No single indicator reliably calls tops or bottoms. But when several independent indicators — spanning valuation, sentiment, and on-chain holder behaviour — line up in the same zone, the signal becomes far more trustworthy than any one metric alone.
Why On-Chain Data Matters
Bitcoin's blockchain is a public ledger — every transaction, every coin's age, every wallet balance is visible to anyone who wants to look. That transparency is unique among major assets: you can't see the order book of every gold vault or every S&P 500 shareholder, but you can see exactly how Bitcoin's supply is distributed and moving at any moment.
On-chain indicators turn that raw data into something usable. Combined with price-based statistical tools (moving averages, volatility bands, drawdown measures), they help answer a question price alone can't: is the current price historically cheap, fair, or expensive relative to how Bitcoin actually behaves?
The ten below are grouped loosely into three families — valuation (is price cheap or expensive), sentiment (is the market fearful or greedy), and cycle timing (where are we relative to the halving). Each links through to a live, continuously updated page where you can see today's actual reading.
1. AHR999 Index
AHR999 compares Bitcoin's price to both its 200-day geometric mean price and a long-term power-law fair value estimate. It was originally designed as a dollar-cost-averaging timing tool: readings below roughly 0.45 have historically marked periods worth accumulating more aggressively, while readings above 1.2 have coincided with late-cycle euphoria.
It's one of the more forgiving indicators for beginners because it's built specifically around a DCA mindset rather than precise top/bottom calling. See the live reading on our AHR999 page, or read our full AHR999 guide for how it's calculated.
2. MVRV Z-Score
MVRV compares Bitcoin's market cap to its realized cap — the value of every coin priced at the moment it last moved, rather than at today's price. It's a proxy for the market's average unrealized profit or loss. The Z-Score version standardizes that ratio against its own historical volatility, which is what makes extreme readings comparable across different market eras.
High Z-Scores have historically clustered near cycle tops (the market is sitting on large aggregate paper profits, which is when selling pressure tends to build). Low or negative scores have clustered near capitulation lows. Check the live MVRV page for today's value, or read our full MVRV Z-Score guide for how realized cap works.
3. Fear & Greed Index
A composite sentiment score built from volatility, momentum, social media activity, and market dominance. It doesn't measure valuation at all — purely how emotional the market currently is. Extreme fear has historically been a better time to be adding than extreme greed, which is the basis of the well-known contrarian framing: "be fearful when others are greedy, and greedy when others are fearful."
It moves fast and can swing within days, which makes it more useful as a short-term temperature check than a long-term valuation tool. Live reading on the Fear & Greed Index page, or read our full Fear & Greed Index guide for how it's calculated.
4. Pi Cycle Top
Pi Cycle Top tracks the crossover between Bitcoin's 111-day moving average and 2× its 350-day moving average. Historically, when the shorter average crosses above the longer one, it has landed within days of major cycle tops — a track record that has held across multiple cycles.
It's a lagging, confirmation-style signal rather than a predictive one: by the time it fires, the top may already be forming. It's best used as a warning to tighten risk management, not a precise sell trigger. See it live on the Pi Cycle page, or read our full Pi Cycle Top guide for its track record.
5. Mayer Multiple
A simple but durable metric: current price divided by the 200-day moving average. Above roughly 2.4, Bitcoin has historically been in overheated territory; below 1.0, it has historically traded at a discount to its own trend. Its simplicity is the appeal — no exotic on-chain data required, just price.
Because it's purely trend-based, it works well as a quick trend-vs-price check alongside more data-heavy indicators. Live value on the Mayer Multiple page, or read our full Mayer Multiple guide for why simplicity has held up so well.
6. NUPL
Net Unrealized Profit/Loss measures, across the entire circulating supply, how much of it is sitting in profit versus loss relative to the price each coin last moved at. It's typically read through named zones — Capitulation, Hope/Fear, Optimism/Anxiety, Belief/Denial, and Euphoria/Greed — each historically associated with a different stage of the cycle.
NUPL is closely related to MVRV (they're derived from similar underlying data) but is scaled differently and easier to read at a glance thanks to its named zones. Live reading on the NUPL page, or read our full NUPL guide for what each zone means.
7. Puell Multiple
Puell Multiple looks at miner economics: the USD value of daily-issued Bitcoin divided by its 365-day moving average. It captures miner revenue stress and windfall — miners are structural sellers (they have real-world costs to cover), so periods of extreme miner profitability have historically coincided with market tops, and periods of miner stress with market bottoms.
It's one of the few indicators that looks at the supply side of the market rather than demand or holder sentiment. Live value on the Puell Multiple page, or read our full Puell Multiple guide for why miner economics matter.
8. Rainbow Chart
The Bitcoin Rainbow Chart plots price on a logarithmic scale against a long-term power-law growth curve, divided into coloured bands from "Fire Sale" (deep blue) to "Maximum Bubble" (red). It's more of a visual, intuitive framing than a precise trading signal — but the underlying power-law model is the same mathematical basis used more rigorously in indicators like AHR999 and Power Law Deviation.
It's a good entry point for beginners because the colour-coded bands are immediately understandable without needing to know the math behind them. See it on the Rainbow Chart page, or read our full Rainbow Chart guide for the power-law math behind the colours.
9. 200-Week Moving Average
The 200-week (roughly 4-year) moving average has, notably, never been broken to the downside on a sustained basis across Bitcoin's trading history — every major bear market has found support at or near this line. It's one of the simplest and most widely watched long-term trend indicators in the entire market.
Because it averages four full years of price action, it moves slowly and is far less prone to whipsaws than shorter-term moving averages. Live reading on the 200-Week MA page, or read our full 200-Week MA guide for its remarkable track record.
10. RHODL Ratio
RHODL Ratio compares the realized value held by coins moved in the last week against coins moved 1–2 years ago, weighted by coin age. In plain terms: it tracks the balance between short-term speculative activity and long-term holder conviction. Elevated readings have historically flagged short-term speculative excess relative to long-term holding behaviour.
It's a more specialized, holder-behaviour-focused metric than the others on this list, useful once you're comfortable with the basics. Live value on the RHODL Ratio page, or read our full RHODL Ratio guide for its cycle-top track record.
Using Them Together
The real value of on-chain indicators isn't any single reading — it's confluence. One indicator flashing "cheap" could be noise. Five or six independent indicators, built from different data (price trend, holder profit/loss, miner economics, sentiment) all pointing the same direction is a genuinely stronger signal.
This is exactly the idea behind our Cycle Compass — a composite score built from 21 of these indicators, weighted equally, that condenses them into a single 0–10 reading so you don't have to manually cross-check each one.
A practical way to use all of this as a long-term investor: don't try to time an exact top or bottom. Use the indicators to lean into accumulation when several agree the market is historically cheap, and to size down new buying when several agree it's historically stretched. That's a meaningfully better process than guessing off headlines or price alone.