Predictive Strength

Negligible
Bitcoin historically had 9.21% 30 days returns when Miner Profitability was▆ High (0.6 - 0.8). It indicates higher than average expected returns.
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BTC Price with Miner Profitability

Factor Plot

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▆ Very Low▆ Low▆ Moderate▆ High▆ Very High

Predictive Strength

Negligible

Puell Multiple measures the ratio of the daily issuance value of Bitcoin to its 365-day moving average, providing insights into mining profitability dynamics and their cascading impact on market selling pressure.

High values indicate miners are greatly profitable, which may reduce immediate sell pressure (as miners hold). Low Values frequently correlates with capitulation events, where miners sell reserves to cover costs, exacerbating downward price pressure.

Potential Edge

Forced Selling During Profitability Squeezes

Miners facing negative cash flow (e.g., post-halving block reward reductions or rising energy costs) become forced sellers to cover operational expenses. Historical patterns show such capitulation often precedes local price bottoms, offering a contrarian signal for accumulation.

Hash Rate as a Reflexive Feedback Loop

Declining profitability forces inefficient miners offline, reducing network hash rate. While this temporarily lowers mining difficulty, it also weakens Bitcoin's security budget, potentially spooking institutional investors. Conversely, rising hash rate (driven by profitability) signals long-term confidence but increases operational costs industry-wide.

Data Collection Methodology

The Puell Multiple calculates Bitcoin's daily issuance value (block rewards + fees in USD) divided by its 365-day moving average, sourced from real-time blockchain data tracking miner revenue and historical issuance records. This ratio normalizes current miner profitability against long-term trends, scaling from 0 to 1.

Read more about our methodology

Track this predictive factor on your dashboard

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Scatter plot - Miner Profitability and BTC 30 and 90 Day Average Returns

Backtest - Strategy Performance

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To understand a predictive factors predictive power, we create a simple long/short strategy and simulate its past performance (with daily rebalancing):

  • 100% Long when the predictive factor is close to 1, with a position size equivalent to the predictive factor value.
  • Flat when the predictive factor is close to 0, with a position size equivalent to the predictive factor value.

The strategy is rebalanced daily, on a continuous basis. There are 0.5% transaction costs applied on each position adjustment.

API

Get started by validating the historical performance of the strategy with our transparent code snippets.
Copy and paste the code snippets below into your Python environment or download the files below.

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Our Methodology