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Yrd. Doç. Dr. Dyt. Esin Şeker

Yrd. Doç. Dr. Dyt. Esin Şeker

13 Temmuz 2025 Pazar

How I Track Tokens, DeFi Flows, and Wallets on Solana (Practical, No-Fluff)

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Whoa! I remember the first time a token transfer flashed by and I had no clue what it meant. My instinct said: this is important. Seriously? Yeah—because on Solana, a single transaction can ripple across dozens of accounts in seconds. Initially I thought block explorers were all the same, but then I dug into tooling, UX, and performance and realized they aren’t even close.

Here’s the thing. If you care about tokens, DeFi positions, or wallet behavior, you need fast, reliable traces. My bias is toward tools that show provenance clearly, not just balances. This part bugs me: too many trackers show numbers without telling the story behind them. On one hand you get raw data; on the other hand you get visualizations that gloss over crucial on-chain events. Though actually—some explorers now combine both, which helps a ton.

Quick practical tip: when scanning a token, look at mint activity first, then recent transfers, then program interactions. Hmm… that ordering saved me from chasing phantoms. Check the token’s supply changes. Check program logs. Follow the money, not the label—because labels lie sometimes, and they lie convincingly.

Screenshot of transaction inspection with token transfer highlighted

What I watch and why it matters

Short answer: provenance, program calls, and cross-account flows. Long answer: start with the mint address, then inspect each account interacting with that mint, and finally read the inner instruction logs to see which programs actually moved funds. My approach is hands-on. I open a few recent swaps or liquidity events and track instructions backwards. It feels like detective work—thrilling and occasionally maddening.

I use explorers that let me jump between accounts and programs without losing context. One useful resource is https://sites.google.com/walletcryptoextension.com/solscan-explore/ which I often consult for quick lookups and visual traces. I’m not pushing a product; I’m sharing what works. I’m biased, but that link has saved me hours more than once.

Wallet tracking matters because patterns reveal intent. Short trades repeated quickly? Maybe market making. Big one-off transfers to a cold wallet? Could be vesting or profit-taking. Watch timing. Watch counterparties. Somethin’ about repeated tiny transfers always looks suspicious to me. Also watch for program-derived addresses; they often mask the true logic flow behind a transaction.

Pro tip: set up alerts on on-chain events rather than price alerts. Yep. Alerts for token mints, account closures, or program upgrades catch unusual activity fast. They’re not perfect, but they beat waking up to a rugpull tweet. Very very simple to do if you use explorers with webhook integrations or built-in watchlists.

When evaluating DeFi analytics, don’t trust shiny dashboards alone. The aggregates can hide front-running, sandwich attacks, or odd slippage. Instead, inspect raw swap transactions around suspicious price moves. You can reconstruct the sequence: who swapped first, who took liquidity, and who paid the price. That sequence tells the story better than any chart.

Okay, so check this out—on Solana, transactions can include multiple inner instructions that involve several programs. If you only look at the outer instruction, you miss the meat. For example, a single “swap” might call a router, which then calls multiple pool programs and token transfers. The best explorers expose those inner calls. If yours doesn’t, switch.

I’m often asked about wallet privacy. Here’s my take: privacy on Solana is weak by default. Reuse of addresses, obvious chain-of-control, and program-derived addresses that tie behavior together make deanonymization straightforward if you know what you’re doing. On the flip side, that visibility is useful for auditors, DAO treasuries, and compliance teams—so it’s a trade-off. I’m not 100% sure where regulation will land, though, and that uncertainty is why monitoring tools are becoming more critical.

Workflow I use (step-by-step, practical)

First: identify the action I care about—transfer, swap, mint, or program upgrade. Second: open the transaction and expand inner instructions. Third: follow token accounts and PDAs to their owners. Fourth: cross-check with recent blocks and other associated transactions. Initially I thought this would be tedious each time, but with saved queries and watchlists it becomes routine and fast.

Tools that let you export CSVs or push JSON webhooks are invaluable for automation. I feed selected events into small scripts that flag anomalies. On weekends I’ll scan flagged events for patterns. That human step catches context machines miss—like when an airdrop suddenly looks targeted because of off-chain coordination. (oh, and by the way… sometimes you just need to ask in a Discord)

Also: don’t ignore token metadata. Metadata fetches often reveal project links, creators, and initial mint history. Those breadcrumbs matter when you’re vetting a token. But be wary—metadata endpoints can be spoofed, and some projects change official pages after launch, so cross-verify when in doubt.

FAQ

How can I spot a rugpull early?

Watch for sudden changes in token liquidity paired with large transfers to unknown wallets. Look at who added initial liquidity and whether they immediately transferred ownership. Also watch for token mint increases and account closures within tight time windows—those are red flags. My rule: if the team wallets are anonymous and liquidity is controllable, assume risk.

Which metrics matter most for DeFi health?

Liquidity depth, recent trade sizes, median slippage on typical trades, and number of unique LPs. Program upgrades and multisig changes also matter because they change trust assumptions. Don’t obsess over one metric; patterns across several indicators are more telling.

To wrap up—well, not wrap up exactly, because this is ongoing—use explorers that reveal inner instructions, set smart alerts, and follow token provenance. My approach is pragmatic: watch, automate, and validate. It sounds simple. In practice it requires patience and curiosity. Really—keep poking at things. You’ll learn patterns. You’ll miss some. And you’ll get better.



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