Asset research, answered.

No. Concentrating an entire portfolio in any single asset class, particularly one as volatile as crypto, exposes you to the full drawdown of that asset class with no diversification. Crypto has historically experienced drawdowns of 75 percent or more from cycle peaks. A 100 percent crypto portfolio is single-asset-class speculation, not investing. A diversified portfolio with a meaningful but bounded crypto allocation is the responsible approach.

Holding multiple crypto assets through a Crypto Index reduces concentration in any single asset. A 3 percent allocation to Bitcoin is a concentrated bet on one asset within the category; the same 3 percent in a diversified Crypto Index is a basket exposure to the category. The basket approach hedges against the risk that the next cycle's strongest asset differs from the previous cycle's strongest. The single-asset approach concentrates exposure if you have specific conviction in one asset.

Ethereum's INR-denominated rolling returns have been strongly positive across most long-window holding periods, comparable to Bitcoin in direction but more volatile in both directions. Ethereum's cycle peaks and troughs have been deeper than Bitcoin's in percentage terms over the analysed window. The two assets are highly correlated (0.7 to 0.9 across rolling windows) but show meaningful divergence periods. An equally weighted basket has historically produced slightly lower volatility than either standalone position.

The Merge (September 2022) transitioned Ethereum from Proof of Work to Proof of Stake. Energy consumption fell by approximately 99.95 percent. Issuance to validators replaced issuance to miners, at a structurally lower rate. Combined with the EIP-1559 fee burn introduced in August 2021, the post-Merge supply trajectory has been close to flat or modestly deflationary in high-activity periods, depending on network usage. The change reduced Ethereum's inflation rate significantly and tied the supply mechanics to actual network activity.

Ethereum provides exposure that is correlated but not identical to Bitcoin. The two assets together capture more of the crypto asset class's drivers than either standalone position. As a portfolio diversifier within an overall portfolio, Ethereum sits inside the broader crypto allocation, sized according to the investor's overall crypto allocation target. Most institutional frameworks place crypto at 1 to 5 percent of total portfolio, with the split between Bitcoin and Ethereum (or a broader basket) being a secondary decision within that.

Documented institutional backing is the verifiable presence of an asset across five evidence categories: regulated investment products (ETFs and similar), corporate treasury allocations disclosed in audited financial statements, payment and settlement integrations with regulated processors, traditional financial firm research coverage, and regulated platform listings. The phrase only carries weight when it is backed by evidence from at least several of these categories. A claim of institutional backing that is not backed by category-level evidence is a marketing claim, not a research signal.

The answer depends on the role the asset is filling in your portfolio. For a diversifying satellite where the value comes from non-correlation with the rest of the portfolio, Bitcoin is typically the cleaner fit because its documented non-correlation pattern is more developed. For a long-horizon emerging-asset exposure, both assets typically have complementary roles, and a split that reflects your specific conviction balance is structurally sound. For a return-enhancement satellite, a 60/40 or 70/30 split (Bitcoin-heavier) is a common default. The four-characteristic framework converts the choice into a portfolio-fit decision rather than a universal ranking.

Four key differences. Value-accrual mechanism: Bitcoin's capped supply drives scarcity-based value accrual; Ethereum's EIP-1559 burn plus network-activity-driven mechanics drive a different model. Volatility profile: Ethereum is typically more volatile than Bitcoin across regimes. Correlation behaviour: high BTC-ETH correlation in normal periods, with divergence during specific regimes (DeFi activity for Ethereum, store-of-value moments for Bitcoin). Adoption trajectory: Bitcoin has the deeper institutional-treasury footprint; Ethereum has the broader operational use-case footprint.

Yes, and for investors with substantive crypto allocation, holding both is typically the structural answer. The two assets have complementary roles in the crypto allocation, and holding both produces within-crypto diversification through the divergent-regime episodes. Many investors hold both at a split that reflects their balance of conviction between the store-of-value thesis (Bitcoin) and the application-layer thesis (Ethereum). Structured baskets like QSI Core hold both alongside non-crypto diversifying allocations.

Ethereum is typically more volatile than Bitcoin across regimes, with annualized volatility historically in the 70 to 90 percent range versus Bitcoin's 50 to 70 percent range. The differential is most pronounced in strong bull periods and during regulatory or macro stress, where Ethereum often moves more sharply in both directions. The volatility differential matters for portfolio sizing: a portfolio with a meaningful crypto allocation needs to consider how the position-level volatility translates into portfolio-level swings.

No. The either/or framing is a common misframe. For most investors with substantive crypto allocation, holding both is structurally sound because the two assets have complementary roles. The choice is between the proportional split, not between holding one and excluding the other. An investor with very limited allocation (1 percent or less) may find single-asset simplicity preferable, in which case Bitcoin is typically the default choice for the cleaner adoption signal and the somewhat lower volatility. Above that, the structural answer involves both. **Disclaimer** Crypto investments are subject to market risk and volatility. Past performance is not indicative of future returns. This is not investment advice. Please consult a qualified financial advisor before investing. *Written by [Sneha](/about/team/sneha), Content Strategist, Qatobit Research Team.*

No. Any rupee amount buys a fraction of a unit at any price. A two thousand rupee investment costs the same two thousand rupees whether the coin is priced at five paise or five lakh rupees. Price per unit decides only how many units that investment converts into.

Circulating supply is the count of coins actually available to buy or sell right now, excluding units still locked in a team allocation, reserved, or scheduled for future release. This is the number that feeds the market cap calculation.

Market capitalization is price multiplied by circulating supply. A coin priced at five rupees with two hundred crore units in circulation carries a market cap of one thousand crore rupees. A coin priced at five hundred rupees with two crore units in circulation carries the exact same figure.

A very large circulating supply divides the same total value into a very large number of units. That pushes the price per unit down without changing the market cap. The unit count is a design choice made at issuance.

Float is the share of circulating supply not parked long term in a small number of wallets, the part realistically available to trade on a given day. A coin can carry a large circulating supply and market cap while its float stays thin, because wallet concentration does not lower the circulating count.

Circulating supply is the number of coins currently in public hands and available to trade. Max supply is a hard cap coded into the protocol itself, such as Bitcoin's 21 million limit, that circulating supply can near but never exceed. The gap between the two signals how much dilution still lies ahead. Not every token has a max supply at all. Ethereum has no hard cap coded into its protocol. XRP holds a large portion of its total supply in escrow instead, under contractual release terms rather than a coded limit. That makes its effective max supply a matter of contract, rather than code. A token with circulating supply far below its max supply still has a large amount of future issuance scheduled to enter the market. That supply arrives through mining rewards, staking rewards, or a scheduled token release. This future supply can weigh on price over time even if demand stays constant, because more coins entering circulation dilutes each existing holder's share. A token trading at a price that values its circulating supply at 5 lakh rupees of market cap can look very different. Its max supply changes that picture. Say only 20 percent of the max supply is currently circulating. The fully diluted value, the price times the max supply, comes to 25 lakh rupees, five times the current market cap figure. A coin with no max supply at all, like Ethereum, cannot be evaluated on a dilution basis the same way. There is no ceiling to compare circulating supply against. In that case, the annual issuance rate itself becomes the relevant number for judging future dilution, rather than a fixed cap.

Market cap is a stock measure, a snapshot of price times circulating supply at one single point in time. Trading volume is a flow measure instead, the total value traded over a period, usually the last 24 hours. Comparing the two flags liquidity problems that neither number reveals on its own. When 24 hour volume runs far above a token's market cap, especially for a small token, it often flags wash trading. That is where the same capital cycles rapidly between a small number of wallets, to inflate apparent activity. Genuine trading interest rarely produces a volume-to-market-cap ratio that high, for more than a brief spike. The opposite pattern, a high market cap paired with thin daily volume, signals low liquidity instead. A trader trying to buy or sell a meaningful position in that token risks larger slippage. There simply are not enough active buyers and sellers on the order book, at any given moment, to absorb a large order cleanly. A token with a 5 lakh rupees market cap that shows 4 lakh rupees of 24 hour volume is likely seeing unusually intense trading. That is large, relative to its size, and worth investigating before treating the volume as evidence of real demand. A token with the same 5 lakh rupees market cap, showing only 5,000 rupees of daily volume, signals something else. Nobody is actively trading it, however large it looks on paper. Both numbers can be manipulated or misread in isolation. Neither should be treated as a complete picture of a token's health, without checking the other alongside it. A sudden volume spike with no corresponding market cap change is also worth checking against news or an exchange listing. That check comes before assuming it reflects organic demand. Qatobit's QSI Crypto Indices are built from established assets like Bitcoin, Ethereum and Solana rather than thinly traded tokens. This sidesteps the wash-trading and slippage concerns that come up most often on small tokens carrying an unusual volume-to-market-cap ratio.

Tokenomics is the economic design of a project's own native token. It covers the supply schedule, team and public allocation, emissions rate, and utility within the protocol. Tokenization is a different process, representing an external asset, like real estate or a bond, as a token on a blockchain. A project can have both. Tokenomics answers questions about a project's own coin, such as how many exist, who holds them, and how new units get created. It also covers what the token is actually used for within its own ecosystem. Get the tokenomics wrong, with too much supply concentrated in early holders or an emissions schedule that floods the market. Even a strong project can see its token price struggle in that case, regardless of adoption. Tokenization instead takes something that already exists outside crypto, a building, a government bond, a share of company equity. It issues a token that represents fractional ownership, or a claim on that underlying asset. The token's value tracks the real-world asset behind it, rather than being driven by a separate token supply and demand dynamic of its own. A project's native token might be worth 5 lakh rupees in total market value at launch. All of that is governed by its own tokenomics, such as vesting schedules, staking rewards, and a fixed emissions curve. That same project could separately launch a tokenized government bond product worth 5 lakh rupees in underlying assets. There, the token's value simply tracks the bond's own price and coupon, rather than any emissions schedule. The two get confused because both use the word token. A native token's price depends on its own supply and demand within its protocol. A tokenized asset's price depends instead on whatever it represents outside the blockchain entirely. Evaluating one on the other's terms, like judging a tokenized bond on emissions schedule, is a category error. Qatobit's Real World Assets product is a tokenization offering rather than a tokenomics one. It represents fractional ownership of assets like tokenized gold, real estate bonds and government-backed instruments. Each one tracks the value of what it actually represents, rather than running on a native emissions schedule.

M2 measures a country's money supply: physical cash plus bank deposits and easily accessible savings, tracked by that country's central bank. Crypto market cap instead measures a token's price times its circulating supply. That is a valuation figure, distinct from a measure of money actually transacted in an economy. M2 is measured in a specific national currency. It represents money that is actively used to buy goods, pay debts, and settle transactions across an entire economy. It grows or shrinks based on lending activity, central bank policy, and how much cash the banking system creates through deposits and loans. Crypto market cap does not represent money in circulation in that same sense. It is simply the last traded price multiplied by however many coins exist. That figure can rise sharply on light trading volume, without any comparable amount of new capital actually entering the asset. A market cap figure can move far faster than any real economy's money supply ever could. An investor holding 5 lakh rupees of a token can watch its price swing sharply within a single day, on relatively thin trading volume. Some Bitcoin valuation models compare its total market cap against a small fraction of global M2, the world's money supply. They argue Bitcoin could capture some share of that pool as a store of value. These models use M2 only as a ceiling, to size Bitcoin's potential market cap against. That is a different exercise from claiming the two measure the same thing today. Treating market cap as if it were real, spendable money overstates how much capital actually sits behind a token. Market cap prices every existing coin at the last trade. Selling even a fraction of that supply at once would move the price well below that headline number. M2, in contrast, reflects money that genuinely exists in accounts, and can be spent without moving its own price.

A price aggregator pulls and averages quotes from many exchanges to display a single reference price, but it never executes a trade itself. An exchange matches actual buy and sell orders, and either custodies or settles the trade. The aggregator's price can lag or diverge from what any exchange would actually fill at. Sites like CoinMarketCap and CoinGecko are aggregators. They collect price data from dozens of exchanges, and weight it by volume or another methodology. The result is a blended figure that nobody can actually transact at directly. They are data infrastructure, built for reference and research, rather than for placing an order. An exchange is trading infrastructure instead. It holds an actual order book, and matches a specific buyer to a specific seller in real time. It then either takes custody of the resulting assets, or settles the trade to each party's account. The price shown on an exchange is the price a real order actually filled at. It comes directly from that exchange's own book, rather than an average pulled from elsewhere. A trader checking an aggregator sees Bitcoin quoted at one figure. Placing an order worth 5 lakh rupees on a specific exchange can fill at a slightly different price. That exchange's own order book determines the actual fill, rather than the aggregator's blended figure. The gap is usually small for a liquid asset like Bitcoin, but can widen considerably for a thinner token. An aggregator's price can also lag during a fast move. It takes a moment to pull and average fresh quotes from every exchange it tracks. A live exchange order book, by contrast, updates instantly with every new trade. Relying on an aggregator's price to time an actual order can mean acting on a number that is already a few seconds stale. Qatobit's Quick Buy/Sell functions as an exchange in this sense rather than a price tracker. It executes a real purchase or sale against deep liquidity behind every pair. The actual price and fee show before the investor confirms, rather than only displaying a blended reference figure.

AI agent tokenomics ties a token's value to real usage. The token pays for compute credits, model inference, or micropayments between autonomous agents settling transactions on-chain. The design test is whether that usage generates enough fee demand to offset the tokens the network keeps issuing. Most tokens carry governance rights, letting holders vote on protocol changes. AI agent tokens add a second job on top of that: covering the cost of running an AI workload. An agent might spend the token to buy compute time, call a paid API, or pay another agent for a completed task. That spending is the demand side of the token's economy. New tokens still enter the market through staking rewards or ecosystem grants, and that supply is the other side of the equation. When agent usage grows faster than new issuance, demand can outpace supply. When it does not, price tends to drift down even if the technology works. Say an investor is evaluating an AI-agent token before allocating 25,000 rupees a month toward it. If agents on that network spend fees that remove 2 percent of supply a year, that demand offsets new issuance. If the network instead issues 8 percent more tokens a year in rewards, and agents barely use it, the token simply dilutes. The gap between usage-driven demand and reward-driven supply is the number worth checking first. Most AI-agent tokens are new, so usage numbers are thin and easy to overstate. A project can report agent transactions that are really internal test traffic, not paying users. Checking on-chain data for actual fee volume is the more reliable check, rather than trusting a project's own dashboard.

Bitcoin's protocol code hard-caps total issuance at 21 million coins, a limit written into the software from the 2009 launch. The cap is enforced by the halving, an event roughly every four years that cuts the new-coin reward miners earn in half. The final fractions of a coin are expected around the year 2140. A halving is a scheduled rule change coded into Bitcoin from the start. Every 210,000 blocks, roughly four years at current speeds, the reward paid to miners for confirming a block is cut in half. The reward started at 50 bitcoin per block in 2009 and has halved several times since, most recently in 2024. Each halving slows the rate at which new coins enter circulation. By around 2140, the block reward shrinks to fractions of a satoshi, the smallest unit Bitcoin divides into. New issuance effectively stops at that point, and miners are expected to earn from transaction fees instead. The 21 million figure is a hard limit in the code, not a target or an estimate. No vote or upgrade has ever changed it. An investor holding 5 lakh rupees in Bitcoin today owns a claim on a fixed, shrinking pipeline of new supply. Roughly 94 percent of the 21 million cap has already been mined. That 5 lakh rupees is not competing against a large wave of coins still to be issued. Compare that to a token with no cap or a slow initial mine. The 21 million figure counts whole bitcoin. Each coin splits into 100 million satoshis, so scarcity at the coin level does not automatically mean scarcity of usable units. A lost private key removes coins from practical circulation forever, and estimates put millions of bitcoin permanently inaccessible this way. The effective supply available to buy or sell is smaller than 21 million. Qatobit's QSI Core, QSI Growth and QSI VRION indexes all hold Bitcoin as part of a basket. Each pairs it with assets like Ethereum, Gold or Solana, depending on the index. An investor gets exposure to Bitcoin's fixed supply within a diversified position rather than a single coin holding.

Bitcoin's market cap is price multiplied by circulating supply, capped at 21 million coins. Gold's total value comes from multiplying its estimated 215,000 tonnes of above-ground stock by spot price. Analysts use the comparison to size how large Bitcoin could grow if it captured a share of gold's store-of-value role. The two supplies behave differently. Bitcoin's supply is fixed by code. It slows further through halvings until it nears 21 million. Gold's supply keeps growing every year as mines extract more, so the above-ground stock estimate shifts over time. Estimates vary by source, which is why gold's total value is never as precise as a market cap read straight off an exchange. The comparison asks a simple question. What happens to Bitcoin's price if its market cap reached a fraction of gold's total value, say five or twenty percent? Divide that target market cap by 21 million coins to get an implied price. This is a sizing exercise, not a forecast, and it depends entirely on the addressable-market assumption chosen. Consider a portfolio holding 5 lakh rupees split across Bitcoin and Gold as separate positions. The investor is not betting one replaces the other. Each is held for what it structurally does inside the basket. Bitcoin is a fixed-supply digital asset, Gold is a separately tracked reserve. Position sizes are set by allocation weights, not by a bet on which one outperforms. The comparison breaks down if gold's above-ground stock estimate is wrong. Most of it sits in jewelry and central bank vaults that are never audited in real time. It also assumes Bitcoin could absorb demand at that scale without changing gold's own price or supply behavior. Treat the resulting number as a rough ceiling for a thesis, not a price target. Qatobit's QSI Core index holds both Bitcoin and Gold as separate components of one basket. Neither stands in for the other. Gold acts as a structural buffer, rebalanced against Bitcoin and the other holdings each month on a published schedule.

A low circulating-to-max-supply ratio means most of a token's eventual supply is still locked or unminted. That locked supply enters the market later, through vesting unlocks or staking emissions. Analysts size this risk with the FDV-to-market-cap ratio, where a wide gap signals heavy future dilution. Circulating supply is what is tradable today. Max supply is the total that will ever exist once every token is minted and every vesting schedule finishes. Say circulating supply is 10 percent of max supply. The other 90 percent is still coming, through a team's vesting cliff, an investor unlock, or ongoing staking rewards paid in new tokens. Each unlock event adds fresh, sellable tokens on a known date. A team member or early investor who received tokens at a low cost often sells into that new liquidity. That can push price down, even if nothing else about the project changed. A high FDV against a small circulating supply means the market is pricing in tokens nobody can sell yet. An investor puts 25,000 rupees into a token with a 10 percent circulating ratio. That means nine times as much supply is still waiting to unlock. Say the unlock schedule releases 5 percent of max supply every month for two years. That 25,000 rupees position then sits against a steady stream of new sellers, long after the investor buys in. A low ratio is not automatically bad. A young network with most tokens locked to fund years of development can outgrow the dilution. That happens if usage and demand grow faster than the unlock schedule. Check the unlock calendar and the size of each cliff before reading the ratio as a warning sign on its own.

Circulating supply is the number of a cryptocurrency's tokens actually in public hands and available to trade right now. It excludes tokens locked in a project's treasury, unvested team allocations, and reserves not yet minted. Market cap is price multiplied by this circulating figure, not by total or max supply. A project typically reports its own circulating supply, pulled from its smart contracts or vesting records. Trackers like CoinMarketCap and CoinGecko then display that self-reported figure, sometimes adjusting it with their own methodology. Because there is no single audited standard, the same coin can show a slightly different circulating supply on two different trackers. This differs from total supply, which includes locked and unvested tokens, and from max supply, the hard ceiling a project can ever mint. A coin can have a huge max supply but a small circulating supply, if most tokens are still vesting. That is why circulating supply, not the bigger numbers, drives the market cap figure investors actually see quoted. An investor compares two coins before putting 25,000 rupees into one. One coin shows a 10 billion token max supply but only 1 billion circulating, a tenth of the total. The other shows 900 million circulating out of a 1 billion max supply, nearly fully released. The market caps might look similar, but the first one carries far more future dilution. Circulating supply figures can lag reality. A project that burns tokens, or re-locks them into a new staking contract, may not update its reported number right away. The figure quoted on a tracker can then be stale by days or weeks. Cross-check a coin's own transparency page against the tracker's number before relying on either one alone. Qatobit's Quick Buy and Sell feature lists supported crypto assets ranked by market capitalization, a figure built directly from each asset's circulating supply. An investor choosing an asset there is looking at a number calculated the same way: price times the tokens actually in public hands.

Yes, a token's circulating supply can fall. Three mechanisms do it. A burn permanently destroys coins. Re-locking pulls tokens back into staking or a new vesting contract. An exchange or protocol freeze removes coins from the tradable float, without destroying them or changing total supply. A burn sends tokens to an address nobody can access, removing them from circulating supply for good. Ethereum burns a portion of every transaction fee automatically. Re-locking works differently. A holder who stakes previously liquid tokens pulls them out of the tradable float without destroying anything. A project that moves treasury tokens into a fresh multi-year vesting contract does the same. Total supply stays unchanged either way. A freeze is a third path. An exchange can pause withdrawals on a token during an investigation, or a smart contract can be paused after an exploit. Both actions take coins out of active circulation without burning or re-locking them. The reported circulating supply number often does not reflect a freeze right away, which is a gap worth checking directly on-chain. An investor holds 25,000 rupees in a token that announces a burn of 2 percent of circulating supply. Every other holder's share, including this investor's, effectively grows relative to the shrinking total. The 25,000 rupees position is unchanged in token count, but its share of the remaining supply is now slightly larger. A burn only helps price if demand for the token stays the same or grows. Burning supply while usage and demand fall faster than the burn rate still leaves a token trending down in price. A shrinking supply number on its own is not a reason to expect the price to rise.

A token's circulating supply grows through three channels. Staking or mining rewards mint brand new coins. Vesting cliffs and linear unlocks release team and investor allocations on a set schedule. Bridge minting creates a wrapped version of a token on a new chain. Staking and mining rewards pay validators or miners in newly minted tokens for securing the network, adding fresh supply with every block or epoch. A vesting cliff releases a large block of tokens at once, on a set date. That is often a team's or an early investor's allocation reaching the end of a lockup. Linear vesting drips tokens out gradually over months instead. Bridge minting is a separate channel. When a token moves to another blockchain through a bridge, the bridge typically locks the original token. It mints a wrapped copy on the destination chain instead. That wrapped copy counts as new circulating supply on the new chain, even though the underlying original token is locked rather than newly created. An investor holds 25,000 rupees in a token facing a 10 million token cliff unlock next month. They are watching how that unlock compares to daily trading volume. Say daily volume runs 2 million tokens and the cliff releases 10 million at once. That 25,000 rupees position then sits in a market that could see five days of ordinary volume added in a single event. Not every unlock hits the market at once. Some teams and funds spread their sales across weeks even after tokens vest. A scheduled unlock date is not always the day supply pressure actually shows up in price. Watch on-chain wallet movement after an unlock, rather than assuming the calendar date and the sell-off are the same day.

A crypto token's market cap is current price multiplied by circulating supply, the tokens actually available to trade. Using total or max supply in that formula instead produces a different number, fully diluted valuation, not market cap. Trackers can show different market caps for the same coin because they use different circulating-supply snapshots. The formula itself is simple. Take the last traded price and multiply it by however many tokens are circulating at that moment. The complexity sits entirely in the second number. Circulating supply changes constantly, as tokens vest, get burned, or get staked, and different trackers update that figure on different schedules. Fully diluted valuation swaps circulating supply for total or max supply in the same formula. It answers a different question: what would the market cap be if every token that will ever exist were already circulating, priced at today's level? A large gap between market cap and FDV signals a large amount of supply still to come. An investor screening a 25,000 rupees position compares two tokens, each priced at 10 rupees. Token A has 100 crore tokens circulating out of a 100 crore max supply, a 1,000 crore rupee market cap that matches its FDV. Token B also shows 100 crore circulating, but out of a 1,000 crore max supply. Its market cap looks the same, 1,000 crore rupees, but its FDV runs to 10,000 crore rupees, ten times the visible number. Two reputable trackers can list different market caps for the same coin on the same day. One counted a recent unlock as circulating; the other had not updated yet. Check a tracker's own methodology page. Compare circulating supply against total and max supply directly, rather than trusting one market cap figure at face value. Qatobit's Quick Buy and Sell ranks supported assets by market cap, using this same price-times-circulating-supply method. QSI GEQ8 weights its equity holdings by a free-float market cap approach, adjusted quarterly. Both use the calculation as a sizing tool, not as a return promise.

No, Solana has no coded maximum supply, unlike Bitcoin's fixed 21 million cap. Its supply grows through inflationary issuance that disinflates every year, stepping down toward a long-term steady annual rate. The design trades a hard scarcity ceiling for a permanent, shrinking stream of validator rewards. Every year, new SOL is minted and paid to validators for confirming transactions and producing blocks. The rate of that new issuance falls by a fixed percentage annually. The pace of dilution slows over time, even though total supply keeps rising. This differs from Bitcoin, where the reward halves in sudden steps rather than shrinking gradually. The reasoning behind an uncapped design is validator incentives. A network needs enough new-token rewards flowing to make running a validator worthwhile. That matters most in the early years, before transaction fees alone could cover the cost. Solana's founders chose ongoing issuance over a hard cap, betting that usage and fee revenue eventually take over as the main incentive. An investor holds 25,000 rupees in SOL, a token whose supply grows every year. Compare that to a Bitcoin position of the same size, sitting against a fixed cap nearing its limit. Say Solana's annual inflation rate runs at 4 to 5 percent in a given year. That 25,000 rupees position then faces roughly that much new supply added annually, before accounting for demand or burns. Solana also burns a portion of every transaction fee, which offsets some of the new issuance. The net inflation rate investors actually experience usually runs lower than the gross issuance rate alone suggests. Check the net figure, gross issuance minus burns, rather than the headline inflation percentage, when comparing Solana's real dilution to a fixed-supply asset. Solana sits in two of Qatobit's QSI indexes, Growth and VRION, held alongside Bitcoin and other assets rather than as a standalone position. An investor gets Solana's growth-token profile inside a basket that also carries assets with different supply mechanics.

A token's float is the portion of its supply actually available for public trading, once locked insider and treasury tokens are excluded. Fully diluted valuation, FDV, is total or max supply multiplied by current price, counting every token whether tradable yet or not. A small float against a large FDV prices coins nobody can trade. Float is close to circulating supply, but stricter. It strips out tokens that are technically circulating on-chain but effectively unavailable. That includes tokens held in a project's own wallet, or committed to a market maker under contract. What remains is the amount actual buyers and sellers can move on any given day. FDV runs the market cap formula on the largest possible supply number instead. It answers a hypothetical: what would this token be worth if every future token already existed and traded at today's price? A token with a tiny float and a huge FDV can turn a thin trickle of money into a large headline valuation. So few tokens are actually setting that price. A token priced at 50 rupees, with 2 crore tokens in float, shows a 100 crore rupee market cap from float alone. Its max supply is 40 crore tokens, which runs the FDV to 2,000 crore rupees, twenty times the float-based number. An investor putting 25,000 rupees into that token is buying into the thinly traded float, not the large FDV figure headlines tend to quote. A wide float-to-FDV gap is not automatically a red flag. A young project with a long vesting runway for its team will always show one. What matters more is the unlock schedule behind that gap. A two-year linear release is a slower drip than a single cliff dumping a fifth of supply on one date. Qatobit's QSI GEQ8 index applies a related free-float method to its equity holdings. It weights by market cap adjusted for shares actually available to trade, rather than every share a company has issued. The same float logic used to judge a token's real tradable size sits behind that equity weighting.

Minting calls a smart contract that creates a unique token ID linked to metadata, recorded on-chain. Primary sale happens through that minting contract, at a fixed price or auction. Secondary resale then moves to a marketplace contract, which can trigger a royalty back to the creator. Minting is the creation step. A creator uploads artwork or another asset, the metadata describing it gets stored, and the smart contract assigns a token ID that points to it. No two token IDs from the same contract are identical. That is what makes each NFT unique, even inside a large collection. After minting, the primary sale is the first sale, whether bought directly at a set price or won at auction. Every sale after that is a secondary sale, handled by a marketplace contract rather than the original minting contract. Marketplaces like OpenSea or Blur read the token's metadata and list it for the current holder to sell again. A collector spends 25,000 rupees minting an NFT at launch, paying that amount to the project's minting contract. That is the primary sale. If the collector later resells it for 40,000 rupees on a marketplace, the smart contract can route a royalty back to the creator. That royalty commonly runs 5 to 10 percent of the resale, paid before the seller receives the rest. Royalty payment is not guaranteed the way it once was. Several major marketplaces made royalty enforcement optional starting in 2022 and 2023, letting buyers pay zero at checkout. A creator's expected income from resales can fall sharply. That happens if the marketplace a token trades on does not enforce the royalty the smart contract was built to pay.

Check four things first: the allocation table, showing the split across team, investors, community and treasury. Check vesting cliff dates and unlock cadence next. Then check the emission curve. Finally check the FDV-to-market-cap ratio, which sizes how much supply is still to come. The allocation table shows who holds what share before anyone else can buy in. A project where team and early investors hold 40 or 50 percent combined carries concentrated sell pressure. One where community and treasury hold the larger share carries less. Vesting cliffs and unlock cadence then tell you when those concentrated holders are actually free to sell. The emission curve describes whether new supply issues on a shrinking schedule, a fixed schedule, or an open-ended perpetual one. A disinflationary curve, like Solana's, front-loads issuance and tapers it. A fixed curve pays the same reward every period indefinitely. A perpetual curve with no cap at all needs real usage growth to keep pace with new supply. An investor deciding whether to put 25,000 rupees into a new token pulls up its allocation table first. Say the team holds 25 percent on a four-year linear vest. That is a far slower drip than a team holding the same 25 percent that unlocks after a one-year cliff. Same allocation percentage, very different sell-pressure timeline for that 25,000 rupees position. A project's official tokenomics page is not always the most current source. Allocation percentages get renegotiated, and vesting schedules get amended after a project raises additional funding rounds. Cross-check the allocation and vesting claims against the token contract itself, or a third-party tracker. A whitepaper can be a year or more out of date.

A token at 100 percent circulating supply has no scheduled dilution left from team or investor unlocks. For an established network nearing its full mined supply, that reflects a mature position, not a warning sign. For a brand-new token, it can instead mean no team allocation was ever locked to reward long-term performance. Context is what separates the two readings. Bitcoin has approached its near-full circulating supply gradually, over sixteen years. Roughly 94 percent of its 21 million cap is mined so far, under a public halving schedule anyone could audit in advance. A brand-new token hitting 100 percent circulating on day one skipped that process. It distributed everything at launch rather than over years. A team with no locked allocation has no long-term financial stake tied to the token's future price, since nothing they hold vests later. That absence of a lockup is the real signal worth checking, not the circulating supply percentage itself. A mature network's full circulation and a new token's full circulation look identical on a supply chart, but they mean very different things. An investor comparing two tokens before allocating 25,000 rupees checks the age and history behind each supply figure. One token is sixteen years old, nearing full circulation, with a publicly documented issuance history. The other launched three weeks ago, with its entire supply already circulating and no team vesting disclosed anywhere. Both show little scheduled dilution ahead, though one carries sixteen years of documented history and the other carries none. A fully circulating supply removes one kind of risk: the sudden unlock. It does nothing about ordinary sell pressure from existing holders, who remain free to trade at any time. Full circulation is not the same as low volatility. A token can still swing sharply in price with zero scheduled unlocks left to worry about.

Market cap alone can mislead because it multiplies price by circulating supply without checking how much of that supply actually trades. A large FDV against a small tradable float prices in coins nobody can sell yet. Thin order-book liquidity then lets small trades move price, and the market cap built on it, out of proportion. Price is the weak link in the formula. A token with most of its supply locked away can see its last-traded price set by a handful of small trades on a low-volume exchange. Multiply that thin price by billions of theoretical tokens, and the resulting market cap can look enormous while representing almost no real capital actually invested. Circulating supply is the other weak link. Projects self-report this figure, with no external audit standard behind it. Two projects can define what counts as circulating differently: one might exclude a foundation's holdings, another might count them. A market cap comparison between two tokens using different self-reported definitions is not really comparing like with like. A token trading at 500 rupees, with a self-reported 2 crore circulating supply, shows a 1,000 crore rupee market cap. Say daily trading volume on that token is only 5 lakh rupees. A single sell order of 25,000 rupees can move the price noticeably. That new price then recalculates the full 1,000 crore rupee market cap off a trade a fraction of its size. Pair market cap with 24-hour trading volume and order book depth before trusting it as a size comparison. A coin with a smaller headline market cap, but deep and consistent volume, is often the more liquid asset. It is usually priced more accurately than one with a larger cap and a thin order book.

A token can carry a high market cap and low liquidity at once, since the two measure different things. Market cap is price times circulating supply, a snapshot valuation. Liquidity is different: how much of that supply sits on exchange order books, rather than locked in a few wallets that never trade. Market cap needs only a price and a supply figure to calculate, and price can be set by the last trade, however small. Liquidity needs real buyers and sellers standing ready at nearby prices. A token can carry a billion-rupee market cap built on one trade an hour ago, while its order book has almost nothing resting behind it. Concentrated holder distribution usually causes the gap. Say a small number of wallets hold most of the circulating supply and rarely trade. The order book then only ever sees a thin slice of it. A single mid-size sell order can then cause double-digit slippage, moving price far more than the token's headline market cap would suggest was possible. An investor places a 25,000 rupees order in a token with a large market cap but a thin order book. The price can move against them mid-trade, filling part of the order at one price and the rest noticeably lower. The same 25,000 rupees order in a deep, liquid market fills close to the quoted price the whole way through. Analysts pair market cap with 24-hour trading volume and order book depth, precisely because cap alone hides this gap. A token showing a large market cap and near-zero daily volume is worth checking directly on an exchange's order book, not just its headline valuation. Qatobit's Quick Buy and Sell shows the real-time price and fee before an order confirms. An investor sees the actual price an order would fill at, rather than reading price only off a market cap headline. That confirmation step is one way the product surfaces the gap between a token's valuation and its live tradable price.

A cryptocurrency shows a market cap near zero for one of three reasons. A data provider marks it dead once it loses a reliable price feed after delisting. Its supply can hyperinflate until the per-token price crashes toward zero, even with billions outstanding. Or its contract gets paused or abandoned and stops reporting trades. Delisting is the most common path. Once no major exchange lists a token, price trackers lose a reliable feed to pull a price from. Many mark the listing as dead, or archive it, rather than showing a stale number. The token may still technically exist and even trade on a small exchange the tracker does not monitor. Hyperinflation is a slower death. A token minting new supply faster than any real demand sees its price fall continuously, sometimes to fractions of a rupee. The raw token count outstanding can climb into the billions or trillions. Multiply a near-zero price by even a huge supply, and the resulting market cap still reads as close to zero. An investor puts 25,000 rupees into a token during a launch hype phase. The project's contract is later paused after an exploit, and that position now shows a near-zero market cap on every tracker. The tokens may still sit in the wallet, so the 25,000 rupees is not necessarily gone on-chain. But no market exists to sell them into anymore. A near-zero market cap does not always mean the tokens are worthless. A paused contract can sometimes resume, and a delisted token occasionally relists on a smaller exchange. Treat a near-zero reading as a signal to check the project's own contract and channels directly, rather than assuming the position is unrecoverable.

NFT marketplace trading volume fell after 2021 for three reasons. Major marketplaces made creator royalties optional, removing an incentive that had propped up floor prices. Exchanges cracked down on wash trading, which had inflated reported volume with fake demand. Speculative flipping also cooled once the 2021 to 2022 peak passed. Royalties had funded a feedback loop. Creators and communities actively marketed their own collections to keep floor prices supported, since a resale royalty was real income for them. Once platforms like OpenSea and Blur made royalty optional at checkout, starting in 2022 and 2023, many buyers simply paid zero. That price-support incentive for creators largely disappeared. Wash trading is when a trader buys from their own second wallet to fake volume. It is often done to farm a marketplace's reward token, or to manufacture the appearance of demand. Marketplaces tightened rules and reward programs to filter this out, starting in 2022. A meaningful share of what had counted as real 2021 volume turned out to be this kind of self-dealing. A collector pays 25,000 rupees for an NFT at the height of 2021 hype, when daily category volume runs into the hundreds of crores. Today, that same collection might trade a small fraction of that daily volume. The 25,000 rupees purchase still exists as an asset, but the liquid market that once surrounded it has thinned considerably. Falling volume is not the same as a dead category. Utility-linked and collection-driven trading has held up better than the purely speculative flipping that dominated 2021. This is trading where an NFT unlocks access to a game, event or community. Check whether volume in a specific collection is utility-driven, before assuming the whole category is finished.

Standard vesting releases tokens gradually to a holder, as they earn them over time. Reverse vesting flips that order. It grants the full allocation upfront. That allocation is subject to clawback if the holder, often a founder or core contributor, leaves before the vesting period ends. In standard vesting, nothing is handed over until it is earned, so leaving early simply means forfeiting tokens not yet released. Reverse vesting gives someone the entire allocation on day one, fully theirs to hold or even transfer. The project then keeps a contractual right to buy back the unearned portion, if that person departs early. DAOs and web3 projects use reverse vesting to keep flexibility over an allocation already distributed. It matters most for founders and early team members, who received tokens at formation before a formal vesting contract existed. It gives the project a way to reclaim value from someone who leaves in year one, rather than losing the allocation entirely. A contributor is granted tokens worth 25,000 rupees at a project's founding, under reverse vesting, holding the full amount immediately. The contributor leaves after six months of a planned two-year term. The project's clawback clause can then repurchase roughly three-quarters of that 25,000 rupees worth of tokens, the portion tied to time not yet served. Reverse vesting only works if the clawback right is actually enforceable. That usually runs through a legal agreement sitting alongside the token grant, not the smart contract alone. A purely on-chain transfer, with no accompanying contract, gives a project no real mechanism to claw anything back once tokens have left the treasury wallet.

Solana launched at about 8 percent annual inflation, disinflating by a fixed yearly percentage rather than a Bitcoin-style halving. It originally cut 15 percent a year toward a 1.5 percent floor. A validator vote in August 2026 doubled that cut to 30 percent a year, reaching the floor in about 2.8 years. Every year, new SOL is minted and paid to validators for confirming transactions and producing blocks. The pace of that new issuance falls by a set percentage annually, under a schedule fixed in Solana's code but adjustable by validator governance. This differs from Bitcoin, where the reward halves in sudden steps that no vote can alter. The original curve would have cut an 8 percent starting rate to roughly 6.8 percent in year two, stepping down by 15 percent annually. The August 2026 vote passed with just over 67 percent support among validators, doubling that annual cut to 30 percent. Solana now reaches its 1.5 percent floor in about 2.8 years instead of the 5.7 years the original schedule needed. An investor holds 25,000 rupees in SOL during Solana's early years, when the inflation rate ran closer to 8 percent. That position faced roughly that much annual dilution, before accounting for any transaction-fee burns. The same 25,000 rupees position, held once the rate nears its 1.5 percent floor in a few years, faces smaller dilution from new issuance. Solana's schedule is not immutable the way Bitcoin's halving is. It sits in the protocol as a governance parameter validators can vote to change, and did, in August 2026. A future vote could slow the pace back down just as easily. The rate an investor sees today is not guaranteed for the life of the position. Solana sits inside two of Qatobit's QSI indexes, Growth and VRION. It is held and rebalanced monthly there, alongside Bitcoin and other assets, rather than tracked as a standalone position against its own inflation schedule.

Solana tokens burn through a native SPL burn instruction, a manual, project-triggered action. Ethereum burns a portion of every transaction's base fee automatically, under EIP-1559, tying the burn directly to network usage. One mechanism is manual and project-triggered. The other runs automatically at the protocol level. The SPL burn instruction is built into Solana's token standard. Any project can call it whenever they decide to reduce a token's supply, whether as a one-time event or a recurring policy. Nothing forces a Solana project to burn tokens. It happens only when a specific transaction triggers that instruction. Ethereum's burn is not optional and not project-specific. Every transaction pays a base fee, and since the 2021 EIP-1559 upgrade, that fee is destroyed automatically rather than paid entirely to validators. More network activity means more ETH burned, tying the burn rate directly to actual usage rather than a decision anyone makes transaction by transaction. An investor holds 25,000 rupees in ETH. That position benefits from a burn rate that rises automatically whenever network activity picks up, with no announcement needed. The same 25,000 rupees held in a Solana-based token only sees a burn if that specific project schedules and executes one. That can happen on any cadence the project chooses, or not at all. Ethereum's burn does not guarantee falling supply, since it only offsets the new ETH issued to validators for staking. When network activity is low, the burn can fall below new issuance, and total ETH supply rises rather than shrinks. Check net issuance, new supply minus burns, rather than assuming a burn mechanism alone means supply is shrinking. Ethereum and Solana sit together in Qatobit's QSI Growth index, alongside Bitcoin, Gold and a stable reserve. All five are held and rebalanced monthly under one published methodology, rather than tracked separately by each asset's own burn mechanics.

Stock market cap is calculated from shares outstanding, often free-float adjusted to exclude shares locked up by insiders. Crypto market cap uses circulating supply instead, a figure self-reported by each project with no standard audit behind it. That inconsistency means two market caps that look identical can carry very different dilution risk underneath. A public company's shares outstanding are disclosed in regulatory filings, audited by outside accountants, so every investor works from the same verified number. Free-float adjustment then strips out shares held by founders, the company itself, or governments. It isolates the portion actually available for public trading, a process applied consistently across most stock markets. Crypto has no equivalent audit requirement. A project decides for itself what counts as circulating supply, and different projects draw that line differently, some excluding a foundation's holdings, others not. Two tokens can report the same circulating supply number using different definitions underneath. A market cap comparison between them is then not really comparing the same thing. An investor compares a stock position and a crypto position, each worth 25,000 rupees. Very different levels of verification sit behind the market cap each reports. The stock's free-float figure came from an audited filing. The token's circulating supply figure came from the project's own dashboard, unaudited and self-defined. Some data providers now apply their own free-float-style adjustments to crypto market caps. They try to strip out foundation and team holdings, the way stock markets do for insiders. These adjustments vary by provider and are not standardized the way equity free-float rules are. Even an adjusted crypto market cap still needs its methodology checked before it is trusted. Qatobit's QSI GEQ8 index applies the equity-market version of this approach directly. It weights its global technology and digital-finance holdings by free-float market cap, adjusted by a quarterly score. That is the standardized, audited version of market cap the crypto side of this comparison is still working to catch up to.

SUI has a fixed total supply of 10 billion tokens. That supply splits across five categories: a community reserve, core contributors, investors, a community access program, and the Mysten Labs treasury. The community-reserve share releases gradually over time, funding grants, staking rewards, and other ecosystem incentives, rather than all at once. Core contributors and investors received allocations under vesting schedules typical of a new network. Tokens release gradually, rather than the full amount handed over at launch. The Mysten Labs treasury holds a separate share, earmarked for the company's own operating needs. That is distinct from tokens set aside to grow the broader Sui ecosystem. The community reserve is the largest lever for ongoing supply growth. It funds staking rewards and grants that pay out continuously, rather than on one fixed date. As that reserve releases tokens over years, it adds to circulating supply steadily. That differs from a single cliff unlock that dumps a large amount on one specific day. An investor puts 25,000 rupees into SUI, buying against a fixed 10 billion token ceiling. The pace at which that ceiling gets reached depends heavily on how fast the community reserve releases staking rewards and grants. A faster release schedule means more competing supply reaching the market sooner, even though the total ceiling itself never moves. A fixed total supply caps how much SUI can ever exist, but it says nothing about how fast the remaining tokens reach circulation. Two tokens can share the same 10 billion cap and still carry very different near-term dilution. That depends entirely on how aggressively their reserve and vesting schedules release tokens over the next year or two.

A token sink is any mechanism that permanently removes tokens from circulating supply, or locks them out of it for a period. Burning fees, the EIP-1559 pattern, removes supply automatically as the network gets used. Staking is a softer sink, locking tokens for a period and reducing the tradable float without destroying the tokens outright. Projects add sinks to counter the new supply their own emission schedule creates. Without a sink, a token paying out staking or mining rewards every day simply grows its circulating supply forever. It adds sellers faster than any fixed level of demand can absorb. A burn or a lockup gives the design something pulling supply back the other way. Burning is the permanent version. Tokens sent to an unusable address are gone for good, tied directly to real usage under a fee-burn design like Ethereum's. Staking is the temporary version. Tokens are locked voluntarily by holders chasing a reward, reducing what is available to sell today, while total supply stays unchanged underneath. An investor holds 25,000 rupees in a token that stakes 40 percent of circulating supply. Those tokens lock out of the tradable float for a set period. Only the remaining 60 percent can realistically be bought or sold on short notice. That locked 40 percent behaves like reduced supply, until holders choose to unstake. A sink only offsets emission if it operates at a comparable scale. A token burning a tiny fraction of fees, while emitting large staking rewards every day, still sees circulating supply grow overall. The sink is present but too small to matter. Compare the actual burn or lock rate against the emission rate, before assuming a sink is doing real work.

A tokenomics model needs four components. A supply rule sets how many tokens can exist, and how fast. An allocation table shows the split across team, investors, community and treasury. An emission schedule sets the pace of new supply. A demand mechanism gives the token a reason to be held. The supply rule and allocation table answer who gets what, and how much can ever exist. A capped supply, like Bitcoin's, behaves differently from an open-ended issuance, like Solana's. An allocation table skewed heavily toward team and investors carries concentrated sell pressure. One weighted toward community and treasury carries less. The emission schedule and demand mechanism answer the harder question: what happens after launch. Emission sets how fast new tokens hit the market, through staking rewards, mining, or a fixed unlock calendar. The demand mechanism can be fee payment, staking lockups, or governance rights. It is what is supposed to create buyers willing to absorb the new supply. An investor screens a project before putting 25,000 rupees in, checking all four pieces together rather than any one alone. A token with a hard supply cap but no real demand mechanism can still drift down in price. A token with heavy emission but strong fee-paying usage can hold steady, if that demand keeps pace with new supply. The four components interact, so reading any one in isolation misses the picture. A generous allocation to community and treasury looks healthy, until the emission schedule reveals those tokens release faster than any realistic demand mechanism can absorb. Model all four together, ideally with the actual unlock and emission dates, before judging a project's tokenomics as sound.

Total crypto market cap sums price times circulating supply across every coin a data aggregator tracks. Thousands of individual market caps add into one headline number. Wrapped and bridged versions of the same asset, like wrapped Bitcoin on Ethereum, can get counted twice across chains. That double-counting inflates the true total somewhat. An aggregator like CoinMarketCap or CoinGecko pulls price and circulating supply data for every coin it lists, then adds every individual market cap together. The number moves every time any listed coin's price or supply changes. With thousands of coins tracked at once, the total figure is essentially never still. Double counting happens because a wrapped token represents value already locked in the original asset elsewhere. Trackers often list the wrapped version's market cap separately, rather than netting it against the original. The same underlying Bitcoin can effectively show up in the total twice, once as native BTC and again as wrapped BTC circulating on another chain. An investor watches total crypto market cap rise by an amount roughly equal to a fund's 5 lakh rupee allocation moving in. The figure is still an aggregate estimate across thousands of coins, not a single verified balance sheet. It is directionally useful for gauging overall market mood, far less useful as a precise measurement. The total figure shifts not just from price moves but from methodology changes. An aggregator adding newly launched coins, delisting dead ones, or reclassifying a token's category can move the headline total. That happens without any real money entering or leaving the market at all. Read the total as a rough sentiment gauge, rather than a precise sum.

A trading volume spike usually follows a specific trigger: a new exchange listing, a delisting announcement, or major news. A sustained drop often means large holders have shifted coins into cold storage, or that speculative interest has faded. Confirming either reading means checking order book depth, not just the headline volume figure. An exchange listing brings new buyers who could not access the token before. A delisting announcement often triggers a rush of selling as holders exit before trading closes. Major news, a partnership, a hack, a regulatory update, pulls in traders reacting to the headline rather than the fundamentals. A sustained drop usually means fewer people want to buy at the current price. It can follow a whale moving coins to cold storage for long term holding. It can also follow fading hype once a news cycle passes. Wash trading, where an entity trades with itself to inflate volume, is one reason raw numbers mislead. Checking order book depth shows whether the volume reflects genuine two sided interest, rather than one entity trading with itself. Say an investor tracking a token for a possible 5 lakh rupee allocation sees its daily volume triple overnight. The news shows a major exchange added the token that morning. Checking the order book shows bids spread across several price levels, not stacked at one price. That spread confirms real buyer interest rather than a single wash traded spike. Volume figures from small or unregulated exchanges are the easiest to fake, since a wash trade needs no real counterparty. A token can also show high volume concentrated in a narrow price band. That looks active but offers little real liquidity to exit a position at that price. Reading volume in isolation, without the order book behind it, is the mistake that catches most people out. Qatobit's Crypto Indices are designed and rebalanced monthly on a published methodology. An investor holding a basket is not reacting to a single coin's volume spike day to day. The decision sits in the methodology rather than in the headline number.

Different crypto trackers show different market caps mainly because they use different circulating supply figures. Each tracker sources that number from a project's own self-reporting, updated on its own schedule. A newly listed token can also show a market cap on one site before a slower tracker has indexed it at all. Circulating supply sounds like a fixed number. In reality it depends on which coins a project reports as locked, burned, or held in a team wallet. Projects update these figures on their own timeline, sometimes monthly, sometimes only when asked. A tracker that has not refreshed its figure yet will show a market cap based on stale supply data. Trackers also disagree on which trading volume counts. Some filter out suspected wash trading before ranking coins by volume or market cap. Wash trading is where an entity trades with itself to inflate activity. A coin that clears one tracker's filter can fail another's. That changes where it ranks, or whether it appears in a top list at all. Say an investor compares two trackers before a 5 lakh rupee allocation. The trackers show a 15 percent difference in a token's reported market cap. One tracker counts locked team tokens as circulating, the other excludes them. Checking each tracker's methodology page, rather than trusting the headline figure, explains the gap before any money moves. The gap is usually largest for newly launched tokens, where supply figures change often and self-reporting lags the real release schedule. For established, high volume coins like Bitcoin or Ethereum, the figures across major trackers usually agree closely. The supply data for these is settled and widely verified.

Some tokens, like Shiba Inu, launch with a supply in the hundreds of trillions. The goal is a per-token price of a fraction of a cent. Market cap, price multiplied by supply, is unaffected by how that value is split. A trillion token supply is a marketing choice, not an economic one. A token's price is just its market cap divided by its supply. Splitting the same value across a hundred trillion units instead of a hundred million makes each unit cost almost nothing. Nothing about the project's actual value changed, only the size of the number stamped on each unit. A low per-unit price creates a feeling of affordability that a two dollar token does not. Both can represent the same total market value. This is a behavioral effect on the buyer. The project's structural value is unchanged. Two tokens with identical market caps can attract very different buying interest purely because of this framing. Say an investor compares two tokens for a 25,000 rupee purchase. One costs 0.000008 rupees per unit with a trillion token supply. The other costs 8,000 rupees per unit with a supply of ten thousand. Both purchases buy the same fraction of the project's total market value, even though one feels like it buys far more. A very large supply can also signal inflationary tokenomics, where new tokens are minted continuously, diluting existing holders over time. The supply can be fixed at launch or still expanding through ongoing emissions. Checking which one applies matters more than the size of the number itself.

A project's tokenomics affects its long term value. It decides who can move the price and how fast new supply dilutes existing holders. A token concentrated in a few wallets can move the price sharply when they sell. An emission schedule that outpaces demand steadily dilutes everyone who already holds the token. Allocation concentration means checking who holds the largest wallets: the team, early investors, or the public. When a small number of wallets control a large share of supply, their decision to sell can move the price. It moves far more than ordinary trading activity would. This is usually visible in the wallet distribution shown on a blockchain explorer. Emission schedule means the rate at which new tokens enter circulation, whether through mining, staking rewards, or a fixed release calendar. When that rate outpaces genuine demand for the token, each existing holder's share is steadily diluted. Utility that creates real demand, such as paying network fees, staking for rewards, or voting on governance, can offset that dilution. Say an investor is deciding between two tokens for a 25,000 rupee purchase. One has 70 percent of its supply held by ten wallets and mints new tokens with no real use case behind them. The other spreads supply across thousands of wallets and uses the token to pay network fees. The second token's demand mechanism gives it a structural reason to hold its value that the first token lacks. A token can have excellent tokenomics on paper and still fail if nobody uses the underlying network. Utility only offsets dilution when real demand exists. Reading the tokenomics without checking actual usage, active wallets and real transaction volume, tells only half the story. Qatobit's QSI Crypto Indices are designed and rebalanced monthly on a published methodology. An investor is not required to individually vet each holding's emission schedule or wallet concentration before allocating.

XRP's full supply of 100 billion tokens was minted at the network's genesis, with no ongoing mining or staking issuance. Ripple holds a large share locked in on-ledger escrow contracts, which release up to 1 billion XRP monthly. Unused amounts are re-locked rather than released. Most cryptocurrencies add new supply gradually, through mining rewards like Bitcoin or staking rewards like Ethereum. XRP took a different approach: the entire 100 billion supply was created in one genesis transaction, with none created afterward. This is called a fixed, pre-mined supply. Ripple wanted to avoid placing all 100 billion tokens onto the market at once. It locked 55 billion of them into escrow contracts in 2017. Each contract releases up to 1 billion XRP on the first day of the month. Whatever Ripple does not use from that release goes back into a new escrow contract. That contract sits at the end of the chain, extending the schedule. Say an investor holds a 5 lakh rupee position in XRP and wants to judge future selling pressure. Checking the public escrow release schedule shows exactly how many tokens can enter circulation that month. The contracts and their release dates are visible on-ledger. This is different from a token whose next release size is undisclosed. The visible schedule does not mean the full 1 billion released each month actually reaches the open market. Ripple has historically re-locked most of it rather than selling it. Reading the schedule as a guaranteed monthly sell pressure of 1 billion tokens overstates the real effect.