Deployment and Upgrade for Multi-Financial Yield Data

Multi-financial yield-related data comes primarily from public financial market APIs, daily settlement files from custodian institutions, and exchange

What this category of data looks like

Multi-financial yield-related data comes primarily from public financial market APIs, daily settlement files from custodian institutions, and exchange market data feeds. Full daily profit calculation and data organization for all covered products is completed T+1 each trading day. Documents use a structured table format. Each row corresponds to one single product. Fields include product code, full product name, statistical period, profit calculation value, product scale, and custodian institution identifier, among others. Profit calculation values are floating-point numeric types. Product scale values are integer numeric types. No additional identifier fields are included. Data is aggregated by product category. Multiple products under the same period are sorted by scale.

What constraints these characteristics impose on deployment and upgrade

Multi-source access, T+1 update cycle, and fixed structured field characteristics of multi-financial yield data impose clear constraints on deployment and upgrade processes. Multi-source data access requires configuring multiple interface authentication and data merging rules during deployment, to avoid service interruptions caused by single data source failures. The T+1 update cycle requires scheduled task trigger times to match the standard window after trading day close. Adjustable time parameters must be preset during deployment. Fixed structured fields require parsing rules to bind specified column names. General fuzzy matching is not allowed, to avoid field misalignment. During upgrades, retain adapted configurations for old data sources, and add connection parameters for new interfaces, to avoid data pull failures after upgrade.

How to set configurations

Configuration ItemRecommended ValueRationale
DATA_SYNC_SOURCE_LIST["market_api", "trust_settlement", "exchange_feed"]Matches the access requirements of multi-financial multi-data sources, covers three types of data sources: market data, custodian settlement, and exchange feeds
SYNC_TASK_TRIGGER_TIME"18:30-20:00"Matches the standard window for T+1 updates on trading days, avoids data fluctuations during daytime trading hours
PARSE_STRUCTURED_COLUMN_MAP{"product_code":"A", "product_name":"B", "period":"C", "profit_value":"D", "scale":"E", "trust_id":"F"}Binds fixed column positions of multi-financial structured documents, avoids field parsing misalignment
UPLOAD_FILE_MAX_SIZE1000 MBSingle batch custodian settlement files are usually hundreds of MB, reserves sufficient space to avoid import interruptions
PARSE_FILE_TIMEOUT_SECONDS600 secondsStructured document parsing requires field verification and aggregation for multiple products, which takes longer than general document parsing
MULTI_SOURCE_MERGE_STRATEGY"priority_by_trust_id"Merges duplicate product data by custodian institution priority, ensures data consistency

The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require case-by-case analysis. It is recommended to test on your own samples before finalizing settings.

Three common configuration errors

  • Accessing the deployed service returns 405 Method Not Allowed. Cause: FastGPT reverse proxy rules are not configured correctly. The request path of the data interface is mapped to the static resource directory, causing POST requests to be rejected.
  • The team management function cannot be enabled after local deployment. Cause: The ENABLE_TEAM_MANAGEMENT=true environment variable is not added when starting the container. This function is disabled by default.
  • Duplicate entries appear in pulled yield data. Cause: Multi-source data merging strategy is not configured, and deduplication by product code is not performed, leading to duplicate import of the same product data from different data sources.

How to confirm the configuration is properly applied

  • Run the curl command to call the data synchronization interface, check that the returned response code is 200 OK, confirm that the interface authentication and forwarding configuration takes effect.
  • View the scheduled task log, confirm that data pull and parsing tasks are started within the preset trigger window, confirm that the trigger time configuration is correct.
  • Randomly select a structured data entry, check that the parsed fields match the preset column mapping rules, confirm that the field binding configuration takes effect.
  • View the multi-source data merged statistics panel, confirm that duplicate product data has been correctly merged, confirm that the merging strategy configuration takes effect.

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.