MAMA — MESA Adaptive Moving Average¶
MAMA (MESA Adaptive Moving Average) computes adaptive moving averages for price series.
Inputs¶
real(price series)
Options¶
(None — the Go tab calls Indicator(close, nil, nil))
Outputs¶
mama(MESA adaptive moving average)fiba(FIBA - second MESA component)
Optional Outputs¶
The indicator exposes two optional outputs: dc_period and alpha. Pass a boolean mask as the third argument — one bool per optional output, in order.
Basic¶
use tulip_rs::indicators::mama::{Mama, Indicator, TIndicatorState};
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) = Mama::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) = Mama::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]);
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[] = {86.59, 86.06, 87.87, 88.00, 88.61, 88.15, 87.84, 88.99, 89.55, 89.36};
double a3[] = {78.59, 78.06, 79.87, 80.00, 80.61, 80.15, 79.84, 80.99, 81.55, 81.36};
double a4[] = {83.22, 82.68, 84.53, 84.66, 85.28, 84.81, 84.50, 85.67, 86.24, 86.05};
/* one [INPUTS]-long pointer array per asset */
const double *asset1[MAMA_INPUTS] = {a1};
const double *asset2[MAMA_INPUTS] = {a2};
const double *asset3[MAMA_INPUTS] = {a3};
const double *asset4[MAMA_INPUTS] = {a4};
const double *const *const simd_inputs[4] = {asset1, asset2, asset3, asset4};
bool optional_outputs[2] = {true, true}; // dc_period, alpha
CSimdResult r = mama_simd_by_assets(simd_inputs, 4, 10, options, optional_outputs, 2);
for (uintptr_t i = 0; i < r.num_results; i++) {
/* r.outputs[i][0] -> asset i's mama series, length r.output_lens[i][0] */
/* r.outputs[i][1] -> asset i's fama */
mama_state_free(r.states[i]);
}
tulip_ffi_simd_result_free(r);
By options — same asset, 4 different option sets in one call:
double o03[] = {0.3}, o003[] = {0.03};
double o04[] = {0.4}, o004[] = {0.04};
double o05[] = {0.5}, o005[] = {0.05};
double o06[] = {0.6}, o006[] = {0.06};
const double *const simd_opts[4] = {
(double[]){0.3, 0.03},
(double[]){0.4, 0.04},
(double[]){0.5, 0.05},
(double[]){0.6, 0.06}
};
bool optional_outputs[2] = {true, true};
CSimdResult r = mama_simd_by_options(inputs, 10, simd_opts, 4, optional_outputs, 2);
/* r.outputs[i][0] -> asset i's mama for option set */
for (uintptr_t i = 0; i < r.num_results; i++) mama_state_free(r.states[i]);
tulip_ffi_simd_result_free(r);
import (
"fmt"
"github.com/me60732/tulip_rs_go/indicators"
"github.com/me60732/tulip_rs_go/tulip"
)
close := []float64{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}
options := []float64{0.5, 0.05} // fast_limit, slow_limit
res, st, _ := indicators.Mama.Indicator(close, options, nil)
fmt.Printf("MAMA: %v\n", tulip.AsFloat64(res.Rows[0]))
fmt.Printf("FAMA: %v\n", tulip.AsFloat64(res.Rows[1]))
res.Close()
st.Close()
partial := close[:35]
res2, state2, _ := indicators.Mama.Indicator(partial, options, nil)
fmt.Printf("Partial MAMA: %v\n", tulip.AsFloat64(res2.Rows[0]))
continued, _ := state2.Batch(close[35:], nil)
fmt.Printf("Continued MAMA: %v\n", tulip.AsFloat64(continued.Rows[0]))
continued.Close()
state2.Close()
import org.tuliprs.*;
import org.tuliprs.indicators.Mama;
double[] 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};
double[] options = {0.5, 0.05}; // fast_limit, slow_limit
// Full computation — output rows are zero-copy views, valid until close().
Outcome oc = Mama.indicator(new double[][] {close}, options);
try (Result res = oc.result(); State st = oc.state()) {
System.out.println(java.util.Arrays.toString(res.toDoubleArray(0))); // MAMA values
System.out.println(java.util.Arrays.toString(res.toDoubleArray(1))); // FAMA values
}
// Partial computation + state continuation.
int n = 35;
Outcome p = Mama.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, 40)});
try (br) {
System.out.println(java.util.Arrays.toString(br.toDoubleArray(0))); // continued MAMA
System.out.println(java.util.Arrays.toString(br.toDoubleArray(1))); // continued FAMA
}
}
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.mama.indicator([close], [])
print(outputs[0]) # mama values
print(outputs[1]) # fiba values
# State continuation
continued = state.batch_indicator([np.array([85.20], dtype=np.float64)])
print(continued[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.mama.indicator([close], []);
console.log('MAMA:', outputs[0]);
console.log('FIBA:', outputs[1]);
// State continuation
const n = close.length - 5;
const [, state2] = ti.mama.indicator([close.slice(0, n)], []);
const continued = state2.batchIndicator([close.slice(n)]);
console.log('Continued MAMA:', 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.mama.indicator([close], []);
console.log('MAMA:', outputs[0]);
console.log('FIBA:', outputs[1]);
// State continuation
const n = close.length - 5;
const [, state2] = ti.mama.indicator([close.slice(0, n)], []);
const continued = state2.batchIndicator([close.slice(n)]);
console.log('Continued MAMA:', continued[0]);
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::{Mama, Indicator, TIndicatorState};
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) = Mama::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)
mama exposes 2 optional outputs: dc_period, alpha. Pass a boolean mask as the third argument — one bool per optional output, in order.
#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,
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};
double options[MAMA_OPTIONS] = {0.5, 0.05}; // fast_limit, slow_limit
const double *inputs[MAMA_INPUTS] = {close};
bool mask[2] = {true, true}; // one per optional output
CIndicatorResult r = mama_indicator(inputs, 40, options, mask, 2);
/* r.outputs[0] -> mama (primary) */
/* r.outputs[1] -> fama (primary) */
/* r.outputs[2] -> dc_period (optional — requested) */
/* r.outputs[3] -> alpha (optional — requested) */
tulip_ffi_result_free(r);
mama_state_free(r.state);
import (
"fmt"
"github.com/me60732/tulip_rs_go/indicators"
"github.com/me60732/tulip_rs_go/tulip"
)
close := []float64{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}
options := []float64{0.5, 0.05} // fast_limit, slow_limit
mask := []bool{true, true} // dc_period, alpha
res, st, _ := indicators.Mama.Indicator(close, options, mask)
fmt.Printf("MAMA: %v\n", tulip.AsFloat64(res.Rows[0]))
fmt.Printf("FAMA: %v\n", tulip.AsFloat64(res.Rows[1]))
fmt.Printf("dc_period: %v\n", tulip.AsFloat64(res.Rows[2]))
fmt.Printf("alpha: %v\n", tulip.AsFloat64(res.Rows[3]))
res.Close()
st.Close()
mama exposes 2 optional outputs: dc_period, alpha. Pass a boolean mask as the third argument — one boolean per optional output, in order.
import org.tuliprs.*;
import org.tuliprs.indicators.Mama;
// (close/options series as in the Basic tab)
boolean[] mask = {true, true}; // dc_period, alpha
Outcome oc = Mama.indicator(new double[][] {close}, new double[] {0.5, 0.05}, mask);
try (Result res = oc.result()) {
// row 0 = mama (primary), row 1 = fama (primary), row 2 = dc_period (optional), row 3 = alpha
System.out.println(java.util.Arrays.toString(res.toDoubleArray(2))); // dc_period
}
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,
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::{Mama, 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![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 = Mama::indicator_by_assets::<4>(&inputs, &[0.5, 0.05], None).unwrap();
for (i, asset_outputs) in results.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::{Mama, 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; 2]; 4] = [
&[0.3, 0.03],
&[0.4, 0.04],
&[0.5, 0.05],
&[0.6, 0.06],
];
let results = Mama::indicator_by_options::<4>(&[close.as_slice()], &opts, None).unwrap();
for (i, opt_outputs) in results.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 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[] = {86.59, 86.06, 87.87, 88.00, 88.61, 88.15, 87.84, 88.99, 89.55, 89.36};
double a3[] = {78.59, 78.06, 79.87, 80.00, 80.61, 80.15, 79.84, 80.99, 81.55, 81.36};
double a4[] = {83.22, 82.68, 84.53, 84.66, 85.28, 84.81, 84.50, 85.67, 86.24, 86.05};
/* one [INPUTS]-long pointer array per asset */
const double *asset1[MAMA_INPUTS] = {a1};
const double *asset2[MAMA_INPUTS] = {a2};
const double *asset3[MAMA_INPUTS] = {a3};
const double *asset4[MAMA_INPUTS] = {a4};
const double *const *const simd_inputs[4] = {asset1, asset2, asset3, asset4};
bool optional_outputs[2] = {true, true}; // dc_period, alpha
CSimdResult r = mama_simd_by_assets(simd_inputs, 4, 10, options, optional_outputs, 2);
for (uintptr_t i = 0; i < r.num_results; i++) {
/* r.outputs[i][0] -> asset i's mama series, length r.output_lens[i][0] */
/* r.outputs[i][1] -> asset i's fama */
mama_state_free(r.states[i]);
}
tulip_ffi_simd_result_free(r);
By options — same asset, 4 different option sets in one call:
double o03[] = {0.3}, o003[] = {0.03};
double o04[] = {0.4}, o004[] = {0.04};
double o05[] = {0.5}, o005[] = {0.05};
double o06[] = {0.6}, o006[] = {0.06};
const double *const simd_opts[4] = {
(double[]){0.3, 0.03},
(double[]){0.4, 0.04},
(double[]){0.5, 0.05},
(double[]){0.6, 0.06}
};
bool optional_outputs[2] = {true, true};
CSimdResult r = mama_simd_by_options(inputs, 10, simd_opts, 4, optional_outputs, 2);
/* r.outputs[i][0] -> asset i's mama for option set */
for (uintptr_t i = 0; i < r.num_results; i++) mama_state_free(r.states[i]);
tulip_ffi_simd_result_free(r);
By assets — same options applied to 4 assets in parallel (lane counts 2/4/8/16):
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,
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}
options := []float64{0.5, 0.05} // fast_limit, slow_limit
// Reuse the same data for assets 2–4 in this example
a2, a3, a4 := close, close, close
assets := [][indicators.MamaInputs][]float64{{close}, {a2}, {a3}, {a4}}
sim, _ := indicators.Mama.SimdByAssets(assets, options, nil)
for i, lanes := range sim.Results {
fmt.Printf("Asset %d MAMA: %v\n", i+1, lanes[0])
fmt.Printf("Asset %d FAMA: %v\n", i+1, lanes[1])
}
sim.Close()
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}
options := [][]float64{{0.3, 0.03}, {0.4, 0.04}, {0.5, 0.05}, {0.6, 0.06}}
sim, _ := indicators.Mama.SimdByOptions(close, options, nil)
for i, lanes := range sim.Results {
fmt.Printf("Option set %d MAMA: %v\n", i+1, lanes[0])
fmt.Printf("Option set %d FAMA: %v\n", i+1, lanes[1])
}
sim.Close()
By assets — same options applied to 4 assets in parallel (N must be 2, 4, 8, or 16):
import org.tuliprs.*;
import org.tuliprs.indicators.Mama;
double[] a1 = {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};
double[] a2 = {86.59, 86.06, 87.87, 88.00, 88.61, 88.15, 87.84, 88.99, 89.55, 89.36};
double[] a3 = {78.59, 78.06, 79.87, 80.00, 80.61, 80.15, 79.84, 80.99, 81.55, 81.36};
double[] a4 = {83.22, 82.68, 84.53, 84.66, 85.28, 84.81, 84.50, 85.67, 86.24, 86.05};
// One entry per asset; each asset lists its INPUTS series.
double[][][] assets = {{a1}, {a2}, {a3}, {a4}};
try (SimdResult sim = Mama.simdByAssets(assets, new double[] {0.5, 0.05}, null)) {
for (int i = 0; i < sim.numResults(); i++) {
System.out.printf("Asset %d MAMA: %s%n", i + 1,
java.util.Arrays.toString(sim.toDoubleArray(i, 0))); // mama
System.out.printf("Asset %d FAMA: %s%n", i + 1,
java.util.Arrays.toString(sim.toDoubleArray(i, 1))); // fama
}
} // 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.Mama;
double[] close = {81.59, 81.06, 82.87, 83.00, 83.61};
try (SimdResult sim = Mama.simdByOptions(new double[][] {close},
new double[][] {{0.3, 0.03}, {0.4, 0.04}, {0.5, 0.05}, {0.6, 0.06}}, null)) {
for (int i = 0; i < sim.numResults(); i++) {
System.out.printf("Option set %d MAMA: %s%n", i + 1,
java.util.Arrays.toString(sim.toDoubleArray(i, 0))); // mama
System.out.printf("Option set %d FAMA: %s%n", i + 1,
java.util.Arrays.toString(sim.toDoubleArray(i, 1))); // fama
}
}
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: