Onchain analysis has emerged as a vital discipline for cryptocurrency investors, offering a data-driven window into market behavior. While relatively young, this field has evolved rapidly since its foundational developments around late 2018. Early innovations, such as the Realized Cap introduced by leading analytics firms, sparked a wave of research and new methodologies for interpreting Bitcoin’s rich transactional data.
Among the most sought-after tools in onchain analysis are pricing models. These models resonate widely because price is a universal metric—central to any asset’s story and crucial for traders and investors. They help market participants gauge value, identify opportunities, and understand market phases.
This guide delves into several foundational onchain pricing models, often called the "Onchain Originals." These models have stood the test of time and are categorized based on the market dynamics they capture:
- Floor Models: These often come into play during the later stages of bear markets, signaling potential areas of price support.
- Mean Reversion Models: Price tends to oscillate around these levels over the long term, highlighting equilibrium zones.
- Euphoria Models: These indicate when prices become attractive enough for long-term holders to start taking profits.
Understanding why these models matter is more important than merely knowing their values. They approximate zones where significant shifts in investor behavior are likely to occur. It is these behavioral changes—not just the mechanical intersection of price and a model—that often establish market tops and bottoms.
Exploring Floor Models
Floor models are designed to identify potential market bottoms. They often represent cost bases for long-term holders or aggregate measures of the market’s realized value. During deep bear markets, the spot price sometimes approaches or even dips below these levels, suggesting severe undervaluation.
These models are not precise predictors but rather zones where the probability of a reversal increases. For instance, when the market price trades near the realized price of the network, it often indicates that a significant portion of holders are at a break-even point or loss, which can reduce selling pressure.
Another common floor model is based on the production cost of miners. While less directly tied to investor behavior, it provides a fundamental economic floor beneath which mining becomes unprofitable, potentially reducing sell pressure from this cohort.
Understanding Mean Reversion Models
Mean reversion models are based on the idea that asset prices tend to revert to a historical average or a middle ground over time. In onchain analysis, this often takes the form of a long-term moving average of the realized cap or a similar macro-value indicator.
These models help identify fair value estimates. When the market price trades significantly above these means, it may signal overvaluation, and when it trades below, it may signal undervaluation. These are not timing tools but rather guides for understanding broader market cycles.
Traders often use these levels to inform their long-term accumulation or distribution strategies, buying when price is below the mean and considering taking profits when it is extended above. For a deeper look at how these mechanics play out in real-time, you can explore more strategies that incorporate these principles.
Identifying Euphoria with Top Models
Euphoria models are designed to spot potential market tops. They typically measure when the market price has deviated significantly from a fundamental baseline, such as the realized price or a long-term trend. These deviations often coincide with periods of high investor profit-taking and market exuberance.
A classic example is the MVRV ratio, which compares the market cap to the realized cap. High values suggest that the average holder is sitting on significant unrealized gains, which may create an incentive to sell. These models help identify when the market is in a state of profit-taking.
It is crucial to remember that these models indicate zones of probability, not certainty. A high MVRV reading doesn’t guarantee an immediate top, but it does suggest that the risk/reward ratio for new long positions is becoming less favorable.
The Psychology Behind the Models
The true power of these models lies not in the lines they draw on a chart but in the collective investor psychology they represent. A floor model breaking doesn’t cause a bottom; it reflects a point where selling exhaustion may be reaching a climax.
Similarly, a euphoria model hitting an extreme level reflects a state of widespread investor greed and profit-taking. The models are a lagging reflection of behavior, not a leading cause of it. This is why they are best used as contextual tools within a broader analysis framework.
Understanding this distinction is key to applying onchain analysis effectively. The goal is to understand the underlying supply and demand dynamics playing out between different investor cohorts.
Applying Models in Practice
Successfully using these models requires more than just watching for crossovers. It involves understanding the context of the market cycle, the strength of the trend, and broader macroeconomic conditions.
For example, a mean reversion model might be more reliable after a significant price move has extended the market far from its historical average. A floor model might be more relevant after a long-term downtrend has flushed out weak hands. Combining these models with other indicators, such as exchange flows or network activity, creates a more robust picture.
The most effective approach is often a holistic one, where onchain pricing models provide the "why" behind potential support and resistance zones, and other analyses help with the "when" and "how."
Frequently Asked Questions
What is the most reliable onchain price model?
There is no single most reliable model. Each captures a different aspect of market behavior. The Realized Price is a strong foundational model for establishing a macro fair value, while the MVRV Z-Score is excellent for identifying statistical extremes. Reliability comes from understanding what each model measures and using them in concert.
Can these models be used for short-term trading?
Most classic onchain pricing models are designed for longer-term, macro perspective analysis. Their signals are often too slow for effective short-term trading. They are better suited for informing strategic decisions about portfolio allocation and identifying major cycle phases rather than timing entries and exits down to the day or week.
How do I know if a model signal is valid?
A signal is generally stronger if it is confirmed by multiple models pointing to the same conclusion (e.g., price is below multiple floor models) and is accompanied by relevant shifts in investor behavior, such as a decrease in exchange inflows or a spike in illiquid supply. Context is everything.
Have these models changed over time?
While the core principles remain sound, the absolute values of some models can drift over multiple cycles as the market matures, investor profiles change, and new capital enters. It's important to view them through a relative, rather than an absolute, lens across different epochs.
What is the biggest mistake people make with these models?
The biggest mistake is treating them as precise price predictions. They are probabilistic zones, not exact lines. Another common error is using them in isolation without considering other factors like liquidity, derivatives market conditions, or macroeconomic trends, which can all override onchain signals.
Do these models work for altcoins?
The principles can be applied, but the models often work best for Bitcoin, which has the most robust and liquid onchain data. For altcoins, the data can be noisier, and models may need to be adjusted for different tokenomics, vesting schedules, and investor behavior patterns.