QStick — qstick¶
A moving average of (Close - Open) over period bars, summarising buying or selling pressure.
Inputs: [open, close] | Options: [period] | Outputs: [qstick]
Basic¶
use tulip_rs::indicators::qstick::{QStick, Indicator, TIndicatorState};
let open_ = vec![81.85, 81.20, 81.55, 82.91, 83.10,
83.41, 82.71, 82.70, 84.20, 84.25_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 = [open_.as_slice(), close.as_slice()];
let (outputs, mut state) = QStick::indicator(&inputs, &[14.0], None).unwrap();
println!("{:?}", outputs[0]);
// State continuation — feed new bars without reprocessing history
let partial_open = open_[..8].to_vec();
let partial_close = close[..8].to_vec();
let (outputs2, mut state) = QStick::indicator(&[partial_open.as_slice(), partial_close.as_slice()], &[14.0], None).unwrap();
println!("{:?}", outputs2[0]);
let new_open = vec![84.03_f64];
let new_close = vec![85.53_f64];
let continued = state.batch_indicator(&[new_open.as_slice(), new_close.as_slice()], &[14.0], None).unwrap();
println!("{:?}", continued[0]);
#include "tulip_rs_ffi.h"
double open_[] = {81.85, 81.20, 81.55, 82.91, 83.10, 83.41, 82.71, 82.70, 84.20, 84.25};
double close[] = {81.59, 81.06, 82.87, 83.00, 83.61, 83.15, 82.84, 83.99, 84.55, 84.36};
double options[QSTICK_OPTIONS] = {5.0}; // period
const double *inputs[QSTICK_INPUTS] = {open_, close};
/* Full computation */
CIndicatorResult r = qstick_indicator(inputs, 10, options, NULL, 0);
/* r.outputs[0] -> QStick(5) series, length r.output_lens[0] */
tulip_ffi_result_free(r);
qstick_state_free(r.state);
/* Partial computation + state continuation */
CIndicatorResult p = qstick_indicator(inputs, 8, options, NULL, 0);
double new_open[] = {84.03};
double new_close[] = {85.53};
const double *new_inputs[QSTICK_INPUTS] = {new_open, new_close};
CBatchResult b = qstick_batch(p.state, new_inputs, 1, NULL, 0);
/* b.outputs[0] -> QStick for the new bar */
tulip_ffi_batch_result_free(b);
tulip_ffi_result_free(p);
qstick_state_free(p.state);
import "github.com/me60732/tulip_rs_go/indicators"
open := []float64{81.85, 81.20, 81.55, 82.91, 83.10,
83.41, 82.71, 82.70, 84.20, 84.25}
close := []float64{81.59, 81.06, 82.87, 83.00, 83.61,
83.15, 82.84, 83.99, 84.55, 84.36}
options := []float64{5.0}
// Full computation — Rows are zero-copy views, valid until Close.
res, st, _ := indicators.Qstick.Indicator(open, close, options, nil)
fmt.Println(res.Rows[0]) // QStick(5) values
res.Close()
st.Close()
// Partial computation + state continuation.
res2, st2, _ := indicators.Qstick.Indicator(open[:8], close[:8], options, nil)
res2.Close() // outputs consumed or closed; state stays live
batch, _ := st2.Batch(open[8:], close[8:], nil)
fmt.Println(batch.Rows[0]) // continued QStick values
batch.Close()
st2.Close()
import org.tuliprs.*;
import org.tuliprs.indicators.Qstick;
double[] open_ = {81.85, 81.20, 81.55, 82.91, 83.10, 83.41, 82.71, 82.70, 84.20, 84.25};
double[] close = {81.59, 81.06, 82.87, 83.00, 83.61, 83.15, 82.84, 83.99, 84.55, 84.36};
double[] options = {5.0};
// Full computation — output rows are zero-copy views, valid until close().
Outcome oc = Qstick.indicator(new double[][] {open_, close}, options);
try (Result res = oc.result(); State st = oc.state()) {
System.out.println(java.util.Arrays.toString(res.toDoubleArray(0))); // QStick(5) values
}
// Partial computation + state continuation.
int n = 8;
Outcome p = Qstick.indicator(new double[][] {
java.util.Arrays.copyOfRange(open_, 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(open_, n, 10),
java.util.Arrays.copyOfRange(close, n, 10)});
try (br) {
System.out.println(java.util.Arrays.toString(br.toDoubleArray(0))); // continued QStick values
}
}
import numpy as np
import tulip_rs
open_ = np.array([81.85, 81.20, 81.55, 82.91, 83.10, 83.41, 82.71, 82.70, 84.20, 84.25], 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.qstick.indicator([open_, close], [14.0])
print(outputs[0])
import * as ti from 'tulip-rs-node';
const open_ = [81.85, 81.20, 81.55, 82.91, 83.10, 83.41, 82.71, 82.70, 84.20, 84.25, 84.03, 85.45, 86.18, 88.00, 87.30];
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.qstick.indicator([open_, close], [14]);
console.log('QStick(14):', outputs[0]);
// State continuation
const n = close.length - 5;
const [, state2] = ti.qstick.indicator([open_.slice(0, n), close.slice(0, n)], [14]);
const continued = state2.batchIndicator([open_.slice(n), close.slice(n)]);
console.log('Continued QStick:', continued[0]);
import { init } from 'tulip-rs-wasm';
import * as ti from 'tulip-rs-wasm';
await init(); // bundler resolves the WASM asset automatically
const open_ = [81.85, 81.20, 81.55, 82.91, 83.10, 83.41, 82.71, 82.70, 84.20, 84.25, 84.03, 85.45, 86.18, 88.00, 87.30];
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.qstick.indicator([open_, close], [14]);
console.log('QStick(14):', outputs[0]);
// State continuation
const n = close.length - 5;
const [, state2] = ti.qstick.indicator([open_.slice(0, n), close.slice(0, n)], [14]);
const continued = state2.batchIndicator([open_.slice(n), close.slice(n)]);
console.log('Continued QStick:', continued[0]);
SIMD¶
By assets — same options, N assets in parallel:
use tulip_rs::indicators::qstick::{QStick, Indicator};
let inputs: [&[&[f64]; 2]; 4] = [
&[o1.as_slice(), c1.as_slice()],
&[o2.as_slice(), c2.as_slice()],
&[o3.as_slice(), c3.as_slice()],
&[o4.as_slice(), c4.as_slice()],
];
let results = QStick::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:
use tulip_rs::indicators::qstick::{QStick, IndicatorByOptions};
let inputs_single = [open_.as_slice(), close.as_slice()];
let opts: [&[f64; 1]; 4] = [&[5.0], &[10.0], &[14.0], &[20.0]];
let results = QStick::indicator_by_options::<4>(&inputs_single, &opts, None).unwrap();
for (i, asset_outputs) in results.iter().enumerate() {
println!("Option {}: {:?}", i + 1, asset_outputs[0]);
}
By assets — same options applied to 4 assets in one call (N must be 2/4/8/16):
double a1_open[] = {81.85, 81.20, 81.55, 82.91, 83.10, 83.41, 82.71, 82.70, 84.20, 84.25};
double a1_close[] = {81.59, 81.06, 82.87, 83.00, 83.61, 83.15, 82.84, 83.99, 84.55, 84.36};
const double *const asset1[QSTICK_INPUTS] = {a1_open, a1_close};
// Asset 2: scaled up (+20%)
double a2_open[10], a2_close[10];
for (size_t i = 0; i < 10; i++) {
a2_open[i] = a1_open[i] * 1.2;
a2_close[i] = a1_close[i] * 1.2;
}
const double *const asset2[QSTICK_INPUTS] = {a2_open, a2_close};
// Asset 3: different upward trend
double a3_open[10], a3_close[10];
for (size_t i = 0; i < 10; i++) {
a3_open[i] = 90.0 + (double)i * 0.5 + a1_open[i] * 0.1;
a3_close[i] = 90.0 + (double)i * 0.5 + a1_close[i] * 0.1;
}
const double *const asset3[QSTICK_INPUTS] = {a3_open, a3_close};
// Asset 4: downward trend
double a4_open[10], a4_close[10];
for (size_t i = 0; i < 10; i++) {
a4_open[i] = 100.0 - (double)i * 0.3 + a1_open[i] * 0.05;
a4_close[i] = 100.0 - (double)i * 0.3 + a1_close[i] * 0.05;
}
const double *const asset4[QSTICK_INPUTS] = {a4_open, a4_close};
const double *const *const simd_inputs[4] = {asset1, asset2, asset3, asset4};
CSimdResult r = qstick_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] */
qstick_state_free(r.states[i]);
}
tulip_ffi_simd_result_free(r);
By options — same asset, 4 different periods in one call:
double o3[] = {3.0}, o5[] = {5.0}, o7[] = {7.0}, o10[] = {10.0};
const double *const simd_opts[4] = {o3, o5, o7, o10};
CSimdResult r = qstick_simd_by_options(inputs, 10, simd_opts, 4, NULL, 0);
/* r.outputs[i] -> results for option set i (periods 3/5/7/10) */
for (uintptr_t i = 0; i < r.num_results; i++) qstick_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):
a1_open := []float64{81.85, 81.20, 81.55, 82.91, 83.10, 83.41, 82.71, 82.70, 84.20, 84.25}
a1_close := []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
a2_open, a2_close := a1_open, a1_close
a3_open, a3_close := a1_open, a1_close
a4_open, a4_close := a1_open, a1_close
assets := [][indicators.QstickInputs][]float64{{a1_open, a1_close}, {a2_open, a2_close},
{a3_open, a3_close}, {a4_open, a4_close}}
sim, _ := indicators.Qstick.SimdByAssets(assets, []float64{5.0}, 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:
open_ := []float64{81.85, 81.20, 81.55, 82.91, 83.10, 83.41, 82.71, 82.70, 84.20, 84.25}
close := []float64{81.59, 81.06, 82.87, 83.00, 83.61, 83.15, 82.84, 83.99, 84.55, 84.36}
sim2, _ := indicators.Qstick.SimdByOptions(open_, close, [][]float64{{5}, {10}, {14}, {20}}, 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 (lane counts 2/4/8/16):
import org.tuliprs.*;
import org.tuliprs.indicators.Qstick;
double[] a1_open = {81.85, 81.20, 81.55, 82.91, 83.10, 83.41, 82.71, 82.70, 84.20, 84.25};
double[] a1_close = {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
double[] a2_open = a1_open;
double[] a2_close = a1_close;
double[] a3_open = a1_open;
double[] a3_close = a1_close;
double[] a4_open = a1_open;
double[] a4_close = a1_close;
// One entry per asset; each asset lists its INPUTS series.
double[][][] assets = {{a1_open, a1_close}, {a2_open, a2_close},
{a3_open, a3_close}, {a4_open, a4_close}};
try (SimdResult sim = Qstick.simdByAssets(assets, new double[] {5.0}, 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.Qstick;
double[] open_ = {81.85, 81.20, 81.55, 82.91, 83.10, 83.41, 82.71, 82.70, 84.20, 84.25};
double[] close = {81.59, 81.06, 82.87, 83.00, 83.61, 83.15, 82.84, 83.99, 84.55, 84.36};
try (SimdResult sim = Qstick.simdByOptions(new double[][] {open_, close},
new double[][] {{5}, {10}, {14}, {20}}, 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 = [[o1, c1], [o2, c2], [o3, c3], [o4, c4]]
outputs_list, states = tulip_rs.indicators.qstick.simd_by_assets(simd_inputs, [14.0])
By options — same asset, N option sets in parallel:
By assets — same period applied to 4 assets in parallel:
const simdInputs = [
[[...open_], close.slice()],
[open_.map(v => v * 1.1), close.map(v => v * 1.1)],
[open_.map(v => v * 0.9), close.map(v => v * 0.9)],
[open_.map(v => v * 1.02), close.map(v => v * 1.02)],
];
const [results] = ti.qstick.simdByAssets(simdInputs, [14]);
results.forEach((out, i) => console.log(`Asset ${i + 1}:`, out[0]));
By options — same asset, 4 different periods in parallel: