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System Evaluator

Statistics system performance ehlers statistics

Calculates robust statistical performance metrics for a trading system based on a stream of trade profits.

Visual Example

System Evaluator — annotated preview mapping to core implementation

Synthetic ideal per library logic. Generated 2026-06-25 IST via docs/generate_all_previews.py (reproducible; maps to core Next<T> implementation).

Description

The System Evaluator indicator is a technical analysis tool that calculates robust statistical performance metrics for a trading system based on a stream of trade profits.

This indicator is primarily used for identifying key market conditions. It provides a robust signal that can be easily integrated into both simple strategies and more complex machine learning feature pipelines. Compared to its alternatives, it offers a distinct balance of responsiveness and stability.

Traders often combine this with other metrics to confirm signals and avoid false positives during sideways market regimes. It remains a standard tool for systematic trading models.

Use to assess the performance quality of a trading system output using signal processing metrics. Helps distinguish systems with genuine edge from those that merely overfit.

Ehlers applies signal processing metrics to evaluate trading system quality in Cybernetic Analysis. Metrics such as the Signal-to-Noise Ratio of the equity curve quantify whether a system is generating genuine signal above the noise floor of random entry and exit.

QuantWave implements this indicator via the universal Next<T> trait, guaranteeing bit-identical results between Rust streaming, Python streaming, and Polars batch (.ta() / map_batches) surfaces.

Formula / Specification

Implementation (quantwave-core/src/indicators/system_evaluator.rs):

[ AveTrade = \% \cdot (PF + 1) - 1 ] [ PF_{breakeven} = \frac{1 - \%}{\%} ] [ N_{losers} = \frac{\ln(0.0027)}{\ln(1 - \%)} ]

Gold-standard parity vectors: quantwave-core/tests/gold_standard/system_evaluation.json.

Parameters

Parameter Default Description
(none) — No tunable parameters for this detector.

Usage Examples

Streaming (Rust)

use quantwave_core::indicators::SYSTEM_EVALUATOR;
use quantwave_core::traits::Next;

let mut ind = SYSTEM_EVALUATOR::new(14);
for price in &prices {
    let value = ind.next(price);
}

Streaming (Python)

from quantwave import SYSTEM_EVALUATOR

ind = SYSTEM_EVALUATOR(14)
for price in prices:
    value = ind.next(price)

Polars Batch (Python)

import polars as pl
import quantwave as qw

def apply_system_evaluator(series: pl.Series) -> pl.Series:
    ind = qw.SYSTEM_EVALUATOR(14)
    return pl.Series([ind.next(float(v)) for v in series.to_list()])

df = (
    pl.read_csv('ohlcv.csv')
    .lazy()
    .with_columns(
        pl.col("close").map_batches(apply_system_evaluator, return_dtype=pl.Float64).alias("system_evaluator")
    )
    .collect()
)

All surfaces are bit-identical via the single Next<T> implementation and proptests.

Edge Cases & Limitations

  • Recursive DSP filters require a warm-up period; first N bars may be unstable or raw-pass-through.
  • Designed for cyclic/mean-reverting regimes; trending markets can produce lag or drift.
  • Parameter period (or equivalent) controls cutoff — too small adds noise, too large adds lag.
  • Prefer chaining with other Ehlers tools (Roofing Filter, SuperSmoother) on noisy inputs.
  • Validated via proptests against gold-standard vectors where available.
  • No look-ahead bias; suitable for live streaming and batch feature pipelines.

Boundary Behavior

Condition Behavior
Warm-up Leading bars return NaN until warmup_bars is satisfied.
period > len When period exceeds series length, output is all NaN.
NaN inputs NaN in input propagates to output (NaN out).
Invalid params Non-positive period or missing required params raise ValueError.
Empty data Empty input returns an empty result series.

Sources & References

Primary Source: https://github.com/lavs9/quantwave/blob/main/references/Ehlers%20Papers/SystemEvaluation.pdf

Implementation: quantwave-core/src/indicators/system_evaluator.rs (SYSTEM_EVALUATOR / SYSTEM_EVALUATOR_METADATA). Parity: quantwave-core/tests/gold_standard/system_evaluation.json

Provenance: Standards bulk upgrade 2026-06-25 IST — see docs/DOCUMENTATION_STANDARDS.md.