Bearish Marubozu
A bearish marubozu is a candlestick pattern with a long body and little or no upper or lower shadow. The open is near the high and the close is near the low, showing strong selling pressure throughout the period.
Interpretation
- Indicates decisive control by sellers
- Often appears during downtrends or at the start of a bearish move
- Can signal continuation if confirmed by follow-through
Use in Trading
Traders may use the bearish marubozu as a confirmation candle after a break of support. It can also serve as a warning sign when it appears after an extended rally.
Limitations
Single-candle patterns can be noisy. Confirmation from volume, trend context, or additional price action is recommended.
Validation Checklist
A high quality pattern usually has a clear preceding trend, a well defined structure, and a logical breakout level. The consolidation should be proportionate to the prior move rather than a random cluster of bars.
It is helpful to check the number of touches on the boundaries, the duration of the pattern, and whether the price action is compressing or expanding in a consistent way.
Target Projection
A common projection method is to measure the prior impulse move and add or subtract it from the breakout point. For a continuation pattern, target = breakout price + or - pole length, depending on direction.
Targets are guidelines rather than guarantees. Partial exits or trailing stops can reduce the risk of missing reversals.
Volume Behavior
Volume often contracts during the consolidation and expands on the breakout. A breakout on weak volume can still work, but it is more prone to failure.
In markets without reliable volume data, use price velocity or volatility expansion as a proxy for participation.
Failure Signs
Common failure signs include quick reversals back inside the pattern, lack of follow through after the breakout, and false breaks around major news.
If the breakout fails, the opposite move can be fast. A failed continuation pattern can act as a reversal signal.
Timeframe and Context
Short term patterns are more sensitive to noise and require tighter risk control. Higher timeframe patterns generally have more significance but can take longer to resolve.
Context matters. Patterns that align with the broader trend or a key fundamental driver tend to perform better.
Operational Notes
Definitions and conventions should be consistent across datasets and venues. A small difference in data fields or session boundaries can change outcomes, especially for short term strategies. Document inputs and assumptions so results can be reproduced.
If the concept depends on exchange rules or broker behavior, confirm those rules for the specific venue. Operational details often explain why a trade behaved differently than expected.
Stress Scenarios
During volatility spikes, liquidity can evaporate and price gaps can appear. Under these conditions, indicators can lag, order types can misfire, and spreads can widen sharply.
Stress testing the concept against fast markets, thin liquidity, and sudden news helps reveal hidden risks. If a strategy only works in calm conditions, size and timing should reflect that.
Documentation Tips
Keep a short checklist of the rules, parameters, and decision points. Record how the concept is used in live trading and compare it to backtest assumptions. This makes future refinement easier and reduces drift in execution.
Common Questions
Traders often ask how sensitive results are to parameter choices, how the concept behaves in different regimes, and whether it scales with size. Answering these questions early improves reliability and prevents overfitting.
Checklist
- Define the exact rule in plain language
- Validate data quality and timing
- Quantify execution costs
- Set risk limits and stop logic
- Review performance by regime
Checklist
- Define the exact rule in plain language
- Validate data quality and timing
- Quantify execution costs
- Set risk limits and stop logic
- Review performance by regime
Checklist
- Define the exact rule in plain language
- Validate data quality and timing
- Quantify execution costs
- Set risk limits and stop logic
- Review performance by regime
Checklist
- Define the exact rule in plain language
- Validate data quality and timing
- Quantify execution costs
- Set risk limits and stop logic
- Review performance by regime
Checklist
- Define the exact rule in plain language
- Validate data quality and timing
- Quantify execution costs
- Set risk limits and stop logic
- Review performance by regime
Checklist
- Define the exact rule in plain language
- Validate data quality and timing
- Quantify execution costs
- Set risk limits and stop logic
- Review performance by regime
Checklist
- Define the exact rule in plain language
- Validate data quality and timing
- Quantify execution costs
- Set risk limits and stop logic
- Review performance by regime