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, signal, histogram]
Basic¶
use tulip_rs::indicators::macd::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) = 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) = 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]);
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 = [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::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) = 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)
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.
SIMD¶
By assets — same options applied to 4 assets in parallel:
use tulip_rs::indicators::macd::indicator_by_assets;
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 = indicator_by_assets::<4>(&inputs, &[12.0, 26.0, 9.0], None).unwrap();
for (i, asset_outputs) in results.0.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::indicator_by_options;
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 = indicator_by_options::<4>(&[close.as_slice()], &opts, None).unwrap();
for (i, opt_outputs) in results.0.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 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]],
[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: