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Gravestone Doji

Patterns candlestick indecision bearish

A Doji variant with a long upper wick, a very small body located near the low of the range, and little or no lower wick. It represents a rejection of higher prices and carries bearish implications when appearing after an advance.

Visual Example

Gravestone Doji: small body at the low of the range with an extremely long upper wick and virtually no lower wick. Annotation emphasizes the upper rejection.

Synthetic ideal matching TA-Lib CDLGRAVESTONEDOJI logic (long upper shadow + open\approx close\approx low). Generated 2026-05-31 IST via docs/gen_candle_previews.py.

Description

The Gravestone Doji forms when buyers push price significantly higher during the period but are unable to sustain the advance; price collapses back to the opening level by the close. The long upper shadow visually records the failed attempt to move higher.

It is most meaningful after a sustained up-move or at resistance. Alone it is still only a warning; confirmation on the subsequent bar (close below the Gravestone low) materially increases its reliability. Quant developers frequently use it as a sparse directional feature in models that already contain Market Structure bias or ATR-based volatility regime.

Formula / Specification

Recognition Rules (exact implementation in QuantWave / TA-Lib CDLGRAVESTONEDOJI):

  1. Body length = |close − open| is negligible (Doji condition).
  2. Upper shadow (high − max(open, close)) is long relative to body and range.
  3. Lower shadow (min(open, close) − low) is negligible or zero.
  4. The pattern is identified when the above geometry holds; output follows TA-Lib sign convention (+100 / −100 / 0).
  5. Stateless single-bar test after OHLC history buffering in the streaming wrapper.

Parameters

Parameter Default Description
(none) — Pattern recognition only; no tunable parameters.

Usage Examples

Streaming (Rust)

use quantwave_core::indicators::CDLGRAVESTONEDOJI;
use quantwave_core::traits::Next;

let mut g = CDLGRAVESTONEDOJI::new();
for (o, h, l, c) in &ohlcv {
    let sig = g.next((o, h, l, c));
    if sig < 0.0 { /* bearish gravestone context */ }
}

Streaming (Python)

from quantwave import CDLGRAVESTONEDOJI
g = CDLGRAVESTONEDOJI()
for o, h, l, c in ohlcv:
    sig = g.next((o, h, l, c))

Polars Batch (Python)

df.lazy().with_columns(
    pl.col("open").ta.cdl_gravestonedoji("open", "high", "low", "close").alias("gravestone")
).collect()

Bit-identical across surfaces.

Edge Cases & Limitations

  • Requires a completed bar; first bar yields no signal.
  • Can appear in strong uptrends without reversal follow-through (continuation risk).
  • Very low range bars distort the "long shadow" perception; combine with ATR.
  • TA-Lib tolerance for "long" vs. body is reference-implementation specific.
  • Highest value when the prior trend was up and the pattern occurs at a known resistance or structure level.
  • Confirmation on next bar strongly recommended; isolated Gravestones in chop are low-value.

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: TA-Lib CDLGRAVESTONEDOJI (ta-lib.org) wrapped in quantwave-core/src/indicators/pattern.rs.

Visual: docs/gen_candle_previews.py, 2026-05-31 IST.

Context: Nison (1991) for rejection psychology only. MQL5 PA series for confluence with confirmed structure levels.

Provenance: Universal Next trait + Polars parity.