- Rust core: cmf.rs (Chaikin Money Flow — summed money-flow volume over summed volume, bounded to [-1, +1]), chaikin_oscillator.rs (Chaikin Oscillator — the MACD of the ADL, EMA(ADL, fast) - EMA(ADL, slow)), force_index.rs (Elder's Force Index — EMA of price change scaled by volume), ease_of_movement.rs (Arms' Ease of Movement — SMA of distance travelled per unit of volume). Each with a full Indicator impl, runnable doctest and reference / property / warmup / reset / batch==streaming tests. - Python: PyChaikinMoneyFlow / PyChaikinOscillator / PyForceIndex / PyEaseOfMovement PyO3 classes + module registration + .pyi stubs. - Node: explicit ChaikinMoneyFlowNode / ChaikinOscillatorNode / ForceIndexNode / EaseOfMovementNode; index.d.ts and index.js updated. - WASM: WasmChaikinMoneyFlow / WasmChaikinOscillator / WasmForceIndex / WasmEaseOfMovement. - Wiki: Indicator-ChaikinMoneyFlow/ChaikinOscillator/ForceIndex/ EaseOfMovement.md plus a new "Oscillators" sub-table in Indicators-Overview.md and entries in Home.md. cargo fmt + clippy (core/wickra/data/wasm/node) clean; 402 core tests, 25 data tests and 57 doctests green.
4.7 KiB
ChaikinMoneyFlow
Chaikin Money Flow (CMF) — the ratio of money-flow volume to total volume over a rolling window, bounded to
[−1, +1].
Quick reference
| Field | Value |
|---|---|
| Family | Volume |
| Sub-category | Oscillators |
| Input type | Candle (uses high, low, close, volume) |
| Output type | f64 |
| Output range | [−1, +1] |
| Default parameters | period = 20 (Python) |
| Warmup period | period |
| Interpretation | Window accumulation/distribution balance; sign and magnitude both matter. |
Formula
MFM_t = ((close − low) − (high − close)) / (high − low) (money-flow multiplier, −1..+1)
MFV_t = MFM_t · volume_t (money-flow volume)
CMF_t = Σ(MFV, period) / Σ(volume, period)
CMF is the Adl increment averaged the way RSI averages
gains: rather than a running total, it divides the summed money-flow volume
of the last period bars by the summed volume of those bars. The result is
volume-normalised, so it lives in [−1, +1] regardless of how heavily the
instrument trades. A bar with high == low carries no positional information
and contributes a money-flow volume of 0.
Parameters
period — the lookback window. The Python binding defaults it to 20; the
Rust and Node constructors require it explicitly.
Inputs / Outputs
From crates/wickra-core/src/indicators/cmf.rs:
impl Indicator for ChaikinMoneyFlow {
type Input = Candle;
type Output = f64;
// update(&mut self, input: Candle) -> Option<f64>
}
ChaikinMoneyFlow is a candle-input indicator: it reads high, low,
close and volume. In Python the streaming update accepts a 6-tuple or a
dict; the batch helper takes high, low, close, volume numpy arrays.
Node and WASM expose update(high, low, close, volume) and the matching
batch.
Warmup
ChaikinMoneyFlow::new(20).warmup_period() == 20. The first value lands once
the window holds a full period bars — on input index period − 1.
Edge cases
- Zero-range bar. A bar with
high == lowcontributesMFV = 0. - Empty-volume window. If the whole window traded zero volume, the
0/0ratio is defined as0.0(zero_volume_window_yields_zeropins this). - Saturated flow. Every bar closing on its high gives
MFM = +1, so CMF saturates at+1(closes_at_high_yield_cmf_onepins this). - Reset.
cmf.reset()clears the window and both running sums.
Examples
Rust
use wickra::{BatchExt, Candle, Indicator, ChaikinMoneyFlow};
fn main() -> Result<(), Box<dyn std::error::Error>> {
let mut cmf = ChaikinMoneyFlow::new(2)?;
let out = cmf.batch(&[
Candle::new(8.0, 10.0, 8.0, 10.0, 100.0, 0)?, // close at high -> MFV +100
Candle::new(10.0, 12.0, 8.0, 10.0, 100.0, 1)?, // close mid-range -> MFV 0
]);
println!("{:?}", out);
Ok(())
}
Output:
[None, Some(0.5)]
Bar 1 closes at its high (MFM = +1, MFV = +100); bar 2 closes mid-range
(MFM = 0, MFV = 0). CMF(2) = (100 + 0) / (100 + 100) = 0.5. This matches
the reference_values test in crates/wickra-core/src/indicators/cmf.rs.
Python
import numpy as np
import wickra as ta
cmf = ta.ChaikinMoneyFlow(2)
high = np.array([10.0, 12.0])
low = np.array([8.0, 8.0])
close = np.array([10.0, 10.0])
volume = np.array([100.0, 100.0])
print(cmf.batch(high, low, close, volume))
Output:
[nan 0.5]
Node
const ta = require('wickra');
const cmf = new ta.ChaikinMoneyFlow(2);
console.log(cmf.batch([10, 12], [8, 8], [10, 10], [100, 100]));
Output:
[ NaN, 0.5 ]
Interpretation
CMF reads as a balance: sustained positive values mean closes are clustering
near bar highs on real volume (accumulation), sustained negative values mean
the opposite (distribution). Crosses of the zero line are the textbook signal;
the ±0.05 band is often treated as a neutral zone. Because CMF is
volume-normalised it is comparable across instruments — unlike the raw
Adl, whose level is arbitrary.
Common pitfalls
- Confusing it with the ADL. CMF is a bounded ratio; the ADL is an unbounded running total. They share the money-flow multiplier and nothing else.
- Feeding it scalar prices. It needs the full OHLCV bar.
References
Marc Chaikin's Chaikin Money Flow; the money-flow-multiplier formulation here matches the standard definition (StockCharts).
See also
- Indicator-Adl.md — the cumulative line CMF is built on.
- Indicator-ChaikinOscillator.md — the EMA-difference oscillator on the ADL.
- Indicators-Overview.md — the full taxonomy.