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MD — Mean Deviation — md

The mean of the absolute deviations of each bar from the rolling mean over period bars. Similar to standard deviation but uses absolute rather than squared differences.

Inputs: [real] | Options: [period] | Outputs: [md]

Basic

use tulip_rs::indicators::md::{Md, Indicator, TIndicatorState};

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 (outputs, mut state) = Md::indicator(&[close.as_slice()], &[14.0], None).unwrap();
println!("{:?}", outputs[0]);

// State continuation — feed new bars without reprocessing history
let partial = close[..8].to_vec();
let (outputs2, mut state) = Md::indicator(&[partial.as_slice()], &[14.0], None).unwrap();
println!("{:?}", outputs2[0]);

let new_close = vec![85.53_f64];
let continued = state.batch_indicator(&[new_close.as_slice()], None).unwrap();
println!("{:?}", continued[0]);

outputs, state = tulip_rs.indicators.md.indicator([close], [14.0])
print(outputs[0])

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

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.md.indicator([close], [14]);
console.log('MD(14):', outputs[0]);

// State continuation
const [, state2] = ti.md.indicator([close.slice(0, -5)], [14]);
const continued = state2.batchIndicator([close.slice(-5)]);
console.log('Continued MD:', 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.md.indicator([close], [14]);
console.log('MD(14):', outputs[0]);

// State continuation
const [, state2] = ti.md.indicator([close.slice(0, -5)], [14]);
const continued = state2.batchIndicator([close.slice(-5)]);
console.log('Continued MD:', continued[0]);

Optional Outputs

md exposes 1 optional output: sma. Pass a boolean mask as the third argument — one bool per optional output, in order.

use tulip_rs::indicators::md::{Md, Indicator, TIndicatorState};

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]; // one per optional output
let (outputs, _state) = Md::indicator(&[close.as_slice()], &[10.0], Some(&mask)).unwrap();

let md  = &outputs[0]; // md (primary)
let sma = &outputs[1]; // sma (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.md.indicator(
    [close], [10.0],
    optional_outputs=[True],
)

md  = outputs[0]  # md (primary)
sma = outputs[1]  # sma (optional — requested)

md exposes 1 optional output: sma.

const [allOut] = ti.md.indicator([close], [14], [true]);
const md  = allOut[0]; // primary
const sma = allOut[1]; // optional 0: sma

The WASM API is identical to Node.js — pass the boolean mask as the third argument.

const [allOut] = ti.md.indicator([close], [14], [true]);
const md  = allOut[0]; // primary
const sma = allOut[1]; // optional 0: sma

SIMD

By assets — same options, N assets in parallel:

use tulip_rs::indicators::md::{Md, Indicator};

let inputs: [&[&[f64]; 1]; 4] = [&[a1.as_slice()], &[a2.as_slice()], &[a3.as_slice()], &[a4.as_slice()]];
let results = Md::indicator_by_assets::<4>(&inputs, &[14.0], 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::md::{Md, IndicatorByOptions};

let opts: [&[f64; 1]; 4] = [&[7.0], &[14.0], &[21.0], &[28.0]];
let results = Md::indicator_by_options::<4>(&[close.as_slice()], &opts, None).unwrap();
for (i, asset_outputs) in results.iter().enumerate() {
    println!("Option {}: {:?}", i + 1, asset_outputs[0]);
}

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

simd_inputs = [[a1], [a2], [a3], [a4]]
outputs_list, states = tulip_rs.indicators.md.simd_by_assets(simd_inputs, [14.0])

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.md.simd_by_options([close], simd_options)

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

const simdInputs = [[close.slice()], [close.map(v => v * 1.1)], [close.map(v => v * 0.9)], [close.map(v => v * 1.02)]];
const [results] = ti.md.simdByAssets(simdInputs, [14]);
results.forEach((out, i) => console.log(`Asset ${i + 1}:`, out[0]));

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

const simdOptions = [[7], [14], [21], [28]];
const [results] = ti.md.simdByOptions([close], simdOptions);
results.forEach((out, i) => console.log(`Period ${simdOptions[i][0]}:`, out[0]));