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Bearish Engulfing pattern

A Bearish Engulfing is a two-candle reversal pattern where a large red candle completely engulfs the prior green candle’s body. It signals that sellers have taken control and often marks the end of a short-term uptrend.

Historical performance

Bearish Engulfing historical win-rate

Follow-through rate — how often price moved in the predicted direction within each window — across 14,723 historical occurrences on 20+ exchanges. Computed June 2026.

Bearish Engulfing: historical follow-through win-rate by horizon (n = 14,723).
HorizonHistorical win-rate
1 hour34%
4 hours34%
24 hours39%
7 days50%
Sample size14,723 occurrences

This is a historical follow-through rate, not a trade simulation, and does not guarantee future results. See methodology →

How the bearish engulfing pattern forms

After an advance, a small green candle is followed by a red candle whose body opens at or above the prior close and closes below the prior open, engulfing it. A larger engulfing body and higher volume strengthen the signal.

How traders use the bearish engulfing pattern

Traders typically look to enter short (or exit longs) on a close below the engulfing candle’s low, with a stop above its high. It is most reliable at resistance, after an extended up-move, and when the higher timeframe agrees.

CryptoPatterns’ scanner detects the bearish engulfing live across 20+ exchanges and every timeframe, tagging each occurrence with the historical win-rate above so you can weigh it in context. See how the scanner works →

FAQ

Bearish Engulfing — common questions

Is a bearish engulfing pattern bearish or bullish?

It is bearish — a red candle engulfing the prior green candle shows sellers overpowering buyers, typically near the top of a move. Its weight increases at resistance and with confirming volume.

How accurate is the bearish engulfing pattern?

Like most single candlestick signals it is a probabilistic edge, not a certainty. The historical follow-through rate above shows how often price actually moved down after it in our dataset; always pair it with trend and level context.

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