RSI — Relative Strength Index

Measures the speed and magnitude of price changes, oscillating between 0 and 100. Values above 70 suggest overbought conditions; values below 30 suggest oversold conditions.

Inputs: [real]  |  Options: [period]  |  Outputs: [rsi]

Basic

RustCGoJavaPythonNode.jsWASM

use tulip_rs::indicators::rsi::{Rsi, TIndicatorState, 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 (outputs, _state) = Rsi::indicator(&[close.as_slice()], &[14.0], None).unwrap();
println!("RSI(14): {:?}", outputs[0]);

// State continuation
let partial = close[..8].to_vec();
let (outputs2, mut state) = Rsi::indicator(&[partial.as_slice()], &[14.0], None).unwrap();
println!("Partial RSI: {:?}", outputs2[0]);

let new_close = close[8..].to_vec();
let continued = state.batch_indicator(&[new_close.as_slice()], None).unwrap();
println!("Continued RSI: {:?}", continued[0]);

#include "tulip_rs_ffi.h"

double close[] = {81.59, 81.06, 82.87, 83.00, 83.61,
                  83.15, 82.84, 83.99, 84.55, 84.36};
double options[RSI_OPTIONS] = {14.0};
const double *inputs[RSI_INPUTS] = {close};

/* Full computation */
CIndicatorResult r = rsi_indicator(inputs, 10, options, NULL, 0);
/* r.outputs[0] -> the RSI(14) series, length r.output_lens[0] */
tulip_ffi_result_free(r);
rsi_state_free(r.state);

/* Partial computation + state continuation */
CIndicatorResult p = rsi_indicator(inputs, 8, options, NULL, 0);
double new_close[] = {84.55, 84.36};
const double *new_inputs[RSI_INPUTS] = {new_close};
CBatchResult b = rsi_batch(p.state, new_inputs, 2, NULL, 0);
/* b.outputs[0] -> RSI values for just the two new bars */
tulip_ffi_batch_result_free(b);
tulip_ffi_result_free(p);
rsi_state_free(p.state);

import "github.com/me60732/tulip_rs_go/indicators"

close := []float64{81.59, 81.06, 82.87, 83.00, 83.61,
                   83.15, 82.84, 83.99, 84.55, 84.36}
options := []float64{14.0}

// Full computation — Rows are zero-copy views, valid until Close.
res, st, _ := indicators.Rsi.Indicator(close, options, nil)
fmt.Println(res.Rows[0]) // RSI(14) values
res.Close()
st.Close()

// Partial computation + state continuation.
res2, st2, _ := indicators.Rsi.Indicator(close[:8], options, nil)
res2.Close() // outputs consumed or closed; state stays live
batch, _ := st2.Batch(close[8:], nil)
fmt.Println(batch.Rows[0]) // continued RSI values
batch.Close()
st2.Close()

import org.tuliprs.*;
import org.tuliprs.indicators.Rsi;

double[] close = {81.59, 81.06, 82.87, 83.00, 83.61,
                  83.15, 82.84, 83.99, 84.55, 84.36};
double[] options = {14.0};

// Full computation — output rows are zero-copy views, valid until close().
Outcome oc = Rsi.indicator(new double[][] {close}, options);
try (Result res = oc.result(); State st = oc.state()) {
    System.out.println(java.util.Arrays.toString(res.toDoubleArray(0))); // RSI(14) values
}

// Partial computation + state continuation.
Outcome p = Rsi.indicator(
    new double[][] {java.util.Arrays.copyOfRange(close, 0, 8)}, options);
try (Result pr = p.result(); State st = p.state()) {
    Result br = st.batch(
        new double[][] {java.util.Arrays.copyOfRange(close, 8, 10)});
    try (br) {
        System.out.println(java.util.Arrays.toString(br.toDoubleArray(0))); // continued RSI values
    }
}

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)

outputs, state = tulip_rs.indicators.rsi.indicator([close], [14.0])
print("RSI(14):", outputs[0])

# State continuation
partial = close[:8]
outputs2, state = tulip_rs.indicators.rsi.indicator([partial], [14.0])
new_close = close[8:]
continued = state.batch_indicator([new_close])
print("Continued RSI:", continued[0])

import * as ti from 'tulip-rs-node';

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.rsi.indicator([close], [14]);
console.log('RSI(14):', outputs[0]);

// State continuation
const [, state2] = ti.rsi.indicator([close.slice(0, -5)], [14]);
const continued = state2.batchIndicator([close.slice(-5)]);
console.log('Continued RSI:', continued[0]);

import { init } from 'tulip-rs-wasm';
import * as ti from 'tulip-rs-wasm';

await init(); // bundler resolves the WASM asset automatically

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.rsi.indicator([close], [14]);
console.log('RSI(14):', outputs[0]);

// State continuation
const [, state2] = ti.rsi.indicator([close.slice(0, -5)], [14]);
const continued = state2.batchIndicator([close.slice(-5)]);
console.log('Continued RSI:', continued[0]);

SIMD

RustCGoJavaPythonNode.js

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

use tulip_rs::indicators::rsi::{Rsi, Indicator};

let a1 = vec![81.59, 81.06, 82.87, 83.00, 83.61, 83.15, 82.84, 83.99, 84.55, 84.36_f64];
let a2 = vec![72.10, 72.85, 73.40, 73.00, 74.20, 74.85, 75.10, 75.60, 76.00, 76.50_f64];
let a3 = vec![55.30, 55.80, 56.10, 56.40, 56.90, 57.20, 57.50, 57.80, 58.10, 58.40_f64];
let a4 = vec![100.1, 100.5, 101.0, 101.3, 101.8, 102.0, 102.5, 103.0, 103.3, 103.8_f64];

let inputs: [&[&[f64]; 1]; 4] = [
    &[a1.as_slice()],
    &[a2.as_slice()],
    &[a3.as_slice()],
    &[a4.as_slice()],
];

let results = Rsi::indicator_by_assets::<4>(&inputs, &[14.0], None).unwrap();
for (i, asset_outputs) in results.iter().enumerate() {
    println!("Asset {}: {:?}", i + 1, asset_outputs[0]);
}

By options — same asset, 4 different periods in parallel:

use tulip_rs::indicators::rsi::{Rsi, IndicatorByOptions};

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 opts: [&[f64; 1]; 4] = [&[7.0], &[14.0], &[21.0], &[28.0]];

let results = Rsi::indicator_by_options::<4>(&[close.as_slice()], &opts, None).unwrap();
for (i, opt_outputs) in results.iter().enumerate() {
    println!("Period set {}: {:?}", i + 1, opt_outputs[0]);
}

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

double a1[] = {81.59, 81.06, 82.87, 83.00, 83.61, 83.15, 82.84, 83.99, 84.55, 84.36};
double a2[] = {72.10, 72.85, 73.40, 73.00, 74.20, 74.85, 75.10, 75.60, 76.00, 76.50};
double a3[] = {55.30, 55.80, 56.10, 56.40, 56.90, 57.20, 57.50, 57.80, 58.10, 58.40};
double a4[] = {100.1, 100.5, 101.0, 101.3, 101.8, 102.0, 102.5, 103.0, 103.3, 103.8};

const double *asset1[RSI_INPUTS] = {a1};
const double *asset2[RSI_INPUTS] = {a2};
const double *asset3[RSI_INPUTS] = {a3};
const double *asset4[RSI_INPUTS] = {a4};
const double *const *const simd_inputs[4] = {asset1, asset2, asset3, asset4};

CSimdResult r = rsi_simd_by_assets(simd_inputs, 4, 10, options, NULL, 0);
for (uintptr_t i = 0; i < r.num_results; i++) {
    /* r.outputs[i][0] -> asset i's RSI series */
    rsi_state_free(r.states[i]);
}
tulip_ffi_simd_result_free(r);

By options — same asset, 4 different periods in parallel:

double close[] = {81.59, 81.06, 82.87, 83.00, 83.61,
                  83.15, 82.84, 83.99, 84.55, 84.36};

/* Tile the series 20x so longer-period option sets have enough data */
#define EXPANDED_LEN (10 * 20)
static double close_expanded[EXPANDED_LEN];
for (size_t i = 0; i < 20; i++) {
    for (size_t j = 0; j < 10; j++) {
        close_expanded[i * 10 + j] = close[j];
    }
}
const double *expanded_inputs[RSI_INPUTS] = {close_expanded};

static const double o7[RSI_OPTIONS]  = {7.0};
static const double o14[RSI_OPTIONS] = {14.0};
static const double o21[RSI_OPTIONS] = {21.0};
static const double o28[RSI_OPTIONS] = {28.0};
const double *const simd_opts[4] = {o7, o14, o21, o28};

CSimdResult r2 = rsi_simd_by_options(expanded_inputs, EXPANDED_LEN, simd_opts, 4, NULL, 0);
for (uintptr_t i = 0; i < r2.num_results; i++) {
    /* r2.outputs[i][0] -> RSI for period simd_opts[i] */
    rsi_state_free(r2.states[i]);
}
tulip_ffi_simd_result_free(r2);

By assets — same period applied to 4 assets in parallel (lane counts 2/4/8/16):

c1 := []float64{81.59, 81.06, 82.87, 83.00, 83.61, 83.15, 82.84, 83.99, 84.55, 84.36}

// Reuse the same data for assets 2–4 in this example
c2 := c1
c3 := c1
c4 := c1

assets := [][indicators.RsiInputs][]float64{{c1}, {c2}, {c3}, {c4}}
sim, _ := indicators.Rsi.SimdByAssets(assets, []float64{14.0}, nil)
for i, lanes := range sim.Results {
    fmt.Printf("Asset %d: %v\n", i+1, lanes[0])
}
sim.Close() // frees every lane state, then the SIMD buffers

By options — same asset, 4 different periods in parallel:

close := []float64{81.59, 81.06, 82.87, 83.00, 83.61,
                   83.15, 82.84, 83.99, 84.55, 84.36}

sim2, _ := indicators.Rsi.SimdByOptions(close, [][]float64{{7}, {14}, {21}, {28}}, nil)
for i, lanes := range sim2.Results {
    fmt.Printf("Period set %d: %v\n", i+1, lanes[0])
}
sim2.Close()

By assets — same period applied to 4 assets in parallel (N must be 2, 4, 8, or 16):

import org.tuliprs.*;
import org.tuliprs.indicators.Rsi;

double[] a1 = {81.59, 81.06, 82.87, 83.00, 83.61, 83.15, 82.84, 83.99, 84.55, 84.36};
double[] a2 = {72.10, 72.85, 73.40, 73.00, 74.20, 74.85, 75.10, 75.60, 76.00, 76.50};
double[] a3 = {55.30, 55.80, 56.10, 56.40, 56.90, 57.20, 57.50, 57.80, 58.10, 58.40};
double[] a4 = {100.1, 100.5, 101.0, 101.3, 101.8, 102.0, 102.5, 103.0, 103.3, 103.8};

// One entry per asset; each asset lists its INPUTS series.
double[][][] assets = {{a1}, {a2}, {a3}, {a4}};
try (SimdResult sim = Rsi.simdByAssets(assets, new double[] {14.0}, null)) {
    for (int i = 0; i < sim.numResults(); i++) {
        System.out.printf("Asset %d: %s%n", i + 1,
            java.util.Arrays.toString(sim.toDoubleArray(i, 0)));    }
}   // frees every lane state, then the SIMD buffers (contractual order)

By options — same asset, 4 different periods in parallel:

import org.tuliprs.*;
import org.tuliprs.indicators.Rsi;

double[] close = {81.59, 81.06, 82.87, 83.00, 83.61,
                  83.15, 82.84, 83.99, 84.55, 84.36};

try (SimdResult sim = Rsi.simdByOptions(new double[][] {close},
        new double[][] {{7}, {14}, {21}, {28}}, null)) {
    for (int i = 0; i < sim.numResults(); i++) {
        System.out.printf("Period set %d: %s%n", i + 1,
            java.util.Arrays.toString(sim.toDoubleArray(i, 0)));    }
}

By assets — same period applied to N assets in parallel (must be 2, 4, 8, or 16):

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)

simd_inputs = [[close], [close + 5.0], [close - 5.0], [close * 1.02]]
outputs_list, states = tulip_rs.indicators.rsi.simd_by_assets(simd_inputs, [14.0])
for i, out in enumerate(outputs_list):
    print(f"Asset {i + 1}: {out[0]}")

By options — same asset, N different periods in parallel:

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)

simd_options = [[7.0], [14.0], [21.0], [28.0]]
outputs_list, states = tulip_rs.indicators.rsi.simd_by_options([close], simd_options)
for i, out in enumerate(outputs_list):
    print(f"Period set {i + 1}: {out[0]}")

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

const simdInputs = [
    [close.slice()],
    [close.map(v => v * 1.1)],
    [close.map(v => v * 0.9)],
    [close.map(v => v * 1.02)],
];
const [results] = ti.rsi.simdByAssets(simdInputs, [14]);
results.forEach((out, i) => console.log(`Asset ${i + 1}:`, out[0]));

By options — same asset, 4 different periods in parallel:

const simdOptions = [[7], [14], [21], [28]];
const [results] = ti.rsi.simdByOptions([close], simdOptions);
results.forEach((out, i) => console.log(`Period ${simdOptions[i][0]}:`, out[0]));