HOMODYNEDISCRIMINATOR — Ehlers Homodyne Discriminator¶
Measures the instantaneous dominant cycle period by comparing successive bar phase using phase accumulation; outputs the dominant cycle period in bars.
Inputs: [real] | Options: [] (none) | Outputs: [dc_period]
Basic¶
use tulip_rs::indicators::homodynediscriminator::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];
// homodynediscriminator takes no options — pass an empty slice
let (outputs, _state) = indicator(&[close.as_slice()], &[], None).unwrap();
println!("DC Period: {:?}", outputs[0]);
// State continuation
let partial = close[..35].to_vec();
let (outputs2, mut state) = indicator(&[partial.as_slice()], &[], None).unwrap();
println!("Partial DC Period: {:?}", outputs2[0]);
let new_close = close[35..].to_vec();
let continued = state.batch_indicator(&[new_close.as_slice()], None).unwrap();
println!("Continued DC Period: {:?}", 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)
# homodynediscriminator takes no options — pass an empty list
outputs, state = tulip_rs.indicators.homodynediscriminator.indicator([close], [])
print("DC Period:", outputs[0])
# State continuation
partial = close[:35]
outputs2, state = tulip_rs.indicators.homodynediscriminator.indicator([partial], [])
new_close = close[35:]
continued = state.batch_indicator([new_close])
print("Continued DC Period:", 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.homodynediscriminator.indicator([close], []);
console.log('DC Period:', outputs[0]);
// State continuation
const [, state2] = ti.homodynediscriminator.indicator([close.slice(0, -5)], []);
const continued = state2.batchIndicator([close.slice(-5)]);
console.log('Continued DC Period:', continued[0]);
import { init, homodynediscriminator } 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] = homodynediscriminator.indicator([close], []);
console.log('DC Period:', outputs[0]);
// State continuation
const [, state2] = homodynediscriminator.indicator([close.slice(0, -5)], []);
const continued = state2.batchIndicator([close.slice(-5)]);
console.log('Continued DC Period:', continued[0]);
SIMD¶
By assets — applied to 4 assets in parallel:
use tulip_rs::indicators::homodynediscriminator::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,
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 a2 = vec![72.10, 72.85, 73.40, 73.00, 74.20, 74.85, 75.10, 75.60, 76.00, 76.50,
77.00, 77.50, 78.00, 78.50, 79.00, 79.50, 80.00, 80.50, 81.00, 81.50,
82.00, 82.50, 83.00, 83.50, 84.00, 84.50, 85.00, 85.50, 86.00, 86.50,
87.00, 87.50, 88.00, 88.50, 89.00, 89.50, 90.00, 90.50, 91.00, 91.50_f64];
let a3 = vec![55.30, 55.80, 56.10, 56.40, 56.90, 57.20, 57.50, 57.80, 58.10, 58.40,
58.70, 59.00, 59.30, 59.60, 59.90, 60.20, 60.50, 60.80, 61.10, 61.40,
61.70, 62.00, 62.30, 62.60, 62.90, 63.20, 63.50, 63.80, 64.10, 64.40,
64.70, 65.00, 65.30, 65.60, 65.90, 66.20, 66.50, 66.80, 67.10, 67.40_f64];
let a4 = vec![100.1, 100.5, 101.0, 101.3, 101.8, 102.0, 102.5, 103.0, 103.3, 103.8,
104.1, 104.5, 105.0, 105.3, 105.8, 106.0, 106.5, 107.0, 107.3, 107.8,
108.1, 108.5, 109.0, 109.3, 109.8, 110.0, 110.5, 111.0, 111.3, 111.8,
112.1, 112.5, 113.0, 113.3, 113.8, 114.0, 114.5, 115.0, 115.3, 115.8_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, &[], None).unwrap();
for (i, asset_outputs) in results.0.iter().enumerate() {
println!("Asset {}: {:?}", i + 1, asset_outputs[0]);
}
This indicator has no options, so by-options SIMD does not apply.
By assets — 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 - 5.0], [close * 1.02]]
outputs_list, states = tulip_rs.indicators.homodynediscriminator.simd_by_assets(simd_inputs, [])
for i, out in enumerate(outputs_list):
print(f"Asset {i + 1}: {out[0]}")
This indicator has no options, so by-options SIMD does not apply.
By assets — applied to 4 assets in parallel:
const simdInputs = [
[close.slice()],
[close.map(v => v * 1.1)],
[close.map(v => v * 0.9)],
[close.map(v => v * 1.02)],
];
const [results] = ti.homodynediscriminator.simdByAssets(simdInputs, []);
results.forEach((out, i) => console.log(`Asset ${i + 1}:`, out[0]));
This indicator has no options, so by-options SIMD does not apply.