Implied Volatility (3 Months)
options
61
Sell-Side Risk Ratio
on-chain-liquidity
10
Money Supply (Global M2)
macro
56
Implied Volatility (1 Month)
options
10
Instantenous Volatility Factor
technicals
56
Net Realized Profit/Loss
on-chain
10
Fast Momentum Factor
technicals
53
BTC Market Dominance
valuation
10
Puell Multiple
miners
52
Implied Volatility (1 Week)
options
10
Reddit Mentions
social
43
Asset Value-Investor Value (AVIV) Ratio
valuation
10
Exchange Outflow
exchanges
36
Short Term Realized Cap HODL Waves Index
on-chain
10
Instantenous Momentum Factor
technicals
33
Futures Premium (CME)
market-stats
10
High Yield Sensitivity
cross-asset
29
Reserve Risk
on-chain-liquidity
10
Fear & Greed Index
market-stats
28
Attention Index
social
10
Stablecoin Supply
stablecoin
26
MVRV
valuation
10
ETF Net Flow
market-stats
14
Hash Price
miners
10
Skew Factor
technicals
0
US Dollar / Bitcoin Ratio
cross-asset
10
Slow Momentum Factor
technicals
0
Unemployment - Initial Claims
macro
10
US Cloud Computing Sector
cross-asset
10
Equities Market Breadth
market-stats
10
US Bank Assets: Securities in Custody for Foreign and International Accounts
macro
10
Corporate Bond Sensitivity
cross-asset
10
Inflation Linked Bonds Sensitivity
cross-asset
10
Telegram Mentions
social
10
Stablecoin Supply Ratio
stablecoin
10
Exchange Balances
exchanges
10
Collateralization of Currency (US Dollar)
macro
10
Unemployment - Continued Claims (Insured Unemployment)
macro
10
Currency Component of M1
macro
10
Active Addresses
on-chain
10
Equities Sensitivity
cross-asset
10
Memecoin Index
cross-asset
10
Aggregate Sentiment
sentiment
10
Open Interest (Aggregate)
market-stats
10
New Users
on-chain
10
Whale Transactions Index
on-chain
10
Bitcoin / Gold Correlation
cross-asset
10
Development Effort
social
10
Stablecoin Transfer Volumes
stablecoin
10
Youtube Mentions
social
10
MSTR Premium
market-stats
10
Treasury and Agency Securities, Overall Level
macro
10
Funding Rates (Aggregate)
market-stats
10
Demand Deposits
macro
10
Gold Certificates
macro
10
Aggregate Futures Volume
market-stats
10
Coin Days Destroyed
on-chain
10
Retail Money Market Funds
macro
10
Hashrate
miners
10
Overall Level Bank Credit
macro
10
Telegram Sentiment
sentiment
10
Median Consumer Price Index
macro
10
Reddit Sentiment
sentiment
10
The Puell Multiple was invented by analyst David Puell, back in March 2019. He developed the metric as a way to quantify miner revenue in relation to historical averages: it compares the daily USD value of Bitcoin mined through block rewards to its 365-day moving average, directly meauring whether miners are earning above or below their annual average income. Why could it be important?
Miners operate on razor-thin margins, with costs tied to energy, hardware, and debt obligations. Their need to liquidate holdings to cover expenses makes them “compulsory sellers,” whose actions reverberate through the market:
High Puell Multiple: Indicate miner revenues far exceed their operational costs, often preceding market tops as miners sell reserves to lock in profits, starting to create downward pressure.
Low Puell Multiple: force inefficient miners offline, reducing competition and allowing surviving miners to accumulate at lower costs — historically, a precursor to renewed bullish momentum. In theory, this dynamic creates a self-reinforcing cycle: miner sell-offs amplify price declines during downturns, while accumulation phases starve the market of supply during recoveries.
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Predictive Correlation measures the rolling Pearson correlation coefficient between Puell Multiple and 90 Day BTC forward returns. 1 Indicates a perfect positive correlation, -1 indicates a perfect negative correlation, and 0 indicates no correlation.
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We group the factor's values into ranges, to make sure we can analyze its behaviour when it's at the extremes - or somewhere in the middle.
BTC’s 30-day return was highest (12.00%) when Puell Multiple was in the 0.8 - 1.0 range. The lowest returns (2.33%) occurred when Puell Multiple was in the 0.01 - 0.2 range.
This graph shows the average cumulative BTC returns over subsequent 30 days when a factor was in a specific range.
The Puell Multiple’s predictive strength is an interesting puzzle. Periods when mining rewards becoming unsustainable relative to historical norms indicating a further rally may not seem like a causal connection. Why would more miner revenue, manifesting itself in sellable Bitcoin lead to price increases, and isn’t the opposite more plausible? We don’t have a clear answer — one explanation is that the Puell Multiple is very similar to a simple “Price oscillator”, reflecting market extremes.
Nevertheless, while an interesting lens, the Puell Multiple is a model that’s exclusively focused on the internal supply dynamics of Bitcoin, and as institutional players start to dominate trading volumes, miner influence on price may wane in the future, and may become a lot less reliable going forward.