Plotting for Research

Under development

This lesson is part of the course scaffold and is being actively written. The learning objectives and outline below define its final scope.

Learning objectives

By the end of this lesson you will be able to:

• Build equity-curve and drawdown charts in matplotlib with correct scales (log where appropriate) and annotated summary statistics.
• Plot return distributions against fitted reference densities and construct parameter-grid and correlation heatmaps.
• Choose between matplotlib and plotly for a given audience and justify when interactivity earns its complexity.
• Apply a publication/IC-readiness checklist — titles, units, axes, sample periods, and data sources — to your own charts and fix what fails it.

Outline

1. matplotlib fundamentals — figures, axes, styles, and layout for research plots
2. Equity curves — log scaling, benchmark overlays, annotation of key statistics
3. Drawdown charts — underwater plots, drawdown duration and recovery
4. Return distributions — histograms, kernel density estimates, tail views
5. Heatmaps — parameter grids, correlation matrices, colormap choice
6. Interactive plots with plotly — when interactivity is worth it
7. Publication/IC-ready standards — labeling, scales, honesty, and the review checklist

Prerequisites

• Pandas and Polars