What the data for this category looks like
Financing daily report data for investment platforms is primarily sourced from public margin trading data APIs of stock exchanges and trading system APIs of partner securities firms. Data is synced during a fixed daily window after market close, covering full market and specified underlying margin trading data for the current day. Each daily report uses a structured table format. Core fields include statistical date, financing purchase amount, financing balance, securities lending sold volume, and securities lending remaining volume. Their respective units are CNY, CNY, shares, shares, and shares. Some platforms add supplementary related data such as daily price change range and turnover rate for margin trading targets. The total length of the document fluctuates based on the number of covered underlyings.
What constraints these characteristics impose on workflow orchestration
Multiple data sources and fixed update times require workflows to include a scheduled trigger node aligned with the daily fixed execution cycle. A data timeliness verification step must also be added to filter non-current historical data. Structured data with multiple fields and clear units requires that exact field names and unit verification rules are specified when configuring field extraction nodes, to avoid cross-unit calculation errors. Fluctuating numbers of covered underlyings require workflows to support dynamic field mapping, to adapt to input data with varying numbers of underlyings and prevent execution failures caused by hard-coded fields. External API calls carry volatility risks, so retry nodes and timeout thresholds must be configured to ensure stable data pulling.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
schedule_cron | 0 17 * * * (17:00 Beijing Time) | Margin trading data from domestic stock exchanges is typically updated after 17:00 daily. This time allows pulling the latest current-day data |
api_request_timeout | 600 seconds | Pulling data from multiple APIs requires sufficient response time to avoid execution failures due to interface delays |
field_validate_rules | Includes statistical date, financing purchase amount (CNY), financing balance (CNY) | Core fields of financing daily reports are fixed. Verification of field completeness and unit correctness is required |
dynamic_field_switch | Enabled | Fluctuating number of covered underlyings requires adaptation to input data with varying numbers of underlyings |
retry_max_times | 3 times | External APIs may experience temporary volatility. 3 retries can improve pulling success rate |
response_format | structured_table | Financing daily reports use structured data. Preserving the original table format facilitates subsequent processing |
The parameter values provided on this page are common recommended 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 mistakes
- Phenomenon: When the
temperatureparameter of an AI chat node is set to 0, the output result differs from that when the temperature switch is disabled. Cause: Disabling the temperature switch actually uses the platform's default temperature parameter, and does not force a fixed value of 0. The generation logic of the two configurations differs. - Phenomenon: When multiple lines of text are entered in a specified reply node, only a single line is displayed after publishing, or line breaks are lost. Cause: The "preserve original line breaks" configuration item within the node is not enabled. By default, line breaks are converted to spaces.
- Phenomenon: A question classification node takes too long to execute, exceeding the preset threshold, leading to overall workflow timeout. Cause: The
batch_sizeparameter is not set to a reasonable value adapted to the current data volume, resulting in too much data processed in a single batch and increased classification delay.
How to confirm correct configuration
- Run a single workflow test, check if the pulled data is the current-day financing daily report data with the statistical date, and verify that the units of core fields match the configured verification rules.
- Simulate interface timeout or temporary failure, check if the workflow triggers the retry mechanism, and confirm that the number of retries matches the preset configuration.
- Adjust the number of covered underlyings, test whether the workflow can normally adapt to input data of varying lengths, with no missing fields or mapping errors.
- Check the
temperatureparameter and switch status of the AI chat node, verify that the output result conforms to the expected generation logic, with no extra redundant brand information.
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.