| Chart | What it shows | Why it matters to a DeFi user |
|---|---|---|
| Current Volatility Snapshot | Three gauges for whichever asset you select. The first shows total volatility, meaning how much the price has been moving overall. The other two split that movement into upside volatility, the part that came from rises, and downside volatility, the part that came from falls. Each needle is placed against that asset's own range for the past year. | These gauges tell you whether the asset is moving calmly or wildly compared with its own past year, and whether that movement is coming from rises, which make holders money, or from falls, which cost them. The comparison against the asset's own history matters because a reading that is normal for one coin can be extreme for another. |
| Volatility Over Time | A line chart tracking three measures through time: the asset's total volatility, the part coming from rises, and the part coming from falls. | The direction of these lines usually tells you more than their level, because a market that is becoming more volatile behaves very differently from one that is calming down, even when both pass through the same reading. Following the trend also shows whether a burst of movement was a one-off event or the start of a rougher stretch. |
| Volatility Spread Over Time | A single line measuring rise-driven volatility minus fall-driven volatility, day by day. The line sits above zero when rallies are the bigger moves and below zero when falls are. | This line works as an early warning, because it often changes sign before the price trend itself turns. When a market gradually stops rallying hard and its falls become the bigger moves, that shift appears here while the price chart still looks steady. |
| Rolling Return Over Time | A line chart of the asset's gain or loss over a rolling window of the past 1, 7 or 30 days, drawn through time. | This chart answers whether the movement actually paid, because two assets can swing equally hard over the same stretch while one ends higher and the other ends lower. Volatility on its own cannot tell those two apart. |
| Chart | What it shows | Why it matters to a DeFi user |
|---|---|---|
| Forecast Volatility | Three gauges showing how much movement the models expect from the asset over the next day, the next 7 days and the next 30 days, each placed against that asset's own past year. | Almost every other number on a market page describes what has already happened, which makes this the one to check when deciding what to do next: it estimates whether the coming days are likely to be calm or rough before they arrive. |
| Forecast Volatility Term Structure | A curve of expected volatility at every horizon from tomorrow out to 90 days. Fainter lines behind it show the same curve as it looked 7, 30 and 90 days ago. | The shape of the curve says when the movement is expected, because a curve that rises with the horizon means the models see bigger moves further out, while one that falls means the current stress is expected to fade. The older curves behind it add context by showing whether those expectations have been building or easing over recent weeks. |
| Volatility Regime Heatmap | A colour-coded grid with one row per asset. Six cells per row show past and expected volatility at three time horizons, each cell coloured from Very Low to Very High by how unusual that reading is for that asset against its own past year. | Because every asset appears in one grid, this is the quickest way to tell whether a move belongs to one coin or to the whole market. A column of hot cells means the movement is market-wide, while a single hot row means something is happening to that coin alone, and those two situations usually deserve different reactions. |
| Chart | What it shows | Why it matters to a DeFi user |
|---|---|---|
| Sector Performance & Risk | A table grouping the 100 largest cryptocurrencies into nine sectors by what they actually do, from blockchain infrastructure and DeFI through to gaming and meme coins, with each sector's return, volatility and risk-adjusted score in the columns. | Crypto stopped moving as a single block years ago, so a rally or a selloff usually lives in one corner of the market rather than everywhere at once. This table shows which corner that is, and it also reveals whether your own holdings are spread across different sectors or concentrated in a single one. |
| Historical Sector Performance (six charts) | Six line charts, each drawing one line per sector through time. One chart each for: returns, total volatility, upside volatility (from rises), downside volatility (from falls), and two risk-adjusted scores (the Sharpe and Sortino ratios). | Because sectors take turns leading the market, these lines are where rotation first becomes visible: when money moves from one theme to another, the crossover shows up here before it is obvious in the headlines. |
| Chart | What it shows | Why it matters to a DeFi user |
|---|---|---|
| Aggregated Group Performance & Risk | A table splitting the 100 largest cryptocurrencies into five groups of twenty by market value, from the biggest coins down to the smallest, with each group's return, volatility and risk-adjusted score. | This table answers whether the extra risk of holding smaller coins is currently being rewarded. When the biggest coins are earning more with less movement, moving down the size curve adds risk without adding return, whereas a stretch in which the smallest coins out-earn the majors is one of the earlier signs that a speculative phase is building. |
| Historical Size Cohort Performance (six charts) | Six line charts, each drawing one line per size group through time. One chart each for: returns, total volatility, upside volatility (from rises), downside volatility (from falls), and two risk-adjusted scores (the Sharpe and Sortino ratios). | The smallest group deserves particular attention, because it tends to move first and hardest in both directions. When its lines start running ahead of the rest, a boom or a bust is often further along than the large coins alone would suggest. |
| Chart | What it shows | Why it matters to a DeFi user |
|---|---|---|
| VaR Analysis: Daily Returns Histogram | A bar chart stacking up the asset's daily moves from the recent past. Most bars cluster around small moves, with a thin tail of extreme days, and a marked line shows the loss a single day is not expected to exceed at your chosen confidence level. | The marked line gives you a working boundary between an ordinary bad day and a genuinely exceptional one. When a day lands beyond it, the more likely explanation is that market conditions have changed, rather than that you were simply unlucky. |
| VaR Analysis: Returns Density vs. Normal Distribution | Two curves laid over each other: the smooth bell curve a calm, orderly market would produce, and the shape this asset's returns actually trace. | Wherever the real curve sits fatter at the edges than the bell curve, big days happen more often than a calm model would predict, and that gap is why crypto keeps surprising people who expect it to behave like a stock index. |
| Distribution Shape | Two numbers summarising the pattern of an asset's returns: skewness, which says whether the big surprises tend to be rises or falls, and kurtosis, which says how much of the risk is packed into a few rare days. | A negative skewness tells you that when this asset produces a big surprise, the surprise is usually a fall, while a high kurtosis tells you that its risk arrives in bursts, with long calm stretches punctuated by enormous days. Together they explain why a quiet month is weak evidence that an asset is safe. |
| Historical Drawdowns | A chart tracking how far the asset sat below its own highest price so far, continuously through history. The line touches zero at every new peak and dips with every decline until the old peak is regained. | This chart is worth studying before buying, because it shows what holding the asset through its worst stretches would actually have felt like, including how deep the falls went and how long the wait was before the price returned to its old peak. |
| Top 20 Drawdown Events by Magnitude | A table of the asset's twenty deepest falls on record: how far the price fell from peak to bottom, how long the fall lasted, and how long it took to climb back to the old peak. | The duration columns matter as much as the depth, because a 40% fall that recovers within a month is a very different experience from one that drags on for a year. Long recoveries are usually what make holders give up, so the length of past falls is a fair test of whether you could hold through the next one. |
| 30-Day Rolling Ulcer Index | A line tracking a single score that combines how deep recent declines have been with how long they have lasted, over a rolling 30-day window. Deeper falls count extra, and the score stays near zero while the price sits at or near its highs. | Ordinary volatility counts rises and falls alike, whereas this score counts only the part that hurts to hold, which is time spent below a previous high. When it climbs while volatility stays flat, the market is becoming more painful to hold even though it does not look any wilder. |
| Frequency of Extreme Moves (Last 30 Days) | A count of how many of the last 30 days moved more than 5%, how many moved more than 10%, and how many moved more than 2.5 times that asset's own usual daily swing. | This turns tail risk into something you can simply count. A month without a single extreme day suggests the market has been unusually compressed, while a cluster of extreme days tells you conditions have already shifted and the recent past is a poor guide to the days ahead. |
Every measure the charts rely on, defined once: what it measures, how it is worked out, and why it matters.
| Term | What it measures | How it is worked out | Why a Cointelegraph reader should care |
|---|---|---|---|
| Log return | Log returns measure the percentage change in price on a logarithmic scale rather than a standard percentage scale. Unlike simple returns, log returns are additive over time, making them ideal for statistical analysis or volatility estimation. | return = ln( price now ÷ price before ) log returns can be summed across hours, days and weeks | Anyone who has watched a position fall by half knows it takes a double to get back to even, and plain percentages quietly hide that asymmetry. If you are comparing a figure here against one from another site, it is worth checking which convention that site uses, because the two drift furthest apart in exactly the volatile stretches you most want to measure. |
| Realized volatility | This is how much an asset has actually been moving. The dashboards measure it as the standard deviation of hourly returns over the window you choose, then scale the result to a yearly rate. | volatility = typical size of an hourly move × √8760 8760 hours in a year; the window is the past 1, 7 or 30 days | If one asset is running at twice the volatility of another, the same money in each swings twice as hard. Watching the number over several weeks also separates a market that is genuinely settling down from one that is only pausing. |
| Annualization | Every volatility figure is quoted as a yearly rate so that different assets and different windows can be compared on a single scale. Because crypto trades continuously, the conversion uses all 365 days rather than the 252 trading days that equity markets assume. | yearly volatility = k-day volatility × √( 365 ÷ k ) typical day ≈ yearly figure ÷ 19 · typical week ≈ yearly figure ÷ 7 | A reading of 30% does not mean the asset will move 30% this week. The conversion back down is easy: divide by about 19 for a typical day and by 7 for a typical week. That turns an abstract yearly figure into a daily range you can actually picture. |
| Upside volatility | Upside volatility measures how much of an asset's movement has come from rising prices: the standard deviation of hourly returns counting only the hours in which the price rose, scaled to a yearly rate. | upside volatility = the realized-volatility calculation, keeping only the up-hours | Volatility tends to get treated as a synonym for danger, when half of it is the movement that makes holders money. An asset whose swings are mostly upward is being called risky for what is really a strong rally. The more useful signal is which half is growing: when upside is climbing while downside stays flat, the market is becoming more rewarding rather than more dangerous. |
| Downside volatility | Downside volatility measures how much of an asset's movement has come from falling prices: the standard deviation of hourly returns counting only the hours in which the price fell, scaled to a yearly rate. Upside and downside together account for an asset's total volatility exactly. | downside volatility = the realized-volatility calculation, keeping only the down-hours the halves reunite: total² = upside² + downside² | This is the half that loses holders money. When downside runs above upside, the falls are sharper than the rallies even if the headline volatility number has not moved—the market is getting more dangerous without looking more volatile. |
| Volatility spread | The volatility spread is upside volatility minus downside volatility. A positive number says the rallies have been the sharper moves, while a negative one says the falls have been doing more of the work. | spread = upside volatility − downside volatility | It compresses a great deal into a single figure: whether the market is currently paying holders or punishing them. What matters most is the moment the sign changes, because the spread tends to turn before the price trend does. A market that quietly stops rallying hard and starts falling hard is changing character while the chart still looks flat. |
| Quintile regime bands | Rather than comparing assets against one another, every reading is ranked against that same asset's own trailing year and sorted into one of five bands, running from Very Low to Very High. | band = where today sits in that asset's own past year, split into fifths bottom fifth = Very Low · top fifth = Very High | This is what lets you judge a number without already knowing the asset well. A reading of 90% volatility might be an ordinary week for a meme coin while 40% is an emergency for BTC, and the bands make that translation for you. It is the only practical way to scan a watchlist of assets that otherwise have nothing in common. |
| Measurement period | Every figure is calculated over a rolling window of either 1 day, 7 days or 30 days of hourly data. | window = the last 1, 7 or 30 days of hourly data | The window is part of the number, so it pays to match it to how long you actually hold. Someone holding for a week should be reading the 7-day figures, and someone trading intraday the 1-day. Setting a 1-day reading for one asset against a 30-day reading for another is the easiest way to talk yourself into a conclusion the data does not support. |
| Term | What it measures | How it is worked out | Why a Cointelegraph reader should care |
|---|---|---|---|
| Current Volatility Snapshot | This shows the live total, upside and downside volatility for whichever asset you have selected, with each figure placed against that asset's own range over the past year. | today's total, upside and downside volatility, each vs its one-year bands | Before reacting to a move, it is worth knowing whether the move is actually unusual. The bands run that comparison for you, so a day that sounds dramatic can be checked against the asset's own normal in a couple of seconds. |
| Volatility Over Time | Total, upside and downside volatility are plotted as lines through time, which makes the shifts between calm and turbulent stretches visible. | total, upside and downside volatility, drawn through time | A number on its own cannot tell you which way conditions are heading. The same reading means a market settling down if it follows a turbulent month, and one waking up if it follows a quiet one. It is the direction of travel, rather than the level, that tells you whether to give a position more room or less. |
| Rolling Return | This tracks the cumulative gain or loss over the preceding window, plotted through time. | return = price now ÷ price k days ago − 1 | Read alongside volatility, it answers whether the turbulence was worth enduring. Two assets can have moved with identical violence and delivered opposite results, and this is the line that tells them apart. |
| Term | What it measures | How it is worked out | Why a Cointelegraph reader should care |
|---|---|---|---|
| Forecast volatility | This is the volatility the model expects over the next 1, 7 or 30 days. It is produced fresh every hour for each covered asset and quoted annualized, exactly like the realized figures sitting beside it. | expected volatility over the next 1, 7 or 30 days | Everything else on these dashboards describes weeks already gone. The comparison that matters is against the realized figure next to it: when the forecast sits higher, the models expect a livelier week than the one just finished—a reason to prepare before the move, not after it. |
| GARCH model | The forecast comes from a GARCH model, a design that has been standard on institutional desks since the 1990s. It captures two things markets reliably do: turbulence arrives in clusters, and a fall raises expected volatility more than a rally of the same size. | next volatility = a base level + today's move (falls count extra) + a memory of recent volatility | This explains behaviour that would otherwise look like the model overreacting, when the forecast jumps the morning after a selloff and then drifts back down through a quiet fortnight. Because falls count for more, a forecast climbing while the price sits still usually means recent downside is being carried forward. |
| Term structure | The forecast is calculated at every horizon from 1 to 90 days and drawn as a curve, with the same curve from 7, 30 and 90 days ago laid behind it. | one forecast per horizon, 1 to 90 days out, joined into a curve | The shape reads as a timeline. A curve sloping upward says the market is quiet now but expects more movement later, while one sloping downward says the current stress is expected to pass. Comparing today's curve against last month's shows whether expectations are building or draining, which is usually what decides between hedging now and waiting. |
| Forecast dial | The gauge places each forecast within a coloured risk band drawn from three years of history, which is a longer memory than the single year the heatmap works from. | band = where the forecast sits within three years of that asset's history | The two views can disagree, and the disagreement is informative rather than a fault. A reading that looks extreme against the past year but ordinary against three years describes a very different situation from one that looks extreme against both. |
| Volatility Regime Heatmap | The heatmap shows realized and forecast volatility for every covered asset at 1, 7 and 30 days, with each of the six cells per asset coloured by its band. | each cell = the band for one asset at one horizon, realized or forecast | Reading down a column is the quickest way to tell whether a move belongs to one asset or to the whole market. The pattern worth watching for is a board where the realized side is calm and the forecast side is hot, which is the signature of a compressed market in which ranges are more likely to break than to hold. |
| Term | What it measures | How it is worked out | Why a Cointelegraph reader should care |
|---|---|---|---|
| Sector classification | The top 100 assets are grouped into nine sectors according to what they actually do, running from Blockchain Infrastructure and DeFI through to Gaming and Meme. | top 100 assets → 9 sectors | Crypto stopped trading as a single block years ago. Setting an asset against its own sector is what separates a move that belongs to that asset from a rotation the whole sector is taking part in, and it often reveals that a view someone holds about crypto is really a view about one corner of it. |
| Market-cap weighting | Each sector and size figure is a weighted average of its members, with larger assets counting proportionally more. | group figure = average of members, weighted by market cap | It is worth knowing what a group number actually represents. In a sector dominated by one or two large assets, the figure is largely describing those assets, so a sector reading can be a single coin wearing a disguise. A glance at the members first tells you whether to treat it as a broad signal. |
| Sharpe Ratio | Sharpe divides the return earned above the risk-free rate by the total volatility taken to earn it. It has been the standard risk-adjusted measure since the 1960s. | Sharpe = extra return earned ÷ total volatility taken | It puts a violent asset and a placid one on the same footing. As a rough guide, a reading near zero means the risk went unrewarded, above 1 is respectable and above 2 is strong. Two assets that returned the same amount are not the same trade if one of them managed it at half the Sharpe. |
| Sortino Ratio | Sortino divides the return earned above the risk-free rate by downside volatility only, so an asset is not penalised for moving sharply upward. | Sortino = extra return earned ÷ downside volatility only | In a market where the best days are also the wildest, Sharpe quietly punishes assets for rallying. Reading the two together is what makes them useful: when Sortino sits well above Sharpe, the volatility that made an asset look dangerous was mostly gains, and its headline risk figure overstated the real threat. |
| Term | What it measures | How it is worked out | Why a Cointelegraph reader should care |
|---|---|---|---|
| Size cohorts | The top 100 are divided by market-cap rank into five groups of twenty, running from the majors down to the tail. | five groups of twenty, by market-cap rank | This settles the recurring question of whether moving further down the size curve is being rewarded. When the majors deliver more return with less volatility, the extra risk in small caps is not being paid for and there is little reason to reach for it. When the tail begins to out-earn the majors on a risk-adjusted basis, that is one of the earlier signs of a speculative phase. |
| Term | What it measures | How it is worked out | Why a Cointelegraph reader should care |
|---|---|---|---|
| Value at Risk (VaR) | This is the loss a single day is not expected to exceed, measured over a 24-hour horizon at whichever confidence level you have selected. | VaR = the loss level a day is not expected to break through the confidence setting decides how far out the line sits | It draws the line between an ordinary bad day and a genuinely exceptional one. A day that breaks through the line is worth treating as a signal about conditions rather than as simple bad luck. |
| Filtered Historical Simulation | The estimate comes from Filtered Historical Simulation. Past returns are first stripped of their own volatility to leave the raw surprises behind, and those surprises are then re-sized to the volatility expected tomorrow. | take history's surprises → re-size them to tomorrow's conditions → read off the worst days | This is why the figure moves with conditions when simpler versions do not. A plain historical estimate treats a crash from three years ago as just as relevant as a quiet Sunday, whereas this one re-dresses every past shock in tomorrow's expected weather. The result tightens in calm markets and widens in stressed ones, which is what you want it to do. |
| Confidence level | This is how rare the day is that the VaR figure describes. The same asset can be quoted at 95%, 99% or 99.5%, and each setting reaches further into the tail. | 95% = 1 day in 20 · 99% = 1 in 100 · 99.5% = 1 in 200 | A 99.5% figure is not a gloomier forecast than a 95% one; it is simply a rarer event. The two are not comparable, so check that any figures you set side by side are quoted at the same level. |
| Expected Shortfall | Expected Shortfall averages the losses on the days that break through the VaR threshold, answering the question VaR deliberately leaves open. | Expected Shortfall = the average of the days beyond VaR | VaR tells you where the tail begins and this tells you how far into it you typically go, which makes it the better number to plan around. Working from VaR alone means planning for the mildest of the bad days. |
| Expected Tail Median | Expected Tail Median is the middle value of the losses on the days that break through the VaR threshold, rather than their average. | Expected Tail Median = the middle of the days beyond VaR | Reading it next to Expected Shortfall shows you the shape of the tail. When the average is much worse than the middle, a handful of extreme days are doing most of the damage and the risk is lumpier than the average alone suggests, which is an argument for holding a smaller position than that average would imply. |
| Skewness | This shows which direction an asset's biggest surprises tend to come from. A reading of 0 means large gains and large losses are equally likely, while a negative reading means the big moves are more often falls. | skewness = the lean of the returns 0 = evenly balanced; negative = big losses more common than equally big gains | It tells you which direction to expect the shocks from. In a negatively skewed market the big moves are falls, so sell orders tend to execute at worse prices than expected during declines. |
| Kurtosis | This is how much of an asset's total risk is packed into a handful of rare days. A calm, bell-curve market scores 3, and anything higher means the extremes carry more of the weight. | kurtosis = the weight of the tails a calm, bell-curve market scores 3; crypto usually scores far higher. Some sites quote “excess kurtosis”, which is this figure minus 3, so their calm benchmark is 0 | Crypto sits well above that benchmark, which is a formal way of saying it spends long stretches quiet and then moves enormously. A calm month is therefore weak evidence that an asset is safe: judge it by its rare days, not its typical ones. |
| Drawdown | A drawdown is how far an asset currently sits below its own previous peak, tracked continuously on hourly prices. | drawdown = price now ÷ highest price so far − 1 | Volatility is the statistician's measure of risk, while drawdown is the one holders actually live through. It is what decides whether somebody sits tight or gives up, and looking at an asset's drawdown history before buying is the most direct test of whether you are taking on more than you could sit through. |
| Maximum Drawdown | The maximum drawdown is the deepest peak-to-trough fall over the period being examined. | max drawdown = the deepest such fall in the period | The question to ask yourself is whether you could have held through it. The consequence for a leveraged position follows immediately: at two times leverage, a 50% drawdown is not a painful stretch but a total loss. |
| Duration and recovery | For each decline, the dashboard records how long the fall lasted and how long the price then took to climb back to its old peak. | days from peak to bottom · days from bottom back to the old peak | Depth is only half of what breaks a holder. The same fall is a very different experience depending on whether it recovered in a fortnight or ground on for a year, and it is the long ones that shake people out at the bottom. This is the number that tests whether your intended holding period is realistic. |
| Ulcer Index | The Ulcer Index scores the depth and persistence of declines over a rolling 30-day window, counting deeper falls more heavily. | Ulcer = average depth of the last 30 days' drawdowns, with big falls counted extra | Unlike volatility it ignores the upside entirely and penalises time spent below the previous high. Two assets with identical volatility can look completely different here, one dipping and recovering while the other sinks and stays down. When the Ulcer reading rises while volatility holds steady, the market is becoming more painful to hold without looking any more volatile. |
| Frequency of extreme moves | This counts how often, over the last 30 days, the 24-hour move exceeded 5%, 10%, or 2.5 times that asset's own usual swing. The final threshold adapts to each asset. | the share of the last 30 days' moves beyond ±5%, ±10%, or 2.5× the asset's usual swing | It turns the tail statistics into something you can simply count. A month without a single extreme day points to a compressed market, while a cluster of them marks one in an altogether different state. Because the last threshold scales to the asset, it flags moves that are genuinely unusual for that coin instead of penalising the ones that are always volatile. |