Comparison

Hunter Killer vs TensorCharts: Order-Book Charting vs Liquidation Positioning (2026)

Updated 2026-07-23

What are Hunter Killer and TensorCharts actually built for?

TensorCharts (tensorcharts.com) is an order-book and market-microstructure charting tool. Its core products are order-book heatmaps, footprint charts, volume bubbles, and liquidation visualizations layered onto price, aimed at traders who want to see the book itself: where resting orders sit, how they move, and how volume prints against price at the tick level.

Hunter Killer is built around a single question: where are leveraged positions clustered, and what happens when price reaches them? The centerpiece is the liquidation Money Map, paired with a public walk-forward accuracy scorecard that shows per-symbol sample sizes and 95% confidence intervals rather than an unverifiable accuracy claim. The two products overlap on the word "liquidation" but solve different problems: one is a microstructure lens, the other is a positioning and risk lens.

What TensorCharts does well

TensorCharts has a real, earned reputation in order-book microstructure charting. Its footprint charts and volume-bubble visualizations give a granular read on where volume actually traded within a candle, which is useful for traders scalping short-timeframe entries or reading absorption at a level. Its order-book heatmap renders resting liquidity depth in real time, which is the kind of data a pure liquidation map does not attempt to show.

For traders whose edge depends on reading the live book, order flow, and tick-level volume distribution, that toolset is genuinely differentiated and not something a liquidation-focused product is trying to replace. If your process starts with "what is the book doing right now," TensorCharts' strength is squarely there.

Where Hunter Killer focuses differently

  • Liquidation Money Map: a dedicated view of estimated liquidation clusters across leverage tiers, built to answer "where would forced selling or forced buying show up" rather than "what does the current book look like."
  • Public walk-forward accuracy scorecard: at /proof, every headline number ships with the sample size (n) and a 95% confidence interval, computed walk-forward rather than fit to history after the fact.
  • Descriptive, not predictive framing: the Money Map and its supporting metrics (funding, open interest) are presented as a description of positioning and its historical resolution, not as a forecast or a signal to auto-trade.

This is a narrower scope than a full microstructure charting suite by design. It exists to make one thing (liquidation positioning) as honest and well-documented as possible rather than covering every chart type.

Feature and philosophy comparison

  • Primary lens: TensorCharts = order book and tape (footprint, bubbles, depth heatmap). Hunter Killer = liquidation clustering and positioning (Money Map, funding, open interest).
  • Time horizon: TensorCharts leans toward intraday and tick-level reads. Hunter Killer's Money Map is typically read on higher timeframes where liquidation clusters build up over hours to days.
  • Accuracy claims: Hunter Killer publishes a walk-forward scorecard with n and 95% CI per symbol at /proof so the numbers are checkable rather than asserted. For TensorCharts' own methodology and any accuracy claims around its liquidation visualizations, check their site directly.
  • Pricing: check each provider's site for current pricing, since both change tiers over time.

Who each tool is for

TensorCharts fits traders whose process is order-flow-driven: scalpers, tape readers, and anyone who wants to see footprint and book depth directly, independent of what liquidation levels are doing.

Hunter Killer fits traders who want a positioning-first view: where is leverage stacked, how has price historically resolved around those clusters, and what does an honestly-scored, sample-sized track record look like rather than a marketing number. It is built for reading the liq tab as a decision-support layer, not a signal generator, and it pairs naturally with a separate execution or order-flow tool for the final entry timing.

The honest bottom line

These are not head-to-head competitors on every feature. TensorCharts is a mature order-book charting product; Hunter Killer is a liquidation-and-positioning product with a public, walk-forward accuracy record. If your process needs footprint and book-depth reads, TensorCharts covers that ground well. If your process needs a liquidation Money Map with sample-sized, confidence-interval-backed numbers you can check yourself at /proof, that is what Hunter Killer is built for. Many traders reasonably use both.

Frequently asked questions

Is Hunter Killer a replacement for TensorCharts?

Not necessarily. Many traders use order-book microstructure tools like TensorCharts for execution timing and a liquidation Money Map for positioning context side by side. They answer different questions: TensorCharts shows what is happening in the book right now, Hunter Killer shows where leveraged positions cluster and how that has resolved historically.

Does Hunter Killer have footprint charts or volume bubbles?

No. Hunter Killer is purpose-built around the liquidation Money Map, funding, open interest, and a public walk-forward accuracy scorecard. It does not attempt to replicate order-book footprint or bid/ask bubble charting, which is TensorCharts' core strength.

Which tool is more accurate for liquidations?

Compare methodologies, not marketing claims. Hunter Killer publishes a walk-forward accuracy scorecard at /proof with per-symbol sample sizes and 95% confidence intervals so you can judge the numbers yourself. Check TensorCharts' own site for how it documents its liquidation visualization methodology.

Can I use both tools together?

Yes, and it is a common setup. Traders often watch TensorCharts for real-time order-book flow and footprint confirmation while using a liquidation Money Map for the higher-timeframe positioning picture, cross-referencing the two before entries.

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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