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MSW — Mesa Sine Wave

Fits a sine wave to the recent price data over period bars. The crossover of the sine and lead (phase-advanced) lines can signal cycle turns and potential entry/exit points.

Inputs: [real]  |  Options: [period]  |  Outputs: [msw_sine, msw_lead]

Basic

use tulip_rs::indicators::msw::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, _state) = indicator(&[close.as_slice()], &[10.0], None).unwrap();
println!("MSW Sine: {:?}", outputs[0]);
println!("MSW Lead: {:?}", outputs[1]);

// State continuation
let partial = close[..8].to_vec();
let (outputs2, mut state) = indicator(&[partial.as_slice()], &[10.0], None).unwrap();
println!("Partial MSW Sine: {:?}", outputs2[0]);
println!("Partial MSW Lead: {:?}", outputs2[1]);

let new_close = close[8..].to_vec();
let continued = state.batch_indicator(&[new_close.as_slice()], None).unwrap();
println!("Continued MSW Sine: {:?}", continued[0]);
println!("Continued MSW Lead: {:?}", continued[1]);

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.msw.indicator([close], [10.0])
print("MSW Sine:", outputs[0])
print("MSW Lead:", outputs[1])

# State continuation
partial = close[:8]
outputs2, state = tulip_rs.indicators.msw.indicator([partial], [10.0])
new_close = close[8:]
continued = state.batch_indicator([new_close])
print("Continued MSW Sine:", continued[0])
print("Continued MSW Lead:", continued[1])

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.msw.indicator([close], [5]);
console.log('MSW Sine:', outputs[0]);
console.log('MSW Lead:', outputs[1]);

// State continuation
const [, state2] = ti.msw.indicator([close.slice(0, -5)], [5]);
const continued = state2.batchIndicator([close.slice(-5)]);
console.log('Continued Sine:', continued[0], 'Lead:', continued[1]);

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.msw.indicator([close], [5]);
console.log('MSW Sine:', outputs[0]);
console.log('MSW Lead:', outputs[1]);

// State continuation
const [, state2] = ti.msw.indicator([close.slice(0, -5)], [5]);
const continued = state2.batchIndicator([close.slice(-5)]);
console.log('Continued Sine:', continued[0], 'Lead:', continued[1]);

SIMD

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

use tulip_rs::indicators::msw::indicator_by_assets;

let a1 = vec![81.59, 81.06, 82.87, 83.00, 83.61, 83.15, 82.84, 83.99, 84.55, 84.36_f64];
let a2 = vec![72.10, 72.85, 73.40, 73.00, 74.20, 74.85, 75.10, 75.60, 76.00, 76.50_f64];
let a3 = vec![55.30, 55.80, 56.10, 56.40, 56.90, 57.20, 57.50, 57.80, 58.10, 58.40_f64];
let a4 = vec![100.1, 100.5, 101.0, 101.3, 101.8, 102.0, 102.5, 103.0, 103.3, 103.8_f64];

let inputs: [&[&[f64]; 1]; 4] = [
    &[a1.as_slice()],
    &[a2.as_slice()],
    &[a3.as_slice()],
    &[a4.as_slice()],
];

let results = indicator_by_assets::<4>(&inputs, &[10.0], None).unwrap();
for (i, asset_outputs) in results.0.iter().enumerate() {
    println!("Asset {} Sine: {:?}", i + 1, asset_outputs[0]);
    println!("Asset {} Lead: {:?}", i + 1, asset_outputs[1]);
}

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

use tulip_rs::indicators::msw::indicator_by_options;

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 opts: [&[f64; 1]; 4] = [&[5.0], &[10.0], &[14.0], &[20.0]];

let results = indicator_by_options::<4>(&[close.as_slice()], &opts, None).unwrap();
for (i, opt_outputs) in results.0.iter().enumerate() {
    println!("Option set {} Sine: {:?}", i + 1, opt_outputs[0]);
    println!("Option set {} Lead: {:?}", i + 1, opt_outputs[1]);
}

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

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)

simd_inputs = [[close], [close + 5.0], [close - 5.0], [close * 1.02]]
outputs_list, states = tulip_rs.indicators.msw.simd_by_assets(simd_inputs, [10.0])
for i, out in enumerate(outputs_list):
    print(f"Asset {i + 1} Sine: {out[0]}")
    print(f"Asset {i + 1} Lead: {out[1]}")

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

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)

simd_options = [[5.0], [10.0], [14.0], [20.0]]
outputs_list, states = tulip_rs.indicators.msw.simd_by_options([close], simd_options)
for i, out in enumerate(outputs_list):
    print(f"Option set {i + 1} Sine: {out[0]}")
    print(f"Option set {i + 1} Lead: {out[1]}")

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.msw.simdByAssets(simdInputs, [5]);
results.forEach((out, i) => console.log(`Asset ${i + 1} Sine:`, out[0], 'Lead:', out[1]));

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

const simdOptions = [[5], [10], [14], [20]];
const [results] = ti.msw.simdByOptions([close], simdOptions);
results.forEach((out, i) => console.log(`Option set ${i + 1} Sine:`, out[0], 'Lead:', out[1]));