Comparison

Hunter Killer vs Aggr: Trade Tape vs Liquidation Map

Updated 2026-07-23

Two tools that answer different questions

Aggr and Hunter Killer get compared because both live in a scalper's second monitor, but they are built to answer different questions. Aggr answers "what is the tape doing right now, across every exchange, in one stream?" It aggregates real-time market-order flow (buys, sells, size, exchange source) into a single live visualization, so you can watch aggressive order flow hit the market tick by tick.

Hunter Killer answers a different question: "where does open leverage sit, and where does it break?" It builds a liquidation Money Map from open interest, funding, and estimated position clustering, and it publishes a walk-forward accuracy scorecard so you can see how that mapping has actually performed, not just how it looks. Neither tool replaces the other; they sit at different layers of the stack (flow vs positioning).

What Aggr does well

Aggr earned its place in the scalper toolkit for good reasons. It is open-source, which means the community can audit exactly what it does and extend it. It is genuinely real-time: the trade tape renders as prints happen, with per-exchange color coding, so you can see whether size is hitting bid or ask and on which venue.

  • Fast, low-latency tape aggregation across many exchanges in one view
  • Free and self-hostable, with an active open-source community
  • Purpose-built for order-flow and volume-profile style scalping
  • Highly configurable panes for traders who want to build their own layout

If your edge is reading aggressive order flow in the moment, tape aggregation is exactly the right tool, and Aggr is a well-regarded one for that job.

Where Hunter Killer focuses differently

Hunter Killer is not trying to be a faster tape. It focuses on liquidation positioning: the Money Map plots where leveraged positions are estimated to cluster and where a cascade of forced closes becomes more likely, layered with funding rate and open interest context (see funding rate explained and open interest explained).

The other difference is methodology transparency. Instead of asking you to trust the map on faith, Hunter Killer publishes a public walk-forward accuracy scorecard at /proof, broken down per symbol with sample sizes and 95% confidence intervals. It is framed honestly as descriptive, not predictive: it shows where crowding has existed historically and how that has held up out-of-sample, not a promise of what price will do next. That framing matters more than any single number.

Feature and philosophy comparison

  • Core object: Aggr aggregates the live trade tape (executed orders). Hunter Killer maps estimated liquidation clusters (unexecuted, at-risk positions).
  • Time horizon: Aggr is built for the present tick. Hunter Killer's Money Map is built for the next few hours to days of positioning risk.
  • Validation: Aggr does not publish an accuracy claim because a trade tape is a direct data feed, not a prediction. Hunter Killer's liquidation mapping is a modeled estimate, so it is validated against a public walk-forward scorecard with per-symbol n and confidence intervals.
  • Openness: Aggr is open-source and self-hostable. Hunter Killer is a hosted product with its methodology and accuracy record made public instead of its source code.
  • Best combined use: many traders run tape aggregation for entry timing and a liquidation map for context on where crowded leverage sits, since the two are complementary, not competing.

Who each is for

Aggr fits you if your process is order-flow and tape-reading driven: you want to watch aggressive buying or selling hit multiple venues in real time and react within seconds, and you are comfortable self-hosting or configuring an open-source tool.

Hunter Killer fits you if you want a positioning-and-risk layer: where is leverage clustered, what does the funding rate say about crowding (see short squeeze / long squeeze explained), and how has that mapping actually performed historically. It is built for traders who want the liquidation side of the picture with the accuracy record shown, not asserted.

The honest bottom line

This is not really an apples-to-apples comparison. Aggr is a trade-tape aggregator; Hunter Killer is a liquidation-mapping and analytics product. If you need both, plenty of traders run a tape tool alongside a liquidation map, since they answer different questions at different time horizons. Whichever you use, check any tool's methodology before trusting its output. Hunter Killer's is public at /proof; for Aggr, the open-source codebase itself is the transparency layer, check their site and repo directly.

Frequently asked questions

Is Aggr free?

Aggr is open-source and self-hostable, and offers a hosted version as well. Check aggr.trade directly for current pricing and hosting options.

Does Aggr show liquidations?

Aggr's core focus is aggregating the real-time trade tape across exchanges. It is not built around a dedicated liquidation-clustering map the way Hunter Killer's Money Map is; check their current feature set directly for any liquidation-adjacent overlays.

Can I use Aggr and Hunter Killer together?

Yes. They cover different layers: Aggr shows executed order flow in real time, Hunter Killer shows estimated liquidation positioning and a public accuracy record. Many traders use tape tools for entry timing and a liquidation map for broader positioning context.

Which one is more accurate?

That comparison does not quite apply. Aggr streams real executed trades, so there is no prediction to score. Hunter Killer's liquidation mapping is a model, so it publishes a walk-forward accuracy scorecard with sample sizes and confidence intervals at /proof so you can judge it yourself rather than take a claim on faith.

Is Hunter Killer open-source like Aggr?

No, Hunter Killer is a hosted product. Instead of publishing source code, it publishes its methodology framing and a live, per-symbol accuracy scorecard so the model's real-world performance is checkable.

See the numbers before you pay: our walk-forward accuracy scorecard is public, with sample sizes and 95% confidence intervals per symbol.

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