Super Trend¶
A trend-following overlay that plots above price in a downtrend and below price in an uptrend; direction flips when price crosses the band. Built on ATR-based dynamic bands, it adapts volatility automatically.
Inputs: [high, low, close] | Options: [period, step] | Outputs: [supertrend] | Optional: [atr, tr, medprice]
Basic¶
use tulip_rs::indicators::supertrend::indicator;
let high = vec![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, 88.20, 88.70, 89.10, 88.50, 89.00,
89.60, 89.90, 89.30, 90.10, 90.50, 91.00, 90.30, 91.00, 91.60, 92.00,
91.30, 92.00, 92.60, 93.00, 92.30, 93.00, 93.60, 94.00, 93.30, 94.10_f64];
let low = vec![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, 87.20, 87.80, 88.20, 87.60, 88.00,
88.60, 88.90, 88.30, 89.00, 89.40, 89.80, 89.20, 89.90, 90.50, 90.80,
90.20, 90.90, 91.50, 91.80, 91.20, 91.90, 92.50, 92.80, 92.20, 93.00_f64];
let close = vec![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, 87.50, 88.10, 88.50, 87.90, 88.20,
88.80, 89.10, 88.70, 89.30, 89.70, 90.10, 89.50, 90.20, 90.80, 91.10,
90.50, 91.20, 91.80, 92.10, 91.50, 92.20, 92.80, 93.10, 92.50, 93.20_f64];
// options: [period, step] — step is the ATR multiplier
let inputs = [high.as_slice(), low.as_slice(), close.as_slice()];
let (outputs, _state) = indicator(&inputs, &[10.0, 3.0], None).unwrap();
println!("Super Trend: {:?}", outputs[0]);
// State continuation
let n = high.len() - 5;
let partial_inputs = [&high[..n], &low[..n], &close[..n]];
let (outputs2, mut state) = indicator(&partial_inputs, &[10.0, 3.0], None).unwrap();
println!("Partial Super Trend: {:?}", outputs2[0]);
let rest_inputs = [&high[n..], &low[n..], &close[n..]];
let continued = state.batch_indicator(&rest_inputs, None).unwrap();
println!("Continued Super Trend: {:?}", 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,
85.90, 86.58, 86.98, 88.00, 87.87, 88.20, 88.70, 89.10, 88.50, 89.00,
89.60, 89.90, 89.30, 90.10, 90.50, 91.00, 90.30, 91.00, 91.60, 92.00,
91.30, 92.00, 92.60, 93.00, 92.30, 93.00, 93.60, 94.00, 93.30, 94.10], 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,
84.03, 85.39, 85.76, 87.17, 87.01, 87.20, 87.80, 88.20, 87.60, 88.00,
88.60, 88.90, 88.30, 89.00, 89.40, 89.80, 89.20, 89.90, 90.50, 90.80,
90.20, 90.90, 91.50, 91.80, 91.20, 91.90, 92.50, 92.80, 92.20, 93.00], 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,
85.53, 86.54, 86.89, 87.77, 87.29, 87.50, 88.10, 88.50, 87.90, 88.20,
88.80, 89.10, 88.70, 89.30, 89.70, 90.10, 89.50, 90.20, 90.80, 91.10,
90.50, 91.20, 91.80, 92.10, 91.50, 92.20, 92.80, 93.10, 92.50, 93.20], dtype=np.float64)
# options: [period, step] — step is the ATR multiplier
outputs, state = tulip_rs.indicators.supertrend.indicator([high, low, close], [10.0, 3.0])
print("Super Trend:", outputs[0])
# State continuation
n = len(high) - 5
outputs2, state = tulip_rs.indicators.supertrend.indicator(
[high[:n], low[:n], close[:n]], [10.0, 3.0]
)
print("Partial Super Trend:", outputs2[0])
continued = state.batch_indicator([high[n:], low[n:], close[n:]])
print("Continued Super Trend:", continued[0])
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, 88.20, 88.70, 89.10, 88.50, 89.00,
89.60, 89.90, 89.30, 90.10, 90.50, 91.00, 90.30, 91.00, 91.60, 92.00,
91.30, 92.00, 92.60, 93.00, 92.30, 93.00, 93.60, 94.00, 93.30, 94.10]);
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, 87.20, 87.80, 88.20, 87.60, 88.00,
88.60, 88.90, 88.30, 89.00, 89.40, 89.80, 89.20, 89.90, 90.50, 90.80,
90.20, 90.90, 91.50, 91.80, 91.20, 91.90, 92.50, 92.80, 92.20, 93.00]);
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, 87.50, 88.10, 88.50, 87.90, 88.20,
88.80, 89.10, 88.70, 89.30, 89.70, 90.10, 89.50, 90.20, 90.80, 91.10,
90.50, 91.20, 91.80, 92.10, 91.50, 92.20, 92.80, 93.10, 92.50, 93.20]);
// options: [period, step] — step is the ATR multiplier
const [outputs, state] = ti.supertrend.indicator([high, low, close], [10, 3.0]);
console.log('Super Trend:', outputs[0]);
// State continuation
const n = high.length - 5;
const [, state2] = ti.supertrend.indicator([high.slice(0, n), low.slice(0, n), close.slice(0, n)], [10, 3.0]);
const continued = state2.batchIndicator([high.slice(n), low.slice(n), close.slice(n)]);
console.log('Continued Super Trend:', 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, 88.20, 88.70, 89.10, 88.50, 89.00,
89.60, 89.90, 89.30, 90.10, 90.50, 91.00, 90.30, 91.00, 91.60, 92.00,
91.30, 92.00, 92.60, 93.00, 92.30, 93.00, 93.60, 94.00, 93.30, 94.10];
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, 87.20, 87.80, 88.20, 87.60, 88.00,
88.60, 88.90, 88.30, 89.00, 89.40, 89.80, 89.20, 89.90, 90.50, 90.80,
90.20, 90.90, 91.50, 91.80, 91.20, 91.90, 92.50, 92.80, 92.20, 93.00];
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, 87.50, 88.10, 88.50, 87.90, 88.20,
88.80, 89.10, 88.70, 89.30, 89.70, 90.10, 89.50, 90.20, 90.80, 91.10,
90.50, 91.20, 91.80, 92.10, 91.50, 92.20, 92.80, 93.10, 92.50, 93.20];
const [outputs, state] = ti.supertrend.indicator([high, low, close], [10, 3.0]);
console.log('Super Trend:', outputs[0]);
// State continuation
const n = high.length - 5;
const [, state2] = ti.supertrend.indicator([high.slice(0, n), low.slice(0, n), close.slice(0, n)], [10, 3.0]);
const continued = state2.batchIndicator([high.slice(n), low.slice(n), close.slice(n)]);
console.log('Continued Super Trend:', continued[0]);
Optional Outputs¶
supertrend exposes 3 optional outputs: atr, tr, medprice. Pass a boolean mask as the third argument — one bool per optional output, in order.
use tulip_rs::indicators::supertrend::indicator;
// ... (same high, low, close data as above)
let mask = [true, true, true];
let (outputs, _state) = indicator(
&[high.as_slice(), low.as_slice(), close.as_slice()],
&[10.0, 3.0],
Some(&mask),
).unwrap();
let supertrend = &outputs[0]; // supertrend (primary)
let atr = &outputs[1]; // atr (optional — requested)
let tr = &outputs[2]; // tr (optional — requested)
let medprice = &outputs[3]; // medprice (optional — requested)
import numpy as np
import tulip_rs
# ... (same high, low, close data as above)
outputs, state = tulip_rs.indicators.supertrend.indicator(
[high, low, close], [10.0, 3.0],
optional_outputs=[True, True, True],
)
supertrend = outputs[0] # supertrend (primary)
atr = outputs[1] # atr (optional — requested)
tr = outputs[2] # tr (optional — requested)
medprice = outputs[3] # medprice (optional — requested)
supertrend exposes 3 optional outputs: atr, tr, medprice.
The WASM API is identical to Node.js — pass the boolean mask as the third argument.
SIMD¶
By assets — same options applied to 4 assets in parallel:
use tulip_rs::indicators::supertrend::indicator_by_assets;
let h1 = high.clone(); let l1 = low.clone(); let c1 = close.clone();
let h2 = h1.clone(); let l2 = l1.clone(); let c2 = c1.clone();
let h3 = h1.clone(); let l3 = l1.clone(); let c3 = c1.clone();
let h4 = h1.clone(); let l4 = l1.clone(); let c4 = c1.clone();
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, &[10.0, 3.0], None).unwrap();
for (i, asset_outputs) in results.0.iter().enumerate() {
println!("Asset {}: {:?}", i + 1, asset_outputs[0]);
}
By options — same asset, 4 different option sets in parallel:
use tulip_rs::indicators::supertrend::indicator_by_options;
let opts: [&[f64; 2]; 4] = [&[7.0, 2.0], &[10.0, 3.0], &[14.0, 3.5], &[20.0, 4.0]];
let inputs = [high.as_slice(), low.as_slice(), close.as_slice()];
let results = indicator_by_options::<4>(&inputs, &opts, None).unwrap();
for (i, out) in results.0.iter().enumerate() {
println!("Period/Step {}/{}: {:?}", opts[i][0], opts[i][1], out[0]);
}
By assets — same options applied to N assets in parallel (must be 2, 4, 8, or 16):
import numpy as np
import tulip_rs
simd_inputs = [
[high, low, close],
[high + 0.5, low + 0.5, close + 0.5],
[high - 0.5, low - 0.5, close - 0.5],
[high * 1.01, low * 1.01, close * 1.01],
]
outputs_list, states = tulip_rs.indicators.supertrend.simd_by_assets(simd_inputs, [10.0, 3.0])
for i, out in enumerate(outputs_list):
print(f"Asset {i + 1}: {out[0]}")
By options — same asset, N different option sets in parallel:
By assets — same options 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.supertrend.simdByAssets(simdInputs, [10, 3.0]);
results.forEach((out, i) => console.log(`Asset ${i + 1}:`, out[0]));
By options — same asset, 4 different option sets in parallel: