VWMA — Volume Weighted Moving Average¶
Moving average weighted by trading volume so that high-volume bars have more influence on the average than low-volume bars.
Inputs: [real, volume] | Options: [period] | Outputs: [vwma]
Basic¶
use tulip_rs::indicators::vwma::indicator;
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 volume = vec![5653100.0, 6447400.0, 7690900.0, 3831400.0, 4455100.0,
3798000.0, 3936200.0, 4732000.0, 4841300.0, 3915300.0_f64];
let inputs = [close.as_slice(), volume.as_slice()];
let (outputs, _state) = indicator(&inputs, &[14.0], None).unwrap();
println!("VWMA(14): {:?}", outputs[0]);
// State continuation
let partial_close = close[..8].to_vec();
let partial_volume = volume[..8].to_vec();
let inputs2 = [partial_close.as_slice(), partial_volume.as_slice()];
let (outputs2, mut state) = indicator(&inputs2, &[14.0], None).unwrap();
println!("Partial VWMA: {:?}", outputs2[0]);
let new_close = close[8..].to_vec();
let new_volume = volume[8..].to_vec();
let new_inputs = [new_close.as_slice(), new_volume.as_slice()];
let continued = state.batch_indicator(&new_inputs, None).unwrap();
println!("Continued VWMA: {:?}", continued[0]);
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)
volume = np.array([5653100.0, 6447400.0, 7690900.0, 3831400.0, 4455100.0,
3798000.0, 3936200.0, 4732000.0, 4841300.0, 3915300.0], dtype=np.float64)
outputs, state = tulip_rs.indicators.vwma.indicator([close, volume], [14.0])
print("VWMA(14):", outputs[0])
# State continuation
partial_close = close[:8]
partial_volume = volume[:8]
outputs2, state = tulip_rs.indicators.vwma.indicator([partial_close, partial_volume], [14.0])
new_close = close[8:]
new_volume = volume[8:]
continued = state.batch_indicator([new_close, new_volume])
print("Continued VWMA:", continued[0])
import * as ti from 'tulip-rs-node';
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 volume = [5653100, 6447400, 7690900, 3831400, 4455100,
3798000, 3936200, 4732000, 4841300, 3915300,
6830800, 6694100, 5293600, 7985800, 4807900];
const [outputs, state] = ti.vwma.indicator([close, volume], [14]);
console.log('VWMA(14):', outputs[0]);
// State continuation
const n = close.length - 5;
const [, state2] = ti.vwma.indicator([close.slice(0, n), volume.slice(0, n)], [14]);
const continued = state2.batchIndicator([close.slice(n), volume.slice(n)]);
console.log('Continued VWMA:', 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 volume = [5653100, 6447400, 7690900, 3831400, 4455100,
3798000, 3936200, 4732000, 4841300, 3915300,
6830800, 6694100, 5293600, 7985800, 4807900];
const [outputs, state] = ti.vwma.indicator([close, volume], [14]);
console.log('VWMA(14):', outputs[0]);
// State continuation
const n = close.length - 5;
const [, state2] = ti.vwma.indicator([close.slice(0, n), volume.slice(0, n)], [14]);
const continued = state2.batchIndicator([close.slice(n), volume.slice(n)]);
console.log('Continued VWMA:', continued[0]);
SIMD¶
By assets — same period applied to 4 assets (each with close + volume) in parallel:
use tulip_rs::indicators::vwma::indicator_by_assets;
let a1_close = vec![81.59, 81.06, 82.87, 83.00, 83.61, 83.15, 82.84, 83.99, 84.55, 84.36_f64];
let a1_vol = vec![5653100.0, 6447400.0, 7690900.0, 3831400.0, 4455100.0,
3798000.0, 3936200.0, 4732000.0, 4841300.0, 3915300.0_f64];
let a2_close = a1_close.iter().map(|x| x + 5.0).collect::<Vec<_>>();
let a2_vol = a1_vol.clone();
let a3_close = a1_close.iter().map(|x| x - 5.0).collect::<Vec<_>>();
let a3_vol = a1_vol.clone();
let a4_close = a1_close.iter().map(|x| x * 1.02).collect::<Vec<_>>();
let a4_vol = a1_vol.clone();
let inputs: [&[&[f64]; 2]; 4] = [
&[a1_close.as_slice(), a1_vol.as_slice()],
&[a2_close.as_slice(), a2_vol.as_slice()],
&[a3_close.as_slice(), a3_vol.as_slice()],
&[a4_close.as_slice(), a4_vol.as_slice()],
];
let results = indicator_by_assets::<4>(&inputs, &[14.0], None).unwrap();
for (i, asset_outputs) in results.0.iter().enumerate() {
println!("Asset {}: {:?}", i + 1, asset_outputs[0]);
}
By options — same asset, 4 different periods in parallel:
use tulip_rs::indicators::vwma::indicator_by_options;
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 volume = vec![5653100.0, 6447400.0, 7690900.0, 3831400.0, 4455100.0,
3798000.0, 3936200.0, 4732000.0, 4841300.0, 3915300.0_f64];
let opts: [&[f64; 1]; 4] = [&[5.0], &[10.0], &[14.0], &[20.0]];
let results = indicator_by_options::<4>(&[close.as_slice(), volume.as_slice()], &opts, None).unwrap();
for (i, opt_outputs) in results.0.iter().enumerate() {
println!("Period set {}: {:?}", i + 1, opt_outputs[0]);
}
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], dtype=np.float64)
volume = np.array([5653100.0, 6447400.0, 7690900.0, 3831400.0, 4455100.0,
3798000.0, 3936200.0, 4732000.0, 4841300.0, 3915300.0], dtype=np.float64)
a1_close, a1_vol = close, volume
a2_close, a2_vol = close + 5.0, volume
a3_close, a3_vol = close - 5.0, volume
a4_close, a4_vol = close * 1.02, volume
simd_inputs = [
[a1_close, a1_vol],
[a2_close, a2_vol],
[a3_close, a3_vol],
[a4_close, a4_vol],
]
outputs_list, states = tulip_rs.indicators.vwma.simd_by_assets(simd_inputs, [14.0])
for i, out in enumerate(outputs_list):
print(f"Asset {i + 1}: {out[0]}")
By options — same asset, N different periods in parallel:
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)
volume = np.array([5653100.0, 6447400.0, 7690900.0, 3831400.0, 4455100.0,
3798000.0, 3936200.0, 4732000.0, 4841300.0, 3915300.0], dtype=np.float64)
simd_options = [[5.0], [10.0], [14.0], [20.0]]
outputs_list, states = tulip_rs.indicators.vwma.simd_by_options([close, volume], 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], [...volume]],
[close.map(v => v * 1.1), volume.map(v => v * 1.1)],
[close.map(v => v * 0.9), volume.map(v => v * 0.9)],
[close.map(v => v * 1.02), volume.map(v => v * 1.02)],
];
const [results] = ti.vwma.simdByAssets(simdInputs, [14]);
results.forEach((out, i) => console.log(`Asset ${i + 1}:`, out[0]));
By options — same asset, 4 different periods in parallel: