Predictive Strength

Negligible
Bitcoin historically had 5.18% 30 days returns when Net Realized Profit/Loss was▆ Moderate (0.4 - 0.6). It indicates average expected returns.
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BTC Price with Net Realized Profit/Loss

Factor Plot

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

Predictive Strength

Negligible

Net Realized Profit/Loss Index measures the profit and loss of all market participants.

Potential Edge

Captures Sentiment Extremes

NRPL quantifies aggregate profit/loss realization, acting as a psychological barometer. Extreme positive values (profit-taking frenzies) often precede local tops, while extreme negatives (capitulation) signal bottoms. This mirrors Warren Buffett's "be fearful when others are greedy" principle, as NRPL objectively measures greed/capitulation thresholds.

Data Collection Methodology

Net Realized Profit/Loss is sourced by analyzing on-chain transactions to calculate the difference between realized profits (coins sold above acquisition cost) and realized losses (coins sold below acquisition cost). We track coins' movement history to determine cost basis and current value, aggregating these values across all transactions. The raw data is then normalized against historical averages to create a relative index.

Read more about our methodology

Track this predictive factor on your dashboard

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Scatter plot - Net Realized Profit/Loss 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