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StdDev — Standard Deviation

Rolling standard deviation of the price series over period bars.

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

Basic

use tulip_rs::indicators::stddev::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 (outputs, mut state) = indicator(&[close.as_slice()], &[20.0], None).unwrap();
println!("{:?}", outputs[0]); // StdDev 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)

outputs, state = tulip_rs.indicators.stddev.indicator([close], [20.0])
print(outputs[0])  # StdDev 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.stddev.indicator([close], [20]);
console.log('StdDev(20):', outputs[0]);

// State continuation
const [, state2] = ti.stddev.indicator([close.slice(0, -5)], [20]);
const continued = state2.batchIndicator([close.slice(-5)]);
console.log('Continued StdDev:', 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.stddev.indicator([close], [20]);
console.log('StdDev(20):', outputs[0]);

// State continuation
const [, state2] = ti.stddev.indicator([close.slice(0, -5)], [20]);
const continued = state2.batchIndicator([close.slice(-5)]);
console.log('Continued StdDev:', continued[0]);

Optional Outputs

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

use tulip_rs::indicators::stddev::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]; // request sma
let (outputs, _state) = indicator(&[close.as_slice()], &[5.0], Some(&mask)).unwrap();

let stddev = &outputs[0]; // stddev (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.stddev.indicator(
    [close], [5.0],
    optional_outputs=[True],
)

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

stddev exposes 1 optional output: sma.

const [allOut] = ti.stddev.indicator([close], [20], [true]);
const stddev = allOut[0]; // primary
const sma    = allOut[1]; // optional 0: sma

SIMD

By assets — same options, N assets in parallel:

use tulip_rs::indicators::stddev::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, &[20.0], 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::stddev::indicator_by_options;

let opts: [&[f64; 1]; 4] = [&[10.0], &[20.0], &[30.0], &[50.0]];
let results = indicator_by_options::<4>(&[close.as_slice()], &opts, None).unwrap();
for (i, out) in results.iter().enumerate() {
    println!("Period {}: {:?}", opts[i][0], 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.stddev.simd_by_assets(simd_inputs, [20.0])
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 = [[10.0], [20.0], [30.0], [50.0]]
outputs_list, states = tulip_rs.indicators.stddev.simd_by_options([close], simd_options)
for i, out in enumerate(outputs_list):
    print(f"Period {simd_options[i][0]}: {out[0]}")

By assets — same period 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.stddev.simdByAssets(simdInputs, [20]);
results.forEach((out, i) => console.log(`Asset ${i + 1}:`, out[0]));

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

const simdOptions = [[10], [20], [30], [50]];
const [results] = ti.stddev.simdByOptions([close], simdOptions);
results.forEach((out, i) => console.log(`Period ${simdOptions[i][0]}:`, out[0]));