Ultimate Oscillator¶
Combines momentum from three different time periods (short, medium, and long) to reduce the false signals that arise from using any single timeframe alone.
Inputs: [high, low, close] | Options: [short_period, medium_period, long_period] | Outputs: [ultosc]
Basic¶
use tulip_rs::indicators::ultosc::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];
// Options: [short_period, medium_period, long_period]
let inputs = [high.as_slice(), low.as_slice(), close.as_slice()];
let (outputs, _state) = indicator(&inputs, &[7.0, 14.0, 28.0], None).unwrap();
println!("Ultimate Oscillator: {:?}", outputs[0]);
// State continuation
let inputs2 = [&high[..8], &low[..8], &close[..8]];
let (outputs2, mut state) = indicator(&inputs2, &[7.0, 14.0, 28.0], None).unwrap();
println!("Partial Ultimate Oscillator: {:?}", outputs2[0]);
let new_inputs = [&high[8..], &low[8..], &close[8..]];
let continued = state.batch_indicator(&new_inputs, None).unwrap();
println!("Continued Ultimate Oscillator: {:?}", 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)
# Options: [short_period, medium_period, long_period]
outputs, state = tulip_rs.indicators.ultosc.indicator([high, low, close], [7.0, 14.0, 28.0])
print("Ultimate Oscillator:", outputs[0])
# State continuation
outputs2, state = tulip_rs.indicators.ultosc.indicator([high[:8], low[:8], close[:8]], [7.0, 14.0, 28.0])
continued = state.batch_indicator([high[8:], low[8:], close[8:]])
print("Continued Ultimate Oscillator:", 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.ultosc.indicator([high, low, close], [7, 14, 28]);
console.log('UltOsc:', outputs[0]);
// State continuation
const n = high.length - 5;
const [, state2] = ti.ultosc.indicator([high.slice(0, n), low.slice(0, n), close.slice(0, n)], [7, 14, 28]);
const continued = state2.batchIndicator([high.slice(n), low.slice(n), close.slice(n)]);
console.log('Continued UltOsc:', 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.ultosc.indicator([high, low, close], [7, 14, 28]);
console.log('UltOsc:', outputs[0]);
// State continuation
const n = high.length - 5;
const [, state2] = ti.ultosc.indicator([high.slice(0, n), low.slice(0, n), close.slice(0, n)], [7, 14, 28]);
const continued = state2.batchIndicator([high.slice(n), low.slice(n), close.slice(n)]);
console.log('Continued UltOsc:', continued[0]);
SIMD¶
By assets — same options applied to 4 assets in parallel:
use tulip_rs::indicators::ultosc::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, &[7.0, 14.0, 28.0], None).unwrap();
for (i, asset_outputs) in results.0.iter().enumerate() {
println!("Asset {}: {:?}", i + 1, asset_outputs[0]);
}
By options — same asset, 4 different option sets in parallel:
use tulip_rs::indicators::ultosc::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; 3]; 4] = [
&[7.0, 14.0, 28.0],
&[5.0, 10.0, 20.0],
&[10.0, 20.0, 40.0],
&[4.0, 8.0, 16.0],
];
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!("Option set {}: {:?}", i + 1, opt_outputs[0]);
}
By assets — same options 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.ultosc.simd_by_assets(simd_inputs, [7.0, 14.0, 28.0])
for i, out in enumerate(outputs_list):
print(f"Asset {i + 1}: {out[0]}")
By options — same asset, N different option sets 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, 28.0],
[5.0, 10.0, 20.0],
[10.0, 20.0, 40.0],
[4.0, 8.0, 16.0],
]
outputs_list, states = tulip_rs.indicators.ultosc.simd_by_options([high, low, close], simd_options)
for i, out in enumerate(outputs_list):
print(f"Option set {i + 1}: {out[0]}")
By assets — same options 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.ultosc.simdByAssets(simdInputs, [7, 14, 28]);
results.forEach((out, i) => console.log(`Asset ${i + 1}:`, out[0]));
By options — same asset, 4 different option sets in parallel: