Behavioral Analysis

Identifies recurring transaction sequences to classify wallet behavior. Common patterns include:

• DEX Swapping: Frequent token trades on decentralized exchanges.
• Yield Farming: Depositing and withdrawing liquidity across protocols.
• Arbitrage: Exploiting price differences between markets.
• NFT Minting & Flipping: Participating in NFT drops and secondary sales.
• Money Flow: Tracking funds between wallets to map relationships.

Groups addresses controlled by a single entity and assigns labels based on aggregated activity. This involves:

• Heuristic Analysis: Linking addresses via shared funding sources or contract interactions.
• Entity Resolution: Identifying if a wallet belongs to a whale, retail trader, smart contract developer, or DAO treasury.
• Sybil Detection: Flagging networks of wallets designed to mimic organic activity for governance or airdrop farming.

Flags statistically significant deviations from established behavioral baselines to surface potential risks or opportunities. Examples include:

• Sudden Liquidation: A large, unexpected withdrawal from a lending protocol.
• Rug Pull Indicators: Developers removing liquidity and selling treasury tokens.
• Wash Trading: Artificial volume generation via circular trades.
• Flash Loan Attacks: Complex, multi-step transactions that exploit protocol logic.

Analyzes the timing, frequency, and duration of activities to gauge strategy sophistication and market impact. Key metrics are:

• Transaction Cadence: Are interactions periodic (e.g., daily harvesting) or event-driven?
• Latency: Speed of reaction to on-chain events (e.g., oracle updates).
• Holding Periods: Duration of asset custody before sale or transfer.
• Gas Price Bidding: Use of priority fees to front-run or ensure timely execution.

Examines interactions unique to particular DeFi primitives to assess expertise and risk exposure. For instance:

• Leverage Cycling: Repeated borrowing and collateral management on platforms like Aave or Compound.
• Option Strategies: Complex positions using protocols like Lyra or Dopex.
• Cross-Chain Bridging: Monitoring asset migration patterns between Layer 1s and Layer 2s.
• Governance Participation: Voting history and delegation patterns within DAOs.

Relies on raw, immutable on-chain data to construct behavioral models. Primary inputs include:

• Transaction Logs: From blocks, containing from, to, value, and data fields.
• Event Logs: Emitted by smart contracts (e.g., Transfer, Swap, Deposit).
• Internal Transactions: Calls between contracts during a single transaction.
• State Changes: Differences in wallet balances and contract storage before/after blocks.

The NVT Ratio is a valuation metric for blockchain networks, analogous to the price-to-earnings (P/E) ratio in traditional finance. It compares the network's market capitalization to the value being transferred on-chain. A high ratio suggests the network may be overvalued relative to its economic utility, while a low ratio may indicate undervaluation. It's calculated as:

• Market Cap / On-Chain Transaction Volume (USD). Used to gauge long-term trends rather than short-term price movements.

Active Addresses count the number of unique addresses that were active as a sender or receiver in a given period (daily, weekly). It's a fundamental gauge of network adoption and user engagement. Analysts track:

• Daily Active Addresses (DAA): For short-term momentum.
• Monthly Active Addresses (MAA): For broader adoption trends. A divergence where price rises but active addresses fall can signal a speculative bubble driven by fewer participants.

Exchange Net Flow measures the net movement of a cryptocurrency into or out of centralized exchange wallets. It's a key sentiment indicator for holder behavior.

• Positive Net Flow (Inflow): More coins moving to exchanges. Often precedes selling pressure, as users deposit to trade.
• Negative Net Flow (Outflow): More coins moving off exchanges. Suggests accumulation or long-term holding (HODLing). Sharp, sustained inflows can signal impending sell-offs, while large outflows may indicate bullish accumulation phases.

Realized Capitalization values each coin at the price it last moved (its 'realized price'), not the current market price. This aggregates the cost basis of all holders. The MVRV Ratio (Market Value to Realized Value) compares market cap to realized cap.

• MVRV > 3: Historically indicates a market top (holders are deeply in profit).
• MVRV < 1: Suggests the market is at or below aggregate cost basis, a potential buying zone. These metrics help identify overbought or oversold conditions from an on-chain cost perspective.

This metric segments the total token supply based on wallet balance sizes (e.g., whales, retail). Tracking changes in these cohorts reveals power dynamics and conviction.

• Whale Accumulation (>1% supply): Large wallets buying can signal strong belief but also centralization risk.
• Retail Distribution: An increase in small wallets ("shrimps") can indicate broadening adoption.
• Exchange Balance: The percentage held on exchanges (see Exchange Net Flow) for liquidity and potential sell pressure.

Analyzing the count and average size of transactions provides insight into usage patterns.

• High Count, Small Size: Typical of retail adoption, micro-transactions, or certain DeFi and NFT activity.
• Low Count, Large Size: Often indicates institutional or whale movements, OTC desk activity, or collateral shifts. Sudden spikes in either metric, especially large transactions, can precede significant market moves and are monitored for whale alert services.

This process creates a unique profile for a blockchain address based on its historical behavior. Fingerprinting examines:

• Asset composition: The types and ratios of tokens held.
• Interaction patterns: Frequent dApps, protocols, and counterparties.
• Temporal activity: Time-of-day patterns and transaction frequency. These profiles enable predictive risk assessment, allowing protocols to flag wallets exhibiting behavior similar to known hackers or scammers before an attack occurs.

Behavioral analysis is a cornerstone of blockchain surveillance for regulatory compliance. Entities like exchanges and financial institutions use it to fulfill Anti-Money Laundering (AML) and Know Your Transaction (KYT) obligations. This involves:

• Screening transactions against sanctions lists and blacklisted addresses.
• Monitoring for patterns indicative of market manipulation or terrorist financing.
• Generating suspicious activity reports (SARs) for regulators. This creates a tension between regulatory oversight, user privacy, and the permissionless nature of public blockchains.

Sophisticated actors employ methods to evade behavioral analysis, highlighting its limitations:

• Chain Hopping: Moving funds across multiple blockchains to break analysis trails.
• Cross-DEX Arbitrage: Using decentralized exchanges with low KYC to obfuscate origins.
• Address Rotation: Frequently generating new wallets to avoid profiling.
• Obfuscation via Bridges & Wrappers: Converting assets across different standards (e.g., ERC-20 to BEP-20). These techniques demonstrate that analysis is an ongoing adversarial game, not a definitive solution.

The raw, immutable record of all transactions and state changes on a blockchain. This is the primary data source for behavioral analysis, providing transparency into wallet interactions, smart contract calls, and asset movements. Key data types include:

• Transaction logs: Records of function calls and events.
• Wallet balances and histories: Track asset holdings over time.
• Smart contract state: The current configuration and stored data of decentralized applications.

The process of clustering addresses and labeling them based on observed activity to identify entities like exchanges, whales, or smart contracts. This is a critical step in moving from raw addresses to actionable intelligence. Common labels include:

• CEX Deposit/Withdrawal Wallets: Identified by high-volume, regular inflows/outflows.
• Smart Money: Wallets associated with successful investors or funds.
• Contract Creators: The deployer addresses of prominent dApps.

A network analysis technique that models wallets as nodes and transactions as edges. This reveals the flow of value and connectivity within the ecosystem. Analysts use it to:

• Identify central hubs (e.g., major exchanges, bridge contracts).
• Trace fund movements for forensic investigations.
• Detect wash trading or circular transaction patterns.

Pre-defined rules and common behavioral signatures used to classify activity automatically. These are the building blocks of automated analysis systems. Examples include:

• Dusting Attacks: Sending tiny amounts of tokens to map address clusters.
• Profit-Taking: Identifying when a wallet sells a large portion of a recently appreciated asset.
• Airdrop Farming: Patterns of interaction designed to qualify for token distributions.

The identification of statistically significant deviations from established behavioral baselines or network norms. This is crucial for security and risk management. Anomalies can signal:

• Security Exploits: Unusual outflows from a protocol treasury or liquidity pool.
• Market Manipulation: Sudden, coordinated wash trading on a low-liquidity asset.
• Novel Attack Vectors: Previously unseen interaction patterns with a smart contract.

Mechanisms designed to prevent a single entity from controlling multiple identities (Sybils) to gain disproportionate influence. Behavioral analysis is a key tool for detecting and mitigating Sybil attacks in areas like:

• Governance: Identifying voters controlled by a single entity.
• Airdrops & Incentives: Filtering out farmers from legitimate users.
• Credit Scoring: Assessing the uniqueness and authenticity of a user's on-chain history.