Vortex¶
Identifies trend direction and strength. VM+ = |high − prev_low|, VM− = |low − prev_high|. vi_up = sum(VM+, period) / sum(TR, period), vi_down = sum(VM−, period) / sum(TR, period). vi_up > vi_down signals a bullish trend; the reverse indicates a bearish trend. Optionally emits the raw True Range series.
Inputs: [high, low, close] | Options: [period] | Outputs: [vi_up, vi_down]
Basic¶
use tulip_rs::indicators::vortex::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 close = vec![81.59, 81.06, 82.87, 83.00, 83.61,
83.15, 82.84, 83.99, 84.55, 84.36_f64];
let inputs = [high.as_slice(), low.as_slice(), close.as_slice()];
let (outputs, mut state) = indicator(&inputs, &[14.0], None).unwrap();
println!("VI+: {:?}", outputs[0]); // vi_up values
println!("VI-: {:?}", outputs[1]); // vi_down values
// State continuation — feed new bars without reprocessing history
let new_high = vec![85.20_f64];
let new_low = vec![84.50_f64];
let new_close = vec![85.00_f64];
let continued = state.batch_indicator(
&[new_high.as_slice(), new_low.as_slice(), new_close.as_slice()],
None,
).unwrap();
println!("VI+ continued: {:?}", continued[0]);
println!("VI- 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)
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.vortex.indicator([high, low, close], [14.0])
print(outputs[0]) # VI+ values
print(outputs[1]) # VI- values
# State continuation
new_high = np.array([85.20], dtype=np.float64)
new_low = np.array([84.50], dtype=np.float64)
new_close = np.array([85.00], dtype=np.float64)
continued = state.batch_indicator([new_high, new_low, new_close])
print(continued[0]) # VI+ continued
print(continued[1]) # VI- continued
import * as ti from 'tulip-rs-node';
const high = Float64Array.from([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 = Float64Array.from([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 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.vortex.indicator([high, low, close], [14]);
console.log('VI+(14):', outputs[0]);
console.log('VI-(14):', outputs[1]);
// State continuation
const n = high.length - 5;
const [, state2] = ti.vortex.indicator([high.slice(0, n), low.slice(0, n), close.slice(0, n)], [14]);
const continued = state2.batchIndicator([high.slice(n), low.slice(n), close.slice(n)]);
console.log('Continued VI+:', continued[0]);
console.log('Continued VI-:', continued[1]);
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 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.vortex.indicator([high, low, close], [14]);
console.log('VI+(14):', outputs[0]);
console.log('VI-(14):', outputs[1]);
// State continuation
const n = high.length - 5;
const [, state2] = ti.vortex.indicator([high.slice(0, n), low.slice(0, n), close.slice(0, n)], [14]);
const continued = state2.batchIndicator([high.slice(n), low.slice(n), close.slice(n)]);
console.log('Continued VI+:', continued[0]);
console.log('Continued VI-:', continued[1]);
Optional Outputs¶
vortex exposes 1 optional output: tr. Pass a boolean mask as the third argument — one bool per optional output, in order.
use tulip_rs::indicators::vortex::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 high = close.iter().map(|x| x + 1.0).collect::<Vec<_>>();
let low = close.iter().map(|x| x - 1.0).collect::<Vec<_>>();
let mask = [true];
let (outputs, _state) = indicator(
&[high.as_slice(), low.as_slice(), close.as_slice()],
&[14.0],
Some(&mask),
).unwrap();
let vi_up = &outputs[0]; // vi_up (primary)
let vi_down = &outputs[1]; // vi_down (primary)
let tr = &outputs[2]; // tr (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)
high = close + 1.0
low = close - 1.0
outputs, state = tulip_rs.indicators.vortex.indicator(
[high, low, close], [14.0],
optional_outputs=[True],
)
vi_up = outputs[0] # vi_up (primary)
vi_down = outputs[1] # vi_down (primary)
tr = outputs[2] # tr (optional — requested)
vortex exposes 1 optional output: tr.
SIMD¶
By assets — same options, N assets in parallel:
use tulip_rs::indicators::vortex::indicator_by_assets;
let inputs: [&[&[f64]; 3]; 4] = [
&[h1.as_slice(), l1.as_slice(), c1.as_slice()],
&[h2.as_slice(), l2.as_slice(), c2.as_slice()],
&[h3.as_slice(), l3.as_slice(), c3.as_slice()],
&[h4.as_slice(), l4.as_slice(), c4.as_slice()],
];
let results = indicator_by_assets::<4>(&inputs, &[14.0], None).unwrap();
for (i, asset_outputs) in results.iter().enumerate() {
println!("Asset {} VI+: {:?}", i + 1, asset_outputs[0]);
println!("Asset {} VI-: {:?}", i + 1, asset_outputs[1]);
}
By options — same asset, N option sets in parallel:
use tulip_rs::indicators::vortex::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 {} VI+: {:?}", opts[i][0], out[0]);
println!("Period {} VI-: {:?}", opts[i][0], out[1]);
}
By assets — same options, N assets in parallel (must be 2, 4, 8, or 16):
simd_inputs = [
[h1, l1, c1],
[h2, l2, c2],
[h3, l3, c3],
[h4, l4, c4],
]
outputs_list, states = tulip_rs.indicators.vortex.simd_by_assets(simd_inputs, [14.0])
for i, asset_outputs in enumerate(outputs_list):
print(f"Asset {i+1} VI+: {asset_outputs[0]}")
print(f"Asset {i+1} VI-: {asset_outputs[1]}")
By options — same asset, N option sets in parallel:
By assets — same period applied to 4 assets in parallel:
const simdInputs = [
[high.slice(), low.slice(), close.slice()],
[high.map(v => v * 1.1), low.map(v => v * 1.1), close.map(v => v * 1.1)],
[high.map(v => v * 0.9), low.map(v => v * 0.9), close.map(v => v * 0.9)],
[high.map(v => v * 1.02), low.map(v => v * 1.02), close.map(v => v * 1.02)],
];
const [results] = ti.vortex.simdByAssets(simdInputs, [14]);
results.forEach((out, i) => console.log(`Asset ${i + 1} VI+:`, out[0], 'VI-:', out[1]));
By options — same asset, 4 different periods in parallel: