A solution for efficiently processing financial data with Qlib
Qlib can effectively solve the problem of inefficient financial data storage and processing through its optimized data processing module. The following are the specific solutions:
- Built-in high performance data storage systemQlib uses a file-based hierarchical storage structure to efficiently store massive financial data, increasing query speed by more than 10 times compared to traditional databases.
- batch processing engine: With predefined dataset processors such as Alpha158, 158 common factor metrics can be batch-calculated to avoid double-counting.
- Intelligent Caching Mechanism: The system automatically caches frequently used query results, with almost zero latency for subsequent calls. Use
qlib.init(provider_uri=)It can be activated automatically upon initialization. - Distributed Computing Support: Large organizations can deploy Qlib-Server to achieve multi-node parallel computing. Processing power can be linearly scaled after deployment via Docker.
Implementation Suggestions: First download the official sample data to understand the storage structure, then plan the data directory according to the actual needs, and finally call through the API interface to avoid direct operation of the file system.
This answer comes from the articleQlib: an AI quantitative investment research tool developed by MicrosoftThe
May not be reproduced without permission:AI productivity tools " How to solve the problem of inefficient data storage and processing in quantitative investment research?
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