Since I'm not a trader, when developing a BTC trading strategy, I must be clear about one thing: what data can predict Bitcoin, and what data only adds confusion to the prediction.

Conclusion first: After completing it, I tested this system for a week, and at every key signal point, it gave me the direction in advance.

Below is the complete logic.

1. Research Background: I Reviewed All Methods of "Predicting BTC"

I am not a professional secondary market trader. So I didn't start by selecting indicators; instead, I did a笨事 (dumb thing) first—

I reviewed all methods of Bitcoin prediction available in the market from 2017 to 2025.

Divided into three categories:

First category: Celebrity opinions. VanEck said $180K by 2025. Didn't happen. Bitwise said $200K. Didn't happen. Tom Lee, Arthur Hayes, Novogratz, Cathie Wood—almost all major price predictions with records over the past 8 years, systematically overestimated, with an average deviation exceeding 50%.

Second category: Analytical methods. Stock-to-Flow model (PlanB's approach), logarithmic growth curve, cycle theory, Wyckoff method, Elliott Wave Theory... Each has its own "historical accuracy," but if you run them post-2024, almost all fail.

Third category: On-chain signals. MVRV Z-Score, SOPR, NUPL, Puell Multiple, Hash Ribbon, Reserve Risk... This category was the one I researched the longest. Because it's not "prediction," it's "state description."

After going through all three categories, I started filtering.

2. Screening and Analysis: More Data Doesn't Mean More Accuracy, It Means More Confusion

After screening, I discovered a counterintuitive thing:

When massive amounts of data point in different directions simultaneously, your judgment actually worsens.

After analysis, I divided them into two categories—

Unreliable Category (Discard)

Celebrity predictions. The incentive structure dictates that they must make bold statements. Saying "$500K" gets headlines, gains followers, and gets repeatedly quoted. Saying "$80K sideways" gets no shares. There are no consequences for being wrong, and being right always makes them a "guru." This structure won't change, so the predictions won't be accurate.

Pure models like Stock-to-Flow. Had high precision before 2021, but collapsed directly after 2022. Why? Because the model's assumption is that "the supply curve determines price," but after ETFs entered the market, what determines price is capital flow, not supply. The model itself isn't wrong; it's that the world it describes has changed.

Single sentiment-based indicators (pure Fear & Greed). Historically, when Fear & Greed has been below 20 for a long time, sometimes it's the bottom, sometimes it's the prelude to "falling to -30." Used alone, there are too many false signals.

Reliable Category (Keep)

MVRV Z-Score. Measures the deviation of the current market cap relative to the average cost basis of all holders. Historically, every time it entered the green zone, it precisely corresponded to a cycle bottom within ±2 weeks—2018, March 2020, 2022, all three hits. But note: After 2024, its ability to judge tops failed (it signaled overheating at $73K, but BTC rose to $126K), because ETF trading happens off-chain, it can't see the institutional portion of the筹码 (chips/coins). So only retain its bottom-judging ability.

SOPR 28-day moving average. Measures how much of the moving BTC is being sold at a loss. Consistently below 1.0 = holders are capitulating = nearing a bottom. This indicator has been historically very stable for judging bottoms.

ETF net flow. A new core indicator post-2024. Must look here for marginal institutional behavior, on-chain data can't see it. Net inflow for 5+ consecutive days cumulative >$1 billion = institutions are accumulating; Net outflow for 5+ consecutive days = institutions are withdrawing.

Macro liquidity. Federal Reserve direction + M2 growth rate. Go long in easing cycles, reduce exposure in tightening cycles. Not for short-term timing, only for setting the major direction.

Fear & Greed as auxiliary. Not used alone, only weighted when resonating with other signals.

After screening, four dimensions remain. Any more is too many.

3. Strategy Formation: Four-Dimensional Resonance, Act Only When Three or More Point in the Same Direction

After clarifying "which are accurate and why," I turned it into a trading strategy.

Core logic: Don't chase price targets, only judge direction and position.

Bottom judgment: MVRV enters green zone + SOPR falls below 1.0 → On-chain holders are capitulating, historical high-probability buying window

Top judgment: On-chain signals indicate overheating + ETF continuous net outflow → Institutions are withdrawing, reduce position

Macro background: Federal Reserve direction → Easing: go long, Tightening: reduce exposure

Sentiment auxiliary: Fear & Greed < 20 → Extreme fear, auxiliary weighting

No single signal is sufficient for action. Only when three or more point in the same direction is it a true basis for entry.

Then I made it into an automated monitoring system:

· Automatically pulls BTC price, Fear & Greed, on-chain data, ETF flow daily

· No push notification if signal not triggered

· Triggered? Directly notifies me via Telegram

· Not a daily report, not noise. Only alerts when it's truly worth paying attention to

Current Signal (April 15, 2026)

The reading this system gives me currently:

BTC $71,631. Fear & Greed = 12, historically extreme fear. MVRV Z-Score in the green buy zone. SOPR below 1.0, holders are selling at a loss.

On-chain triple resonance all confirmed.

The only counter signal: ETF flow has been weak recently, institutions haven't clearly started accumulating yet.

Historically, on-chain triple resonance (extreme fear + MVRV green zone + SOPR < 1) has only occurred three times: end of 2018 bottom, March 2020, end of 2022 bottom. All produced 100%+ returns in the following 12 months.

This is not predicting how high BTC will go. This is an objective description of the current market state.

My biggest feeling after this research is:

Predictions are others' opinions; a framework is your own judgment tool.

If a prediction is wrong, you have nothing. If a framework is wrong, you at least know where the problem is and can iterate.

You can incorporate your own preferences, such as contract leverage and cycle preferences, so the signals AI pushes to you are most suitable for your own operating style.

Note: The above is based on historical patterns, not financial advice.