SterlingRatio

Return per unit of typical pain — mean return over the average drawdown of the compounded equity curve.

Quick reference

Field	Value
Family	Risk / Performance
Input type	f64 (per-period returns)
Output type	f64
Output range	unbounded (negative for net-losing windows)
Default parameters	(period = 36) (Python)
Warmup period	period
Interpretation	Higher = more return per average drawdown.

Formula

equity_t  = Π_{i<=t} (1 + return_i)          (compounded curve)
peak_t    = max_{s<=t} equity_s
dd_t      = (peak_t − equity_t) / peak_t      (fractional drawdown, >= 0)
Sterling  = mean(returns) / mean(dd_t)

The Sterling Ratio divides the average per-period return by the average drawdown along the compounded equity curve. Of Wickra's three drawdown-based ratios it is the gentlest on outliers: averaging the drawdowns means one deep crater does not dominate, unlike the BurkeRatio (which sums squared drawdowns) or the MartinRatio (which uses the root-mean-square percentage drawdown). A window that never draws down has zero average drawdown and reports 0.0. Source: crates/wickra-core/src/indicators/sterling_ratio.rs.

Parameters

Name	Type	Default	Valid range	Source	Description
period	usize	36 (Python)	>= 2	sterling_ratio.rs:50	Window of returns (e.g. 36 months). < 2 errors with Error::InvalidPeriod.

The period getter returns the window.

Inputs / Outputs

From crates/wickra-core/src/indicators/sterling_ratio.rs:

use wickra::{Indicator, SterlingRatio};
// SterlingRatio: Input = f64, Output = f64
const _: fn(&mut SterlingRatio, f64) -> Option<f64> = <SterlingRatio as Indicator>::update;

An f64 return in, an Option<f64> out. Python update(ret) / batch(returns) (NaN warmup); Node update(ret) / batch(returns[]) (null warmup).

Warmup

warmup_period() == period. The first value lands once period returns are seen (reference_value exercises the emission at index period − 1).

Edge cases

• Reference value. [0.1, −0.1, 0.1] → drawdowns [0, 0.1, 0.01], (0.1/3) / (0.11/3) = 0.1/0.11 (reference_value pins this).
• No drawdown. A monotonically rising window reports 0.0 (no_drawdown_is_zero pins this).
• Losing window. A net-losing window gives a negative ratio (losing_window_is_negative pins this).
• Non-finite input. A NaN/∞ return is skipped (ignores_non_finite_input).
• Reset. sr.reset() clears the window (reset_clears_state pins this).

Examples

Rust

use wickra::{BatchExt, Indicator, SterlingRatio};

fn main() -> Result<(), Box<dyn std::error::Error>> {
    let mut sr = SterlingRatio::new(3)?;
    let out = sr.batch(&[0.1, -0.1, 0.1]);
    println!("{:?}", out[2]); // Some(0.9090...)
    Ok(())
}

Output:

Some(0.9090909090909091)

Python

import numpy as np
import wickra as ta

sr = ta.SterlingRatio(36)
returns = np.random.randn(60) * 0.05
print(sr.batch(returns)[-1])

Node

const ta = require('wickra');
const sr = new ta.SterlingRatio(3);
console.log(sr.batch([0.1, -0.1, 0.1]).at(-1)); // ~0.909

Streaming

use wickra::{Indicator, SterlingRatio};

let mut sr = SterlingRatio::new(36).unwrap();
let monthly_returns: Vec<f64> = Vec::new(); // your live stream
for r in monthly_returns {
    if let Some(ratio) = sr.update(r) {
        // higher ratio -> more return per average drawdown
    }
}

Streaming update and batch are equivalent tick-for-tick (batch_equals_streaming pins this).

Interpretation

1. Smoothness reward. The Sterling Ratio favours strategies whose equity grinds higher with shallow, frequent dips over those with rare but brutal crashes.
2. The drawdown family. Read it next to the BurkeRatio and MartinRatio: a high Sterling but low Burke flags that the average drawdown is benign while a few are severe.
3. Classic definition note. The original Sterling Ratio used the average of the largest annual drawdowns; this rolling form averages every per-period drawdown in the window — comparable in spirit, simpler to stream.

Common pitfalls

• No-drawdown anomaly. A window that only rises reports 0.0 (undefined), not infinity.
• Frequency. mean(returns) is per-period — annualise consistently if you quote an annualised Sterling Ratio.
• Outlier blindness. Because it averages drawdowns, a single catastrophic drawdown is under-weighted — pair with Burke/Martin to catch tail risk.

References

Deane Sterling Jones; popularised via Kestner, L. N., Quantitative Trading Strategies (2003) — the Sterling Ratio.

See also

• Indicator-BurkeRatio — sum of squared drawdowns.
• Indicator-MartinRatio — return over the Ulcer Index.
• Indicator-SharpeRatio — mean over total volatility.
• Indicators-Overview — the full taxonomy.