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PSAR — Parabolic SAR

A trailing stop-and-reverse indicator. The SAR dot flips below or above price to signal trend direction.

Inputs: [high, low] | Options: [acceleration_factor_step, acceleration_factor_maximum] | Outputs: [psar]

Basic

use tulip_rs::indicators::psar::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];

// options: [acceleration_factor_step, acceleration_factor_maximum]
let inputs = [high.as_slice(), low.as_slice()];
let (outputs, mut state) = indicator(&inputs, &[0.02, 0.2], None).unwrap();
println!("{:?}", outputs[0]); // PSAR values

// 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!("{:?}", continued[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)

# options: [acceleration_factor_step, acceleration_factor_maximum]
outputs, state = tulip_rs.indicators.psar.indicator([high, low], [0.02, 0.2])
print(outputs[0])  # PSAR values

# 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])

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.psar.indicator([high, low], [0.02, 0.2]);
console.log('PSAR:', outputs[0]);

// State continuation
const n = high.length - 5;
const [, state2] = ti.psar.indicator([high.slice(0, n), low.slice(0, n)], [0.02, 0.2]);
const continued = state2.batchIndicator([high.slice(n), low.slice(n)]);
console.log('Continued PSAR:', 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.psar.indicator([high, low], [0.02, 0.2]);
console.log('PSAR:', outputs[0]);

// State continuation
const n = high.length - 5;
const [, state2] = ti.psar.indicator([high.slice(0, n), low.slice(0, n)], [0.02, 0.2]);
const continued = state2.batchIndicator([high.slice(n), low.slice(n)]);
console.log('Continued PSAR:', continued[0]);

SIMD

By assets — same options, N assets in parallel:

use tulip_rs::indicators::psar::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, &[0.02, 0.2], 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::psar::indicator_by_options;

let opts: [&[f64; 2]; 4] = [&[0.01, 0.1], &[0.02, 0.2], &[0.03, 0.3], &[0.04, 0.4]];
let results = indicator_by_options::<4>(&inputs, &opts, None).unwrap();
for (i, out) in results.iter().enumerate() {
    println!("Step/Max {}/{}: {:?}", opts[i][0], opts[i][1], out[0]);
}

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.psar.simd_by_assets(simd_inputs, [0.02, 0.2])
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 = [[0.01, 0.1], [0.02, 0.2], [0.03, 0.3], [0.04, 0.4]]
outputs_list, states = tulip_rs.indicators.psar.simd_by_options([high, low], simd_options)
for i, out in enumerate(outputs_list):
    print(f"Step/Max {simd_options[i][0]}/{simd_options[i][1]}: {out[0]}")

By assets — same options 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.psar.simdByAssets(simdInputs, [0.02, 0.2]);
results.forEach((out, i) => console.log(`Asset ${i + 1}:`, out[0]));

By options — same asset, 4 different option sets in parallel:

const simdOptions = [[0.01, 0.1], [0.02, 0.2], [0.03, 0.3], [0.04, 0.4]];
const [results] = ti.psar.simdByOptions([high, low], simdOptions);
results.forEach((out, i) => console.log(`Step ${simdOptions[i][0]}:`, out[0]));