Combining DataFrame Filters to Screen Stocks and Retrieve Their History

Summary

This short programming example shows how to screen daily stock data with several conditions at once, then select only the desired columns. The example combines a date cutoff, a minimum trading amount, and a maximum closing price using boolean masks joined with logical AND. It returns the matching instruments alongside their dates, closes, and amounts.

The discussion also explains how to reuse the selected instrument identifiers in a second data-source read when additional history or fields are needed. If the first filtered table already contains the required observations, another read is unnecessary; the second query is useful when a separate retrieval is desired for the chosen instruments. The example teaches a basic data-handling pattern rather than an investment signal. It does not address point-in-time data quality, survivorship bias, execution assumptions, or whether the sample filters have predictive value.

Key ideas

• Create a boolean condition for each screening rule and combine conditions with logical AND.
• Use row filtering to retain observations that satisfy all specified rules.
• Select instrument identifiers and required columns to keep the filtered dataset focused.
• A second data read can be restricted to the instruments selected in the first pass.
• The example demonstrates data manipulation, not evidence that the screening rules predict returns.

Tags

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