Stock Screen Using Prior-Day Leaderboard Activity and Bollinger Bands

Summary

This post proposes screening stocks with a large daily range that appeared on the previous day’s trading leaderboard, then keeping names whose close is above the Bollinger middle band and below the upper band. It presents the combination as a way to focus on active, volatile stocks while using Bollinger Bands to locate price within its recent distribution. Formula and Python examples show a rolling average and standard deviation for the bands, plus a leaderboard filter and a ranking step.

The author flags volatility and possible band deviations as risks, and notes that ranking, timing, selection count, market conditions, and sector exposure affect how the screen behaves. Suggested refinements include other technical indicators, fundamental and industry analysis, dynamic adjustment, and stop or limit controls. The post supplies no backtest or evidence of returns. Its sample calculations also leave implementation choices to the user, including how to align the prior-day range and leaderboard data with the current screening date, so reproducibility and out-of-sample performance need evaluation.

Key ideas

• The screen combines a large daily range with appearance on the previous day’s trading leaderboard.
• It selects closes above the Bollinger middle band and below the upper band.
• The examples calculate Bollinger Bands from a rolling mean and standard deviation.
• The post identifies volatility, market shifts, and sector conditions as factors that can affect the screen.
• No backtest evidence is provided, and date alignment requires careful implementation.

Tags

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