What data for this use case looks like
This category's data primarily comes from standardized APIs of licensed financial data service providers and reconciliation logs from internal trading systems. Full batch updates are completed within one hour after daily market close. Individual data entries are small in size, but batch requests generate data packets containing multiple products. The data uses a structured format, with fields including product_id, product_name, prev_day_return, net_value, trading_volume, update_time. The unit for net_value is yuan per share, trading_volume is measured in issued shares, and update_time is an ISO 8601 formatted timestamp.
Constraints imposed by these characteristics on model integration and configuration
The batch update feature requires model integration to support batch request adaptation, to avoid performance loss from single-item calls. The structured field format requires pre-defining field mapping rules during configuration, to ensure the model can accurately recognize the meaning of core fields such as product_id and net_value. The fixed daily update schedule requires binding a scheduled trigger logic in the access configuration, to avoid pulling outdated unupdated data during non-fixed periods. Additionally, APIs from licensed data sources have call frequency limits, so rate limiting parameters must be configured to prevent interface bans, and signature verification logic must be added to ensure data transmission security.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
batch_request_max_count | 20–50 items per request | Adapts to the single-batch limit of licensed financial data APIs to avoid triggering rate limits |
field_mapping_template | Configure according to the data source's standard fields | Structured data requires strict matching of core fields such as product_id and net_value to ensure accurate model reading |
schedule_trigger_time | 17:30–18:30 daily | Aligns with the conventional data update window after domestic financial market close, ensuring pulling the latest daily report data |
api_request_timeout | 30 seconds | Reserves response buffer time for batch requests to avoid timeout errors caused by network fluctuations |
rate_limit_quota | 10 calls per minute | Matches the basic call frequency limit of most licensed data service providers to prevent interface bans |
data_validate_enabled | Enabled | Validates the completeness of required fields to avoid model output anomalies caused by missing data |
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 case-by-case analysis, and it is recommended to test on your own samples before finalizing settings.
Three common configuration mistakes
- The symptom is that the model stops outputting mid-generation when creating yield daily reports, with no complete result. The cause is failure to configure
batch_request_max_countwithin the data source's limit, triggering interface rate limiting and interrupting the request. - The symptom is that returned fields are empty or do not match expected formats. The cause is incorrect configuration of
field_mapping_template, failure to match the data source's standard field names, leading to the model being unable to recognize valid data. - The symptom is that the scheduled task pulls previous day's outdated data. The cause is that the
schedule_trigger_timewindow is later than the actual data source update completion time, resulting in pulling unupdated historical data.
How to confirm successful configuration
- Call the test API to pull a single data entry, verify that the returned fields match those configured in
field_mapping_template. - Initiate a batch request to confirm that the number of returned data entries falls within the range configured for
batch_request_max_count. - Trigger the scheduled task, check that the
update_timeof the pulled data falls within the post-market close window of the current day. - Simulate high-frequency calls to confirm that the
rate_limit_quotalimit is not triggered, with no error returns.
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.