Garbage In, Confidence Out: The New Retail Trap

I’m not in SaaS or dev tools. I’m in trading. But the pattern I’m seeing with AI and bad data is universal, especially when you build based on false assumptions.

This post is about that.

Most retail traders are feeding junk data into AI, trusting the output without question, and calling it research.

They export raw price data, feed it into AI, and trust whatever comes out.
No validation. No structure checks. Just blind faith.

The result?

Seemingly well-written outputs. Backtests that sound convincing. Clean tables.
Sometimes the conclusions are right. Other times, they’re way off.

You’re not building a system.

You’re playing Russian roulette with your logic, because AI doesn’t flag bad inputs. It just makes them look sharp.

This kind of blind trust in “AI magic” is the same mindset behind trading bot subscriptions. People pay for bots they don’t understand, fed by signals they don’t understand, chasing a shortcut that doesn’t exist.

Garbage In, Confidence Out.

Most retail workflows are built like this:
• Export excel file (CSV) with zero structure checks
• Copy & Paste into AI
• Get a result that feels intelligent
• Act on it without verifying a single number

But those exports often contain:
• Misaligned rows
• Extra headers
• Duplicate bars
• Indicators that don’t match what’s on the chart
• Subtle timezone mismatches or adjustment errors

The bigger the dataset, the more likely something breaks. AI doesn’t fix these issues. It hides them under confident language.
This Isn’t About Being Smart. It’s About Being Precise.
Most retail workflows collapse the moment you stop assuming the data is good.

How Institutions Handle It:

Institutions don’t trust raw data.
They cross-check multiple feeds, flag anomalies, filter outliers, and keep human oversight in the loop. Most of that process exists before any strategy logic begins.

Retail traders? Most don’t even know they should be checking. If AI says it’s clean, it’s good to go… Until it isn’t.

If You’re Retail, What Can Actually Do? You don’t need Bloomberg terminal.
You just need to stop trusting raw files blindly.

Here are a few ways to validate:

• Compare today’s open vs yesterday’s close (gap logic)
• Calculate high – low range per bar to catch spikes or zero-move days
• Overlay a 20EMA or 200SMA and match it to what you see on your chart
• Check if indicators are actually computed on clean input
• Scan for missing rows or volume = 0 anomalies

This isn’t about perfection. It’s about removing landmines.

The Real Issue: Performance vs Optics

This isn’t just a technical mistake. It’s a cultural one.

Retail traders tend to trust:

• Clean dashboards
• Bold headlines
• Polished charts
• Confident voices
• Visual noise that looks like signal

Nobody asks, “Are your inputs clean?”
But everyone clicks when they see a dashboard that looks like alpha.

Final Thought:

If you're building anything in the financial data space, especially tools powered by AI, make sure the foundation isn't rotting underneath.
Because at that point, you’re not trading.
You’re automating noise and packaging it like edge.

I know this isn’t a typical IndieHackers topic, but the principle applies across different domains.

If the input is flawed, everything downstream is noise. Curious if others here have run into similar issues applying AI in their own field.

(For Thinkorswim users who actively backtest: I built a tool that cuts the cleanup time of raw TOS exports from 10–15 minutes per dataset down to just a few seconds. No manual formatting, no Excel gymnastics. If that’s a bottleneck you’ve run into, I’m open to sharing more.)