跳到正文
cccskills
免费在 GitHub 公开

onchain-analysis

Analyze public blockchain records to assess crypto market conditions. Combine network activity, large-holder transfers, decentralized finance liquidity, and valuation measures into a report with indicator comparisons, a composite score, and an interpretation of market signals.

中文简介正在准备中。当前显示英语简介。

原文查看原文说明

On-chain data analysis — active addresses / whale tracking / TVL / DEX liquidity, interpretation and signal generation using on-chain valuation metrics such as MVRV / NVT / SOPR.

查看安装方法

适用场景

  • Compare blockchain activity with price trends
  • Examine large-holder transfers and exchange flows
  • Review funds held in decentralized finance and trading liquidity
  • Assess crypto valuations using blockchain indicators

包含的文件(1)

  • SKILL.md9.6 KB

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

On-Chain Data Analysis

Overview

Use transparent public blockchain data for analysis, covering network activity, whale-behavior tracking, DeFi liquidity analysis, and on-chain valuation metrics. This provides crypto investing with data dimensions unavailable in traditional finance.

Network Activity Metrics

Core Activity Indicators

MetricMeaningBullish SignalBearish Signal
Active addressesUnique addresses interacting with the network each daySustained increase (network adoption rising)Sustained decline
New addressesAddresses appearing for the first time each dayAccelerating growth (new users entering)Shrinkage
Transaction countNumber of on-chain transactions per daySteady growthSharp decline
Transfer valueTotal value transferred on-chain per dayLarge-value transfer activityInactive
Activity / price ratioGrowth in active addresses vs growth in priceActivity growth outpaces price growthPrice rises but activity does not

Activity Analysis Framework

Healthy bull market:
  Price↑ + active addresses↑ + new addresses↑ = driven by real demand

Bubble signal:
  Price↑ + active addresses↓ or flat = capital-driven, not user-driven

Bottoming signal:
  Price↓ + active addresses bottom out and stabilize = speculators exit, real users remain

Historical Reference for BTC Active Addresses

PhaseActive Addresses (daily avg)BTC PriceMeaning
2020 bear-to-bull transition0.8-1.0 million$10k-$20kBottom stabilization
2021 bull market1.0-1.3 million$30k-$69kHealthy growth
2022 bear market0.8-0.9 million$16k-$30kPulled back but did not collapse
2024 bull market0.9-1.2 million$40k-$100kInstitution-driven

Whale Tracking

Whale Definition

TierBTC HoldingsETH HoldingsEstimated Count
Super whales>10,000 BTC>100,000 ETH~100
Large whales1,000-10,000 BTC10,000-100,000 ETH~2,000
Mid whales100-1,000 BTC1,000-10,000 ETH~15,000
Small whales10-100 BTC100-1,000 ETH~150,000

Whale Behavior Signals

Bullish signals:
1. Whales withdraw from exchanges -> intent to hold long term
2. Whale-wallet count rises -> institutions / large holders accumulating
3. Exchange BTC balance keeps falling -> lower available supply
4. Long-term holder (LTH) balances rise -> smart money buying

Bearish signals:
1. Large whale transfers into exchanges -> preparing to sell
2. Dormant addresses wake up and transfer -> early holders taking profits
3. Exchange balances surge -> sell pressure is about to be released
4. Miner balances fall and move to exchanges -> miner capitulation / profit taking

Large Transfer Monitoring

Thresholds to watch:
- BTC: single transfer > 500 BTC (about $50M)
- ETH: single transfer > 10,000 ETH (about $30M)
- USDT: single transfer > $50M

Transfer-direction analysis:
- Wallet -> exchange: potential selling (bearish)
- Exchange -> wallet: withdrawal to hold (bullish)
- Exchange -> exchange: arbitrage transfer (neutral)
- Wallet -> wallet: OTC trade (watch follow-up behavior)

DeFi Liquidity Analysis

TVL (Total Value Locked)

TVL = total value of assets locked in DeFi protocols

TVL analysis dimensions:
1. Total TVL trend: rising = capital flowing into the DeFi ecosystem
2. Cross-chain TVL: market-share changes among ETH vs Solana vs Arbitrum
3. Protocol TVL ranking: leading protocols such as Aave / Lido / Maker
4. TVL / market cap ratio: similar to asset-utilization rate in traditional finance

DEX Liquidity Metrics

MetricMeaningFocus
DEX volumeDaily decentralized-exchange volumeTrend in DEX / CEX ratio
Liquidity depthSize of AMM liquidity poolsBigger pools = lower slippage = better
LP yieldAnnualized return for liquidity providersAbnormally high = unsustainable
Impermanent lossOpportunity cost for LPsMore severe when volatility is higher

Stablecoin Liquidity

Stablecoin inflows = "dry powder" for the crypto market

Metrics to watch:
1. Changes in total USDT / USDC supply
2. Stablecoin balances on exchanges
3. Stablecoin mint / burn activity (Tether / Circle)
4. Stablecoin market-cap share (lower = higher risk appetite)

Signals:
- Heavy stablecoin minting -> capital preparing to enter
- Exchange stablecoin balances up -> buying power is accumulating
- Stablecoin share rising quickly -> risk-off market (capital rotating from coins into stablecoins)

On-Chain Valuation Metrics

MVRV (Market Value to Realized Value)

MVRV = market cap / realized cap

Realized cap = Σ(each UTXO × price at last movement)
             = sum of all holders' cost basis

| MVRV | Meaning | Historical Signal |
|------|------|---------|
| > 3.5 | Severely overvalued, large unrealized profits across holders | Historical top zone |
| 2.0-3.5 | Richly valued, most holders in profit | Mid-to-late bull market |
| 1.0-2.0 | Reasonable range | Normal market or early bull market |
| < 1.0 | Undervalued, most holders underwater | Bear-market bottom zone |

Signal logic: MVRV > 3.5 -> most holders have large unrealized gains -> strong incentive to sell -> potential top
              MVRV < 1.0 -> most holders are losing money -> unwilling to sell -> potential bottom

NVT (Network Value to Transactions)

NVT = market cap / daily on-chain transfer value

Similar to a PE ratio in traditional finance:
- High NVT: market cap is high relative to on-chain activity (overvalued or optimistic on future growth)
- Low NVT: market cap is low relative to on-chain activity (undervalued or value-like)

NVT Signal (improved):
NVT_Signal = market cap / MA(90, daily on-chain transfer value)
Use a 90-day moving average to smooth noise

| NVT Signal | Meaning |
|-----------|------|
| > 150 | Severely overvalued |
| 65-150 | Normal range |
| < 65 | Undervalued |

SOPR (Spent Output Profit Ratio)

SOPR = realized value of spent outputs / creation value of spent outputs

Simply put: on average, are the coins sold today being sold at a profit or a loss?

| SOPR | Meaning | Signal |
|------|------|------|
| > 1.05 | Sellers are realizing 5%+ profit on average | Profit-taking pressure |
| 1.0-1.05 | Small-profit selling | Normal |
| = 1.0 | Break-even | Key support / resistance |
| < 1.0 | Selling at a loss | Panic selling (bottom signal) |

In bull markets: a pullback to SOPR = 1.0 is a buy opportunity (cost-basis support)
In bear markets: a rebound to SOPR = 1.0 is a sell opportunity (cost-basis resistance)

Other On-Chain Valuation Metrics

MetricFormulaPurpose
Puell MultipleDaily miner revenue / MA(365, daily miner revenue)Miner-income cycle
Stock-to-Flowstock / annual productionBTC scarcity model
Reserve RiskHODL Bank / priceHolder confidence
Exchange balancetotal BTC held on exchangesSupply-side pressure

Analysis Framework

Composite On-Chain Score

Score each indicator from 1 to 5 and combine with weights:

| Dimension | Weight | Indicators |
|------|------|------|
| Valuation | 30% | MVRV, NVT |
| Activity | 25% | active addresses, new addresses |
| Capital flow | 25% | exchange balances, stablecoins |
| Whale behavior | 20% | whale holdings, large transfers |

Total score > 4.0: strongly bullish
Total score 3.0-4.0: bullish bias
Total score 2.0-3.0: neutral
Total score < 2.0: bearish / leaning bearish

Output Format

## On-Chain Analysis Report: BTC

### On-Chain Snapshot
| Metric | Current Value | Historical Percentile | Signal |
|------|--------|---------|------|
| MVRV | 2.1 | 65% | Elevated but not at a top |
| NVT Signal | 85 | 50% | Fair |
| SOPR(7d avg) | 1.02 | 55% | Slight profit-taking state |
| Active addresses | 950k/day | 45% | Relatively low |
| Exchange balance | 2.30M BTC | 30% | Low level (bullish) |

### Whale Activity
- Last 7 days: net withdrawal of +15,000 BTC (bullish)
- Large transfers: 3 transfers >1000 BTC into cold wallets
- Miners: holdings stable, no major outflows observed

### Composite Score: 3.5/5 (bullish bias)

### Conclusion
On-chain data is broadly bullish. MVRV is not yet in an extreme zone, exchange balances are low,
and whales continue to accumulate. But active addresses remain soft, so monitor whether this is an
"institution-driven bull market without retail participation". Maintain long exposure, but keep
position sizing controlled and avoid leverage.

Notes

  1. Data-source limitations: on-chain data is most reliable for BTC and ETH; data quality for other chains is uneven
  2. Entity identification is difficult: one entity can control multiple addresses, so whale analysis contains error
  3. UTXO vs account model: BTC (UTXO) analysis methods cannot be directly applied to ETH (account model)
  4. Missing Layer-2 data: many transactions occur on L2s (Arbitrum / Optimism), so L1 data is incomplete
  5. On-chain data lag: block confirmation takes time, so this is not suitable for short-term trading decisions
  6. Data acquisition: the built-in OKX data source provides candles / trade data, but on-chain data requires extra APIs (Glassnode / Nansen)
  7. Metric desensitization: as market structure changes (ETFization / institutionalization), historical thresholds may need adjustment

评价

还没有评价。欢迎分享你的使用感受。

评论

无需登录即可发表。欢迎分享使用感受、提问,或说明在哪些环境下无法运行。

还没有评论。来写第一条吧。

提交后会立即公开。请勿填写邮箱、电话等个人信息。URL 最多 2 个。

0 / 1000 字

此表单受 reCAPTCHA 保护,适用 Google 的隐私权政策和服务条款。

同一仓库的技能

Compare a Chinese company's US depositary receipts, Hong Kong shares, and mainland shares after adjusting for exchange rates and share ratios. Analyze pricing gaps, valuation differences, and delisting risks to support research into its different listings.

中文简介正在准备中。当前显示英语简介。

  • Compare A-share and H-share pricing gaps
  • Measure ADR premiums against Hong Kong shares
  • Review US delisting risks and Hong Kong listing alternatives
HKUDS/Vibe-Trading3.6万2026年10月21日 更新

akshare

免费

Retrieve stock prices, futures quotes, exchange rates, and economic indicators with the free Python library AKShare. The skill explains market-specific functions, symbol formats, and data preparation for analysis and testing strategies against historical data.

中文简介正在准备中。当前显示英语简介。

  • Retrieve historical prices for Chinese, US, and Hong Kong stocks
  • Collect Chinese GDP and consumer price data
  • Check futures quotes and foreign exchange rates
HKUDS/Vibe-Trading3.6万2026年10月21日 更新

alpha-zoo

免费

Explore bundled libraries of investment factors, signals used to compare securities, and inspect individual formulas and metadata. Evaluate a factor or an entire library across a chosen universe and period, producing aggregate performance statistics and an HTML report.

中文简介正在准备中。当前显示英语简介。

  • Find bundled momentum or reversal factors
  • Inspect a named factor's formula and metadata
  • Compare a factor library across a chosen universe and period
HKUDS/Vibe-Trading3.6万2026年10月21日 更新

Assess whether a Chinese A-share company may receive ST or *ST risk warnings next financial year. Review financial forecasts, dividends, audit opinions, and regulatory penalties, then report risk severity separately from confidence in the prediction.

中文简介正在准备中。当前显示英语简介。

  • Reviewing a Chinese A-share company's future ST risk
  • Assessing escalation risk for an existing ST or *ST stock
  • Checking revenue, equity, and dividend warning signs
HKUDS/Vibe-Trading3.6万2026年10月21日 更新

Plan portfolio allocations across assets using risk, correlations, return forecasts, and trading costs. Compare five optimization methods, produce settings for config.json, and define rebalancing rules that adjust holdings back toward their target weights.

中文简介正在准备中。当前显示英语简介。

  • Comparing allocations across stocks, bonds, and gold
  • Choosing an optimizer based on return forecasts and correlations
  • Preparing portfolio settings for config.json
HKUDS/Vibe-Trading3.6万2026年10月21日 更新

Diagnose failed or abnormal backtests, which test trading strategies on historical data. Inspect result files, strategy code, and configuration to identify bugs, make targeted repairs, rerun tests, and verify the updated metrics.

中文简介正在准备中。当前显示英语简介。

  • Investigating backtest execution errors
  • Finding why a strategy produces no trades
  • Checking delayed trades or low capital usage
HKUDS/Vibe-Trading3.6万2026年10月21日 更新

查看 HKUDS 的全部技能

报告此技能的问题