# MOM > Momentum — the raw price change over a fixed lookback, > `price_t − price_{t−period}`, in absolute price units. ## Quick reference | Field | Value | |-------|-------| | Family | Momentum Oscillators | | Input type | `f64` (single close) | | Output type | `f64` | | Output range | unbounded around zero (price-difference scale) | | Default parameters | `period = 10` (Python) | | Warmup period | `period + 1` | | Interpretation | Sign and size of the move over the last `period` bars. | ## Formula ``` MOM_t = price_t − price_{t−period} ``` The simplest momentum primitive. Positive output means price is higher than it was `period` bars ago, negative means lower, and the magnitude is the change in raw price units. [`Roc`](../momentum-oscillators/Indicator-Roc.md) is the same idea expressed as a percentage of the old price. ## Parameters | Name | Type | Default | Valid range | Description | |----------|---------|----------------|-------------|-------------| | `period` | `usize` | `10` (Python) | `>= 1` | Lookback distance in bars. `period = 0` errors with `Error::PeriodZero`. | The Python binding defaults `period` to `10` via `#[pyo3(signature = (period=10))]`. ## Inputs / Outputs From `crates/wickra-core/src/indicators/mom.rs`: ```rust impl Indicator for Mom { type Input = f64; type Output = f64; // update(&mut self, input: f64) -> Option } ``` A single `f64` close in, an `Option` out. Python maps this to `float | None` / `numpy.ndarray` (NaN warmup); Node to `number | null` / `Array` (NaN warmup). ## Warmup `Mom::new(period).warmup_period() == period + 1`. The output needs both the current price and the price `period` bars back, so the window must hold `period + 1` values — the first non-`None` output lands on input `period + 1`. ## Edge cases - **Constant series.** A flat series yields `0.0` from input `period + 1` onward (`constant_series_yields_zero` pins this). - **NaN / infinity inputs.** Non-finite inputs are silently dropped: the rolling window is not advanced and the previous value is returned. The next finite input still references the correct historical price. - **Reset.** `mom.reset()` clears the window and restarts the warmup. ## Examples ### Rust ```rust use wickra::{BatchExt, Indicator, Mom}; fn main() -> Result<(), Box> { let mut mom = Mom::new(3)?; let out: Vec> = mom.batch(&[1.0, 2.0, 3.0, 4.0, 7.0]); println!("{:?}", out); Ok(()) } ``` Output: ``` [None, None, None, Some(3.0), Some(5.0)] ``` `MOM(3)` first emits on input 4: `4 − 1 = 3`. The fifth input gives `7 − 2 = 5`. This matches the `reference_values` test in `crates/wickra-core/src/indicators/mom.rs`. ### Python ```python import numpy as np import wickra as ta mom = ta.MOM(3) print(mom.batch(np.array([1.0, 2.0, 3.0, 4.0, 7.0]))) ``` Output: ``` [nan nan nan 3. 5.] ``` ### Node ```javascript const ta = require('wickra'); const mom = new ta.MOM(3); console.log(mom.batch([1, 2, 3, 4, 7])); ``` Output: ``` [ NaN, NaN, NaN, 3, 5 ] ``` ## Interpretation `Mom` is a zero-centred oscillator. The textbook reads are the zero-line cross (momentum flipping sign) and divergence (price making a new high while `Mom` makes a lower high — a stalling trend). Because the output is in price units, `Mom` values are not comparable across instruments at different price levels; use [`Roc`](../momentum-oscillators/Indicator-Roc.md) when you need a scale-free percentage instead. ## Common pitfalls - **Comparing `Mom` across instruments.** A `Mom` of `5` means very different things on a $10 stock and a $5000 index. Normalise with `Roc` for cross-asset work. - **Forgetting the `+1` warmup.** `warmup_period()` is `period + 1`, not `period`. ## References Momentum is one of the oldest technical studies; the implementation here is the standard `price − price[period]` difference, matching TA-Lib's `MOM`. ## See also - [Indicator-Roc.md](../momentum-oscillators/Indicator-Roc.md) — the percentage-scaled counterpart. - [Indicator-Cmo.md](../momentum-oscillators/Indicator-Cmo.md) — bounded momentum from summed changes. - [Indicators-Overview.md](../../Indicators-Overview.md) — the full taxonomy.