EF — Efficiency Ratio

Measures how efficiently price moves in one direction over period bars; values near 1 indicate a strong trending market, values near 0 indicate a choppy, sideways market.

Inputs: [real]  |  Options: [period]  |  Outputs: [ef]

Basic

RustCGoJavaPythonNode.jsWASM

use tulip_rs::indicators::ef::{Ef, 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,
                 85.53, 86.54, 86.89, 87.77, 87.29_f64];

let (outputs, mut state) = Ef::indicator(&[close.as_slice()], &[5.0], None).unwrap();
println!("EF(5): {:?}", outputs[0]);

// State continuation — feed new bars without reprocessing history
let partial_close = close[..8].to_vec();
let (outputs2, mut state) = Ef::indicator(&[partial_close.as_slice()], &[5.0], None).unwrap();
println!("Partial EF: {:?}", outputs2[0]);

let new_close = vec![86.54_f64];
let continued = state.batch_indicator(&[new_close.as_slice()], None).unwrap();
println!("Continued EF: {:?}", continued[0]);

#include "tulip_rs_ffi.h"

static const double close[] = {81.59, 81.06, 82.87, 83.00, 83.61,
                               83.15, 82.84, 83.99, 84.55, 84.36};

const double options[EF_OPTIONS] = {5.0}; // period
const double *inputs[EF_INPUTS] = {close};

/* Full computation (check r.error == C_INDICATOR_ERROR_OK in real code) */
CIndicatorResult r = ef_indicator(inputs, 10, options, NULL, 0);
/* r.outputs[0] -> ef */
tulip_ffi_result_free(r);
ef_state_free(r.state);

/* Partial computation + state continuation */
CIndicatorResult pr = ef_indicator(inputs, 8, options, NULL, 0);
const double *rest_inputs[EF_INPUTS] = {close + 8};
CBatchResult br = ef_batch(pr.state, rest_inputs, 2, NULL, 0);
tulip_ffi_batch_result_free(br);
tulip_ffi_result_free(pr);
ef_state_free(pr.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}

// Full computation — Rows are zero-copy views, valid until Close.
res, st, _ := indicators.Ef.Indicator(close, options, nil)
fmt.Println(res.Rows[0]) // EF(5) values
res.Close()
st.Close()

// Partial computation + state continuation.
res2, st2, _ := indicators.Ef.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 EF values
batch.Close()
st2.Close()

import org.tuliprs.*;
import org.tuliprs.indicators.Ef;

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

// Full computation — output rows are zero-copy views, valid until close().
Outcome oc = Ef.indicator(new double[][] {close}, options);
try (Result res = oc.result(); State st = oc.state()) {
    System.out.println(java.util.Arrays.toString(res.toDoubleArray(0))); // EF(5) values
}

// Partial computation + state continuation.
int n = 8;
Outcome p = Ef.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 EF
    }
}

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,
                  85.53, 86.54, 86.89, 87.77, 87.29], dtype=np.float64)

outputs, state = tulip_rs.indicators.ef.indicator([close], [5.0])
print("EF(5):", outputs[0])

# State continuation
n = len(close) - 5
outputs2, state = tulip_rs.indicators.ef.indicator([close[:n]], [5.0])
print("Partial EF:", outputs2[0])

continued = state.batch_indicator([close[n:]])
print("Continued EF:", 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.ef.indicator([close], [5]);
console.log('EF(5):', outputs[0]);

// State continuation
const n = close.length - 5;
const [, state2] = ti.ef.indicator([close.slice(0, n)], [5]);
const continued = state2.batchIndicator([close.slice(n)]);
console.log('Continued EF:', 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.ef.indicator([close], [5]);
console.log('EF(5):', outputs[0]);

// State continuation
const n = close.length - 5;
const [, state2] = ti.ef.indicator([close.slice(0, n)], [5]);
const continued = state2.batchIndicator([close.slice(n)]);
console.log('Continued EF:', continued[0]);

SIMD

RustCGoJavaPythonNode.js

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

use tulip_rs::indicators::ef::{Ef, Indicator};

let a1 = vec![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_f64];
let a2 = vec![72.10, 72.85, 73.40, 73.00, 74.20, 74.85, 75.10, 75.60, 76.00, 76.50,
              77.10, 77.80, 78.20, 78.90, 79.30_f64];
let a3 = vec![55.30, 55.80, 56.10, 56.40, 56.90, 57.20, 57.50, 57.80, 58.10, 58.40,
              58.90, 59.20, 59.60, 60.00, 60.30_f64];
let a4 = vec![100.1, 100.5, 101.0, 101.3, 101.8, 102.0, 102.5, 103.0, 103.3, 103.8,
              104.2, 104.7, 105.0, 105.5, 106.0_f64];

let inputs: [&[&[f64]; 1]; 4] = [
    &[a1.as_slice()],
    &[a2.as_slice()],
    &[a3.as_slice()],
    &[a4.as_slice()],
];

let results = Ef::indicator_by_assets::<4>(&inputs, &[5.0], None).unwrap();
for (i, asset_outputs) in results.iter().enumerate() {
    println!("Asset {}: {:?}", i + 1, asset_outputs[0]);
}

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

use tulip_rs::indicators::ef::{Ef, IndicatorByOptions};

let close = vec![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_f64];

let opts: [&[f64; 1]; 4] = [&[3.0], &[5.0], &[7.0], &[10.0]];

let results = Ef::indicator_by_options::<4>(&[close.as_slice()], &opts, None).unwrap();
for (i, opt_outputs) in results.iter().enumerate() {
    println!("Period set {}: {:?}", i + 1, opt_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};

/* one [INPUTS]-long pointer array per asset */
const double *asset1[EF_INPUTS] = {a1};
const double *asset2[EF_INPUTS] = {a2};
const double *asset3[EF_INPUTS] = {a3};
const double *asset4[EF_INPUTS] = {a4};
const double *const *const simd_inputs[4] = {asset1, asset2, asset3, asset4};

CSimdResult r = ef_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 ef */
    ef_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 = ef_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++) ef_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 := []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 := a1
a3 := a1
a4 := a1

assets := [][indicators.EfInputs][]float64{{a1}, {a2}, {a3}, {a4}}
sim, _ := indicators.Ef.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 isn't offered for this indicator.

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.Ef;

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};

// One entry per asset; each asset lists its INPUTS series.
double[][][] assets = {{a1}, {a2}, {a3}, {a4}};
try (SimdResult sim = Ef.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))); // ef
    }
}   // frees every lane state, then the SIMD buffers (contractual order)

By-options isn't offered for this indicator.

By assets — same period applied to N assets in parallel (must be 2, 4, 8, or 16):

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,
                  85.53, 86.54, 86.89, 87.77, 87.29], dtype=np.float64)

simd_inputs = [[close], [close + 5.0], [close - 5.0], [close * 1.02]]
outputs_list, states = tulip_rs.indicators.ef.simd_by_assets(simd_inputs, [5.0])
for i, out in enumerate(outputs_list):
    print(f"Asset {i + 1}: {out[0]}")

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

simd_options = [[3.0], [5.0], [7.0], [10.0]]
outputs_list, states = tulip_rs.indicators.ef.simd_by_options([close], simd_options)
for i, out in enumerate(outputs_list):
    print(f"Period set {i + 1}: {out[0]}")

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.ef.simdByAssets(simdInputs, [5]);
results.forEach((out, i) => console.log(`Asset ${i + 1}:`, out[0]));

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

const simdOptions = [[3], [5], [7], [10]];
const [results] = ti.ef.simdByOptions([close], simdOptions);
results.forEach((out, i) => console.log(`Period ${simdOptions[i][0]}:`, out[0]));