MAMA — MESA Adaptive Moving Average¶
An adaptive moving average that adjusts its smoothing factor in proportion to the instantaneous rate of phase change; FAMA (Following Adaptive Moving Average) is the slower-responding companion line used for crossover signals.
Inputs: [real] | Options: [fast_limit, slow_limit] | Outputs: [mama, fama] | Optional: [dc_period, alpha]
Basic¶
use tulip_rs::indicators::mama::indicator;
let close = vec![
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, 87.50, 88.10, 88.50, 87.90, 88.20,
88.80, 89.10, 88.70, 89.30, 89.70, 90.10, 89.50, 90.20, 90.80, 91.10,
90.50, 91.20, 91.80, 92.10, 91.50, 92.20, 92.80, 93.10, 92.50, 93.20_f64,
];
// Options: [fast_limit, slow_limit]
let (outputs, _state) = indicator(&[close.as_slice()], &[0.5, 0.05], None).unwrap();
println!("MAMA: {:?}", outputs[0]);
println!("FAMA: {:?}", outputs[1]);
// State continuation
let n = close.len() - 5;
let partial = close[..n].to_vec();
let (outputs2, mut state) = indicator(&[partial.as_slice()], &[0.5, 0.05], None).unwrap();
println!("Partial MAMA: {:?}", outputs2[0]);
let rest = close[n..].to_vec();
let continued = state.batch_indicator(&[rest.as_slice()], None).unwrap();
println!("Continued MAMA: {:?}", continued[0]);
println!("Continued FAMA: {:?}", continued[1]);
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,
85.53, 86.54, 86.89, 87.77, 87.29, 87.50, 88.10, 88.50, 87.90, 88.20,
88.80, 89.10, 88.70, 89.30, 89.70, 90.10, 89.50, 90.20, 90.80, 91.10,
90.50, 91.20, 91.80, 92.10, 91.50, 92.20, 92.80, 93.10, 92.50, 93.20,
], dtype=np.float64)
# Options: [fast_limit, slow_limit]
outputs, state = tulip_rs.indicators.mama.indicator([close], [0.5, 0.05])
print("MAMA: ", outputs[0])
print("FAMA: ", outputs[1])
# State continuation
partial = close[:-5]
outputs2, state = tulip_rs.indicators.mama.indicator([partial], [0.5, 0.05])
rest = close[-5:]
continued = state.batch_indicator([rest])
print("Continued MAMA: ", continued[0])
print("Continued FAMA: ", continued[1])
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, 87.50, 88.10, 88.50, 87.90, 88.20,
88.80, 89.10, 88.70, 89.30, 89.70, 90.10, 89.50, 90.20, 90.80, 91.10,
90.50, 91.20, 91.80, 92.10, 91.50, 92.20, 92.80, 93.10, 92.50, 93.20,
]);
const [outputs, state] = ti.mama.indicator([close], [0.5, 0.05]);
console.log('MAMA:', outputs[0]);
console.log('FAMA:', outputs[1]);
// State continuation
const [, state2] = ti.mama.indicator([close.slice(0, -5)], [0.5, 0.05]);
const continued = state2.batchIndicator([close.slice(-5)]);
console.log('Continued MAMA:', continued[0]);
console.log('Continued FAMA:', continued[1]);
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, 87.50, 88.10, 88.50, 87.90, 88.20,
88.80, 89.10, 88.70, 89.30, 89.70, 90.10, 89.50, 90.20, 90.80, 91.10,
90.50, 91.20, 91.80, 92.10, 91.50, 92.20, 92.80, 93.10, 92.50, 93.20,
];
const [outputs, state] = ti.mama.indicator([close], [0.5, 0.05]);
console.log('MAMA:', outputs[0]);
console.log('FAMA:', outputs[1]);
// State continuation
const [, state2] = ti.mama.indicator([close.slice(0, -5)], [0.5, 0.05]);
const continued = state2.batchIndicator([close.slice(-5)]);
console.log('Continued MAMA:', continued[0]);
console.log('Continued FAMA:', continued[1]);
Optional Outputs¶
mama exposes 2 optional outputs: dc_period, alpha. Pass a boolean mask as the third argument — one bool per optional output, in order.
use tulip_rs::indicators::mama::indicator;
let close = vec![
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, 87.50, 88.10, 88.50, 87.90, 88.20,
88.80, 89.10, 88.70, 89.30, 89.70, 90.10, 89.50, 90.20, 90.80, 91.10,
90.50, 91.20, 91.80, 92.10, 91.50, 92.20, 92.80, 93.10, 92.50, 93.20_f64,
];
let mask = [true, true]; // one per optional output
let (outputs, _state) = indicator(&[close.as_slice()], &[0.5, 0.05], Some(&mask)).unwrap();
let mama = &outputs[0]; // mama (primary)
let fama = &outputs[1]; // fama (primary)
let dc_period = &outputs[2]; // dc_period (optional — requested)
let alpha = &outputs[3]; // alpha (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,
85.53, 86.54, 86.89, 87.77, 87.29, 87.50, 88.10, 88.50, 87.90, 88.20,
88.80, 89.10, 88.70, 89.30, 89.70, 90.10, 89.50, 90.20, 90.80, 91.10,
90.50, 91.20, 91.80, 92.10, 91.50, 92.20, 92.80, 93.10, 92.50, 93.20,
], dtype=np.float64)
outputs, state = tulip_rs.indicators.mama.indicator(
[close], [0.5, 0.05],
optional_outputs=[True, True],
)
mama = outputs[0] # mama (primary)
fama = outputs[1] # fama (primary)
dc_period = outputs[2] # dc_period (optional — requested)
alpha = outputs[3] # alpha (optional — requested)
mama exposes 2 optional outputs: dc_period, alpha.
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::mama::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![86.59, 86.06, 87.87, 88.00, 88.61, 88.15, 87.84, 88.99, 89.55, 89.36_f64];
let a3 = vec![78.59, 78.06, 79.87, 80.00, 80.61, 80.15, 79.84, 80.99, 81.55, 81.36_f64];
let a4 = vec![83.22, 82.68, 84.53, 84.66, 85.28, 84.81, 84.50, 85.67, 86.24, 86.05_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, &[0.5, 0.05], None).unwrap();
for (i, asset_outputs) in results.0.iter().enumerate() {
println!("Asset {} MAMA: {:?}", i + 1, asset_outputs[0]);
println!("Asset {} FAMA: {:?}", i + 1, asset_outputs[1]);
}
By options — same asset, 4 different option sets in parallel:
use tulip_rs::indicators::mama::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; 2]; 4] = [
&[0.3, 0.03],
&[0.4, 0.04],
&[0.5, 0.05],
&[0.6, 0.06],
];
let results = indicator_by_options::<4>(&[close.as_slice()], &opts, None).unwrap();
for (i, opt_outputs) in results.0.iter().enumerate() {
println!("Option set {} MAMA: {:?}", i + 1, opt_outputs[0]);
println!("Option set {} FAMA: {:?}", i + 1, opt_outputs[1]);
}
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,
85.53, 86.54, 86.89, 87.77, 87.29, 87.50, 88.10, 88.50, 87.90, 88.20,
88.80, 89.10, 88.70, 89.30, 89.70, 90.10, 89.50, 90.20, 90.80, 91.10,
90.50, 91.20, 91.80, 92.10, 91.50, 92.20, 92.80, 93.10, 92.50, 93.20,
], dtype=np.float64)
simd_inputs = [[close], [close + 5.0], [close - 3.0], [close * 1.02]]
outputs_list, states = tulip_rs.indicators.mama.simd_by_assets(simd_inputs, [0.5, 0.05])
for i, out in enumerate(outputs_list):
print(f"Asset {i + 1} MAMA: {out[0]}")
print(f"Asset {i + 1} FAMA: {out[1]}")
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,
85.53, 86.54, 86.89, 87.77, 87.29, 87.50, 88.10, 88.50, 87.90, 88.20,
88.80, 89.10, 88.70, 89.30, 89.70, 90.10, 89.50, 90.20, 90.80, 91.10,
90.50, 91.20, 91.80, 92.10, 91.50, 92.20, 92.80, 93.10, 92.50, 93.20,
], dtype=np.float64)
simd_options = [
[0.3, 0.03],
[0.4, 0.04],
[0.5, 0.05],
[0.6, 0.06],
]
outputs_list, states = tulip_rs.indicators.mama.simd_by_options([close], simd_options)
for i, out in enumerate(outputs_list):
print(f"Option set {i + 1} MAMA: {out[0]}")
print(f"Option set {i + 1} FAMA: {out[1]}")
By assets — same options applied to 4 assets in parallel:
const simdInputs = [
[close.slice()],
[close.map(v => v + 5.0)],
[close.map(v => v - 3.0)],
[close.map(v => v * 1.02)],
];
const [results] = ti.mama.simdByAssets(simdInputs, [0.5, 0.05]);
results.forEach((out, i) => console.log(`Asset ${i + 1} MAMA:`, out[0], 'FAMA:', out[1]));
By options — same asset, 4 different option sets in parallel: