Coin Metrics

Coin Metrics provide quantitative insight into the performance, volatility, and behavioral dynamics of individual blockchain assets.
They capture how a token trades, fluctuates, and is held across the network—combining price-based indicators, volume-weighted metrics, and on-chain holder statistics.

These metrics serve as the foundation for evaluating token momentum, liquidity, and long-term strength.
They complement Wallet Metrics by focusing on the asset-level perspective, measuring how the market behaves around a coin rather than how individual wallets perform.

Each metric below includes a definition, an example, and an interpretation to help users understand what the value means and how it can be applied for analysis, trading, or research.

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Performance (1h, 1d, 1w, 1m)

Definition
Performance represents the percentage change in a token’s price over a specific period (hour, day, week, or month).
It measures short- and medium-term momentum and helps identify trends or reversals.

$${\mathrm{Performance}}_t=\frac{P_{\text{end}}-P_{\text{start}}}{P_{\text{start}}}\times 100$$

Example
If SOL trades at $130 one hour ago and $135 now,

$${\mathrm{Performance}}_{1h}=\frac{135-130}{130}\times 100=3.85\%.$$

Interpretation

• Positive values indicate upward momentum (bullish trend).
• Negative values indicate downward movement (bearish trend).
• When short-term performance (1h) is negative but long-term (1m) is positive, the market may be consolidating.
• Used for ranking “Top Gainers / Losers”.

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Relative Strength Index (RSI)

Definition
RSI is a momentum oscillator (0–100) comparing average gains and losses over a set number of periods (usually 14).
It helps detect whether an asset is overbought or oversold.

$$\mathrm{RSI}=100-\frac{100}{1+RS},RS=\frac{\text{Avg Gain}}{\text{Avg Loss}}$$

Example
Average gain = 2.5, average loss = 1.5 → ( RS = 1.67 ) → ( RSI = 62.5 ).

Interpretation

• RSI > 70 → overbought (potential correction).
• RSI < 30 → oversold (potential rebound).
• RSI around 50 → neutral or sideways trend.
• Good confirmation signal when combined with price divergence.

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Moving Average Convergence Divergence (MACD)

Definition
MACD shows the relationship between two exponential moving averages (EMAs) of price (fast and slow).
It highlights trend strength and momentum shifts.

$$\begin{array}{rl}\mathrm{MACD}& ={\mathrm{EMA}}_{12}-{\mathrm{EMA}}_{26}\\ \mathrm{Signal}& ={\mathrm{EMA}}_9(\mathrm{MACD})\\ \mathrm{Histogram}& =\mathrm{MACD}-\mathrm{Signal}\end{array}$$

Example
EMA12 = 135, EMA26 = 130, MACD = 5, Signal = 3 → Histogram = 2.

Interpretation

• MACD > Signal → bullish momentum.
• MACD < Signal → bearish momentum.
• A widening histogram indicates strengthening trend.

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Bollinger Bands

Definition
Bollinger Bands consist of a moving average (middle band) and two outer bands set at ±k standard deviations.
They measure volatility and potential price extremes.

$$\text{Upper}={\mathrm{SMA}}_n+k{\sigma }_n,\text{Lower}={\mathrm{SMA}}_n-k{\sigma }_n$$

Example
20-period SMA = 150, standard deviation = 5, k = 2 → Upper = 160, Lower = 140.

Interpretation

• Price touching upper band → overbought signal.
• Price touching lower band → oversold signal.
• Bands widening → higher volatility; narrowing → consolidation.

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200-Day Moving Average (MA200)

Definition
MA200 is the long-term trend line showing the average closing price over the last 200 trading days.

$${\mathrm{MA}}_{200}=\frac{{\sum }_{i=1}^{200}{P}_i}{200}$$

Example
Sum of 200 closes = 28,000 → MA200 = 140.

Interpretation

• Price above MA200 → bullish long-term bias.
• Price below MA200 → bearish market.
• Widely used for institutional trend confirmation.

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Volume Weighted Average Price (VWAP)

Definition
VWAP represents the average price weighted by trade volume within a period (typically 1 day).
It is used as a benchmark for fair value and trading performance.

$$\mathrm{VWAP}=\frac{\sum P_i{V}_i}{\sum V_i}$$

Example
(150×500 + 152×700 + 148×300) / 1500 = 150.53.

Interpretation

• Price > VWAP → intraday bullish sentiment.
• Price < VWAP → intraday bearish sentiment.
• VWAP acts as a dynamic support/resistance during the session.

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Average True Range (ATR)

Definition
ATR measures market volatility as the average of true ranges over n periods.
The true range considers both intraday range and overnight gaps.

$$TR=\max (H-L,|H-C_{prev}|,|L-C_{prev}|),ATR=\frac{1}{n}\sum T{R}_i$$

Example
Total TR over 14 days = 42 → ATR = 3.0.

Interpretation
High ATR = volatile market.
Low ATR = consolidation or quiet phase.
Used to set dynamic stop-loss levels.

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Standard Deviation

Definition
Standard deviation quantifies price variability around its mean return.
It is a core measure of volatility.

$$\sigma =\sqrt{\frac{\sum (R_i-\bar{R}{)}^2}{n}}$$

Example
Daily returns $0.02,-0.01,0.03,0.01,-0.02\to \sigma =2.07$.

Interpretation
Higher σ = more volatility/risk.
Lower σ = stable price behavior.
Used in Sharpe and Sortino calculations.

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Distance from Moving Average

Definition
The percentage distance between the current price and a selected moving average (e.g., MA50 or MA200).

$${\mathrm{Distance}}_{MA}=\frac{P_{\text{current}}-{\mathrm{MA}}_n}{{\mathrm{MA}}_n}\times 100$$

Example
Price = 160, MA200 = 150 → 6.67%.

Interpretation
Large positive distance → price extended above trend (possible overextension).
Large negative distance → below trend (potential accumulation zone).

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Wallet Retention

Definition
The proportion of wallets that continue to hold a given token after a specified period.
It indicates holder commitment and network stickiness.

$${\mathrm{Retention}}_t=\frac{\text{Wallets still holding at }t}{\text{Wallets that bought}}\times 100$$

Example
1,000 buyers; 850 still hold after one month → 85%.

Interpretation
High retention → strong conviction, long-term holders.
Low retention → speculative flow and low loyalty.

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Average Exit Time

Definition
Average time a wallet holds a token before selling or transferring it out.

$${\overline{T}}_{exit}=\frac{1}{n}\sum (T_{exit,i}-T_{entry,i})$$

Example
Holding periods: 5, 10, and 15 days → 10 days average.

Interpretation
Short average → active trader base.
Long average → investor behavior or staking participation.

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Net Inflow

Definition
Net buy volume minus sell volume for a token during a period.
Represents accumulation or distribution activity.

$$\text{Net Inflow}=\text{Buy Volume}-\text{Sell Volume}$$

Example
Buy = 1.5M SOL, Sell = 1.2M SOL → Net = +300k SOL.

Interpretation
Positive → accumulation.
Negative → distribution or selling pressure.

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Holders Count by Value Range

Definition
Counts wallets by the USD value of their holdings (e.g., $0–$10, $10–$100, $100–$1k).
Shows wealth distribution among participants.

Example

Range (USD)	Wallets
0–10	2,000
10–100	1,200
100–1k	700
1k–10k	250
10k–100k	50

Interpretation
A large share of small wallets = retail-driven market.
Concentration in large wallets = whale-dominated market with higher risk of large selloffs.

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Average Weighted Entry Price (AWEP)

Definition
Weighted average entry price across all wallets, by position size.
Reflects the network’s average cost basis.

$$\mathrm{AWEP}=\frac{\sum P_{entry,i}{Q}_i}{\sum Q_i}$$

Example
100 SOL @ $150, 200 SOL @ $140, 50 SOL @ $160 → 145.71.

Interpretation
Price > AWEP → aggregate profit (possible take-profit behavior).
Price < AWEP → unrealized loss (potential capitulation).

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Average Size per Trade

Definition
Average number of tokens traded per transaction within a timeframe.

$$\overline{Q}=\frac{1}{n}\sum Q_i$$

Example
Trades = 50 SOL → 20 SOL average.

Interpretation
Larger trades → institutional or whale activity.
Smaller trades → retail dominance.

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Total Realized and Unrealized Gains

Definition
Sum of all realized profits/losses (closed positions) and unrealized ones (open holdings).
Reflects aggregate profitability across all holders.

$$\text{Total}=\sum (P_{exit}-P_{entry})Q_{realized}+\sum (P_{current}-P_{entry})Q_{unrealized}$$

Example
Realized = $5,000, Unrealized = $1,000 → Total = $6,000.

Interpretation
Positive = overall profitable network.
Negative = aggregate losses; possible selling pressure if prices continue dropping.

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Slippage for $100/$1k / $10k Trades

Definition
The difference between expected and executed price for trades of different sizes, expressed as a percentage.
Measures liquidity efficiency.

$$\text{Slippage}(\%)=\frac{P_{executed}-P_{expected}}{P_{expected}}\times 100$$

Example
Expected = $150, Executed = $152 for a $10k order → 1.33%.

Interpretation
Low slippage = deep liquidity and stable markets.
High slippage = thin order books or volatile environment.