GUSI Pro

GUSI — Adaptive Bitcoin Cycle Risk Model

Most on-chain metrics published on TradingView — such as NUPL, MVRV, or Puell Multiple — were once reliable in past cycles but have lost accuracy. The reason is simple: their trigger levels are static, while Bitcoin’s market structure changes over time. Tops have formed lower each cycle, yet the traditional horizontal thresholds remain unchanged.

What GUSI does differently:

It introduces sloped trigger functions that decrease over time, adapting each metric to Bitcoin’s maturing market.

It applies long-term normalization methods (smoothing and z-score lookups) to reduce distortion from short-term volatility and extreme outliers.

It only includes signals that remain valid across all Bitcoin cycles since 2011, discarding dozens of popular on-chain ideas that fail even after adjustment.

How GUSI is built:
GUSI is not just a mashup of indicators. Each component is a proprietary, modified version of a known on-chain signal:

Logarithmic MACD with declining trigger bands

MVRV-Z Score Regression with cycle-aware slopes

Net Unrealized Profit/Loss Ratio normalized with dynamic z-scores

Puell Multiple with logarithmic decay

Weekly RSI momentum filter for bottoms

Optional Pi Cycle Top logic with sloped moving averages

These are combined into a composite risk scoring system (0–100). Every signal contributes to the score according to user-defined weights, and each can be toggled on/off. The end result is a flexible model that adapts to long-term changes in Bitcoin’s cycles while staying transparent in its logic.

How to use it:

Scores near 97 indicate historically high-risk conditions (cycle tops).

Scores near 2.5 highlight deep accumulation zones (cycle bottoms).

Background colors and labels make the conditions clear, and built-in alerts let you automate your strategy.

GUSI is designed for the INDEX:BTCUSD 1D chart and works best when viewed in that context.

In short: GUSI makes classic on-chain indicators relevant again by adapting them to Bitcoin’s evolving market cycles. Instead of relying on static thresholds that stop working over time, it introduces dynamic slopes, normalization, and a weighted composite framework that traders can adjust themselves.