DX — Directional Movement Index¶
The ratio of the difference to the sum of +DI and -DI, expressing directional movement as a single value. ADX is a smoothed DX.
Inputs: [high, low, close] | Options: [period] | Outputs: [dx]
Basic¶
use tulip_rs::indicators::dx::{Dx, Indicator, TIndicatorState};
let high = vec![82.15, 81.89, 83.03, 83.30, 83.85,
83.90, 83.33, 84.30, 84.84, 85.00_f64];
let low = vec![81.29, 80.64, 81.31, 82.65, 83.07,
83.11, 82.49, 82.30, 84.15, 84.11_f64];
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 inputs = [high.as_slice(), low.as_slice(), close.as_slice()];
let (outputs, mut state) = Dx::indicator(&inputs, &[14.0], None).unwrap();
println!("{:?}", outputs[0]); // DX values
// State continuation — feed new bars without reprocessing history
let partial_high = high[..8].to_vec();
let partial_low = low[..8].to_vec();
let partial_close = close[..8].to_vec();
let (outputs2, mut state) = Dx::indicator(&[partial_high.as_slice(), partial_low.as_slice(), partial_close.as_slice()], &[14.0], None).unwrap();
println!("{:?}", outputs2[0]);
let new_high = vec![85.90_f64];
let new_low = vec![84.03_f64];
let new_close = vec![85.53_f64];
let continued = state.batch_indicator(
&[new_high.as_slice(), new_low.as_slice(), new_close.as_slice()],
None,
).unwrap();
println!("{:?}", continued[0]);
#include "tulip_rs_ffi.h"
/* high, low, close arrays */
const double *inputs[DX_INPUTS] = {high, low, close};
double options[DX_OPTIONS] = {14.0};
/* Full computation (check r.error == C_INDICATOR_ERROR_OK in real code) */
CIndicatorResult r = dx_indicator(inputs, 10, options, NULL, 0);
/* r.outputs[0] -> the DX series, length r.output_lens[0] */
tulip_ffi_result_free(r);
dx_state_free(r.state);
/* Partial computation + state continuation */
CIndicatorResult p = dx_indicator(inputs, 8, options, NULL, 0);
const double *new_inputs[DX_INPUTS] = {new_high, new_low, new_close};
CBatchResult b = dx_batch(p.state, new_inputs, 1, NULL, 0);
/* b.outputs[0] -> DX value for the new bar */
tulip_ffi_batch_result_free(b);
tulip_ffi_result_free(p);
dx_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}
close := []float64{81.59, 81.06, 82.87, 83.00, 83.61,
83.15, 82.84, 83.99, 84.55, 84.36}
options := []float64{14.0}
// Full computation — Rows are zero-copy views, valid until Close.
res, st, _ := indicators.Dx.Indicator(high, low, close, options, nil)
fmt.Println(res.Rows[0]) // DX values
res.Close()
st.Close()
// Partial computation + state continuation.
res2, st2, _ := indicators.Dx.Indicator(high[:8], low[:8], close[:8], options, nil)
res2.Close() // outputs consumed or closed; state stays live
batch, _ := st2.Batch(high[8:], low[8:], close[8:], nil)
fmt.Println(batch.Rows[0]) // continued DX values
batch.Close()
st2.Close()
import org.tuliprs.*;
import org.tuliprs.indicators.Dx;
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[] close = {81.59, 81.06, 82.87, 83.00, 83.61,
83.15, 82.84, 83.99, 84.55, 84.36};
double[] options = {14.0};
// Full computation — output rows are zero-copy views, valid until close().
Outcome oc = Dx.indicator(new double[][] {high, low, close}, options);
try (Result res = oc.result(); State st = oc.state()) {
System.out.println(java.util.Arrays.toString(res.toDoubleArray(0))); // DX values
}
// Partial computation + state continuation.
int n = 8;
Outcome p = Dx.indicator(new double[][] {
java.util.Arrays.copyOfRange(high, 0, n),
java.util.Arrays.copyOfRange(low, 0, n),
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(high, n, 10),
java.util.Arrays.copyOfRange(low, n, 10),
java.util.Arrays.copyOfRange(close, n, 10)});
try (br) {
System.out.println(java.util.Arrays.toString(br.toDoubleArray(0))); // continued DX values
}
}
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)
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.dx.indicator([high, low, close], [14.0])
print(outputs[0]) # DX values
# State continuation
new_high = np.array([85.20], dtype=np.float64)
new_low = np.array([84.50], dtype=np.float64)
new_close = np.array([85.00], dtype=np.float64)
continued = state.batch_indicator([new_high, new_low, new_close])
print(continued[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 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.dx.indicator([high, low, close], [14]);
console.log('DX(14):', outputs[0]);
// State continuation
const n = high.length - 5;
const [, state2] = ti.dx.indicator([high.slice(0, n), low.slice(0, n), close.slice(0, n)], [14]);
const continued = state2.batchIndicator([high.slice(n), low.slice(n), close.slice(n)]);
console.log('Continued DX:', 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 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.dx.indicator([high, low, close], [14]);
console.log('DX(14):', outputs[0]);
// State continuation
const n = high.length - 5;
const [, state2] = ti.dx.indicator([high.slice(0, n), low.slice(0, n), close.slice(0, n)], [14]);
const continued = state2.batchIndicator([high.slice(n), low.slice(n), close.slice(n)]);
console.log('Continued DX:', continued[0]);
Optional Outputs¶
dx exposes 2 optional outputs: atr, tr. Pass a boolean mask as the third argument — one bool per optional output, in order.
use tulip_rs::indicators::dx::{Dx, 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_f64];
let high = close.iter().map(|x| x + 1.0).collect::<Vec<_>>();
let low = close.iter().map(|x| x - 1.0).collect::<Vec<_>>();
let mask = [true, false];
let (outputs, _state) = Dx::indicator(
&[high.as_slice(), low.as_slice(), close.as_slice()],
&[14.0],
Some(&mask),
).unwrap();
let dx = &outputs[0]; // dx (primary)
let atr = &outputs[1]; // atr (optional — requested)
// tr not requested — omitted from outputs
#include "tulip_rs_ffi.h"
/* dx exposes 2 optional outputs, in fixed order: atr, tr */
const double *inputs[DX_INPUTS] = {high, low, close};
double options[DX_OPTIONS] = {14.0};
bool optional_outputs[2] = {true, false}; /* request atr, skip tr */
CIndicatorResult r = dx_indicator(inputs, 10, options, optional_outputs, 2);
/* r.outputs[0] -> dx, r.outputs[1] -> atr (only requested optionals append) */
tulip_ffi_result_free(r);
dx_state_free(r.state);
// dx exposes 2 optional outputs, in fixed order: atr, tr.
res, st, _ := indicators.Dx.Indicator(high, low, close, []float64{14.0}, []bool{true, false})
defer res.Close()
defer st.Close()
dx := res.Rows[0] // dx (primary)
atr := res.Rows[1] // atr (optional — requested; tr skipped, not appended)
fmt.Println(dx, atr)
dx exposes 2 optional outputs: atr, tr. Pass a boolean mask as the third argument — one boolean per optional output, in order.
import org.tuliprs.*;
import org.tuliprs.indicators.Dx;
// (high/low/close series as in the Basic tab)
boolean[] mask = {true, false}; // atr, tr
Outcome oc = Dx.indicator(new double[][] {high, low, close}, new double[] {14.0}, mask);
try (Result res = oc.result()) {
// row 0 = dx (primary), row 1 = atr (optional — requested); tr was not requested
System.out.println(java.util.Arrays.toString(res.toDoubleArray(0))); // dx
System.out.println(java.util.Arrays.toString(res.toDoubleArray(1))); // atr
}
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)
high = close + 1.0
low = close - 1.0
outputs, state = tulip_rs.indicators.dx.indicator(
[high, low, close], [14.0],
optional_outputs=[True, False],
)
dx = outputs[0] # dx (primary)
atr = outputs[1] # atr (optional — requested)
# tr not requested — omitted from outputs
dx exposes 2 optional outputs: atr, tr.
SIMD¶
By assets — same options, N assets in parallel:
use tulip_rs::indicators::dx::{Dx, Indicator};
let inputs: [&[&[f64]; 3]; 4] = [
&[h1.as_slice(), l1.as_slice(), c1.as_slice()],
&[h2.as_slice(), l2.as_slice(), c2.as_slice()],
&[h3.as_slice(), l3.as_slice(), c3.as_slice()],
&[h4.as_slice(), l4.as_slice(), c4.as_slice()],
];
let results = Dx::indicator_by_assets::<4>(&inputs, &[14.0], None).unwrap();
for (i, asset_outputs) in results.iter().enumerate() {
println!("Asset {}: {:?}", i + 1, asset_outputs[0]);
}
By options — same asset, N option sets in parallel:
By assets — same option applied to 4 assets in parallel (N must be 2/4/8/16):
#include "tulip_rs_ffi.h"
/* each asset supplies all DX_INPUTS series: high, low, close */
const double *asset1[DX_INPUTS] = {h1, l1, c1};
const double *asset2[DX_INPUTS] = {h2, l2, c2};
const double *asset3[DX_INPUTS] = {h3, l3, c3};
const double *asset4[DX_INPUTS] = {h4, l4, c4};
const double *const *const simd_inputs[4] = {asset1, asset2, asset3, asset4};
double options[DX_OPTIONS] = {14.0};
CSimdResult r = dx_simd_by_assets(simd_inputs, 4, len, options, NULL, 0);
for (uintptr_t i = 0; i < r.num_results; i++) {
/* r.outputs[i][0] -> asset i's DX series, length r.output_lens[i][0] */
dx_state_free(r.states[i]);
}
tulip_ffi_simd_result_free(r);
By options — same asset, 4 different periods in parallel:
double o7[] = {7.0}, o14[] = {14.0}, o21[] = {21.0}, o28[] = {28.0};
const double *const simd_opts[4] = {o7, o14, o21, o28};
CSimdResult r = dx_simd_by_options(inputs, len, simd_opts, 4, NULL, 0);
/* r.outputs[i] -> DX for period simd_opts[i] */
for (uintptr_t i = 0; i < r.num_results; i++) dx_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}
c1 := []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 assets 2–4 in this example
h2, l2, c2 := h1, l1, c1
h3, l3, c3 := h1, l1, c1
h4, l4, c4 := h1, l1, c1
assets := [][indicators.DxInputs][]float64{{h1, l1, c1}, {h2, l2, c2}, {h3, l3, c3}, {h4, l4, c4}}
sim, _ := indicators.Dx.SimdByAssets(assets, []float64{14.0}, 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}
close := []float64{81.59, 81.06, 82.87, 83.00, 83.61,
83.15, 82.84, 83.99, 84.55, 84.36}
sim2, _ := indicators.Dx.SimdByOptions(high, low, close, [][]float64{{7}, {14}, {21}, {28}}, 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.Dx;
// (h1/l1/c1 data as in the Go tab)
double[] h2 = h1, l2 = l1, c2 = c1; // Reuse same data for assets 2-4
double[] h3 = h1, l3 = l1, c3 = c1;
double[] h4 = h1, l4 = l1, c4 = c1;
double[][][] assets = {{h1, l1, c1}, {h2, l2, c2}, {h3, l3, c3}, {h4, l4, c4}};
try (SimdResult sim = Dx.simdByAssets(assets, new double[] {14.0}, 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.Dx;
// (high/low/close series as in the Go tab)
try (SimdResult sim = Dx.simdByOptions(new double[][] {high, low, close},
new double[][] {{7.0}, {14.0}, {21.0}, {28.0}}, 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, c1],
[h2, l2, c2],
[h3, l3, c3],
[h4, l4, c4],
]
outputs_list, states = tulip_rs.indicators.dx.simd_by_assets(simd_inputs, [14.0])
for i, asset_outputs in enumerate(outputs_list):
print(f"Asset {i+1}: {asset_outputs[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(), close.slice()],
[high.map(v => v * 1.1), low.map(v => v * 1.1), close.map(v => v * 1.1)],
[high.map(v => v * 0.9), low.map(v => v * 0.9), close.map(v => v * 0.9)],
[high.map(v => v * 1.02), low.map(v => v * 1.02), close.map(v => v * 1.02)],
];
const [results] = ti.dx.simdByAssets(simdInputs, [14]);
results.forEach((out, i) => console.log(`Asset ${i + 1}:`, out[0]));
By options — same asset, 4 different periods in parallel: