Fundamental Analysis — Complete Guide

• •FA assesses intrinsic (fair) value through fundamental factors, not price/volume (that's TA).
• •The goal is to find under-/overvalued assets with a horizon of months to years.
• •In crypto there's no classic financial reporting: FA = technology + tokenomics + governance + team + community + on-chain + narrative + macro + regulation.

• •Qualitative (technology, team, use case, narrative) vs quantitative (tokenomics, metrics, multiples).
• •On-chain (blockchain data) vs off-chain (team, regulation, social media).
• •Top-down (macro → narrative → asset) vs bottom-up (protocol → up).

Value

• •Whitepaper / litepaper, use case / value proposition, product-market fit (real users, not speculation; whether a blockchain is even needed here).

Consensus mechanisms

• •PoW (BTC) — security through hashrate; proven but energy-intensive/slow.
• •PoS (ETH, most L1s) — stake + slashing; efficient; risks of 'the rich get richer' and centralization via LSTs.
• •DPoS (Tron) — elected delegates, faster but more centralized; PoH (Solana) — verifiable time on top of PoS; PoA — permissioned networks.
• •BFT (Tendermint/Cosmos) — instant finality; Avalanche — subsampled voting.
• •The trilemma: decentralization / security / scalability — pick 2; how a project balances it is the key question.

Architecture and layers

• •L0 — interoperability (Cosmos IBC, Polkadot); L1 — settlement (BTC, ETH, SOL).
• •L2 rollups: Optimistic (Arbitrum/Optimism — fraud proofs, 7-day challenge) vs ZK (zkSync/StarkNet — validity proofs, faster finality).
• •Sidechains (Polygon PoS) — own security; modular (execution/settlement/consensus/DA — Celestia) vs monolithic (Solana).

Scalability and security

• •TPS (raw is misleading), finality time, block time; sharding (danksharding), parallelization (Sealevel); fees EIP-1559, blob fees, state bloat.
• •Cost of attack: 51% (PoW), stake-to-attack (PoS), 33/66% (BFT); node distribution, client diversity, the Nakamoto coefficient, stake concentration (Lido).

Interoperability and development

• •Bridges (lock-mint/burn-mint/liquidity) — the main vector of hacks; IBC, LayerZero, CCIP, Wormhole.
• •VM: EVM vs SVM vs Move vs CosmWasm; GitHub activity / number of devs (Electric Capital); upgrades, innovation / moat.

• •Governance determines who controls the protocol and the treasury — a factor in both value and risk.

Voting models

• •On-chain (executable, Compound) vs off-chain (Snapshot — signaling); token voting = the risk of plutocracy.
• •Delegation (Uniswap); vote-escrow (veCRV — lock → vote + boost); quadratic voting (Gitcoin); bicameral / council (Optimism, a security council with a veto).

Process and distribution of power

• •Lifecycle: temperature check → discussion → proposal → vote → timelock → execution; parameters: quorum, proposal threshold.
• •Token concentration (whales/VCs), turnout (low turnout → a minority rules), the governance Nakamoto coefficient.

Treasury and risks

• •Treasury: size, composition (own token vs stablecoins); overexposure to the own token = reflexive risk; runway, transparency.
• •Attacks: flash-loan voting (Beanstalk), capture by a whale/VC, vote buying / bribes (Curve wars), griefing.

veTokenomics, decentralization, signals

• •Curve wars: accumulating veCRV/vlCVX to direct emissions; bribe markets (Votium, Hidden Hand); ve(3,3).
• •Progressive decentralization: renounce admin keys → multisig → on-chain; 'sufficient decentralization' (Howey); legal wrappers (Wyoming DAO LLC, Cayman).
• •Strong governance: reasonable turnout, distributed power, active delegates, a timelock, a diversified treasury — but not paralyzed.

• •The most decisive block: whether the protocol's success translates into token value. Three questions: supply, demand, the bridge between them (value accrual).

Supply

• •Emission curves: hard cap (BTC 21M, halvings); tail emission (Monero); fixed inflation; dynamic (Cosmos — a target staking ratio); uncapped (ETH, regulated by burning).
• •Net emission = issuance − burned (true inflation); base-fee burn (EIP-1559), buyback-and-burn (BNB); ETH in periods of activity — net deflation.
• •Sell pressure: miners/validators, farmers (dumping emission rewards), unlocks — the largest predictable pressure.

Distribution and float

• •Shares: team, VC, foundation, treasury, community; launch type (ICO/IDO/fair launch/airdrop); concentration in top wallets.
• •Free float — the actually tradable share; FDV = price × max supply.
• •The low float / high FDV trap: a launch with 5–15% float and a huge FDV → an avalanche of unlocks → a slow bleed. Look at FDV + the unlock schedule, not just MCap.

Unlocks

• •Structures: linear · cliff+linear (a cliff date = risk) · milestone; categories: team, investors (seed/private — different entry prices), ecosystem, airdrop.
• •Pressure assessment: % of circulating; absolute $-value vs daily volume (10× of turnover → won't be 'eaten' without a drop); who receives it; cliff dates on the calendar.
• •OTC sales: investors dump locked tokens at a discount — pressure runs ahead of the formal unlock.

Value accrual

• •'Does the token capture the value the protocol creates?' Mechanisms: fee burn, buyback-and-burn/-distribute, real-yield staking (GMX, dYdX), veTokenomics, collateral/gas demand.
• •The creation vs capture gap: UNI — dominates by volume, but the fee switch is off → holders saw no revenue. A big business ≠ a valuable token.
• •Token velocity (MV=PQ): high V pushes P down; velocity sinks (staking, locks, collateral, governance) give reasons to hold.

Sustainability and checklist

• •Liquidity mining → mercenary capital (rewards drop → capital flees); death spiral; Ponzi-nomics (income from new inflows); sinks vs faucets.
• •🔴 High FDV/MCap · large unlocks in 3–12 months · insiders >40–50% · no value accrual · inflation > demand · income from emissions · a tiny float.
• •🟢 Real accrual (fee→holder) · net deflation under activity · velocity sinks · a transparent unlock schedule · distribution to the community · real yield.
• •Young listings with thin history are typical candidates for this trap — the screener flags such coins with a limited-history tag, so pay attention to it.

Staking (PoS)

• •Staking ratio; validator economics (yield, threshold, slashing); net emission ('ultrasound money'); MEV.
• •LST (Lido stETH, Rocket Pool); restaking (EigenLayer, AVS); real staking yield.

Mining (PoW)

• •Hash rate and difficulty (health/security); mining cost ('the floor'); miner capitulation + Hash Ribbons.
• •Miner wallet flows (to exchanges = pressure); security budget (falls with halvings); ESG/energy (affects adoption and regulation).

• •A transparent ledger — crypto's unique advantage. Each metric = a lens; strength comes from combination + cycle context. Extremes matter more than absolute levels.

Valuation / network

• •Realized Cap — the sum of coins at the price of their last move (≈ the network's cost basis); Realized Price = RC/supply (acts as S/R).
• •MVRV = MCap/RC (>3.5–4 froth, <1 capitulation); MVRV-Z marks tops/bottoms; NVT/NVTS — value vs throughput; Thermocap.

Profitability / cohorts

• •SOPR = sale price / purchase price (>1 in profit; a reset to 1 in a bull = support); NUPL — from capitulation to euphoria.
• •MVRV by cohorts: STH-MVRV (short-term holders' cost basis — a key S/R), LTH-MVRV.
• •LTH vs STH (threshold ~155 days): LTH growth = accumulation; CDD/Dormancy/Liveliness (old coins moving = distribution); RHODL, HODL Waves.

Activity and flows

• •Active/new addresses, tx count, entity-adjusted transfer volume, fees, whale tx count.
• •Exchange netflow (outflow = accumulation/bullish, inflow = possible selling); exchange reserves (a drop = a supply squeeze); SSR; illiquid vs liquid supply.
• •Miners: reserves/outflow, Puell Multiple; DeFi: TVL, fees/revenue, stablecoin market cap.

How to read

• •Entity-adjustment (exchanges' internal transfers inflate the numbers); cohort + cycle context; triangulation (not a single metric); caution with Goodhart (metrics get gamed).

• •DeFi protocols are the closest thing to 'businesses' with revenue. Analyze them like a company: product, how it earns, who captures the income, whether it's sustainable.

Categories and revenue

• •DEX (Uniswap, Curve) — swap fees; lending (Aave) — rate spread + reserve factor; LST (Lido) — a fee on staking rewards.
• •Perps (GMX, dYdX, Hyperliquid) — fees/funding/liquidations; yield aggregators (Yearn) — performance fee; stablecoin issuers (Maker) — stability fee + RWA.

Revenue ≠ fees ≠ profit

• •Total fees (paid by users) ≠ protocol revenue (the protocol's share) ≠ earnings (revenue − token incentives).
• •Supply-side fees (to LPs) vs protocol fees (to treasury/holders). Uniswap: huge fees, almost all to LPs, protocol revenue ≈ 0.

Metrics and multiples

• •TVL (capital, but ≠ value, can be mercenary), volume, OI, borrows; Fees/Revenue/Earnings; P/F, P/S, P/E.
• •Take rate = revenue/volume; capital efficiency = volume/TVL; revenue per TVL/user; utilization = borrowed/supplied.
• •Real yield (from real revenue) vs emissions yield (an inflationary token, unsustainable); a narrative shift toward real yield.

TVL quality and risks

• •Mercenary vs sticky TVL; POL vs rented; TVL composition (own token = reflexive risk); double counting (restaking, LST).
• •DeFi risks: exploit, oracle manipulation, bad debt/liquidation cascades, depeg, governance attack, composability/contagion, regulatory.
• •Red flags: income only from emissions, TVL in the own token, Ponzi design, falling revenue at a high token price.

Smart contracts

• •Audits: who (tier-1: Trail of Bits, OpenZeppelin, Spearbit, Zellic), severity of findings and whether they were fixed; multiple/continuous audit; formal verification (Certora).
• •Bug bounty (Immunefi — the size = confidence); code maturity ('Lindy' — time in production with large TVL without hacks).
• •Vulnerability classes: reentrancy, oracle/price manipulation (flash loan), access control, overflow, logic errors, signature replay, MEV.

Operational security / admin

• •Admin keys: who holds them, multisig (m-of-n), hardware; timelocks on upgrades; upgradability (proxy) — flexibility vs risk; pausability, blacklist, renounced ownership.

Security economics and dependencies

• •Cost of attack (51%, stake-to-attack); security budget and resilience as emissions fall (the question of BTC fees).
• •Bridges — the biggest hacks (Ronin $625M, Wormhole, Nomad); oracle dependence (Chainlink); RPC/front-end centralization; depeg contagion.
• •Incident history and response (reimbursement, fork); whitehat relations; insurance (Nexus Mutual).

• •Determines the ability to exit, price impact, vulnerability to manipulation, and accessibility for institutions.

Depth and venues

• •Order book depth ('2% depth' — how many $ move the price by 2%); bid-ask spread; slippage; market impact.
• •CEX (tier-1 Binance/Coinbase/OKX vs tier-2/3); DEX (pool depth, concentrated liquidity v3); aggregators; the CEX vs DEX split.
• •Market makers (Wintermute, GSR, Jump); token loans to MMs (dump risk); DEX LPs (incentivized vs organic).

Volume quality and red flags

• •Real vs wash trading; volume/MCap (turnover); volume on reputable venues.
• •Red flags: low depth at a high FDV, tokens on loan to MMs, liquidity on a single venue, wash volume, an illiquid supply unlock ahead.

Founders and advisors

• •Founders' experience and track record; doxxed (a public figure with reputation at stake) vs anonymous (higher rug-pull risk, but not always — some anonymous teams prove reliable over years).
• •Advisors — real involvement or just a name for marketing; check activity, not merely presence on the list.

Investors and legal structure

• •VC backers (a16z, Paradigm, Multicoin) — a signal of due-diligence quality, but also a future source of sell pressure after unlocks; check entry rounds and valuations alongside the 'Tokenomics' section.
• •Partnerships — check whether it's a real integration with a measurable effect, or a PR announcement with no follow-through.
• •Legal structure: foundation, jurisdiction, compliance/KYC-AML — affects accessibility for institutions and the regulatory risk covered in 'Macro and regulation'.

• •In crypto, the community = distribution, network effect, and the engine of narrative; critical for memecoins and L1s.

Metrics and quality

• •Social volume/dominance, engagement, sentiment, follower growth (X/Discord/Telegram/Reddit — watch for bots); developer mindshare separate from retail.
• •Organic vs paid shilling/bots; engagement quality; mindshare / share of attention (Kaito); tools: Santiment, LunarCrush, Google Trends, Dune.

Network effects and signals

• •Metcalfe logic (users²); active users vs holders vs speculators; real usage vs vanity metrics.
• •Contrarian signals: extreme optimism = a local top; spikes in social volume often mark tops.
• •Red flags: engagement farming, fake followers, coordinated shilling, a community = only price talk.

Competitive analysis

• •Who the direct competitors are within the same category (e.g. L1 vs L1, DEX vs DEX) — compare only within a category, not L1 vs a memecoin.
• •TAM (total addressable market) — a rough ceiling estimate: if a project claims a market share far larger than the category leader's, that's either an ambitious thesis or an unrealistic expectation.
• •Market share by the category's key metric (TVL for DeFi, TPS/developers for L1, volume for DEX) — the trend in share matters more than a static number.

Moat

• •Network effects — the more users/liquidity, the more valuable the product is for the next user (a strong moat for DEX/L1, weak for forks with no differentiation).
• •Liquidity as a moat — a deep pool is hard for a competitor to pull away even with a better product (classic DeFi liquidity wars over TVL).
• •Brand and the Lindy effect — time in production without incidents becomes a competitive advantage in itself (trust).
• •A weak moat = code that's easy to fork (most of DeFi) — check what actually retains users besides the code itself.

Narratives and sectors

• •The cycle's main narratives: AI, RWA, DePIN, L2/modularity, restaking, memecoins, GameFi, SocialFi, DeFi, BTC L2 — the makeup and leaders of each sector change every cycle.
• •Narratives rotate in waves (capital and attention flow from sector to sector); a narrative's valuation typically runs ahead of its fundamentals — the market pays for the story in advance.

How to gauge narrative strength

• •An early narrative: few projects in the sector, thin media coverage, price hasn't decoupled from fundamentals yet — higher potential, higher risk of picking the wrong leader.
• •A late narrative: the sector is already in every Twitter thread, and openly weak clones start appearing on the hype — a typical sign that most of the move is already behind it.
• •Check whether the narrative is backed by real demand/usage, or whether it's just a rebrand of an old idea (e.g. 'DePIN' partly reissues long-standing distributed-network ideas).
• •In practice: a narrative tells you where to look; the screener and the scoring framework help you pick the specific asset within the sector.

• •Positive: mainnet, upgrades/hardfork, halving, listings, ETF decisions, partnerships.
• •Negative: large unlocks, delisting, regulatory actions, the end of incentive programs.
• •The 'buy the rumor, sell the news' logic; an events calendar as a tool.
• •Don't rely on remembering the date — set an alert for the event or the price level tied to the catalyst.

Macro liquidity

• •Global liquidity = central bank balance sheets + M2; net liquidity (Fed: balance sheet − TGA − RRP) correlates strongly with BTC.
• •Rates (real, the cycle, dot plot); DXY (inverse to crypto); risk-on/off; stablecoin market cap as a crypto-native liquidity indicator.
• •Crypto as a macro asset: correlation with Nasdaq (risk-on), with gold (SoV); 'high-beta tech'; the partial-decoupling thesis.

Regulatory landscape

• •US: SEC (security — Howey, Ripple/Coinbase), CFTC (commodity — BTC/ETH), Treasury/OFAC, FinCEN, states (BitLicense).
• •EU: MiCA (framework, stablecoins, CASP licensing); Asia: Singapore/Hong Kong (pro), China (ban); Travel Rule (FATF), AML/KYC.
• •Howey (4 conditions): money → a common enterprise → expectation of profit → from the efforts of others; 'sufficient decentralization' weakens the 4th; security status → delisting.

Institutional adoption

• •Spot ETFs (BTC Jan 2024, ETH 2024): flows = a demand signal; corporate treasuries (MicroStrategy); custody (Coinbase, BitGo, Fidelity).
• •Geopolitics: legal status (El Salvador), reserves, mining hubs, CBDCs. Regulatory clarity → institutional inflows.

The 4-year cycle

• •Pattern: halving → ~12–18 months bull → blowoff top → ~a year bear → accumulation (2012/16/20/24).
• •Debates: supply shock vs liquidity cycle vs reflexive narrative; declining amplitude; the 'is the cycle alive' question after the ETF.

Phases and indicators

• •Accumulation → markup/early bull → euphoria/blowoff (the peak of alt season) → distribution → markdown/bear.
• •Cycle on-chain: MVRV-Z, NUPL zones, Pi Cycle Top, Puell, Reserve Risk, RHODL, Realized Price; LTHs distribute at the top, accumulate at the bottom; SOPR reset.
• •Sentiment: Fear & Greed extremes; social volume, Google Trends.

Capital rotation and context

• •BTC leads → BTC.D rises → ETH → large caps → alts → memecoins (alt season); BTC.D and ETH/BTC as phase indicators.
• •The same metric reads differently by phase (MVRV≈1 — a bottom in a bear); reflexivity (Soros): price ↔ fundamentals reinforce each other.

• •Turns FA into 'how much it's worth'. No model is 'the truth' — they are triangulated (2–3 relevant ones → a range → a check against the market).

Relative and absolute

• •Multiples: NVT ('crypto P/E'), MVRV (>3.5 tops, <1 bottoms), P/F, P/S, MCap/TVL. Compare only similar assets; the trap: cheap on P/F may be deservedly cheap.
• •DCF — only for protocols that generate revenue AND capture it in the token; forecast fees → share to holders → discount 20–40%+. Doesn't work if the token doesn't capture fees.

Network, monetary, scarcity

• •Metcalfe (V ∝ n², active addresses); NVM ratio; fits BTC/ETH in certain periods.
• •S2F = stock/flow — historically influential, but broke after 2021, ignores demand, not predictive.
• •Cost-of-production (PoW) — a soft floor; MV=PQ (monetary, hypersensitive to V); TAM (% of gold — a rough estimate).

Anchors and model selection

• •On-chain anchors: Realized Cap, Thermocap (floors, not targets); macro: BTC vs gold/M2; the crypto risk-free rate (ETH staking / T-bills).
• •By type: BTC — scarcity+cost+Metcalfe+macro; ETH — monetary+quasi-DCF+network; DeFi — DCF/P-F/P-S; L1 alts — Metcalfe+relative; memecoins — no model (reflexivity, liquidity, attention).
• •Triangulation: 2–3 models → a range → a check against MCap → cycle context. Short-term, narrative rules.

Risk categories

• •Smart-contract (bug/exploit); centralization (admin keys, upgradeability); regulatory (security status, delisting); tokenomics (unlocks, inflation).
• •Liquidity (hard to exit without slippage); rug pull (founder fraud); competitive (product obsolescence); external dependency (bridge, oracle); stablecoin depeg for related assets.

How to assess aggregate risk

• •Score each category separately (low → critical) rather than one overall 'risky / not risky' gut feel — the same principle as the weight categories in the 'Scoring framework'.
• •One critical flag (e.g. an unaudited contract holding large TVL) outweighs several medium ones — aggregate risk doesn't always average out, sometimes it multiplies.
• •Risk and position size are linked exactly as in TA: the higher an asset's aggregate risk score, the smaller a share of the portfolio it should occupy — see the sizing mechanics in the TA Guide's 'Risk management' section.

• •A guaranteed '100x'; an excessive insider share; no audit/product; plagiarism; wash trading; token concentration; vague tokenomics with no unlock calendar; paid shilling/bots; an anonymous team; an inability to withdraw.
• •Our screener already surfaces some of these signals (holder concentration, listing age) as columns — that's a starting point, not a replacement for manual due diligence.

On-chain forensics (new tokens, memecoins)

• •LP lock; honeypot detection; the contract owner (renounced? mint/pausable/blacklist); holder distribution (dev wallet, sniper bots).
• •Tools: Token Sniffer, GoPlus, Honeypot.is, DEXScreener.

Types and reserves

• •Fiat-backed (USDT, USDC) — 1:1 reserves, centralized; crypto-backed (DAI, LUSD) — overcollateralized, a depeg when collateral crashes.
• •Algorithmic (UST — collapse; FRAX — hybrid) — the highest risk; yield-bearing (USDe, sDAI) — yield with source risk.
• •Fiat-backed reserves: attestations vs a full audit (the Tether debate); composition (cash+T-bills safe vs commercial paper); counterparty risk (USDC depeg during SVB).

Peg, depeg, metrics, regulation

• •Peg via mint/redeem arbitrage; PSM (Maker); collateral ratio + liquidations; algo mechanics (seigniorage, rebase).
• •Depeg history: UST/LUNA (death spiral), USDC/SVB (recovered), DAI via USDC (contagion).
• •FA metrics: market cap and growth, peg history, reserve quality, redemption volume, dominance (USDT vs USDC); aggregate market cap = 'dry powder' (SSR).
• •MiCA (EMT/ART, reserves, EU limits); red flags: opaque reserves, risky collateral, algo without backing, unsustainable yield.

• •BTC: SoV, S2F, scarcity, ETF, halving, Lindy; ETH: ultrasound money, staking, L2, settlement.
• •L1 alts: TPS, TVL, developers, competition; DeFi: revenue, real yield, fee switch, multiples.
• •Memecoins: community, narrative, liquidity, dev wallet, locks; NFT/gaming: users, royalties, utility.
• •Privacy (Monero/Zcash): resilience of anonymity, delisting risk, anonymity set; RWA: real collateral, legal wrapper, counterparty/custodial risk, KYC.

• •Market: CoinGecko, CoinMarketCap. On-chain: Glassnode, Nansen, IntoTheBlock, Santiment, Dune, Artemis.
• •Fundamentals: Token Terminal, Messari, DefiLlama. Explorers: Etherscan, Solscan. Unlocks: TokenUnlocks, CryptoRank. Code: GitHub.

• •Weighting (by asset type): technology 15% · tokenomics 20% · team 10% · adoption/metrics 20% · security 10% · liquidity 10% · narrative/catalysts 10% · risks (penalty) 5%.
• •Each factor 1–10 → weight → sum = a score. Memecoins → community/liquidity; L1 → technology/adoption; DeFi → tokenomics/revenue.
• •A score is a way to compare on equal terms, not 'the truth'.
• •This is a different, more detailed manual framework than the automated Score column in our screener — that score blends technical, momentum, patterns, structure, derivatives, on-chain and sentiment pillars using our own weighting, not this fundamentals checklist. Use this section to build your OWN fundamental view of any coin, including ones not in our database.

• •1. Narrative and sector → 2. Problem/solution → 3. Technology → 4. Governance → 5. Tokenomics (+unlock calendar) → 6. Metrics and unit economics.
• •7. Security → 8. Liquidity → 9. Team/community → 10. Catalysts → 11. Macro/cycle → 12. Risks/red flags → valuation (models) → conclusion.
• •Formula: narrative → problem/solution → technology → governance → tokenomics → metrics → security → liquidity → team → community → catalysts → macro/cycle → risks → valuation. TA tells you when; FA — what and why.