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::{Ichimoku, Indicator, TIndicatorState};
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, mut state) = Ichimoku::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 — feed new bars without reprocessing history
let partial_high = high[..8].to_vec();
let partial_low = low[..8].to_vec();
let partial_close = close[..8].to_vec();
let (outputs2, mut state) = Ichimoku::indicator(&[partial_high.as_slice(), partial_low.as_slice(), partial_close.as_slice()], &[9.0, 26.0], None).unwrap();
println!("Conversion: {:?}", outputs2[0]);
let new_high = vec![85.90_f64];
let new_low = vec![84.03_f64];
let new_close = vec![85.53_f64];
let continued = state.batch_indicator(&[new_high.as_slice(), new_low.as_slice(), new_close.as_slice()], None).unwrap();
println!("Continued Conversion: {:?}", continued[0]);
#include "tulip_rs_ffi.h"
double 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};
double 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};
double 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};
double options[ICHIMOKU_OPTIONS] = {9.0, 26.0}; // short_period, long_period
const double *inputs[ICHIMOKU_INPUTS] = {high, low, close};
/* Full computation */
CIndicatorResult r = ichimoku_indicator(inputs, 40, options, NULL, 0);
tulip_ffi_result_free(r);
ichimoku_state_free(r.state);
/* Partial + continuation */
CIndicatorResult p = ichimoku_indicator(inputs, 35, options, NULL, 0);
const double *rest_inputs[ICHIMOKU_INPUTS] = {high + 35, low + 35, close + 35};
CBatchResult b = ichimoku_batch(p.state, rest_inputs, 5, NULL, 0);
tulip_ffi_batch_result_free(b);
tulip_ffi_result_free(p);
ichimoku_state_free(p.state);
import "github.com/me60732/tulip_rs_go/indicators"
high := []float64{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}
low := []float64{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}
close := []float64{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 := []float64{9.0, 26.0} // short_period, long_period
// Full computation — Rows are zero-copy views, valid until Close.
res, st, _ := indicators.Ichimoku.Indicator(high, low, close, options, nil)
fmt.Println(res.Rows[0]) // conversion
fmt.Println(res.Rows[1]) // base
fmt.Println(res.Rows[2]) // leading_span_a
fmt.Println(res.Rows[3]) // leading_span_b
res.Close()
st.Close()
// Partial computation + state continuation.
res2, st2, _ := indicators.Ichimoku.Indicator(high[:8], low[:8], close[:8], options, nil)
res2.Close() // outputs consumed or closed; state stays live
batch, _ := st2.Batch(high[8:], low[8:], close[8:], nil)
fmt.Println(batch.Rows[0]) // continued conversion
batch.Close()
st2.Close()
import org.tuliprs.*;
import org.tuliprs.indicators.Ichimoku;
double[] 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};
double[] 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};
double[] 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};
double[] options = {9.0, 26.0}; // short_period, long_period
// Full computation — output rows are zero-copy views, valid until close().
Outcome oc = Ichimoku.indicator(new double[][] {high, low, close}, options);
try (Result res = oc.result(); State st = oc.state()) {
System.out.println(java.util.Arrays.toString(res.toDoubleArray(0))); // conversion
System.out.println(java.util.Arrays.toString(res.toDoubleArray(1))); // base
System.out.println(java.util.Arrays.toString(res.toDoubleArray(2))); // leading_span_a
System.out.println(java.util.Arrays.toString(res.toDoubleArray(3))); // leading_span_b
}
// Partial computation + state continuation.
int n = 8;
Outcome p = Ichimoku.indicator(new double[][] {
java.util.Arrays.copyOfRange(high, 0, n),
java.util.Arrays.copyOfRange(low, 0, n),
java.util.Arrays.copyOfRange(close, 0, n)}, options);
try (Result pr = p.result(); State st = p.state()) {
Result br = st.batch(new double[][] {
java.util.Arrays.copyOfRange(high, n, 10),
java.util.Arrays.copyOfRange(low, n, 10),
java.util.Arrays.copyOfRange(close, n, 10)});
try (br) {
System.out.println(java.util.Arrays.toString(br.toDoubleArray(0))); // continued conversion
}
}
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::{Ichimoku, Indicator, TIndicatorState};
// ... (same high, low, close data as above)
let mask = [true];
let (outputs, _state) = Ichimoku::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)
ichimoku exposes 1 optional output: lagging_span. Pass a boolean mask as the third argument.
#include "tulip_rs_ffi.h"
// ... (same high, low, close data as above)
bool optional_outputs[1] = {true}; // lagging_span
CIndicatorResult r = ichimoku_indicator(inputs, 40, options, optional_outputs, 1);
/* r.outputs[0..3] -> primary outputs (conversion, base, leading_a, leading_b) */
/* r.outputs[4] -> lagging_span (optional — requested) */
tulip_ffi_result_free(r);
ichimoku_state_free(r.state);
ichimoku exposes 1 optional output: lagging_span. Pass a boolean mask as the third argument.
import "github.com/me60732/tulip_rs_go/indicators"
// ... (same high, low, close data as above)
mask := []bool{true} // lagging_span
res, st, _ := indicators.Ichimoku.Indicator(high, low, close, options, mask)
conversion := res.Rows[0] // conversion (primary)
base := res.Rows[1] // base (primary)
leadingA := res.Rows[2] // leading_span_a (primary)
leadingB := res.Rows[3] // leading_span_b (primary)
laggingSpan := res.Rows[4] // lagging_span (optional — requested)
res.Close()
st.Close()
ichimoku exposes 1 optional output: lagging_span. Pass a boolean mask as the third argument.
import org.tuliprs.*;
import org.tuliprs.indicators.Ichimoku;
// ... (same high, low, close data as above)
double[] options = {9.0, 26.0};
boolean[] mask = {true}; // lagging_span
Outcome oc = Ichimoku.indicator(new double[][] {high, low, close}, options, mask);
try (Result res = oc.result()) {
System.out.println(java.util.Arrays.toString(res.toDoubleArray(0))); // conversion (primary)
System.out.println(java.util.Arrays.toString(res.toDoubleArray(1))); // base (primary)
System.out.println(java.util.Arrays.toString(res.toDoubleArray(2))); // leading_span_a (primary)
System.out.println(java.util.Arrays.toString(res.toDoubleArray(3))); // leading_span_b (primary)
System.out.println(java.util.Arrays.toString(res.toDoubleArray(4))); // lagging_span (optional — requested)
}
oc.state().close();
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.
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::ichimoku::{Ichimoku, Indicator};
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 = Ichimoku::indicator_by_assets::<4>(&inputs, &[9.0, 26.0], None).unwrap();
for (i, asset_outputs) in results.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::{Ichimoku, IndicatorByOptions};
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 = Ichimoku::indicator_by_options::<4>(&inputs, &opts, None).unwrap();
for (i, out) in results.iter().enumerate() {
println!("Short/Long {}/{}: Conversion={:?}", opts[i][0], opts[i][1], out[0]);
}
By assets — same options applied to 4 assets in parallel:
double h1[] = {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};
double l1[] = {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};
double c1[] = {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};
double h2[40], l2[40], c2[40];
for (int i = 0; i < 40; i++) { h2[i] = h1[i] * 1.2; l2[i] = l1[i] * 1.2; c2[i] = c1[i] * 1.2; }
double h3[40], l3[40], c3[40];
for (int i = 0; i < 40; i++) { h3[i] = 90.0 + i * 0.5 + h1[i] * 0.1; l3[i] = 90.0 + i * 0.5 + l1[i] * 0.1; c3[i] = 90.0 + i * 0.5 + c1[i] * 0.1; }
double h4[40], l4[40], c4[40];
for (int i = 0; i < 40; i++) { h4[i] = 100.0 - i * 0.3 + h1[i] * 0.05; l4[i] = 100.0 - i * 0.3 + l1[i] * 0.05; c4[i] = 100.0 - i * 0.3 + c1[i] * 0.05; }
const double *asset1[ICHIMOKU_INPUTS] = {h1, l1, c1};
const double *asset2[ICHIMOKU_INPUTS] = {h2, l2, c2};
const double *asset3[ICHIMOKU_INPUTS] = {h3, l3, c3};
const double *asset4[ICHIMOKU_INPUTS] = {h4, l4, c4};
const double *const *const simd_inputs[4] = {asset1, asset2, asset3, asset4};
CSimdResult r = ichimoku_simd_by_assets(simd_inputs, 4, 40, options, NULL, 0);
for (uintptr_t i = 0; i < r.num_results; i++) {
ichimoku_state_free(r.states[i]);
}
tulip_ffi_simd_result_free(r);
By options — same asset, 4 different option sets in parallel:
double high_expanded[400], low_expanded[400], close_expanded[400];
for (int i = 0; i < 10; i++) {
for (int j = 0; j < 40; j++) {
high_expanded[i * 40 + j] = h1[j];
low_expanded[i * 40 + j] = l1[j];
close_expanded[i * 40 + j] = c1[j];
}
}
const double *expanded_inputs[ICHIMOKU_INPUTS] = {high_expanded, low_expanded, close_expanded};
double o5_10[] = {5.0, 10.0}, o7_14[] = {7.0, 14.0}, o9_18[] = {9.0, 18.0}, o9_26[] = {9.0, 26.0};
const double *const simd_opts[4] = {o5_10, o7_14, o9_18, o9_26};
CSimdResult r = ichimoku_simd_by_options(expanded_inputs, 400, simd_opts, 4, NULL, 0);
for (uintptr_t i = 0; i < r.num_results; i++) {
ichimoku_state_free(r.states[i]);
}
tulip_ffi_simd_result_free(r);
By assets — same options applied to 4 assets in parallel (lane counts 2/4/8/16):
import "github.com/me60732/tulip_rs_go/indicators"
h1 := []float64{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}
l1 := []float64{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}
c1 := []float64{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 := []float64{9.0, 26.0}
// Reuse the same data for assets 2–4 in this example
h2, l2, c2 := h1, l1, c1
h3, l3, c3 := h1, l1, c1
h4, l4, c4 := h1, l1, c1
assets := [][indicators.IchimokuInputs][]float64{{h1, l1, c1}, {h2, l2, c2}, {h3, l3, c3}, {h4, l4, c4}}
sim, _ := indicators.Ichimoku.SimdByAssets(assets, options, nil)
for i, lanes := range sim.Results {
fmt.Printf("Asset %d Conversion: %v\n", i+1, lanes[0])
fmt.Printf("Asset %d Base: %v\n", i+1, lanes[1])
}
sim.Close() // frees every lane state, then the SIMD buffers
By options — same asset, 4 different option sets in parallel:
import "github.com/me60732/tulip_rs_go/indicators"
high := []float64{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}
low := []float64{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}
close := []float64{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}
// Tile to ensure longer-period option sets have enough data.
tiledLen := len(high) * 3
highTiled := make([]float64, tiledLen)
lowTiled := make([]float64, tiledLen)
closeTiled := make([]float64, tiledLen)
for i := 0; i < 3; i++ {
copy(highTiled[i*len(high):(i+1)*len(high)], high)
copy(lowTiled[i*len(high):(i+1)*len(high)], low)
copy(closeTiled[i*len(high):(i+1)*len(high)], close)
}
optionSets := [][]float64{{5.0, 10.0}, {7.0, 14.0}, {9.0, 18.0}, {9.0, 26.0}}
sim2, _ := indicators.Ichimoku.SimdByOptions(highTiled, lowTiled, closeTiled, optionSets, nil)
for i, lanes := range sim2.Results {
fmt.Printf("Short/Long %v/%v: Conversion=%v\n", optionSets[i][0], optionSets[i][1], lanes[0])
}
sim2.Close()
By assets — same options applied to 4 assets in parallel (N must be 2, 4, 8, or 16):
import org.tuliprs.*;
import org.tuliprs.indicators.Ichimoku;
double[] h1 = {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};
double[] l1 = {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};
double[] c1 = {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};
double[] options = {9.0, 26.0};
// Reuse the same data for assets 2–4 in this example.
double[] h2 = h1, l2 = l1, c2 = c1;
double[] h3 = h1, l3 = l1, c3 = c1;
double[] h4 = h1, l4 = l1, c4 = c1;
// One entry per asset; each asset lists its INPUTS series.
double[][][] assets = {{h1, l1, c1}, {h2, l2, c2}, {h3, l3, c3}, {h4, l4, c4}};
try (SimdResult sim = Ichimoku.simdByAssets(assets, options, null)) {
for (int i = 0; i < sim.numResults(); i++) {
System.out.printf("Asset %d Conversion: %s%n", i + 1,
java.util.Arrays.toString(sim.toDoubleArray(i, 0)));
System.out.printf("Asset %d Base: %s%n", i + 1,
java.util.Arrays.toString(sim.toDoubleArray(i, 1)));
}
} // frees every lane state, then the SIMD buffers (contractual order)
By options — same asset, 4 different option sets in parallel:
import org.tuliprs.*;
import org.tuliprs.indicators.Ichimoku;
double[] 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};
double[] 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};
double[] 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};
// Tile to ensure longer-period option sets have enough data.
int tiledLen = high.length * 3;
double[] highTiled = new double[tiledLen];
double[] lowTiled = new double[tiledLen];
double[] closeTiled = new double[tiledLen];
for (int i = 0; i < 3; i++) {
System.arraycopy(high, 0, highTiled, i * high.length, high.length);
System.arraycopy(low, 0, lowTiled, i * high.length, high.length);
System.arraycopy(close, 0, closeTiled, i * high.length, high.length);
}
double[][] optionSets = {{5.0, 10.0}, {7.0, 14.0}, {9.0, 18.0}, {9.0, 26.0}};
try (SimdResult sim = Ichimoku.simdByOptions(new double[][] {highTiled, lowTiled, closeTiled},
optionSets, null)) {
for (int i = 0; i < sim.numResults(); i++) {
System.out.printf("Short/Long %v/%v: Conversion=%s%n", optionSets[i][0], optionSets[i][1],
java.util.Arrays.toString(sim.toDoubleArray(i, 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:
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: