Building an Opening 15-Minute Volume Factor with Minute Data

Summary

This tutorial develops a stock factor from the sum of trading volume during the first 15 minutes of a session. It explains why opening activity may be informative: overnight news is reflected in early trading, the opening period contributes to price discovery, and its volume can indicate market participation and short-term activity. It defines opening turnover as that volume divided by floating shares, multiplied by 100%, and gives a numerical illustration.

The article outlines two data-processing routes: use a computing cluster for minute-level factor development, or aggregate one-minute stock bars through a conventional workflow. Floating share counts can then be joined to calculate turnover. It also describes uploading and appending factor datasets. The tutorial presents implementation steps and a sample calculation, but supplies no backtest or evidence that the factor predicts returns. The claimed relevance of opening volume should therefore be treated as a motivation for research, not as demonstrated trading performance.

Key ideas

• Opening 15-minute volume can be aggregated from one-minute stock bars to form a daily factor.
• Opening turnover is calculated by dividing that volume by floating shares and multiplying by 100%.
• The article presents cluster-based and conventional data-processing workflows for factor construction.
• Factor datasets can be written to a data source and later extended with appended records.
• The document gives a rationale for the signal but no test of its predictive performance.

Tags

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