CryptoQuant
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BTC’s Rally Was a Short Squeeze

“As long as price depends more on derivatives than on solid spot validation, the structure will remain vulnerable to reversal.” – Read More

By Carmelo_Alemán | @cryptoquant_official
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Bullish Divergence in Ethereum: Active Addresses Hit Record Highs Amid Price Decline

“From an on-chain analysis perspective, this glaring divergence implies that Ethereum may currently be undervalued.” – Read More

By CryptoOnchain | @cryptoquant_official
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Bitcoin’s Sudden Drop Explained — Structural Downside Driven by Leverage Liquidations

“Weekend market structure played a key role. With reduced participation from institutions and liquidity providers, order books become thin, making prices more sensitive to market orders.” – Read More

By XWIN Japan | @cryptoquant_official
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Exchange Inflow Spike: A Warning Sign or Just Noise?

“The combination of a whale-driven inflow spike and rising exchange reserves suggests that smart money is preparing to sell into any strength. Unless this inflow is quickly absorbed, a retest of the $74K–$75K support zone is increasingly likely in the near term.” – Read More

By Woominkyu | @cryptoquant_official
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🎙️ New podcast: The Macro Forces Driving Crypto

Jamie Coutts (Chief Crypto Analyst, Real Vision) joins Julio Moreno and Ben Sizelove to break down where Bitcoin and the broader market stand right now.

Inside:

• Why global liquidity is expanding but BTC has lagged

• Q4 2024 deleveraging and what actually broke

• Whether Feb 2025 was the real bottom

• The four-year cycle, MVRV, SOPR, and what the data says

• Altcoin tokenomics — why Tron, BNB and Solana hit ATHs

👉 First episode of Unbiased.

@cryptoquant_official
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BTC: Current Drawdown Remains Above Prior Capitulation Levels

“This does not guarantee further downside, but current conditions still differ materially from past cyclical lows.” – Read More

By Zizcrypto | @cryptoquant_official
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Perp demand is rising. Spot demand is still contracting.

That exact setup appeared in 2022 and preceded the next leg down.

It doesn’t guarantee the same outcome, but structurally, this is a bearish demand signal.

👉 Full breakdown.

@cryptoquant_official
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Bitcoin Is Close to Flipping the Market Structure

“A sustained reclaim of the Realized Price, paired with the MVRV stabilizing and trending above 1.0, would signal a structural regime change.” – Read More

By MorenoDV_ | @cryptoquant_official
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Rebounding at the Average Cost Basis of Early ETF Buyers

“The average cost basis of investors who entered the market following the ETF approval is now acting as a key support level for Bitcoin's current price, and we are seeing a rebound emerge from this zone.” – Read More

By Crypto Dan | @cryptoquant_official
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Deconstructing April’s Recovery — The Divergence in Supply-Demand Structure Between Bitcoin and Ethereum

“If ETH begins to show sustained spot demand similar to BTC, broader altcoin participation may follow. Until then, Bitcoin dominance is likely to persist.” – Read More

By XWIN Japan | @cryptoquant_official
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BTC Breaks $81K as Dominance Rises, but Altcoins Show Early Stabilization

“Trading volumes for altcoins on Binance are also showing signs of gradual recovery. Their share relative to the combined BTC and ETH volumes on Binance has increased from 31% to 49%.” – Read More

By Darkfost | @cryptoquant_official
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How do you actually use the CryptoQuant MCP for trading?

Plug it into n8n, let it fetch on-chain data, convert it into trading signals, run it through risk management, and execute orders on your exchange.

Step-by-step example 👇

Step 1: trigger + data fetch

Start with a Cron node, which run every day/hour.

Two parallel MCP calls to CryptoQuant:

• Taker Volume
• BTC Price (OHLCV)

This is raw aggressive order flow, who's hitting the bid vs lifting the offer.

Step 2: 7-period MA

Compute a 7-period moving average on the buy/sell ratio. This filters the chop and surfaces the actual trend.

Step 3: z-score

Z-score tells you how extreme the current reading is vs recent history:

z = (value - 30p mean) / 30p std

• z > +2 → unusually heavy buy pressure
• z < -2 → unusually heavy sell pressure
• in between → noise, do nothing

This is your signal engine.

Step 4: signal generation

Simple branching node:

• IF z > +2 → emit BUY
• IF z < -2 → emit SELL
• ELSE → wait

One variable, one threshold, one decision. Keep it simple.

Step 5: risk gate

Take the recent price range versus the median range of the last 14 periods.

If the market is moving > 2x normal → SKIP the trade or halve the position size.

Never catch a falling knife.

Step 6: define next steps

For example:

🔴 Execute trades on exchange via API/MCP
🔵 Log every trade for monitoring
🟡 Send real-time Telegram alerts

What you have now:

→ Fully autonomous flow

→ Decision based on one meaningful signal (taker imbalance + z-score)

→ Risk management baked in

👉 See CryptoQuant MCP for more.

For educational purposes only — not financial advice

@cryptoquant_official
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