Using Public Trade Data for Order Flow Analysis in Freqtrade
Summary
The guide explains how to enable public trade downloads in Freqtrade and configure order flow processing. Settings control cached candles, trade history depth, footprint price-bin size, and the volume and ratio thresholds used to identify imbalances. Historical trades can be downloaded for backtesting where the exchange supports them.
It describes dataframe fields for raw trades, footprint summaries, bid and ask volume, delta, trade counts, and stacked imbalances. Footprints aggregate trade amounts and counts by price bin; delta measures the difference between ask and bid volume, and can be accumulated across candles for a cumulative delta series. The guide provides data structures and examples, but no performance evidence or trading rules. It cautions that the feature is experimental, consumes substantial memory, can slow initial startup, has exchange-dependent data availability, and has not been tested in combination with FreqAI.
Key ideas
- Public trade data supports price-level footprint and order flow analysis in Freqtrade.
- Footprint bins summarize bid and ask amounts, trade counts, total volume, and delta at each price level.
- Candle-level order flow fields include aggregate bid and ask volume, delta, trade counts, and stacked imbalance levels.
- Configuration thresholds determine which price-level imbalances are retained.
- Trade data can increase startup time and memory use, and availability depends on the exchange.
Tags
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.