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::{UltOsc, TIndicatorState, 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) = UltOsc::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) = UltOsc::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]);
#include "tulip_rs_ffi.h"
double high[] = {82.15, 81.89, 83.03, 83.30, 83.85,
83.90, 83.33, 84.30, 84.84, 85.00};
double low[] = {81.29, 80.64, 81.31, 82.65, 83.07,
83.11, 82.49, 82.30, 84.15, 84.11};
double close[] = {81.59, 81.06, 82.87, 83.00, 83.61,
83.15, 82.84, 83.99, 84.55, 84.36};
double options[ULTOSC_OPTIONS] = {7.0, 14.0, 28.0}; // short_period, medium_period, long_period
const double *inputs[ULTOSC_INPUTS] = {high, low, close};
/* Full computation */
CIndicatorResult r = ultosc_indicator(inputs, 10, options, NULL, 0);
/* r.outputs[0] -> the Ultimate Oscillator series, length r.output_lens[0] */
tulip_ffi_result_free(r);
ultosc_state_free(r.state);
/* Partial computation + state continuation */
CIndicatorResult p = ultosc_indicator(inputs, 8, options, NULL, 0);
double new_high[] = {84.55, 85.00};
double new_low[] = {84.15, 84.11};
double new_close[] = {84.55, 84.36};
const double *new_inputs[ULTOSC_INPUTS] = {new_high, new_low, new_close};
CBatchResult b = ultosc_batch(p.state, new_inputs, 2, NULL, 0);
/* b.outputs[0] -> Ultimate Oscillator values for just the two new bars */
tulip_ffi_batch_result_free(b);
tulip_ffi_result_free(p);
ultosc_state_free(p.state);
import "github.com/me60732/tulip_rs_go/indicators"
high := []float64{82.15, 81.89, 83.03, 83.30, 83.85,
83.90, 83.33, 84.30, 84.84, 85.00}
low := []float64{81.29, 80.64, 81.31, 82.65, 83.07,
83.11, 82.49, 82.30, 84.15, 84.11}
close := []float64{81.59, 81.06, 82.87, 83.00, 83.61,
83.15, 82.84, 83.99, 84.55, 84.36}
options := []float64{7.0, 14.0, 28.0} // short_period, medium_period, long_period
// Full computation — Rows are zero-copy views, valid until Close.
res, st, _ := indicators.Ultosc.Indicator(high, low, close, options, nil)
fmt.Println(res.Rows[0]) // Ultimate Oscillator values
res.Close()
st.Close()
// Partial computation + state continuation.
res2, st2, _ := indicators.Ultosc.Indicator(high[:8], low[:8], close[:8], options, nil)
res2.Close() // outputs consumed or closed; state stays live
batch, _ := st2.Batch(high[8:], low[8:], close[8:], nil)
fmt.Println(batch.Rows[0]) // continued Ultimate Oscillator values
batch.Close()
st2.Close()
import org.tuliprs.*;
import org.tuliprs.indicators.Ultosc;
double[] high = {82.15, 81.89, 83.03, 83.30, 83.85,
83.90, 83.33, 84.30, 84.84, 85.00};
double[] low = {81.29, 80.64, 81.31, 82.65, 83.07,
83.11, 82.49, 82.30, 84.15, 84.11};
double[] close = {81.59, 81.06, 82.87, 83.00, 83.61,
83.15, 82.84, 83.99, 84.55, 84.36};
double[] options = {7.0, 14.0, 28.0}; // short_period, medium_period, long_period
// Full computation — output rows are zero-copy views, valid until close().
Outcome oc = Ultosc.indicator(new double[][] {high, low, close}, options);
try (Result res = oc.result(); State st = oc.state()) {
System.out.println(java.util.Arrays.toString(res.toDoubleArray(0))); // Ultimate Oscillator values
}
// Partial computation + state continuation.
int n = 8;
Outcome p = Ultosc.indicator(new double[][] {
java.util.Arrays.copyOfRange(high, 0, n),
java.util.Arrays.copyOfRange(low, 0, n),
java.util.Arrays.copyOfRange(close, 0, n)}, options);
try (Result pr = p.result(); State st = p.state()) {
Result br = st.batch(new double[][] {
java.util.Arrays.copyOfRange(high, n, 10),
java.util.Arrays.copyOfRange(low, n, 10),
java.util.Arrays.copyOfRange(close, n, 10)});
try (br) {
System.out.println(java.util.Arrays.toString(br.toDoubleArray(0))); // continued Ultimate Oscillator
}
}
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 = 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 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]);
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::{UltOsc, Indicator};
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 = UltOsc::indicator_by_assets::<4>(&inputs, &[7.0, 14.0, 28.0], None).unwrap();
for (i, asset_outputs) in results.iter().enumerate() {
println!("Asset {}: {:?}", i + 1, asset_outputs[0]);
}
By options — same asset, 4 different option sets in parallel:
use tulip_rs::indicators::ultosc::{UltOsc, IndicatorByOptions};
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 = UltOsc::indicator_by_options::<4>(&inputs, &opts, None).unwrap();
for (i, opt_outputs) in results.iter().enumerate() {
println!("Option set {}: {:?}", i + 1, opt_outputs[0]);
}
By assets — same options applied to 4 assets in parallel:
double h1[] = {82.15, 81.89, 83.03, 83.30, 83.85, 83.90, 83.33, 84.30, 84.84, 85.00};
double l1[] = {81.29, 80.64, 81.31, 82.65, 83.07, 83.11, 82.49, 82.30, 84.15, 84.11};
double c1[] = {81.59, 81.06, 82.87, 83.00, 83.61, 83.15, 82.84, 83.99, 84.55, 84.36};
double h2[] = {h1[0]*1.1, h1[1]*1.1, h1[2]*1.1, h1[3]*1.1, h1[4]*1.1,
h1[5]*1.1, h1[6]*1.1, h1[7]*1.1, h1[8]*1.1, h1[9]*1.1};
double l2[] = {l1[0]*1.1, l1[1]*1.1, l1[2]*1.1, l1[3]*1.1, l1[4]*1.1,
l1[5]*1.1, l1[6]*1.1, l1[7]*1.1, l1[8]*1.1, l1[9]*1.1};
double c2[] = {c1[0]*1.1, c1[1]*1.1, c1[2]*1.1, c1[3]*1.1, c1[4]*1.1,
c1[5]*1.1, c1[6]*1.1, c1[7]*1.1, c1[8]*1.1, c1[9]*1.1};
double h3[] = {h1[0]*0.9, h1[1]*0.9, h1[2]*0.9, h1[3]*0.9, h1[4]*0.9,
h1[5]*0.9, h1[6]*0.9, h1[7]*0.9, h1[8]*0.9, h1[9]*0.9};
double l3[] = {l1[0]*0.9, l1[1]*0.9, l1[2]*0.9, l1[3]*0.9, l1[4]*0.9,
l1[5]*0.9, l1[6]*0.9, l1[7]*0.9, l1[8]*0.9, l1[9]*0.9};
double c3[] = {c1[0]*0.9, c1[1]*0.9, c1[2]*0.9, c1[3]*0.9, c1[4]*0.9,
c1[5]*0.9, c1[6]*0.9, c1[7]*0.9, c1[8]*0.9, c1[9]*0.9};
double h4[] = {h1[0]*1.02, h1[1]*1.02, h1[2]*1.02, h1[3]*1.02, h1[4]*1.02,
h1[5]*1.02, h1[6]*1.02, h1[7]*1.02, h1[8]*1.02, h1[9]*1.02};
double l4[] = {l1[0]*1.02, l1[1]*1.02, l1[2]*1.02, l1[3]*1.02, l1[4]*1.02,
l1[5]*1.02, l1[6]*1.02, l1[7]*1.02, l1[8]*1.02, l1[9]*1.02};
double c4[] = {c1[0]*1.02, c1[1]*1.02, c1[2]*1.02, c1[3]*1.02, c1[4]*1.02,
c1[5]*1.02, c1[6]*1.02, c1[7]*1.02, c1[8]*1.02, c1[9]*1.02};
const double *const asset1[ULTOSC_INPUTS] = {h1, l1, c1};
const double *const asset2[ULTOSC_INPUTS] = {h2, l2, c2};
const double *const asset3[ULTOSC_INPUTS] = {h3, l3, c3};
const double *const asset4[ULTOSC_INPUTS] = {h4, l4, c4};
const double *const *const simd_inputs[4] = {asset1, asset2, asset3, asset4};
CSimdResult r = ultosc_simd_by_assets(simd_inputs, 4, 10, options, NULL, 0);
for (uintptr_t i = 0; i < r.num_results; i++) {
/* r.outputs[i][0] -> asset i's ultosc series */
ultosc_state_free(r.states[i]);
}
tulip_ffi_simd_result_free(r);
h1 := []float64{82.15, 81.89, 83.03, 83.30, 83.85, 83.90, 83.33, 84.30, 84.84, 85.00}
l1 := []float64{81.29, 80.64, 81.31, 82.65, 83.07, 83.11, 82.49, 82.30, 84.15, 84.11}
c1 := []float64{81.59, 81.06, 82.87, 83.00, 83.61, 83.15, 82.84, 83.99, 84.55, 84.36}
// Reuse the same data for assets 2–4 in this example
h2, l2, c2 := h1, l1, c1
h3, l3, c3 := h1, l1, c1
h4, l4, c4 := h1, l1, c1
assets := [][indicators.UltoscInputs][]float64{{h1, l1, c1}, {h2, l2, c2}, {h3, l3, c3}, {h4, l4, c4}}
sim, _ := indicators.Ultosc.SimdByAssets(assets, []float64{7.0, 14.0, 28.0}, nil)
for i, lanes := range sim.Results {
fmt.Printf("Asset %d: %v\n", i+1, lanes[0])
}
sim.Close() // frees every lane state, then the SIMD buffers
By options — same asset, 4 different option sets in parallel:
high := []float64{82.15, 81.89, 83.03, 83.30, 83.85,
83.90, 83.33, 84.30, 84.84, 85.00}
low := []float64{81.29, 80.64, 81.31, 82.65, 83.07,
83.11, 82.49, 82.30, 84.15, 84.11}
close := []float64{81.59, 81.06, 82.87, 83.00, 83.61,
83.15, 82.84, 83.99, 84.55, 84.36}
sim2, _ := indicators.Ultosc.SimdByOptions(high, low, close, [][]float64{{7, 14, 28}, {5, 10, 20}, {10, 20, 40}, {4, 8, 16}}, nil)
for i, lanes := range sim2.Results {
fmt.Printf("Option set %d: %v\n", i+1, lanes[0])
}
sim2.Close()
By assets — same options applied to 4 assets in parallel (N must be 2, 4, 8, or 16):
import org.tuliprs.*;
import org.tuliprs.indicators.Ultosc;
double[] h1 = {82.15, 81.89, 83.03, 83.30, 83.85, 83.90, 83.33, 84.30, 84.84, 85.00};
double[] l1 = {81.29, 80.64, 81.31, 82.65, 83.07, 83.11, 82.49, 82.30, 84.15, 84.11};
double[] c1 = {81.59, 81.06, 82.87, 83.00, 83.61, 83.15, 82.84, 83.99, 84.55, 84.36};
double[] h2 = {83.15, 82.89, 84.03, 84.30, 84.85, 84.90, 84.33, 85.30, 85.84, 86.00};
double[] l2 = {82.29, 81.64, 82.31, 83.65, 84.07, 84.11, 83.49, 83.30, 85.15, 85.11};
double[] c2 = {82.59, 82.06, 83.87, 84.00, 84.61, 84.15, 83.84, 84.99, 85.55, 85.36};
double[] h3 = {84.15, 83.89, 85.03, 85.30, 85.85, 85.90, 85.33, 86.30, 86.84, 87.00};
double[] l3 = {83.29, 82.64, 83.31, 84.65, 85.07, 85.11, 84.49, 84.30, 86.15, 86.11};
double[] c3 = {83.59, 83.06, 84.87, 85.00, 85.61, 85.15, 84.84, 85.99, 86.55, 86.36};
double[] h4 = {85.15, 84.89, 86.03, 86.30, 86.85, 86.90, 86.33, 87.30, 87.84, 88.00};
double[] l4 = {84.29, 83.64, 84.31, 85.65, 86.07, 86.11, 85.49, 85.30, 87.15, 87.11};
double[] c4 = {84.59, 84.06, 85.87, 86.00, 86.61, 86.15, 85.84, 86.99, 87.55, 87.36};
// One entry per asset; each asset lists its INPUTS series.
double[][][] assets = {{h1, l1, c1}, {h2, l2, c2}, {h3, l3, c3}, {h4, l4, c4}};
try (SimdResult sim = Ultosc.simdByAssets(assets, new double[] {7.0, 14.0, 28.0}, null)) {
for (int i = 0; i < sim.numResults(); i++) {
System.out.printf("Asset %d: %s%n", i + 1,
java.util.Arrays.toString(sim.toDoubleArray(i, 0)));
}
} // frees every lane state, then the SIMD buffers (contractual order)
By options — same asset, 4 different option sets in parallel:
import org.tuliprs.*;
import org.tuliprs.indicators.Ultosc;
double[] high = {82.15, 81.89, 83.03, 83.30, 83.85,
83.90, 83.33, 84.30, 84.84, 85.00};
double[] low = {81.29, 80.64, 81.31, 82.65, 83.07,
83.11, 82.49, 82.30, 84.15, 84.11};
double[] close = {81.59, 81.06, 82.87, 83.00, 83.61,
83.15, 82.84, 83.99, 84.55, 84.36};
try (SimdResult sim = Ultosc.simdByOptions(new double[][] {high, low, close},
new double[][] {{7, 14, 28}, {5, 10, 20}, {10, 20, 40}, {4, 8, 16}}, null)) {
for (int i = 0; i < sim.numResults(); i++) {
System.out.printf("Option set %d: %s%n", i + 1,
java.util.Arrays.toString(sim.toDoubleArray(i, 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.slice(), low.slice(), close.slice()],
[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: