bottrading

#BotTrading
Vibecoding a Trading Bot -
Part 1: The Architecture Nobody Talks About
$BTC
Everyone wants a trading bot. Almost nobody talks about what's actually inside one before you write a single line of code.
I've been building a memecoin sniper bot on Solana and before I show any of the build (that's coming in later parts), I want to break down the mental model. Because most people jump straight to "let's code it" and skip the part that actually determines whether the bot works: the architecture.
A trading bot isn't one thing. It's three systems stitched together.
1. The data feed
This is the bot's eyes. It needs to see the market in real time - new token launches, price movement, liquidity changes, wallet activity. Garbage data in means garbage decisions out, no matter how smart the logic downstream is. This is the layer people underestimate most.
2. The scoring/decision logic
This is the bot's judgment. Given what it just saw, is this worth acting on? This is where you encode what "good" looks like - not as a single hard rule, but usually as a weighted combination of signals. No single indicator tells the whole story; the skill is in how you combine them.
3. The execution layer
This is the bot's hands. Once it's decided something's worth acting on, it needs to actually place the trade fast, and without falling over when the network's congested or a transaction fails.
Why this framing matters
Most people who fail at building a bot don't fail at coding - they fail at skipping straight to layer 3 without a real layer 2. A fast execution engine with weak decision logic just loses money quickly instead of slowly.
I'll go deeper into how I actually built each layer including how I used AI to scaffold the code itself ("vibecoding") - in the next part of this series.
For now: if you're thinking about building something like this, start by sketching these three layers on paper before you touch a keyboard. It'll save you weeks.
Part 2 - how I actually vibecoded the scaffold - coming soon.

Disclaimer:
Not financial advice. Educational content only. Trading carries risk.

#memecoin🚀🚀🚀
#BotTrading
Vibecoding a Trading Bot -
Part 2: Turning the
Architecture into Code
$SOL
In Part 1, I broke down the three layers every trading bot needs - data feed, decision logic, execution. If you missed it, go back and read that first, because this part builds directly on it.
Now let's talk about actually turning that into working code - and specifically, how AI fits into that process. This is what people mean when they say "vibecoding."
Vibecoding isn't "AI writes the bot for you"
A lot of people hear "vibecoding" and picture typing "build me a trading bot" into a prompt and walking away with a finished product. That's not how it works, and treating it that way is how people end up with bots that silently fail or leak funds.
What vibecoding actually looks like in practice:
1. Scaffold first, logic later
I started by getting the skeleton in place - file structure, how the data feed connects to the decision layer, how the decision layer hands off to execution. AI is genuinely fast at this part. It's boilerplate, and boilerplate is exactly where AI assistance shines.
2. Read everything it gives you. This is the part people skip. Every function AI generates, I went through line by line - not because I didn't trust it, but because I needed to actually understand what my own bot does. If you can't explain why a piece of your own code works, you can't fix it when it breaks at 2am with real money on the line.
3. Iterate in small pieces, not one giant promptInstead of asking for the whole bot at once, I built it layer by layer - data feed working and tested before touching decision logic, decision logic tested with fake data before wiring up real execution. Vibecoding works best as a tight loop: prompt, read, test, adjust, repeat. Not one shot.
4. Treat AI like a fast junior dev, not an oracleIt'll confidently write things that are subtly wrong especially around edge cases like failed transactions, rate limits, or bad data. Catching those isn't optional. That's still your job.
Why this matters for the decision layer specifically The scoring logic - the part that decides what's actually worth acting on - is where I spent the most time refining, because this is where a bot's real edge lives (or doesn't). I won't get into the specific weighting here, but the short version: no single signal tells the whole story, and getting that combination right took a lot more iteration than the scaffold itself.
Part 3 is going to go into how I actually trained and tuned that decision-making layer - what "training" even means for a bot like this, and how backtesting shaped it.
Part 3 - training the bot's decision-making - coming soon.

Disclaimer:
Not financial advice. Educational content only. Trading carries risk.