CMF — Chaikin Money Flow¶
Measures buying and selling pressure. For each bar: MFV = ((close − low) − (high − close)) / (high − low) × volume. CMF = sum(MFV, period) / sum(volume, period). Values near +1 indicate strong buying pressure; values near −1 indicate strong selling pressure.
Inputs: [high, low, close, volume] | Options: [period] | Outputs: [cmf]
Basic¶
use tulip_rs::indicators::chaikinmf::indicator;
let high = vec![82.15, 81.89, 83.03, 83.30, 83.85,
83.90, 83.33, 84.30, 84.84, 85.00_f64];
let low = vec![81.29, 80.64, 81.31, 82.65, 83.07,
83.11, 82.49, 82.30, 84.15, 84.11_f64];
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![1200.0, 1500.0, 1300.0, 1100.0, 1600.0,
1400.0, 1200.0, 1700.0, 1800.0, 1500.0_f64];
let inputs = [high.as_slice(), low.as_slice(), close.as_slice(), volume.as_slice()];
let (outputs, mut state) = indicator(&inputs, &[14.0], None).unwrap();
println!("{:?}", outputs[0]); // CMF values
// State continuation — feed new bars without reprocessing history
let new_high = vec![85.20_f64];
let new_low = vec![84.50_f64];
let new_close = vec![85.00_f64];
let new_volume = vec![1550.0_f64];
let continued = state.batch_indicator(
&[new_high.as_slice(), new_low.as_slice(),
new_close.as_slice(), new_volume.as_slice()],
None,
).unwrap();
println!("{:?}", continued[0]);
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], 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], dtype=np.float64)
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([1200.0, 1500.0, 1300.0, 1100.0, 1600.0,
1400.0, 1200.0, 1700.0, 1800.0, 1500.0], dtype=np.float64)
outputs, state = tulip_rs.indicators.chaikinmf.indicator([high, low, close, volume], [14.0])
print(outputs[0]) # CMF values
# State continuation
new_high = np.array([85.20], dtype=np.float64)
new_low = np.array([84.50], dtype=np.float64)
new_close = np.array([85.00], dtype=np.float64)
new_volume = np.array([1550.0], dtype=np.float64)
continued = state.batch_indicator([new_high, new_low, new_close, new_volume])
print(continued[0])
import * as ti from 'tulip-rs-node';
const high = Float64Array.from([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 = Float64Array.from([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 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 volume = Float64Array.from([1200.0, 1500.0, 1300.0, 1100.0, 1600.0, 1400.0, 1200.0, 1700.0, 1800.0, 1500.0, 1350.0, 1650.0, 1450.0, 1750.0, 1600.0]);
const [outputs, state] = ti.chaikinmf.indicator([high, low, close, volume], [14]);
console.log('CMF(14):', outputs[0]);
// State continuation
const n = high.length - 5;
const [, state2] = ti.chaikinmf.indicator([high.slice(0, n), low.slice(0, n), close.slice(0, n), volume.slice(0, n)], [14]);
const continued = state2.batchIndicator([high.slice(n), low.slice(n), close.slice(n), volume.slice(n)]);
console.log('Continued CMF:', 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 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 = [1200.0, 1500.0, 1300.0, 1100.0, 1600.0, 1400.0, 1200.0, 1700.0, 1800.0, 1500.0, 1350.0, 1650.0, 1450.0, 1750.0, 1600.0];
const [outputs, state] = ti.chaikinmf.indicator([high, low, close, volume], [14]);
console.log('CMF(14):', outputs[0]);
// State continuation
const n = high.length - 5;
const [, state2] = ti.chaikinmf.indicator([high.slice(0, n), low.slice(0, n), close.slice(0, n), volume.slice(0, n)], [14]);
const continued = state2.batchIndicator([high.slice(n), low.slice(n), close.slice(n), volume.slice(n)]);
console.log('Continued CMF:', continued[0]);
SIMD¶
By assets — same options, N assets in parallel:
use tulip_rs::indicators::chaikinmf::indicator_by_assets;
let inputs: [&[&[f64]; 4]; 4] = [
&[h1.as_slice(), l1.as_slice(), c1.as_slice(), v1.as_slice()],
&[h2.as_slice(), l2.as_slice(), c2.as_slice(), v2.as_slice()],
&[h3.as_slice(), l3.as_slice(), c3.as_slice(), v3.as_slice()],
&[h4.as_slice(), l4.as_slice(), c4.as_slice(), v4.as_slice()],
];
let results = 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:
By assets — same options, N assets in parallel (must be 2, 4, 8, or 16):
simd_inputs = [
[h1, l1, c1, v1],
[h2, l2, c2, v2],
[h3, l3, c3, v3],
[h4, l4, c4, v4],
]
outputs_list, states = tulip_rs.indicators.chaikinmf.simd_by_assets(simd_inputs, [14.0])
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 period applied to 4 assets in parallel:
const simdInputs = [
[high.slice(), low.slice(), close.slice(), volume.slice()],
[high.map(v => v * 1.1), low.map(v => v * 1.1), close.map(v => v * 1.1), volume.map(v => v * 1.1)],
[high.map(v => v * 0.9), low.map(v => v * 0.9), close.map(v => v * 0.9), volume.map(v => v * 0.9)],
[high.map(v => v * 1.02), low.map(v => v * 1.02), close.map(v => v * 1.02), volume.map(v => v * 1.02)],
];
const [results] = ti.chaikinmf.simdByAssets(simdInputs, [14]);
results.forEach((out, i) => console.log(`Asset ${i + 1}:`, out[0]));
By options — same asset, 4 different periods in parallel: