> ## Documentation Index
> Fetch the complete documentation index at: https://docs.sequency.sh/llms.txt
> Use this file to discover all available pages before exploring further.

# Check Data Freshness

> Health check for data pipeline freshness - market-hours aware.

AUDIT-001: Uses trading calendar to correctly determine expected freshness.

Behavior:
- Trading days (pre-market, intraday, after-hours): Expects TODAY's data
  to detect same-day ingestion outages throughout the day
- Non-trading days: Expects previous trading day's data

This ensures outages during active trading are detected immediately,
while avoiding false alarms on weekends/holidays.

Returns:
    dict with status, freshness_mode, expected_date, checks, and issues



## OpenAPI

````yaml /api/openapi.json get /health/data-freshness
openapi: 3.1.0
info:
  title: Sequency Graph API
  description: >

    # Sequency Graph-Native Trading API


    Graph-native API for trading intelligence, built on FalkorDB.


    ## Features


    - **Screener**: Filter stocks using graph-native queries with relationship
    expansion

    - **Stock Detail**: Complete stock context with all relationships

    - **Confluence Scoring**: Setup quality assessment with weighted components

    - **Strategy Matching**: Find matching options strategies via graph
    traversal

    - **Market Context**: Current market-wide context for trading decisions


    ## Data Sources


    All data is sourced from the FalkorDB knowledge graph, populated by:

    - Pattern detector (Go) - Technical indicators, patterns

    - Graph sync service - Levels, volume profiles, news, day classification
  version: 1.0.0
servers: []
security: []
paths:
  /health/data-freshness:
    get:
      tags:
        - Health
      summary: Check Data Freshness
      description: >-
        Health check for data pipeline freshness - market-hours aware.


        AUDIT-001: Uses trading calendar to correctly determine expected
        freshness.


        Behavior:

        - Trading days (pre-market, intraday, after-hours): Expects TODAY's data
          to detect same-day ingestion outages throughout the day
        - Non-trading days: Expects previous trading day's data


        This ensures outages during active trading are detected immediately,

        while avoiding false alarms on weekends/holidays.


        Returns:
            dict with status, freshness_mode, expected_date, checks, and issues
      operationId: check_data_freshness_health_data_freshness_get
      responses:
        '200':
          description: Successful Response
          content:
            application/json:
              schema:
                additionalProperties: true
                type: object
                title: Response Check Data Freshness Health Data Freshness Get

````