Day Low
The day low is the lowest price reached by a security during a trading session. It is often used to gauge intraday weakness and to identify breakdown levels.
Significance
- A new day low can signal strong selling pressure
- The level can serve as support or a breakdown trigger
- It is commonly used in intraday risk management
Trading Applications
- Breakdown entry: sell or short when price drops below the day low
- Stop placement: set protective stops slightly above the day low for shorts
- Session analysis: use day low to define the lower boundary of the range
Relationship to Volatility
The day low contributes to the total range of the session and helps measure intraday volatility. Large deviations from the open often signal directional conviction.
Interaction with Opening Range
A day low set early can indicate a weak session. Late session tests of the low reveal whether sellers maintain control or whether short covering begins.
Context and Confirmation
Breaks of the day low are more reliable when supported by broader market weakness, negative news flow, or rising volume. Isolated dips without follow through often reverse.
Risk Management
Stops are often placed above the breakdown level or above the most recent swing high. Position sizing should allow for quick reversals in volatile sessions.
Limitations
Day lows can be influenced by single trades or data errors. Traders often require confirmation or a close below the level before acting.
Practical Notes
Ensure consistency in session boundaries. Including overnight or premarket prices can change what is considered the day low.
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