Building a Stock Factor Correlation Heatmap in BigQuant

Summary

This short BigQuant forum post shows an approach for visualizing correlations among several fields for a single Chinese stock. Its example queries a prefactor table for closing price, volume, and rolling turnover sums, retrieves the data as a dataframe, computes a correlation matrix, and passes that matrix to a heatmap chart. This illustrates a basic workflow for inspecting how candidate factors and market variables move together over a selected period.

The author reports that the example fails because the instrument identifier is a string that cannot be converted to a float, and says a small modification made it usable. However, the post does not show the corrected version, explain which columns to exclude or convert, or discuss missing values and time alignment. It is therefore a useful pointer to the plotting workflow and a common data-type issue, but not a complete, reproducible solution or an analysis of the resulting correlations.

Key ideas

• A factor correlation heatmap can be built by querying data, computing a dataframe correlation matrix, and plotting it.
• The example uses price, volume, and rolling turnover fields for one Chinese stock.
• A string instrument identifier causes a numeric conversion error when included in the correlation input.
• The post mentions a correction but does not provide the working modification or interpret any heatmap results.

Tags

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