Automated High-Frequency Trading Bot

Technical Challenge

Arbitrage opportunities between decentralized AMMs and centralized orderbook exchanges exist for milliseconds. A price that is stale on one venue and live on another is not an insight — it is a race. Building a profitable bot in that window required minimizing RPC latency, mitigating mempool front-running (MEV), and managing gas spikes automatically. The stack we used was Node.js and Web3.js, which is not the language of HFT folklore, but it was the language of the Ethereum tooling we already trusted in 2021.

This was a development case, not a fund. Capital at risk was capped. The bot was allowed to miss trades; it was not allowed to lose the wallet on a reverted multi-leg swap or a gas auction it could not win.

Engineering Architecture

Private RPC mesh. Public endpoints are a shared waiting room. We ran clustered WebSocket nodes in Frankfurt, Singapore, and Tokyo, close to the matching engines and to the geography of the CEX APIs we compared against. Each region subscribed to the same pair set; a local process computed a spread; only the region that saw a live edge was allowed to sign. Clock sync and a shared kill switch mattered more than any single strategy parameter.

Atomic execution. A profitable-looking path that dies on the second hop is a loss. Smart contracts bundled swap legs so that either the whole cycle landed or nothing did — zero capital loss on reverted trades, minus gas. That still meant we had to decide when gas was worth it. A “free revert” is not free.

Dynamic EIP-1559 estimator. Priority fees in 2021 were a mempool weather report. We estimated tip from recent blocks and pending congestion, then refused to bid when the remaining spread after fees was below a floor. The bot is not brave. It is a spreadsheet with a signer.

MEV awareness. Sending a juicy arbitrage to the public mempool is an invitation. Where we could, we used private relay paths so the transaction was less visible to generalized searchers. That does not eliminate MEV; it changes who sees you first. We also avoided strategies that only work if you assume nobody else is looking.

Operations

Node.js is single-threaded enough to hurt you if JSON-RPC parsing and strategy share a loop. The hot path was thin: decode, compare, maybe sign. Everything else (logging, Postgres, dashboards) sat off the trade process. WebSocket reconnects were treated as an incident, not a retry toast. If a region went dark, it stopped trading; it did not trade on stale books.

Risk controls were boring and non-negotiable: max notional per block, max failed txs per minute, a hard wallet balance floor, and a human panic button. The bot could be profitable and still be shut off if the error budget burned.

Outcome

The system made money on a subset of CEX–DEX basis trades when latency and fees lined up, and it sat idle the rest of the time — which is the correct behavior. The engineering residue is the mesh, the atomic legs, and the fee estimator, not a magic alpha. In 2021 that was enough to learn that most “HFT on-chain” is gas and location, and that Node.js is fine if you do not ask it to be a matching engine.