Tick Data
Tick data is the most granular market data, recording each individual trade or quote update. It includes price, size, time, and sometimes bid and ask changes.
Uses
- High frequency research and backtesting.
- Building tick charts and order flow indicators.
- Estimating execution quality and slippage.
Example
A dataset contains each trade in a futures contract for a day, with timestamps to the millisecond. A trader uses it to reconstruct the order flow around a news event.
Practical notes
Tick data is large and requires careful cleaning. Different venues may report trades and quotes with slight timing differences.
Practical checklist
- Define the time horizon for Tick Data and the market context.
- Identify the data inputs you trust, such as price, volume, or schedule dates.
- Write a clear entry and exit rule before committing capital.
- Size the position so a single error does not damage the account.
- Document the result to improve repeatability.
Common pitfalls
- Treating Tick Data as a standalone signal instead of context.
- Ignoring liquidity, spreads, and execution friction.
- Using a rule on a different timeframe than it was designed for.
- Overfitting a small sample of past examples.
- Assuming the same behavior in abnormal volatility.
Data and measurement
Good analysis starts with consistent data. For Tick Data, confirm the data source, the time zone, and the sampling frequency. If the concept depends on settlement or schedule dates, align the calendar with the exchange rules. If it depends on price action, consider using adjusted data to handle corporate actions.
Risk management notes
Risk control is essential when applying Tick Data. Define the maximum loss per trade, the total exposure across related positions, and the conditions that invalidate the idea. A plan for fast exits is useful when markets move sharply.
Variations and related terms
Many traders use Tick Data alongside broader concepts such as trend analysis, volatility regimes, and liquidity conditions. Similar tools may exist with different names or slightly different definitions, so clear documentation prevents confusion.
Practical checklist
- Define the time horizon for Tick Data and the market context.
- Identify the data inputs you trust, such as price, volume, or schedule dates.
- Write a clear entry and exit rule before committing capital.
- Size the position so a single error does not damage the account.
- Document the result to improve repeatability.
Common pitfalls
- Treating Tick Data as a standalone signal instead of context.
- Ignoring liquidity, spreads, and execution friction.
- Using a rule on a different timeframe than it was designed for.
- Overfitting a small sample of past examples.
- Assuming the same behavior in abnormal volatility.
Data and measurement
Good analysis starts with consistent data. For Tick Data, confirm the data source, the time zone, and the sampling frequency. If the concept depends on settlement or schedule dates, align the calendar with the exchange rules. If it depends on price action, consider using adjusted data to handle corporate actions.
Risk management notes
Risk control is essential when applying Tick Data. Define the maximum loss per trade, the total exposure across related positions, and the conditions that invalidate the idea. A plan for fast exits is useful when markets move sharply.
Variations and related terms
Many traders use Tick Data alongside broader concepts such as trend analysis, volatility regimes, and liquidity conditions. Similar tools may exist with different names or slightly different definitions, so clear documentation prevents confusion.
Practical checklist
- Define the time horizon for Tick Data and the market context.
- Identify the data inputs you trust, such as price, volume, or schedule dates.
- Write a clear entry and exit rule before committing capital.
- Size the position so a single error does not damage the account.
- Document the result to improve repeatability.
Common pitfalls
- Treating Tick Data as a standalone signal instead of context.
- Ignoring liquidity, spreads, and execution friction.
- Using a rule on a different timeframe than it was designed for.
- Overfitting a small sample of past examples.
- Assuming the same behavior in abnormal volatility.
Data and measurement
Good analysis starts with consistent data. For Tick Data, confirm the data source, the time zone, and the sampling frequency. If the concept depends on settlement or schedule dates, align the calendar with the exchange rules. If it depends on price action, consider using adjusted data to handle corporate actions.
Risk management notes
Risk control is essential when applying Tick Data. Define the maximum loss per trade, the total exposure across related positions, and the conditions that invalidate the idea. A plan for fast exits is useful when markets move sharply.
Variations and related terms
Many traders use Tick Data alongside broader concepts such as trend analysis, volatility regimes, and liquidity conditions. Similar tools may exist with different names or slightly different definitions, so clear documentation prevents confusion.