Author: Frank, PANews

In early 2026, a sense of frustration and confusion permeated the crypto market.

Bitcoin has retreated approximately 36% from its all-time high reached in October 2025, with the market fluctuating between bulls and bears. However, what worries many crypto investors more is not the price itself, but rather the fact that the indicator system they used to determine market positions has almost entirely failed.

The S2F model's $500,000 prediction deviated from reality by more than three times. The four-year cycle failed to deliver the explosive price surge expected after the halving. The Pi Cycle Top indicator remained silent throughout the cycle, the fixed threshold of the MVRV Z-Score was no longer triggered, and the top area of the rainbow chart became unreachable. Meanwhile, the contrarian signals from the Fear & Greed Index repeatedly failed to be accurate, and the much-anticipated "altcoin season" never materialized.

Why are these indicators collectively failing? Is it a temporary deviation, or has the market structure undergone a fundamental change? PANews has systematically reviewed and analyzed eight failing indicators that are currently being widely discussed.

Four-year cycle theory: The supply shock from halving is becoming negligible.

The four-year cycle theory is the most widely accepted pattern in the crypto market. This theory posits that Bitcoin follows a fixed rhythm driven by halving events: accumulation before the halving, a surge 12-18 months after the halving, a peak drop of 75%-90%, a bear market bottoming out, and a restart. The halvings in 2012, 2016, and 2020 have all effectively validated this pattern.

However, after the halving in April 2024, the market did not experience the explosive surge typical of previous cycles. Bitcoin's annualized volatility has decreased from over 100% historically to around 50%, exhibiting more of a "slow bull" characteristic. The bear market decline is also narrowing; the drop from peak to trough in 2022 was 77%, smaller than the 86% in 2014 and 84% in 2018.

Discussions about the failure of the four-year cycle theory are widespread on social media, with the prevailing view being that the entry of institutional funds has fundamentally altered the market's microstructure.

First, the Bitcoin spot ETF has continuously attracted funds since its listing in the United States, creating sustained demand and breaking the simplistic narrative that it is driven solely by halving.

Secondly, on the supply side, the 2024 halving will reduce the block reward to 3.125 BTC, decreasing the daily new supply from approximately 900 BTC to 450 BTC, resulting in an annualized supply reduction of approximately 164,000 coins. This reduction lowers Bitcoin's annualized inflation rate (supply growth rate) from 1.7% before the halving to approximately 0.85%, with the annual supply reduction representing only 0.78% of the total 21 million coins issued. Compared to Bitcoin's market capitalization of trillions of dollars, the actual impact of this supply reduction is negligible.

Pi Cycle Top: Decreasing volatility makes it impossible for moving averages to cross.

Developed by Philip Swift, the Pi Cycle Top identifies market tops by observing when the 111-day moving average crosses above twice the 350-day moving average. This indicator accurately signaled market tops three times: in 2013, 2017, and April 2021.

During the bull market cycle of 2025, the two moving averages never formed a valid crossover, and the indicators remained "silent." However, the downward trend in the market was already quite evident.

The failure of this indicator may be due to the fact that Pi Cycle Top relies on sharp price fluctuations that cause short-term moving averages to deviate significantly from long-term moving averages before crossing. With the structural decrease in Bitcoin volatility and the participation of ETFs and institutions, BTC price movements have become smoother, and retail-driven parabolic rises have decreased, making the preconditions for moving average crossovers less readily met. Furthermore, this indicator is essentially a curve fit to data from its early adoption phase (2013-2021), and with the qualitative changes in the market participant structure, the parameters fitted in those early stages are likely no longer applicable.

MVRV Z-Score: Market size and holding patterns have changed the foundation of computing.

The MVRV Z-Score is an on-chain valuation metric that assesses market valuation by comparing the deviation between Bitcoin's market value (current market capitalization) and its realized value (the total value of each Bitcoin calculated based on its price at the time of its last on-chain movement). Traditionally, a Z-Score above 7 is considered a sell signal indicating an overheated market, while a score below 0 is considered a buy signal indicating extreme undervaluation.

In terms of performance, even at the peak of the 2021 bull market, the Z-Score did not reach the heights of previous cycles, and the traditional fixed threshold (>7) was no longer triggered. By 2025, although the price of Bitcoin had peaked, the highest Z-Score was only 2.69.

The reasons for this may include the following aspects:

1. Institutional investors buy at high prices and hold for the long term, systematically raising the Realized Value (MVRV) to a level closer to market value, thus compressing the volatility of MVRV.

2. The high-frequency movement of short-term active traders continues to "refresh" the RV of active supply to near the current price level, further narrowing the MV-RV gap.

3. As the market capitalization expands, the amount of capital required to achieve the same extreme Z-Score as in the early stages increases exponentially.

The combined result of these three factors is that the ceiling of Z-Score has been structurally lowered, and the original fixed threshold of "7 = overheating" can no longer be reached.

Rainbow chart: The logarithmic growth assumption is being broken.

The Bitcoin Rainbow Chart uses a logarithmic growth curve to fit long-term price trends, dividing price ranges into colored bands ranging from "extremely undervalued" to "bubble-driven." Investors use this to determine buying and selling opportunities. In 2017 and 2021, the price indeed reached the top of the cycle when it hit the higher colored bands.

However, throughout the entire 2024-2025 bull market cycle, the price of Bitcoin remained only in the neutral "HODL!" zone, never approaching the deep red zone representing an extreme bubble. The chart's top predictive function was almost entirely ineffective.

For the Rainbow Bridge indicator, this model treats price only as a function of time. It doesn't consider halvings, ETFs, institutional funds, macroeconomic policies, or any other variables. Furthermore, the decreased volatility brought about by institutionalization systematically reduces the price's deviation from the trend line, making the fixed-width band no longer reachable. Additionally, Bitcoin's growth is transitioning from a "steep segment of the S-curve of adoption" to a "slow growth segment of a mature asset," with the logarithmic extrapolation of the growth rate systematically exceeding the actual growth rate, causing the price to consistently linger below the center line.

Altcoin Seasonal Index and BTC Dominance: The Premise of "Capital Rotation" Has Changed

The Altcoin Season Index measures the percentage of the top 100 altcoins that outperformed BTC over the past 90 days; a percentage exceeding 75% is considered an "altcoin season." BTC Dominance (BTC market capitalization as a percentage of total market capitalization) is seen as a signal of funds flowing from BTC to altcoins when it falls below 50% or even 40%. In 2017, BTC Dominance dropped from 85% to 33%, and in 2021 it fell from 70% to the 40% range, both corresponding to large-scale altcoin rallies.

However, throughout 2025, the altcoin seasonality index remained below 30, indicating a persistent "Bitcoin season." The BTC Dominance reached a high of 64.34% and never fell below 50%. By early 2026, the so-called "Altseason" manifested more as localized rotations driven by precise narratives, benefiting only specific sectors such as AI and RWA, rather than the broad-based rallies of the previous two rounds.

The underlying reason for the failure of these two indicators is also due to the current market structure. With institutional and ETF funds becoming dominant, these funds have a significantly higher risk appetite for Bitcoin than for altcoins. Furthermore, a large amount of capital has been siphoned off by the market frenzy surrounding AI and precious metals, further reducing the inflow of funds into the crypto market. The incremental funds attracted by Bitcoin ETFs flow directly into BTC; structurally, these funds do not "rotate" to altcoins. ETF holders are buying financial products, not tickets to the crypto ecosystem. Additionally, the stagnation of the altcoin ecosystem narrative and the weakening liquidity support for new projects are also important reasons why the altcoin boom has been delayed.

Fear & Greed Index: Retail Investor Sentiment No Longer the Decisive Force in Prices

The Crypto Fear & Greed Index combines multiple factors, including volatility, market momentum, social media sentiment, and Google Trends, to arrive at a score from 0 to 100. A classic approach is to trade contrarian: buy when there is extreme fear and sell when there is extreme greed.

In April 2025, the index fell below 10, lower than during the FTX crash, but BTC did not subsequently experience the expected significant rebound. The 30-day average for the year was only 32, with 27 days in the fear or extreme fear range. As a top signal, this indicator is also unreliable; at the market high in October 2025, the index was only around 70.

The core reason why the Crypto Fear & Greed Index fails is that institutional funds disrupt the transmission mechanism between sentiment and price. When retail investors are fearful, institutions may be buying on dips; when retail investors are greedy, institutions may be hedging with derivatives. This makes retail investor sentiment no longer the dominant force in price movements.

NVT Ratio: On-chain transaction volume no longer accurately reflects real economic activity.

The NVT ratio is known as the "crypto version of the price-to-earnings ratio." It is calculated by dividing the network's market capitalization by the daily on-chain transaction volume. A high NVT may indicate an overvaluation, while a low NVT may indicate an undervaluation.

In 2025, the indicator showed conflicting signals. In April, before prices had risen significantly, the NVT Golden Cross reached a high of 58, but by October, when the price reached around $120,000, it indicated that the price was undervalued.

The fundamental reason for NVT's failure lies in its denominator, on-chain transaction volume, which can no longer represent the real economic activity of the Bitcoin network.

S2F model: The cost of only looking at supply and not demand

The Stock-to-Flow (S2F) model, proposed by anonymous analyst PlanB in 2019, borrows from the valuation logic of precious metals. It measures scarcity by the ratio of Bitcoin's existing supply to its annual increase and uses logarithmic regression to fit a price prediction curve. The core assumption is that the S2F ratio doubles after each halving, and the price should rise exponentially.

In terms of performance failure, the model predicted that BTC should reach about $100,000 in December 2021, while the actual price was about $47,000, a deviation of more than 50%. In 2025, the model target was $500,000, while the actual price was about $120,000, and the gap further widened to more than 3 times.

The fundamental reason for the failure of S2F lies in the fact that it is a purely supply-side model, completely ignoring demand-side variables. Furthermore, once Bitcoin's market capitalization reaches trillions, exponential growth becomes increasingly unsustainable physically, and diminishing marginal returns are an unavoidable reality.

What's failing isn't a single indicator, but rather the market assumptions upon which these indicators collectively rely.

Examining the failures of these indicators together reveals that their malfunctions are not isolated events, but rather point to the same set of structural changes:

Institutionalization has altered the market microstructure: the entry of Bitcoin ETFs, corporate treasury allocations, CME derivatives, and pension funds has collectively changed the capital structure and price discovery mechanism. Institutions tend to buy on dips and hold for the long term, smoothing out the previously sharp fluctuations driven by retail sentiment. This makes it difficult for all indicators that rely on extreme volatility or sentiment signals to function as before. Furthermore, the siphoning of funds by AI and precious metals has reduced liquidity in the crypto market.

The structural decline in volatility is the direct technical reason for the failure of several indicators: Pi Cycle Top and Rainbow Chart require extreme price increases to trigger signals, MVRV requires a huge deviation between market capitalization and cost basis, and funding rates require extreme long-short imbalance. When volatility drops from 100% to 50%, these conditions become more difficult to meet.

Bitcoin's "asset type" is shifting: from digital goods to macro financial assets, the price drivers of Bitcoin are moving from on-chain variables (halving, on-chain activity) to macro factors such as Federal Reserve policy, global liquidity, and geopolitics. Those metrics focused on analyzing on-chain data are facing a market increasingly dominated by off-chain factors.

The representativeness of on-chain data itself is declining: Layer 2 transactions, exchange internal settlement, and ETF custody models are all eroding the data foundation of on-chain metrics, making it increasingly difficult for metrics such as NVT and MVRV, which rely on on-chain transaction data, to capture the full picture.

Furthermore, most classic indicators are essentially curve fitting based on 3-4 halving cycles, with a very small sample size, and are prone to failure after a qualitative change in the market environment.

For ordinary investors, the collective failure of these indicators may convey a simpler message: understanding the assumptions and limitations of each indicator is perhaps more important than pursuing a universal predictive tool. Over-reliance on any single indicator can lead to misjudgments. In a phase where the underlying rules of the market are being rewritten, maintaining cognitive flexibility may be more pragmatic than searching for the next "universal indicator."

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