PPO — Percentage Price Oscillator¶
Expresses the MACD as a percentage of the slow EMA, making it comparable across different price levels.
Inputs: [real] | Options: [short_period, long_period] | Outputs: [ppo]
Basic¶
use tulip_rs::indicators::ppo::{Ppo, 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];
// options: [short_period, long_period]
let (outputs, mut state) = Ppo::indicator(&[close.as_slice()], &[12.0, 26.0], None).unwrap();
println!("{:?}", outputs[0]); // PPO values
// State continuation — feed new bars without reprocessing history
let partial = close[..8].to_vec();
let (outputs2, mut state) = Ppo::indicator(&[partial.as_slice()], &[12.0, 26.0], 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[PPO_OPTIONS] = {12.0, 26.0}; // short_period, long_period
const double *inputs[PPO_INPUTS] = {close};
/* Full computation (check r.error == C_INDICATOR_ERROR_OK in real code) */
CIndicatorResult r = ppo_indicator(inputs, 10, options, NULL, 0);
/* r.outputs[0] -> the PPO series, length r.output_lens[0] */
tulip_ffi_result_free(r);
ppo_state_free(r.state);
/* Partial computation + state continuation */
CIndicatorResult pr = ppo_indicator(inputs, 8, options, NULL, 0);
double new_close[] = {85.53};
const double *new_inputs[PPO_INPUTS] = {new_close};
CBatchResult br = ppo_batch(pr.state, new_inputs, 1, NULL, 0);
/* br.outputs[0] -> PPO values for just the new bar */
tulip_ffi_batch_result_free(br);
tulip_ffi_result_free(pr);
ppo_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{12.0, 26.0} // short_period, long_period
// Full computation — Rows are zero-copy views, valid until Close.
res, st, _ := indicators.Ppo.Indicator(close, options, nil)
fmt.Println(res.Rows[0]) // PPO values
res.Close()
st.Close()
// Partial computation + state continuation.
res2, st2, _ := indicators.Ppo.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 PPO values
batch.Close()
st2.Close()
import org.tuliprs.*;
import org.tuliprs.indicators.Ppo;
double[] close = {81.59, 81.06, 82.87, 83.00, 83.61,
83.15, 82.84, 83.99, 84.55, 84.36};
double[] options = {12.0, 26.0}; // short_period, long_period
// Full computation — output rows are zero-copy views, valid until close().
Outcome oc = Ppo.indicator(new double[][] {close}, options);
try (Result res = oc.result(); State st = oc.state()) {
System.out.println(java.util.Arrays.toString(res.toDoubleArray(0))); // PPO values
}
// Partial computation + state continuation.
int n = 8;
Outcome p = Ppo.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 PPO 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)
# options: [short_period, long_period]
outputs, state = tulip_rs.indicators.ppo.indicator([close], [12.0, 26.0])
print(outputs[0]) # PPO 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.ppo.indicator([close], [12, 26]);
console.log('PPO:', outputs[0]);
// State continuation
const [, state2] = ti.ppo.indicator([close.slice(0, -3)], [12, 26]);
const continued = state2.batchIndicator([close.slice(-3)]);
console.log('Continued PPO:', 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.ppo.indicator([close], [12, 26]);
console.log('PPO:', outputs[0]);
// State continuation
const [, state2] = ti.ppo.indicator([close.slice(0, -3)], [12, 26]);
const continued = state2.batchIndicator([close.slice(-3)]);
console.log('Continued PPO:', continued[0]);
Optional Outputs¶
ppo exposes 2 optional outputs: short_ema, long_ema. Pass a boolean mask as the third argument — one bool per optional output, in order.
use tulip_rs::indicators::ppo::{Ppo, 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];
let (outputs, _state) = Ppo::indicator(&[close.as_slice()], &[5.0, 20.0], Some(&mask)).unwrap();
let ppo = &outputs[0]; // PPO values (primary)
let short_ema = &outputs[1]; // short_ema (optional — requested)
let long_ema = &outputs[2]; // long_ema (optional — requested)
#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[PPO_OPTIONS] = {5.0, 20.0}; // short_period, long_period
const double *inputs[PPO_INPUTS] = {close};
/* request both optional outputs: short_ema and long_ema */
bool optional_outputs[2] = {true, true};
CIndicatorResult r = ppo_indicator(inputs, 10, options, optional_outputs, 2);
if (r.error != C_INDICATOR_ERROR_OK) {
fprintf(stderr, "ppo_indicator failed: error=%d\n", r.error);
return 1;
}
/* r.outputs[0] -> PPO values (primary) */
/* r.outputs[1] -> short_ema (optional — requested) */
/* r.outputs[2] -> long_ema (optional — requested) */
tulip_ffi_result_free(r);
ppo_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, 20.0} // short_period, long_period
// request both optional outputs: short_ema and long_ema (mask order matches optional_outputs)
mask := []bool{true, true}
res, st, _ := indicators.Ppo.Indicator(close, options, mask)
ppo := res.Rows[0] // PPO values (primary)
shortEma := res.Rows[1] // short_ema (optional — requested)
longEma := res.Rows[2] // long_ema (optional — requested)
fmt.Println("PPO:", ppo)
fmt.Println("short_ema:", shortEma)
fmt.Println("long_ema:", longEma)
res.Close()
st.Close()
import org.tuliprs.*;
import org.tuliprs.indicators.Ppo;
double[] close = {81.59, 81.06, 82.87, 83.00, 83.61, 83.15, 82.84, 83.99, 84.55, 84.36};
boolean[] mask = {true, true}; // short_ema, long_ema
Outcome oc = Ppo.indicator(new double[][] {close}, new double[] {5.0, 20.0}, mask);
try (Result res = oc.result()) {
// row 0 = ppo (primary), row 1 = short_ema (optional — requested), row 2 = long_ema (optional — requested)
System.out.println(java.util.Arrays.toString(res.toDoubleArray(1))); // short_ema
System.out.println(java.util.Arrays.toString(res.toDoubleArray(2))); // long_ema
}
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.ppo.indicator(
[close], [5.0, 20.0],
optional_outputs=[True, True],
)
ppo = outputs[0] # PPO values (primary)
short_ema = outputs[1] # short_ema (optional — requested)
long_ema = outputs[2] # long_ema (optional — requested)
ppo exposes 2 optional outputs: short_ema, long_ema.
SIMD¶
By assets — same options, N assets in parallel:
use tulip_rs::indicators::ppo::{Ppo, Indicator};
let inputs: [&[&[f64]; 1]; 4] = [
&[asset1_close.as_slice()],
&[asset2_close.as_slice()],
&[asset3_close.as_slice()],
&[asset4_close.as_slice()],
];
let results = Ppo::indicator_by_assets::<4>(&inputs, &[12.0, 26.0], 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::ppo::{Ppo, IndicatorByOptions};
let opts: [&[f64; 2]; 4] = [&[6.0, 13.0], &[12.0, 26.0], &[19.0, 39.0], &[24.0, 52.0]];
let results = Ppo::indicator_by_options::<4>(&[close.as_slice()], &opts, None).unwrap();
for (i, out) in results.iter().enumerate() {
println!("Option set {}: {:?}", i + 1, out[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[PPO_INPUTS] = {a1};
const double *asset2[PPO_INPUTS] = {a2};
const double *asset3[PPO_INPUTS] = {a3};
const double *asset4[PPO_INPUTS] = {a4};
const double *const *const simd_inputs[4] = {asset1, asset2, asset3, asset4};
CSimdResult r = ppo_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 PPO series, length r.output_lens[i][0] */
ppo_state_free(r.states[i]);
}
tulip_ffi_simd_result_free(r);
By options — same asset, 4 different periods in one call:
double o6[] = {6.0}, o13[] = {13.0};
double o12[] = {12.0}, o26[] = {26.0};
double o19[] = {19.0}, o39[] = {39.0};
double o24[] = {24.0}, o52[] = {52.0};
const double *const simd_opts[4] = {o6, o12, o19, o24};
CSimdResult r = ppo_simd_by_options(inputs, 10, simd_opts, 4, NULL, 0);
/* r.outputs[i] -> results for option set i */
for (uintptr_t i = 0; i < r.num_results; i++) ppo_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 := a1
a3 := a1
a4 := a1
assets := [][indicators.PpoInputs][]float64{{a1}, {a2}, {a3}, {a4}}
sim, _ := indicators.Ppo.SimdByAssets(assets, []float64{12.0, 26.0}, 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:
close := []float64{81.59, 81.06, 82.87, 83.00, 83.61,
83.15, 82.84, 83.99, 84.55, 84.36}
sim2, _ := indicators.Ppo.SimdByOptions(close, [][]float64{{6.0, 13.0}, {12.0, 26.0}, {19.0, 39.0}, {24.0, 52.0}}, nil)
for i, lanes := range sim2.Results {
fmt.Printf("Period set %d: %v\n", i+1, lanes[0])
}
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.Ppo;
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 = Ppo.simdByAssets(assets, new double[] {12.0, 26.0}, 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.Ppo;
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 = Ppo.simdByOptions(new double[][] {close},
new double[][] {{6.0, 13.0}, {12.0, 26.0}, {19.0, 39.0}, {24.0, 52.0}}, 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.ppo.simd_by_assets(simd_inputs, [12.0, 26.0])
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 options 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.ppo.simdByAssets(simdInputs, [12, 26]);
results.forEach((out, i) => console.log(`Asset ${i + 1}:`, out[0]));
By options — same asset, 4 different option sets in parallel: