Cross-ratio failure and collective confusion in the crypto indicator system: the fundamental transformation of the market structure in 2026

At the beginning of 2026, the classic set of crypto market indicators that once guided investors faced an unprecedented crisis. Predictions from the supply-side Stock-to-Flow model reaching absurd levels of 3x, silence from the Pi Cycle Top indicator that signals market tops via moving average crossovers, and the on-chain MVRV Z-Score failing to reach its previous extreme thresholds—all these tools, which proved reliable in past cycles, are now signaling to market participants: the old rules of the game are being rewritten.

Bitcoin retraced about 36% from its October 2025 all-time high. The market oscillated repeatedly across multiple spaces, but what truly unsettled investors was that this indicator system, which relies on specific market assumptions, could no longer accurately describe the current market reality. This is not an isolated failure of a single indicator but points to a deeper change: the market structure itself is undergoing a qualitative leap.

Supply-Side Logic Breakdown: From Halving-Driven to Macro Assets

The four-year cycle theory is the first “iron law” established in the crypto market—Bitcoin follows a fixed rhythm driven by halving events: accumulation before halving, explosion 12-18 months after, peaks with 75%-90% crashes, and a new bottoming phase leading to the next cycle. The three halvings in 2012, 2016, and 2020 all played out according to this script, earning widespread acceptance.

However, after the April 2024 halving, the market did not follow the expected explosive rally. Bitcoin’s annualized volatility dropped from over 100% historically to about 50%, making a “slow bull” the new norm. The depth of bear market declines also shrank—77% from peak to trough in 2022, less than 86% in 2014 and 84% in 2018.

The core reason is that Bitcoin’s market cap has now reached trillions of dollars. The 2024 halving reduced the block reward from 6.25 BTC to 3.125 BTC, and daily new supply from about 900 BTC to 450 BTC. Although this halving seems significant, it only reduces annual supply by about 164,000 BTC, lowering the inflation rate from 1.7% to roughly 0.85%. Relative to a market cap in the trillions, this supply reduction’s actual impact is negligible—like throwing a grain of sand into the ocean.

Meanwhile, Bitcoin spot ETFs in the U.S. have continuously absorbed capital, creating sustained demand. This means that price drivers have shifted from solely “halving scarcity” to macro factors such as “Federal Reserve liquidity,” “geopolitical situations,” and “inflation expectations.” The gap between the $500,000 forecast of the Stock-to-Flow model and the actual $120,000 reflects this shift—an indicator that looks only at supply, ignoring demand, is doomed to be proven wrong in an era where demand is dominated by macro asset attributes.

Silence of Moving Average Crossovers: How Lower Volatility Breaks the Cross Indicators

The Pi Cycle Top indicator appears elegant: it signals a market top when the 111-day moving average crosses above twice the 350-day moving average. This crossover accurately signaled tops in 2013, 2017, and April 2021, making many investors expect this signal to be reliable.

But throughout the entire 2025 bull cycle, these two moving averages never produced a valid crossover; the indicator remained “silent.” Despite a clear downtrend, it gave no warning.

The reason for failure is that Pi Cycle Top fundamentally depends on sharp price volatility. When parabolic rises driven by retail speculation dominate, short-term moving averages diverge significantly from long-term averages, creating crossovers. But as ETFs and institutional funds dominate, Bitcoin’s price trend becomes smoother, with volatility structurally declining to around 50%. In such a low-volatility environment, short-term averages cannot diverge enough from long-term ones to produce crossovers; the precondition for the indicator’s signal no longer exists.

Deeper still, this indicator is essentially a curve fit based on early market data from 2013-2021. When the market structure changes—funds shifting from retail to institutions—the parameters that worked in early phases no longer apply. It’s like a production model designed for artisanal workshops being used in industrial manufacturing—ineffective.

Market Cap and Cost Disconnection: How the MVRV Z-Score’s Evaluation Foundation Is Rewritten

The MVRV Z-Score is a classic on-chain valuation metric, comparing market value to realized value to assess over- or undervaluation. A Z-Score above 7 signals overheat and a sell, below 0 indicates extreme undervaluation and a buy. This logic has proven reliable across cycles.

But in recent cycles, the Z-Score’s behavior has been perplexing. Even at the 2021 top, it did not reach previous cycle highs. By 2025, although Bitcoin topped out, the highest Z-Score was only 2.69—far from the traditional “overheated” threshold of 7.

This is due to three structural changes:

First, institutional buying and long-term holding strategies have systematically elevated realized value closer to market value. When institutions buy at current prices and hold long-term, they anchor realized value at high levels.

Second, high-frequency trading by retail traders continually pushes on-chain active supply’s realized value near current prices, further narrowing the gap between market cap and realized value.

Third, as market cap expands from billions to trillions, generating the same extreme Z-Score as in early cycles requires exponentially larger capital inflows—an almost impossible feat in reality.

The combined effect is that the Z-Score’s ceiling is structurally lowered. The once “ironclad” thresholds are now relics of the past.

The Illusion of Logarithmic Curves: Why Rainbow Charts Fail to Predict

Bitcoin’s rainbow chart uses a log growth model to fit long-term price trends, dividing the price range into color bands from “deep undervaluation” to “bubble extremes.” In 2017 and 2021, touching the high bands coincided with cycle tops, fostering confidence in the chart.

But during the 2024-2025 bull cycle, Bitcoin’s price only hovered in the neutral “HODL” zone, never approaching the deep red “extreme bubble” band. The chart lost its core function—predicting cycle tops.

The reasons are threefold: First, this model treats price solely as a function of time, ignoring external variables like halving, ETFs, institutional flows, and macro policies. Second, the decline in volatility due to institutionalization causes price deviations from trend lines to shrink systematically, making fixed-width bands less meaningful. Third, Bitcoin’s growth is transitioning from the steep part of the adoption S-curve to a slow, mature asset phase. When growth slows, the exponential extrapolation overestimates actual expansion, placing the price persistently below the centerline.

The Myth of Capital Rotation: Failures of Altcoin Seasons and BTC Dominance

The Altcoin Season Index measures the proportion of top 100 altcoins outperforming BTC; over 75% indicates “altcoin season.” BTC Dominance falling below 50% or even 40% is seen as a sign of capital rotating into altcoins. Historically, in 2017, BTC dominance dropped from 85% to 33%, and in 2021 from 70% to 40%, corresponding to major altcoin rallies.

But throughout 2025, the Altcoin Season Index remained below 30, and BTC dominance stayed above 64%, never falling below 50%. The so-called “Altseason” in early 2026 is more a narrative-driven, localized rotation—only select sectors like AI and RWA benefited, not a broad market surge.

This reflects a deeper change: institutional and ETF capital now dominate market liquidity, and these funds have a higher risk appetite for Bitcoin than for altcoins. Capital inflows into Bitcoin ETFs go directly into BTC holdings, not into altcoins. Meanwhile, market enthusiasm for AI and precious metals siphons off liquidity from crypto altogether. The narrative exhaustion and reduced liquidity in altcoin ecosystems further diminish the likelihood of a true altcoin season.

Sentiment Indicators Reversed: Retail Psychology No Longer Drives Prices

The Crypto Fear & Greed Index combines volatility, market momentum, social media sentiment, Google Trends, etc., into a 0-100 score. Traditionally, extreme fear signals buying opportunities; extreme greed signals selling.

But in 2025, this approach failed. In April, the index dropped below 10—lower than during the FTX collapse—yet Bitcoin did not rebound as expected. The 30-day moving average averaged only 32, with 27 days in fear or extreme fear zones. Similarly, at the 2025 top, the index was only around 70—far from the extreme greed levels that historically marked tops.

The core reason is that the transmission of emotion to price has been broken by institutional activity. When retail fear peaks, institutions may be buying the dip; when retail greed peaks, institutions may be hedging via derivatives. Retail sentiment no longer drives price movements and often becomes the prey of repeated market cycles.

On-Chain Data and Its Representativeness Crisis: NVT and Cost Basis Indicators

The NVT ratio, dubbed “crypto PE ratio,” divides network market cap by daily on-chain transaction volume. High NVT suggests overvaluation; low NVT suggests undervaluation. But in 2025, the indicator gave contradictory signals: before a major rally, NVT Golden Cross reached 58; at a $120,000 price, it indicated undervaluation.

The failure stems from the fact that on-chain transaction volume no longer fully captures Bitcoin’s real economic activity. Layer 2 transactions, exchange internal settlements, ETF custody—these new forms erode the relevance of pure on-chain data, causing all on-chain valuation metrics to face a representativeness crisis.

Structural Breakdowns and Adaptive Thinking: From Single Indicators to Multi-Frameworks

Viewing these eight indicators collectively reveals that their failures are not isolated but point to a common set of deep structural changes:

Institutionalization has transformed the microstructure of markets. Bitcoin spot ETFs, corporate treasury holdings, CME derivatives, pension fund inflows—these have fundamentally altered capital sources and price discovery. Institutions tend to buy on dips and hold long-term, smoothing out the volatility once driven by retail sentiment. Indicators relying on extreme volatility or sentiment signals are thus rendered ineffective.

The structural decline in volatility is a direct technical cause of many indicator failures. Pi Cycle Top and rainbow charts require extreme price moves to trigger signals; MVRV needs large deviations between market cap and cost basis. With volatility dropping from over 100% to around 50%, these conditions are rarely met.

Bitcoin’s asset nature is shifting from a digital commodity to a macro financial asset. Price drivers are moving from on-chain variables (halving, active addresses) to macro factors like Fed policies, global liquidity, and geopolitics. Indicators focused solely on on-chain data face a market increasingly dominated by external macro influences.

On-chain data’s representativeness is declining systemically. The proliferation of Layer 2 ecosystems, exchange liquidity concentration, ETF custody systems—all dilute the ability of on-chain metrics to capture the full market picture.

Furthermore, many classic indicators are based on curve fitting of just 3-4 halving cycles, with very limited samples. As market environments change qualitatively, these historical fits become less valid.

For ordinary investors, the collective failure of these indicators signals a more fundamental lesson: understanding each indicator’s assumptions and boundaries may be more valuable than chasing a universal predictive tool. Overreliance on any single indicator risks misjudgment. When the underlying rules are being rewritten, maintaining cognitive flexibility and a multi-framework approach is likely more practical than obsessively seeking the next “crossing level” or “magic indicator.”