StdDev — Standard Deviation¶
Rolling standard deviation of the price series over period bars.
Inputs: [real] | Options: [period] | Outputs: [stddev]
Basic¶
use tulip_rs::indicators::stddev::{StdDev, 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 (outputs, mut state) = StdDev::indicator(&[close.as_slice()], &[20.0], None).unwrap();
println!("{:?}", outputs[0]); // StdDev values
// State continuation — feed new bars without reprocessing history
let partial = close[..8].to_vec();
let (outputs2, mut state) = StdDev::indicator(&[partial.as_slice()], &[20.0], None).unwrap();
println!("{:?}", outputs2[0]);
let new_close = vec![85.53_f64];
let continued = state.batch_indicator(&[new_close.as_slice()], None).unwrap();
println!("{:?}", continued[0]);
#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};
double options[STDDEV_OPTIONS] = {20.0}; // period
const double *inputs[STDDEV_INPUTS] = {close};
/* Full computation */
CIndicatorResult r = stddev_indicator(inputs, 10, options, NULL, 0);
printf("StdDev[0]: %.4f\n", r.outputs[0][0]); // outputs[0] is the stddev series
tulip_ffi_result_free(r);
stddev_state_free(r.state);
/* Partial computation + state continuation */
CIndicatorResult p = stddev_indicator(inputs, 8, options, NULL, 0);
double new_close[] = {85.53};
const double *new_inputs[STDDEV_INPUTS] = {new_close};
CBatchResult b = stddev_batch(p.state, new_inputs, 1, NULL, 0);
printf("Continued StdDev[0]: %.4f\n", b.outputs[0][0]);
tulip_ffi_batch_result_free(b);
tulip_ffi_result_free(p);
stddev_state_free(p.state);
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}
options := []float64{20.0} // period
// Full computation — Rows are zero-copy views, valid until Close.
res, st, _ := indicators.Stddev.Indicator(close, options, nil)
fmt.Println(res.Rows[0]) // StdDev values
res.Close()
st.Close()
// Partial computation + state continuation.
res2, st2, _ := indicators.Stddev.Indicator(close[:8], options, nil)
res2.Close() // outputs consumed or closed; state stays live
batch, _ := st2.Batch(close[8:], nil)
fmt.Println(batch.Rows[0]) // continued StdDev values
batch.Close()
st2.Close()
import org.tuliprs.*;
import org.tuliprs.indicators.Stddev;
double[] close = {81.59, 81.06, 82.87, 83.00, 83.61,
83.15, 82.84, 83.99, 84.55, 84.36};
double[] options = {20.0}; // period
// Full computation — output rows are zero-copy views, valid until close().
Outcome oc = Stddev.indicator(new double[][] {close}, options);
try (Result res = oc.result(); State st = oc.state()) {
System.out.println(java.util.Arrays.toString(res.toDoubleArray(0))); // StdDev values
}
// Partial computation + state continuation.
Outcome p = Stddev.indicator(
new double[][] {java.util.Arrays.copyOfRange(close, 0, 8)}, options);
try (Result pr = p.result(); State st = p.state()) {
Result br = st.batch(
new double[][] {java.util.Arrays.copyOfRange(close, 8, 10)});
try (br) {
System.out.println(java.util.Arrays.toString(br.toDoubleArray(0))); // continued StdDev values
}
}
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.stddev.indicator([close], [20.0])
print(outputs[0]) # StdDev values
# State continuation
new_close = np.array([85.10, 85.72], dtype=np.float64)
continued = state.batch_indicator([new_close])
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.stddev.indicator([close], [20]);
console.log('StdDev(20):', outputs[0]);
// State continuation
const [, state2] = ti.stddev.indicator([close.slice(0, -5)], [20]);
const continued = state2.batchIndicator([close.slice(-5)]);
console.log('Continued StdDev:', 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.stddev.indicator([close], [20]);
console.log('StdDev(20):', outputs[0]);
// State continuation
const [, state2] = ti.stddev.indicator([close.slice(0, -5)], [20]);
const continued = state2.batchIndicator([close.slice(-5)]);
console.log('Continued StdDev:', continued[0]);
Optional Outputs¶
stddev exposes 1 optional output: "sma". Pass a boolean mask as the third argument — one bool per optional output, in order.
use tulip_rs::indicators::stddev::{StdDev, 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 mask = [true]; // request sma
let (outputs, _state) = StdDev::indicator(&[close.as_slice()], &[5.0], Some(&mask)).unwrap();
let stddev = &outputs[0]; // stddev (primary)
let sma = &outputs[1]; // sma (optional — requested)
stddev exposes 1 optional output: sma. Pass a boolean mask as the third argument.
#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};
double options[STDDEV_OPTIONS] = {5.0}; // period
const double *inputs[STDDEV_INPUTS] = {close};
bool optional_outputs[1] = {true}; // request sma
CIndicatorResult r = stddev_indicator(inputs, 10, options, optional_outputs, 1);
printf("stddev[0]: %.4f\n", r.outputs[0][0]); // primary
printf("sma[0]: %.4f\n", r.outputs[1][0]); // optional 0: sma
tulip_ffi_result_free(r);
stddev_state_free(r.state);
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}
options := []float64{5.0} // period
mask := []bool{true} // request sma
res, st, _ := indicators.Stddev.Indicator(close, options, mask)
fmt.Println(res.Rows[0]) // stddev (primary)
fmt.Println(res.Rows[1]) // sma (optional — requested)
res.Close()
st.Close()
import org.tuliprs.*;
import org.tuliprs.indicators.Stddev;
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}; // period
boolean[] mask = {true}; // request sma
Outcome oc = Stddev.indicator(new double[][] {close}, options, mask);
try (Result res = oc.result()) {
System.out.println(java.util.Arrays.toString(res.toDoubleArray(0))); // stddev (primary)
System.out.println(java.util.Arrays.toString(res.toDoubleArray(1))); // sma (optional — requested)
}
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)
outputs, state = tulip_rs.indicators.stddev.indicator(
[close], [5.0],
optional_outputs=[True],
)
stddev = outputs[0] # stddev (primary)
sma = outputs[1] # sma (optional — requested)
stddev exposes 1 optional output: sma.
SIMD¶
By assets — same options, N assets in parallel:
use tulip_rs::indicators::stddev::{StdDev, Indicator};
let inputs: [&[&[f64]; 1]; 4] = [
&[asset1_close.as_slice()],
&[asset2_close.as_slice()],
&[asset3_close.as_slice()],
&[asset4_close.as_slice()],
];
let results = StdDev::indicator_by_assets::<4>(&inputs, &[20.0], 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::stddev::{StdDev, IndicatorByOptions};
let opts: [&[f64; 1]; 4] = [&[10.0], &[20.0], &[30.0], &[50.0]];
let results = StdDev::indicator_by_options::<4>(&[close.as_slice()], &opts, None).unwrap();
for (i, out) in results.iter().enumerate() {
println!("Period {}: {:?}", opts[i][0], out[0]);
}
By assets — same options applied to 4 assets in parallel:
double a1[] = {81.59, 81.06, 82.87, 83.00, 83.61, 83.15, 82.84, 83.99, 84.55, 84.36};
double a2[] = {72.10, 72.85, 73.40, 73.00, 74.20, 74.85, 75.10, 75.60, 76.00, 76.50};
double a3[] = {55.30, 55.80, 56.10, 56.40, 56.90, 57.20, 57.50, 57.80, 58.10, 58.40};
double a4[] = {100.1, 100.5, 101.0, 101.3, 101.8, 102.0, 102.5, 103.0, 103.3, 103.8};
const double *asset1[STDDEV_INPUTS] = {a1};
const double *asset2[STDDEV_INPUTS] = {a2};
const double *asset3[STDDEV_INPUTS] = {a3};
const double *asset4[STDDEV_INPUTS] = {a4};
const double *const *const simd_inputs[4] = {asset1, asset2, asset3, asset4};
double options[STDDEV_OPTIONS] = {20.0}; // period
CSimdResult r = stddev_simd_by_assets(simd_inputs, 4, 10, options, NULL, 0);
for (uintptr_t i = 0; i < r.num_results; i++) {
printf("Asset %zu stddev[0]: %.4f\n", i + 1, r.outputs[i][0][0]);
stddev_state_free(r.states[i]);
}
tulip_ffi_simd_result_free(r);
By options — same asset, 4 different periods in parallel:
double close[] = {81.59, 81.06, 82.87, 83.00, 83.61,
83.15, 82.84, 83.99, 84.55, 84.36};
#define EXPANDED_LEN (10 * 20)
double close_exp[EXPANDED_LEN];
for (uintptr_t i = 0; i < 20; i++) {
for (uintptr_t j = 0; j < 10; j++) {
close_exp[i*10+j] = close[j];
}
}
const double *inputs[STDDEV_INPUTS] = {close_exp};
double o10[] = {10.0}, o20[] = {20.0}, o30[] = {30.0}, o50[] = {50.0};
const double *const simd_opts[4] = {o10, o20, o30, o50};
CSimdResult r = stddev_simd_by_options(inputs, EXPANDED_LEN, simd_opts, 4, NULL, 0);
for (uintptr_t i = 0; i < r.num_results; i++) {
printf("Period %g: %.4f\n", simd_opts[i][0], r.outputs[i][0][0]);
stddev_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):
real1 := []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
real2 := real1
real3 := real1
real4 := real1
assets := [][indicators.StddevInputs][]float64{{real1}, {real2}, {real3}, {real4}}
sim, _ := indicators.Stddev.SimdByAssets(assets, []float64{20.0}, 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:
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.Stddev;
// One entry per asset; each asset lists its INPUTS series.
// Lanes 2–4 reuse real1 in this example.
double[] real1 = {81.59, 81.06, 82.87, 83.00, 83.61, 83.15, 82.84, 83.99, 84.55, 84.36};
double[][][] assets = {{real1}, {real1}, {real1}, {real1}};
try (SimdResult sim = Stddev.simdByAssets(assets, new double[] {20.0}, 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.Stddev;
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 = Stddev.simdByOptions(new double[][] {close},
new double[][] {{10}, {20}, {30}, {50}}, 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 = [
[np.array(asset1_close, dtype=np.float64)],
[np.array(asset2_close, dtype=np.float64)],
[np.array(asset3_close, dtype=np.float64)],
[np.array(asset4_close, dtype=np.float64)],
]
outputs_list, states = tulip_rs.indicators.stddev.simd_by_assets(simd_inputs, [20.0])
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 = [[close.slice()], [close.map(v => v * 1.1)], [close.map(v => v * 0.9)], [close.map(v => v * 1.02)]];
const [results] = ti.stddev.simdByAssets(simdInputs, [20]);
results.forEach((out, i) => console.log(`Asset ${i + 1}:`, out[0]));
By options — same asset, 4 different periods in parallel: