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Super Smoother — Ehlers Super Smoother

A two-pole Butterworth filter with no phase lag that provides smoother output than a simple moving average with the same length; useful as a drop-in substitute for the SMA where lag is critical.

Inputs: [real]  |  Options: [period]  |  Outputs: [supersmoother]

Basic

use tulip_rs::indicators::supersmoother::indicator;

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 (outputs, _state) = indicator(&[close.as_slice()], &[10.0], None).unwrap();
println!("Super Smoother(10): {:?}", outputs[0]);

// State continuation
let n = close.len() - 5;
let partial = close[..n].to_vec();
let (outputs2, mut state) = indicator(&[partial.as_slice()], &[10.0], None).unwrap();
println!("Partial Super Smoother: {:?}", outputs2[0]);

let rest = close[n..].to_vec();
let continued = state.batch_indicator(&[rest.as_slice()], None).unwrap();
println!("Continued Super Smoother: {:?}", continued[0]);

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.supersmoother.indicator([close], [10.0])
print("Super Smoother(10):", outputs[0])

# State continuation
partial = close[:-5]
outputs2, state = tulip_rs.indicators.supersmoother.indicator([partial], [10.0])
rest = close[-5:]
continued = state.batch_indicator([rest])
print("Continued Super Smoother:", continued[0])

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.supersmoother.indicator([close], [10]);
console.log('Super Smoother(10):', outputs[0]);

// State continuation
const [, state2] = ti.supersmoother.indicator([close.slice(0, -5)], [10]);
const continued = state2.batchIndicator([close.slice(-5)]);
console.log('Continued Super Smoother:', continued[0]);

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.supersmoother.indicator([close], [10]);
console.log('Super Smoother(10):', outputs[0]);

// State continuation
const [, state2] = ti.supersmoother.indicator([close.slice(0, -5)], [10]);
const continued = state2.batchIndicator([close.slice(-5)]);
console.log('Continued Super Smoother:', continued[0]);

SIMD

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

use tulip_rs::indicators::supersmoother::indicator_by_assets;

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 = indicator_by_assets::<4>(&inputs, &[10.0], 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::supersmoother::indicator_by_options;

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] = [&[5.0], &[10.0], &[14.0], &[20.0]];

let results = indicator_by_options::<4>(&[close.as_slice()], &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

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.supersmoother.simd_by_assets(simd_inputs, [10.0])
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

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], [14.0], [20.0]]
outputs_list, states = tulip_rs.indicators.supersmoother.simd_by_options([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 = [
    [close.slice()],
    [close.map(v => v + 5.0)],
    [close.map(v => v - 3.0)],
    [close.map(v => v * 1.02)],
];
const [results] = ti.supersmoother.simdByAssets(simdInputs, [10]);
results.forEach((out, i) => console.log(`Asset ${i + 1}:`, out[0]));

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

const simdOptions = [[5], [10], [14], [20]];
const [results] = ti.supersmoother.simdByOptions([close], simdOptions);
results.forEach((out, i) => console.log(`Period ${simdOptions[i][0]}:`, out[0]));