Skip to content

Mass Index — mass

Uses the high-low trading range to identify potential trend reversals via range expansion. Watch for values rising above 27 then falling below 26.5 — this "reversal bulge" signals a likely trend change.

Inputs: [high, low] | Options: [period] | Outputs: [mass]

Basic

use tulip_rs::indicators::mass::indicator;

let high = [82.15_f64, 81.89, 83.03, 83.30, 83.85, 83.90, 83.33, 84.30, 84.84, 85.00];
let low  = [81.29_f64, 80.64, 81.31, 82.65, 83.07, 83.11, 82.49, 82.30, 84.15, 84.11];

let inputs = [high.as_slice(), low.as_slice()];
let (outputs, _) = indicator(&inputs, &[25.0], None).unwrap();
println!("{:?}", outputs[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)

outputs, state = tulip_rs.indicators.mass.indicator([high, low], [25.0])
print(outputs[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 [outputs, state] = ti.mass.indicator([high, low], [25]);
console.log('Mass(25):', outputs[0]);

// State continuation
const n = high.length - 5;
const [, state2] = ti.mass.indicator([high.slice(0, n), low.slice(0, n)], [25]);
const continued = state2.batchIndicator([high.slice(n), low.slice(n)]);
console.log('Continued Mass:', 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 [outputs, state] = ti.mass.indicator([high, low], [25]);
console.log('Mass(25):', outputs[0]);

// State continuation
const n = high.length - 5;
const [, state2] = ti.mass.indicator([high.slice(0, n), low.slice(0, n)], [25]);
const continued = state2.batchIndicator([high.slice(n), low.slice(n)]);
console.log('Continued Mass:', continued[0]);

SIMD

By assets — same options, N assets in parallel:

use tulip_rs::indicators::mass::indicator_by_assets;

let inputs: [&[&[f64]; 2]; 4] = [
    &[h1.as_slice(), l1.as_slice()],
    &[h2.as_slice(), l2.as_slice()],
    &[h3.as_slice(), l3.as_slice()],
    &[h4.as_slice(), l4.as_slice()],
];
let results = indicator_by_assets::<4>(&inputs, &[25.0], None).unwrap();

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

use tulip_rs::indicators::mass::indicator_by_options;

let opts: [&[f64; 1]; 4] = [&[15.0], &[20.0], &[25.0], &[30.0]];
let results = indicator_by_options::<4>(&inputs_single, &opts, None).unwrap();

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

simd_inputs = [[h1, l1], [h2, l2], [h3, l3], [h4, l4]]
outputs_list, states = tulip_rs.indicators.mass.simd_by_assets(simd_inputs, [25.0])

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

simd_options = [[15.0], [20.0], [25.0], [30.0]]
outputs_list, states = tulip_rs.indicators.mass.simd_by_options([high, low], simd_options)

By assets — same period applied to 4 assets in parallel:

const simdInputs = [
    [[...high], [...low]],
    [high.map(v => v * 1.1), low.map(v => v * 1.1)],
    [high.map(v => v * 0.9), low.map(v => v * 0.9)],
    [high.map(v => v * 1.02), low.map(v => v * 1.02)],
];
const [results] = ti.mass.simdByAssets(simdInputs, [25]);
results.forEach((out, i) => console.log(`Asset ${i + 1}:`, out[0]));

By options — same asset, 4 different periods in parallel:

const simdOptions = [[15], [20], [25], [30]];
const [results] = ti.mass.simdByOptions([high, low], simdOptions);
results.forEach((out, i) => console.log(`Period ${simdOptions[i][0]}:`, out[0]));