Many profitable trades on Solana are actually controlled by insiders. They collude with project parties, buy tokens in advance, and sell at a high price, making millions of dollars. Projects like LIBRA, HAWK, etc., have been exposed for insider wallet operations, trapping ordinary investors.
🥦 On-chain transparency allows you to discover potential insider wallets and their behaviors within the Solana ecosystem in a timely manner, providing strong evidence for investment decisions.
🎣 How to track insider wallets (addresses) and operations on Solana
🪻1. Use on-chain browsers to query fund flows
Tool recommendations: Solscan, Solana Explorer, SolanaFM, etc.
Operational method:
Monitor the main contracts of popular projects (such as IDOs/airdrops/newly launched tokens) to view the main wallets involved, receiving, and distributing funds.
Track wallets with large amounts entering and exiting in a short time, deeply analyze their historical transactions, and mark key addresses that may be related to project parties, venture capital, and development teams.
🪻2: Use whale and tagging tools to automatically monitor behaviors
Tool recommendations: Whale Alert, Arkham Intelligence, Nansen (if already supports Solana), Solscan Label, etc.
Operational method:
Set up automatic alerts for large whale transfers and abnormal amounts entering and exiting.
Subscribe to transfer notifications for key project wallets to check for frequent large interactions with non-public addresses.
Cite on-chain data APIs to analyze fund inflow and outflow nodes, transfer patterns, and time distribution.
🪻3: Community collaboration for discovery and information cross-validation
Follow Twitter, Discord, Telegram, and other communities to collect public tags about 'project parties/VCs/whale wallets'.
Pay close attention to the flow of funds in related wallets after on-chain events, such as large transfer records before and after new tokens are launched.
🦑 Solana insider address analysis theory and methodology
🪻1: Tag induction method
Quickly identify the ownership of core addresses through on-chain browsers and community public wallet tags.
Historical data review, analyzing the transfer frequency and funding patterns between wallets and known 'teams/VCs/market makers/MEV bots' and other labeled addresses.
🪻2: Identification of abnormal large-scale behaviors
Statistics on large SOL or USDT/USDC operations that enter and exit non-exchanges in a short time, combined with announcements, news, and on-chain major event timelines, to determine if it is insider trading.
🪻3: Behavioral link tracing
Combining the 'football passing' model — the same large amount of funds is repeatedly split and transferred, layer upon layer, until it flows into centralized exchanges/or cash-out addresses, we can speculate the risks of 'money laundering/cashing out/nesting dolls'.
🪻4: Event correlation analysis
For example, large on-chain transfers before and after price fluctuations, main chain upgrades, important announcements, and before and after IDO/IEO, tracing the end-to-end related behaviors of funds.
Summary of practical tracking steps 🩷
🪻1: Select events of interest (such as new token launches, airdrop distributions, price fluctuations, etc.), and locate relevant main contracts and key wallets.
🪻2: Continuously monitor these wallets' large cross-account actions using tools like Solscan, Whale Alert, etc.
🪻3: Use tags and transfer path analysis to find addresses related to project parties, old VCs, and previous offenders.
🪻4: Form a wallet relationship network (Graph), assisting in discovering unknown insider addresses.
🪻5: Regularly review data to filter suspicious patterns and reuse tags.
🩷 Risks & Precautions
Insider address with no 100% public identity, often uses 'annotation-aggregation-cross-reasoning'
Advanced insider behaviors often use methods like 'multi-hop splitting/mixing coins/flash loans' to confuse tracking clues.
🪻 Combine on-chain big data signals and community revelations to improve judgment accuracy.