Min — Lowest Value Over Period — `min`¶

The lowest value in the input series over a rolling period window.

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

Basic¶

RustCGoJavaPythonNode.jsWASM

use tulip_rs::indicators::min::{Min};

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, mut state) = Min::indicator(&[close.as_slice()], &[14.0], None).unwrap();
println!("{:?}", outputs[0]);

// State continuation — feed new bars without reprocessing history
let partial = close[..8].to_vec();
let (outputs2, mut state) = Min::indicator(&[partial.as_slice()], &[14.0], None).unwrap();
println!("{:?}", outputs2[0]);

let new_close = vec![85.53_f64];
let continued = state.batch_indicator(&[new_close.as_slice()], None).unwrap();
println!("{:?}", 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};
const double *inputs[MIN_INPUTS] = {close};
double options[MIN_OPTIONS] = {14.0}; // period

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

/* Partial computation + state continuation */
CIndicatorResult p = min_indicator(inputs, 8, options, NULL, 0);
double new_close[] = {85.53};
const double *new_inputs[MIN_INPUTS] = {new_close};
CBatchResult b = min_batch(p.state, new_inputs, 1, NULL, 0);
/* b.outputs[0] -> MIN values for just the one new bar */
tulip_ffi_batch_result_free(b);
tulip_ffi_result_free(p);
min_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.Min.Indicator(close, options, nil)
fmt.Println(res.Rows[0]) // Min(14) values
res.Close()
st.Close()

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

import org.tuliprs.*;
import org.tuliprs.indicators.Min;

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}; // period

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

// Partial computation + state continuation.
Outcome p = Min.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 Min values
    }
}

outputs, state = tulip_rs.indicators.min.indicator([close], [14.0])
print(outputs[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.min.indicator([close], [14]);
console.log('Min(14):', outputs[0]);

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

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

SIMD¶

RustCGoJavaPythonNode.js

By assets — same options, N assets in parallel:

use tulip_rs::indicators::min::{Min, Indicator};

let inputs: [&[&[f64]; 1]; 4] = [&[a1.as_slice()], &[a2.as_slice()], &[a3.as_slice()], &[a4.as_slice()]];
let results = Min::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 assets — same period applied to 4 assets in parallel (N must be 2/4/8/16):

double a1[] = {81.59, 81.06, 82.87, 83.00, 83.61, 83.15, 82.84, 83.99, 84.55, 84.36};
double a2[] = {81.59*1.2, 81.06*1.2, 82.87*1.2, 83.00*1.2, 83.61*1.2, 83.15*1.2, 82.84*1.2, 83.99*1.2, 84.55*1.2, 84.36*1.2};
double a3[] = {90.0+0.5*0+81.59*0.1, 90.0+0.5*1+81.06*0.1, 90.0+0.5*2+82.87*0.1, 90.0+0.5*3+83.00*0.1, 90.0+0.5*4+83.61*0.1, 90.0+0.5*5+83.15*0.1, 90.0+0.5*6+82.84*0.1, 90.0+0.5*7+83.99*0.1, 90.0+0.5*8+84.55*0.1, 90.0+0.5*9+84.36*0.1};
double a4[] = {100.0-0.3*0+81.59*0.05, 100.0-0.3*1+81.06*0.05, 100.0-0.3*2+82.87*0.05, 100.0-0.3*3+83.00*0.05, 100.0-0.3*4+83.61*0.05, 100.0-0.3*5+83.15*0.05, 100.0-0.3*6+82.84*0.05, 100.0-0.3*7+83.99*0.05, 100.0-0.3*8+84.55*0.05, 100.0-0.3*9+84.36*0.05};

/* one [INPUTS]-long pointer array per asset */
const double *asset1[MIN_INPUTS] = {a1};
const double *asset2[MIN_INPUTS] = {a2};
const double *asset3[MIN_INPUTS] = {a3};
const double *asset4[MIN_INPUTS] = {a4};
const double *const *const simd_inputs[4] = {asset1, asset2, asset3, asset4};

CSimdResult r = min_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 series, length r.output_lens[i][0] */
    min_state_free(r.states[i]);
}
tulip_ffi_simd_result_free(r);

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

double o5[] = {5.0}, o10[] = {10.0}, o20[] = {20.0}, o50[] = {50.0};
const double *const simd_opts[4] = {o5, o10, o20, o50};

CSimdResult r = min_simd_by_options(inputs, 10, simd_opts, 4, NULL, 0);
/* r.outputs[i] -> results for option set i (periods 5/10/20/50) */
for (uintptr_t i = 0; i < r.num_results; i++) min_state_free(r.states[i]);
tulip_ffi_simd_result_free(r);

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

a1 := []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
a2, a3, a4 := a1, a1, a1

assets := [][indicators.MinInputs][]float64{{a1}, {a2}, {a3}, {a4}}
sim, _ := indicators.Min.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.Min.SimdByOptions(close, [][]float64{{5}, {10}, {20}, {50}}, 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 (lane counts 2/4/8/16):

import org.tuliprs.*;
import org.tuliprs.indicators.Min;

double[] a1 = {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
double[] a2 = a1;
double[] a3 = a1;
double[] a4 = a1;

// One entry per asset; each asset lists its INPUTS series.
double[][][] assets = {{a1}, {a2}, {a3}, {a4}};
try (SimdResult sim = Min.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))); // Min(14) values
    }
}   // 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.Min;

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 = Min.simdByOptions(new double[][] {close},
        new double[][] {{5}, {10}, {20}, {50}}, 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))); // Min values
    }
}

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

simd_inputs = [[a1], [a2], [a3], [a4]]
outputs_list, states = tulip_rs.indicators.min.simd_by_assets(simd_inputs, [14.0])

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

simd_options = [[5.0], [10.0], [20.0], [50.0]]
outputs_list, states = tulip_rs.indicators.min.simd_by_options([close], simd_options)

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.min.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 = [[5], [10], [20], [50]];
const [results] = ti.min.simdByOptions([close], simdOptions);
results.forEach((out, i) => console.log(`Period ${simdOptions[i][0]}:`, out[0]));

Min — Lowest Value Over Period — min¶

Basic¶

SIMD¶

Min — Lowest Value Over Period — `min`¶