Intro

Charts in roboquant are all based on matplotlib. Either by directly invoking methods or via the Pandas dateframe plot method.

Using matplotlib for trading charts brings several benefits:

• Mature & battle-tested — matplotlib has been the go-to Python plotting library for over two decades, with a vast ecosystem of tutorials, extensions, and community support.

• Full control over every visual element — you can customize tick labels, grid lines, annotations, legends, and layouts down to the pixel, which is essential when you need to highlight specific trade signals or events.

• Seamless Pandas integration — price and trade data naturally lives in pandas DataFrames, and matplotlib can plot directly from them with a single .plot() call.

• Multiple export formats — charts can be saved as PDF, PNG, SVG, or EPS, making them suitable for inclusion in research papers, reports, or presentations.

• Jupyter-native — charts render inline in notebooks, enabling fast, iterative exploration of backtest results without leaving the development environment.

• Extensibility — if you ever need candlestick charts or more advanced financial visualizations, libraries like mplfinance build directly on top of matplotlib.

Styles¶

Roboquant has a light and dark style for the charts, which can be enabled by calling the set_dark_style() and set_light_style() function. Besides the dark background, it also sets some other parameters for the charts, like the figure size, dpi and grids.

import roboquant as rq

rq.set_light_style()
rq.set_dark_style()

The following charts shows these two styles in action.

Light style¶

Great for exporting to PDF and printing.

import roboquant as rq

rq.set_light_style()
feed = rq.feeds.YahooFeed("MSFT")
feed.plot("MSFT");

Dark style¶

Great for developing late at night or in a dark mode editor.

rq.set_dark_style()
feed.plot("MSFT");