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Explore the Bitcoin Stock to Flow indicator on TradingView. Learn how this scarcity-based model tracks BTC price trends and market supply dynamics.
The Stock to Flow (S2F) indicator, developed by user DMT62DMT, provides a technical framework for modeling Bitcoin’s value based on its scarcity and issuance rate [3]. The tool, which is available as an open-source script on TradingView, allows traders to visualize the relationship between Bitcoin’s circulating supply and its periodic production flow [3].
| At a glance | |
|---|---|
| Indicator Type | Scarcity-based Price Model |
| Primary Asset | Bitcoin (BTC) |
| Recommended Timeframe | Monthly |
| Core Methodology | Stock-to-Flow Ratio |
The S2F indicator functions by calculating the ratio between Bitcoin's existing supply—the "stock"—and the new supply entering the market—the "flow" [3]. By analyzing these metrics, the script attempts to project a model price for Bitcoin that reflects its diminishing issuance schedule [3]. The author of the script notes that the indicator has been updated to account for approximately 1,000,000 lost Bitcoins, adjusting the yearly flow rate to provide a more accurate representation of available supply [3].
Because the indicator relies on long-term supply dynamics, the developer specifies that it is designed to function most accurately on the monthly timeframe [3]. Users have the option to modify the underlying code to plot either the standard stock-to-flow ratio or the number of blocks generated per month [3]. As an open-source project, the script allows for community verification, enabling traders to inspect the logic behind the model's price projections [3].
The release of such indicators coincides with a broader expansion of analytical tools on the TradingView platform. Recently, the platform introduced new global screeners for coins, stocks, and ETFs, designed to help users identify assets breaking out or emerging in the market [2]. These tools complement existing technical analysis features, such as automated Fibonacci levels and Smart Money Concepts (SMC) integration, which some traders report have improved their win rates and trading consistency [1].
While the S2F model focuses on long-term scarcity, the broader crypto market remains influenced by diverse factors, including institutional flows and market capitalization shifts. Bitcoin recently neared a $1 trillion market capitalization, maintaining its position as the largest cryptocurrency by a significant margin over Ethereum [2]. As traders continue to utilize both fundamental scarcity models and technical price action indicators, the interplay between supply-side economics and real-time market sentiment remains a primary focus for the community [2, 3].
The utility of the Stock to Flow model remains a subject of ongoing debate among market participants, serving as one of many tools used to interpret Bitcoin's price history. Whether this scarcity-based approach provides actionable insight or remains a theoretical framework depends on how well it aligns with future market volatility and institutional adoption.
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AI-assisted synthesis by the TrendWatcher Editorial Desk · sourced from 3 outlets · Sep 1, 2026 · How we report
The Stock To Flow model is a valuation framework that estimates the price of an asset based on its scarcity, defined as the ratio between the total existing supply and the amount of new supply produced each year.
The Stock To Flow model for Bitcoin uses regression analysis to project price based on the network's fixed supply schedule, which decreases the rate of new Bitcoin issuance during halving events that occur roughly every four years.
The Stock To Flow model is not considered a reliable standalone price prediction tool because Bitcoin's market price has frequently deviated from its projections in recent years, leading many to view it primarily as a historical reference.
People compare Bitcoin to gold using Stock To Flow because both assets are considered scarce, have a low rate of new supply, and are viewed by some as 'store of value' commodities that retain value over long time frames.