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Fisher Transform

Converts prices into a Gaussian normal distribution. Sharp moves in the Fisher value can signal potential price reversals; the signal line is a one-bar lag of the Fisher line.

Inputs: [high, low]  |  Options: [period]  |  Outputs: [fisher, fisher_signal]

Basic

use tulip_rs::indicators::fisher::indicator;

let high = vec![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_f64];
let low  = vec![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_f64];

let inputs = [high.as_slice(), low.as_slice()];
let (outputs, _state) = indicator(&inputs, &[10.0], None).unwrap();
println!("Fisher:        {:?}", outputs[0]);
println!("Fisher Signal: {:?}", outputs[1]);

// State continuation
let inputs2 = [&high[..10], &low[..10]];
let (outputs2, mut state) = indicator(&inputs2, &[10.0], None).unwrap();
println!("Partial Fisher: {:?}", outputs2[0]);

let new_inputs = [&high[10..], &low[10..]];
let continued = state.batch_indicator(&new_inputs, None).unwrap();
println!("Continued Fisher:        {:?}", continued[0]);
println!("Continued Fisher Signal: {:?}", continued[1]);

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,
                 85.90, 86.58, 86.98, 88.00, 87.87], 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,
                 84.03, 85.39, 85.76, 87.17, 87.01], dtype=np.float64)

outputs, state = tulip_rs.indicators.fisher.indicator([high, low], [10.0])
print("Fisher:        ", outputs[0])
print("Fisher Signal: ", outputs[1])

# State continuation
outputs2, state = tulip_rs.indicators.fisher.indicator([high[:10], low[:10]], [10.0])
continued = state.batch_indicator([high[10:], low[10:]])
print("Continued Fisher:        ", continued[0])
print("Continued Fisher Signal: ", continued[1])

import * as ti from 'tulip-rs-node';

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 [outputs, state] = ti.fisher.indicator([high, low], [9]);
console.log('Fisher:', outputs[0]);
console.log('Signal:', outputs[1]);

// State continuation
const n = high.length - 5;
const [, state2] = ti.fisher.indicator([high.slice(0, n), low.slice(0, n)], [9]);
const continued = state2.batchIndicator([high.slice(n), low.slice(n)]);
console.log('Continued Fisher:', 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 [outputs, state] = ti.fisher.indicator([high, low], [9]);
console.log('Fisher:', outputs[0]);
console.log('Signal:', outputs[1]);

// State continuation
const n = high.length - 5;
const [, state2] = ti.fisher.indicator([high.slice(0, n), low.slice(0, n)], [9]);
const continued = state2.batchIndicator([high.slice(n), low.slice(n)]);
console.log('Continued Fisher:', continued[0]);

SIMD

By assets — same period applied to 4 assets in parallel:

use tulip_rs::indicators::fisher::indicator_by_assets;

let h1 = vec![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_f64];
let l1 = vec![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_f64];
let h2 = h1.clone(); let l2 = l1.clone();
let h3 = h1.clone(); let l3 = l1.clone();
let h4 = h1.clone(); let l4 = l1.clone();

let inputs: [&[&[f64]; 2]; 4] = [
    &[h1.as_slice(), l1.as_slice()],
    &[h2.as_slice(), l2.as_slice()],
    &[h3.as_slice(), l3.as_slice()],
    &[h4.as_slice(), l4.as_slice()],
];

let results = indicator_by_assets::<4>(&inputs, &[10.0], None).unwrap();
for (i, asset_outputs) in results.0.iter().enumerate() {
    println!("Asset {} Fisher:        {:?}", i + 1, asset_outputs[0]);
    println!("Asset {} Fisher Signal: {:?}", i + 1, asset_outputs[1]);
}

By options — same asset, 4 different periods in parallel:

use tulip_rs::indicators::fisher::indicator_by_options;

let high = vec![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_f64];
let low  = vec![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_f64];

let opts: [&[f64; 1]; 4] = [&[5.0], &[10.0], &[14.0], &[20.0]];
let inputs = [high.as_slice(), low.as_slice()];
let results = indicator_by_options::<4>(&inputs, &opts, None).unwrap();
for (i, opt_outputs) in results.0.iter().enumerate() {
    println!("Option set {} Fisher:        {:?}", i + 1, opt_outputs[0]);
    println!("Option set {} Fisher Signal: {:?}", i + 1, opt_outputs[1]);
}

By assets — same period 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,
                 85.90, 86.58, 86.98, 88.00, 87.87], 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,
                 84.03, 85.39, 85.76, 87.17, 87.01], dtype=np.float64)

simd_inputs = [
    [high,        low],
    [high + 0.5,  low + 0.5],
    [high - 0.5,  low - 0.5],
    [high * 1.01, low * 1.01],
]
outputs_list, states = tulip_rs.indicators.fisher.simd_by_assets(simd_inputs, [10.0])
for i, out in enumerate(outputs_list):
    print(f"Asset {i + 1} Fisher:        {out[0]}")
    print(f"Asset {i + 1} Fisher Signal: {out[1]}")

By options — same asset, N different periods 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,
                 85.90, 86.58, 86.98, 88.00, 87.87], 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,
                 84.03, 85.39, 85.76, 87.17, 87.01], dtype=np.float64)

simd_options = [[5.0], [10.0], [14.0], [20.0]]
outputs_list, states = tulip_rs.indicators.fisher.simd_by_options([high, low], simd_options)
for i, out in enumerate(outputs_list):
    print(f"Option set {i + 1} Fisher:        {out[0]}")
    print(f"Option set {i + 1} Fisher Signal: {out[1]}")

By assets — same period applied to 4 assets in parallel:

const simdInputs = [
    [[...high], [...low]],
    [high.map(v => v * 1.1), low.map(v => v * 1.1)],
    [high.map(v => v * 0.9), low.map(v => v * 0.9)],
    [high.map(v => v * 1.02), low.map(v => v * 1.02)],
];
const [results] = ti.fisher.simdByAssets(simdInputs, [9]);
results.forEach((out, i) => console.log(`Asset ${i + 1} Fisher:`, out[0], 'Signal:', out[1]));

By options — same asset, 4 different periods in parallel:

const simdOptions = [[5], [10], [14], [20]];
const [results] = ti.fisher.simdByOptions([high, low], simdOptions);
results.forEach((out, i) => console.log(`Period ${simdOptions[i][0]} Fisher:`, out[0], 'Signal:', out[1]));