TSF — Time Series Forecast — tsf¶
Projects the linear regression line one bar forward, giving a one-period-ahead price forecast.
Inputs: [real] | Options: [period] | Outputs: [tsf]
Basic¶
use tulip_rs::indicators::tsf::{Tsf, 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) = Tsf::indicator(&[close.as_slice()], &[14.0], None).unwrap();
println!("{:?}", outputs[0]);
// State continuation — feed new bars without reprocessing history
let partial = close[..8].to_vec();
let (outputs2, mut state) = Tsf::indicator(&[partial.as_slice()], &[14.0], None).unwrap();
println!("{:?}", outputs2[0]);
let new_close = vec![85.53_f64];
let continued = state.batch_indicator(&[new_close.as_slice()], &[14.0], 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[TSF_OPTIONS] = {14.0}; // period
const double *inputs[TSF_INPUTS] = {close};
/* Full computation */
CIndicatorResult r = tsf_indicator(inputs, 10, options, NULL, 0);
/* r.outputs[0] -> TSF(14) series, length r.output_lens[0] */
tulip_ffi_result_free(r);
tsf_state_free(r.state);
/* Partial computation + state continuation */
CIndicatorResult p = tsf_indicator(inputs, 8, options, NULL, 0);
double new_close[] = {85.53};
const double *new_inputs[TSF_INPUTS] = {new_close};
CBatchResult b = tsf_batch(p.state, new_inputs, 1, NULL, 0);
/* b.outputs[0] -> TSF for the new bar */
tulip_ffi_batch_result_free(b);
tulip_ffi_result_free(p);
tsf_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{14.0} // period
// Full computation — Rows are zero-copy views, valid until Close.
res, st, _ := indicators.Tsf.Indicator(close, options, nil)
fmt.Println(res.Rows[0]) // TSF(14) values
res.Close()
st.Close()
// Partial computation + state continuation.
res2, st2, _ := indicators.Tsf.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 TSF values
batch.Close()
st2.Close()
import org.tuliprs.*;
import org.tuliprs.indicators.Tsf;
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}; // period
// Full computation — output rows are zero-copy views, valid until close().
Outcome oc = Tsf.indicator(new double[][] {close}, options);
try (Result res = oc.result(); State st = oc.state()) {
System.out.println(java.util.Arrays.toString(res.toDoubleArray(0))); // TSF(14) values
}
// Partial computation + state continuation.
int n = 8;
Outcome p = Tsf.indicator(new double[][] {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(close, n, 10)});
try (br) {
System.out.println(java.util.Arrays.toString(br.toDoubleArray(0))); // continued TSF values
}
}
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.tsf.indicator([close], [14]);
console.log('TSF(14):', outputs[0]);
// State continuation
const [, state2] = ti.tsf.indicator([close.slice(0, -5)], [14]);
const continued = state2.batchIndicator([close.slice(-5)]);
console.log('Continued TSF:', 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.tsf.indicator([close], [14]);
console.log('TSF(14):', outputs[0]);
// State continuation
const [, state2] = ti.tsf.indicator([close.slice(0, -5)], [14]);
const continued = state2.batchIndicator([close.slice(-5)]);
console.log('Continued TSF:', continued[0]);
Optional Outputs¶
tsf exposes 3 optional outputs: linreg, linregslope, linregintercept. Pass a boolean mask as the third argument — one bool per optional output, in order.
use tulip_rs::indicators::tsf::{Tsf, 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, true, false]; // one per optional output
let (outputs, _state) = Tsf::indicator(&[close.as_slice()], &[14.0], Some(&mask)).unwrap();
let tsf = &outputs[0]; // tsf (primary)
let linreg = &outputs[1]; // linreg (optional — requested)
let linregslope = &outputs[2]; // linregslope (optional — requested)
// linregintercept not requested
tsf exposes 3 optional outputs: linreg, linregslope, linregintercept. 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[TSF_OPTIONS] = {14.0}; // period
const double *inputs[TSF_INPUTS] = {close};
bool optional_outputs[3] = {true, true, false}; // linreg, linregslope, linregintercept
CIndicatorResult r = tsf_indicator(inputs, 10, options, optional_outputs, 3);
/* r.outputs[0] -> tsf (primary) */
/* r.outputs[1] -> linreg (optional — requested) */
/* r.outputs[2] -> linregslope (optional — requested) */
tulip_ffi_result_free(r);
tsf_state_free(r.state);
tsf exposes 3 optional outputs: linreg, linregslope, linregintercept. Pass a boolean mask as the third argument.
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{14.0} // period
mask := []bool{true, true, false} // linreg, linregslope, linregintercept
res, st, _ := indicators.Tsf.Indicator(close, options, mask)
fmt.Println(res.Rows[0]) // tsf (primary)
fmt.Println(res.Rows[1]) // linreg (optional — requested)
fmt.Println(res.Rows[2]) // linregslope (optional — requested)
res.Close()
st.Close()
tsf exposes 3 optional outputs: linreg, linregslope, linregintercept. Pass a boolean mask as the third argument — one boolean per optional output, in order.
import org.tuliprs.*;
import org.tuliprs.indicators.Tsf;
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}; // period
boolean[] mask = {true, true, false}; // linreg, linregslope, linregintercept
Outcome oc = Tsf.indicator(new double[][] {close}, options, mask);
try (Result res = oc.result()) {
System.out.println(java.util.Arrays.toString(res.toDoubleArray(0))); // tsf (primary)
System.out.println(java.util.Arrays.toString(res.toDoubleArray(1))); // linreg (optional — requested)
System.out.println(java.util.Arrays.toString(res.toDoubleArray(2))); // linregslope (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.tsf.indicator(
[close], [14.0],
optional_outputs=[True, True, False],
)
tsf = outputs[0] # tsf (primary)
linreg = outputs[1] # linreg (optional — requested)
linregslope = outputs[2] # linregslope (optional — requested)
# linregintercept not requested
tsf exposes 3 optional outputs: linreg, linregslope, linregintercept.
The WASM API is identical to Node.js — pass the boolean mask as the third argument.
SIMD¶
By assets — same options, N assets in parallel:
use tulip_rs::indicators::tsf::{Tsf, Indicator};
let inputs: [&[&[f64]; 1]; 4] = [&[a1.as_slice()], &[a2.as_slice()], &[a3.as_slice()], &[a4.as_slice()]];
let results = Tsf::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::tsf::{Tsf, IndicatorByOptions};
let opts: [&[f64; 1]; 4] = [&[7.0], &[14.0], &[21.0], &[28.0]];
let results = Tsf::indicator_by_options::<4>(&[close.as_slice()], &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[] = {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[TSF_INPUTS] = {a1};
const double *asset2[TSF_INPUTS] = {a2};
const double *asset3[TSF_INPUTS] = {a3};
const double *asset4[TSF_INPUTS] = {a4};
const double *const *const simd_inputs[4] = {asset1, asset2, asset3, asset4};
double options[TSF_OPTIONS] = {14.0};
CSimdResult r = tsf_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 TSF series */
tsf_state_free(r.states[i]);
}
tulip_ffi_simd_result_free(r);
By options — same asset, 4 different periods in one call:
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 = tsf_simd_by_options(inputs, 10, simd_opts, 4, NULL, 0);
for (uintptr_t i = 0; i < r.num_results; i++) {
/* r.outputs[i][0] -> period set i results */
tsf_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):
a1 := []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, a3, a4 := a1, a1, a1
assets := [][indicators.TsfInputs][]float64{{a1}, {a2}, {a3}, {a4}}
sim, _ := indicators.Tsf.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:
By assets — same options applied to 4 assets in parallel (lane counts 2/4/8/16):
import org.tuliprs.*;
import org.tuliprs.indicators.Tsf;
double[] a1 = {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 = a1;
double[] a3 = a1;
double[] a4 = a1;
// One entry per asset; each asset lists its INPUTS series.
double[][][] assets = {{a1}, {a2}, {a3}, {a4}};
try (SimdResult sim = Tsf.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.Tsf;
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 = Tsf.simdByOptions(new double[][] {close},
new double[][] {{7}, {14}, {21}, {28}}, 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 = [[a1], [a2], [a3], [a4]]
outputs_list, states = tulip_rs.indicators.tsf.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 = [[close.slice()], [close.map(v => v * 1.1)], [close.map(v => v * 0.9)], [close.map(v => v * 1.02)]];
const [results] = ti.tsf.simdByAssets(simdInputs, [14]);
results.forEach((out, i) => console.log(`Asset ${i + 1}:`, out[0]));
By options — same asset, 4 different periods in parallel: