Okay, so check this out—I’ve spent years poking around Ethereum blocks, watching trades and contract calls like a hawk. Whoa! My instinct said early on that most people only scratch the surface. Really. At first glance a transaction hash looks like a string of chaos. But when you learn the patterns, things snap into place, and you start to see the narrative behind money moving on-chain. Hmm… somethin’ about that detective work is addictive.

Here’s the thing. You don’t need to be an on-chain wizard to follow a DeFi flow. You do need the right tools and a little method. I lean on a good explorer, transaction decoding, logs, and a few heuristics born from repeated errors. Initially I thought raw logs were impenetrable, but then realized that event topics and indexed parameters tell most of the story if you parse them methodically. On one hand the blockchain is transparent—though actually, the signal-to-noise ratio is often very low. On the other hand, with practice, wallets, bots, and contracts reveal predictable habits.

Start with the hash — then zoom out

Transaction hash first. Paste it into the explorer and breathe. Short. Look at the status. Look at the gas used. Look at the “internal transactions” tab if it’s there. Many DeFi interactions hide most of their value inside internal calls. If you only look at top-level transfers you miss swaps that happen inside router contracts, and those are the ones that move millions.

Check the “Logs” or “Event” section. These are your breadcrumbs. Each ERC-20 Transfer, Approval, or custom event is a snapshot of state change. You can decode an event by its topic and indexed parameters. For ERC-20 tokens, a single Transfer event tells you who sent what to whom. For AMMs, look for PairSwap events or Sync events to infer reserves and slippage. Initially I thought you needed to read Solidity to follow this, but actually pattern recognition goes a long way. Pay attention to the tokens’ decimal places—values look huge otherwise.

Watch the “From” and “To” addresses. If a router, aggregator, or lending pool appears repeatedly, tag it mentally. (Oh, and by the way… keep a tiny spreadsheet. Trust me.)

Contract verification — the single most useful habit

Verified source code is gold. If a contract is verified on an explorer, you can inspect functions, modifiers, and comments. That often tells you whether transfers are balanced or if there are admin backdoors. Wow! I’ve bailed out of many a token simply because the verified code had an owner-only mint function. If the contract isn’t verified, behave as if you’re blindfolded—assume the worst.

Look for common red flags: owner-only transfers, arbitrary blacklist lists, self-destruct functions, or hidden fees. Also scan the constructor for initial token allocations. A large initial allocation to a single address often means the deployer holds leash power. You can also find whether liquidity is locked by checking for LP token transfers to timelock contracts. If liquidity was added and LP tokens were immediately moved to a dead address or a known lock contract, that’s a positive signal—though not a guarantee.

Follow the approvals — they’re very very important

Approvals matter. An approved allowance is permission to move tokens. Watch who received allowances and how large. If a random contract has an infinite approval for a million tokens, consider revoking or flagging it. Many rug operations begin with approvals to malicious contracts. Track approval patterns and expiration events if you can. My checklist here: check timestamp, spender address, allowance size, and whether this spender has a history of suspicious transfers.

Pro tip: some explorers let you revoke approvals directly or generate a transaction to set allowance to zero. If you interact with a ton of DeFi dApps, periodic cleanup is prudent. I’m biased, but I tidy allowances monthly—less drama that way.

Use the token tracker and holder distribution

Token holder lists are telling. If one address holds 70% of the supply, that’s a concentration risk. Watch for sudden shifts: large transfers from a deployer to exchanges may signal an exit. Watch the top holders over time. Many rug pulls feature an initial distribution to a few wallets that later funnel to DEXes.

Also, the “Transfers” tab for a token often shows liquidity events. Search for Pair creation or addLiquidity calls; they frequently happen in sequence and sometimes in the same transaction as the token mint. That sequence is a recipe for short-lived tokens that explode and vanish.

Decode internal transactions and trace money flow

Internal txs reveal swapped paths, loan borrowings, and flash loan flows. They can show how a single top-level action triggered dozens of on-chain effects—liquidity pulls, arbitrage trades, and collateral seizures. When I trace a suspicious event, I follow internal txs to find the ultimate beneficiary. On one hand this is tedious, though actually it’s the only way to see what’s really happening after complex router calls.

Remember to look at gas price patterns. Front-runners and sandwich bots use higher gas to jump queues. If you see a block where a transaction paid 100–200 gwei when normal gas was 20 gwei, assume bot activity and look for paired incoming/outgoing transfers around that tx. Often you’ll find two or three transactions forming a sandwich around a swap, and that speaks volumes about who profited.

APIs, alerts, and programmatic tracking

Human eyeballs are great. Automation is better. Most explorers provide APIs to pull transactions, token transfers, and event logs. Use them to build watchlists, trigger alerts for approvals, or detect large transfers from deployer addresses. I have a small script that hits the events API and looks for Transfers > X ETH or token value > $Y. When something trips the threshold, I get a push alert. Pretty handy.

Some things are tricky to automate though—context matters. A large transfer might be a legitimate market maker rebalancing; sometimes it’s an exit. Initially I thought thresholds alone would catch scams, but patterns and counterparties are just as important. So the script reports, I inspect, and then I decide.

Spotting scams and rug pulls

Common patterns: unverified contracts, concentrated initial ownership, unlimited approvals, sudden liquidity removal, and owner functions that can alter balances. Also watch for unrealistic tokenomics. If a token promises ridiculous yields with no clear mechanism, be skeptical—I’m not 100% sure on every nuance, but those promises often fail a sanity check.

Another pattern is repeated creation of new tokens that all route through the same deployer or factory address. That tells you the actor is testing social engineering tactics across many projects. On-chain labels help: explorers label known scam addresses, front-running bots, and honeypots. Use those labels, but also verify independently; labels can be slow or incomplete.

Deeper tracing: forks, simulations, and local tracing

For high-stakes analysis, I occasionally fork mainnet locally and replay transactions in a debugger to see state changes in real time. This reveals hidden behaviors—reentrancy, balance manipulation, or oracle manipulations—that logs alone may not show. It’s technical, requires a dev environment, and is overkill for casual tracking, but it’s invaluable when you suspect sophisticated fraud.

On the less nerdy side, use the explorer’s “read contract” and “write contract” tabs to see public getters and state variables before you interact. That often answers questions like “Is there a max supply?” or “Who is the owner?”