Struct MaxDrawdown 

pub struct MaxDrawdown { /* private fields */ }

Rolling Maximum Drawdown — the deepest peak-to-trough decline within the trailing window.

The input is treated as an equity-curve sample (or any non-negative value series). For each bar the indicator computes the largest fractional decline from any prior peak inside the trailing period-bar window:

drawdown_t = (equity_t − peak_t) / peak_t        (a negative number)
MaxDrawdown = min(drawdown_t over window)        (most-negative value)

peak_t is the running maximum from the oldest value in the window up to t, and only a positive peak counts.

Output is the magnitude of the worst drawdown as a non-negative fraction (0.20 = 20 % drop from peak). A monotonically rising equity curve has a max drawdown of 0. Setting period greater than or equal to the number of bars you will ever feed makes the metric effectively cumulative — the indicator never forgets the global peak.

Each update is amortised O(1). Within the window the running peaks split the values into segments, each a peak followed by the values below it, and a segment’s deepest drawdown is (peak − low) / peak. The window is held as two halves: the older one is rebuilt from the newer one every period updates, storing for each value the peak and worst drawdown from it to the end of the half, and the newer one keeps its own running peaks with the low of each segment. Because the subtraction and the division both round monotonically, the result is bit for bit the one a rescan of the window gives.

Example

use wickra_core::{Indicator, MaxDrawdown};

let mut mdd = MaxDrawdown::new(10).unwrap();
// Equity peaks at 110 then drops to 88 — a 20% drawdown.
for v in [100.0, 110.0, 100.0, 95.0, 88.0, 90.0, 92.0, 95.0, 100.0, 105.0] {
    mdd.update(v);
}
assert!((mdd.update(106.0).unwrap() - 0.20).abs() < 1e-9);

Implementations

impl MaxDrawdown

pub fn new(period: usize) -> Result<Self>

Construct a new rolling Max Drawdown.

Errors

Returns Error::PeriodZero if period == 0.

pub const fn period(&self) -> usize

Configured rolling-window length.

pub const fn value(&self) -> Option<f64>

Current value if available.

Trait Implementations

impl Clone for MaxDrawdown

fn clone(&self) -> Self

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for MaxDrawdown

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Indicator for MaxDrawdown

type Input = f64

Type of one input data point (typically f64 for a price, or Candle / Tick).

type Output = f64

Type of one output value.

fn update(&mut self, input: f64) -> Option<f64>

Feed one new data point into the indicator and return the freshly computed output, or None if there is no value for this input.

fn reset(&mut self)

Reset all internal state, leaving the indicator equivalent to a freshly constructed instance with the same parameters.

fn warmup_period(&self) -> usize

Number of inputs required before the first non-None output can be produced.

fn is_ready(&self) -> bool

Whether the indicator has emitted at least one value since the last reset.

fn name(&self) -> &'static str

Stable, human-readable indicator name. Used by chaining and diagnostics.

fn batch_nan_into(&mut self, inputs: &[Self::Input], out: &mut [f64])

where Self::Input: Copy, Self::Output: Into<f64>,

Run the indicator over inputs, writing one output per input into the caller-owned out buffer (NaN where update returns None). It exists for every indicator whose output converts to an f64; the batch pipelines use it for the scalar f64 -> f64 ones.

fn batch_fast_into(&mut self, inputs: &[Self::Input], out: &mut [f64])

where Self::Input: Copy, Self::Output: Into<f64>,

Opt-in fast batch: like batch_nan_into, but an indicator with a vectorised kernel may reassociate its arithmetic to run it in SIMD lanes. Each value then agrees with the exact batch to within the tolerance the indicator documents (a few units in the last place), not bit for bit; warmup positions, NaN placement and the output length are identical. The kernels are deterministic: the same input produces the same bits on every platform, with or without SIMD hardware.