Caching iFinD Industry Data for Faster Backtests

Summary

The note describes a workflow for handling large datasets or repeated data requests during quantitative research. Such requests can slow backtests or cause errors, so it recommends downloading the required data into the research environment first and then reading it from there during backtesting.

The example uses iFinD data for first-level Shenwan industry indexes. It points readers to the Python interface documentation and the relevant indicator codes, then outlines downloading the data through the iFinD API for later use in a backtest. The post provides no actual code, benchmark, or comparison of runtime, so the benefit is presented as practical guidance rather than measured evidence. The method also depends on preparing and maintaining a local or research-environment copy of the data; the note does not discuss update frequency, storage, or data-quality checks.

Key ideas

• Repeated or large API requests can slow a backtest or cause errors.
• Downloading data into the research environment first can reduce repeated requests during backtesting.
• The example applies this workflow to iFinD first-level Shenwan industry index data.
• The post gives no code or measured performance results, and it does not address data refresh or validation.

Tags

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.