Freqtrade Advanced Strategy Tools: Trade Data, Tags, Dataframes, and Reuse

Summary

This Freqtrade documentation page describes strategy customization features beyond basic entry and exit signals. It explains how to store small JSON-serializable values persistently on individual trades, access analyzed candle data in callbacks, and use entry and exit tags to distinguish among signals. It also covers strategy inheritance, embedding strategy files in configuration, and reducing pandas dataframe fragmentation during indicator calculations.

The page cautions that custom trade data should remain small and serializable, while in-memory strategy dictionaries are deprecated and are lost after a restart. It highlights callback and dataframe timing details, including that the latest available candle may not correspond to the current time. Tag length and shared-column behavior can also affect which signal is retained. The examples are implementation guidance rather than trading research: they offer no evidence about profitability, and the supplied sample contains apparent syntax and formatting errors, so it should not be treated as ready-to-run code.

Key ideas

• Persistent custom trade data must be JSON serializable and is best kept small.
• The documentation discourages non-persistent strategy dictionaries because they are lost on restart and can consume memory.
• Callbacks can retrieve analyzed candles, but the latest available candle may not match the current time.
• Entry tags can help callbacks distinguish signals, though they have a length limit and may be overwritten when signals collide.
• Strategy inheritance and dataframe concatenation are presented as ways to reuse logic and reduce indicator-building overhead.

Tags

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.