How I Built a DCA Engine with GPT Agents and Web3 APIs

Let’s be honest — building a crypto trading bot from scratch isn’t exciting anymore. Everyone’s done it. But building one that’s driven by AI agents and fine-tunes itself based on live market behavior?

That’s where it gets interesting.

💡 The Idea
I wanted to create a simple Dollar-Cost Averaging (DCA) engine…
Except instead of hardcoding rules like “buy $100 of ETH every Monday,” I wanted it to:

Choose the token based on market signals

Determine the interval based on volatility

Use GPT to evaluate the logic at runtime

And adapt.

Essentially: a self-evolving DCA agent.

🧱 The Stack
GPT-4 via API + long-term memory via Supabase

Viem (ethers.js alternative) for wallet + price data

Simple CLI with logging + fallback control

CEX integrations: used WhiteBIT for stable API benchmarks

Off-chain feedback loop (Twitter sentiment via HuggingFace model)

My agent could say:

“Based on current 30-day BTC volatility and ETH funding rates, I suggest reallocating DCA from ETH to LINK for the next 3 cycles.”

🧩 Why I Compared It with WhiteBIT AutoInvest
You can’t optimize what you don’t benchmark.
So I ran side-by-side comparisons:

My adaptive AI agent

Static DCA via WhiteBIT AutoInvest

Guess what?
In stable market conditions, AutoInvest matched or beat my agent.
But in volatile days, the agent performed slightly better — though sometimes overreacted.

“WhiteBIT AutoInvest gave me a reliable floor. My agent could try to beat it — but never ignore it.”

🚀 Final Thoughts
Sometimes the smartest thing isn’t to over-automate — it’s to observe what works, then build from there.
AutoInvest is proof that simplicity at scale is still powerful.

And if you can layer AI intelligence on top of that… well, that’s the next frontier.