analyze_data_quality
Detect time gaps, price outliers, null values, and duplicate records in market data to assess quality with a score and detailed issue breakdown.
Instructions
Analyze data quality and detect issues in market data.
Detects:
Time gaps in data
Price outliers (>3 standard deviations)
Null values and missing data
Duplicate records
Returns:
Quality score (0-100)
List of issues and warnings
Detailed breakdown of problems
Example:
First retrieve data with get_historical_data
Then analyze_data_quality(dataset="GLBX.MDP3", symbols="ES.FUT", start="2024-01-15", end="2024-01-15")
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| dataset | Yes | Dataset name | |
| symbols | Yes | Comma-separated list of symbols | |
| start | Yes | Start date in YYYY-MM-DD format | |
| end | Yes | End date in YYYY-MM-DD format | |
| schema | No | Data schema (default: 'trades') | trades |
| limit | No | Maximum records to analyze (default: 10000) |