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ADOSC — Accumulation/Distribution Oscillator

The difference between a short and long EMA of the A/D line, used to confirm price trends with volume.

Inputs: [high, low, close, volume] | Options: [short_period, long_period] | Outputs: [adosc]

Basic

use tulip_rs::indicators::adosc::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];
let volume = vec![1200.0, 1400.0, 1100.0, 1600.0, 1300.0,
                  900.0, 1500.0, 1800.0, 1000.0, 1700.0_f64];

// options: [short_period, long_period]
let inputs = [high.as_slice(), low.as_slice(), close.as_slice(), volume.as_slice()];
let (outputs, mut state) = indicator(&inputs, &[3.0, 10.0], None).unwrap();
println!("{:?}", outputs[0]); // ADOSC values

// State continuation — feed new bars without reprocessing history
let new_high   = vec![85.20_f64];
let new_low    = vec![84.50_f64];
let new_close  = vec![85.00_f64];
let new_volume = vec![1550.0_f64];
let continued = state.batch_indicator(
    &[new_high.as_slice(), new_low.as_slice(),
      new_close.as_slice(), new_volume.as_slice()],
    None,
).unwrap();
println!("{:?}", 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)
volume = np.array([1200.0, 1400.0, 1100.0, 1600.0, 1300.0,
                   900.0, 1500.0, 1800.0, 1000.0, 1700.0], dtype=np.float64)

# options: [short_period, long_period]
outputs, state = tulip_rs.indicators.adosc.indicator(
    [high, low, close, volume], [3.0, 10.0]
)
print(outputs[0])  # ADOSC values

# State continuation
new_high   = np.array([85.20], dtype=np.float64)
new_low    = np.array([84.50], dtype=np.float64)
new_close  = np.array([85.00], dtype=np.float64)
new_volume = np.array([1550.0], dtype=np.float64)
continued = state.batch_indicator([new_high, new_low, new_close, new_volume])
print(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 volume = [5653100, 6447400, 7690900, 3831400, 4455100, 3798000, 3936200, 4732000, 4841300, 3915300, 6830800, 6694100, 5293600, 7985800, 4807900];

const [outputs, state] = ti.adosc.indicator([high, low, close, volume], [3, 10]);
console.log('ADOSC:', outputs[0]);

// State continuation
const n = high.length - 5;
const [, state2] = ti.adosc.indicator([high.slice(0, n), low.slice(0, n), close.slice(0, n), volume.slice(0, n)], [3, 10]);
const continued = state2.batchIndicator([high.slice(n), low.slice(n), close.slice(n), volume.slice(n)]);
console.log('Continued ADOSC:', 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 volume = [5653100, 6447400, 7690900, 3831400, 4455100, 3798000, 3936200, 4732000, 4841300, 3915300, 6830800, 6694100, 5293600, 7985800, 4807900];

const [outputs, state] = ti.adosc.indicator([high, low, close, volume], [3, 10]);
console.log('ADOSC:', outputs[0]);

// State continuation
const n = high.length - 5;
const [, state2] = ti.adosc.indicator([high.slice(0, n), low.slice(0, n), close.slice(0, n), volume.slice(0, n)], [3, 10]);
const continued = state2.batchIndicator([high.slice(n), low.slice(n), close.slice(n), volume.slice(n)]);
console.log('Continued ADOSC:', continued[0]);

Optional Outputs

adosc exposes 3 optional outputs: short_ema, long_ema, ad. Pass a boolean mask as the third argument — one bool per optional output, in order.

use tulip_rs::indicators::adosc::indicator;

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 high   = close.iter().map(|x| x + 1.0).collect::<Vec<_>>();
let low    = close.iter().map(|x| x - 1.0).collect::<Vec<_>>();
let volume = vec![10000.0, 12000.0, 9500.0, 11000.0, 13000.0, 9800.0, 10500.0, 12500.0, 11800.0, 10200.0_f64];

let mask = [true, false, true];
let (outputs, _state) = indicator(
    &[high.as_slice(), low.as_slice(), close.as_slice(), volume.as_slice()],
    &[6.0, 20.0],
    Some(&mask),
).unwrap();

let adosc     = &outputs[0]; // adosc (primary)
let short_ema = &outputs[1]; // short_ema (optional — requested)
let long_ema  = &outputs[2]; // long_ema (optional — not requested)
let ad        = &outputs[3]; // ad (optional — requested)

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], dtype=np.float64)
high   = close + 1.0
low    = close - 1.0
volume = np.array([10000.0, 12000.0, 9500.0, 11000.0, 13000.0, 9800.0, 10500.0, 12500.0, 11800.0, 10200.0], dtype=np.float64)

outputs, state = tulip_rs.indicators.adosc.indicator(
    [high, low, close, volume], [6.0, 20.0],
    optional_outputs=[True, False, True],
)

adosc     = outputs[0]  # adosc (primary)
short_ema = outputs[1]  # short_ema (optional — requested)
long_ema  = outputs[2]  # long_ema (optional — not requested)
ad        = outputs[3]  # ad (optional — requested)

adosc exposes 3 optional outputs: short_ema, long_ema, ad.

const [allOut] = ti.adosc.indicator([high, low, close, volume], [3, 10], [true, true, true]);
const adosc    = allOut[0]; // primary
const shortEma = allOut[1]; // optional 0: short_ema
const longEma  = allOut[2]; // optional 1: long_ema
const ad       = allOut[3]; // optional 2: ad

SIMD

By assets — same options, N assets in parallel:

use tulip_rs::indicators::adosc::indicator_by_assets;

let inputs: [&[&[f64]; 4]; 4] = [
    &[h1.as_slice(), l1.as_slice(), c1.as_slice(), v1.as_slice()],
    &[h2.as_slice(), l2.as_slice(), c2.as_slice(), v2.as_slice()],
    &[h3.as_slice(), l3.as_slice(), c3.as_slice(), v3.as_slice()],
    &[h4.as_slice(), l4.as_slice(), c4.as_slice(), v4.as_slice()],
];
let results = indicator_by_assets::<4>(&inputs, &[3.0, 10.0], None).unwrap();
for (i, asset_outputs) in results.iter().enumerate() {
    println!("Asset {}: {:?}", i + 1, asset_outputs[0]);
}

By options — same asset, N option sets in parallel:

use tulip_rs::indicators::adosc::indicator_by_options;

let opts: [&[f64; 2]; 4] = [&[2.0, 5.0], &[3.0, 10.0], &[5.0, 20.0], &[7.0, 28.0]];
let results = indicator_by_options::<4>(&inputs, &opts, None).unwrap();
for (i, out) in results.iter().enumerate() {
    println!("Option set {}: {:?}", i + 1, out[0]);
}

By assets — same options, N assets in parallel (must be 2, 4, 8, or 16):

simd_inputs = [
    [h1, l1, c1, v1],
    [h2, l2, c2, v2],
    [h3, l3, c3, v3],
    [h4, l4, c4, v4],
]
outputs_list, states = tulip_rs.indicators.adosc.simd_by_assets(simd_inputs, [3.0, 10.0])
for i, asset_outputs in enumerate(outputs_list):
    print(f"Asset {i+1}: {asset_outputs[0]}")

By options — same asset, N option sets in parallel:

simd_options = [[2.0, 5.0], [3.0, 10.0], [5.0, 20.0], [7.0, 28.0]]
outputs_list, states = tulip_rs.indicators.adosc.simd_by_options(
    [high, low, close, volume], 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], [...volume]],
    [high.map(v => v * 1.1), low.map(v => v * 1.1), close.map(v => v * 1.1), volume.map(v => v * 1.1)],
    [high.map(v => v * 0.9), low.map(v => v * 0.9), close.map(v => v * 0.9), volume.map(v => v * 0.9)],
    [high.map(v => v * 1.02), low.map(v => v * 1.02), close.map(v => v * 1.02), volume.map(v => v * 1.02)],
];
const [results] = ti.adosc.simdByAssets(simdInputs, [3, 10]);
results.forEach((out, i) => console.log(`Asset ${i + 1}:`, out[0]));

By options — same asset, 4 different option sets in parallel:

const simdOptions = [[2, 5], [3, 10], [5, 20], [7, 28]];
const [results] = ti.adosc.simdByOptions([high, low, close, volume], simdOptions);
results.forEach((out, i) => console.log(`Option set ${i + 1}:`, out[0]));