Simulations

Statistical Analysis & Monte Carlo Projections for Bitcoin Hashprice

SIMULATION FRAMEWORK

Hashprice Modeling Framework

This simulation framework models the three key drivers of mining economics:

BTC Price
Primary revenue driver. Highly volatile with fat-tailed distributions.
Network Hashrate
Competition intensity. Generally trends upward with periodic corrections.
Hashprice
$/PH/day. The key metric for mining economics = f(Price, Hashrate).
Current BTC Price
-USD
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Current Hashrate
-EH/s
Network total
Current Hashprice
-$/PH/d
Revenue per PH/s per day
Price 1Y Return
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BTC price change
Hashrate 1Y Change
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Network growth
Hashprice 1Y Change
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Mining economics
ANALYSIS MODULES
Historical Analysis

Comprehensive statistical analysis of historical returns for BTC price, network hashrate, and hashprice.

  • Distribution fitting (Normal, t, Hyperbolic, etc.)
  • Return statistics: daily, weekly, monthly, annual
  • Probability estimates and confidence intervals
  • Performance analysis across time horizons
Monte Carlo Simulations

Forward-looking projections using Monte Carlo simulation with fitted statistical distributions.

  • Price, hashrate, and hashprice projections
  • Confidence intervals (50%, 80%, 95%)
  • Probability of various outcomes
  • Combined hashprice model with correlation
Mining Fleet Simulator

Build a custom mining fleet and simulate economic performance with Monte Carlo methods.

  • Select ASICs from database
  • HODL vs Sell strategy comparison
  • Mining vs Just Buying BTC analysis
  • Portfolio optimization & risk metrics
METHODOLOGY
Statistical Approach

Distribution Fitting

Bitcoin returns exhibit "fat tails" (excess kurtosis) and cannot be accurately modeled with normal distributions. We fit multiple distributions and rank them using AIC and KS tests:

  • Generalized Hyperbolic - flexible, captures skewness and kurtosis
  • Student's t - heavy tails, commonly used in finance
  • Laplace - double-exponential, moderate tails
  • Normal - baseline comparison (typically poor fit)

Monte Carlo Simulation

Monte Carlo uses random sampling from fitted distributions to generate thousands of possible future paths, providing probability estimates for outcomes:

  • Generate returns from best-fit distribution
  • Compound log returns to project prices
  • Calculate confidence intervals from percentiles
  • Use correlated simulations for hashprice

Important Disclaimer

These simulations use historical data to estimate probability distributions. Past performance does not guarantee future results. Bitcoin and mining economics are influenced by factors not captured in price history alone, including halvings, regulatory changes, and technology shifts.