Downside Gap
A downside gap occurs when the opening price of a session is significantly below the prior session’s low, leaving a gap on the chart. It often signals strong negative sentiment or news impact.
Types
- Common gap: small and often filled quickly
- Breakaway gap: starts a new downtrend
- Exhaustion gap: may signal the end of a downtrend
Trading Considerations
Downside gaps can attract momentum traders and trigger stop losses. They can also create oversold conditions if the move is extreme.
Risk Management
Gap trading is risky because price can reverse quickly or continue sharply. Liquidity at the open may be thin, leading to slippage.
Practical Notes
Confirm gap type with context and volume. A gap after a long decline can be exhaustion rather than continuation.
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
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
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