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DM — Directional Movement

Raw directional movement values before smoothing. +DM captures upward movement; -DM captures downward movement.

Inputs: [high, low] | Options: [period] | Outputs: [plus_dm, minus_dm]

Basic

use tulip_rs::indicators::dm::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 inputs = [high.as_slice(), low.as_slice()];
let (outputs, mut state) = indicator(&inputs, &[14.0], None).unwrap();
println!("+DM: {:?}", outputs[0]);
println!("-DM: {:?}", outputs[1]);

// State continuation — feed new bars without reprocessing history
let new_high = vec![85.30_f64];
let new_low  = vec![84.60_f64];
let continued = state.batch_indicator(
    &[new_high.as_slice(), new_low.as_slice()],
    None,
).unwrap();
println!("+DM continued: {:?}", continued[0]);
println!("-DM continued: {:?}", continued[1]);

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.dm.indicator([high, low], [14.0])
print(outputs[0])  # Plus DM
print(outputs[1])  # Minus DM

# State continuation
new_high = np.array([85.30], dtype=np.float64)
new_low  = np.array([84.60], dtype=np.float64)
continued = state.batch_indicator([new_high, new_low])
print(continued[0])  # Plus DM continued
print(continued[1])  # Minus DM continued

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.dm.indicator([high, low], [14]);
console.log('+DM:', outputs[0]);
console.log('-DM:', outputs[1]);

// State continuation
const n = high.length - 5;
const [, state2] = ti.dm.indicator([high.slice(0, n), low.slice(0, n)], [14]);
const continued = state2.batchIndicator([high.slice(n), low.slice(n)]);
console.log('Continued +DM:', 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.dm.indicator([high, low], [14]);
console.log('+DM:', outputs[0]);
console.log('-DM:', outputs[1]);

// State continuation
const n = high.length - 5;
const [, state2] = ti.dm.indicator([high.slice(0, n), low.slice(0, n)], [14]);
const continued = state2.batchIndicator([high.slice(n), low.slice(n)]);
console.log('Continued +DM:', continued[0]);

SIMD

By assets — same options, N assets in parallel:

use tulip_rs::indicators::dm::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, &[14.0], None).unwrap();
for (i, asset_outputs) in results.iter().enumerate() {
    println!("Asset {} +DM: {:?}", i + 1, asset_outputs[0]);
    println!("Asset {} -DM: {:?}", i + 1, asset_outputs[1]);
}

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

use tulip_rs::indicators::dm::indicator_by_options;

let opts: [&[f64; 1]; 4] = [&[7.0], &[14.0], &[21.0], &[28.0]];
let results = indicator_by_options::<4>(&inputs, &opts, None).unwrap();
for (i, out) in results.iter().enumerate() {
    println!("Period {} +DM: {:?}", opts[i][0], out[0]);
    println!("Period {} -DM: {:?}", opts[i][0], out[1]);
}

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.dm.simd_by_assets(simd_inputs, [14.0])
for i, asset_outputs in enumerate(outputs_list):
    print(f"Asset {i+1} +DM: {asset_outputs[0]}")
    print(f"Asset {i+1} -DM: {asset_outputs[1]}")

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

simd_options = [[7.0], [14.0], [21.0], [28.0]]
outputs_list, states = tulip_rs.indicators.dm.simd_by_options([high, low], simd_options)
for i, out in enumerate(outputs_list):
    print(f"Period {simd_options[i][0]} +DM: {out[0]}")
    print(f"Period {simd_options[i][0]} -DM: {out[1]}")

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.dm.simdByAssets(simdInputs, [14]);
results.forEach((out, i) => console.log(`Asset ${i + 1} +DM:`, out[0], '-DM:', out[1]));

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

const simdOptions = [[7], [14], [21], [28]];
const [results] = ti.dm.simdByOptions([high, low], simdOptions);
results.forEach((out, i) => console.log(`Period ${simdOptions[i][0]} +DM:`, out[0], '-DM:', out[1]));