Looking for options to visualize large market timeseries data


I have a large timeseries dataset that includes stock market data. The dataset includes 5 seconds candle data (much larger than daily bars)

I’ve built a Mathplotlib visualization for my data. While my visualization works, working with Mathplotlib charts is tedious. I like to have something like Plotly.py’s Range Sliders shown here below but with larger dataset:

I understand Plotly Python transforms and serializes data sources to Javascript arrays and that concerns me when the a dataset is large.

Before I spend time and migrate my visualization from mathplotlib to Plotly Python, I wanted to ask what is dataset size limitations for Plotly Python. Is there any guideline how workaround data size limitations and how to handle large datasets.

Thank you,


I think that plotly-resampler might help with the mentioned scalability issues.

Plotly-resampler solves plotly’s scalability issues by downsampling (aggregating) the data respective to the view and then plotting the aggregated points. When you interact with the plot (panning, zooming, …), callbacks are used to aggregate data and update the figure.

However, (as for now), there is now candlestick-chart support.
Will update this post accordingly when we will support this kind of visualization.