low over period bars. upper = max(high, period), lower = min(low, period), middle = (upper + lower) / 2. Used to identify breakouts — a close above the upper band signals bullish momentum. Rendered as a price overlay.

Inputs: [high, low]  |  Options: [period]  |  Outputs: [lower, middle, upper]

Basic

use tulip_rs::indicators::donchianchannel::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 inputs = [high.as_slice(), low.as_slice()];
let (outputs, _state) = indicator(&inputs, &[14.0], None).unwrap();
println!("Lower:  {:?}", outputs[0]);
println!("Middle: {:?}", outputs[1]);
println!("Upper:  {:?}", outputs[2]);

// State continuation
let inputs2 = [&high[..8], &low[..8]];
let (outputs2, mut state) = indicator(&inputs2, &[14.0], None).unwrap();
println!("Partial Lower:  {:?}", outputs2[0]);
println!("Partial Middle: {:?}", outputs2[1]);
println!("Partial Upper:  {:?}", outputs2[2]);

let new_inputs = [&high[8..], &low[8..]];
let continued = state.batch_indicator(&new_inputs, None).unwrap();
println!("Continued Lower:  {:?}", continued[0]);
println!("Continued Middle: {:?}", continued[1]);
println!("Continued Upper:  {:?}", continued[2]);

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)

outputs, state = tulip_rs.indicators.donchianchannel.indicator([high, low], [14.0])
print("Lower: ", outputs[0])
print("Middle:", outputs[1])
print("Upper: ", outputs[2])

# State continuation
outputs2, state = tulip_rs.indicators.donchianchannel.indicator([high[:8], low[:8]], [14.0])
print("Partial Lower: ", outputs2[0])
print("Partial Middle:", outputs2[1])
print("Partial Upper: ", outputs2[2])

continued = state.batch_indicator([high[8:], low[8:]])
print("Continued Lower: ", continued[0])
print("Continued Middle:", continued[1])
print("Continued Upper: ", continued[2])

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 [outputs, state] = ti.donchianchannel.indicator([high, low], [14]);
console.log('Donchian Lower:', outputs[0]);
console.log('Donchian Middle:', outputs[1]);
console.log('Donchian Upper:', outputs[2]);

// State continuation
const n = high.length - 5;
const [, state2] = ti.donchianchannel.indicator([high.slice(0, n), low.slice(0, n)], [14]);
const continued = state2.batchIndicator([high.slice(n), low.slice(n)]);
console.log('Continued Lower:', continued[0]);
console.log('Continued Middle:', continued[1]);
console.log('Continued Upper:', continued[2]);

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 [outputs, state] = ti.donchianchannel.indicator([high, low], [14]);
console.log('Donchian Lower:', outputs[0]);
console.log('Donchian Middle:', outputs[1]);
console.log('Donchian Upper:', outputs[2]);

// State continuation
const n = high.length - 5;
const [, state2] = ti.donchianchannel.indicator([high.slice(0, n), low.slice(0, n)], [14]);
const continued = state2.batchIndicator([high.slice(n), low.slice(n)]);
console.log('Continued Lower:', continued[0]);
console.log('Continued Middle:', continued[1]);
console.log('Continued Upper:', continued[2]);

SIMD

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

use tulip_rs::indicators::donchianchannel::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 h2 = h1.clone(); let l2 = l1.clone();
let h3 = h1.clone(); let l3 = l1.clone();
let h4 = h1.clone(); let l4 = l1.clone();

let inputs: [&[&[f64]; 2]; 4] = [
    &[h1.as_slice(), l1.as_slice()],
    &[h2.as_slice(), l2.as_slice()],
    &[h3.as_slice(), l3.as_slice()],
    &[h4.as_slice(), l4.as_slice()],
];

let results = indicator_by_assets::<4>(&inputs, &[14.0], None).unwrap();
for (i, asset_outputs) in results.0.iter().enumerate() {
    println!("Asset {} Lower:  {:?}", i + 1, asset_outputs[0]);
    println!("Asset {} Middle: {:?}", i + 1, asset_outputs[1]);
    println!("Asset {} Upper:  {:?}", i + 1, asset_outputs[2]);
}

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

use tulip_rs::indicators::donchianchannel::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 opts: [&[f64; 1]; 4] = [&[7.0], &[14.0], &[21.0], &[28.0]];
let inputs = [high.as_slice(), low.as_slice()];
let results = indicator_by_options::<4>(&inputs, &opts, None).unwrap();
for (i, opt_outputs) in results.0.iter().enumerate() {
    println!("Period {} Lower:  {:?}", opts[i][0], opt_outputs[0]);
    println!("Period {} Middle: {:?}", opts[i][0], opt_outputs[1]);
    println!("Period {} Upper:  {:?}", opts[i][0], opt_outputs[2]);
}

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)

simd_inputs = [
    [high,        low],
    [high + 0.5,  low + 0.5],
    [high - 0.5,  low - 0.5],
    [high * 1.01, low * 1.01],
]
outputs_list, states = tulip_rs.indicators.donchianchannel.simd_by_assets(simd_inputs, [14.0])
for i, out in enumerate(outputs_list):
    print(f"Asset {i + 1} Lower:  {out[0]}")
    print(f"Asset {i + 1} Middle: {out[1]}")
    print(f"Asset {i + 1} Upper:  {out[2]}")

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

simd_options = [[7.0], [14.0], [21.0], [28.0]]
outputs_list, states = tulip_rs.indicators.donchianchannel.simd_by_options([high, low], simd_options)
for i, out in enumerate(outputs_list):
    print(f"Period {simd_options[i][0]} Lower:  {out[0]}")
    print(f"Period {simd_options[i][0]} Middle: {out[1]}")
    print(f"Period {simd_options[i][0]} Upper:  {out[2]}")

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

const simdInputs = [
    [high.slice(), low.slice()],
    [high.map(v => v * 1.1), low.map(v => v * 1.1)],
    [high.map(v => v * 0.9), low.map(v => v * 0.9)],
    [high.map(v => v * 1.02), low.map(v => v * 1.02)],
];
const [results] = ti.donchianchannel.simdByAssets(simdInputs, [14]);
results.forEach((out, i) => {
    console.log(`Asset ${i + 1} Lower:`, out[0]);
    console.log(`Asset ${i + 1} Middle:`, out[1]);
    console.log(`Asset ${i + 1} Upper:`, out[2]);
});

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

const simdOptions = [[7], [14], [21], [28]];
const [results] = ti.donchianchannel.simdByOptions([high, low], simdOptions);
results.forEach((out, i) => console.log(`Period ${simdOptions[i][0]}:`, out[0], out[1], out[2]));