Mass Index — mass¶
Uses the high-low trading range to identify potential trend reversals via range expansion. Watch for values rising above 27 then falling below 26.5 — this "reversal bulge" signals a likely trend change.
Inputs: [high, low] | Options: [period] | Outputs: [mass]
Basic¶
use tulip_rs::indicators::mass::{Mass, Indicator, TIndicatorState};
let high = [82.15_f64, 81.89, 83.03, 83.30, 83.85, 83.90, 83.33, 84.30, 84.84, 85.00];
let low = [81.29_f64, 80.64, 81.31, 82.65, 83.07, 83.11, 82.49, 82.30, 84.15, 84.11];
let inputs = [high.as_slice(), low.as_slice()];
let (outputs, mut state) = Mass::indicator(&inputs, &[25.0], None).unwrap();
println!("{:?}", outputs[0]);
// State continuation — feed new bars without reprocessing history
let partial_high = high[..8].to_vec();
let partial_low = low[..8].to_vec();
let (outputs2, mut state) = Mass::indicator(&[partial_high.as_slice(), partial_low.as_slice()], &[25.0], None).unwrap();
println!("{:?}", outputs2[0]);
let new_high = vec![86.54_f64];
let new_low = vec![85.39_f64];
let continued = state.batch_indicator(&[new_high.as_slice(), new_low.as_slice()], None).unwrap();
println!("{:?}", 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};
double low[] = {81.29, 80.64, 81.31, 82.65, 83.07, 83.11, 82.49, 82.30, 84.15, 84.11};
const double *inputs[MASS_INPUTS] = {high, low};
double options[MASS_OPTIONS] = {25.0}; // period
/* Full computation */
CIndicatorResult r = mass_indicator(inputs, 10, options, NULL, 0);
/* r.outputs[0] -> the MASS(25) series, length r.output_lens[0] */
tulip_ffi_result_free(r);
mass_state_free(r.state);
/* Partial computation + state continuation */
CIndicatorResult p = mass_indicator(inputs, 8, options, NULL, 0);
double new_high[] = {86.54};
double new_low[] = {85.39};
const double *new_inputs[MASS_INPUTS] = {new_high, new_low};
CBatchResult b = mass_batch(p.state, new_inputs, 1, NULL, 0);
/* b.outputs[0] -> MASS values for just the one new bar */
tulip_ffi_batch_result_free(b);
tulip_ffi_result_free(p);
mass_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}
low := []float64{81.29, 80.64, 81.31, 82.65, 83.07, 83.11, 82.49, 82.30, 84.15, 84.11}
options := []float64{25.0}
// Full computation — Rows are zero-copy views, valid until Close.
res, st, _ := indicators.Mass.Indicator(high, low, options, nil)
fmt.Println(res.Rows[0]) // Mass(25) values
res.Close()
st.Close()
// Partial computation + state continuation.
res2, st2, _ := indicators.Mass.Indicator(high[:8], low[:8], options, nil)
res2.Close() // outputs consumed or closed; state stays live
batch, _ := st2.Batch(high[8:], low[8:], nil)
fmt.Println(batch.Rows[0]) // continued Mass values
batch.Close()
st2.Close()
import org.tuliprs.*;
import org.tuliprs.indicators.Mass;
double[] high = {82.15, 81.89, 83.03, 83.30, 83.85, 83.90, 83.33, 84.30, 84.84, 85.00};
double[] low = {81.29, 80.64, 81.31, 82.65, 83.07, 83.11, 82.49, 82.30, 84.15, 84.11};
double[] options = {25.0};
// Full computation — output rows are zero-copy views, valid until close().
Outcome oc = Mass.indicator(new double[][] {high, low}, options);
try (Result res = oc.result(); State st = oc.state()) {
System.out.println(java.util.Arrays.toString(res.toDoubleArray(0))); // Mass(25) values
}
// Partial computation + state continuation.
int n = 8;
Outcome p = Mass.indicator(new double[][] {
java.util.Arrays.copyOfRange(high, 0, n),
java.util.Arrays.copyOfRange(low, 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)});
try (br) {
System.out.println(java.util.Arrays.toString(br.toDoubleArray(0))); // continued Mass
}
}
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], 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], dtype=np.float64)
outputs, state = tulip_rs.indicators.mass.indicator([high, low], [25.0])
print(outputs[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]);
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]);
const [outputs, state] = ti.mass.indicator([high, low], [25]);
console.log('Mass(25):', outputs[0]);
// State continuation
const n = high.length - 5;
const [, state2] = ti.mass.indicator([high.slice(0, n), low.slice(0, n)], [25]);
const continued = state2.batchIndicator([high.slice(n), low.slice(n)]);
console.log('Continued Mass:', 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];
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];
const [outputs, state] = ti.mass.indicator([high, low], [25]);
console.log('Mass(25):', outputs[0]);
// State continuation
const n = high.length - 5;
const [, state2] = ti.mass.indicator([high.slice(0, n), low.slice(0, n)], [25]);
const continued = state2.batchIndicator([high.slice(n), low.slice(n)]);
console.log('Continued Mass:', continued[0]);
SIMD¶
By assets — same options, N assets in parallel:
By assets — same period applied to 4 assets in parallel (N must be 2/4/8/16):
double h1[] = {82.15, 81.89, 83.03, 83.30, 83.85, 83.90, 83.33, 84.30, 84.84, 85.00};
double l1[] = {81.29, 80.64, 81.31, 82.65, 83.07, 83.11, 82.49, 82.30, 84.15, 84.11};
double h2[] = {82.15*1.2, 81.89*1.2, 83.03*1.2, 83.30*1.2, 83.85*1.2, 83.90*1.2, 83.33*1.2, 84.30*1.2, 84.84*1.2, 85.00*1.2};
double l2[] = {81.29*1.2, 80.64*1.2, 81.31*1.2, 82.65*1.2, 83.07*1.2, 83.11*1.2, 82.49*1.2, 82.30*1.2, 84.15*1.2, 84.11*1.2};
double h3[] = {90.0+0.5*0+82.15*0.1, 90.0+0.5*1+82.15*0.1, 90.0+0.5*2+83.03*0.1, 90.0+0.5*3+83.30*0.1, 90.0+0.5*4+83.85*0.1, 90.0+0.5*5+83.90*0.1, 90.0+0.5*6+83.33*0.1, 90.0+0.5*7+84.30*0.1, 90.0+0.5*8+84.84*0.1, 90.0+0.5*9+85.00*0.1};
double l3[] = {90.0+0.5*0+81.29*0.1, 90.0+0.5*1+80.64*0.1, 90.0+0.5*2+81.31*0.1, 90.0+0.5*3+82.65*0.1, 90.0+0.5*4+83.07*0.1, 90.0+0.5*5+83.11*0.1, 90.0+0.5*6+82.49*0.1, 90.0+0.5*7+82.30*0.1, 90.0+0.5*8+84.15*0.1, 90.0+0.5*9+84.11*0.1};
double h4[] = {100.0-0.3*0+82.15*0.05, 100.0-0.3*1+81.89*0.05, 100.0-0.3*2+83.03*0.05, 100.0-0.3*3+83.30*0.05, 100.0-0.3*4+83.85*0.05, 100.0-0.3*5+83.90*0.05, 100.0-0.3*6+83.33*0.05, 100.0-0.3*7+84.30*0.05, 100.0-0.3*8+84.84*0.05, 100.0-0.3*9+85.00*0.05};
double l4[] = {100.0-0.3*0+81.29*0.05, 100.0-0.3*1+80.64*0.05, 100.0-0.3*2+81.31*0.05, 100.0-0.3*3+82.65*0.05, 100.0-0.3*4+83.07*0.05, 100.0-0.3*5+83.11*0.05, 100.0-0.3*6+82.49*0.05, 100.0-0.3*7+82.30*0.05, 100.0-0.3*8+84.15*0.05, 100.0-0.3*9+84.11*0.05};
/* one [INPUTS]-long pointer array per asset */
const double *asset1[MASS_INPUTS] = {h1, l1};
const double *asset2[MASS_INPUTS] = {h2, l2};
const double *asset3[MASS_INPUTS] = {h3, l3};
const double *asset4[MASS_INPUTS] = {h4, l4};
const double *const *const simd_inputs[4] = {asset1, asset2, asset3, asset4};
CSimdResult r = mass_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] */
mass_state_free(r.states[i]);
}
tulip_ffi_simd_result_free(r);
By options — same asset, 4 different periods in parallel:
double o15[] = {15.0}, o20[] = {20.0}, o25[] = {25.0}, o30[] = {30.0};
const double *const simd_opts[4] = {o15, o20, o25, o30};
CSimdResult r = mass_simd_by_options(inputs, 10, simd_opts, 4, NULL, 0);
/* r.outputs[i] -> results for option set i (periods 15/20/25/30) */
for (uintptr_t i = 0; i < r.num_results; i++) mass_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):
h1 := []float64{82.15, 81.89, 83.03, 83.30, 83.85, 83.90, 83.33, 84.30, 84.84, 85.00}
l1 := []float64{81.29, 80.64, 81.31, 82.65, 83.07, 83.11, 82.49, 82.30, 84.15, 84.11}
// Reuse the same data for assets 2–4 in this example
h2, l2 := h1, l1
h3, l3 := h1, l1
h4, l4 := h1, l1
assets := [][indicators.MassInputs][]float64{{h1, l1}, {h2, l2}, {h3, l3}, {h4, l4}}
sim, _ := indicators.Mass.SimdByAssets(assets, []float64{25.0}, 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:
high := []float64{82.15, 81.89, 83.03, 83.30, 83.85, 83.90, 83.33, 84.30, 84.84, 85.00}
low := []float64{81.29, 80.64, 81.31, 82.65, 83.07, 83.11, 82.49, 82.30, 84.15, 84.11}
sim2, _ := indicators.Mass.SimdByOptions(high, low, [][]float64{{15}, {20}, {25}, {30}}, 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.Mass;
double[] h1 = {82.15, 81.89, 83.03, 83.30, 83.85, 83.90, 83.33, 84.30, 84.84, 85.00};
double[] l1 = {81.29, 80.64, 81.31, 82.65, 83.07, 83.11, 82.49, 82.30, 84.15, 84.11};
// Reuse the same data for assets 2–4 in this example
double[] h2 = h1;
double[] l2 = l1;
double[] h3 = h1;
double[] l3 = l1;
double[] h4 = h1;
double[] l4 = l1;
// One entry per asset; each asset lists its INPUTS series.
double[][][] assets = {{h1, l1}, {h2, l2}, {h3, l3}, {h4, l4}};
try (SimdResult sim = Mass.simdByAssets(assets, new double[] {25.0}, 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.Mass;
double[] high = {82.15, 81.89, 83.03, 83.30, 83.85, 83.90, 83.33, 84.30, 84.84, 85.00};
double[] low = {81.29, 80.64, 81.31, 82.65, 83.07, 83.11, 82.49, 82.30, 84.15, 84.11};
try (SimdResult sim = Mass.simdByOptions(new double[][] {high, low},
new double[][] {{15}, {20}, {25}, {30}}, 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 options, N assets in parallel (must be 2, 4, 8, or 16):
simd_inputs = [[h1, l1], [h2, l2], [h3, l3], [h4, l4]]
outputs_list, states = tulip_rs.indicators.mass.simd_by_assets(simd_inputs, [25.0])
By options — same asset, N option sets in parallel:
By assets — same period applied to 4 assets in parallel:
const simdInputs = [
[high.slice(), low.slice()],
[high.map(v => v * 1.1), low.map(v => v * 1.1)],
[high.map(v => v * 0.9), low.map(v => v * 0.9)],
[high.map(v => v * 1.02), low.map(v => v * 1.02)],
];
const [results] = ti.mass.simdByAssets(simdInputs, [25]);
results.forEach((out, i) => console.log(`Asset ${i + 1}:`, out[0]));
By options — same asset, 4 different periods in parallel: