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Open-Close Average (OC2)

Ehlers DSP price-transform ehlers smoothing dsp

A simple average of the Open and Close prices.

Visual Example

Open-Close Average (OC2) — 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 Open-Close Average (OC2) indicator is a technical analysis tool that a simple average of the open and close prices.

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 reduce noise in technical indicators. Based on John Ehlers' recent research, averaging the open and close can significantly improve signal-to-noise ratios in DSP-based indicators.

In his 2023 paper 'Every Little Bit Helps', John Ehlers demonstrates that using the average of the Open and Close as an input can enhance the performance of various filters and oscillators by providing a cleaner signal with reduced aliasing. — John Ehlers

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/price_transform.rs):

\[ OC2 = \frac{Open + Close}{2} \]

Gold-standard parity vectors: quantwave-core/tests/gold_standard/oc2.json.

Parameters

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

Usage Examples

Streaming (Rust)

use quantwave_core::indicators::OC2;
use quantwave_core::traits::Next;

let mut ind = OC2::new(14);
for price in &prices {
    let value = ind.next(price);
}

Streaming (Python)

from quantwave import OC2

ind = OC2(14)
for price in prices:
    value = ind.next(price)

Polars Batch (Python)

import polars as pl
import quantwave as qw

def apply_open_close_average_oc2(series: pl.Series) -> pl.Series:
    ind = qw.OC2(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_open_close_average_oc2, return_dtype=pl.Float64).alias("open_close_average_oc2")
    )
    .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: Every Little Bit Helps (John Ehlers, 2023)

Implementation: quantwave-core/src/indicators/price_transform.rs (OC2 / OC2_METADATA). Parity: quantwave-core/tests/gold_standard/oc2.json

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