What data for this category looks like
Teams source multi-financial yield data from three primary channels: self-operated product disclosures by non-bank financial institutions, over-the-counter derivative trading market interfaces, and record and public disclosure data from industry self-regulatory organizations. Fixed-income products update data after market close each trading day. Equity and alternative products publish their latest net value and yield figures weekly. Data documents use standard JSON format. Fields include product unique identifier, full product name, 7-day annualized yield value, 1-year total return value, unit net value value, and information disclosure date. Yield-related fields use proportional values. Net value uses standard currency units. Dates follow ISO 8601 format strings.
Constraints imposed on HTTP interfaces and external systems
The distributed nature of multiple data sources requires multi-source aggregation synchronization logic. This logic must adapt to three distinct data source types: institutional disclosures, over-the-counter markets, and filing interfaces. Different update rhythms require differentiated scheduled pull cycles. This prevents excessive API calls and missed data updates. Fields contain mixed numerical and date formats. Teams must configure strict validation rules to ensure consistent formatting for downstream processing. Over-the-counter market interfaces typically enforce call rate limits. Teams must set throttling parameters to avoid triggering account bans. Some filing interfaces require exclusive authentication methods. Teams must configure corresponding request headers and signature rules to meet compliance requirements for data acquisition.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
multi_source_sync_enable | true | Adapts to multi-financial multi-data source pull requirements, covering institutional disclosure, over-the-counter market, and filing interface data sources |
task_schedule_interval | 3600 seconds / 604800 seconds | Differentiates update rhythms for fixed-income products (pull once per hour) and alternative products (pull once per week), matching data source disclosure cycles |
field_validation_mode | strict | Validates formats for yield values, net values, and dates, preventing downstream processing failures caused by abnormal API return formats |
api_rate_limit | 100 requests / minute | Matches throttling rules for over-the-counter market interfaces, preventing call bans |
auth_sign_type | HMAC-SHA256 | Meets authentication requirements for most non-bank financial data interfaces, ensuring request legitimacy |
data_sync_filter | Deduplicate by disclosure date | Avoids repeated pulls of already synchronized historical data, reducing API call costs |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require individual analysis. It is recommended to test on your own samples before finalizing settings.
Three common configuration errors
- Symptom: The API returns duplicate product yield data. The status code is 200, but the result set includes multiple records with the same disclosure date. Cause: No deduplication filter rule configured by disclosure date, leading to repeated pulls of historical data.
- Symptom: A
429 Too Many Requestserror appears after a scheduled task runs. Cause: No throttling parameters set to match data source requirements, causing call frequency to exceed interface limits. - Symptom: Yield fields for some alternative products are empty. Task execution logs show format validation failures. Cause: Validation mode set to
loose, not strictly validating field formats for non-public products, resulting in skipped abnormal data.
How to confirm correct configuration
- Initiate a single synchronization test request, verify that returned field formats match preset validation rules, and confirm validation logic is active.
- Review scheduled task execution records, confirm that products of different categories trigger pulls according to their corresponding update cycles.
- Check data aggregation results, confirm that records from multiple data sources are correctly integrated with no duplicate entries.
- Simulate API calls, confirm that throttling configurations are active and will not trigger call restrictions.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.