How I Watch Wallets, Track SOL Transactions, and Hunt NFTs on Solana

Whoa! This felt overdue. I was poking around a messy mempool one night and somethin’ clicked. Tracking wallets isn’t glamorous, but it matters. My instinct said: build a tidy way to follow activity without losing my mind.

Okay, so check this out—there are a few layers to what I do when I’m watching a wallet on Solana. First, you want reliable real-time visibility. Second, you want context about who interacted with whom and why. Third, you need signals for NFTs and token flows, because those tell stories that plain balances do not.

At a glance, Solana transactions are fast and cheap. Really fast. That speed is both a blessing and a curse for sleuthing. You can see a flurry of micro-transfers that mean nothing, or you can catch a single swap that indicates a big game is afoot.

Here’s what bugs me about many explorers: they dump data without storytelling. Hmm… you get timestamps and base58 blobs. That’s useful but it lacks the “why” and the “next likely move.” Initially I thought raw logs would be enough, but then realized that human-readable heuristics are needed to convert noise into signals.

So I built a checklist in my head. It’s simple. Watch: the incoming signatures, outgoing program calls, token mints, and NFT metadata updates. Also track rent-exempt account creations. Small detail, but often very telling. On one hand those micro-details feel tedious, though actually they’re often the smoking gun.

Using tools and one clean reference: solscan

I’m biased toward tooling that surfaces intent, not just numbers. That’s why I often jump to solscan for a quick profile view—transactions, token holdings, program interactions, and NFT lineage all in one place. The UI helps me spot patterns quickly, like repeated program IDs or a recurring marketplace address, which is huge when you’re hunting a wash sale or wash trade pattern.

How I triage wallets: first filter by recent activity. Then scan for program calls that match marketplaces or swaps. Next check token account initializations. These steps are medium-effort but yield high signal-to-noise. For devs, this is also a debugging shortcut; you can confirm program behavior without attaching a full debugger.

One trick: watch for consistent lamport transfers that come in round numbers. Those often indicate bot-driven activity. Also, if a wallet repeatedly creates token accounts for the same mint, that’s a clue about automated flows.

Another practical angle is NFT tracing. NFTs on Solana are usually underpinned by token metadata updates and creators referenced in the metadata. Watch the metadata update calls and the associated signature batch. Sometimes a creator will retag or delegate, and that tells you about secondary market strategies or reveal mechanics.

I’m not 100% sure about every projector—some marketplaces obfuscate a bit. But you can still infer a lot from adjoining transactions and rent payments. I usually open the preceding and following transactions and read them like a thread, which helps reconstruct an intent timeline.

On the technical side, program IDs are your north star. Once you memorize 4–6 common ones (DEXes, marketplaces, token programs), you can eyeball activity quickly. For instance, a sudden burst of calls to a staking program paired with mass token transfers is very different than many small swaps to a DEX address.

Okay, one more practical note: set alerts. Seriously? Yes. You can subscribe to signature feeds or use webhooks. That saves you from constantly refreshing the block explorer when something important happens. I get pinged for large outgoing transfers and for new token mints associated with wallets I monitor.

Sometimes the signals are subtle. A wallet that just pays rent-exempt balances for others might be a delegate or a factory for ephemeral accounts. At first glance it looks like philanthropy, but then you see a pattern of funneling funds. Initially I misread such wallets as benign. Then I tracked longer, and the pattern became obvious.

On the dev side: instrument your programs to emit structured logs. This is low-key the best help for anyone debugging or monitoring interactions. Logs make it far faster to map function calls to wallet behavior. Without that, you’re playing detective with 1s and 0s.

One failed approach I tried was obsessing over token balance deltas alone. That flopped. Why? Because balances change for reasons unrelated to intent: airdrops, rent refunds, and batch cleanups. Instead I pair deltas with program signatures and metadata changes. That combo tells the real tale.

There’s also value in looking at a wallet’s “neighborhood.” Who funds it? Who signs alongside it? Shared transaction patterns—like multiple wallets participating in the same set of transactions—often reveal botnets or syndicates. It’s a little like social graph analysis, but for keys and programs.

I’m biased, but I recommend keeping a small local cache of address metadata. Name tags, first-seen timestamps, and linked program IDs are very useful. When you load a wallet and see a known marketplace or a tagged scam address, your brain saves time and you act faster.

And don’t forget privacy watchers. Some wallets hop through mixers or use ephemeral accounts to hide flows. Those are the trickiest. My approach there is patience: follow small trails across epochs until a consistent pattern emerges. It’s painstaking, but it works.

For NFT explorers specifically, follow creator royalties and metadata updates. A sudden change in royalty parameters can mean a drop in the pipeline or a contract migration. Also note when an NFT’s metadata URI shifts to a new domain; that tells you someone moved asset hosting or updated reveal images.

One more thing—watch for “reversible” transactions in transaction history. If you see a sequence of trades that net to zero, but fees paid, someone tested a strategy or probed liquidity. Those tests can foreshadow bigger moves when the attacker finds a vulnerability or a costless arbitrage.

Look, I won’t pretend I catch everything. I’m not omniscient. But following these heuristics has saved me hours and sometimes cash. I’m writing this as advice, not a rulebook. Use your judgment and adjust thresholds for alerts and heuristics to your risk tolerance.