ffn

ffn is useful when prices are already in a notebook and you want to understand their behavior. Load a local CSV or request data through a provider, convert prices with to_returns or to_log_returns, and use calc_stats for a grouped performance report. Rebasing places several price series on a comparable starting scale; correlation tables and heatmaps show how their returns move together.

The quickstart supports explicit fields such as Open, High, Low and Close. Its Yahoo provider uses adjusted closing prices when a field is omitted. A CSV can be merged into the existing table, so the workflow can combine a downloaded series with your own data.

Install ffn locally for free. Yahoo examples need the separate ffn[yahoo] dependency; other providers can be callables or plugins. No ffn account is required for local files, although external providers have their own access conditions.

The library supplies calculations and plots, not a complete trading simulation. Use its companion bt when you need strategy rules and portfolio rebalancing. Importing ffn also adds methods to pandas objects, a detail to consider when integrating it into an existing Python project.