Bitcoin (BTC) Bull Market Top Checklist: Peak Zone Signals (2024-2025/2026)

I've created a tiered checklist with scoring in attempt to identify when Bitcoin (BTC) is in "mania/euphoria" for the 2024-2025/2026 cycle.

For this Bitcoin (BTC) bull market cycle, I created a “bull cycle top zone” checklist that aims to determine whether we are likely in the top zone of the bull run.

Disclaimer: Nothing here is investment advice or should be used to make investment decisions. Don’t be dumb.

I wanted to make an efficient, structured checklist that can be used as a basic heuristic to gauge whether we are likely at or near the top zone (not the “peak price” - nobody knows this, but within the upper bounds of the cycle).

By “top zone” I mean the time that BTC remains within ~10% of the peak price during the cycle… in 2021 there were 2 peak zones lasting ~93 days and ~45 days, respectively (with ~161 days between the peaks).

(Related: Triple Lens Strategy for Bitcoin/Crypto Cycles)

Methods for Developing the Bitcoin Bull Cycle Top Zone Checklist

To create the BTC bull cycle top checklist, I first brainstormed a F-ton of things that I thought might correlated with bull cycle top zones.

Think: Google Search trends, NFT values, on-chain metrics, whale flows, fear/greed index, YouTube views, economic data, stock market data, M2, etc.

Then I used advanced AIs to refine the list and identify specific datapoints/metrics that had highest odds of providing high quality signals without unnecessary noise (i.e. muddying the waters).

I intentionally did not “back test” this checklist because I didn’t want it overfitted - as “this time is different” in terms of political & regulatory climate.

1. Historical Foundations & Empirical Observations

We started with widely recognized signals that have repeatedly appeared around past cycle peaks (e.g., 2013, 2017, 2021).

Indicators like exchange app rankings, peak Google search interest, mainstream media frenzy, and extreme Fear & Greed readings are not arbitrary—they’re drawn from documented historical coincidences where these signals aligned closely with market tops.

Rationale: By beginning with known, historically correlated signals, we ensure that the framework is grounded in actual past events rather than speculation.

2. Tiers Based on Correlation Strength

We then grouped signals into tiers (Tier 1, Tier 2, etc.) reflecting their estimated correlation strength and timing proximity to previous peaks.

Tier 1 signals represent those that historically fired very close to the top (e.g., top-ranked exchange apps, record search volume), while Tier 2 signals are strong but slightly less precise, and Tier 3 or contextual factors are supportive rather than definitive.

Rationale: Organizing signals by their historical closeness to the peak helps prioritize the most reliable indicators and prevents overreliance on weaker or more ambiguous metrics.

3. Quantification of Criteria & Durations

To move beyond vague “often occurs near the top” statements, I introduced specific thresholds and sustained durations.

For example, requiring “7 consecutive days” of extreme Fear & Greed or “3 consecutive days” of an exchange app holding the #1 ranking.

I also set numerical benchmarks (e.g., Google Trends ≥90% of previous peak) and included historically tested on-chain metrics (like MVRV-Z≥7 or NUPL≥0.75).

Rationale: Clear, objective criteria reduce guesswork. Historical tops usually weren’t marked by one-day anomalies; they were sustained euphoric conditions. Setting quantifiable thresholds and timeframes helps filter out short-lived noise.

4. Multi-Domain Cross-Confirmation

We combined signals from different domains: retail sentiment (app ranks, Google searches), mainstream media coverage, on-chain fundamentals (MVRV-Z, NUPL, Puell Multiple), market structure shifts (BTC dominance drop, whale inflows, derivatives leverage), and optional context (macro/regulatory environment).

Rationale: True cycle tops are rarely defined by a single factor; they emerge from a confluence of broad mania, extreme sentiment, and fundamental distribution patterns. Cross-verification across multiple domains increases accuracy and reduces the risk of being misled by any single indicator.

5. Weighted Scoring & Probability Estimates

Instead of a binary “signal present or not,” we assigned point values to each signal, reflecting our estimation of its historical reliability.

We then used logical inference to estimate probabilities.

For example, having most Tier 1 signals simultaneously is rare and strongly suggests a peak, thus meriting higher probability scores.

Adding Tier 2 confirmations and on-chain clusters further increases confidence.

Rationale: Market tops are probabilistic, not certain. By translating signal combinations into approximate probability ranges, we acknowledge uncertainty while giving structured, evidence-based guidance. This approach mirrors how analysts often stack multiple indicators to increase confidence.

6. Incorporation of Well-Studied On-Chain Metrics

The list improved upon prior frameworks by including a cluster of on-chain indicators (MVRV-Z score, NUPL, Puell Multiple) known from historical research to mark macro cycle extremes.

By requiring at least two of these to align for a set duration, we reduce noise and significantly improve peak detection accuracy.

Rationale: On-chain metrics provide a fundamental, data-driven dimension to market psychology indicators. They reflect actual profit-taking, valuation extremes, and miner behavior—hard data that complements sentiment-based signals.

7. Iterative Refinement & Logical Consistency

Throughout the process, we questioned the weighting, criteria, and role of each signal, refining the framework to be as logical and historically consistent as possible.

If a signal lacked strong historical precedent (like stablecoin inflows) or was too vague, we downgraded or removed it.

If a known metric (like MVRV-Z) had proven reliability, we prioritized it.

Rationale: The creation of this framework was an iterative, reasoned process. We aimed for “optimal” accuracy given known historical patterns, always leaning towards signals and methods with the strongest past track record.

8. Limitations & Non-Absolute Confidence

We noted throughout that these are heuristics and educated estimates, not guarantees.

We mentioned the rarity of actual peaks and the evolving nature of crypto markets.

Rationale: Transparency about uncertainty prevents over-reliance on the checklist. Markets change, and no system can predict with 100% certainty.

(Related: AI Predicts Bitcoin, Ethereum, Solana, Chainlink Peaks in 2025/2026)

Bitcoin (BTC) Bull Market Peak Zone Checklist

Aim: Identify the “peak zone” near a major Bitcoin market top (within -/+ 10%), not necessarily the exact top day, but the final euphoric stage that historically precedes a significant cyclical downturn.

Method: Use a weighted checklist of signals proven to correlate strongly with previous BTC cycle peaks (e.g., 2013, 2017, 2021). Group signals into tiers, require sustained conditions over multiple days, and combine market sentiment, retail mania, on-chain fundamentals, and market structure. Add minimal contextual modifiers to fine-tune confidence.

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Principles for Accuracy

1. Focus on Historically Reliable Indicators: Only use signals repeatedly seen at past tops: extreme retail interest (app rankings, Google searches), mainstream frenzy, alt/NFT mania, extreme sentiment, on-chain valuation extremes (MVRV-Z, NUPL, Puell), whale and leverage activity.

2. Sustained Confirmation: Require signals to hold for multiple consecutive days (3 or 7 days) to filter out short-lived spikes.

3. Multi-Domain Confirmation: Confirm across multiple domains: retail sentiment (Google searches, app rankings), mainstream coverage, on-chain metrics, derivatives, and whales. This reduces noise and increases confidence.

4. Minimal Contextual Influence: Contextual factors (pro-crypto regulation, macro tailwinds) matter but should not overshadow direct market signals.

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Tier 1: Core Mania Indicators (Strongest Historical Correlation)

Maximum Tier 1 Points: 20 points (21 with the exchange app 7-day bonus).

1. Exchange App #1 Ranking (4 to 5 Points)

• Criteria: A major crypto exchange app (e.g., Coinbase) must rank #1 in the overall U.S. App Store “Top Free” category.

• 4 Points: If it holds #1 for at least 3 consecutive days.

• +1 Bonus Point (Total 5): If it maintains #1 for 7 consecutive days.

Rationale: Historically, major peaks occurred when retail FOMO was so intense that exchange apps topped general app charts.

2. Peak Google Search Volume for “Bitcoin” (4 Points)

• Data Source: Google Trends (Worldwide, Past 5 Years, Web Search).

• Criteria: Current weekly average (7-day) hits ≥90% of previous cycle’s highest recorded peak (which is typically normalized as 100) and sustains this level for 7 consecutive days.

Rationale: Mass global interest via search typically climaxes near cycle tops.