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Ichimoku — Ichimoku Kinkō Hyō

A comprehensive trend-following system that defines support/resistance, trend direction, and momentum in a single glance; the conversion and base lines act like short- and long-period midpoint averages, while the leading spans project a "cloud" forward by long_period bars.

Inputs: [high, low, close]  |  Options: [short_period, long_period]  |  Outputs: [conversion, base, leading_span_a, leading_span_b]  |  Optional: [lagging_span]

Basic

use tulip_rs::indicators::ichimoku::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: [short_period, long_period]
let inputs = [high.as_slice(), low.as_slice(), close.as_slice()];
let (outputs, _state) = indicator(&inputs, &[9.0, 26.0], None).unwrap();
println!("Conversion:     {:?}", outputs[0]);
println!("Base:           {:?}", outputs[1]);
println!("Leading Span A: {:?}", outputs[2]);
println!("Leading Span B: {:?}", outputs[3]);

// State continuation
let n = high.len() - 5;
let partial_inputs = [&high[..n], &low[..n], &close[..n]];
let (outputs2, mut state) = indicator(&partial_inputs, &[9.0, 26.0], None).unwrap();
println!("Partial Conversion: {:?}", outputs2[0]);

let rest_inputs = [&high[n..], &low[n..], &close[n..]];
let continued = state.batch_indicator(&rest_inputs, None).unwrap();
println!("Continued Conversion: {:?}", 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: [short_period, long_period]
outputs, state = tulip_rs.indicators.ichimoku.indicator([high, low, close], [9.0, 26.0])
print("Conversion:    ", outputs[0])
print("Base:          ", outputs[1])
print("Leading Span A:", outputs[2])
print("Leading Span B:", outputs[3])

# State continuation
n = len(high) - 5
outputs2, state = tulip_rs.indicators.ichimoku.indicator(
    [high[:n], low[:n], close[:n]], [9.0, 26.0]
)
print("Partial Conversion:", outputs2[0])

continued = state.batch_indicator([high[n:], low[n:], close[n:]])
print("Continued Conversion:", 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: [short_period, long_period]
const [outputs, state] = ti.ichimoku.indicator([high, low, close], [9, 26]);
console.log('Conversion:    ', outputs[0]);
console.log('Base:          ', outputs[1]);
console.log('Leading Span A:', outputs[2]);
console.log('Leading Span B:', outputs[3]);

// State continuation
const n = high.length - 5;
const [, state2] = ti.ichimoku.indicator([high.slice(0, n), low.slice(0, n), close.slice(0, n)], [9, 26]);
const continued = state2.batchIndicator([high.slice(n), low.slice(n), close.slice(n)]);
console.log('Continued Conversion:', 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.ichimoku.indicator([high, low, close], [9, 26]);
console.log('Conversion:    ', outputs[0]);
console.log('Base:          ', outputs[1]);
console.log('Leading Span A:', outputs[2]);
console.log('Leading Span B:', outputs[3]);

// State continuation
const n = high.length - 5;
const [, state2] = ti.ichimoku.indicator([high.slice(0, n), low.slice(0, n), close.slice(0, n)], [9, 26]);
const continued = state2.batchIndicator([high.slice(n), low.slice(n), close.slice(n)]);
console.log('Continued Conversion:', continued[0]);

Optional Outputs

ichimoku exposes 1 optional output: lagging_span. The lagging span is the close price shifted back by long_period bars, useful for confirming trend signals against historical price. Pass a boolean mask as the third argument — one bool per optional output, in order.

use tulip_rs::indicators::ichimoku::indicator;

// ... (same high, low, close data as above)
let mask = [true];
let (outputs, _state) = indicator(
    &[high.as_slice(), low.as_slice(), close.as_slice()],
    &[9.0, 26.0],
    Some(&mask),
).unwrap();

let conversion   = &outputs[0]; // conversion (primary)
let base         = &outputs[1]; // base (primary)
let leading_a    = &outputs[2]; // leading_span_a (primary)
let leading_b    = &outputs[3]; // leading_span_b (primary)
let lagging_span = &outputs[4]; // lagging_span (optional — requested)

import numpy as np
import tulip_rs

# ... (same high, low, close data as above)
outputs, state = tulip_rs.indicators.ichimoku.indicator(
    [high, low, close], [9.0, 26.0],
    optional_outputs=[True],
)

conversion   = outputs[0]  # conversion (primary)
base         = outputs[1]  # base (primary)
leading_a    = outputs[2]  # leading_span_a (primary)
leading_b    = outputs[3]  # leading_span_b (primary)
lagging_span = outputs[4]  # lagging_span (optional — requested)

ichimoku exposes 1 optional output: lagging_span.

const [allOut] = ti.ichimoku.indicator([high, low, close], [9, 26], [true]);
const conversion  = allOut[0]; // primary
const base        = allOut[1]; // primary
const leadingA    = allOut[2]; // primary
const leadingB    = allOut[3]; // primary
const laggingSpan = allOut[4]; // optional 0: lagging_span

The WASM API is identical to Node.js — pass the boolean mask as the third argument.

const [allOut] = ti.ichimoku.indicator([high, low, close], [9, 26], [true]);
const conversion  = allOut[0]; // primary
const base        = allOut[1]; // primary
const leadingA    = allOut[2]; // primary
const leadingB    = allOut[3]; // primary
const laggingSpan = allOut[4]; // optional 0: lagging_span

SIMD

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

use tulip_rs::indicators::ichimoku::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, &[9.0, 26.0], None).unwrap();
for (i, asset_outputs) in results.0.iter().enumerate() {
    println!("Asset {} Conversion: {:?}", i + 1, asset_outputs[0]);
    println!("Asset {} Base:       {:?}", i + 1, asset_outputs[1]);
}

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

use tulip_rs::indicators::ichimoku::indicator_by_options;

let opts: [&[f64; 2]; 4] = [&[7.0, 22.0], &[9.0, 26.0], &[11.0, 30.0], &[13.0, 34.0]];
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!("Short/Long {}/{}: Conversion={:?}", 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.ichimoku.simd_by_assets(simd_inputs, [9.0, 26.0])
for i, out in enumerate(outputs_list):
    print(f"Asset {i + 1} Conversion: {out[0]}")
    print(f"Asset {i + 1} Base:       {out[1]}")

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

simd_options = [[7.0, 22.0], [9.0, 26.0], [11.0, 30.0], [13.0, 34.0]]
outputs_list, states = tulip_rs.indicators.ichimoku.simd_by_options(
    [high, low, close], simd_options
)
for i, out in enumerate(outputs_list):
    print(f"Short/Long {simd_options[i][0]}/{simd_options[i][1]}: {out[0]}")

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.ichimoku.simdByAssets(simdInputs, [9, 26]);
results.forEach((out, i) => {
    console.log(`Asset ${i + 1} Conversion:`, out[0]);
    console.log(`Asset ${i + 1} Base:      `, out[1]);
});

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

const simdOptions = [[7, 22], [9, 26], [11, 30], [13, 34]];
const [results] = ti.ichimoku.simdByOptions([high, low, close], simdOptions);
results.forEach((out, i) => console.log(`Short/Long ${simdOptions[i][0]}/${simdOptions[i][1]}:`, out[0]));