Stochastic Oscillator¶
Compares a security's closing price to its price range over a given period. %K is the raw stochastic value; %D is a smoothed moving average of %K.
Inputs: [high, low, close] | Options: [k_period, k_slowing_period, d_period] | Outputs: [stoch_k, stoch_d]
Basic¶
use tulip_rs::indicators::stoch::{Stoch, TIndicatorState, Indicator};
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()];
// Options: [k_period, k_slowing_period, d_period]
let (outputs, _state) = Stoch::indicator(&inputs, &[14.0, 3.0, 3.0], None).unwrap();
println!("Stoch %K: {:?}", outputs[0]);
println!("Stoch %D: {:?}", outputs[1]);
// State continuation
let inputs2 = [&high[..8], &low[..8], &close[..8]];
let (outputs2, mut state) = Stoch::indicator(&inputs2, &[14.0, 3.0, 3.0], None).unwrap();
println!("Partial %K: {:?}", outputs2[0]);
let new_inputs = [&high[8..], &low[8..], &close[8..]];
let continued = state.batch_indicator(&new_inputs, None).unwrap();
println!("Continued %K: {:?}", continued[0]);
println!("Continued %D: {:?}", continued[1]);
#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 options[STOCH_OPTIONS] = {14.0, 3.0, 3.0}; // k_period, k_slowing_period, d_period
const double *inputs[STOCH_INPUTS] = {high, low, close};
/* Full computation */
CIndicatorResult r = stoch_indicator(inputs, 10, options, NULL, 0);
/* r.outputs[0] -> slowk (stoch_k), length r.output_lens[0] */
/* r.outputs[1] -> slowd (stoch_d), length r.output_lens[1] */
tulip_ffi_result_free(r);
stoch_state_free(r.state);
/* Partial computation + state continuation */
CIndicatorResult p = stoch_indicator(inputs, 8, options, NULL, 0);
double new_high[] = {84.55, 85.00};
double new_low[] = {84.15, 84.11};
double new_close[] = {84.55, 84.36};
const double *new_inputs[STOCH_INPUTS] = {new_high, new_low, new_close};
CBatchResult b = stoch_batch(p.state, new_inputs, 2, NULL, 0);
/* b.outputs[0] -> slowk for just the two new bars */
/* b.outputs[1] -> slowd for just the two new bars */
tulip_ffi_batch_result_free(b);
tulip_ffi_result_free(p);
stoch_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, 3.0, 3.0} // k_period, k_slowing_period, d_period
// Full computation — Rows are zero-copy views, valid until Close.
res, st, _ := indicators.Stoch.Indicator(high, low, close, options, nil)
fmt.Println(res.Rows[0]) // Stoch %K values
fmt.Println(res.Rows[1]) // Stoch %D values
res.Close()
st.Close()
// Partial computation + state continuation.
res2, st2, _ := indicators.Stoch.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 %K values
fmt.Println(batch.Rows[1]) // continued %D values
batch.Close()
st2.Close()
import org.tuliprs.*;
import org.tuliprs.indicators.Stoch;
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, 3.0, 3.0}; // k_period, k_slowing_period, d_period
// Full computation — output rows are zero-copy views, valid until close().
Outcome oc = Stoch.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))); // Stoch %K values
System.out.println(java.util.Arrays.toString(res.toDoubleArray(1))); // Stoch %D values
}
// Partial computation + state continuation.
int n = 8;
Outcome p = Stoch.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 %K values
System.out.println(java.util.Arrays.toString(br.toDoubleArray(1))); // continued %D 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)
# Options: [k_period, k_slowing_period, d_period]
outputs, state = tulip_rs.indicators.stoch.indicator([high, low, close], [14.0, 3.0, 3.0])
print("Stoch %K:", outputs[0])
print("Stoch %D:", outputs[1])
# State continuation
outputs2, state = tulip_rs.indicators.stoch.indicator([high[:8], low[:8], close[:8]], [14.0, 3.0, 3.0])
continued = state.batch_indicator([high[8:], low[8:], close[8:]])
print("Continued %K:", continued[0])
print("Continued %D:", continued[1])
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.stoch.indicator([high, low, close], [5, 3, 3]);
console.log('%K:', outputs[0]);
console.log('%D:', outputs[1]);
// State continuation
const n = high.length - 5;
const [, state2] = ti.stoch.indicator([high.slice(0, n), low.slice(0, n), close.slice(0, n)], [5, 3, 3]);
const continued = state2.batchIndicator([high.slice(n), low.slice(n), close.slice(n)]);
console.log('Continued %K:', 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.stoch.indicator([high, low, close], [5, 3, 3]);
console.log('%K:', outputs[0]);
console.log('%D:', outputs[1]);
// State continuation
const n = high.length - 5;
const [, state2] = ti.stoch.indicator([high.slice(0, n), low.slice(0, n), close.slice(0, n)], [5, 3, 3]);
const continued = state2.batchIndicator([high.slice(n), low.slice(n), close.slice(n)]);
console.log('Continued %K:', continued[0]);
SIMD¶
By assets — same options applied to 4 assets in parallel:
use tulip_rs::indicators::stoch::{Stoch, Indicator};
let h1 = vec![82.15, 81.89, 83.03, 83.30, 83.85, 83.90, 83.33, 84.30, 84.84, 85.00_f64];
let l1 = vec![81.29, 80.64, 81.31, 82.65, 83.07, 83.11, 82.49, 82.30, 84.15, 84.11_f64];
let c1 = vec![81.59, 81.06, 82.87, 83.00, 83.61, 83.15, 82.84, 83.99, 84.55, 84.36_f64];
// Reuse the same data for assets 2–4 in this example
let h2 = h1.clone(); let l2 = l1.clone(); let c2 = c1.clone();
let h3 = h1.clone(); let l3 = l1.clone(); let c3 = c1.clone();
let h4 = h1.clone(); let l4 = l1.clone(); let c4 = c1.clone();
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 = Stoch::indicator_by_assets::<4>(&inputs, &[14.0, 3.0, 3.0], None).unwrap();
for (i, asset_outputs) in results.iter().enumerate() {
println!("Asset {} %K: {:?}", i + 1, asset_outputs[0]);
println!("Asset {} %D: {:?}", i + 1, asset_outputs[1]);
}
By options — same asset, 4 different option sets in parallel:
use tulip_rs::indicators::stoch::{Stoch, IndicatorByOptions};
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 opts: [&[f64; 3]; 4] = [
&[5.0, 3.0, 3.0],
&[9.0, 3.0, 3.0],
&[14.0, 3.0, 3.0],
&[21.0, 3.0, 3.0],
];
let inputs = [high.as_slice(), low.as_slice(), close.as_slice()];
let results = Stoch::indicator_by_options::<4>(&inputs, &opts, None).unwrap();
for (i, opt_outputs) in results.iter().enumerate() {
println!("Option set {} %K: {:?}", i + 1, opt_outputs[0]);
println!("Option set {} %D: {:?}", i + 1, opt_outputs[1]);
}
By assets — same period applied to 4 assets in parallel:
double h1[] = {82.15, 81.89, 83.03, 83.30, 83.85, 83.90, 83.33, 84.30, 84.84, 85.00};
double l1[] = {81.29, 80.64, 81.31, 82.65, 83.07, 83.11, 82.49, 82.30, 84.15, 84.11};
double c1[] = {81.59, 81.06, 82.87, 83.00, 83.61, 83.15, 82.84, 83.99, 84.55, 84.36};
double h2[] = {h1[0]*1.1, h1[1]*1.1, h1[2]*1.1, h1[3]*1.1, h1[4]*1.1,
h1[5]*1.1, h1[6]*1.1, h1[7]*1.1, h1[8]*1.1, h1[9]*1.1};
double l2[] = {l1[0]*1.1, l1[1]*1.1, l1[2]*1.1, l1[3]*1.1, l1[4]*1.1,
l1[5]*1.1, l1[6]*1.1, l1[7]*1.1, l1[8]*1.1, l1[9]*1.1};
double c2[] = {c1[0]*1.1, c1[1]*1.1, c1[2]*1.1, c1[3]*1.1, c1[4]*1.1,
c1[5]*1.1, c1[6]*1.1, c1[7]*1.1, c1[8]*1.1, c1[9]*1.1};
double h3[] = {h1[0]*0.9, h1[1]*0.9, h1[2]*0.9, h1[3]*0.9, h1[4]*0.9,
h1[5]*0.9, h1[6]*0.9, h1[7]*0.9, h1[8]*0.9, h1[9]*0.9};
double l3[] = {l1[0]*0.9, l1[1]*0.9, l1[2]*0.9, l1[3]*0.9, l1[4]*0.9,
l1[5]*0.9, l1[6]*0.9, l1[7]*0.9, l1[8]*0.9, l1[9]*0.9};
double c3[] = {c1[0]*0.9, c1[1]*0.9, c1[2]*0.9, c1[3]*0.9, c1[4]*0.9,
c1[5]*0.9, c1[6]*0.9, c1[7]*0.9, c1[8]*0.9, c1[9]*0.9};
double h4[] = {h1[0]*1.02, h1[1]*1.02, h1[2]*1.02, h1[3]*1.02, h1[4]*1.02,
h1[5]*1.02, h1[6]*1.02, h1[7]*1.02, h1[8]*1.02, h1[9]*1.02};
double l4[] = {l1[0]*1.02, l1[1]*1.02, l1[2]*1.02, l1[3]*1.02, l1[4]*1.02,
l1[5]*1.02, l1[6]*1.02, l1[7]*1.02, l1[8]*1.02, l1[9]*1.02};
double c4[] = {c1[0]*1.02, c1[1]*1.02, c1[2]*1.02, c1[3]*1.02, c1[4]*1.02,
c1[5]*1.02, c1[6]*1.02, c1[7]*1.02, c1[8]*1.02, c1[9]*1.02};
const double *const asset1[STOCH_INPUTS] = {h1, l1, c1};
const double *const asset2[STOCH_INPUTS] = {h2, l2, c2};
const double *const asset3[STOCH_INPUTS] = {h3, l3, c3};
const double *const asset4[STOCH_INPUTS] = {h4, l4, c4};
const double *const *const simd_inputs[4] = {asset1, asset2, asset3, asset4};
CSimdResult r = stoch_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 slowk, length r.output_lens[i][0] */
/* r.outputs[i][1] -> asset i's slowd, length r.output_lens[i][1] */
stoch_state_free(r.states[i]);
}
tulip_ffi_simd_result_free(r);
By options — same asset, 4 different option sets in parallel:
double h[] = {82.15, 81.89, 83.03, 83.30, 83.85, 83.90, 83.33, 84.30, 84.84, 85.00};
double l[] = {81.29, 80.64, 81.31, 82.65, 83.07, 83.11, 82.49, 82.30, 84.15, 84.11};
double c[] = {81.59, 81.06, 82.87, 83.00, 83.61, 83.15, 82.84, 83.99, 84.55, 84.36};
/* Tile the series 20x so longer-period option sets have enough data */
#define EXPANDED_LEN (10 * 20)
static double high_expanded[EXPANDED_LEN];
static double low_expanded[EXPANDED_LEN];
static double close_expanded[EXPANDED_LEN];
for (size_t i = 0; i < 20; i++) {
for (size_t j = 0; j < 10; j++) {
high_expanded[i * 10 + j] = h[j];
low_expanded[i * 10 + j] = l[j];
close_expanded[i * 10 + j] = c[j];
}
}
const double *inputs[STOCH_INPUTS] = {high_expanded, low_expanded, close_expanded};
static const double o5[STOCH_OPTIONS] = {5.0, 3.0, 3.0};
static const double o9[STOCH_OPTIONS] = {9.0, 3.0, 3.0};
static const double o14[STOCH_OPTIONS] = {14.0, 3.0, 3.0};
static const double o21[STOCH_OPTIONS] = {21.0, 3.0, 3.0};
const double *const simd_opts[4] = {o5, o9, o14, o21};
CSimdResult r = stoch_simd_by_options(inputs, EXPANDED_LEN, simd_opts, 4, NULL, 0);
for (uintptr_t i = 0; i < r.num_results; i++) {
/* r.outputs[i][0] -> asset i's slowk, length r.output_lens[i][0] */
/* r.outputs[i][1] -> asset i's slowd, length r.output_lens[i][1] */
stoch_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}
// 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.StochInputs][]float64{{h1, l1, c1}, {h2, l2, c2}, {h3, l3, c3}, {h4, l4, c4}}
sim, _ := indicators.Stoch.SimdByAssets(assets, []float64{14.0, 3.0, 3.0}, nil)
for i, lanes := range sim.Results {
fmt.Printf("Asset %d %%K: %v\n", i+1, lanes[0])
fmt.Printf("Asset %d %%D: %v\n", i+1, lanes[1])
}
sim.Close() // frees every lane state, then the SIMD buffers
By options — same asset, 4 different option sets 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.Stoch.SimdByOptions(high, low, close, [][]float64{{5, 3, 3}, {9, 3, 3}, {14, 3, 3}, {21, 3, 3}}, nil)
for i, lanes := range sim2.Results {
fmt.Printf("Option set %d %%K: %v\n", i+1, lanes[0])
fmt.Printf("Option set %d %%D: %v\n", i+1, lanes[1])
}
sim2.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.Stoch;
double[] h1 = {82.15, 81.89, 83.03, 83.30, 83.85, 83.90, 83.33, 84.30, 84.84, 85.00};
double[] l1 = {81.29, 80.64, 81.31, 82.65, 83.07, 83.11, 82.49, 82.30, 84.15, 84.11};
double[] c1 = {81.59, 81.06, 82.87, 83.00, 83.61, 83.15, 82.84, 83.99, 84.55, 84.36};
double[] h2 = {h1[0]*1.1, h1[1]*1.1, h1[2]*1.1, h1[3]*1.1, h1[4]*1.1,
h1[5]*1.1, h1[6]*1.1, h1[7]*1.1, h1[8]*1.1, h1[9]*1.1};
double[] l2 = {l1[0]*1.1, l1[1]*1.1, l1[2]*1.1, l1[3]*1.1, l1[4]*1.1,
l1[5]*1.1, l1[6]*1.1, l1[7]*1.1, l1[8]*1.1, l1[9]*1.1};
double[] c2 = {c1[0]*1.1, c1[1]*1.1, c1[2]*1.1, c1[3]*1.1, c1[4]*1.1,
c1[5]*1.1, c1[6]*1.1, c1[7]*1.1, c1[8]*1.1, c1[9]*1.1};
double[] h3 = {h1[0]*0.9, h1[1]*0.9, h1[2]*0.9, h1[3]*0.9, h1[4]*0.9,
h1[5]*0.9, h1[6]*0.9, h1[7]*0.9, h1[8]*0.9, h1[9]*0.9};
double[] l3 = {l1[0]*0.9, l1[1]*0.9, l1[2]*0.9, l1[3]*0.9, l1[4]*0.9,
l1[5]*0.9, l1[6]*0.9, l1[7]*0.9, l1[8]*0.9, h1[9]*0.9};
double[] c3 = {c1[0]*0.9, c1[1]*0.9, c1[2]*0.9, c1[3]*0.9, c1[4]*0.9,
c1[5]*0.9, c1[6]*0.9, c1[7]*0.9, c1[8]*0.9, c1[9]*0.9};
double[] h4 = {h1[0]*1.02, h1[1]*1.02, h1[2]*1.02, h1[3]*1.02, h1[4]*1.02,
h1[5]*1.02, h1[6]*1.02, h1[7]*1.02, h1[8]*1.02, h1[9]*1.02};
double[] l4 = {l1[0]*1.02, l1[1]*1.02, l1[2]*1.02, l1[3]*1.02, l1[4]*1.02,
l1[5]*1.02, l1[6]*1.02, l1[7]*1.02, l1[8]*1.02, l1[9]*1.02};
double[] c4 = {c1[0]*1.02, c1[1]*1.02, c1[2]*1.02, c1[3]*1.02, c1[4]*1.02,
c1[5]*1.02, c1[6]*1.02, c1[7]*1.02, c1[8]*1.02, c1[9]*1.02};
// One entry per asset; each asset lists its INPUTS series.
double[][][] assets = {{h1, l1, c1}, {h2, l2, c2}, {h3, l3, c3}, {h4, l4, c4}};
try (SimdResult sim = Stoch.simdByAssets(assets, new double[] {14.0, 3.0, 3.0}, null)) {
for (int i = 0; i < sim.numResults(); i++) {
System.out.printf("Asset %d %%K: %s%n", i + 1,
java.util.Arrays.toString(sim.toDoubleArray(i, 0)));
System.out.printf("Asset %d %%D: %s%n", i + 1,
java.util.Arrays.toString(sim.toDoubleArray(i, 1)));
}
} // 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.Stoch;
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};
try (SimdResult sim = Stoch.simdByOptions(new double[][] {high, low, close},
new double[][] {{5, 3, 3}, {9, 3, 3}, {14, 3, 3}, {21, 3, 3}}, null)) {
for (int i = 0; i < sim.numResults(); i++) {
System.out.printf("Option set %d %%K: %s%n", i + 1,
java.util.Arrays.toString(sim.toDoubleArray(i, 0)));
System.out.printf("Option set %d %%D: %s%n", i + 1,
java.util.Arrays.toString(sim.toDoubleArray(i, 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
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)
h1, l1, c1 = high, low, close
h2, l2, c2 = high + 0.5, low + 0.5, close + 0.5
h3, l3, c3 = high - 0.5, low - 0.5, close - 0.5
h4, l4, c4 = high * 1.01, low * 1.01, close * 1.01
simd_inputs = [[h1, l1, c1], [h2, l2, c2], [h3, l3, c3], [h4, l4, c4]]
outputs_list, states = tulip_rs.indicators.stoch.simd_by_assets(simd_inputs, [14.0, 3.0, 3.0])
for i, out in enumerate(outputs_list):
print(f"Asset {i + 1} %K: {out[0]}")
print(f"Asset {i + 1} %D: {out[1]}")
By options — same asset, N different option sets in parallel:
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)
simd_options = [
[5.0, 3.0, 3.0],
[9.0, 3.0, 3.0],
[14.0, 3.0, 3.0],
[21.0, 3.0, 3.0],
]
outputs_list, states = tulip_rs.indicators.stoch.simd_by_options([high, low, close], simd_options)
for i, out in enumerate(outputs_list):
print(f"Option set {i + 1} %K: {out[0]}")
print(f"Option set {i + 1} %D: {out[1]}")
By assets — same options 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.stoch.simdByAssets(simdInputs, [5, 3, 3]);
results.forEach((out, i) => console.log(`Asset ${i + 1} %K:`, out[0], '%D:', out[1]));
By options — same asset, 4 different option sets in parallel: