Using Bokeh to Visualize Stock Returns, Prices, and Fundamentals
Summary
This tutorial introduces Bokeh as a Python tool for interactive browser-based plots and dashboards. It distinguishes server-connected plots, whose data can update the interface, from standalone plots, which still support interactions such as linked navigation and hover details. It also outlines Bokeh's Python model and plotting interfaces and ways to display charts in HTML or notebooks. Examples apply these tools to equity analysis: a dot plot compares sector returns, a scatter plot compares stocks within a sector, stacked time-series charts show a stock beside an index, and bars display quarterly sales and profit growth. The article interprets a stock's price rise alongside stronger results and favorable news, illustrating how charts can help explore possible context. These examples are descriptive rather than a tested trading strategy; they do not establish causal links or evaluate predictive performance. The tutorial is also limited to selected chart types and a historical market illustration.
Key ideas
- Bokeh creates interactive visualizations in a browser from Python model objects.
- Standalone plots can offer interactions without requiring a Bokeh server.
- Sector and stock return charts can help compare equity performance.
- Price series and company fundamentals can be viewed together to investigate market moves.
- The examples illustrate charting workflows but do not test a trading rule.
Tags
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.