MACD — Moving Average Convergence Divergence¶
Shows the relationship between two EMAs of different periods. The histogram visualises the difference between the MACD line and its signal line, highlighting momentum shifts.
Inputs: [real] | Options: [fast_period, slow_period, signal_period] | Outputs: [macd_line, signal_line, histogram]
Basic¶
use tulip_rs::indicators::macd::{Macd, 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];
// Options: [fast_period, slow_period, signal_period]
let (outputs, _state) = Macd::indicator(&[close.as_slice()], &[12.0, 26.0, 9.0], None).unwrap();
println!("MACD line: {:?}", outputs[0]);
println!("Signal: {:?}", outputs[1]);
println!("Histogram: {:?}", outputs[2]);
// State continuation
let partial = close[..8].to_vec();
let (outputs2, mut state) = Macd::indicator(&[partial.as_slice()], &[12.0, 26.0, 9.0], None).unwrap();
println!("Partial MACD: {:?}", outputs2[0]);
let new_close = close[8..].to_vec();
let continued = state.batch_indicator(&[new_close.as_slice()], None).unwrap();
println!("Continued MACD: {:?}", continued[0]);
println!("Continued Signal: {:?}", continued[1]);
println!("Continued Histogram: {:?}", continued[2]);
#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[MACD_INPUTS] = {close};
double options[MACD_OPTIONS] = {12.0, 26.0, 9.0};
/* Full computation with all optional outputs */
bool optional_outputs[2] = {true, true}; /* short_ema, long_ema */
CIndicatorResult r = macd_indicator(inputs, 10, options, optional_outputs, 2);
/* r.outputs[0] -> macd_line (primary) */
/* r.outputs[1] -> signal_line (primary) */
/* r.outputs[2] -> histogram (primary) */
/* r.outputs[3] -> short_ema (optional) */
/* r.outputs[4] -> long_ema (optional) */
tulip_ffi_result_free(r);
macd_state_free(r.state);
/* Partial computation + state continuation (no optional outputs) */
CIndicatorResult p = macd_indicator(inputs, 8, options, NULL, 0);
double new_close[] = {84.55, 84.36};
const double *new_inputs[MACD_INPUTS] = {new_close};
CBatchResult b = macd_batch(p.state, new_inputs, 2, NULL, 0);
/* b.outputs[0] -> MACD line */
/* b.outputs[1] -> Signal */
/* b.outputs[2] -> Histogram */
tulip_ffi_batch_result_free(b);
tulip_ffi_result_free(p);
macd_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{12.0, 26.0, 9.0} // fastperiod,slowperiod,signalperiod
// Full computation — Rows are zero-copy views, valid until Close.
res, st, _ := indicators.Macd.Indicator(close, options, nil)
fmt.Println(res.Rows[0]) // MACD line values
fmt.Println(res.Rows[1]) // Signal line values
fmt.Println(res.Rows[2]) // Histogram values
res.Close()
st.Close()
// Partial computation + state continuation.
res2, st2, _ := indicators.Macd.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 MACD line values
fmt.Println(batch.Rows[1]) // continued Signal line values
fmt.Println(batch.Rows[2]) // continued Histogram values
batch.Close()
st2.Close()
import org.tuliprs.*;
import org.tuliprs.indicators.Macd;
double[] close = {81.59, 81.06, 82.87, 83.00, 83.61,
83.15, 82.84, 83.99, 84.55, 84.36};
double[] options = {12.0, 26.0, 9.0}; // fastperiod,slowperiod,signalperiod
// Full computation — output rows are zero-copy views, valid until close().
Outcome oc = Macd.indicator(new double[][] {close}, options);
try (Result res = oc.result(); State st = oc.state()) {
System.out.println(java.util.Arrays.toString(res.toDoubleArray(0))); // MACD line values
System.out.println(java.util.Arrays.toString(res.toDoubleArray(1))); // Signal line values
System.out.println(java.util.Arrays.toString(res.toDoubleArray(2))); // Histogram values
}
// Partial computation + state continuation.
int n = 8;
Outcome p = Macd.indicator(new double[][] {
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(close, n, 10)});
try (br) {
System.out.println(java.util.Arrays.toString(br.toDoubleArray(0))); // continued MACD line values
System.out.println(java.util.Arrays.toString(br.toDoubleArray(1))); // continued Signal line values
System.out.println(java.util.Arrays.toString(br.toDoubleArray(2))); // continued Histogram 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)
# Options: [fast_period, slow_period, signal_period]
outputs, state = tulip_rs.indicators.macd.indicator([close], [12.0, 26.0, 9.0])
print("MACD line: ", outputs[0])
print("Signal: ", outputs[1])
print("Histogram: ", outputs[2])
# State continuation
partial = close[:8]
outputs2, state = tulip_rs.indicators.macd.indicator([partial], [12.0, 26.0, 9.0])
new_close = close[8:]
continued = state.batch_indicator([new_close])
print("Continued MACD: ", continued[0])
print("Continued Signal: ", continued[1])
print("Continued Histogram: ", continued[2])
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.macd.indicator([close], [12, 26, 9]);
console.log('MACD line:', outputs[0]);
console.log('Signal:', outputs[1]);
console.log('Histogram:', outputs[2]);
// State continuation
const [, state2] = ti.macd.indicator([close.slice(0, -1)], [12, 26, 9]);
const continued = state2.batchIndicator([close.slice(-1)]);
console.log('Continued MACD:', 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.macd.indicator([close], [12, 26, 9]);
console.log('MACD line:', outputs[0]);
console.log('Signal:', outputs[1]);
console.log('Histogram:', outputs[2]);
// State continuation
const [, state2] = ti.macd.indicator([close.slice(0, -1)], [12, 26, 9]);
const continued = state2.batchIndicator([close.slice(-1)]);
console.log('Continued MACD:', continued[0]);
Optional Outputs¶
macd exposes 2 optional outputs: short_ema, long_ema. Pass a boolean mask as the third argument — one bool per optional output, in order.
use tulip_rs::indicators::macd::{Macd, 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 mask = [true, true]; // one per optional output
let (outputs, _state) = Macd::indicator(&[close.as_slice()], &[12.0, 26.0, 9.0], Some(&mask)).unwrap();
let macd_line = &outputs[0]; // macd_line (primary)
let signal_line = &outputs[1]; // signal_line (primary)
let histogram = &outputs[2]; // histogram (primary)
let short_ema = &outputs[3]; // short_ema (optional — requested)
let long_ema = &outputs[4]; // long_ema (optional — requested)
macd exposes 2 optional outputs: short_ema, long_ema. Pass a boolean mask in header order.
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[MACD_INPUTS] = {close};
double options[MACD_OPTIONS] = {12.0, 26.0, 9.0};
bool mask[2] = {true, true}; /* one per optional output */
CIndicatorResult r = macd_indicator(inputs, 10, options, mask, 2);
/* r.outputs[0] -> macd_line (primary) */
/* r.outputs[1] -> signal_line (primary) */
/* r.outputs[2] -> histogram (primary) */
/* r.outputs[3] -> short_ema (optional — requested) */
/* r.outputs[4] -> long_ema (optional — requested) */
tulip_ffi_result_free(r);
macd_state_free(r.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{12.0, 26.0, 9.0} // fastperiod,slowperiod,signalperiod
// Full computation with optional outputs — Rows are zero-copy views.
res, st, _ := indicators.Macd.Indicator(close, options, []bool{true, true})
fmt.Println(res.Rows[0]) // macd_line (primary)
fmt.Println(res.Rows[1]) // signal_line (primary)
fmt.Println(res.Rows[2]) // histogram (primary)
fmt.Println(res.Rows[3]) // short_ema (optional — requested)
fmt.Println(res.Rows[4]) // long_ema (optional — requested)
res.Close()
st.Close()
macd exposes 2 optional outputs: short_ema, long_ema. Pass a boolean mask as the third argument — one boolean per optional output, in order.
import org.tuliprs.*;
import org.tuliprs.indicators.Macd;
// (close series and options as in the Basic tab)
boolean[] mask = {true, true}; // short_ema, long_ema
Outcome oc = Macd.indicator(new double[][] {close}, new double[] {12.0, 26.0, 9.0}, mask);
try (Result res = oc.result()) {
// row 0 = macd_line, row 1 = signal_line, row 2 = histogram;
// rows 3 = short_ema, row 4 = long_ema (optional — requested)
System.out.println(java.util.Arrays.toString(res.toDoubleArray(0))); // macd_line
}
oc.state().close();
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.macd.indicator(
[close], [12.0, 26.0, 9.0],
optional_outputs=[True, True],
)
macd_line = outputs[0] # macd_line (primary)
signal_line = outputs[1] # signal_line (primary)
histogram = outputs[2] # histogram (primary)
short_ema = outputs[3] # short_ema (optional — requested)
long_ema = outputs[4] # long_ema (optional — requested)
macd exposes 2 optional outputs: short_ema, long_ema.
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::macd::{Macd, 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 = Macd::indicator_by_assets::<4>(&inputs, &[12.0, 26.0, 9.0], None).unwrap();
for (i, asset_outputs) in results.iter().enumerate() {
println!("Asset {} MACD: {:?}", i + 1, asset_outputs[0]);
println!("Asset {} Signal: {:?}", i + 1, asset_outputs[1]);
println!("Asset {} Histogram: {:?}", i + 1, asset_outputs[2]);
}
By options — same asset, 4 different option sets in parallel:
use tulip_rs::indicators::macd::{Macd, 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; 3]; 4] = [
&[6.0, 13.0, 5.0],
&[12.0, 26.0, 9.0],
&[19.0, 39.0, 14.0],
&[24.0, 52.0, 18.0],
];
let results = Macd::indicator_by_options::<4>(&[close.as_slice()], &opts, None).unwrap();
for (i, opt_outputs) in results.iter().enumerate() {
println!("Option set {} MACD: {:?}", i + 1, opt_outputs[0]);
println!("Option set {} Signal: {:?}", i + 1, opt_outputs[1]);
println!("Option set {} Histogram: {:?}", i + 1, opt_outputs[2]);
}
By assets — same options applied to 4 assets in one call (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[] = {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 *const asset1[MACD_INPUTS] = {a1};
const double *const asset2[MACD_INPUTS] = {a2};
const double *const asset3[MACD_INPUTS] = {a3};
const double *const asset4[MACD_INPUTS] = {a4};
const double *const *const simd_inputs[4] = {asset1, asset2, asset3, asset4};
CSimdResult r = macd_simd_by_assets(simd_inputs, 4, 10, options, NULL, 0);
for (uintptr_t i = 0; i < r.num_results; i++) {
/* r.outputs[i][0] -> MACD line */
/* r.outputs[i][1] -> Signal */
/* r.outputs[i][2] -> Histogram */
macd_state_free(r.states[i]);
}
tulip_ffi_simd_result_free(r);
By options — same asset, 4 different option sets in one call:
double o12[] = {12.0, 26.0, 9.0}, o19[] = {19.0, 39.0, 14.0};
double o24[] = {24.0, 52.0, 18.0}, o6[] = {6.0, 13.0, 5.0};
const double *const simd_opts[4] = {o12, o19, o24, o6};
CSimdResult r = macd_simd_by_options(inputs, 10, simd_opts, 4, NULL, 0);
/* r.outputs[i][0] -> MACD line */
/* r.outputs[i][1] -> Signal */
/* r.outputs[i][2] -> Histogram */
for (uintptr_t i = 0; i < r.num_results; i++) macd_state_free(r.states[i]);
tulip_ffi_simd_result_free(r);
By assets — same options applied to 2 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 asset 2 in this example
a2 := a1
assets := [][indicators.MacdInputs][]float64{{a1}, {a2}}
sim, _ := indicators.Macd.SimdByAssets(assets, []float64{12.0, 26.0, 9.0}, nil)
for i, lanes := range sim.Results {
fmt.Printf("Asset %d MACD: %v\n", i+1, lanes[0])
fmt.Printf("Asset %d Signal: %v\n", i+1, lanes[1])
fmt.Printf("Asset %d Histogram: %v\n", i+1, lanes[2])
}
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"
close := []float64{81.59, 81.06, 82.87, 83.00, 83.61,
83.15, 82.84, 83.99, 84.55, 84.36}
sim2, _ := indicators.Macd.SimdByOptions(close, [][]float64{{10, 20, 5}, {12, 26, 9}, {15, 30, 8}, {20, 40, 10}}, nil)
for i, lanes := range sim2.Results {
fmt.Printf("Option set %d MACD: %v\n", i+1, lanes[0])
fmt.Printf("Option set %d Signal: %v\n", i+1, lanes[1])
fmt.Printf("Option set %d Histogram: %v\n", i+1, lanes[2])
}
sim2.Close()
By assets — same options applied to 2 assets in parallel (N must be 2, 4, 8, or 16):
import org.tuliprs.*;
import org.tuliprs.indicators.Macd;
// One entry per asset; each asset lists its INPUTS series.
double[] a1 = {81.59, 81.06, 82.87, 83.00, 83.61, 83.15, 82.84, 83.99, 84.55, 84.36};
double[] a2 = a1; // reuse the same data for asset 2
double[][][] assets = {{a1}, {a2}};
try (SimdResult sim = Macd.simdByAssets(assets, new double[] {12.0, 26.0, 9.0}, null)) {
for (int i = 0; i < sim.numResults(); i++) {
System.out.printf("Asset %d MACD: %s%n", i + 1,
java.util.Arrays.toString(sim.toDoubleArray(i, 0)));
System.out.printf("Asset %d Signal: %s%n", i + 1,
java.util.Arrays.toString(sim.toDoubleArray(i, 1)));
System.out.printf("Asset %d Histogram: %s%n", i + 1,
java.util.Arrays.toString(sim.toDoubleArray(i, 2)));
}
} // 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.Macd;
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 = Macd.simdByOptions(new double[][] {close},
new double[][] {{10, 20, 5}, {12, 26, 9}, {15, 30, 8}, {20, 40, 10}}, null)) {
for (int i = 0; i < sim.numResults(); i++) {
System.out.printf("Option set %d MACD: %s%n", i + 1,
java.util.Arrays.toString(sim.toDoubleArray(i, 0)));
System.out.printf("Option set %d Signal: %s%n", i + 1,
java.util.Arrays.toString(sim.toDoubleArray(i, 1)));
System.out.printf("Option set %d Histogram: %s%n", i + 1,
java.util.Arrays.toString(sim.toDoubleArray(i, 2)));
}
}
By assets — same options 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.macd.simd_by_assets(simd_inputs, [12.0, 26.0, 9.0])
for i, out in enumerate(outputs_list):
print(f"Asset {i + 1} MACD: {out[0]}")
print(f"Asset {i + 1} Signal: {out[1]}")
print(f"Asset {i + 1} Histogram: {out[2]}")
By options — same asset, N different option sets 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 = [
[6.0, 13.0, 5.0],
[12.0, 26.0, 9.0],
[19.0, 39.0, 14.0],
[24.0, 52.0, 18.0],
]
outputs_list, states = tulip_rs.indicators.macd.simd_by_options([close], simd_options)
for i, out in enumerate(outputs_list):
print(f"Option set {i + 1} MACD: {out[0]}")
print(f"Option set {i + 1} Signal: {out[1]}")
print(f"Option set {i + 1} Histogram: {out[2]}")
By assets — same options 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.macd.simdByAssets(simdInputs, [12, 26, 9]);
results.forEach((out, i) => console.log(`Asset ${i + 1} MACD:`, out[0], 'Signal:', out[1]));
By options — same asset, 4 different option sets in parallel: