Highpass — Ehlers High Pass Filter¶
Removes low-frequency trend components from price by applying Ehlers' two-pole high-pass filter; the period controls the cutoff frequency.
Inputs: [real] | Options: [period] | Outputs: [highpass]
Basic¶
use tulip_rs::indicators::highpass::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()], &[20.0], None).unwrap();
println!("Highpass(20): {:?}", outputs[0]);
// State continuation
let n = close.len() - 5;
let partial = close[..n].to_vec();
let (outputs2, mut state) = indicator(&[partial.as_slice()], &[20.0], None).unwrap();
println!("Partial Highpass: {:?}", outputs2[0]);
let rest = close[n..].to_vec();
let continued = state.batch_indicator(&[rest.as_slice()], None).unwrap();
println!("Continued Highpass: {:?}", 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.highpass.indicator([close], [20.0])
print("Highpass(20):", outputs[0])
# State continuation
partial = close[:-5]
outputs2, state = tulip_rs.indicators.highpass.indicator([partial], [20.0])
rest = close[-5:]
continued = state.batch_indicator([rest])
print("Continued Highpass:", 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.highpass.indicator([close], [20]);
console.log('Highpass(20):', outputs[0]);
// State continuation
const [, state2] = ti.highpass.indicator([close.slice(0, -5)], [20]);
const continued = state2.batchIndicator([close.slice(-5)]);
console.log('Continued Highpass:', 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.highpass.indicator([close], [20]);
console.log('Highpass(20):', outputs[0]);
// State continuation
const [, state2] = ti.highpass.indicator([close.slice(0, -5)], [20]);
const continued = state2.batchIndicator([close.slice(-5)]);
console.log('Continued Highpass:', continued[0]);
SIMD¶
By assets — same period applied to 4 assets in parallel:
use tulip_rs::indicators::highpass::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, &[20.0], 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::highpass::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] = [&[10.0], &[20.0], &[30.0], &[40.0]];
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.highpass.simd_by_assets(simd_inputs, [20.0])
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 = [[10.0], [20.0], [30.0], [40.0]]
outputs_list, states = tulip_rs.indicators.highpass.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.highpass.simdByAssets(simdInputs, [20]);
results.forEach((out, i) => console.log(`Asset ${i + 1}:`, out[0]));
By options — same asset, 4 different periods in parallel: