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Counterattack

Patterns pattern candlestick classic

A reversal pattern where the second candle closes at the same level as the first.

Visual Example

Counterattack — 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 Counterattack indicator is a technical analysis tool that a reversal pattern where the second candle closes at the same level as the first.

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.

Signals a stalemate after a strong move.

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

Recognition Rules (TA-Lib-compatible, CDLCOUNTERATTACK in quantwave-core/src/indicators/pattern.rs):

  1. Stateless candlestick pattern evaluated on OHLC windows.
  2. Returns a signed signal (+100 bullish, −100 bearish, 0 none) on the completion bar.
  3. Exact threshold geometry (body ratios, gap requirements, shadow lengths) matches the TA-Lib reference implementation wrapped via talib_cdl! in quantwave-core/src/indicators/pattern.rs.
  4. Validate against quantwave-core/tests/gold_standard/ vectors where present.

Parameters

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

Usage Examples

Streaming (Rust)

use quantwave_core::indicators::CDLCOUNTERATTACK;
use quantwave_core::traits::Next;

let mut det = CDLCOUNTERATTACK::new();
for (o, h, l, c) in &ohlcv {
    let sig = det.next((o, h, l, c));
}

Streaming (Python)

from quantwave import CDLCOUNTERATTACK

det = CDLCOUNTERATTACK()
for o, h, l, c in ohlcv:
    sig = det.next((o, h, l, c))

Polars Batch (Python)

import polars as pl

df = (
    pl.read_csv('ohlcv.csv')
    .lazy()
    .with_columns(
        pl.col("open").ta.cdl_counterattack("open", "high", "low", "close").alias("counterattack")
    )
    .collect()
)

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

Edge Cases & Limitations

  • Requires sufficient complete OHLC bars; early bars yield no signal.
  • False positives are common in sideways markets — gate with trend or structure filters.
  • Pattern semantics follow TA-Lib body/shadow rules; literature variants may differ.
  • Signed output (+/−/0) should be consumed as events, not continuous features without encoding.
  • Combine with volume expansion or higher-timeframe confirmation for production use.
  • No look-ahead bias; signal is known only after the pattern window closes.

Boundary Behavior

Condition Behavior
Warm-up Pattern functions emit 0 (no pattern) until enough bars exist.
period > len Short series returns all zeros (no pattern detected).
NaN inputs Bars with NaN OHLC are treated as no pattern (0).
Invalid params N/A for most candlestick patterns.
Empty data Empty input returns an empty integer series.

Sources & References

Primary Source: https://www.investopedia.com/articles/active-trading/062315/using-bullish-candlestick-patterns-buy-stocks.asp

Implementation: quantwave-core/src/indicators/pattern.rs (CDLCOUNTERATTACK / CDLCOUNTERATTACK_METADATA). Pattern reference: TA-Lib CDL family via talib_cdl! in pattern.rs. Nison (1991) cited for psychology only — no duplicated boilerplate. Parity: quantwave-core/tests/gold_standard/cdlcounterattack.json

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