Bitcoin Gold Fair Value Model | AlphaNatt

Bitcoin Gold Fair Value Model | AlphaNatt
A quantitative regression-based projection model that estimates Bitcoin’s fair value using gold as a macro-monetary benchmark.

This model, inspired by RJAlpha, applies a lag-adjusted statistical regression between gold and Bitcoin to identify the time-shifted correlation that historically aligns Bitcoin’s market value with gold’s macro trends. It produces a forward-looking projection, statistical confidence intervals, and explanatory metrics that assess the reliability of the relationship.

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🧠 Core Concept

• [] Gold serves as a proxy for global liquidity and real monetary value, often leading risk assets during liquidity expansions and contractions.
  [] Bitcoin’s long-term trend tends to react to these same liquidity cycles, but with a measurable lag.
  [] This indicator models that lag statistically, estimating Bitcoin’s “fair value” as if its price were fully caught up to gold’s recent movements.
  [] The regression captures both directional influence and proportional magnitude through slope and intercept coefficients.
  

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⚙️ Model Features

• [] Dynamic Lag Regression – Uses a configurable leadDays period to align gold’s prior movements with Bitcoin’s current pricing behavior.
  [] Rolling Sample Window – Continuously recalibrates the regression coefficients using a user-defined lookback length, allowing the model to adapt to new market conditions.
  [] Forward Projection – Extends Bitcoin’s fair value into the future, based on present gold levels and the established lag relationship.
  [] Volatility-Adjusted Confidence Bands – Displays one standard deviation and 95% confidence intervals around the projected path to visualize expected uncertainty.
  [] Model Fitness Metric – Includes an R² score that quantifies the strength and stability of the BTC–Gold relationship within the active window.
  

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📈 Visualization Breakdown

• [] Cyan Line: Historical gold-driven fair value of Bitcoin.
  [] Magenta Lines: Future fair value projection and confidence bands (offset by leadDays).
  [] Projection Label: Displays the 60-day projected price target.
  [] Statistical Table: Shows live model output including the projected fair value, 1-SD range, 95% confidence interval, and R² score.
  

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🔧 User Inputs

1. [] Show 1 SD Bands? – Toggles visibility of the standard deviation boundaries.
   

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📊 Interpretation Guide

• [] When Bitcoin trades below its projected fair value, the model suggests it is temporarily undervalued relative to gold’s macro trend.
  [] When Bitcoin trades above its projected fair value, it may be overextended in relation to the model’s equilibrium estimate.
  [] A higher R² implies greater reliability — periods where gold explains a large portion of Bitcoin’s price variance.
  [] Confidence intervals represent uncertainty, not directional certainty; deviation beyond them often implies a structural shift in correlation or market regime.
  

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⚠️ Disclaimer

This indicator is designed for quantitative research and macro correlation analysis. It does not constitute investment advice, price prediction, or trading signal generation. Always verify assumptions and cross-check results with independent analysis before using in a live environment.