AD — Accumulation/Distribution¶
A cumulative indicator that uses price and volume to assess whether a security is being accumulated (bought) or distributed (sold).
Inputs: [high, low, close, volume] | Options: [] | Outputs: [ad]
Basic¶
use tulip_rs::indicators::ad::{Ad, 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 volume = vec![1200.0, 1400.0, 1100.0, 1600.0, 1300.0,
900.0, 1500.0, 1800.0, 1000.0, 1700.0_f64];
let inputs = [high.as_slice(), low.as_slice(), close.as_slice(), volume.as_slice()];
let (outputs, mut state) = Ad::indicator(&inputs, &[], None).unwrap();
println!("{:?}", outputs[0]); // A/D line values
// State continuation — feed new bars without reprocessing history
let new_high = vec![85.20_f64];
let new_low = vec![84.50_f64];
let new_close = vec![85.00_f64];
let new_volume = vec![1550.0_f64];
let continued = state.batch_indicator(
&[new_high.as_slice(), new_low.as_slice(),
new_close.as_slice(), new_volume.as_slice()],
None,
).unwrap();
println!("{:?}", continued[0]);
#include "tulip_rs_ffi.h"
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 volume[] = {1200.0, 1400.0, 1100.0, 1600.0, 1300.0,
900.0, 1500.0, 1800.0, 1000.0, 1700.0};
const double *inputs[AD_INPUTS] = {high, low, close, volume};
const double options[AD_OPTIONS] = {}; // no options
/* Full computation (check r.error == C_INDICATOR_ERROR_OK in real code) */
CIndicatorResult r = ad_indicator(inputs, 10, options, NULL, 0);
/* r.outputs[0] -> the AD series, length r.output_lens[0] */
tulip_ffi_result_free(r);
ad_state_free(r.state);
/* Partial computation + state continuation */
CIndicatorResult p = ad_indicator(inputs, 8, options, NULL, 0);
double new_high[] = {85.20};
double new_low[] = {84.50};
double new_close[] = {85.00};
double new_volume[] = {1550.0};
const double *new_inputs[AD_INPUTS] = {new_high, new_low, new_close, new_volume};
CBatchResult b = ad_batch(p.state, new_inputs, 1, NULL, 0);
/* b.outputs[0] -> AD value for the single new bar */
tulip_ffi_batch_result_free(b);
tulip_ffi_result_free(p);
ad_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}
volume := []float64{1200.0, 1400.0, 1100.0, 1600.0, 1300.0,
900.0, 1500.0, 1800.0, 1000.0, 1700.0}
options := []float64{} // no options
// Full computation — Rows are zero-copy views, valid until Close.
res, st, _ := indicators.Ad.Indicator(high, low, close, volume, options, nil)
fmt.Println(res.Rows[0]) // AD values
res.Close()
st.Close()
// Partial computation + state continuation.
res2, st2, _ := indicators.Ad.Indicator(high[:8], low[:8], close[:8], volume[:8], options, nil)
res2.Close() // outputs consumed or closed; state stays live
batch, _ := st2.Batch(high[8:], low[8:], close[8:], volume[8:], nil)
fmt.Println(batch.Rows[0]) // continued AD values
batch.Close()
st2.Close()
import org.tuliprs.*;
import org.tuliprs.indicators.Ad;
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[] volume = {1200.0, 1400.0, 1100.0, 1600.0, 1300.0,
900.0, 1500.0, 1800.0, 1000.0, 1700.0};
double[] options = {}; // no options
// Full computation — output rows are zero-copy views, valid until close().
Outcome oc = Ad.indicator(new double[][] {high, low, close, volume}, options);
try (Result res = oc.result(); State st = oc.state()) {
System.out.println(java.util.Arrays.toString(res.toDoubleArray(0))); // AD values
}
// Partial computation + state continuation.
int n = 8;
Outcome p = Ad.indicator(new double[][] {
java.util.Arrays.copyOfRange(high, 0, n),
java.util.Arrays.copyOfRange(low, 0, n),
java.util.Arrays.copyOfRange(close, 0, n),
java.util.Arrays.copyOfRange(volume, 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),
java.util.Arrays.copyOfRange(volume, n, 10)});
try (br) {
System.out.println(java.util.Arrays.toString(br.toDoubleArray(0))); // continued AD
}
}
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)
volume = np.array([1200.0, 1400.0, 1100.0, 1600.0, 1300.0,
900.0, 1500.0, 1800.0, 1000.0, 1700.0], dtype=np.float64)
outputs, state = tulip_rs.indicators.ad.indicator([high, low, close, volume], [])
print(outputs[0]) # A/D line 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)
new_volume = np.array([1550.0], dtype=np.float64)
continued = state.batch_indicator([new_high, new_low, new_close, new_volume])
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 volume = Float64Array.from([5653100, 6447400, 7690900, 3831400, 4455100, 3798000, 3936200, 4732000, 4841300, 3915300, 6830800, 6694100, 5293600, 7985800, 4807900]);
const [outputs, state] = ti.ad.indicator([high, low, close, volume], []);
console.log('AD:', outputs[0]);
// State continuation
const n = high.length - 5;
const [, state2] = ti.ad.indicator([high.slice(0, n), low.slice(0, n), close.slice(0, n), volume.slice(0, n)], []);
const continued = state2.batchIndicator([high.slice(n), low.slice(n), close.slice(n), volume.slice(n)]);
console.log('Continued AD:', 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 volume = [5653100, 6447400, 7690900, 3831400, 4455100, 3798000, 3936200, 4732000, 4841300, 3915300, 6830800, 6694100, 5293600, 7985800, 4807900];
const [outputs, state] = ti.ad.indicator([high, low, close, volume], []);
console.log('AD:', outputs[0]);
// State continuation
const n = high.length - 5;
const [, state2] = ti.ad.indicator([high.slice(0, n), low.slice(0, n), close.slice(0, n), volume.slice(0, n)], []);
const continued = state2.batchIndicator([high.slice(n), low.slice(n), close.slice(n), volume.slice(n)]);
console.log('Continued AD:', continued[0]);
SIMD¶
By assets — same options, N assets in parallel:
use tulip_rs::indicators::ad::{Ad, Indicator};
let inputs: [&[&[f64]; 4]; 4] = [
&[h1.as_slice(), l1.as_slice(), c1.as_slice(), v1.as_slice()],
&[h2.as_slice(), l2.as_slice(), c2.as_slice(), v2.as_slice()],
&[h3.as_slice(), l3.as_slice(), c3.as_slice(), v3.as_slice()],
&[h4.as_slice(), l4.as_slice(), c4.as_slice(), v4.as_slice()],
];
let results = indicator_by_assets::<4>(&inputs, &[], None).unwrap();
for (i, asset_outputs) in results.iter().enumerate() {
println!("Asset {}: {:?}", i + 1, asset_outputs[0]);
}
This indicator has no options, so by-options SIMD does not apply.
By assets — same option applied to 4 assets in one call (N must be 2/4/8/16):
double a1_high[] = {82.15, 81.89, 83.03, 83.30, 83.85, 83.90, 83.33, 84.30, 84.84, 85.00};
double a1_low[] = {81.29, 80.64, 81.31, 82.65, 83.07, 83.11, 82.49, 82.30, 84.15, 84.11};
double a1_close[] = {81.59, 81.06, 82.87, 83.00, 83.61, 83.15, 82.84, 83.99, 84.55, 84.36};
double a1_volume[] = {1200.0, 1400.0, 1100.0, 1600.0, 1300.0, 900.0, 1500.0, 1800.0, 1000.0, 1700.0};
double a2_high[] = {84.30, 83.78, 86.06, 86.60, 87.70, 87.80, 86.66, 88.60, 89.68, 90.00};
double a2_low[] = {82.58, 81.28, 83.62, 84.30, 85.14, 85.22, 83.48, 85.70, 87.30, 87.22};
double a2_close[] = {83.18, 82.12, 85.74, 86.00, 87.22, 86.30, 85.68, 87.98, 89.10, 88.72};
double a2_volume[] = {2400.0, 2800.0, 2200.0, 3200.0, 2600.0, 1800.0, 3000.0, 3600.0, 2000.0, 3400.0};
double a3_high[] = {75.00, 74.50, 76.00, 76.30, 76.85, 76.90, 76.33, 77.30, 77.84, 78.00};
double a3_low[] = {74.29, 73.64, 75.31, 75.65, 76.07, 76.11, 75.49, 75.30, 77.15, 77.11};
double a3_close[] = {74.59, 74.06, 76.87, 76.00, 76.61, 76.15, 75.84, 76.99, 77.55, 77.36};
double a3_volume[] = {600.0, 700.0, 550.0, 800.0, 650.0, 450.0, 750.0, 900.0, 500.0, 850.0};
double a4_high[] = {90.00, 89.25, 91.50, 91.80, 92.30, 92.35, 91.75, 92.75, 93.25, 93.40};
double a4_low[] = {88.65, 88.00, 90.00, 90.20, 91.00, 91.05, 90.40, 91.30, 92.10, 92.25};
double a4_close[] = {89.30, 88.60, 91.00, 91.20, 91.75, 91.70, 91.10, 92.20, 92.65, 92.80};
double a4_volume[] = {3600.0, 4200.0, 3300.0, 4800.0, 3900.0, 2700.0, 4500.0, 5400.0, 3000.0, 5100.0};
const double *asset1[AD_INPUTS] = {a1_high, a1_low, a1_close, a1_volume};
const double *asset2[AD_INPUTS] = {a2_high, a2_low, a2_close, a2_volume};
const double *asset3[AD_INPUTS] = {a3_high, a3_low, a3_close, a3_volume};
const double *asset4[AD_INPUTS] = {a4_high, a4_low, a4_close, a4_volume};
const double *const *const simd_inputs[4] = {asset1, asset2, asset3, asset4};
CSimdResult r = ad_simd_by_assets(simd_inputs, 4, 10, options, NULL, 0);
for (uintptr_t i = 0; i < r.num_results; i++) {
/* r.outputs[i][0] -> asset i's series, length r.output_lens[i][0] */
ad_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):
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}
v1 := []float64{1200.0, 1400.0, 1100.0, 1600.0, 1300.0, 900.0, 1500.0, 1800.0, 1000.0, 1700.0}
// Reuse the same data for assets 2–4 in this example
h2, l2, c2, v2 := h1, l1, c1, v1
h3, l3, c3, v3 := h1, l1, c1, v1
h4, l4, c4, v4 := h1, l1, c1, v1
assets := [][indicators.AdInputs][]float64{{h1, l1, c1, v1}, {h2, l2, c2, v2}, {h3, l3, c3, v3}, {h4, l4, c4, v4}}
sim, _ := indicators.Ad.SimdByAssets(assets, []float64{}, 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 assets — same options applied to 4 assets in parallel (N must be 2, 4, 8, or 16):
import org.tuliprs.*;
import org.tuliprs.indicators.Ad;
// Each entry lists one asset's input series (h1..v4 as in the C tab).
double[][][] assets = {{h1, l1, c1, v1}, {h2, l2, c2, v2}, {h3, l3, c3, v3}, {h4, l4, c4, v4}};
try (SimdResult sim = Ad.simdByAssets(assets, new double[] {}, 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)
This indicator has no options, so by-options SIMD does not offer.
By assets — applied to 4 assets in parallel:
const simdInputs = [
[high.slice(), low.slice(), close.slice(), volume.slice()],
[high.map(v => v * 1.1), low.map(v => v * 1.1), close.map(v => v * 1.1), volume.map(v => v * 1.1)],
[high.map(v => v * 0.9), low.map(v => v * 0.9), close.map(v => v * 0.9), volume.map(v => v * 0.9)],
[high.map(v => v * 1.02), low.map(v => v * 1.02), close.map(v => v * 1.02), volume.map(v => v * 1.02)],
];
const [results] = ti.ad.simdByAssets(simdInputs, []);
results.forEach((out, i) => console.log(`Asset ${i + 1}:`, out[0]));
This indicator has no options, so by-options SIMD does not apply.