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APO — Absolute Price Oscillator

The raw difference between two EMAs (short minus long). Positive values indicate upward momentum.

Inputs: [real] | Options: [short_period, long_period] | Outputs: [apo]

Basic

use tulip_rs::indicators::apo::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];

// options: [short_period, long_period]
let (outputs, mut state) = indicator(&[close.as_slice()], &[12.0, 26.0], None).unwrap();
println!("{:?}", outputs[0]); // APO values

// State continuation — feed new bars without reprocessing history
let new_close = vec![85.10, 85.72_f64];
let continued = state.batch_indicator(&[new_close.as_slice()], None).unwrap();
println!("{:?}", 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], dtype=np.float64)

# options: [short_period, long_period]
outputs, state = tulip_rs.indicators.apo.indicator([close], [12.0, 26.0])
print(outputs[0])  # APO values

# State continuation
new_close = np.array([85.10, 85.72], dtype=np.float64)
continued = state.batch_indicator([new_close])
print(continued[0])

import * as ti from 'tulip-rs-node';

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.apo.indicator([close], [12, 26]);
console.log('APO:', outputs[0]);

// State continuation
const [, state2] = ti.apo.indicator([close.slice(0, -3)], [12, 26]);
const continued = state2.batchIndicator([close.slice(-3)]);
console.log('Continued APO:', 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];

const [outputs, state] = ti.apo.indicator([close], [12, 26]);
console.log('APO:', outputs[0]);

// State continuation
const [, state2] = ti.apo.indicator([close.slice(0, -3)], [12, 26]);
const continued = state2.batchIndicator([close.slice(-3)]);
console.log('Continued APO:', continued[0]);

Optional Outputs

apo exposes 2 optional outputs: short_ema, long_ema. Pass a boolean mask as the third argument — one bool per optional output, in order.

use tulip_rs::indicators::apo::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 mask = [true, true];
let (outputs, _state) = indicator(&[close.as_slice()], &[5.0, 20.0], Some(&mask)).unwrap();

let apo       = &outputs[0]; // APO values (primary)
let short_ema = &outputs[1]; // short_ema (optional — requested)
let long_ema  = &outputs[2]; // long_ema (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)

outputs, state = tulip_rs.indicators.apo.indicator(
    [close], [5.0, 20.0],
    optional_outputs=[True, True],
)

apo       = outputs[0]  # APO values (primary)
short_ema = outputs[1]  # short_ema (optional — requested)
long_ema  = outputs[2]  # long_ema (optional — requested)

apo exposes 2 optional outputs: short_ema, long_ema.

const [allOut] = ti.apo.indicator([close], [12, 26], [true, true]);
const apo      = allOut[0]; // primary
const shortEma = allOut[1]; // optional 0: short_ema
const longEma  = allOut[2]; // optional 1: long_ema

SIMD

By assets — same options, N assets in parallel:

use tulip_rs::indicators::apo::indicator_by_assets;

let inputs: [&[&[f64]; 1]; 4] = [
    &[asset1_close.as_slice()],
    &[asset2_close.as_slice()],
    &[asset3_close.as_slice()],
    &[asset4_close.as_slice()],
];
let results = indicator_by_assets::<4>(&inputs, &[12.0, 26.0], 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::apo::indicator_by_options;

let opts: [&[f64; 2]; 4] = [&[6.0, 13.0], &[12.0, 26.0], &[19.0, 39.0], &[24.0, 52.0]];
let results = indicator_by_options::<4>(&[close.as_slice()], &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 = [
    [np.array(asset1_close, dtype=np.float64)],
    [np.array(asset2_close, dtype=np.float64)],
    [np.array(asset3_close, dtype=np.float64)],
    [np.array(asset4_close, dtype=np.float64)],
]
outputs_list, states = tulip_rs.indicators.apo.simd_by_assets(simd_inputs, [12.0, 26.0])
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 = [[6.0, 13.0], [12.0, 26.0], [19.0, 39.0], [24.0, 52.0]]
outputs_list, states = tulip_rs.indicators.apo.simd_by_options([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 = [[[...close]], [close.map(v => v * 1.1)], [close.map(v => v * 0.9)], [close.map(v => v * 1.02)]];
const [results] = ti.apo.simdByAssets(simdInputs, [12, 26]);
results.forEach((out, i) => console.log(`Asset ${i + 1}:`, out[0]));

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

const simdOptions = [[6, 13], [12, 26], [19, 39], [24, 52]];
const [results] = ti.apo.simdByOptions([close], simdOptions);
results.forEach((out, i) => console.log(`Option set ${i + 1}:`, out[0]));